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RESEARCH AND PRACTICE |
Joseph J. Sudano Jr is with the Center for Health Care Research and Policy, Case Western Reserve University at the MetroHealth System, and the Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio. David W. Baker is with the Feinberg School of Medicine, Northwestern University, Evanston, Ill.
Correspondence: Requests for reprints should be sent to Joseph J. Sudano Jr, PhD, MetroHealth System, Rammelkamp 236, 2500 MetroHealth Dr, Cleveland, OH 44109-1998 (e-mail: jsudano{at}metrohealth.org).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. This study examined the association between intermittent lack of health insurance coverage and use of preventive health services.
Methods. Analyses focused on longitudinal data on insurance status and preventive service use among a national sample of US adults who participated in the Health and Retirement Study.
Results. Findings showed that, among individuals who obtain insurance coverage after histories of intermittent coverage, relatively long periods may be necessary to reestablish clinically appropriate care patterns. Increasing periods of noncoverage led to successively lower rates of use of most preventive services.
Conclusions. Intermittent lack of insurance coverage—even across a relatively long period—results in less use of preventive services. Studies that examine only current insurance status may underestimate the population at risk from being uninsured.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Most studies of the uninsured population have focused on individuals who lack insurance coverage at the time they are interviewed or who have been uninsured for all of the previous year. In fact, health insurance coverage is quite dynamic, with many people being uninsured for substantial portions of a year or intermittently uninsured over several years.6–9 Recent studies have shown that among individuals who lack health insurance coverage for even relatively short periods of time, there is a significantly increased likelihood of delays in seeking recommended follow-up care,10 care for chronic conditions, and clinically indicated preventive services.11 Health insurance status can change frequently within a given year and over a period of several years; thus, most figures may substantially underestimate the pool of individuals who are potentially vulnerable to the effects of noncoverage.8,9,11–13
We recently reported that being continuously or intermittently uninsured increases the likelihood of adverse health outcomes among the near-elderly population.14 To examine possible mechanisms in the causal pathway between insurance status and health outcomes, we undertook an analysis of the relationship between lack of health insurance coverage and use of preventive services. In contrast to previous studies, we used longitudinal data, available in the national Health and Retirement Study (HRS), to focus on how 2 different measures of insurance status—insurance status in 1994 and 1996 and episodes of noncoverage during 1992 through 1996—predicted use of preventive services among older adults over a 2-year reference period. These 2 insurance status measures allowed us to answer the following questions: (1) How does loss or acquisition of insurance affect the use of preventive services? and (2) Do multiple episodes of noncoverage increase the risk of underuse of preventive services?
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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In all analyses, we used the survey subroutines available in Stata 7.0 (Stata Corp, College Station, Tex) to account for sample weighting and to adjust variances for the complex survey design of the HRS. Descriptive statistics presented here include frequency distributions for all of the study variables and cross tabulations of preventive service use by each of the various insurance status measures. Cross-tabulation significance levels were based on second-order corrected Pearson 
2 statistics for categorical variables.16,17
Multivariate logistic regression models were constructed via the Stata (version 7.0) "svylogit" procedure. This logistic regression subroutine simultaneously applies analytic weights (i.e., adjustments for probability of selection into the sample and for differential response rates, and poststratification adjustments to Bureau of the Census population estimates for age, race/ethnicity, and geographic location) and adjusts variances for complex survey sampling designs. A P value of .05 was used to define statistical significance.
Because the outcomes of interest (use of various types of preventive services) were common in this study, reliance on odds ratios (ORs) would exaggerate the true relative risk. We corrected for this effect by converting adjusted odd ratios to "corrected relative risks," using the following formula provided by Zhang and Yu18: corrected relative risk = OR/(1 – PO) + (PO x OR), where OR is the adjusted odds ratio obtained from the exponentiated logistic regression coefficients and PO is the prevalence of the outcome of interest for the referent category, expressed as a proportion.
Outcome Measures
All of the outcome measures included in this study were assessed at the 1996 interview (wave 3) and were based on selfreports. The preventive service use section of the questionnaire begins as follows: "In the last two years, have you had any of the following medical tests or procedures?" We refer to this 2-year time period preceding the 1996 interview as the "reference period." Subsequent questions ask about specific procedures and tests obtained during the reference period, such as flu shots and cholesterol blood tests. Also, female respondents are asked whether they check their breasts for lumps monthly, as well as whether they have had a Pap test or a mammogram or x-ray of the breast to search for cancer in the previous 2 years. Male respondents are asked whether they have had a prostate examination to screen for cancer.
Responses to these items were coded as 0 (no) or 1 (yes). We refer to our outcome variables as (1) mammography, (2) Pap test, (3) cholesterol test, (4) influenza vaccination, (5) prostate examination, and (6) breast self-examination. Because it is unlikely that lack of insurance coverage poses any constraint or barrier per se on breast self-examination, in that access to a provider is not necessary to engage in this preventive health behavior, we included this outcome variable as a source of "internal control," thereby providing a test of the validity of the findings.
Insurance Status
First, we determined each respondent’s insurance status at the 1992, 1994, and 1996 interviews. On the basis of self-reports, we categorized individuals as privately insured (individually purchased or employment-based coverage), publicly insured (coverage via Medicaid, Medicare, veterans’, CHAMPUS [Civilian Health and Medical Program of the Uniformed Services], or other government insurance), or uninsured (no health insurance of any kind or only catastrophic coverage). In several analyses not described here, there was little difference in the use of preventive services between privately insured and publicly insured individuals (in fact, publicly insured individuals were more likely than privately insured individuals to use some of the preventive services examined). Therefore, we combined privately and publicly insured individuals into a single "insured" category for all analyses conducted in this study.
Next, we categorized respondents according to their insurance status at the beginning and end of the reference period (1994 and 1996): insured at both interviews, lost insurance between 1994 and 1996, obtained insurance between 1994 and 1996, or uninsured at both interviews (note that persons with either private or public insurance coverage at the time of the interview were classified as insured). We hypothesized that, in our analyses, the group of individuals who lost insurance during the reference period would exhibit less use of preventive services than the group that obtained insurance.
Finally, we categorized respondents according to the number of times (0 [referent], 1, 2, or 3) they were uninsured at each of the 3 interviews (1992, 1994, and 1996). Again, those with either public or private insurance coverage at the time of the interview were categorized as being insured. Initially, we conducted several analyses with the categorical variable of all possible insurance "states" over the 3 interviews (insured/insured/uninsured, insured/uninsured/insured, etc.). However, there were too few individuals in some of the categories to provide sufficient power for stable and accurate estimates. Thus, we used number of episodes of noncoverage in our final classification. This final classification allowed us to determine whether the risk pool of individuals underusing preventive services is, in fact, larger when intermittent insurance status over a relatively long time period is taken into account and whether number of episodes of noncoverage has a "dose–response" effect on service use.
Sociodemographic Characteristics, Health Behaviors, and Health Status Measures
We selected variables a priori according to the categories of predisposing, enabling, and need-related characteristics of Andersen’s behavioral model of health service use and included them in all of our multivariate models.19–21 We also included several "unique" measures in our analysis to better control for the effects of socioeconomic status and educational attainment. For example, we included net worth because we wanted to complement the use of total family income adjusted for family size with another measure of wealth, thereby reducing the "residual confounding" associated with inclusion of only income.
Likewise, we included a measure of cognitive ability as an additional control factor beyond educational attainment. Because higher levels of educational attainment affect use of services both through their influence on income and through their contribution to a sense of efficacy and control over life,22,23 some residual confounding could occur if, among a cohort of individuals whose educational opportunities have historically been limited, only an education variable were included. Cognitive ability may also influence efficacy and control. Our inclusion of cognitive ability, which some might refer to as "intelligence,"24–26 provided an additional measure of control beyond the enabling factor of education alone.
All variables were based on self-reports. Predisposing characteristics were as follows: age in years (discrete, as measured at the time of the 1996 interview), sex (male as referent), and racial/ethnic group membership (non-Hispanic White/"other" [referent], nonHispanic Black, Hispanic). Enabling factors were insurance status (as described earlier), educational attainment (0–8 years [referent], 9–11 years, high school diploma or equivalent, some college or college degree), cognitive ability quartile (lowest quartile as referent; based on scores on immediate and delayed word recall and word similarity items included in the HRS; see Wray et al.27 for further details), 1994 income-to-needs ratio adjusted for family size (less than 100% of poverty [referent], 100%–149%, 150%–199%, 200%–299%, 300%–499%, 500% or more), change in income-to-needs ratio during 1994 through 1996 (continuous measure derived by subtracting 1996 ratio from 1994 ratio; range: –5 to 5), and 1994 total net worth quintile in $1000s (less than $20.2 [referent], $20.2–$69.0, $69.1–$138.0, $138.1–$286.0, more than $286.0).
Need-related factors included 1994 smoking status (never smoked [referent], past smoker, current smoker), 1994 alcohol consumption pattern (abstainer [no alcohol consumption], moderate drinker [1 or 2 drinks per day; referent], heavy drinker [3 or more drinks per day]), and past or current problem drinking behavior (coded as 0 [referent], 1, or 2 or more CAGE score positive indicators).28 (The CAGE is a 4-question alcoholism screening instrument whose name is a mnemonic designating an individual who has attempted to "cut down" on alcohol consumption, is "annoyed" by criticism of his or her drinking, feels "guilty," and needs an "eye-opener" drink in the morning. The presence of 2 or more of these characteristics is considered indicative of an alcohol use disorder. CAGE score information was available only from the 1992 interview.) Other need-related factors were 1994 weight status (underweight/normal weight [body mass index below 25 kg/m2; referent], overweight [body mass index of 25–25.9 kg/m2], or obese [body mass index of 30 kg/m2 or above]),29 number of chronic diseases reported in 1994 (hypertension, diabetes, heart disease, chronic lung disease, cancer, arthritis, stroke, or difficulties with vision, summed and coded as 0, 1, 2, or 3 or more), and both 1994 and 1996 self-reported overall health (excellent, very good, good, fair, poor [referent]).
Health behaviors or characteristics such as tobacco use patterns, alcohol use patterns, and body mass index were not included as intrinsically indicative per se of factors affecting use of services. Negative health behaviors were included instead as possible indicators of an individual’s having a less healthy approach to life in general and, hence, being less likely to participate in or seek out preventive services.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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). Overall, 21.2% (weighted value: 19.1%) of the respondents reported having at least 1 episode of noncoverage between 1992 and 1996 (Table 1). Thus, the pool of individuals who were continuously or intermittently uninsured was approximately 40% greater than the estimated number of uninsured individuals derived from data on baseline 1992 insurance status alone. Additional participant characteristics are shown in Table 1.
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Use of Preventive Services
Table 2 presents unweighted and weighted percentages of participants who reported use of preventive services during the reference period. Rates of preventive service use varied from 38.8% for influenza vaccinations to 71.8% for mammograms. Prevalence data from the Behavioral Risk Factor Surveillance System for the years 1995 and 199630 indicate that the overall preventive service use rates observed in this study are comparable to nationwide rates (data not shown). No national data on breast self-examination were available for comparison.
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presents bivariate and multivariate logistic regression results in the form of crude and adjusted relative risks for use of services, by insurance status, in 1994 and 1996. According to the bivariate results, those who reported being uninsured at both the 1994 and 1996 interviews were significantly less likely than those reporting coverage to use preventive services requiring access to a health provider (i.e., mammography, Pap test, cholesterol test, influenza vaccination, and prostate examination) (Table 3). The multivariate results exhibited the same pattern, with those who were uninsured at both interviews having a relative risk of receiving services 29% to 47% less than that of those who were insured at both interviews. Although this group tended to be less likely to perform breast self-examinations, the difference was far less pronounced than the reductions in preventive service use (Table 3).
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). The same pattern persisted in multivariate analyses for all services other than influenza vaccination (results for influenza vaccination were not statistically significant but exhibited the same trend). In the multivariate analyses, results for respondents who obtained insurance coverage during the reference period (and whom we hypothesized might "catch up" in terms of service use) were only marginally better than those for respondents who lost insurance in regard to mammography (RR = 0.83; 95% CI = 0.71, 0.95 vs RR = 0.86; 95% CI = 0.75, 0.95) and Pap tests (RR = 0.86; 95% CI = 0.71, 0.99 vs RR = 0.89; 95% CI = 0.76, 0.99). Multivariate results for cholesterol test use showed that respondents who obtained insurance had rates of use that were approximately halfway between those of respondents who lost insurance and those of respondents who had continuous coverage. Their rates "caught up" with the rates of those with continuous coverage only in the case of influenza vaccination. Results for prostate examinations were not statistically significant in the multivariate analysis but again exhibited the same trend as that for cholesterol testing and mammography. Finally, there was no relationship between breast self-examination and insurance status, which again suggests that our finding of less use of preventive services among those with intermittent coverage was not spurious or due to reporting bias.
Episodes of Noncoverage
Table 4 presents bivariate and multivariate logistic regression results in the form of crude and adjusted relative risks according to number of episodes of noncoverage between 1992 and 1996. All statistical tests involved comparisons with the referent (no episodes of noncoverage) group. In the case of all preventive services requiring access to a health care provider (influenza vaccination, cholesterol test, mammography, Pap test, prostate examination), crude prevalence ratios were statistically significant for respondents in all categories of noncoverage (relative to those reporting no episodes of noncoverage).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Persons who obtained insurance during the reference period did not rapidly "catch up" with their already-insured peers in regard to use of preventive services. At the time of their 1996 interview, individuals who acquired insurance coverage between 1994 and 1996 were still less likely than individuals who were insured at both interviews to report that they had obtained necessary preventive services over the previous 2 years. This finding suggests that a relatively long period of time may elapse before appropriate patterns of care can be reestablished. We were unable to accurately estimate the interval typically required for this process, but our data suggest that a period in excess of 2 years may be necessary.
Our study involved a number of limitations. First, insurance status was assessed at only 3 points. It is likely that a significant number of people classified as continuously insured experienced changes in coverage that were not reflected in our data. In addition, many individuals who are insured are underinsured and may face high deductibles and copayments that deter their use of preventive services. Such effects can lead to underestimates of the true differences between individuals who are intermittently uninsured and individuals who have continuous insurance with adequate coverage for preventive services. Moreover, some uninsured individuals can take advantage of free cancer screening programs available in their communities. Previous studies have revealed large discrepancies in access to care among uninsured persons in several communities across the country,31 so that our results may significantly underestimate the effects of intermittent lack of insurance coverage on use of preventive services in areas that do not have established, well-publicized free screening programs.
Second, there is significant debate about whether prostate examinations are beneficial, raising questions about their utility as an outcome measure. Healthy People 2000 did not recommend that questions regarding prostate examinations be included in health surveys.32 In addition, routine rectal examination to screen for prostate cancer is not recommended by the US Preventive Services Task Force.1 However, these changes in thinking about the appropriateness of rectal examination cancer screening were not published until 1996, after the data for this study had been collected. Thus, although rectal examination of the prostate is no longer recommended as a screening measure, its use in our study reflects the standard of care at the time the data were collected. The lower rates of use observed here among uninsured individuals may not have any clinical significance, but they nevertheless exemplify the relationship between insurance coverage and use of preventive services.1 Finally, our results are not generalizable to adults in age groups different from those in our study population.
In summary, our results provide additional evidence that intermittent lack of health insurance coverage undermines access to preventive services, even after adjustment for a complex array of variables to account for the potentially confounding effects of socioeconomic status and health behaviors. Because both individuals who are continuously uninsured and those who experience intermittent periods of noncoverage are at higher risk for underuse of clinical preventive services and for other problems with access to care, the United States should begin formal tracking of the number of individuals who are intermittently uninsured at any point in time over a period of several years. Additional policy initiatives are needed to promote stability in insurance coverage.
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Acknowledgments |
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Human Participant Protection
No protocol approval was needed for this study, as the data are publically available and contain no personal identifiers.
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Footnotes |
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J. J. Sudano Jr led the writing and completed the analyses. Both authors helped in conceiving the study, conceptualizing ideas, interpreting results, and reviewing drafts of the article.
Accepted for publication August 23, 2002.
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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2. Kiefe CI, Funkhouser E, Fouad MN, May DS. Chronic disease as a barrier to breast and cervical cancer screening. J Gen Intern Med.1998;13:357–365.[ISI][Medline]
3. Potosky AL, Breen N, Graubard BI, Parsons PE. The association between health care coverage and the use of cancer screening tests: results from the 1992 National Health Interview Survey. Med Care.1998;36:257–270.[ISI][Medline]
4. Schoen C, DesRoches C. Uninsured and unstably insured: the importance of continuous insurance coverage. Health Serv Res.2000;35:187–206.[ISI][Medline]
5. Hsia J, Kemper E, Kiefe C, et al. The importance of health insurance as a determinant of cancer screening: evidence from the Women’s Health Initiative. Prev Med.2000;31:261–270.[ISI][Medline]
6. Bennefield RL. Health Insurance Coverage: 1997. Washington, DC: US Dept of Commerce; 1998. Current Population Reports, Series P-60, No. 202.
7. Bennefield RL. Dynamics of Economic Well-Being: Health Insurance, 1993–1995. Who Loses Coverage and for How Long? Washington, DC: US Dept of Commerce; 1998. Current Population Reports, Series P-70, No. 64.
8. Jensen GA. The dynamics of health insurance among the near elderly. Med Care.1992;30:598–614.[ISI][Medline]
9. Sloan FA, Conover CJ. Life transitions and health insurance coverage of the near elderly. Med Care.1998;36:110–125.[ISI][Medline]
10. Burstin HR, Swartz K, O’Neil AC, Orav EJ, Brennan TA. The effect of change of health insurance on access to care. Inquiry.1998;35:389–397.
11. Ayanian JZ, Weissman JS, Schneider EC, Ginsburg JA, Zaslavsky AM. Unmet health needs of uninsured adults in the United States. JAMA.2000;284:2061–2069.
12. Swartz K, Marcotte J, McBride TD. Personal characteristics and spells without health insurance. Inquiry.1993;30:64–76.[ISI][Medline]
13. Swartz K, Marcotte J, McBride TD. Spells without health insurance: the distribution of durations when left-censored spells are included. Inquiry.1993;30:77–83.[ISI][Medline]
14. Baker DW, Sudano JJ, Albert JM, Borawski EA, Dor A. Lack of health insurance and decline in overall health in late middle age. N Engl J Med.2001;345:1106–1112.
15. Heeringa SG, Connor JH. Technical description of the Health and Retirement Study sample design. Available at: http://hrsonline.isr.umich.edu/intro/sho_intro.php?hfyle=uinfo. Accessed October 14, 2002.
16. Rao JNK, Scott AJ. On 2 tests for multiway contingency tables with cell proportions estimated from survey data. Ann Statistics.1984;12:46–60.
17. Rao JNK, Scott AJ. The analysis of categorical data from complex sample surveys: 2 test for goodness of fit and independence in two-way tables. J Am Stat Assoc.1981;76:221–230.[ISI]
18. Zhang J, Yu KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA.1998;280:1690–1691.
19. Andersen R, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Q.1973;51:95–124.
20. Aday LA, Andersen R. A framework for the study of access to medical care. Health Serv Res.1974;9:208–220.[Medline]
21. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav.1995;36:1–10.[ISI][Medline]
22. Ross CE, Mirowsky J. Refining the association between education and health: the effects of quantity, credential, and selectivity. Demography.1999;36:445–460.[ISI][Medline]
23. Mirowsky J, Ross CE. Education, personal control, lifestyle, and health: a human capital hypothesis. Res Aging.1998;20:415–449.[Abstract]
24. Gottfredson LS. Why g matters: the complexity of everyday life. Intelligence.1997;24:79–132.[ISI]
25. Hogan RT, Harkness AR, Lubinski D. Personality and individual differences. In: Pawlik K, Rosenzweig MR, eds. International Handbook of Psychology. London, England: Sage Publications Ltd; 2000:283–304.
26. Lubinski D, Humphreys LG. Incorporating general intelligence into epidemiology and the social sciences. Intelligence.1997;24:159–201.
27. Wray LA, Herzog AR, Willis RJ, Wallace RB. The impact of education and heart attack on smoking cessation among middle-aged adults. J Health Soc Behav.1998;39:271–294.[ISI][Medline]
28. Mayfield D, McLeod G, Hall P. The CAGE questionnaire: validation of a new alcoholism screening instrument. Am J Psychiatry.1974;131:1121–1123.
29. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: Evidence Report. Bethesda, Md: National Institutes of Health; 1998. NIH publication 98-4083.
30. National Center for Chronic Disease Prevention and Health Promotion. Behavioral Risk Factor Surveillance System online prevalence data 1995–2000. Available at: http://apps.ncdc.gov/brfss. Accessed October 14, 2002.
31. Cunningham PJ, Kemper P. Ability to obtain medical care for the uninsured: how much does it vary across communities? JAMA.1998;280:921–927.
32. Healthy People 2000 Final Review. Hyattsville, Md: National Center for Health Statistics; 2001.
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