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The Use of Preventive Services by the Elderly January 1989 NTIS order #PB91-192880
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FOREWORD Interest in health promotion and disease prevention strategies for the elderly has grown in the last ten years, partly as a result of advancing knowledge in these areas and partly due to the search for ways to moderate the rising cost of health care in this growing segment of the population. Reflecting this interest, the House Committee on Ways and Means requested that OTA analyze the effectiveness and costs of providing selected preventive health services to the elderly under the Medicare program. The Senate Labor and Human Resources Committee has also requested that OTA provide information on the value of preventive services for the American people. This Staff Paper, The Use of Preventive Services by the Elderly, is the second in a series of papers being prepared in response to these requests. Understanding the use of preventive services by the elderly is an important component of assessing their effectiveness as Medicare benefits. In this paper we review both new and previously published data on the proportions of elderly currently receiving a variety of preventive health services; we examine factors associated with whether the elderly receive these services; and we analyze the likely implications for Medicare if preventive health services were offered as covered benefits. The first paper in this series on Preventive Health Services Under Medicare examined glaucoma screening as a potentialMedicare benefit. Subsequent papers will assess screening for cholesterol, cervical cancer, and colorectal cancer, and will analyze broad issues related to Medicare financing of preventive services for the elderly. u JOHN H. GIBBONS Director
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The Use of Preventive Services by the Elderly by Michael E. Gluck Judith L. Wagner Brigitte M. Duffy Health Program Office of Technology Assessment Congress of the United States Washington, D.C. 20510-8025 January 1989 A Staff Paper in OTAs Series on Preventive Health Services Under Medicare Carol Guntow prepared this Staff Paper for publication. The views expressed in this Staff Paper do not necessarily represent those of the Technology Assessment Board, the Technology Assessment Advisory Council, or their individual members.
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CONTENTS Chapter Page 1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....... 1 2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 5 Types of Preventive Services . . . . . . . . . . . . . . . . . . . . . . . . 5 Preventive Services and Medicare . . . . . . . . . . . . . . . . . . . . . . . 6 3. Recommendations of Professional and Expert Groups for the Use of Preventive Services by Older Adults . . . . . . . . . . . . . . . . . . . . . . 7 4. What Percentage of the Elderly Use Preventive Services? . . . . . . . . . . . . . . 11 Sources of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Estimates of Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Use of Multiple Services . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Time Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5. What Factors Promote or Inhibit Elderly Peoples Use of Preventive Services? l . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Theoretical Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Evidence on Patient Behavior . . . . . . . . . . . . . . . . . . . . . . . . 18 Evidence From Studies of Health Care Provider Behavior . . . . . . . . . . . . 24 6. Implications for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Potential Medicare Coverage . . . . . . . . . . . . . . . . . . . . . . . . 29 Delivery of Preventive Services for the Elderly . . . . . . . . . . . . . . . . 30 Appendix A. Advisory Panel-Project on Preventive Health Services Under Medicare . . . . . . . 33 B. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 C. Empirical Studies of the Use and/or Determinants of Use of Preventive Services by the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 D. Empirical Studies of the Use and/or Determinants of Use of Preventive Services by the Non-elderly . . . . . . . . . . . . . . . . . . . . . . . . . . 38 E. OTA Analysis of Preventive Service Use By the Elderly Using Data from the 1982 National Health Interview Survey . . . . . . . . . . . . . . . . . . . . 46 F. Analysis of Preventive Service Use By Older Adults in a Health Maintenance Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ......... 59 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ......... 60 Tables Table Page 1. Selected Potential Clinical Preventive Services for the Elderly . . . . . . . . . . . 6 2. Published Recommendations for the Use of Selected Preventive Services by Older Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3. Percent of Elderly People Receiving Preventive Services Within Specified Periods of Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4. Percent of Persons Over 65 Using Multiple Preventive Services from the 1982 National Health Interview Survey . . . . . . . . . . . . . . . . . . 15 ii
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CONTENTS (centd) 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. Trends in the Percent of Adults or Older Adults Ever Having Received Selected Preventive Services . . . . . . . . . . . . . . . . . . . . . 16 Selected Factors Hypothesized to Affect Use of Preventive Services by the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Significant Predictors of Use from OTA Multivariate Logit Analysis of 1982 National Health Interview Survey . . . . . . . . . . . . . . . . . . . . 19 Periods of Time Used by OTA and Two Studies to Measure Older Adults Use of Preventive Services . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 1982 National Health Interview Survey: Selected Descriptive Statistics for Persons Over 65 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Elderly Use of Five Screening Services: Logistic Regression Results . . . . . . . . 50 Correlation Matrix for Variables in Logistic Regression Models . . . . . . . . . . 51 Effect of Statistically Significant Binary Variables in Logistic Regressions on Elderly Use of Five Screening Services: Predicted Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Sample Sizes for Each Measurement Period in OTAs Analysis of Preventive Service Use in One HMO . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Percents of Continuously Enrolled HMO Members Receiving Eight Preventive Services During Specified Periods of Time . . . . . . . . . . . . . . . . . . . 57 Figure 1. Effect of 2. Effect of 3. Effect of 4. Effect of Page Age on Use: Predicted Probabilities . . . . . . . . . . . . . . . . . 54 Income on Use: Predicted Probabilities . . . . . . . . . . . . . . . . 54 Education on Use: Predicted Probabilities . . . . . . . . . . . . . . . 54 Bed Days on Use: Predicted Probabilities . . . . . . . . . . . . . . . 54 iii
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1. SUMMARY In recent years, clinicians, academics, and policy makers have begun to examine the potential benefits of services to promote health or to prevent disease, disability, or death in the elderly. Although Medicare, the Federal program responsible for paying the bulk of the noninstitutionalized elderlys health care bills, currently pays for few preventive services, Congress has several proposals pending to expand Medicar e coverage of these procedures. In this paper, OTA examines the implications of potential Medicare coverage for the use of preventive services by analyzing current use and the determinants of that use. How Many Elderly Use Preventive Services? OTA found few sources that measure the use of preventive services by the elderly. The data that are available (summarized in table 3 in the text of the paper) suggest two main n conclusions: The use of preventive services by the elderly varies according to the type of service from a low of 20 to 30 percent for routine fecal occult blood testing in some sites to a high of 93 percent for blood pressure measurement. These differences cannot be explained by differences in the periods of time over which use is measured. Rates of use of specific services show a high level of consistency across studies, despite differences in methods. Trends in available data suggest that the use of these procedures has increased over the last 15 years. Data also indicate that if an elderly person receives any preventive services, he or she is likely to receive multiple services. Which Elderly Use Preventive Services? Studies to isolate factors associated with the use of preventive services fall into two categories: those that focus on the behavior of patients, and l those that focus on the behavior of health care providers and organizations. Most of the studies in both of these categories examine preventive service use among the nonelderly. An analysis of data for the over65 population in the 1982 National Health Interview Survey (NHIS) found that, controlling for other factors, the probability that an elderly person used each of five selected preventive services--glaucoma screening, eye exams, ares, to: l n n n n blood pressure measurement, breast exand Pap smears-was consistently related being male (for the three services available for both men and women), being younger (although still over-65), having more education, having greater family income, having some health insurance in addition to Medicare, living in a metropolitan area, and having spent more days in bed during the previous year. OTA found that receiving health care through a prepaid health plan was not related to the use of any preventive service. However, so few people in the study sample belonged to prepaid plans that it may not have been possible to find a statistically significant effect. Race, living alone, and having some limitation in activity had no clear or consistent effect on the use of the five services studied.
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2 The Use of Preventive Services by the Elderly Other studies of the relationship between patient characteristics and the use of preventive services have had similar findings. Among health care providers, physicians play a key role in the provision of preventive services. The evidence suggests that gaps exist between physicians knowledge and experts recommendations on the use of preventive services as well as betwee n physicians knowledge or beliefs and actual practice. These gaps may be more prominent in relation to elderly patients. While they may suggest a shortcoming in physicians performance, they could also indicate that physicians take individual patients situations into account when ordering preventive services. Other insights into the importance of health care providers in determining whether the elderly receive preventive services come from trials designed to improve compliance with expert recommendations. These studies indicate that health care organizations can organize themselves to affect the percentage of individuals receiving such services Strategies suggested in the literature worthy of further study include: targeting groups in need of prevention, using non-physician medical professionals to deliver services, and generating reminders to physicians and patients about the periodic need for preventive services (especially with the aid of computerized record-keeping systems). Although OTAs analysis of preventive service use showed that health maintenance organizations (HMOs) had no discernible effect on elderly enrollees preventive activities, the review of the literature on provider behavior indicates that HMOs and other group practices with centralized administration and record-keeping may have potential for increasing the use of such services. Implications of Medicare Coverage for the Use of Preventive Services by the Elderly The findings of this study have three main implications for potential Medicare coverage of preventive services: n Reducing patients out-of-pocket expenses for preventive services through Medicare would probably increase the percentage of elderly receiving preventive care. For four of the five services examined in detail by OTA, having some insurance coverage beyond Medicare is associated with about a 10 percent increase in the receiving each service. However, there are three finding: (1) OTAs analysis measured th e likelihood of caveats to this (2) (3) presence of insurance tha t reduced patients total out-ofpocket health care expenditures, not direct coverage of preventive services. The effect of direct coverage on use may be different from the effect observed i n OTAs analysis. The association between insurance and use may not always reflect a direct cause and effect. Rather, some people may be likely both to buy supplemental insurance and use preventive services out of concern for their own health. OTAs analysis suggests that insurance coverage alone would not be sufficient to induce many elderly to avail themselves of preventive services.
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The Use of Preventive Services by the Elderly 3 Medicare coverage of preventive services may indirectly increase the use of preventive services by raising interest in preventive care among non-Medicare health care consumers, providers, and payers. Such coverage would, in effect, place the authority of the Federal Government behind the covered services. OTA found no existing data to estimate the existence or magnitude of this potential effect. Because large numbers of elderly people already use preventive services, expansion of Medicare to cover preventive procedures will represent an immediate boost in the programs financial obligations even if increases in use are minimal or nonexistent. While Medicare may already pay for some screening services incorrectly labeled as diagnostic procedures, Medicare coverage would still transfer a large portion of the current costs of preventive services from patients or other payers to the Federal Government. Other Implications for Policy Among other factors important i n determining whether the elderly receive preventive services, a few such as gender, age, education, income, rural or urban residence, and bed days could be useful in helping policy makers target educational efforts on the need for preventive services to those elderly at highest risk of not complying with expert recommendations. The relationship between use and educational level suggests that policy makers should carefully consider the media they employ to promote preventive service recommendations, benefits, and other programs they undertake. Pamphlets or other materials that rely heavily on the written word are not as effective for the less welleducated who also have a relatively higher risk of not receiving preventive procedures. Policy makers could consider using visual media to communicate their messages to such groups. Some of the factors important to the elderlys use of preventive services, such as income or educational level, are unlikely to be the focus of policy efforts designed solely to increase the use of preventive services. However, changing these factors for some other purpose might result in increases in use. The analysis in this paper concentrates on those services most often raised in congressional discussions of prevention under Medicare--screening and immunizations. The conclusions presented above may have limited applicability to consideration of other preventive services such as health risk appraisals, health education, counseling services, or prevention of disability among elderly suffering from chronic disease.
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2. INTRODUCTION In 1984, personal health care expenditures for the 28 million Americans over the age of 65 totaled $120 billion, nearly all of which went toward the treatment of existing conditions rather than to screening for or preventing health problems (85). Recently, however, policy makers, health advocates, and medical practitioners have begun to focus greater attention upon the potential of preventive medicine for the elderly. As the elderly population has grown, physicians and decisionmakers have looked to preventive services as a possible means of extending life, reducing morbidity and disability, and controlling health care costs (67,50). Congress has recently mandated studies of communitybased preventive health service programs for the elderly and expanded Medicare coverage of certain services, including screening mammography and some immunizations (34). This paper has three purposes: to summarize existing professional recommendations for older adults use of preventive health services, to estimate the percentage of elderly who currently use such services, and to identify the factors related to elderly individuals use of preventive care with particular attention to the potential effects of Medicare coverage. The information brought together in this paper has two major policy implications. First, in order to estimate the impact of Medicare coverage of preventive services on Medicare program expenditures, one must know the number of potential users. While current rates of use alone may not adequately predict use under expanded third-part y financing of preventive services, examination of existing literature and data provides insight into factors associated with use. In particular, such analysis reveals the relative importance of Medicare coverage in removing barriers to use for elderly Americans. Planners and administrators of disease prevention for the elderly also benefit from an analysis of current use. By understanding those factors that affect whether older people accept and receive preventive services, Congress may be able to target initiatives where they will be most effective or most needed. Where supported by the evidence, this paper points out such implications for public policy. Types of Preventive Services The traditional taxonomy of prevention distinguishes among primary, secondary, and tertiary prevention (38,56). Primary pre vention refers to activities designed to avoid disease or other conditions that adversely affect health. Immunizations are one example of primary prevention. Secondary prevention includes efforts to identify existing conditions that could cause illness and disability before the appearance of clinical symptoms, or to minimize the progression of disease. Disease screening is one form of secondary prevention. Tertiary prevention refers to efforts to control irreversible chronic conditions in order to avoid disability or death. Kane, et al., have suggested that this typology does not adequately distinguish among preventive services, especially those targeted toward the chronic conditions common among the elderly. For example, while diet change can be a means of primary prevention of hypertension, treatment of existing hypertension is also primary prevention of stroke. To avoid such ambiguities, this paper simply distinguishes among immunizations, disease screening, and educational or counseling services. Table 1 lists specific examples of each category of prevention. While the list of services in table 1 is not exhaustive of all preventive services applicable, it does include the procedures examined in this paper. 5
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6 n The Use of Preventive Services by the Elderly Table 1. --Selected Potential Clinical Preventive Services for the Elderly Immunizations n Influenz a n Tetanus n Pneunococcua n Hepatitis B b Screening n n n n n n n n n n n n n n Cancer screening: -Breast cancer (clinical examination ; mammography) -Colorectal cancer (occult blood stool; sigmoidoscopy) -Cervical and uterine cancer (clinical examination; Pap smear; endometrial biopsy) -Prostate cancer (clinical examination ; ultrasound ) -Skin cancer (clinical examination ) Blood pressure measurement Vision examination Glaucoma screening Hearing tes t Cholesterol measurement Mental status/dementia Osteoporosis (standard x-ray; quantitative CT; other radiological techniques ) Diabetes screening Asymptomatic coronary artery disease (exercis e stress test ) Dental health assessment Multiple health risks appraisal/assessmen t Functional status assessment Depression screening Screening for hyperthyroidism Education and Counseling n Nutritio n n Weight control m Sinking cessation n Home safety/injury prevention n Stress management n Appropriate use of medications n Alcohol use n Exercis e Abbreviation: CT =computed tomography. a Currently covered by Medicare. Currently covered by Medicare for high risk patients SOURCE: Office of Technology Assessment, 1989. Preventive Services and Medicare In defining preventive services an d measuring their use, this paper focuses on the implications for their potential coverage under Medicare. This perspective limits the preventive interventions analyzed to personal health services offered to individuals. This review does not examine mass media education programs targeted toward the elderly. As enacted in 1965, Medicare covered no preventive services. It paid for procedures on a diagnostic basis only--that is, when the patient has a symptom or a previous diagnosis for a condition. However, because treatment of most diagnosed conditions is covered, Medicare does pay for much tertiary prevention designed to control existing chronic conditions. In addition, some physicians probably receive payment for screening services they incorrectly label as diagnostic. The extent of this de facto coverage of prevention has gone unmeasured. In recent years, however, Congress has incrementally added coverage of some immunizations and screening services. These include hepatitis B immunizations for beneficiaries at high risk of contracting the disease and pneumococcal pneumonia vaccinations for all beneficiaries. The Medicar e Catastrophic Coverage Act of 1988 (Public Law 100-360) includes coverage of up to $50 for biannual screening mammographies beginning in 1990. In addition, Congress has mandated that the Health Care Financing Administration (HCFA) fund demonstrations of influenza immunization coverage, and of therapeutic shoes for diabetics, and several community-based demonstration projects to analyze health outcomes and costs associated with the provision of screening, health risk appraisals, education, and counseling to Medicare beneficiaries, Additional proposals brought before the 100th Congress included coverage of Pap smear screening for cervical cancer and a physical examination with medical history upon enrollment in Medicare or on a periodic basis.
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3. RECOMMENDATIONS OF PROFESSIONAL AND EXPERT GROUPS FOR THE USE OF PREVENTIVE SERVICES BY OLDER ADULTS One way to measure the use of preventive services is to compare the actual behavior of individuals with the frequency of use recommended by expert groups Numerous groups have provided recommendations about the periodicity with which the elderly should receive particular immunizations and screening services. In interpreting the medical evidence on frequency of use, these expert groups vary in the criteria they employ in developing recommendations. Table 2 summarizes several selected sets of recommendations made by professional or expert groups for older adults, primarily for those over 65 years old. The summary is not comprehensive; rather it includes a range of views on the use of preventive services analyzed in this paper. The most comprehensive guidelines come from the two governmental task forces. Over the last ten years, the Canadian Task Force on the Periodic Health Examination has analyzed medical evidence about the effectiveness of preventive services and made recommendations for Canadian citizens ( 18). The U.S. Preventive Services Task Force has engaged in a similar exercise and recently published some of its findings. The task force will publish its full report in 1989 (39,43,79). Among other U.S. governmental organizations, individual institutes within the National Institutes of Health (NIH) have made recommendations for cancer and coronary heart disease screening (37,77,78). Some of these recommendations result from intramural efforts within NIH, while others are the product of consensus development conferences that bring together experts and interested organizations. Additional guidelines come from professional societies such as the American College of Physicians (4,5), the American Medical Association (68), the America n Academy of Ophthalmology (2), the American Optometric Association (9), the American College of Obstetrics and Gynecology (42), and the American College of Radiology (6) as well as health consumer organizations such as the American Cancer Society (3), the American Society to Prevent Blindness (10), and the American Heart Association (8). As table 2 indicates, there is nearly complete agreement among the included groups making recommendations for immunizations for the elderly. For screening services there is a high degree of consistency among groups, but some disagreement does exist. 7
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Table2. --Published Recommendations for the Use of Selected Preventive Services by Older Adults Preventive Professional service CDC a Consumer ACP b NIH c CTF d USPSTF e societies f organizations9 Tetanus Booster every 10 Booster every 10 Booster every 10 Booster every immunization years if primary years years 10 years series has been done ------------------------. --------------------------------------------------------------.... -----------------------------------. ..---Pneumococcal Over age 65--once Over age 65--once High risk Over age 65-immunization patients--once once -------------------------------------------------------------------------------. . ---------------------------------------------Influenza Over age 65-Over age 65-Over age 65-immunization every year every year every year ------------------------------------------------------,.. --------------------------------------------------------------------Occult blood in NCI: over age ACS: over age stool 50--every year 50--every year ---------------------------------------------------------------------------------------------------------------------------------Sigmoidoscopy NCI: over age ACS: over age 50--every 3-5 50--every 3-5 years years after 2 negative tests -------------------------------------------------------------------------------------------. ---------------------------------Digital rectal NCI: over age Not recommended ACS: over age exam 40--every year for prostate 40--every year cancer; no recommendation for enlarged prostate screening ---------------------------------------------------------------------. . ----------------------------------------. . ---------Clinical breast Considered in NCI: over age Every year from Over age 40-ACR: over age 35-ACS: over age examination conjunction with 50--every year age 50 to 59 every year every year (with 40--every year mammography monthly breast self(with monthly examination) breast selfACOG: advises examination) following ACS guidelines ------------------------------------------------------------------------------------------...-.----------------------------------Mammography Says screening NCI: over age Between ages 50 Over age 50-ACR: over age 50-ACS: over age with mammography 50--every year and 59--every every year every year 50--every year is effective; year ACOG: advises does not specify following ACS frequency or when guidelines to start; says AMA: between ages 40 screening women and 49--every one to aged 50 to 59 two years; age 50 saves lives and over--every year
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Table 2. --Published Recommenda tions for the Use of Selected Preventive Services by Older Adults (Continued) Preventive Professional Consumer service CDC a ACP b NIH c CTF d USPSTF e societies f organizations Pap smear MCI: over age 18 Every 5 years ACOG, AMA, ANA, ACS: supports or if sexually from age 35 to AAFP, AND AWJA: NCI guidelines active--3 age 60; screening support NCI consecutive should continue guidelines annual Pap smears if prior smears and pelvic exams have been with negative abnormal results, then less frequently at discretion of physician -------------------------------------------. . --------------------------------------------. . ---Cholesterol NHLBI: over age screening 20--every 5 years ------------------------. . --------------.-----------------------------------------------.... --------Serum glucose Not recommended without family history of diabetes or previous circulatory problems -----------. . ------------. . ------------------------. . ------------------------Blood pressure NHLBI: over age Over age 65-18--at least every 2 years every 2 years, depending previous reading ---------------. . -----. AHA: supports NHLBI recommendations -----------. . ---------. ADA: people at risk should be screened (no frequency specified) AHA: every 5 years from age 20 to 75; optional after age 75 if baselines are uell-documented ---------------------------------------AHA: every 5 years starting at age 20 ---------------------------------------------------------------------------------------------------. . --------------------. EKG Recommended for AHA: at ages 20, symptomatic 40, and 60 adults only ---------------------------------------------------------------------------------------------------------------------------Vision Not recommended AOA: over age ASPB: over age examination 40--every year 35--every 2 years including MO: over age glaucoma 40--every 2 to 5 screening by tonometry years ---------------------------.---------------------------------------------------------------------------------------------
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10 H The Use of Preventive Services by the Elderly ,, II a L 0
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4. WHAT PERCENTAGE OF THE ELDERLY USE PREVENTIVE SERVICES? While a large empirical literature exists on the use of medical services in general, few studies concentrate on preventive services and fewer still analyze use of these services by the elderly 1 Apart from the analyses presented for the first time in this paper, only nine studies offer empirical evidence about the use of preventive services among older adults. Appendix C summarizes the scope and methods of each of these studies. Because of the small volume of research examining preventive service use by the elderly, this paper also draws upon empirical investigations of use by the non-elderly. Appendix D describes 35 studies in this category. Several of these studies examine how age affects the use of preventive services and offer insight into the behavior of older patients in seeking out such care. Sources of Data Table 3 presents comparative estimates of the percentages of elderly people using 17 preventive services within specified periods of time. Three of the seven sources contain national estimates. OTA analyzed data from the 1982 National Health Interview Survey (NHIS). (See appendix E for a description of the NHIS.) Results of this analysis, showing the percentage of the elderly who have received five preventive services--glaucoma screening eye exams, blood pressur e measurement, breast exams, and Pap smears -within periods of time roughly similar to the intervals suggested by expert groups are shown in the sixth column of the table. 1 Many studies have been carried out on the use and correlates of use of non-preventive medica[ ser vices and dental care by the elderly. However, because the purpose, nature, and likel y determinants of use of these services diffe r markedly from those of preventive health services, such studies are not reviewed in this paper. The second source of national estimates in table 3 comes from a survey conducted by the Gallup Organization every 3 or 4 years for the American Cancer Society (ACS). This household, mail survey examines individuals knowledge of cancer risk factors and the frequency with which they receive certai n screening tests (28). Gallup publishes results by gender, age, and selected demographic variables. Although the study does not present findings for Medicare-eligible respondents as a separate group, it does give results for individuals over 50 years old. These two studies rely on respondents self-reported behavior, which may affect the accuracy of the estimates. The direction of this potential bias is unclear. On the one hand, lack of familiarity with medical services may cause respondents not to know that they had received a given service, and hence, to underreport use. On the other hand respondents may perceive preventive behavior to be socially desirable and may inflate the use they report to the interviewer. The relative importance of each of these biases in affecting the estimates is unknown. The third national data source is the U.S. Immunization Survey conducted annually by the Centers for Disease Control (CDC) until 1985. A household survey, it provides data on the percentages of individuals immunized against influenza and pneumococcal pneumonia, broken down by age (including people over 65)(26). Another set of estimates, found in the seventh column of table 3, comes from a large, urban closed-panel* health maintenance organization (HMO). This HMO pro2 In a closed panel HMO, enrollees must receiv e health care from a physician employed directly b y the HMO usually in a clinic run by the organiza tion 1 1
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12 The Use of Preventive Services by the Elderly Table 3--Percent of Elderly People Receiving Preventive Services Within Specified Periods of Time Study a Servic e ACS b Brown c CDC d Chao e Lazaro f OTA g OTA h Rundall i Winawer j Blood pressure Men 93 91 Women 93 9 2 -------------------------........ ....... w k ..... ...-...... : 2 .!:?. . . -------. . . . . . Breast exam Women 48 53 (I
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The Use of Preventive Services by the Elderly 13 Table 3--Percent of Elderly People Receiving Preventive Services Within Specifed Periods of Time (Centd) stud y a Service AC S b Brown c CDC d Chao e Lazaro f OTA g OTA h Rundall i Winawer j Procto exam Men 15 Women 12 (1 yr ) ----. . . . . --------------------------------------------------. . . . . . . . Rectal exam Men 28 Women 28 (1 yr ) . ---------------. . . . . ----------. . . . . . . . -----. . . . . . Sigmoidoscopy Total 95 (N/A ) . . . . . . .-.....--.... . . . . . --------------------------------------------Swine flu vaccine Sample estimate 72 Population est. 6 3 (N/A ) -----. . . . ------------. . ----------------------------. . . . . . . .--------Tetanus vaccine Men 30 Women 26 (10 yr ) Abbreviations: HMO = health maintenance organization; NHIS = National Health Interview Survey ~Fu(( descriptions of methodology of each study can be found in table 4. Ga((up Organization, IiThe 1987 survey of Public Awareness and Use of CanCer Detection Tests: SUIWM ry of Findings,ll Conducted for the American Cancer Society (princeton, NJ: Gallup Organization, January 1988); n=952; age=50+. CJ.T. Brown and B.S. Hulka, ItScreening Mamgra@y in the Elderly: A Case-Control Study, J. Gen. Intern. h4edicine d 3:126-131, 1988; n=309; age=60+. D.S. Fedson, llInf[uenza ad pnemcoccal Invrnmization Strategies for Physicians, it Chest 91:436-443, 1987; n=not given; age=65+. A. Chao, A. Paganini -Hill, R.K. Ross, et al., Illjse of preventive Care by the Elderly, Preventive Medicine ~ 16:710-722, 1987; n=ll,888; age range=48--100; mean age=74.4. C.M. Lazaro, D.N. Logsdon, and R. Meier, IIljti(ization of preventive Health Services by the Elderly, Insure Project, Lifecyc(e Preventive Health Services, New York, NY, presentation to the American Pyschological Association Convention, Aug. 31, 1987, New York, NY; n=713; age=60+. Use rates from Lazaro study are proportions of ail persons invited to receive checkup uho actually received the service. Proportions of persons accepting the invitation who actually received the checkup are as follows: ages 60-74=.65; ages 75+=.53. goffice of Technology Assessment/NHIS, 1988a; n=11434; age=65+. Office of Technology Assessment/HMO, 1988b; n=5394 for checkup, influenza, and fecal occult blood; 3371 for eye exams and Pap smears; 2322 for cholesterol; and 894 for pneunococcal and tetanus; age=65+. T.G. Rundall and J.R.C. Wheeler, ~lFactors Associated Uith Utilization of the Swine Flu Vaccination program Among Senior Citizens in Tompkins County, Medical Care 17:191-200, 1979; sample n=232; population N=5000; age=65+. js.J. Uinawer, M. Baldwin, E. Herbert, et al., IIScreening Experience With Fecal Occult Blood Testing as a Function of Age,n in Prospectiveson Prevention and Treatment of Cancerin the Elderly, R. Yancik (cd.) (Neu York, NY : ~ Raven Press, 1983); n=21,961; age=40+. Notation in parentheses indicates period of time over which use was measured. Key for parenthetical notations: y r = year or years < = up to but not including <= = up to and including N/A= not applicable; study is a single trial conducted over a finite period. SOURCE: Office of Technology Assessment, 1989. L ,~ ,,, 4
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14 The Use of Preventive Services by the Elderly vialed OTA with data on the percentages of adults in various age categories who received each of nine preventive services within periods of time specified in table 8. (See appendix F for a more complete discussion of the data and estimation methods.) Unlike the three national surveys, these estimates come directly from the providers records, thus avoiding the potential inaccuracies of self-reported data. However, the population from which the HMO data are drawn is probably not representative of the national experience or even of other HMOs. The elderly enrolled in this single prepaid plan may be different from the total elderly population in the HMOs market area as well as the elderly population of other areas. In addition, HMOs in general tend to provide better coverage of preventive services than do other insurance plans (46). This HMO in particular engaged in activities to promote the use of some preventive procedures. All of these potential distortions suggest that estimates from this HMO are probably indicative of the upper bound of use attainable under Medicare coverage rather than national estimates of current use. In the four remaining sets of data presented in table 3, estimating use was not the authors primary objective. One paper was a case-control study of breast cancer in elderly women (15). Another looked at the relationship between screening and disease prognosis for colorectal cancer. The thir d examined factors associated with swine flu vaccination during the predicted epidemic of 1977 and 1978 (59), and the fourth presents self-reported data from a retirement community about respondents most recent use of five preventive services (19). Estimates of Use Because of some overlap in the services examined in the seven studies discussed above, one can compare different estimates of use of the same services. These procedures are general examinations, fecal occult blood screening, mammography, breast examinations, Pap smears, eye examinations, and blood pressure checks. For four services, the estimates of use are consistent across data sources. About 92 percent of the elderly report having their blood pressure checked within a l-year period and 74 percent report eye examinations within the previous 2 years. Although estimates for Pap smear use show a bit more variation across studies, the range runs only from about 50 percent of elderly people in the NHIS sample to 71 percent in the HMO data. Differences in the periods of time over which researchers measure use do not account for the variation in estimates that does exist. For example, the ACS estimate of Pap smear use within a 3-year period is actually higher than the NHIS estimate that examines a period of up to 4 years. Hence, these differences reflect either different populations or different survey methods. Despite some consistency across studies for the same service, there is little similarity in rates of use across different services. For example, while less than 15 percent of the elderly report having had annual rectal exams, 92 percent report an annual blood pressure check. Estimates for the remaining services fall within this wide range. These drastic differences in rates of use suggest that preventive services are more different from one another than they are alike. Several studies discussed later in this paper have examined these differences. Use of Multiple Services Measuring the percentage of elderly individuals who receive multiple preventive services provides a slightly different profile of individuals preventive behavior than is revealed by examining one service at a time. As indicated in table 4, a majority of elderly persons report receiving all three services that both sexes can receive (glaucoma screening, eye exams, and blood pressure measurement). One-quarter of men and one-fifth of women
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The Use of Preventive Services by the Elderly 15 report receiving one or fewer of the three services. The extremely small percentages who report using no services reflect the almost universal measurement of blood pressure. Looking only at women and including the two additional services they can receive (Pap smears and breast exams) reveals that only 13 percent report using none or one service. However, only 30 percent report using all five services. These data indicate a great deal of variation in the number of services elderly people receive. In the only other study to examine multiple preventive service use, Calnan found that among middle-aged women, the probability of using one service does not predict whether an individual uses others (17). Time Trends The differences in trends between the ACS sible l n and NHIS data sources have several posexplanations: Only Pap smears and breast exams overlap the NHIS and ACS surveys. The differences between the two data sources could be due to different trends in the particular services each survey examined. NHIS estimates are for individuals over 65 years old, while the ACS data are for wider age ranges. If the trend in the NHIS applies only to the elderly, the inclusion of non-elderly people in the ACS samples might obscure this trend. Table 4--Percent of Persons Over 65 Using Multiple Preventive Services (From the 1982 National Health Interview Survey) a The ACS and NHIS data allow examination of time trends in self-reported use of several services over the period from 1973 through 1987. As shown in table 5, the percentage of older Americans who report ever having received these preventive services grew over the periods measured. Usin g identical questionnaires, the NHIS showed substantial increases in the use of seven services between 1973 and 1982 (72,75). The trends in the ACS data are not quite as dramatic (28). Some procedures show little change between 1980 and 1983 with five services showing a decline in use. The declines between 1980 and 1983 most likely reflect sampling error. While all of the tests except digital rectal exams for women increased between 1980 and 1987, the jumps are less dramatic than those suggested by the NHIS data. Glaucoma, Breast exam, Pap smear eye exam and glaucoma, eye exam Number of blood Pressure a and blood pressure services Men Women Women Zero 4 3 3 One 22 19 10 Two 16 13 11 Three 58 65 26 Four N/A N/A 21 Five N/A N/A 30 Total 100 100 100 Abbreviation: N/A = Not applicable. a For men and women, table presents proportions o f the noninstitutionalized, civilian, over-65 population using none, one, two, or three of th e following services--glaucoma screening, eye ex amination, and blood pressure measurement--within ~the periods of time listed in table 8. For women only, the table also presents the pro portions of this same population using none, one, two three, four or five of the followin g services--glaucoma screening, eye examination blood pressure measurement, Pap smears, and breast examination--within the periods of time listed in table 8. SOURCE: Office of Technology Assessment, 1989.
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16 The Use of Preventive Services by the Elderly Table 5.--Some Trends in the Percent of Adults or Older Adults Ever Having Received Selected Preventive Services a National Health b American Cancer Society/Gallup Interview Survey Organization survey Year Year Servic e 1973 1982 1976 1980 1983 1987 Check-up 45 42 4 6 4 7 (annual) a (All adults ) . . . . . . . . . . . . . . . . . . . . . . . . ---------Breast exam (Women 58 74 79 8 9 81 only ) (Ages 65+ ) (All adult women) . . . . . . . . . . . . . . . . . . . . . . ---------------Pap smear 54 79 8 6 84 8 7 (women (Ages 65+ ) (All adult women) only ) . . . . . . . . . . . . . . -----------------------------. . . . EKG 6 7 82 (Ages 65+) . -----. . -----. . . -----. . . -----. . -----------------------------------Eye exam 94 (Ages 65+ ) . . . . . . . . . . . . . . . . . . . . . . ---------------Glaucoma 56 (Ages 65+ ) . . . . . . . . . . . . . . . ---------------------------------------Fecal occult bloo d Men 17 29 43 Women 20 2 7 4 7 (Ages 50+) . . . . . . . . . . . . . . . . . . . . . . . . . ------. Mammography 43 41 62 (women only) (Ages 50+) . . . . . . . . . . . . . . . . . . . ---------------------------Proctosigmoidoscopy Men 3 7 32 43 Women 35 31 42 \ (Ages 50+) -----. . . . . . . . -----. . . . . . . . . . . . . . . . . Rectal exam Men 54 56 53 Women 52 4 7 5 8 (Ages 40+) Abbreviation: EKG = electrocardiogram With the exception of the medical check-up, this table presents data on the proportion of individuals who having received each service. For medical check-ups, the statistics refer to the proportion ~who report having a regular, annual exam. National Center for Health Statistics, U.S. Department of Health, Education, and Uelfare, Wse of Selected Preventive Services U.S--1973,N Vital and Health Statistics, Series 10, No. 110 (Washington, DC: U.S. Govermnent Printing Office, March 1977); and National Center for Health Statistics, U.S. Department of Health ad Human Services, Wse of Selected Preventive Services U.S--1982,11 Vital and Health Statistics, Series 10, No. 157 (Washington, DC: U.S. Govemnent Printing Office, August 1986). cGallup Organization, llThe 1987 survey of public Awareness and Use of Cancer Detection Tests: SLmIary of Findings,ll conducted for the American Cancer Society (Princeton, NJ: Gallup Organization, January 1988). SOURCE: Office of Technology Assessment, 1989.
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5. WHAT FACTORS PROMOTE OR INHIBIT ELDERLY PEOPLES USE OF PREVENTIVE SERVICES? Theoretical Approaches In addition to providing estimates of the percentage of the population receiving preventive services, the literature laid out in appendixes C and D offers insight into factors associated with use. These studies represent at least two theoretical approaches to explaining the use of preventive services: 1) an preach that emphasizes patient behavior, 2) an approach that emphasizes provider havior. Patient Behavior apand beUnderlying this approach is the assumption that the decision to use a preventive health service is made by the recipient. Receipt of these services results from factors that influence the decision to seek preventive care and the patients ability to carry out that decision. There are two main versions of this approach: 1) a model of medical service utilization first proposed by Andersen and his colleagues (11,12,36), and 2) the Health Belief Model (57,58). The Andersen Model. --According to this model, three types of factors determine an individuals probability of using medical services as well as the volume of use: Predisposing variables includ e demographic factors and the individuals beliefs about the services. Enabling variables that affect th e patients ability to gain access to services include the individuals financia l resources, the availability of the services in the individuals community, and insurance coverage. Need variables include practitioners and patients own perceptions of the patients health status. Poor health status may indicate a need for better health care, including preventive services. Alternatively, variables that measure health status may actually be proxies for the need for nonpreventive health services. To the extent that the need for these other services increases contact with the health care system, individuals may be more likely to receive preventive services that require some health care intervention. Hence, health status variables may enable or predispose individuals to receive preventive services by increasing their contact with the health care system. The Health Belief Model. --This behavioral model arose from an attempt by medical sociologists during the early 1970s to understand patterns of preventive health and health maintenance (48). It is similar to Andersens model in its focus on the patient. However, it posits that patient beliefs and attitudes are the most direct determinants of the decision to receive preventive care. Sociodemographic factors, characteristics of the health care system, and other exogenous variables (such as public education or illness of a family member) all indirectly affect preventive behavior by influencing the individuals beliefs and attitudes (57,58). These attitudinal factors include: n n n n the patients perceived susceptibility to a given disease or condition; the perceived potential severity of that disease; the perceived benefit of preventive action in reducing susceptibility or severity; cues to taking the action such as public education programs, reminders an d physician recommendations; and the perceived barriers to taking the action including cost, inconvenience, and embarrassment. One major limitation of the Health Belief Model in explaining the use of preventive services is the lack of data measuring individual attitudes and perceptions. Only data sets constructed specifically for Health Belief Model analyses are likely to contain the requisite information (23,31 ,54). 17
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18 The Use of Preventive Services by the Elderly However, some researchers have used the underlying relationships suggested by the Health Belief Model to design experiments to improve preventive behavior among patients (70). Provider Behavior While patient behavior models focus on the consumers of health, provider behavior models focus upon providers of such services (86). They suggest that patients receive preventive services as the result of their providers decisions to offer, encourage, and enable their uptake. While the patient behavior models see patients as active decisionmakers, the provider behavior approach sees patients as more passive, less important than their providers. Explanatory studies that use the provider behavior approach examine the effects on use of health care organization, patient contact with the health care system, or with different types of health personnel, and providers knowledge of preventive services (55,92). This approach also underlies experiments and demonstration projects that try to determine how the manner in which services are provided can maximize their use. Mass screening programs or trials that employ physician education are examples of these types of studies (20,24,62,74). Combining the Patient and Provider Approaches The patient and provider approaches need not be mutually exclusive. At least three studies have attempted to combine the provider and patient approaches into a single model (21,29,86). Although each approach places emphasis upon different groups of potential determinants of preventive behavior, they may be valid in explaining different parts of the variation in use. In addition, there is some overlap among the two approaches. Andersens enabling variables represent the same basic ideas that the provider behavior models focus upon. However, in the patient behavior models, characteristics of the health care system affect individual patient decisionmaking. Provider behavior studies implicitly assume a more passive role for patients who respond largely to actions of health providers. Evidence on Patient Behavior This section describes the results of OTAs analysis of the 1982 NHIS data set (see appendix E for detailed discussion of methods) and examines how these results compare with results of studies listed in appendixes C and D. While many of the studies in appendix D are limited in their implications for elderly use of prevention, they provide a general context within which studies of elderly populations can be interpreted. OTAs Analysis of the 1982 NHIS Although the 1982 NHIS does not contain all of the variables described in Andersens approach and in the Health Belief Model, it is the most comprehensive existing source of information about the determinants of preventive behavior among the noninstitutionalized elderly. Table 6 lists potential explanatory variables included in the NHIS data set. Table 6. --Selected Factors Hypothesized To Affect Use of Preventive Services by the Elderly Predisposing factors n Gender n Age n Race (white/non -white) n Education Enabling factors n Family income n Having health insurance in addition to Medicare a n Receiving health care through a prepaid plan n Living alon e n Living in a metropolitan area Measures of health status n Bed days in the previous 12 months n Having some limitation on activity a Potential insurance coverage for the elderly i n addition to Medicare includes privately purchased health insurance, Veterans Administration or mil itary health insurance, or means tested publi c assistance health benefits including Medicaid. SOURCE : Office of Technology Assessment, 1989.
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The Use of Preventive Services by the Elderly 19 The major category of variables missing from these data are the health belief variables: patient perceptions of disease threat, perceived benefits of preventive services, and perceived barriers to their use. OTA used weighted logit models to estimate the independent effects of each of Table 7--Significant Predictors of Use From OTA Multivariate Logit Analysis of 1982 National Health Interview Survey a Glaucoma Eye Blood Pap Breas t Variabl e screening exam pressure smear exam Predisposing factors 1. Sex (male) 2. Age 3. Race (non-white) 4. Education Enabling factors 5. Family income 6. Having health insurance in addition to Medicare 7. Receiving health care through a prepaid plan 8. Living in a metropolitan area 9. Living alone . ++ ++ ++ NS ++ NS Measures of health status 10. Bed days in the previous 12 months ++ 11. Having sane limitatio n on activit y NS NS NS ++ + ++ NS ++ NS ++ ++ NS + NS ++ NS NS NS ++ + N/A N/A . NS NS ++ ++ ++ ++ ++ ++ NS NS + ++ NS ++ ++ ++ NS NS Abbreviations: N S = Estimated coefficient on vari able not statistically significant ; N/A = Not applicable; variable no t included in model. a Use measured according to standards described in appendix E and in table 8. Key to symbols: + =Difference in proportions using service sig nificant at 0.05 level (2 tailed), variabl e positively associated with us e ++ =Difference in proportions using service sig nificant at 0.01 level (2 tailed), variabl e positively associated ~wth use =Difference in proportions using service sig nificant at 0.05 level (2 tailed), variabl e negatively associated with use =Difference in proportions using service sig nificant at 0.01 level (2 tailed), variabl e negatively associated with use SOURCE: Office of Technology Assessment, 1989. the variables listed in table 6 on the use of each screening test or examination included in the NHIS. These models posit that the probability that an individual uses a preventive service within a specified period of time is a function of the variables listed in the table. Appendix E describes each model specification in greater detail and presents actual estimates. Table 7 summarizes the statistically significant predictors of use. Despite the substantial variation that exists across the five services in the percentage of elderly receiving the specified levels of prevention, the estimated models show a great deal of consistency across services in the significant predictors. Only the use of blood pressure checks appears different. The analysis suggests that fewer variables are important in predicting blood pressure checks than in predicting the other services. This is consistent with the relative lack of variation in the use of this service; over 90 percent of the elderly report having had their blood pressure measured within the previous 2-year period. Almost every medical visit includes blood pressure measurement, and individuals can use machines found in many supermarkets and restaurants to screen themselves for hypertension. As expected, OTAs analysis found that younger age, more education, and higher income are all consistently associated with a higher probability of using the five preventive services measured in the NHIS. For the three services applicable to both genders, men over 65 are less likely than women of similar age to receive them. More bed days are consistently related to use of the five services, suggesting that sicker individuals have greater contact with the health care system, and hence, a greater opportunity to be offered preventive services. The analysis showed no relationship between limitation in activity and the use of any of the preventive services except for eye exams, where the direction of the association is neg-
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20 n The Use of Preventive Services by the Elderly ative, and blood pressure measurement, where the direction is positive. l For all services except blood pressure measurement, living in one of the Census Bureaus Standard Metropolitan Statistical Areas (SMSAs), which are defined by geography and population density, is positively and significantly related to the use of preventive services. In urban communities with a large number of health facilities and personnel, individuals are likely to live closer and have more ways to get to appropriate health facilities than their rural counterparts. Having some health insurance in addition to Medicare is also associated with use. Although such Medigap coverage (held by 71 percent of the elderly in the NHIS sample) is unlikely to pay for preventive services, it does lower patients out-of-pocket expenses for medical care, thus making preventive services more affordable In addition, a patients willingness to buy such insurance may indicate a certain concern for his or her own health also found in individuals likely to use preventive services. Among variables not associated with the use of preventive services, membership in an HMO or other prepaid health plan is the most unexpected. Published literature indicates that such health care providers are more likely than others to offer preventive services to ] It is possible that bed days and limitations ar e measuring health status in the same way. If the two variables are measuring the exact same idea, the logit estimation procedure would not be able to distinguish the independent effects of each variable. This could lead to the insignificant coef ficients found for the limitation variable. To test for this potential problem (multicollinearity), we examined the correlation between the bed days and limitations variables. Ue found a correlation coefficient of 0.31 suggesting that uhile the estimated standardemm of the two logit coefficients may be somewhat biased downward (thus creating potential non-significance), the two variables largely measure different notions of health status. their patients in hopes of lowering treatment expenses (46,54). While OTAs analysis suggest that this relationship may not exist, it is also possible that the small number of elderly NHIS respondents enrolled in HMOs did not provide enough statistical power to detect an actual effect of prepaid membership. In 1982 only 2.3 percent (or about 573,000) of the elderly belonged to HMO S 2 and the NHIS sample reflects this relatively small number. The remaining two variables in the logit models are not consistently associated with use: race is positively associated with glaucoma screening; living alone is positively associated with breast examinations. Summary of Evidence on the Determinants of Use of Preventive Services Age. --Age has generally been found to be a negative predictor of the elderlys use of preventive services (16,19,69). Studies of younger adult populations have also found such an association (32,44,80,81,81,88). In the OTA analysis, all services except blood pressure followed this pattern. Two studies have examined the relationship between age and immunization behavior; neither found any strong association between age and swine flu immunization (22,59). Of the other studies that look at age, one found a positive correlation with the likelihood and volume of preventive visits to a single HMO (40). Another study that looked only at fecal occult blood screening within a well-defined trial also found no age effect 2 Since 1982, the number of Medicare beneficiaries has grown due to riskand cost-based Medicare demonstration programs. Under contract with the Health Care Financing Administration, each HMO participating in these programs agrees to provide Medicare benefits to eligible enrollees. As o f January 1988, over 1.7 million elderly were partic ipating in Medicare/HMO demonstration program s (71)
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The Use of Preventive Services by the Elderly n 2 1 Despite these exceptions, the bulk of available evidence suggests that use of preventive services falls with age, especially among the elderly. There are at least three explanations for this observation: As an individual gets older, he or she may perceive fewer benefits and more barriers to receiving commonly recommended levels of prevention. The observed difference may reflect a tendency of individuals who were elderly in the early 1980s not to use prevention -a tendency that will disappear among future groups of elderly. 3 Th e benefits and availability of most preventive services emphasized today have been known for only the past generation. By the time that these services became widespread, older individuals may already have established patterns of health care that did not include prevent ion. According to this reasoning, succeeding generations may have more uniform rates of preventive care over the adult age spectrum. As individuals age, they are more likely to visit the doctor for diagnostic and therapeutic services. While they still may receive preventive services, they may not remember that the clinician performed these procedures. Prevention becomes obscured by treatment. Education .All studies of use that have examined education as an explanatory variable have found it to be a statistically significant predictor of the use of preventive services (22,40,6 1,8 1). The more education a person has, the more likely he or she is to use preventive services. OTAs analysis of the NHIS data set conforms to this observation. There are two possible explanations for the association between education and use: n Education may affect the decision to use preventive services by altering patients 3 This is sometimes cat led a cohort effect. The t rend data presented ear 1 i er i n this paper are consistent with this hypothesis. perceptions about disease and potential services. It increases their general knowledge and ability to evaluate health risks and the net benefits of prevention. In addition, it may increase their knowledge of specific diseases, recommendations for preventive services, an d sources of care. Education and prevention are both investments with expected future payoffs (27). Individuals with more education may be more oriented toward the future than less educated people. Hence, these people tend to seek both education and prevention. Gender. The evidence on the effect of gender on the use of preventive health services is conflicting. Several studies, including OTAs analysis, found a strong significant association between being female and engaging in preventive health measures (31,40,41). An analysis of swine flu vaccinations, however, indicates only a weak correlation between being a woman and use (22). On the basis of two services for the elderly (from OTAsq analysis) and a few other studies of the whole adult population, one cannot conclude that elderly women have a consistently greater predisposition toward the use of preventive services than do their male counterparts. Even if such a conclusion were empirically justified, no explanation for this finding is readily apparent. Race .The relationship between race and the use of preventive services by the elderly or other adults is ambiguous. OTAs multivariate logit analysis revealed that elderly whites are more likely to receive glaucoma screening than are elderly members of other racial groups. However, race was not a significant predictor of any of the other services studied. Other studies that have looked at the relationship between race and the use of preventive services by the non-elderly present inconsistent results. Four studies found a statistically significant negative association
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22 The Use of Preventive Services by the Elderly between being black or non-white and using preventive services (60,62,66,82), but three other studies found no significant relationship (15,31,44). In one study, the results varied according to the preventive service (91). In a review of studies of participation in fecal occult blood screening, Blalock and colleagues report similarly inconsistent results of the effect of ethnicity on the use of this one cancer screening test and draw no conclusions (14). Income-Income is a reflection of the availability of financial resources to purchase health services. Economic theory suggests that the consumption of most goods rises with income. Preventive health services for the elderly may be particularly sensitive to income for two reasons. First, unlike acute illness care and even some types of chronic illness care, preventive care can be put off without short-term consequences. Thus, preventive care may receive a lower priority than other types of health care or other necessary consumption. Second, Medicare, the primary health insurer for the elderly, does not cover most preventive services. Hence, to use such services, the elderly must have private health insurance (discussed be10W), wealth, or income to pay the out-ofpocket expenses. Almost all multivariate studies of preventive use, including the OTA analysis, found that income has a significant positive effect in predicting the use of preventive services (17,22,31,32,40,41,44). Studies examining only the bivariate relationship between prevention and income have also consistently found such an association between prevention and income (54,81,91). Using Michigan survey data, Rundall and Wheeler examined the relationship between income and the use of preventive visits in greater detail. Their analysis indicated that although income has little direct effect on preventive use, it indirectly increases the likelihood of use by altering perceptions about health and susceptibility to illness, and by increasing the probability that individuals have a regular source of care (60). Insurance and Price--Except for OTAs analysis, which found that insurance coverage beyond Medicare had a consistently positive significant effect on the use of preventive services, the effect of insurance has not been studied in elderly populations. The published literature on adults use of preventive services generally supports the contention that the out-of-pocket price is a significant negative predictor of use (16,32,47,61,63,86,91) However, no study has examined the relationship between actual cost to the patient and the use of services. The potential impact of insuranc e coverage on use has important policy implications for consideration of Medicare coverage of preventive services. In assessing the benefits and costs of such a decision, one would want to know the number of new users of covered services as well as the total number of users. The OTA analysis and othe r studies (16,45) suggest that while insurance coverage does increase use, a substantial percentage of individuals do not receive recommended levels of preventive care, even in the presence of generous health insurance. A recent study that compared the use of three preventive services-blood pressure measurement, breast exams, and Pap smears--in Canada, where preventive services are covered by national health insurance, and in the United States found little difference in rates of use by elderly individuals in the two countries (76). Only breast exams were used with statistically significantly greater frequency in Canada. Enrollment in Prepaid Plans.--Except for OTAs analysis, which found no evidence that enrollment in HMOs increases the use of preventive services by the elderly, only one other study has compared preventive care in HMOs with that of traditional insurance plans (66). The researchers in that study found that employed adults in a prepaid group practice had utilization rates for preventive services no different from those of similar individuals in a Blue Cross plan.
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The Use of Preventive Services by the Elderly 23 Living Arrangements and Logistical Barriers. --In addition to the financial costs of preventive services, these procedures also entail time and transportation costs. One would expect the use of preventive services to decline as the distance between services and an individuals home or job increases. Two analyses of adult demand for preventive care in an HMO found that distance to a source of medical care was negatively (but nonsignificantly) related to the probability of use (40,41 ). Two other multivariate analyses that included indexes of time, transportation, and perceived difficulties in obtaining preventive procedures also found no significant relationship between these logistical barriers and the likelihood of using preventive services (16,22). A less perfect measure of logistical barriers to access that may be especially relevant for the elderly is whether or not the individual lives alone. Living with another person could either raise or lower the logistical barriers to preventive services. An additional household member could assist an individual in overcoming immobility or distance; on the other hand, if the additional househol d member is in some way limited in mobility or function, the effect on the healthy members use of preventive services may be negative since it may be difficult to leave a dependent partner to receive preventive care. In OTA's analysis of the NHIS data, living alone was a statistically significant, positive predictor of use for breast exams only. Geographical Location. --The community in which an older person lives may affect his or her access to prevention. In a multivariate analysis of the effect of geographic location on use based on the same data set that OTA used (i.e., the 1982 NHIS), Woolhandler and colleagues found that among middle-aged women, nonrural residence had a negative effect on the likelihood of having a glaucoma test but had no effect on the use of hypertension screening, Pap smears, and clinical breast examinations (91 ). These results conflict with OTAs analysis which found that people in urban communities use more preventive care than do those in non-urban communities. The differences between the two studies may be due to several factors: n n Woolhandler, et al., estimated a logit model with fewer explanatory variables and a slightly different distinction between urban and rural residence from that used by OTA; Woolhandler, et al., used a less sophisticated method of estimating variances from the complex NHIS sample design than OTA did; living in a rural area may be less of a barrier for middle-aged women than for the elderly population in obtaining preventive services. Health Status. --The evidence on the effect of health status on preventive health service use is equivocal. Most multivariate analyses have found no significant effect of health status on use (19,40,41,44,91 ).4 Except for OTAs analysis, which found a strong positive significant relationship between number of bed disability days and use, only Rundall and Wheeler found that reporting relatively poor health has a direct positive effect on the likelihood of receiving preventive care (60). However, variation in measures of health status, model specifications, an d samples make it difficult to draw conclusions from these studies. A few other researchers have measured health status by the presence of chronic disease. Blalock, et al., reported that having a chronic condition increases the likelihood of receiving fecal occult blood screening for colorectal cancer ( 14). Warnecke, et al., found a similar association with the probability of a regular check-up in Illinois adults (86). d In their multi variate models of the use of prevent i ve services by middle-aged women using the 1982 NH I S data, Wool handl er, et a 1., found that being healthy was significant ly and positively related only to blood pressure screening.
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24 The Use of Preventive Services by the Elderly Limitations of the Patient Behavior Analyses of Preventive Service Use The literature and analysis reviewed in this section suggest at least two major limitations of the patient behavior approach to understanding the use of preventive services: n n Existing studies do not account for many factors specific to a particular preventive intervention such as patients perceptions of pain, discomfort, embarrassment, or complexity of administration that may impinge on the willingness to use of a particular procedure. Inclusion of variables that measure these perceptions would be consistent with the Health Belief Model. The models of use examined in this section assume that patients themselves decide whether to receive preventive care. OTAs analysis and most of the published patient behavior literature do not directly examine the role of the primary care physician and the health care organization in the decision to use services. Evidence From Studies of Health Care Provider Behavior The Physician Physicians must perform, supervise, or prescribe most preventive services in order for a patient to receive them. In fact, many adults may depend on their primary care physician to tell them what types of prevention they should receive and how often (92). The literature examining the role of physicians in determining the use of preventive services includes three types of analysis: n n n comparisons of physicians knowledge about appropriate prevention with published sets of recommendations; analyses of actual physician performance; and experiments to increase physician compliance with recommended procedures. Because almost all of the trials designed to narrow the gap between published recommendations and actual practice focus on changes in health care organization or management rather than just physician behavior, this paper considers studies that fall into the third category in the section on health care organization below. None of the published studies analyzed the elderly as a group separate from the general patient population. Physician Knowledge and Actual Practice. -Woo and her colleagues asked 83 physicians in a hospital-based teaching ambulatory care practice about the frequenc y with which they recommend 16 screening procedures to different age groups (92). Across all patient age groups and procedures, physicians with less training recommended with greater frequency. Doctors with a history of cancer in their families recommended more frequent sigmoidoscopies and mammograms. The mean physician recommendation for preventive use was more frequent than the mean of published guidelines in 48 situations and less frequent in 18 situations. The researchers found close agreement among the respondents on Pap smears, blood pressure checks, physical exams, and medical histories, but wide variation in glucose and cholesterol measurement and mammography. Almost half the physicians reported that they knew they recommended preventive services more frequently than publishe d guidelines and cited as reasons patient desires and the belief that the guidelines are insufficient. Woo suggests that despite recommendations published by the Canadian Task Force and others, the physicians in this study may believe it better to err on the conservative side by recommending services for which the supporting medical evidence of effectiveness is inconclusive. 5 A llsi tuationll is a particular screening service for a particular age group.
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The Use of Preventive Services by the Elderly 25 An analysis of patient records in the same study found a higher level of physician compliance with recommended guidelines for those services that doctors could order other health personnel to perform, such as laboratory or radiological tests, than with services that require labor by the primary car e physician, such as sigmoidoscopy and breast exams. Woo and her colleagues infer that because the patients they surveyed desir e screening with appropriate regularity, the gap in performance must be due to doctors failing to offer prevention according to their own or published recommendations. They also suggest that rates of use are partially related to the ease with which physicians can provide them. Other studies support this hypothesis that variation in the use of different preventive services is a function of characteristics of the services themselves (e.g., patients pain, discomfort, and embarrassment) (57, 63,65). Another study compared the preventive care recommendations of 31 physicians practicing general internal medicine in North Carolina with three sets of publishe d guidelines (55). The participating doctors listed procedures they considered essential to a periodic examination for three age groups (30-39; ages 40-49; and ages 50-59). Among the procedures recommended in published guidelines but not chosen as essential or routinely recorded in the medical record by at least two-thirds of the sample physicians were hearing exams, vision exams, fecal occult blood tests, lipid profiles, mammography for women over 50, immunizations, and exams for hypothyroidism. The North Carolina physicians also recommended services not recommended in the published guidelines, including thorough examinations of the major organ systems, measurement of blood urea nitrogen, white blood counts, chest x-rays, and microscopic urinalysis. Examining the records of 334 patients visiting the 31 North Carolina internists for general examinations, the researchers found that, on average, 59 percent of the procedures recommended by expert groups were found in the record. Compliance was greater for younger patients, a result consistent with the estimates of use of several services reviewed earlier in this paper. The researchers also found that compliance was greater fo r laboratory and physical examination procedures than for medical history and counseling services and was inversely related to the number of expert groups recommending each procedure. Noting that the lowest compliance occurred with procedures identified by the Canadian Task Force on the Period Health Examination, one of the recommending bodies, as having strong scientific validity, Romm and colleagues suggest that improving compliance requires physician education. In discussing ways to improve compliance, they did not consider the possibility that physicians may take into account individual patient characteristics and circumstances in deciding not to provide recommended preventive services. In another study, McPhee, et al., found that discrepancies exist between American Cancer Society (ACS) recommendations for the use of seven preventive services and physician performance and that physicians tend to overestimate their own provision of these procedures (51 ). The researchers report that physicians cite four reasons most frequently for not providing recommended services: forgetfulness lack of time, in convenience or logistical difficulties, and patient discomfort or refusal. One study suggests that physicians may differ by specialty in their performance of some preventive services. In a study of Pap smear use by physicians in Maryland, Teitelbaum, et al., found that specialists in obstetrics and gynecology (OB/GYN) were more likely than general practitioners and internists to encourage patients to receive Pap smears, to remind patients by mail or telephone to get a Pap smear, and to achieve compliance with their recommendations (69).
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26 n The Use of Preventive Services by the Elderly Finally, one study currently underway may shed additional light on the role and motivations of the physician in providing preventive services. Schwartz and Lewis in cooperation with the American College of Physicians (ACP) recently surveyed ACP members about preventive practices (64). In addition to examining the frequency with which physicians say they perform a number of services, Schwartz and Lewis will look for relationships between preventive practices for patients and demographic characteristics, the physicians experience, and the physicians preventive practices for themselves. Health Care Organization Because physicians work within a larger health care system with other practitioners and administrators, it is possible that health personnel and characteristics of the office, hospital, clinic, group practice, or HMO providing patients care could affect whether or not older individuals receive preventive services. This section considers the relationship between the use of preventive procedures and the health care organizations that provide them. Health Maintenance Organization s (HMOs) and Other Prepaid Plans. --Some researchers have claimed that HMOs, in general, may promote the use of preventive services (46). To the extent that HMOs stand to gain from potential savings in health care costs resulting from preventive services, these organizations would have an economic incentive to offer more preventive services. In addition, since visits to HMOs are either free or very inexpensive, HMO patients may demand more such visits. Data from a single HMO that provided OTA with estimates of preventive service use suggests that these health care providers may have the ability to organize themselves to provide more preventive services than is now generally received by patients. As table 3 above indicates, the rates of use in the HMO were at least as high as or higher than comparable rates of use reported in other studies. Not only has this HMO made preventive care a stated organizational goal, but it has developed management tools to achieve compliance with some preventive recommendations, including computer-generated reminders to both patients and physicians for immunizations. However, these relatively high rates of use may be achieved by recruiting patients who already have characteristics that make them more likely to use preventive care. If the high levels of use found in the single HMO for which OTA obtained data are related to its organization, it is not clear whether less centralized prepaid health plans such as independent practice associations (IPAs) would be able to do the same. OTAs multivariate analysis of the 1982 NHIS found that the elderly enrolled in an HMO are no more likely to use prevention than their unenrolled counterparts. Given the small number of elderly in HMOsq, however, the NHIS sample may not have been large enough to detect an actual difference. With respect to preventive services other than screening, Riddiough, et al., reported mixed evidence about the relative likelihood of HMOs to provide immunizations (54). Organizational Factors Related to Use--Other characteristics of health providers may also affect the use of preventive procedures. For example, the use of non-physician personnel, cues to compliance such as reminders and media, or health fairs are all organizational strategies that have been employed to increase the use of preventive services. One potential mechanism for increasing the use of preventive services is the health screening fair in which participants can receive selected procedures at a publicized time and place. In an analysis of the costeffectiveness of this screening method, Berwick concluded that fairs work best when the target population is clearly defined, the screening tests are appropriately chosen, reliable and accurate, and the fair provides appropriate guidelines for abnormal results,
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The Use of Preventive Services by the Elderly 27 follow-up, referral, and treatment (13). Although fairs may increase the ability to detect and prevent illness, significant risks may exist if screening in the fair setting is relatively insensitive or unspecific or does not provide adequate follow-up. Among interventions designed to increase patient compliance with recommended services, Thompson, et al., studied the value of combining talks by physicians and nurses, postcard reminders to patients, and phone calls to comply with a fecal occult blood test (70). While those receiving any one of these interventions had an average compliance rate of 89 percent versus 68 percent among the control group, the reminder postcard was especially cost-effective, raising compliance about 25 percent to an overall rate of 93 percent at a relatively small cost. The talk by health personnel, which was somewhat more labor intensive, increased compliance about 13 percent. McDonald and colleagues found similar results in another randomized trial. Interns and resident physicians who received computer-generated reminders provided 49 percent of the preventive services suggested to them, while physicians in the control group provided only 29 percent of the services (49). Among physicians who received the reminder intervention, the researchers found that overall attitudes toward the reminder system and whether the physician read and signed the reminder were statistically significant predictors of use, while years of training and faculty assessments of the physicians were not. The researchers conclude that noncompliance with recommendations is an error of omission that can be mitigated by technological aids. In another study, Satarino and colleagues retrospectively asked patients receiving free breast cancer screening at two clinics in New York City how they learned of the service and their need to be screened (62). Most black screenees with less than a high school education learned of the clinic through television ads followed by word-of-mouth and private physician referrals. While this paper suggests that television may be a useful cue in promoting use, it does not indicate how one might reach individuals who were no t screened at one of the two clinics. Two studies have tested strategies to induce the provision of preventive services to patients. In one study, four clinics were randomly assigned either to participate in a program that combined physician education with a checklist of services due each patient on the medical record or to a control group. Over a 4-month period, the researchers measured rates of mammography and influenza and pneumococcal immunizations among eligible patients in each group. The intervention group had significant increases in the use of these services, ranging from 2 to 40 percent over the control group. The researchers also found significant increases in tests o f physician knowledge and attitudes about prevention among physicians (20). In another randomized trial, nurses who already routinely reviewed patient charts in a university-based internal medicine practice reminded physicians when a patient was due for particular screening services and immunizations (24). For patients receiving the nurse-reminder intervention, the researchers found statistically significant increases in rates of use for fecal occult blood screening (32 to 47 percent), breast exams (29 to 46 percent) and influenza immunizations (18 to 40 percent), but not for Pap smears (13 to 14 percent). The study represents the only randomized experiment to analyze the role of non-physician personnel in providing or boosting the use of preventive services. Physicians believe that such organizational strategies would improve the use of preventive services. In two studies o f physician attitudes, researchers conclude that while most physicians see themselves as ineffective in improving patient compliance with their recommendations for preventive care, they believe that they could be much more effective if they had more resources at
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28 The Use of Preventive Services by the Elderly their disposal including better training for themselves and their support staff, improved reimbursement for preventive services, and better educational materials for patients (84,87). Other research currently underway may enhance our understanding of the impact of organization on use. The Health Services Research Center at the University of North Carolina, with funds from the National Center for Health Services Research and Health Care Technology Assessment (NCHSR), has recently surveyed administrators, medical directors, and staff physicians in 150 large medical practices of different types (25). While most of the study seeks to identify organizational characteristics that contribute to physician satisfaction with his or her work environment, it will also focus on the participating organizations preventive care practices. Limitations of Evidence About Provider Behavior Compared to studies of patient behavior, there are relatively few studies of the structure of the health care system or the role of physicians in the use of preventive services (54), and none focuses exclusively on prevention for an elderly population. A new study of preventive practices among physicians currently underway may shed more light on these issues. However, no current or completed studies to date have examined the role of other potentially important factors, including: potential revenue obtainable from preventive services, potential liability associated with offering or withholding preventive services, and the degree of management control within the health care organization. The evidence about whether or not HMOs provide greater levels of prevention is ambiguous. While HMO enrollees may receive more preventive care than enrollees of other health plans, other predisposing and enabling factors such as gender, education, and income may explain this differential. Controlling for these factors, OTAs analysis of the NHIS data revealed no effect of HMO membership on the probability of using five preventive services. The data gathered by OTA from one closed-panel HMO suggest that prepaid health plans may have th e potential to increase the use of preventive services among older adults. No data exist about whether other prepaid plans achieve a level of preventive care comparable to the one OTA examined. The literature contains several studies of interventions within clinics, ambulatory care practices, and HMOs designed to promote the use of preventive services. They suggest that provider-based strategies can increase the use of preventive strategies. However, the narrowness of these studies indicates the need for more research. In particular, the existing literature does not adequately address: the generalizability of particular interventions to other settings, the most effective means of informing the public about the need for an d availability of screening programs, the role of non-physician personnel in affecting patient use of preventive services, and the potential of technological advances (e.g., the introduction of computerized medical records and new screening technology for the physicians offices) in affecting patient use of preventive services.
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6. IMPLICATIONS FOR POLICY Potential Medicare Coverage Medicare represents the Federal Governments major financial and policy commitment to health care. In 1986, Medicare represented 58 cents of each Federal dollar spent on health care and was the source of payment for 29 percent of all expenditures for hospital care and 21 percent of expenditures for physician services (73). The use and correlates of use of preventive services for the elderly have several important implications for the Medicare program. First, covering preventive services under the Medicare program would probably bring about increases in the percentage of elderly receiving preventive care. However, current evidence suggests that reducing out-of pocket expenses for patients is not sufficient to assure compliance with published preventive recommendations. OTAs analysis of five preventive services suggested th e presence of insurance beyond Medicare is associated with about a 10-percent increase in the percentage of elderly receiving each service during the period of time examined. With the exception of blood pressur e measurement, which almost all elderly already receive on a routine basis, substantial portions of the elderly with additional coverage that defrays out-of-pocket expenses do not use each of the preventive services OTA examined. In addition, because the additional insurance coverage held by Medicare recipients in most instances excludes preventive services, the OTA analysis is not a direct test of the impact of coverage of specific procedures on the rates of use of these services. Other factors enter into the physicians decision about whether to offer or provide the service and the patients decision about whether to seek or use it. Some of these factors may be amenable to change through public policy, while other characteristics describe groups of elderly patients at relatively high or low risk of not receiving adequate preventive care. Second, coverage of preventive services for Medicare beneficiaries could affect preventive use beyond the Medicare population itself. Medicare payment may raise interest in preventive care among health care providers and payers by placing the authority of the Federal Government behind it. In addition, consumers of health care may put more weight on preventive services in managing their own health because of the public discussion and attention focused on Medicare coverage. None of the data or literature currently available allows OTA to estimate the existence or magnitude of this potential indirect effect. A recent analysis of preventive services in Canada where such procedures are paid for by the government revealed rates of use comparable to those in the United States, suggesting that both the direct and indirect effects of government coverage may be small (76). The Medicare Catastrophic Coverage Act of 1988 (Public Law 100-360) may offer an opportunity to assess the full impact of Medicare coverage of breast cancer screening for the elderly by analyzing trends if the use of this service is monitored. Third, expansion of Medicare to cover preventive procedures will represent an immediate boost in the programs financial obligations even if increases in use are minimal or nonexistent. Although gaps exist between experts recommendations and current levels of use, substantial numbers of elderly still receive a variety of preventive services at recommended frequencies. OTAs analysis indicated that at least one-half of the noninstitutionalized elderly receives each of the five services examined on a regular basis. For three of these services (glaucoma screening, eye examinations, and blood pressure measurement), rates of use were even higher. Estimates of costs attributable to expanded Medicare coverage of preventive services must take account of the programs obligation to pay for procedures whose costs are currently borne by other payers. However, some portion of these services are for patients with a related medical history or symptoms and are 29
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30 The Use of Preventive Services by the Elderly diagnostic and already reimbursable under Medicare It is also probable that some physicians may categorize some examinations and screening services as diagnostic so that the procedure will be covered by Medicare. No existing data from published literature or the records of the Health Care Financing Administration (HCFA), the Federal agency that administers Medicare, indicate what portion of all procedures reimbursed by Medicare are actually for screening purposes. Fourth, the conclusions of this paper about the use of medical services such as screening and immunizations may hav e limited applicability for policymakers considering Medicare coverage of other preventive services such as health risk appraisals, education, counseling services, or tertiary prevention of disability among elderly suffering from chronic disease. Many of the services listed in table 1 could rely on non-physician personnel to a greater degree than do screening and immunization, and patients could receive them in a wider array of settings than they receive most medical services. These characteristics suggest that use of preventive services not examined in this paper may be markedly different from those explored here. Hence, an understanding of the implications of Medicare coverage of services other than screening and immunizations use would require additional study. Delivery of Preventive Services for the Elderly Although one of the major focuses of this paper is the potential impact of insurance coverage on the use of preventive services by the elderly, the data and literature reviewed in this paper suggest that other factors are strongly related to use. This information would be useful to public policy makers who seek strategies for altering the elderlys use of preventive services. Of the patient and provider characteristics related to use, a few are immutable, some are amenable to change through policy, and others are theoretically amenable to change, although the policy interventions to accomplish these changes are unlikely to prove cost-effective. Public policy cannot affect age, gender, rural versus urban residence, and usually, days spent in bed during the previous year, three factors correlated with the use of preventive services. However, these demographic and health status characteristics do identify segments of the population particularly at risk of not receiving adequate screening or immunizations. Knowing that on average more women than men receive such care or that recommended prevention appears to decline with age and good health may help policy makers target some of their preventive care efforts toward the more vulnerable groups. Because Medicare is an entitlement program available to all persons over 65 who receive Social Security, it is an unlikely means of focusing efforts on demographically defined subsets of the elderly population. Nevertheless, other government investments in prevention such as mass media campaigns and screening fairs may be able to narrow their target. Policy makers who want to bolster use among elderly groups unlikely to receive preventive services may wish to study the potential costs and effectiveness of such programs in detail. Other factors related to the elderlys use of preventive care do seem amenable to policy interventions. Studies that examine influences on physician behavior suggest that better or more frequent physician education may bring about better compliance with preventive recommendations. In addition, evidence suggests that record-keeping systems and reminders to physicians (possibly aided by computer technology) have positive effects on use. This paper has discussed the potential for insurance coverage to increase somewhat the percentage of elderly receiving prevention. Government and providers could design policies to bring about these changes where they do not already exist. The relationship between use and provider characteristics is not clear. For exam-
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The Use of Preventive Services by the Elderly 31 pie, conflicting published literature and the small number of elderly enrolled in HMOs makes it difficult to determine if the enrollment of older adults in prepaid health plans increases the amount of preventive care they receive. Additional research is also needed to establish the potential of nurses and other non-physician health professionals in providing or promoting the appropriate use of preventive services. Previous research suggests the substantial contact they have with patients in an ambulatory care setting, the availability of new screening technologies (e.g., instruments that can measure cholesterol in a physicians office from a finger prick), and the growth in health fairs that provide some preventive services in alternative settings may enhance the role of these professionals. The remaining variables affecting older individuals use of screening and immunizations -educational level and famil y income --are also potentially susceptible to government interventions. However, public policies designed to change these characteristics are so much more broadly construed that they would never be implemented simply to affect the use of preventive care. If, however, the government decides for some other reason to promote education or supplement income among the elderly, long-term increases in the use of preventive procedures may be an additional benefit.
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Appendix A: ADVISORY PANEL Marianne C. Fahs Department of Health Economics Mt. Sinai Medical Center New York, NY Gordon De Friese, Panel Chair Health Services Research Center University of North Carolina, Chapel Hill, NC John Frank Department of Preventive Medicine & Biostatistics University of Toronto Ontario, Canada Gary D. Friedman Epidemiology and Biostatistics Division Permanence Medical Group, Inc. Oakland, CA Lawrence Gottlieb Clinical Guidelines Program Harvard Community Health Plan Brookline Village, MA Mary Knapp John Whitman and Associates Philadelphia, PA Risa Lavizzo-Mourey Geriatrics Program University of Pennsylvania Philadelphia, PA M. Cristina Leske Department of Preventive Medicine SUNY at Stony Brook Stony Brook, NY Donald Logsdon INSURE Project New York, NY Mildred B. McCauley American Association of Retired Persons Washington, DC Peter McMenamin Chevy Chase, MD Meredith Minkler Center on Aging University of California, Berkeley Berkeley, CA Marilyn Moon Public Policy Institute American Association of Retired Persons Washington, DC George Morley Department of Obstetrics/Gynecology University of Michigan Medical Center Ann Arbor, MI Gilbert Omenn Dean, School of Public Health & Community Medicine University of Washington Seattle, WA George Pickett Department of Public Health Policy School of Public Health University of Michigan Ann Arbor, MI Donald Shepard Department of Health Policy and Management Harvard School of Public Health Boston, MA Barry Stults Division of General Internal Medicine University of Utah Medical Center Salt Lake City, UT Advisory Panel members provide valuable guidance during the preparation of OTA reports. However, the presence of an individual on the Advisory Panel does not mean that individual agrees with or endorses the conclusions of this particular paper. 33
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Appendix B: ACKNOWLEDGMENTS The OTA staff would like to express our appreciation of the following people for their valuable guidance. This acknowledgment should not be construed in any way to imply that the individuals agree with or endorse the conclusions of this paper. Ronald M. Andersen Charlotte Muller The University of Chicago Mt. Sinai Medical Center Chicago, IL New York, NY Robert Burack Annlia Pajanini-Hill Wayne State University University of Southern California Detroit, MI Morris F. Cohen KaiserPermanante Oakland, CA Alan Garber National Bureau of Stanford, CA Pearl S. German The Johns Hopkins Baltimore, MD Robert L. Kane Los Angeles, CA Penelope Pollard Medical Care Program National Health Policy Forum The George Washington University Washington, DC Economic Research, Inc. John A. Sawyer University of Texas Health Science Center San Antonio, TX University F. Douglas Scutchfield American College of Preventive Medicine University of Minnesota Minneapolis, MN Mary Grace Kovar National Center for Health Statistics Washington, DC David R. Lairson Health Science Center Houston, TX Angela Mickalide U.S. Preventive Services Task Force Washington, DC Washington, DC Stephen J. Williams San Diego State University San Diego, CA Steven H. Woolf U.S. Preventive Services Task Force Washington, DC Steffie Woolhandler The Cambridge Hospital Cambridge, MA Steven Moore Office of the Surgeon General Rockville, MD 34
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Appendix C: EMPIRICAL STUDIES DETERMINANTS OF BY THE ELDERLY OF THE USE AND/OR USE OF PREVENTIVE SERVICES 35
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36 n The Use of Preventive Services by the Elderl.y I I t , , , I 1 I , I I 1 1 , t , 0 , , t I , , t t t 0 0 0 1 , , , , 0 0 I t I , 1 1 0 1 , 0 # , , # I , , 0 , , t I 1 , , I t m aJ L .1m -u 2 0 0 0 1 0 0 $ 0 , 0 I 1 I @al :s 14 c u , , ,ig L .t-uw a)aJ >.$ ., 0 0 0 UN I Vvl -, 1 0 c I a).; I cl) .0$ o c1 m: >m m: N O :C la $3 ,1 l-al I cc I -a , & am 1 Oc 10 ~j , I I 1 1 a >-M 1(A AJ c 0 em IC , , , , 0 , g: Lxl maJl E u):: 0.: : (mL, am I , 0 , 0 , , 1 1 t , [ g-, -om 1 u)Iol 10. I A L 4 m 1 ,
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Appendix D: EMPIRICAL STUDIES OF THE USE AND/OR DETERMINANTS OF USE OF PREVENTIVE SERVICES BY THE NON-ELDERLY
PAGE 42
. The Use of Preventive Services by the Elderly 39 a .a l , # m o , , , 0 # I o a aJ L t , , # o , t # 0 , , 0 1 , , , , , ?i , 1 +-I 1 0 lx 1 , , , , o t Q 1 t 1 # # , # I 1 0 o 0 I 0 I/-J
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Appendix D. --cont'd Services Period of time in Sample description study examined which use is measured and design Analyses Cummings et al., Swine flu vaccine Lifetime (albeit Telephone survey of 286 adults Uptake of swine flu vaccine as a function 19797 only offered randomly drawn from all of Health Belief Model variables using within finite households in Oakland City, regression and path analyses period) Michigan ---------. . . . . . . . ----------------------------------------------------------------------. . . ----------. David and Boldt, 1980 8 None; examined patient Not applicable 92 responses to mailed survey of Descriptive statistics of patient attitudes toward preventive random 10% of active patients of attitudes toward prevention, physician care University of Kentucky Medical preventive function, time and cost of Center preventive care; bivariate relationships between attitudes and social position ---------------------------------------------------,----------------------------------------------------------. . . -----Davidson et al., Pap smear 3 years Patients receiving outpatient Effect of nurse-initiated reminder system 19849 Fecal occult blood 1 year care through university based of preventive services due; outcome Breast exam 1 year general internal medicine measure is the difference in rate of Influenza immunization 1 year (for 65+) practice during two l-year compliance with recommenda tions between (or 1 year for periods; randomized trial; n=450 experimental and control periods all services: not clear) ----------------------------------..-.-. . ------------------------------....------------------------------------. . -------Harris and Guten, Health protective Not applicable Interview With 842 randomly Relationships between constructed scales 1979 10 activities including selected adults from Cleveland, of health protective behavior and Health regular medical and dental Ohio area Belief Model variables (bivariate) checkups ---. . --------------------------------------------------------------------. . ------------. . . ----------------------Hayward et al., Pap smear 1 year and 3-5 4659 women from random national Estimates of proportions of women 1988 11 years telephone survey (1986 Access to receiving cancer screening within Breast examination 1 year Care Study) recommended periods of time; bivariate Mammography 1 year and multivariate analysis of correlates Periodic health exam 1 year of use ---------------------------------------------------. . ------------------------------------------------------------------Howard, 1979 12 Mammography Date of last use Not applicable Reviews four studies on use of mammography and three studies of major deterrents to physician use of mammography -------------------------------------------------------------------------------------------------------------------------Larison and Swint, Preventive and non1 year 5% random sample of 3892 Compared likelihood and volune of use of 1976. 1978 13 preventive physician visits individuals enrolled in large preventive and nonpreventive visits in an prepaid plan (Kaiser Portland) HMO as a function of health status, demographic, insurance, and other economic variables ----. -----------------------------------------------------------. . ----. . ------------------------------------------McDonald et al., Variety of screening Not applicable 115 resident physicians in a Estimated the effectiveness of ccmputer198415 services, immunizations, teaching hospital general generated reminder system in achieving and weight reduction medicine practice (61 study and compliance with medical indications for 54 control, randomly assigned) preventive and other procedures; studied over a 2-year period analyzed the correlates of physician use and reasons for nonuse -------------------------------------------------------------------------------------------------------. . . -----------
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44 The Use O f Preventive Services by the Elderly In al m m n o 0 IN = mm %!?! .4-I L% u. In tit
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Appendix D. --centd 18pub(ic Health Service, U.S. Department of Health and Hunan SefViCeS, use of Mammography for Breast Cancer Screening -Rhode Island, 1987, Morbidity and Mortality Ueekly Report 37(23):357-360, 1988. 19p~[ic Health Service, U.S. Department of Health ad Human Services, I!provisima[ Estimates From the Nati~l Health Interviw Survey S~[WIWlt Otl Cancer Control -United States, January-March 1987, Morbidity and Mortality Ueekly Report 37(27):417-425, 1988. 20L-A. Li[[ard, H-G. Manning, C. petersofl, et al., Preventive medical Care: Standards, Usage, and Efficacy (Santa Monica, CA: The Rand Corporation, 1986); and N. Lurie, U.G. Manning, C. Peterson, et al., I$preventive care: Do ue practice what we Preach,ll Am. J. Public Health 77(7):801-804, 1987. 21M.A. Riddiouqh. J.S. Uil(ems, C.R. Sanders, et al., I*Factors Affecting the Use of Vaccines: Considerations for I mnunization Program Planners, Public Health Repor~s.96(6):528-535j 1981 22F-J. R-, S.u. Fletcher, ad B. Hulka, ~The per Journa[ 74(3):265-271, 1981. 23T-G. Rundal[ ad J-R-C. wheeler, ~The Effect of Social Behavior 20:397-406, 1979. 24D.sSa[kever, l)Accessibi lity and the Demand for 25U.A. Satariano, A.G. s~h~artz, ad G.M. s~anson, odic Hea~th Examination: Comparison of Rec onwnendations and Internists Performance, Southern Medical ncom on Use of Preventive Care: An Evaluation of Alternative Explanations, w Journal of Health and Preventive Care,w Social Science ard Medicine 10:469-475, 1976. ItScreening for Cervical Cancer: Results From Several Intervention Strategies, w in Issues in Cancer Screening arid Communications (Neu York, NY: Alan R. Liss, Inc., 1982). 26J.A. Sawer, J. Earp, R.H. Fletcher, et at., IIpap cjmars i n Rural Black u~n,ll fr~ the Department Of Med University of North Carolina at Chapel Hill, Chapel Hi(l, NC, unpublished manuscript. 27D-P. slesinger, R.C. Tessler, and D. Mechanic, llThe Effects of Social Characteristics on the utilization 0< Health Care programs,~ Medical Care 14(5):392-404, 1976. 2~.A. TeiteLba~. C-SUeisman, A.C. K[assen, et al., ~pap Testing Intervals: Specialty Differences in Phys cine and School of Public Health, Preventive Medical Services in Contrasting cians Rec cmnnendations in Re[ation to Uomens Pap Testing Behavior, ~ Medical Care 26(6):607-618, 1988. 29R.S. Thonpson, M.E. Michnich, J. Gray, et al., llMaximizing c~liance Uith Hemoccu[t Screening for colon Cancer in clinical praCtiCe, MediCa[ care 24(10):904-914, 1986. 30c.M. Valente, J. Sobal, H.L. MunCie, et ai., llHealth pr~tion: Physicians I Beliefs, Attitudes, and Practices, AM J. Preventive Medicine 2(2):82-88, 1986.2 31R-B. u arnec k e p.L. Hav[icek, and c. Hanfredi, !IAHareneSS ad use of screening by Older-Age perSOnS,ll in prOSPt? ctives on Prevention and Treatment of Cancer in the Elderly, R. Yancik (cd.) (New York, NY: Raven Press, 1983). 32H. uechsler, S Levine, R.K. Idelson, et al-, llThe physicians Role in Health prOMOtiOn A Survey of Primary-Care Practitioners, N. Eng[. J. Med. 308(2):97-100, 1983. 33p.s. Uilli-on, C.E. Driscoll, L.D. Dvorak, et al., llHea\th Screening Examinations: The patientS %?Wective8 II The Journa{ of Family Practice 27(2):187-192, 1988. 34R.A. wi~sor and J. Morris, Ucms-r particivti~ ad parent for Health pr~ti~ programs at an IJrban Primary care Center, The Alabama Journal O f Medical Sciences 21(2):209-213, 1984. 35B. uoo, 6u~o, E.Fcook, et al., IiScreening proc~ures i n the Asypt~tic Adult: Comparisons of Physicians Reccmnendations, patients Desires, Published Guidelines, and Actual Practice, ti J.A.M.A. 254(11):1480-1484, 1985. SCIJRCE: Office bf Technology Assessment, 1989.
PAGE 49
Appendix E: OTA ANALYSIS OF PREVENTIVE SERVICE USE BY THE ELDERLY WITH DATA FROM THE 1982 NATIONAL HEALTH INTERVIEW SURVEY Methods The Data The National Health Interview Survey (NHIS) is a representative household survey conducted annually since 1957 by the U.S. Department of Health and Human Services National Center for Health Statistics (NCHS). In addition to a core questionnaire that measures the self-reported prevalence of various medical conditions, the use of health services, general health status, disability, and demographic characteristics, NCHS adds supplemental questionnaires on specific topics that vary from year to year. In 1982, the NHIS contained supplemental questionnaires on the use of preventive health services and the types and degree of health insurance coverage. Although NCHS has published some data from the preventive services supplement in tabular form (75), no published work to date has attempted to use these data to understand what factors are associated with the use of preventive services by the elderly. OTA decided to conduct such an analysis. OTA obtained magnetic tapes of the core and supplemental questionnaire data for the 1982 NHIS from NCHS. These data files contained 103,923 observations reflecting respondents of all ages with 11,434 observations for individuals 65 years or older. In addition to responses to survey questionnaires, each observation contained a uniqu e identifier, variables identifying a stratum and cluster from which it was drawn, and the weights necessary to produce representative estimates. Among the variables on the data set, OTA was interested in: n the amount of time elapsed since the respondent last received each of five screening services (glaucoma screening, blood pressure measurement, eye examination, breast examination, and Pap smear); and n factors potentially associated with the use or nonuse of these five services. OTA converted each of the five variables measuring elapsed time since use of preventive services to a binary variable that measures whether or not the individual used the service within a specified period of time. These periods of time are based on the recommendations of expert groups presented in table 2 in the text of this paper. Because there is some variation across the different sets of expert guidelines listed in table 2, OTA summarized the published recommendations in the composite measures presented in table 8. These composite measures do not represent a set of recommendations themselves; rather, they are merely one benchmark for comparing actual use to what is generally considered adequate by recommendin g groups. Where there is disagreement among recommending groups, the composit e measures tend toward longer intervals between screenings in order to measure compliance with minimal recommended levels of prevention These composites of expert recommendations pertain only to the primary analyses conducted by OTA. As a source of comparison, table 8 also includes the periods used by two other studies of the use of preventive procedures. In one of these papers, the authors measured recommended periodicity as the mean of published recommendations (92). The other paper formed a consensus based on their own review of relevant literature (45). Because of the coding scheme of the NHIS, use within an x year period really means that the individual had used the service within a period of less than but not including x+1 years. For example, consider the case of breast examinations. Table 2 suggests that one should measure use within the previous year. However, under the NHIS coding scheme, one would consider an elderly 46
PAGE 50
The Use of Preventive Services by the Elderly 47 woman who had her last breast examination on the data set: 21 months ago to have been adequately screened; a woman whose last breast examn ination was 24 months ago would not. Table 9 lists all variables used in the analysis and also includes appropriately weighted descriptive statistics. n Methods of Analysis OTA used PC-CARP R software (on an 80286 personal computer) to analyze the data. PC-CAR P R was developed by the Statistical Laboratory at Iowa State University especially for analyzing data from surveys with complex designs like the NHIS. It makes use of the sampling information in the data files to produce appropriate point and variance estimates. OTA performed two separate analyses a descriptive summary of the percentages of elderly individuals who reported using each of the five screening services within the specified time; and a multivariate weighted logistic regression analysis of the use or nonuse of these services. In addition, OTA: n examined whether observations dropped from the multivariate analysis because they contained some missing information differ in any important ways from observations included in the analysis, potentially biasing our estimated parameters; Table 8--Periods of Time Used by OTA and Two Studies to Measure Older Adults Use of Preventive Services Period of time to measure use employed by: Service OTA a Lillard, et al., 1986 b woo, et al., 1985 C n Initial or periodic physical exam 1 year . 1.4 years n Blood cholesterol level 5 years . 4.5 years n Fecal occult blood test 1 year 1 year 1.0 year n Pap smear 3 years* 3 years 4.1 years n Glaucoma screening 2 years* . ---n Optometry/ophthalmology exam 2 years* ------n Pneumococcal immunization L i fet i me Lifetime ---n Influenza immunization 1 year 1 year . n Tetanus immunization 10 years 10 years -.. n Hypertension screening 1 year* ..-. 1.4 years n Breast examination 1 year* ..-. ... Abbreviation: HMO= health maintenance organization. ~Intervals listed in this colum represent composites of the expert recommendations surrnarized in table 2. L.A. Li[lard, W.G. Manning, C. Peterson, et al., Preventive Medical Care: Standards Usa~e and Efficacy (Santa Monica, CA: The Rand Corporation, 1986). CB. Woo, B. Woo, E.F. Cook, et al., IIScreening procedures in the Asymptomatic Adult: Comparisons of Physicians Recommendations, Patients Desires, Published Guidelines, and Actual Practice, ti J.A.M.A. 254(11):1480-1484, 1985. *As described in greater detai[ in appendices E and F, OTA eStiIMtd the Use Of preVefltiVe SerViCeS aMOn9 the elderly with two different data sourcesa single HMO and the 1982 National Health Interview Survey (NHIS). The asterisk indicates services included in the NHIS analysis. Because of the coding scheme of the NHIS, use within an xM year period really means that the individual has used the service within a period of less than but not including X+lU years. Under this scheme, we would consider an elderly woman who had her last breast examination 21 months ago to have been adequately screened; a woman whose last breast examination was 24 months ago would not. For the HMO data, use within xM years carries a literal definition Sources: Office of Technology Assessment, 1989.
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48 n The Use of Preventive Services by the Elderly n considered whether multicollinearity in the models might reduce precision; and n examined how the probabilities of use predicted by the models varied with each significant variable. The multivariate analysis uses Taylor series techniques to estimate a weighted logistic regression model of the form: is the probability of elderly person i using service j. Use is measured by binary variable Y ij (=1 if person i had service j within the specified period of time; Y ij O otherwise). is a vector of predisposing characteristics describing elderly person i. E i is a vector of enabling characteristics H i Uj &j describing elderly person i. is a vector of health status characteristics describing elderly person i. is an estimated parameter, and 6 j, 8 j* Yj are vectors of estimated parameters for service j. is an individual, service specific error term. PC-CAR P R produces estimated coefficients that are consistent and appropriatel y weighted. Estimates of asymptotic variances also appropriately reflect the complex survey design. PC-CAR P R requires that no observation in the data matrix contain missing values. For the logistic regression analysis, OTA used SPSS-PC+ to create two data files. OTA purged both files in a listwise fashion of observations containing missing data on any variable in the models. 1 OTA used one o f the data sets to estimate models for preventive services potentially used by both sexes--glaucoma screening, eye examinations, and blood pressure measurement. OTA used the other data set, which contained only the observations for women, to estimate models for breast examinations and Pap smears. The data set for both sexes contained exactly 9,000 out of the original 11,434 observations. The remaining 2,434 observations, which had missing data, represented a weighted 21.5 percent of the over-65 population. The single variable with the most missing observations was family income. This variable alone had missing observations representing 15.3 weighted percent of the elderly population. Of each of the other variables containing missing values, none lacked data on observations representing more than 4 weighted percent of individuals over-65. The data set containing only women had 5,040 observations out of a possible 6,655. The 1,615 observations with missing dat a represented 19.6 weighted percent of all women over 65. Results The estimates of the national proportions of elderly using each of the five screening services within the specified time are presented in table 3 in the text of the paper. Additional descriptive statistics are presented in table 9. Table 10 below presents the parameters of the estimated logistic regressions that attempt to explain the use or nonuse of each service. Table 7 summarizes these results, and the text of the paper discusses their significance. 1 To estimate the descriptive statistics presented in table 9 and the national percentages of elderl y receiving the five screening services, OTA use d data sets that contained all observations for which any data is available for the particular variabl e in question. The Listwise deletion of missing values described here only applies to the logisti c regression analysis.
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The Use of Preventive Services by the Elderly 49 Table 9--1982 National Health Interview Survey: Selected Descriptive Statistics for Persons Over 65 Number of observations: 11,434 Weighted number of observations: 25,391,02 3 Weighted Means and Standard Deviations for Continuous Variables Variabl e Mean Standard deviation ..--.. . . . ------------. . . . AGE -73.39 6.6 3 ..--.. . ------------------------------INCOME 15,217.9 7 13,853.5 6 Weighted Frequency Distributions for Categorical Variables Variabl e Proportion Variabl e Proportion GLAUCOMA EDLEVEL Highest educational level attained: l=screened for glaucoma within O=none or kindergarten 0.02 previous 2 years, 11 months 0.6 6 O=otherwise 0.34 ------------------------. . . . EYE 1=received eye examination within previous 2 years, 11 months 0.75 O=otherwise 0.25 ----. . . . . . . . . . BLOODP l=had blood pressure measured within previous 1 year, 11 months 0.93 O=otherwise 0.07 . . . . . . . . . . ---BREAST l=had clinical breast examination within previous 1 year, 11 months 0.50 O=otherwise 0.50 --.... ------. . . . . . . . PAP 1=had Pap smear within previous 3 years, 11 months 0.52 O=otherwise 0.48 ---. . . . . ----. . . . 1=1 to 8 years (elementary ) 0.3 8 2=9 to 11 years (some high school) 0.1 6 3=12 years (high school graduate) 0.2 6 4=1 to 3 years colleg e 0.09 5=college graduate 0.05 6=post-graduate education 0.04 . . . . . . . . ------. . . SMSA 1=resides in a Census Bureau Standard Metropolitan Statistical Area (urban area) 0.6 4 0=otheruis e 0.3 6 .-.-.. . . . . . . . . . . . PREPAID 1=enrolled in HMO or sane other prepaid health plan 0.02 0=otherwise 0.9 8 . . . . . . . . . . . . HLTHINSR 1=has some health insurance coverage or health benefits beyond Medicare including prepaid, Veteran s i military, or means tested public assistance health benefi t 0.7 8 0=otherwise 0.2 2 -----. . . . . . . ------. . --ALONE 1=lives alon e 0.30 0=otherwise 0.70 . . . . . --------. . . . ------. 0.4 1 0=female 0.59 BEDDAYS days in bed during previous 12 months: O=none 0.6 5 1=1 to 7 days 0.1 6 2=8 to 30 days 0.1 2 3=31 to 180 days 0.0 5 4=181 to 365 days 0.0 2 -----. -----...-.-. . . . . . . . . . . . . . . . . . . . NONWHITE l=nonwhite 0=white LIMITED 0.1 0 1=limited in some activit y 0.9 0 0=otherwise 0.70 0.30 SOURCE: Office of Technology Assessment, 1989.
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50 n The Use of Preventive Services by the Elderly Table 10. --Elderly Use of Five Screening Services: Logistic Regression Results GLAUCOMA BLOOOP EYE -BREAST PAP Independent Mean Estimated Estimated Estimated variable value coefficients coefficients coefficients INTERCEPT 1.0000000 0.4160073 -0.3433352 0.8399042 Predispose ng factors: MALE 0.4162490 AGE 73.2137000 NONWHITE 0.0945106 EDLEVEL 2.3429000 Enabling factors: INCOME 15276.8000000 SMSA 0.6336510 PREPAID 0.0229561 HLTHINSR 0.7896040 ALONE 0.3067190 Health status measures: BEDDAYS 0.6316600 LIMITED 0.2992070 -----. . . . --(0.2927390) (0.7538013) (O.296341O)** -0.3009192 -0.3259628 -0.2439690 (0.0534922)** (0.0957841)** (0.5874069)** -0.0091917 0.0264826 -0.0061867 (0.0038157)* (0.0095720) (0.0039844 ) -0.3735570 -0.0368144 -0.0887188 (0.0838741)** (0.1647689) (0.0923361) 0.1601070 0.1038493 0.1147214 (0.0223536)** (0.0436750)* (0.0229877)** 0.0000114 0.0000106 0.0000066 (0.0000026)** (0.0000058) (0.0000028)* 0.2167839 0.0064913 0.2054843 (0.0571966)** (0.1049219) (0.0589708)** 0.2349249 0.6391572 0.1612398 (0.2200813) (0.5023571) (0.2554439) 0.4306881 0.5616778 0.3853976 (0.0625027)** (0.1248533)** (0.0628222)** 0.0062441 -0.1136007 0.0406232 (0.0589261) (0.0944282) (0.0638674) 0.1227310 0.8908110 0.0776005 (0.0304799)** (O.1O5O414)** (0.0281800)** 0.0003354 0.3276151 -0.1233437 (0.0585122) (0.1317948)* (0.5909522)* . . . . . . . . . . . Model statistics: N 9000.0 9000.0 9000.0 Dependent variable mean 0.6635 0.9319 0.7432 F-statistic 32.41** 15.14** 15.12** Average design effect = 1.32 1.53 1.28 Mean Estimated Estimated value coefficients coefficients 1.0000000 -0.0166337 2.1542170 (0.39609n) (0.3889979)** . . 73.4815000 -0.0167258 -0.0400128 (0.0051317)** (0.0049593)** 0.0937475 0.0263338 0.1506788 (0.0982817) (0.1056188) 2.3367600 0.1132536 0.0962961 (0.0255863)** (0.0249885)** 4059000000 0.0000119 0.0000102 (0.0000027)**(0.0000028)** (0.0000028)** 0.6364580 0.3110662 0.1555873 (0.0694007)** (0.0700830)* 0.0199337 0.3845290 0.4433711 (0.2405225) (0.2723756) (0.2723756 ) 0.7834470 0.3761862 0.3581028 (0.0884009)**(0.0861343)** (0.0861346)** 0.4099680 0.1971576 -0.0397508 (O.O74391O)**(O.O665113)** (0.0651125) 0.6405870 0.3337425 0.1943092 (0.0327194)**(0.0335717)** (0.0333572)** O.3O8W1O 0.0448802 0.0331326 (0.0703522) (0.0762676) (0.0762676 ) -----.-.-... . -----------------5040.0 5040.0 0.4985 0.5243 23.09** 25.87** 1.20 1.23 a As by totic standard errors are in parentheses belou each estimated coefficient. Variable means uere calculatd from data matrices used to estimate logit models (i.e., purged of observations ~ith missing values). cAverage effectU of coaplex survey design on variances of estimated coefficients. This effect is measured as the nunber of times greater the variance from the complex design is than the variance from a simple random design. *Estimated parameter significant at the 0.05 level, t~o-tailed test. **Esti~ted ~r~ter significant at the 0.01 level, tW-tailSd test. Variable Key: ~~v~ GLAUCOMA --l=sc~eened for glaucoma within previous 2 years, 11 months; O=otheruise EYE --l=received eye examination within previous 2 years, 11 months; O=otherwise BLOCX)P --l=had blood pressure measured uithin previous 1 year, 11 months; O=otheruise BREAST --l=had clinical breast examination within previous 1 year, 11 months; O=otheruise PAP --l=had Pap smear within previous 3 years, 11 months; O=otherwise ~tv~ MALE --l=male~ O=female AGE --respondents age in years NONWHITE --l=nonwhite; O=white EDLEVEL --highest educational level attained; O=none or kindergarten; 1=1 to 8 years (elementary); 2=9 to 11 years (sp,e high school); 3=12 years (high school graduate); 4=1 to 3 years college; 5=college graduate; 6=post-graduate education INCOME --family income in dollars SMSA --l=resides in a Census Bureau Standard Metropolitan Statistical Area (urban area); O=otherwise PREPAID --l=enrolled in HMO or some other prepaid health plan; O=otherwise HLTHINSR --l=has some health insurance coverage or health benefits beyond Medicare including prepaid, Veterans, military, or means tested public assistance health benefit; O=otherwise ALONE --l=lives alone; O=otherwise BEDDAYS --days in bed during previews 12 months; O=none; 1=1 to 7 days; 2=8 to 30 days; 3=31 to 180 days; 4=181 to 365 days LIMITED --l=limited in some activity; O=otherwise SOURCE: Office of Technology Assessment, 1989.
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The Use of Preventive Services by the Elderly 51 Table 11. --Correlation Matrix for Variables in Logistic Regression Models a NONEDHLTH GLAUCOMA BLOODP EYE BREAST PAP MALE AGE WHITE LEVEL INCOME SMSA PREPAID INSR ALONE BEDDAYS LIMITED GLAUCOMA 1.00 xx BLOODP 0.19 1.00 xx xx EYE 0.69 0.18 1.00 xx xx xx BREAST xx xx xx xx xx xx xx 1.00 PAP xx xx xx xx xx xx xx xx 0.49 1.00 MALE AGE NONWHITE EDLEVEL INCOME SMSA PREPAID HLTHINSR ALONE BEDDAYS -0.06 xx -0.05 xx -0.09 xx 0.15 xx 0.12 xx 0.07 xx 0.03 xx 0.12 xx -0.01 xx 0.04 xx -0.04 xx 0.03 xx -0.02 xx 0.05 xx -0.05 xx xx 1.00 xx xx xx 1.00 -0.03 xx XX -0.06 XX -0.06 -0.15 XX -0.03 xx xx 0.00 xx -0.02 0.00 xx 0.10 xx xx 0.01 xx 0.11 0.11 xx 0.04 0.07 xx xx 0.10 xx xx 0.09 0.10 xx 0.01 0.06 XX xx -0.01 xx xx 0.09 0.05 xx 0.02 0.02 xx xx 0.03 xx xx 0.04 0.04 xx 0.07 0.09 xx xx 0.02 xx xx 0.10 0.09 xx -0.01 0.01 xx XX -0.26 xx XX 0.01 -0.06 XX 0.13 0.02 xx xx -0.02 xx XX 0.15 0.08 XX 1.00 1.00 -0.01 -0.01 -0.14 -0.14 .11 -0.09 -0.00 0.01 -0.03 -0.03 -0.09 -0.08 0.18 0.21 0.06 0.06 1.00 1.00 -0.16 1.00 -0.18 1.00 -0.13 0.39 1.00 .13 0.32 1.00 0.04 0.09 0.12 1.00 0.02 0.08 0.11 1.00 -0.00 0.04 0.03 0.10 1.00 -0.01 0.03 0.01 0.08 1.00 -0.12 0.18 0.11 0.01 0.08 1.00 .11 0.18 0.07 0.00 0.08 1.00 0.01 -0.02 -0.31 -0.00 -0.04 -0.01 1.00 .01 -0.00 -0.36 -0.01 -0.03 0.03 1.00 0.04 -0.05 -0.03 -0.01 -0.02 -0.01 0.01 1.00 0.04 -0.05 -0.04 -0.00 0.00 -0.02 0.01 1.00 LIMITED -0.00 0.06 -0.03 xx xx -0.04 -0.07 0.06 -0.12 -0.07 -0.04 -0.00 -0.02 -0.03 0.31 1.00 xx xx xx 0.04 0.03 xx -0.04 0.07 -0.12 .05 -0.03 0.01 -0.01 -0.04 0.45 1.00 Pearson correlation coefficients. First row in each cell gives correlation in data set used to estimate models for GLAUCOMA, EYE and BL~P(n=9000). Second row gives correlation for data set used to estimate mode(s for BREAST and PAP (n=5040). s ymbol Key: XX=Not applicable (Both variables not contained on that data set) Variable Key: DependentVariables: GLAUCOMA --l=screened for glaucoma within previous 2 years, 11 months; 0=othewise EYE --l=received eye examination within previous 2 years, 11 months; O=other~ise BL00DP --l=had blood pressure measured within previous 1 year, 11 months; O=otherwise BREAST --l=had clinical breast examination within previous 1 year, 11 months; O=otherwise PAP --l=had Pap smear within previous 3 years, 11 months; O=otherwise Independent Variables: MALE --l=male; O=female AGE --respondents age in years NONWHITE --l=nonwhite; O=white EDLEVEL --highest educational level attained; O=none or kindergarten; 1=1 to 8 years (elementary); 2=9 to 11 years (some high school); 3=12 years (high school graduate); 4=1 to 3 years college; 5=college graduate; 6=post-graduate education INCOME --family income in dollars SMSA --l=resides in a Census Bureau Standard Metropolitan Statistical Area (urban area); O=otherwise PREPAID --l=enrolled in HMO or some other prepaid health plan; O=otherwise HLTHINSR --l=has some health insurance coverage or health benefits beyond Medicare including prepaid, Veterans', military, or means tested public assistance health benefit; O=otherwise ALONE -l=lives alone; O=otherwise BEDDAYS --days in bed during previews 12 months; O=none; 1=1 to 7 days; 2=8 to 30 days; 3=31 to 180 days; 4=181 to 365 days LIMITED --l=limited in some activity; O=otherwise SOURCE: Office of Technology Assessment, 1989.
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52 The Use of Preventive Services by the Elderly To examine the possibility that multicollinearity among the independent variables in the model might preclude precise estimation, OTA estimated the weighted first-order Pearson correlation matrices for the two data sets. Table 11 presents the correlatio n statistics. Only three pairs of independent variables had correlations greater than 0.25 (or less than -0.25): EDLEVEL and INCOME (0.39 in the two-gender data set and 0.32 in the women only data set), ALONE and INCOME (-0.31 and -0.36 respectively), and the two measures of health status LIMITED and BEDDAYS (0.31 and ().43 respectively). In addition, ALONE and AGE have correlations of 0.18 and 0.21 respectively in the two data sets. However, despite the potential effect of this collinearity on the estimated variances, the conclusions are unlikely to change. In all models except blood pressure measurement, EDLEVEL and INCOME are both already significant predictors of preventive service use. ALONE is significant in three out the five (with blood pressure measurement and Pap smears being the exceptions). Although there is a high degree of correlation between BEDDAYS and LIMITED, at least one of them is statistically significant in all of the models except glaucoma screening, thus supporting the notion that health status is associated with preventive service use among the elderly. 2 OTA excluded a substantial proportion of observations because data were missing for one or more variables in the model. In order to examine if these exclusions could have biased the results of the multivariate models, OTA compared the characteristics of the included and excluded groups. In both data sets, the included respondents were significantly different from those eliminated beZ However, because mul ticol 1 i neari ty reduces the precision of the estimator, the stsndard error o f these two variables estimated coefficients may b e biased in the glaucoma screening model. cause of missing data for only two variables: HLTHINSR (the presence of any health insurance beyond Medicare) and INCOME. 3 For each of the other variables (including the dependent variables), the mean for the observations with missing data did not differ statistically from the mean for observations included in our analyses. This analysis suggests that the exclusion of observations with missing data is unlikely to introduce bias into the multivariate models, but OTA cannot rule out the possibility. In order to examine the effect of each significant variable in the estimated models, OTA simulated, one independent variable at a time, how the probability of using each screening service varied with each possible value of the independent variables. In these simulations, all independent variables, except the one whose effect was being simulated, assumed their mean values. Table 12 and figures 1 through 4 present the results of this analysis for each significant variable in our models. Among the independent variables, holding other factors constant, age, education, and health insurance appear to have the greatest overall effect on the probability of receiving each of these services. This analysis also supports the notion that blood pressure measurement is different from other services. Since almost everyone receives it, there is less variation to explain. Hence, the variables in the model appear less important in predicting its use than they do for the other services. 3 The group of observations excluded from the analysis had a louer mean income ($14,475 versu s $15,276 in the two gender data set; pcO.01) and was less likely to have any insurance coverage beyond Medicare (0.70 versus 0.79 in the two gender data set; P
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The Use of Preventive Services by the Elderly n 5 3 Table 12-Effect of Statistically Significant Binary Variables a in Logistic Regressions on Elderly Use of Five Screening Services: Predicted Probabilities b MALE = 0 = 1 . . NONWHITE = 0 q 1 . . SMSA = o = 1 . . HLTHINSR = 0 = 1 . . ALONE = o = 1 ....-. . LIMITED = 0 = 1 GLAUCOMA Predicted probability 0.70 0.63 . -----0.6 8 0.59 ---------. 0.6 4 0.7 7 . . . 0.59 0.69 ----. . . . . . . BLOOOP Predicted probability EYE Predicted probability 0.96 0.94 . . . . ....---..... . . . . 0.92 0.96 . . --. -----. . 0.95 0.96 0.77 0.72 . . . . -. . . . 0.73 0.77 . . . 0.69 0.77 . . . . . . . 0.76 0.74 BREAST Predicted probability xx xx . . . . . . . 0.45 0.53 . . . 0.43 0.52 ----. . 0.48 0.53 . . . . PAP Predicted probability xx xx . . . . . . . 0.50 0.54 . . . 0.46 0.55 . . . . . . . . . . . . a Effect of significant non-binary variables shown in figures 1 through 4. b Predicted probability is estimated as 1/[1 + e-x~l where ~is the vector of estimated coefficients and X is the vector of individual characteristics. Of these characteristics (a[l independent variables included in the estimated model), eac h takes on its mean value except the one designated in that row of the table above; it takes on the va(ue shown in the row header. Symbo l Key: XX=Independent variable not included in model --=Estimated coefficient on independent variable not significant at 0.05 level, two-tailed test Variable Key: Dependent Variables: GLAUCOMA --l=screened for glaucoma within previous 2 years, 11 months; O=otherwise EYE --l=received eye examination within previous 2 years, 11 months; O=otherwise BLOOOP --l=had blood pressure measured within previous 1 year, 11 months; O=otherwise BREAST --l=had clinical breast examination within previous 1 year, 11 months; O=otherwise PAP --l=had Pap smear within previous 3 years, 11 months; O=otherwise Independent Variables: MALE --l=male; O=female NONWHITE --l=nonwhite; O=white SMSA --l=resides in a Census Bureau Standard Metropolitan Statistical Area (urban area); O=otherwise PREPAID --l=enrolled in HMO or some other prepaid health plan; O=otherwise HLTHINSR --l=has some health insurance coverage or health benefits beyond Medicare including prepaid, Veterans!, military, or means tested public assistance health benefit; O=otherwise ALONE --l=lives alone O=otherwise LIMITED --l=limited in some activity; O=otherwise SOURCE: Office of Technology Assessment, 1989.
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54 The Use of Preventive Services by the Elderly 0 0 w 0 0 0 z E w 4 +
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APPENDIX F: ANALYSIS OF PREVENTIVE SERVICE USE BY OLDER ADULTS IN A HEALTH MAINTENANCE ORGANIZATION Methods The Data OTA contracted with a health maintenance organization (HMO) to provide data on the use of eight preventive services by their n n n n n n n n over-65 year old enrollees: Check-up visit, Cholesterol measurement, Eye examination, Fecal occult blood test, Pap smear, Influenza immunization, Pneumococcal immunization, and Tetanus immunization. OTA chose these services in consultation with the HMO to meet the following criteria: they are services often included among discussions or recommendations for elderly preventive health; and the HMOs data system routinely records their use as distinct services. To examine how use varies with age, the HMO also provided comparable data for enrollees between the ages of 40 and 64. The HMO measured the proportions of enrollees using each service within the periods of time presented in table 8. The HMO is a large, urban, staff model health maintenance organization located in the Northeastern United States. It serves enrollees through private employers, government agencies, and individual accounts. Since January 1976, the HMO has served Medicare beneficiaries, initially under a plan where the HMO billed Medicare fo r Medicare-covered procedures on a fee-forservice basis. The HMO provided non covered procedures, including the preventive services examined in this study, through a wraparound or Medi-gap policy purchased by or for the enrollee. Beginning in July 1985, the HMO entered into a Medicare demonstration risk contract with over 80 percent of its 2500 existing Medicare enrollees transferring into this plan within the first three months. All of the services covered under the HMOs basic benefit package, including preventive services, were included in the risk contract plan. The HMO has traditionally encouraged the use of preventive services by at-risk populations through clinical guidelines for preventive care and coverage of regular checkups. Before October 1987, the monitoring of compliance with these guidelines was limited to pediatric screening and immunization, prenatal screening, and influenza immunization. Since that date, the HMO has adopted a program to monitor and inform clinicians at each visit of a patients compliance with the HMOs preventive guidelines. Since OTA believed that this program is not typical of most HMOs, this HMO used October 1987 as the endpoint for measuring rates of use for each preventive services studied. Hence, during the periods of time examined, only influenza immunizations reflect any monitoring by the HMO, and for that service clinicians only received information on aggregate rates of compliance among all enrollees. Methods of Analysis The base population for this study is all present and former HMO enrollees who were age 40 or older as of October 1, 1987. The HMO identified the base population through a computerized search of enrollment records and separated the population into four subgroups on the basis of age: n 40 to 49 years old, 50 to 64 years old, n 65 to 74 years old, and n 75 years and older. 55
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56 n The Use of Preventive Services by the Elderly Because the HMO calculated age at the end of the observation period, some of the enrollees in each group fell below the low age threshold at the time they actually used a specific service. Through computer searches of this base population, the HMO defined a denominator population for each age group and observation period over which the use of a specific preventive service was to be measured. Each denominator poplation consisted of all persons of appropriate age continuously enrolled in the HMO during the observation period. Since two of the HMOs ten clinics did not have computerized records at the level of specific clinical services, enrollees from these sites were excluded from the analysis. Enrollees excluded from the denominator files because they came from one of the non computerized sites or because they were not continuous members represented 20 percent of the base population in each age group. The denominator file for the over-65 age groups consists of all continuously enrolled individuals from the eight sites. The denominator population for the 40 to 64 age groups were so large that the HMO used a random sample of these groups for the analysis. They chose a 10-percent random sample for all but the 10-year observation period, where they chose a 20-percent rando m sample. Table 13 presents the number of observations in each denominator file used to calculate the rates of use. In order to measure the use of each service for each age group, the HMO searched the base population to form numerator files consisting of persons who met both of the following criteria: n the individual was enrolled in one of the eight sites at the time the analysis was conducted (June through Septembe r 1988); and n the individual received the specific preventive service within the observation period. To calculate rates of use, each numerator file was compared to its corresponding denominator file. Individuals in the numerator file who did not appear in the denominator file were discarded. Stratifying by gender, the HMO tallied the number of individuals remaining in each numerator file and divided that number by the number in the corresponding denominator file to calculate a rate of use for each service and agegender group. Table 14 presents the results of this analysis. It is possible that a few continuously enrolled members transferred from one of the two excluded sites to one of the eight included sites before October 1, 1987. While such individuals would be included in the denominator files, they would not appear in the numerator file if they received a preventive service at the excluded site. This would deflate the use rate. However, because the two excluded sites serve geographically distinct communities with most members living in close proximity to the clinic, transfer to another site is relatively rare. Therefore, OTA and the HMO concluded that the potential undercounting in the use rates is minimal.
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Table 13--Sample Sizes for Each Measurement Period in OTAS Analysis of Preventive Service Use in One HMO 3 yr. and 2 yr. ** 10 yr. period 5 yr. period** ( 10/84-10/87) 1 yr. period** Age ( 10/77-10/88) ( 10/82-10/87) and (10/85-10/87) (10/86-10/87) I I 1 1 Male Female Total Male Female Total Male Female Total Male Female Total 40-4P 250 260 510 455 507 962 725 797 1522 995 1068 2063 50-64 246 227 473 282 331 613 466 518 984 654 784 1438 65-74** 329 307 636 849 ,956 1805 1265 1440 2705 1902 2219 4128 75+** 113 145 258 204 313 517 271 395 666 514 752 1266 \ I I I I l The n for 40-49 and 50-64 age groupa represent a 20 percent sqle of menbers continuously enrolled during each period at the eight sites studied. The 65-74 and 75+ age groups represent all mwbers. l *The n for 40-49 ad SO-64 age groups represent a Fpercent sanp~e of menbers continuously enrolled durirw each period at the eight sites studied. The 65-74 and 75+ age groups represent all merbers. Table 14--Percents of Conti nuously Enrolled Members Receiving Eight Preventive Services During Specified Periods of Time P neunococcal Check-up visit Cholesterol Eye exam Fecal occult blood Pap smear Influenza vaccine vaccine Tetanus vaccine Age (1 year period) (5 year period) (2 year period) (1 year period) (3 year period) (1 year period) (lifetime) (10 year period) Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total 40-49 yrs. 24% 39% 32X 68% 67% 67% 39% 50% 44% 21% 32% 27% N/A 80% N/A 4% 5% 4% 1% 1% 20% 17 18% 50-64 yrs. 38 49 44 80 77 78 51 55 53 36 46 41 N/A 73 N/A 14 12 13 7 5 6 13 13 13 Subtotal 40-64 29 43 37 73 71 72 44 52 48 27 38 33 N/A 77 N/A 88 8 32 3 17 15 16 65-74 yrs. 48 52 50 77 76 76 70 74 72 49 53 51 N/A 75 N/A 55 55 55 29 27 28 47 40 44 75+ yrs. 52 47 49 69 64 66 80 79 79 51 46 48 N/A 60 N/A 67 63 64 47 42 44 40 35 37 Subtotal 65+ 49 51 50 75 73 74 72 75 73 49 52 50 N/A 71 N/A 58 57 57 33 30 31 45 38 42 Abbreviation: N/A = Not applicable. SOURCE: Office of Technology Assessment, 1989. n
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ACRONYMS AAO ACOG ACP ACR ACS ADA A HA AMA AMWA ANA AOA ASPB CDC CPS CTF EKG HCFA HMO IPA MMWR NCHS NCHSR NCI NHIS NHLBI NIH OTA SMSA USPSTF --American --American --American --American --American --American --American --American --American --American --American --American Academy of Ophthalmology College of Obstetrics and Gynecology College of Physicians College of Radiologists Cancer Society Diabetes Association Heart Association Medical Association Medical Womens Association Nurses Association Optometric Association Society to Prevent Blindness --Centers for Disease Control --Current Population Survey --Canadian Task Force on the Periodic Health Examination --Electrocardiogram --Health Care Financing Administration --Health maintenance organization --Independent practice association --Morbidity and Mortality Weekly Report --National Center for Health Statistics --National Center for Health Services Research and Health Technology Assessment --National Cancer Institute --National Health Interview Survey --National Heart, Lung, and Blood Institute --National Institutes of Health --Office of Technology Assessment --Standard Metropolitan Statistical Area --United States Preventive Services Task Force 59
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1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 60 Adams, M., and Kerner, J. F., Evaluation of Promotional Strategies To Solve the Problem of Underutilization of Breast Examination/Education Center in a New York City Black Community, in Issues in Cancer Screening and Communications (New York, NY: Alan R. Liss, Inc., 1982). American Academy of Ophthalmology, Policy Statement, Frequency of Ocular Examinations, approved Feb. 6, 1983. American Cancer Society, Summary of Current Guidelines for the Cancer-Related Checkup: Recommendations (New York, NY: ACS Professional Education Publication, 1988). American College of Physicians, Committee on Immunizations, Guide for Adult Immunization, Philadelphia, PA, 1985. American College of Physicians, Health and Public Policy Committee, The Use of Diagnostic Tests for Screening and Evaluating Breast Lesions, Annals of Internal Medicine 103:143-146, 1985. American College of Radiology, Policy Statement, Guidelines for Mammography, adopted Sept. 22, 1982. American Diabetes Association, f A.D.A. Policy on Screening for Hyperglycemia, June 1983. American Heart Association, Public Screening Strategies for Measuring Blood Cholesterol in Adults--Issues for Special Concern, October 1987. American Optometric Association, National Health Division, Optometry and the Nations Health: Recommendations for the Implementation of Congress National Health Priorities, February 1978. American Society for the Prevention of Blindness, Facts on Blindness and Prevention, February 1988. Andersen, R., and Aday, L. A., Access to Medical Care in the U. S.: Realized and Potential, Medical Care 16:533-546, 1978. Andersen, R., and Newman, J., Societal and Individual Determinants of Medical Care Utilization, Milbank Mere. Fund Q. 51:95-125, 1973. Berwick, D., Screening in Health Fairs: A Critical Review of Benefits, Risks and Costs, Office of Disease Prevention and Health Promotion Monograph Series (Washington, DC: U.S. Government Printing Office, 1985). Blalock, S. J., De Vellis, B. M., and Sandier, R. S., Participation in Fecal Occult Blood Screening: A Critical Review, Preventive Medicine 16:9-18, 1987. Brown, J. T., and Hulka, B. S., Screening Mammography in the Elderly: A Case-Control Study, J. Gen. Intern. Medicine 3:126-131, 1988.
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The Use of Preventive Services by the Elderly 61 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. Burack, R. C., and Liang, J., The Early Detection of Cancer in the Primary-Care Setting: Factors Associated With the Acceptance and Completion of Recommended Procedures, Preventive Medicine 16:739-751, 1987. Calnan, M., Patterns in Preventive Behaviour: A Study of Women in Middle Age, Soc. Sci. Med. 20(3):263-268, 1985. Canadian Periodic Health Examination Task Force, The Periodic Health Examination, Can. Med. Assoc. J. 121(9):1 193-1254, 1979; 130(10):1276-1292, 1984; 134(7):724-727, 1986; 138(7):618-626, 1988. Chao, A., Paganini-Hill, A., Ross, R. K., et al., Use of Preventive Care by the Elderly, Preventive Medicine 16:710-722, 1987. Cohen, D. I., Littenberg, B., Wetzel, C., et al., Improving Physician Compliance With Preventive Medicine Guidelines, Medical Care 20(10):1040-1045, 1982. Cummings, K. M., Becker, M. H., and Maile, M. C., Bringing the Models Together: An Empirical Approach to Combining Variables Used to Explain Health Actions, Journal of Behavioral Medicine 3(2):123-145, 1980. Cummings, K. M., Jette, A. M., Brock, B. M., et al., Psychosocial Determinants of Immunization Behavior in a Swine Influenza Campaign, Medical Care 17(6):639-649, 1979. David, A. K., and Boldt, J. S., A Study of Preventive Health Attitudes and Behaviors in a Family Practice Setting, The Journal of Family Practice 11(1):77-84, 1980. Davidson, R. A., Fletcher, S. W., Retchin, S., et al., A Nurse-Initiated Reminder System for the Periodic Health Examination, Arch. Intern. Med. 144:2167-2170, 1984. DeFriese, G., Director, Health Services Research Center, University of North Carolina at Chapel Hill, personal communication, Washington, DC, May 20, 1988. Fedson, D. S., Influenza and Pneumococcal Immunization Strategies for Physicians, Chest 91:436-443, 1987. Fuchs, V., How We Live (Cambridge, MA: Harvard University Press, 1983). Gallup Organization, The 1987 Survey of Public Awareness and Use of Cancer Detection Tests: Summary of Findings, conducted for the American Cancer Society (Princeton, NJ: Gallup Organization, January 1988). Green, L. W., Kreuter, M. W., Deeds, S. G., et al., Health Education Today and the PRECEDE Framework, n in Health Education Planning: A Diagnostic Approach (Palo Alto, CA: Mayfield Publishing Company, 1980). Grundy, S. M., Greenland, P., Herd, A., et al., Cardiovascular and Risk Factor Evaluation of Healthy American Adults, Circulation 75(6):1340A-1362A, 1987. Harris, D. M., and Guten, S., Health-Protective Behavior: An Exploratory Study, Journal of Health and Social Behavior 20:17-29, 1979. Hayward, R. A., Shapiro, M. F., Freeman, H. E., et al., Who Gets Screened for Cervical and Breast Cancer? Arch. Intern. Med. 148:1177-1181, 1988.
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The Use of Preventive Services by the Elderly 63 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. McDonald, C. J., Hui, S. L., Smith, D. M., et al., Reminders to Physicians From an Introspective Computer Medical Record, Annals of Internal Medicine 100:130-138, 1984. McGinnis, M., The Tithonus Syndrome: Health and Aging in America, in Health Promotion and Disease Prevention in the Elderly, R. Chernoff and D.A. Lipschitz (eds.) (New York, NY: Raven Press, Ltd., 1988). McPhee, S. J., Richard, R. J., and Solkowitz, S. N., Performance of Cancer Screening in a University General Internal Medicine Practice: Comparison With the 1980 Cancer Society Guidelines, Journal of General Internal Medicine 1:275-281, 1986. National Society for the Prevention of Blindness, Glaucoma Patient Guide (New York, NY: April 1984). Radecki, S. E., Kane, R. L., Solomon, D. H., et al., Are Physicians Sensitive to the Special Problems of Older Patients? Journal of the American Geriatric Society 36:719-725, 1988. Riddiough, M. A., Willems, J. S., Sanders, C. R., et al., Factors Affecting the Use of Vaccines: Considerations for Immunization Program Planners, Public Health Reports 96(6):528-535, 1981. Romm, F. J., Fletcher, S. W., and Hulka, B., The Periodic Health Examination: Comparison of Recommendations and Internists Performance, Southern Medical Journal 74(3):265-271, 1981. Rosenau, M. J., and Maxey, K. F., Maxcy -Rosenau Public Health and Preventive Medicine, 1lth cd., J.M. Last (cd.) (New York, NY: Appleton-Century-Crofts, 1980). Rosenstock, I. M., Historical Origins of the Health Belief Model, in The Health Belief Model and Personal Health Behavior, M.H. Becker (cd.), Health Education Monograph, vol. 2, No. 4 (Baltimore, MD: Johns Hopkins University, winter 1974), Rosenstock, I. M., The Health Belief Model and Preventive Health Behavior, in The Health Belief Model and Personal Health Behavior, M.H. Becker (cd.), Health Education Monograph, vol. 2, No. 4 (Baltimore, MD: Johns Hopkins University, winter 1974). Rundall, T. G., and Wheeler, J. R. C., Factors Associated With Utilization of the Swine Flu Vaccination Program Among Senior Citizens in Tompkins County, w Medical Care 17:191200, 1979. Rundall, T. G., and Wheeler, J. R. C., The Effect of Income on Use of Preventive Care: An Evaluation of Alternative Explanations, Journal of Health and Social Behavior 20:397-406, 1979. Salkever, D. S., Accessibility and the Demand for Preventive Care, Social Science and Medicine 10:469-475, 1976. Satariano, W. A., Schwartz, A. G., and Swanson, G. M., Screening for Cervical Cancer: Results From Several Intervention Strategies, in Issues in Cancer Screening and Communications (New York, NY: Alan R. Liss, Inc., 1982). Sawyer, J. A., Earp, J., Fletcher, R. H., et al., Pap Smears in Rural Black Women, from the Department of Medicine and School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, unpublished manuscript.
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64 n The Use of Preventive Services by the Elderly 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. Schwartz, S., University of Pennsylvania School of Medicine, personal communication, Philadelphia, PA, Mar. 16, 1988. Selby, J. V., and G.B. Friedman, Sigmoidoscopy in the Periodic Health Examination of Asymptomatic Adults, report for the U.S. Preventive Services Task Force, Oct. 7, 1987. Slesinger, D. P., Tessler, R. C., and Mechanic, D., The Effects of Social Characteristics on the Utilization of Preventive Medical Services in Contrasting Health Care Programs, Medical Care 14(5):392-404, 1976. Stults, B., Preventive Care for the Elderly, Western Journal of Medicine 141(6):832-845, 1984. Technology Reimbursement Reports, Women Age 40 to Every One to Two Years, AMA Delegates Recommend, Reports 4(28):6, 1988. 49 Should Receive a Mammogram Technology Reimbursement Teitelbaum, M. A., Weisman, C. S., Klassen, A. C., et al., Pap Testing Intervals: Specialty Differences in Physicians Recommendations in Relation to Womens Pap Testing Behavior, Medical Care 26(6):607-618, 1988. Thompson, R. S., Michnich, M. E., Gray, J., et al., Maximizing Compliance With Hemoccult Screening for Colon Cancer in Clinical Practice, Medical Care 24(10):904-914, 1986. U.S. Congress, Congressional Research Service, Medicare: Risk Contracts With Health Maintenance Organizations and Competitive Medical Plans, 88-138 EPW, 1988. U.S. Department of Health, Education, and Welfare, National Center for Health Statistics, Use of Selected Preventive Services U.S--1973, Vital and Health Statistics, Series 10, No. 110 (Washington, DC: U.S. Government Printing Office, March 1977). U.S. Department of Health and Human Services, Health Care Financing Administration, Office of the Actuary, Division of National Cost Estimates, National Health Expenditures, 1986 -2000, Health Care Financing Review 8(4):1-36, 1987. U.S. Department of Health and Human Services, National Center for Health Statistics, The National Health Interview Survey Design, 1973-1984, and Procedures, 1975 -1983, Vital and Health Statistics, Series 1, No. 18 (Washington, DC: U.S. Government Printing Office, August 1985). U.S. Department of Health and Human Services, National Center for Health Statistics, Use of Selected Preventive Services U. S--1982, Vital and Health Statistics, Series 10, No. 157 (Washington, DC: U.S. Government Printing Office, August 1986). U.S. Department of Health and Human Services, National Center for Health Statistics, Adult Health Practices in the United States and Canada, Vital and Health Statistics, Series 5, No. 3 (Washington, DC: U.S. Government Printing Office, May 1988). U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute, Division of Cancer Prevention and Control, Early Detection Branch, Working Guidelines for Early Cancer Detection: Rationale and Supporting Evidence to Decrease Mortality (Bethesda, MD: December 1987).
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