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a Center on Aging, East Carolina University, Greenville, NC
b Department of Anthropology, East Carolina University, Greenville, NC
c Department of Sociology, East Carolina University, Greenville, NC
d Department of English, East Carolina University, Greenville, NC
Correspondence: Jim Mitchell, PhD, Center on Aging, School of Medicine, East Carolina University, Physicians Quadrangle, Building N, Greenville, NC 27858-4354. E-mail: mitchellj{at}mail.ecu.edu.
Laurence G. Branch, PhD
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Abstract |
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Key Words: Aging • Prescription drug mismanagement • Noncompliance
I need help bad with my medication. I am on six different kinds of medication which I cannot afford to buy. Social services say they cannot help me. I have tried everything to get help. My blood and sugar stay up at all times because I cannot afford to take a full dose. I have to skip days and take less. I talk to people every day that have never worked and they get doctor care and medicine and checks. I want to know why people who worked all their lives can't get nothing. (nonsolicited correspondence from a 62-year-old woman)
Although those aged 65 years and older make up 12% of the population, they consume over 30% of all prescription medications (Baum, Kennedy, Knapp, Faich, and Nello 1987
; Mueller, Schur, and O'Connell 1997; Soumerai and Ross-Degnan 1999). Many take several medications simultaneously for multiple chronic conditions. Studies show that, depending upon the population sampled, from 75% to 92% of older adults take at least one prescription medication, with the average older person taking between two and four (Fillenbaum et al. 1993; Helling et al. 1987; Moller and Mathiowetz 1989; Stoller 1988). In 1996, an average of 5.5 outpatient prescriptions were written for those aged 55–64, compared with more than 8 for those aged 65 and older (Copeland 1999). Prescription drug expenditures, moreover, grew at double-digit rates during almost every year since 1980, accelerating to 14.1% in 1997 while total national health expenditures decreased (Copeland 1999). These trends translate into greater per capita spending on prescription drugs among the elderly population compared to other age groups (Copeland 1999; Mueller et al. 1997). Researchers, service providers, and policy makers contend that the escalating cost of prescription drugs is a serious problem that threatens health maintenance and recovery from acute illness episodes among elderly people (Blustein 2000; Rogowski, Lillard, and Kington 1997; Soumerai and Ross-Degnan 1999).
At the federal level, Congress is considering revising the Medicare program to include a prescription drug benefit and whether or not to means test such a benefit (Christensen and Wagner 2000; McKercher 1997; Poisal, Murray, Chulis, and Cooper 1999). Some states have adjusted Medicaid drug benefits in an attempt to reduce costs. New Hampshire recently changed its Medicaid policy from unlimited prescription drug coverage to three drugs per month. Those evaluating the effects of this change noted that the use of essential medications among elderly and disabled persons declined significantly (Soumerai, Avorn, Ross-Degnan, and Gortmaker 1987), and they found chronically ill elderly persons to be twice as likely as controls to enter nursing homes, often permanently, costing far more than what was saved in drug expenditures (Soumerai, McLaughlin, Ross-Degnan, Casteris, and Bollini 1994). Glickman, Bruce, Caro, and Avorn 1994 interviewed physicians following the New Hampshire Medicaid policy change, and 85% of them said that patients had told them about their inability to afford prescribed drugs.
Despite concern about the effect of medication noncompliance upon chronic illness trajectory, use of emergency medical care, and premature institutionalization among elderly people, little is known about what older adults themselves do in response to the rising cost of prescription medications. Rafoul 1986 and Giannetti 1983 have outlined numerous strategies that elderly people use to cope with medication costs, including failure to fill prescriptions, saving medication for future use, forgoing one medication for another, taking less than the recommended dosage, and taking medication prescribed for another person. While these strategies can have important and deleterious effects, particularly when the use of essential drugs is curtailed, it is not clear in these two studies if such strategies are being employed deliberately by elderly people to manage costs or result, instead, from lack of understanding about the appropriate use of medications. It is well established in the literature that as the number of medications taken concurrently increases, so does the probability of misuse resulting from patient noncompliance or physician prescribing errors.
Cooper, Love, and Rafoul 1982 categorize such noncompliance with medication regimens among elders as either intentional or nonintentional. The most common reason for intentional noncompliance among older adults is stopping medication use prematurely because they feel better (Rafoul 1986). Other forms of intentional noncompliance include stopping medication use prematurely because of unpleasant side effects (McGrath 1999), failure to understand the necessity of the medication, forgetfulness or cognitive impairment (Hanlon et al. 1996; McGrath 1999) or inability to afford high prescription drug costs (e.g., Bazargan, Barbre, and Hamm 1993; Cooper et al. 1982; Levit et al. 1998).
Our goals in this study were to document the use of medication mismanagement in a sample of noninstitutionalized, rural elderly people. The dependent variable is the self-reported use of nine different noncompliant strategies to manage prescription drug regimens. These strategies, shown in Table 1 , were derived from transcripts of in-depth interviews with 200 community-dwelling elders who reported using alternative health care practices or practitioners (see Mitchell and Mathews 1992). The list is not intended to be exhaustive but to provide an estimate of how widespread the use of mismanagement strategies is in the study population.
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We hypothesized that several socioeconomic characteristics including income, income adequacy, education, residence, and cohabitation as an indicator of social support would affect mismanagement. Specifically, we hypothesized that elderly people who live alone, are rural residents, have less education, and those who have lower and less adequate incomes would be more likely than others to mismanage their prescription medications. We further hypothesized that older adults of advanced age, men, and African Americans would be more likely than their counterparts to mismanage their medications. Several studies report that African American older adults take fewer prescription medications than do Whites (Fillenbaum et al. 1993, Fillenbaum et al. 1996; Khandker and Simoni-Wastila 1998; Kotzan, Carroll, and Kotzan 1989). This may be because fewer African American elders than Whites have supplementary insurance that pays for drugs (Bazargan et al. 1993), or, as suggested by Fillenbaum and colleagues 1996, it may be because poorer communication between typically White physicians and African American patients leads to noncompliance and/or inappropriate treatment.
Health status indicators hypothesized to affect mismanagement include physician visits for acute symptoms as well as chronic comorbidity, self-reported mental health, and the ability to perform selected activities of daily living (ADLs) without assistance. We hypothesize that elderly people with more acute and chronic care physician visits, with poorer self-reported mental health, and those requiring more assistance performing ADLs will be more likely to mismanage medications.
Finally, we include self-reported consultation by a physician about prescription drugs, the number of prescription medications currently taken, knowledge about what the medications are for, the retail cost of medications, the cost of medications to older persons, and the extent to which Medicaid spend-down or the cost of prescription drugs is a problem for the respondent as predictors of mismanagement. We hypothesize that those reporting consultation with their physician and with more knowledge of their medications are less likely than other older adults to mismanage medications, whereas those taking more medications, costlier medications, and those with problems paying for medications will be more likely than others to mismanage their medications.
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Measurement
Table 2 elaborates upon the characteristics of the sample of prescription drug users by describing the distributions of the variables included in the prediction of prescription drug mismanagement. Variables are categorized as socioeconomic, health status, and medication profile indicators. Continuous variables are coded such that a higher number reflects more of an attribute (e.g., better mental health). The coding of categorical variables will be described.
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Educational attainment is measured such that the highest level, post high school, has a value of 3. Consistent with what would be expected in this largely rural region, just over 40% of the older adults had eighth grade educations or less, and one fifth had some post high school educational experience. Gross annual household income is measured by a series of 14 income categories ranging from under $3,000 to over $100,000 per year. They are coded such that a higher number indicates more annual income. About one third of the older adults had household incomes less than $7,000 per year and another one-third had incomes in excess of $10,000. Income adequacy is the summed response to three questions: worry about having enough income in the future, trouble making ends meet, and enough income for little extras. A higher number—between 3 and 10—indicates more adequate income. Table 2 shows the mean value for income adequacy to be 7.1, indicating that the average respondent considers his or her income to be more adequate than inadequate.
Health status indicators in Table 1 begin with yes (coded 1) or no (coded 0) responses to visiting a physician for 13 acute symptoms that respondents had experienced in the past few months. The symptoms were taken from disease-specific (e.g., cancer, heart disease, or diabetes) warning signs. Across the 13 rather serious acute symptoms, the average number reported was 3 and the number resulting in a physician visit, shown in Table 2 , averaged 2.2. Respondents were also asked whether they go to the doctor on a regular basis (e.g., every few months) for nine chronic conditions (arthritis, breathing problems such as asthma or emphysema, extremity circulation problems, diabetes, stomach or bowel problems such as ulcers, high blood pressure, heart trouble, cancer, and anxiety or depression). Responses to regularly seeing a physician for each problem were yes (coded 1) or no (coded 0). The typical respondent reported 2.3 physician visits associated with the chronic conditions. Responses to both measures of physician visits were summed across either acute symptoms or chronic conditions.
A global approach to measuring mental health based upon self-evaluation was used rather than recall of information that may or may not be relevant to the daily lives of the older adults in our study population. Thus, mental health includes summed responses to seven items. They are: "Are you in good spirits most of the time?", "Do you often feel depressed?", "Do you often feel lonely?", "Are you frequently confused?", "Do you often feel anxious or worried about the future?" (yes, not sure, or no for each), overall self-rated mental health (excellent, good, fair, or poor), and mental health compared to 5 years ago (better, about the same, or worse). Responses to the items were coded so that a higher number reflects better mental health.
Self-reported mental health was generally good. The range of mental health values was from 8 to 22, and Table 2 shows that the average respondent had a self-reported mental health value of 18, quite high given the range. Internal consistency among the mental health items is also good (unrotated principal components factor loadings range from .58 to .72), and the internal reliability is reasonable (alpha = .75).
Items indicating ability to perform personal care and independent in-home living activities were drawn from the Older Americans Resource Survey (OARS; Duke University Center for the Study of Aging and Human Development 1978), the National Health and Nutrition Examination Survey (Cornoni-Huntley, Huntley, and Feldman 1990), the Multidimensional Functional Assessment Instrument (Kane, Bell, Riegler, Wilson, and Kane 1983), and Lawton 1972 Philadelphia Geriatric Center Multilevel Assessment Instrument. Items were chosen to represent a full range of abilities conducive to in-home independent living in this community-dwelling population. Principal components factor analysis results (not shown) indicate that there are two dimensions of activities, each suggesting different in-home competencies. The first, physical ability, includes self-reported ability with no difficulty, some difficulty, much difficulty or inability to rise from a straight chair without arms, walk up and down at least two steps, bend down and pick up clothes from the floor, reach up and get down a 5 lb. bag of sugar from just overhead, get in and out of a car, get in and out of a bathtub, and cut toenails (alpha = .93). Personal care activities consist of difficulty dressing, getting in and out of bed, eating food, washing and drying one's body, using the toilet, and brushing one's teeth or dentures (alpha = .92). Responses to each of the summed measures are coded consistent with ability. Those with the least difficulty across the activities in each dimension have the highest numeric score.
Measures used as indicators of the medication profile of respondents include the number of prescription medications currently taken. During the interview, respondents were asked to produce all of the prescription medications, including pills, capsules, ointments and creams, suppositories, and such they were currently taking. Interviewers recorded the form of each medication (e.g., ointment or capsule), the drug name, dosing regimen, and strength from the label. Table 2 shows that the number of medications taken by respondents ranged between 1 and 14, with the average approaching 4 medications.
Physician consultation is self-reported discussion by physicians of four topics, including medicines that should be taken, when to take them, lower cost medicines, and side effects. Yes (coded 2) or no (coded 1) responses to each are summed. During the interview, respondents were asked whether they knew the purpose of each medication that they were taking. A registered pharmacist coded each response using categories of incorrect (coded 1), partially correct or close (coded 2), or correct (coded 3). Respondents who admitted outright that they did not know what a medication was for were combined with those with attempted but incorrect responses. Because respondents took between 1 and 14 medications, the average response across medications was computed for each respondent, resulting in 171 values between 1 and 3. Judging from the range of values summed across medications for each respondent, it appears that the majority of the respondents indicated that they knew the appropriate use of each medication.
Using the Red Book Advisory Board 1996 Red Book for the year the data were gathered, the registered pharmacist recorded the monthly retail cost, minus dispensing fee, of each medication based upon dosage and, using insurance or Medicaid coverage information, the cost of each medication to the respondent. At the time of the interview, Medicaid paid for up to four medications per enrollee. Because the distributions of these variables are skewed (see Table 1 ), each value was converted to its base 10 logarithmic equivalent. The base 10 log was chosen because the effect of a tenfold increase on the dependent variable is easily interpreted. The final medication profile indicator consisted of responses to a query about difficulty paying for prescription medications or, for those enrolled, meeting Medicaid spend-down requirements. Table 2 shows the distribution of respondents according to category of difficulty, coded such that the higher number represents more difficulty.
The dependent variable as noted previously is the self-reported use of the nine strategies indicative of the mismanagement of prescription drug regimens shown in Table 1 . Affirmative responses were coded 1, and the number of strategies used by each respondent were summed. Because over half (281 or 56.2%) of the respondents used no strategies, the distribution was skewed (M = 1.19, SD = 1.87). Consequently, for the multivariate analysis, mismanagement is dichotomized into one or more strategies (coded 1) versus no strategies (coded 0).
Statistical Analysis
Because the dependent variable is dichotomous, the main effects of the socioeconomic, health status, and medication profile indicators were assessed using the LOGISTIC procedure of the SUDAAN (release 7.11) statistical package (Shah, Barnwell, and Bieler 1996). This package adjusts the standard errors of prediction with nonrandom samples by population weighting according to the categories of a constructed variable consistent with sample selection criteria. Our sample size of approximately equal numbers of persons by race and gender was, consequently, adjusted using estimates of race- and gender-specific persons in the 33-county study region. Statistical results provide model parameter estimates that simulate random sample results of tests of the null hypothesis that regression coefficients are equal to 0. The LOGISTIC procedure is appropriate for the analysis of individual main effects and the overall significance of multivariate predictions of dependent Boolean variables with values of 0 and 1.
In order to assess whether predictive effects are spurious and to look at change in predictive effectiveness as additional variables are added to the analysis, we examined the main effects of the predictive variables in three phases. The first includes only socioeconomic characteristics hypothesized to affect self-management strategies. Second, the effects of the health status characteristics are added. The third phase includes the effects of the combined socioeconomic, health status, and medication profile indicators. For each model, we report a chi-square goodness-of-fit value plus a multiple R2 estimate, similar to Aldrich and Nelson 1984 pseudo-R2 analog.
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Table 1 shows that, even when the analysis is not restricted to more expensive medications, older adults use a variety of strategies to manage the cost of their prescription medications. Just under one fifth (83 or 17%) buy part of a prescription instead of all of it; 15% of respondents take less medicine than prescribed to make it last; and 19% ask their doctors for free samples. It is clear that a sizable proportion of respondents have difficulty either obtaining their medication or taking it as prescribed. Although the number of persons using any one strategy is not excessive, 44% use one or more self-management strategies and 19% or about one fifth, use three or more strategies. That almost half of the respondents use at least one strategy is of concern, as the use of even one indicates problems obtaining medication or difficulty complying with their medication regimen as prescribed.
Logistic Regression Models
The hypothesized main effects of the socioeconomic, health status, and medication profile indicators are shown by the odds ratios in Table 3 . The values in the first column suggest that, among African Americans, the odds of mismanaging prescribed medications are increased by 1.40 compared to Whites. Mismanagement is slightly less common among older, compared to younger, respondents although the odds ratio is not particularly large. For each increase of one year in age, the odds of mismanaging prescription drugs decreases by 3%. The model 
2 and R2 estimates indicate that the prediction of mismanagement from only socioeconomic indicators is not particularly effective. Neither income nor income adequacy has much effect on mismanagement.
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2 and R2 estimates in the first and second columns, the addition of health status indicators only slightly improves the prediction. The third column of odds ratios in Table 3 adds the effects of medication profile indicators in the prediction of prescription drug mismanagement. The odds ratios again show that an increase of one year in age results in a 4% decrease in the odds of prescription drug mismanagement. Controlling other socioeconomic indicators as well as health status and medication profile indicators, the odds of prescription medication mismanagement are increased to 1.84 among African American respondent compared to Whites. Other findings in the third column of Table 3 suggest that as acute care physician visits increase by 1, mismanagement of medications increases by 1.2 times. The effect of mental health remains largely unchanged with the addition of the medication profile indicators.
Underscoring the importance of the cost of medications for older adults' compliance with prescribed dosing regimens, a one-unit increase in difficulty paying for prescription medications increases the odds of mismanagement about 2.5 times. An increase of one unit in the measure of knowledge of what medications are for also results in a 3% decrease in prescription drug mismanagement. This third phase of model development provides a reasonable prediction of drug mismanagement, with roughly 33% of the variation in mismanagement explained.
In sum, the third column of odds ratios provides a profile of older persons who are more likely than others to mismanage prescription medications in ways that are detrimental to their treatment effectiveness. These persons are older adults who are African American, younger, with more visits to physicians for acute health problems, poorer self-rated mental health, less knowledge of what drugs are for, and with more difficulty paying for medication, including spend-down requirements for Medicaid eligibility.
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). Such estimates, however, are highly uncertain and do not consider the possible effect that a more comprehensive benefit might have on driving up prices for some drugs. While some have suggested that any drug benefit must be tied to financial need in order to hold down overall program costs, relatively little is known about the characteristics of older adults impacted by this problem. Without such data, it is difficult to formulate meaningful policies that inform which segments of the population should receive such a benefit. In this article, we describe the extent to which prescription medication costs are a problem in a largely rural, noninstitutionalized population of older adults, and we describe a method to assess the impact of medication costs on adherence to prescribed medication regimens.
Findings not shown in the tables indicate that more than half (about 56%) of 499 older adults using one or more prescription medications report no difficulty paying for their medications or paying the annual deductible required for the Medicaid prescription drug benefit ($250 deductible and a four-drug benefit in the study region). In contrast, 149 of 499 respondents (about 30%) said they have a little or some difficulty paying for their medications. Most importantly, 68 (about 14%) of the prescription medication users interviewed reported a great deal of difficulty paying for their prescription medications. Findings from the multiple logistic regression analysis, moreover, demonstrate that older adults who report more difficulty paying for prescription medications are significantly more likely than others to mismanage medications, reducing compliance with medication regimens that can ultimately result in adverse health effects.
Additional analysis not shown also suggests that those reporting either some or a great deal of difficulty paying for their medications are more likely to be African American than White (2 = 29.8, p < .0000, Cramer's V = .24) and they are more likely to live in a rural compared to nonrural area (2 = 19.74, p < .002, Cramer's V = .20). Thus, it would seem prudent to consider targeting African American as well as rural elderly residents, in particular, in efforts to promote a prescription drug benefit.
The findings from this study, therefore, provide preliminary support for the idea of means testing a Medicare prescription drug benefit with targeted intervention. Our data suggest that such a benefit would be of assistance to about 30% of the population studied and of significant importance to the poorest 14% of the sample. Alternatively, 56% of those surveyed reported little difficulty paying for prescription medications and would not immediately benefit from such coverage. Of course, circumstances change and many of those presently able to afford medication might not be if their health profiles worsened or if their financial status changed. Nonetheless, these data suggest that financial assistance for medications should be targeted to reach those most in need of such a benefit—a policy that would also help to contain the overall costs of such a program.
Although well over half of the population surveyed reported little difficulty paying for prescription medications, we still found the mismanagement of prescription medications to be quite widespread (see Table 1 ). Forty-four percent of the older adults interviewed who were taking at least one prescription medication used one or more mismanagement strategies. Almost one fifth of the respondents used three or more strategies. These findings suggest that difficulty paying for medication is not the only factor prompting people to mismanage medications. To explore this issue further, we assessed the effects of characteristics of older adults hypothesized to influence mismanagement, including socioeconomic, health status, and medication profile variables.
Multiple logistic regression results suggest that older adults who are African American, as well as those who are younger in the group aged 66 and over, are more likely than others to mismanage medications. Although the population includes older adults with a wide income range, we found that neither categorical household income nor income adequacy had an effect on mismanagement. One could speculate that this is because controlling race in the analysis accounts for the effects of income and income adequacy. When income and income adequacy are eliminated in the analysis, however, the effects of race and age remain essentially unchanged. This suggests that prescription medication mismanagement is indeed more likely to occur among older people who are African American and younger elderly people regardless of income.
Other studies also document the tendency of African Americans to use fewer prescription medications than White Americans (Fillenbaum et al. 1993; Khandker and Simoni-Wastila 1998; Kotzan, Carroll, and Kotzan 1989) although the reasons for this disparity are not well understood. Fillenbaum and associates 1993 study of a sample of community-residing elders found that health status and use of medical services were the most important predictors of prescription drug use for both African Americans and Whites. Thus, drug use may be lower among African Americans because they are less likely to use health services overall. Bazargan and coworkers 1993 found that the failure to fill prescriptions was greater among the African American elders who consumed more over-the-counter medications, suggesting that they might be resorting to self-treatment as a substitute for medical care. Our data, however, indicate that—with the exception of poor mental health—overall health status, which included an assessment of physician visits, did not affect prescription drug use. Previous research by two of this study's authors (Mitchell and Mathews 1992) would suggest, moreover, that some rural African American elders choose to rely on self-care and the use of alternative medications such as herbs and religious healing instead of biomedical services. In other words, noncompliance with prescription medication regimens, particularly in rural communities with strong alternative healing traditions, may be the result of preferential choice of resources as much as it is due to lack of access to care.
It is important to note, however, that Fillenbaum and colleagues 1993 study did document an additional finding of importance related to finances. The authors found that having Medigap insurance for Whites and being on Medicaid for African Americans were factors predicting prescription drug use. The implication of their finding is that many African Americans may earn too much to qualify for Medicaid but too little to afford Medigap or other supplemental insurance to pay drug costs not covered by Medicare, thus leading them to resort more to medication management strategies to control expenses. Indeed, a recent study of Medicare beneficiaries by Poisal and Chulis 2000 found that those enrollees without drug insurance consistently used fewer prescriptions, spent more out of pocket, and had less in total drug expenditures than their insured peers.
These two factors (health status and insurance coverage) might also account for the somewhat puzzling finding that younger elders are more likely than older elders across both racial groups to mismanage medications. Again, the younger elderly group may have better overall health status and thus tend to use health services less frequently or to perceive less need for supplemental insurance to cover drug costs. Alternatively, those younger elders at the start of a chronic illness trajectory may have more financial resources available than the older group and may not yet have met the spend-down requirements necessary to qualify for Medicaid. Either of these factors could lead these younger elders to mismanage medications.
The multiple logistic regression analysis also found those reporting poorer mental health to be more likely than others to mismanage medications. Elderly people in poorer mental health with multiple prescriptions may find it difficult to remember the physician's instructions for each medication. Forgetfulness has also been cited as a factor in the failure to have prescriptions filled initially (Bazargan et al. 1993). Alternatively, poor mental health may reflect cognitive impairment and an inability or unwillingness to comply with medical regimens as prescribed. A large community-based study by Hanlon and colleagues 1996 determined that elderly participants with cognitive impairment were less likely than cognitively intact subjects to use any prescription medications, and the authors concluded that drug use patterns by elderly people vary with cognitive status. More research needs to be done, however, to determine the extent to which cognitive impairment and/or the presence of specific mental disorders leads to the use of specific self-management strategies and whether such noncompliance with medication regimens is intentional or unintentional.
As hypothesized, those with more acute-care physician visits are 1.2 times more likely than their counterparts to mismanage their medications. On the surface, this finding would seem to contradict a widespread observation in the literature that the cost burden for prescription medication is greatest for elderly people with chronic diseases (Mueller et al. 1997; Steinberg et al. 2000). It has been well documented that among the elderly population, 36% have three or more chronic conditions and account for 57% of drug expenditures for this group (Mueller et al. 1997). Given the statistical controls in our analysis, our finding suggests that for older adults already suffering from chronic disease, the experience of acute illness episodes may exacerbate problems paying for prescription medications, resulting in higher probability that drug mismanagement will occur.
Finally, our study demonstrates that the number of medications currently taken, the retail cost of the medications, and the cost to the patient had no effect on mismanagement. This finding may be an artifact of the similar pattern that results when an older adult takes several lower cost medications or a few expensive ones. It is also possible that the cost of medication can potentially affect older adults across the income spectrum, with lower cost medication being a problem for persons with lower income and higher cost medications being problematic for persons with higher incomes. Alternatively, statistically controlling race and residence, our findings suggest that the national problem of prescription drug affordability is another example of a pervasive pattern in health inequality characterized by poorer access among rural African American older adults. They likely have more trouble paying for drugs or meeting the requirements of Medicaid spend-down, and they are probably more likely to deviate from prescribed drug dosage regimens after obtaining the medications.
Limitations of the Study
We recognize that the work described here has limitations. For example, we did not include the role of informal caregivers who help older adults manage their prescription medication regimens. We also recognize measurement shortcomings, such as our inability to assess the quality of consultation with physicians or pharmacists about dosing regimens. We also recognize that acute episodes resulting in physician visits could result from improperly taking medications as well as the reverse. The cross-sectional design impedes our ability to clarify the time ordering of these events. Finally, a number of factors need further exploration, including the relationship of poor mental health to cognitive impairment, the relationship of health status to health services utilization, and the relationship of age and race to the need for and the ability to purchase supplemental Medicare insurance or other private insurance and to the use of alternative health care resources.
Implications of the Study
Nonetheless, this research provides important empirical data on the degree to which prescription medication costs are a burden for a randomly selected population of noninstitutionalized elderly adults. The findings suggest that any revision of Medicare to include a prescription drug benefit should explore targeting that benefit to those with the greatest need. These data also demonstrate clearly that the mismanagement of medications is not solely the result of the inability to pay for prescription drugs. Rather, African Americans and the younger elderly population, those with poor mental health, those with less knowledge about the purpose of their medications, and those with more acute-care episodes are also more likely to mismanage medications. These observations are important because they demonstrate that the addition of a prescription drug benefit to Medicare will not completely solve the problem of medication noncompliance among elderly people. Additional measures will be necessary, including (a) assistance to those with poor mental health, and (b) special attention to the drug load prescribed for those elderly persons with multiple acute-care episodes, especially if they also suffer from additional chronic conditions. Finally, these data suggest that the gap in the care received between African Americans and Whites is not due solely to socioeconomic factors. Any interventions targeted toward improving compliance with medication regimens must be tailored to the particular needs and concerns of different ethnic groups.
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Received for publication August 7, 2000. Accepted for publication February 1, 2001.
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