drug insurance instability and its correlates: results from the 2000 medical expenditure panel...

Research in Social and Administrative Pharmacy

2 (2006) 232–253

Original research

Drug insurance instability and itscorrelates: Results from the 2000

Medical Expenditure Panel Surveyq

Kiran Gupta, M.S., Richard R. Cline, Ph.D.*,Stephen W. Schondelmeyer, Ph.D.

Department of Pharmaceutical Care and Health Systems, College of Pharmacy,

University of Minnesota, Minneapolis, MN 55455, USA

Abstract

Background: Health insurance instability (ie, temporal gaps in health insurance cov-erage) is a prevalent phenomenon in the United States. To date, most studies havefocused on the factors that affect the intermittent lack of health insurance coverage.

However, no studies known to the authors have examined the factors associated withprescription drug insurance instability (ie, temporal gaps in drug insurance coverage)among working-age adults. Developing an accurate profile of persons with unstable

drug insurance is essential to formulate rational policy to address this problem.Objectives: The objectives of this study were to (1) document the prevalence ofprescription insurance instability among working-age adults and (2) describe theassociation between prescription drug insurance instability and demographic, socio-

economic status, and employment characteristics.Methods: The data source used in this study was the 2000Medical Expenditure PanelSurvey. This study used a cross-sectional design using data provided by respondents at

each of the 3 interviews conducted during the year 2000. Chi-square and hierarchical

q Presented in part at the 152nd Annual Meeting of The American Pharmacists Association,

April 1-5, 2005, Orlando, FL.

* Corresponding author. Department of Pharmaceutical Care and Health Systems, College of

Pharmacy, University of Minnesota, 308 Harvard St. SE, Minneapolis, MN 55455, USA. Tel.:

þ1 612 624 0124; fax: þ1 612 625 9931.

E-mail address: [email protected] (R.R. Cline).

1551-7411/$ - see front matter � 2006 Elsevier Inc. All rights reserved.

doi:10.1016/j.sapharm.2006.02.003

mailto:[email protected]

233K. Gupta et al. / Research in

Social and Administrative Pharmacy 2 (2006) 232–253

multinomial logistic regression analyses were used to describe the associations among

(1) demographics, (2) socioeconomic status, and (3) employment characteristics anddrug insurance status (classified as continuous, absent, or unstable).Results: During the year 2000, 12.5% (21.1 million) of the working-age adults in theUnited States had unstable prescription drug coverage. Persons aged 35-54 years had

lower rates of drug insurance instability compared with those aged 18-24 [adjustedodds ratio 0.66 (95% confidence interval 0.54-0.80)]. The least educated (12 or feweryears of education) were more likely than those with more education (13-16 years) to

experience at least one period without drug coverage (62% vs 32%, P< 0.01). Thepoorest respondents (those at less than 200% of the federal poverty level) were morelikely than the wealthiest respondents (those at more than 400% of the poverty level)

to report at least some time without drug coverage (37% vs 28%, P< 0.01). Thoseexperiencing a divorce or death of a spouse were more than twice as likely as stablymarried persons to experience at least one period without drug insurance [adjusted

odds ratio 2.23 (95% confidence interval 1.68-2.96)]. Adults who were unstably em-ployed during the year and/or who worked for small firms generally experiencedhigher rates of drug insurance instability.Conclusions: Prescription drug insurance instability is a prevalent phenomenon

among working-age adults in the United States, with approximately 1 in 8 experienc-ing this problem during 2000. Our results suggest that demographics, socioeconomicstatus, and employment characteristics all play important roles in predicting pre-

scription drug insurance status, with the least educated and poorest being particu-larly vulnerable to interruptions in drug coverage. Premium assistance programsproviding subsidies to small firms’ low-income employees and permitting small firms

to form insurance pools may help to decrease the number of drug coverage uninsur-ance spells in this population.� 2006 Elsevier Inc. All rights reserved.

Keywords: Prescription drug insurance; Insurance instability; Working-age adults; Medical

Expenditure Panel Survey (MEPS)

1. Introduction

There are a significant number of individuals in the United States whoexperience periods without health insurance.1 Approximately 85.2 millionnonelderly people were without health insurance at some point during the2003-2004 period, which is an increase of 12.7 million from the 1999-2000biennium.2 Of the 85.2 million persons uninsured during 2003-2004, almosttwo-thirds (64.3%) were uninsured for 6 months or more, and more than half(51.3%) were uninsured for 9 months or more. One out of 3 Americans underthe age of 65 (33.3%) was uninsured at some point during this 2-year period.

Health insurance status may change frequently within a given year andover a period of several years. Thus, most figures underestimate the poolof individuals who are potentially vulnerable to the effects of noncoverage.3-7

234 K. Gupta et al. / Research in


Gaps in coverage create health insurance instability. There is an ongoingstream of people who flow quickly into and out of the uninsured ‘‘pool.’’8

Many people who lose their health insurance regain coverage within a rel-atively short time while some others do not. For example, half to two-thirds of the people who are uninsured over the course of any year moveinto or out of coverage during that year. High turnover means that manyof the roughly 43.6 million people who are now uninsured will not beamong the 43.6 million counted as uninsured a year from now.

Point-in-time estimates of the number of uninsured persons disproportion-ately include those persons with the longest uninsured spells.9 Studies pub-lished over the past 10 years consistently show that half of uninsured spellsend within 5 or 6months.8 For example, data from the 1985 Survey of Incomeand Program Participation panel10 provide evidence that point-in-time esti-mates fail to adequately characterize the problem of the uninsured becausemany persons who experience short spells of noncoverage are overlooked.It has been suggested that policy makers should think of ‘‘uninsured’’ as re-ferring not to people, but rather to gaps in coverage over time.8

According to the 2003 Commonwealth Fund Biennial Health InsuranceSurvey, health insurance coverage is becoming increasingly unstable.11

The uninsured population in the United States is a public issue of concernfor at least 2 reasons.12,13 First, health insurance is viewed as necessary toensure that people have access to medical care and financial protectionagainst the risk of costly and unforeseen medical events. The uninsuredoften are turned away for being unable to pay their full medical bill at thetime of service and are more likely to be hospitalized for an ‘‘avoidable con-dition,’’ which could have been prevented had the person received appropri-ate and timely outpatient care.14 For example, long-term uninsured adultswith diabetes are less likely than insured adults with diabetes to receive basicservices such as eye and foot examinations, cholesterol screening, and influ-enza vaccinations, thus resulting in serious complications with this chronicdisease.5 Second, timely and reliable estimates of the population’s health in-surance status are vital to evaluating the costs and expected impact of publicpolicy interventions intended to expand coverage and/or change the waythat private and public insurance is funded and administered.

1.1. Prescription drug insurance instability

The importance of prescription drugs in medical care is increasing withthe increase in both the number of people using prescription drugs andthe number of prescriptions per user.15 However, most studies to date havefocused on the factors that impact the intermittent lack of overall health in-surance coverage, which does not always accord with prescription drug cov-erage. Only one study known to the authors has examined the factorsassociated with prescription drug insurance instability.16 While valuable,this study was conducted among Medicare beneficiaries. Thus, developing



an accurate estimate of the number of working-age Americans (those aged18-64) with unstable prescription drug insurance and a better understandingof the factors associated with gaps in drug insurance coverage are essentialfor rational policy formulation aimed at addressing this problem.

1.2. Past studies

Anumber of studies have examined the various factors that affect the healthinsurance status of individuals. Jensen3 used data from the Survey of Incomeand Program Participation to examine the dynamics of health insuranceamongpersons aged55-64 years. The investigator found that between the sum-mer of 1983 and early 1986, 21%of persons aged 55-64 years experienced sometime without health insurance and that womenwere particularly vulnerable toperiods without insurance, accounting for approximately twice as many of theunstably insured asmen. She also reported that half of the long-termuninsuredand 20% of the short-term uninsured lived at or below poverty in 1983, com-pared with only 7% among persons with continual coverage. More than 50%of those ever having a spell of noncoverage were nonworking.

Short17 used the Survey of Income and Program Participation data toidentify and describe gaps and sources of insurance instability that are im-portant among women. She observed that income and family status aremore important than gender in explaining health insurance differences.The health insurance status of married men and women was found to besimilar. However, married women experienced fewer gaps and changes incoverage than single women (and single men).

Another study18 used data from 3 different databases: the Robert WoodJohnson Foundation 1996-1997 Community Tracking Survey, the Kaiser/Commonwealth 1997 National Survey of Health Insurance, and the 1995-1997 Kaiser/Commonwealth State Low Income Surveys. The investigatorsreported that compared with continuously insured individuals, unstably in-sured adults were disproportionately likely to be African American or His-panic, to be single, and to be younger than 45 years. Also, adults with lowerincomes were at significantly higher risk of having a time uninsured.

Comer et al19 used data from a sample of 5530 adults living in Nebraskato analyze the predictors of health insurance instability. Persons losinginsurance tended to be female, single, live in urban areas, have incomes un-der $25,000, and employed part-time. Having a higher income and workingfor a larger firm also were statistically significant influences on acquiring in-surance, whereas being single had a negative impact on acquiring insurance.

In the most comprehensive study on the topic to date, Short and Graefe8

found that lack of health insurance is especially pronounced among thenear-elderly population (older adults between the ages of 55 and 64) becausethey were the most likely to lack coverage for the entire 4-year study period(1996-1999). Twenty-two percent of the near-elderly group was uninsuredfor the whole study period. People at 400% of poverty or more were more


likely to have a single gap in coverage than those at 200-399% of povertylevel. Moreover, in households with incomes above 200% of poverty levelfewer children were repeatedly uninsured compared with those in house-holds with incomes below 200% of poverty level.

1.3. Research model

Many studies on the topic of health insurance instability have reportedsimilar conclusions. This phenomenon appears to be related to 3 constructs:(1) demographic characteristics such as age, gender, and race/ethnicity,(2) socioeconomic characteristics such as income and education level, and(3) employment characteristics such as job stability and firm size. Given thesefindings, we cast our research within a framework proposed by the Instituteof Medicine for the study of health insurance eligibility and enrollment.13

Briefly, this model posits health insurance availability (and subsequent indi-vidual enrollment decisions) as a function of a variety of macro- and micro-level factors. Macrolevel factors include federal tax policy and other publicpolicies affecting coverage availability. Microlevel factors include demo-graphics such as age and family composition, socioeconomic variables suchas income, and employment characteristics such as work status and employercharacteristics. Our research is also informed by the medical sociology litera-ture, which emphasizes indicators of social class and stratification, such aseducation and income, as determinants of access to health insurance.20,21

Together, these observations helped to form the conceptual basis for this study.

1.4. Study objective

The overall objective of this study was to develop a profile of drug cover-age instability in a representative sample of people aged 18-64 years in theUnited States. Our specific aims were to (1) document the prevalence of pre-scription drug insurance instability among people aged 18-64 years in the year2000 and (2) describe the association between prescription drug insurance in-stability and demographic, socioeconomic, and employment characteristics.

2. Methods

2.1. Design and data collection

This study was conducted using a 1-year, longitudinal panel survey de-sign.22 The data were collected from respondents at 3 separate interviewsspanning the year 2000. Three groups were then defined in the current study:persons with continuous prescription drug insurance, persons with no drugcoverage during 2000, and those with unstable drug insurance (ie, periodswith and without coverage).



The data for this study were obtained from the 2000 Medical ExpenditurePanel Survey (MEPS), a nationally representative survey of health care useand spending in the U.S. civilian, noninstitutionalized population.23 Impor-tantly, MEPS contains detailed information on demographic characteristics,socioeconomic status, and employment characteristics, as well as privateprescription drug coverage.24 The sampling design is a stratified multistagearea probability design in which certain groups (including racial minoritiesand low-income households) are oversampled. The survey consists of anoverlapping panel design in which any given sample panel is intervieweda total of 5 times in person over 30 months over 2 calendar years. Thehousehold component file of MEPS 2000 (HC-050) was used in this study.Respondents were included in the current study if they were betweenthe ages of 18 and 64 and if their drug insurance experience during 2000could be pieced together from available data. Thus, if individuals answered‘‘unknown’’ to all assumed private and public sources of drug coveragein a given interview round, they were excluded from the current study.

2.2. Dependent variable

The primary dependent variable in this study was prescription drug insur-ance status. This variable was created by exploiting the fact that the 2000MEPS questionnaire obtained private prescription coverage information foreach respondent at each of the 3 interviews conducted during 2000. Publiclyprovided prescription coverage was not asked about during these interviews.Therefore, complete prescription drug coverage data were obtained by com-bining data from the private prescription drug insurance variable and severalhealth insurance variables representing programs that frequently, or nearly al-ways, include prescription coverage (see Appendix A for MEPS variablenames and definitions used in this study). These included the Tricare program,state Medicaid programs, and the Veteran’s Administration. For example, theprescription drug insurance status for respondents who answered ‘‘no’’ to theprivate drug coverage question in a given interview round but who werecovered under Medicaid was recoded to ‘‘yes’’ for the purpose of the study.Individuals reporting drug coverage from some source in all 3 interviewrounds were categorized into the ‘‘continuous prescription drug insurance’’category. Those reporting no coverage in all 3 rounds were categorized intothe ‘‘no prescription drug insurance’’ category. Those individuals with differ-ent combinations of ‘‘yes’’ and ‘‘no’’ over the 3 rounds of the 2000 MEPSwere categorized into the ‘‘unstable prescription drug insurance’’ category.

2.3. Independent variables

Based on prior research, we hypothesized that prescription drug coveragestatus would be a function of demographics, socioeconomic status, and em-ployment characteristics. Specifically, demographic variables examined were




age, gender, race, census region, marital status, and health status. Age wascategorized into 4 groups: 18-24, 25-34, 35-54, and 55-64 years. Race wasclassified into 4 categories: white, black, Hispanic, and ‘‘other’’ ethnicity.The ‘‘other’’ ethnicity group included 3 separate racial groups: American In-dian, Aleut/Eskimo, and Asian/Pacific Islander. Marital status was classifiedinto 3 categories: married during 2000, unmarried during 2000 (widowed, di-vorced, separated, and never married individuals), and unstably married(persons whose marital status changed anytime during 2000). Health statusat interview one was classified into 2 categories: excellent/very good/goodand fair/poor.

The socioeconomic characteristics examined in this study included educa-tion and income. Education was categorized as 0-12 years, greater than12 years but less than or equal to 16 years, and those having more than 5 col-lege years. Income was segmented into 3 categories based on federal povertylimits (FPL). These included low income/near poor/poor (<200% FPL),middle income (200-400% FPL), and high income (�400% FPL). Theemployment characteristics examined were the employment status of theindividual during the year 2000 and the firm size. People were categorizedas stably employed if they reported employment at all 3 interviews of theMEPS survey and unemployed if they reported being unemployed at all3 rounds of the survey. Those who were employed in at least one, but notall, rounds of the survey and persons whose employment status was un-known were categorized into the unstably employed group for the purposeof this study. Firm size was categorized as small (<100 employees), medium(100-499 employees), and large (500þ employees).

2.4. Data analysis

To control for the complex survey design used by MEPS, all statisticalanalyses were completed using STATA SE statistical software for WindowsVersion 8.0.25 Survey data commands implemented in STATA allow for ac-curate estimation of standard errors based on individual probabilityweights. The statistical analyses conducted in this study included estimationof descriptive statistics (counts and percentages), bivariate comparisons us-ing the Pearson chi-square statistic, and hierarchical multinomial logistic re-gression analyses. All results are reported as weighted counts and adjustedodds ratios (AOR) with 95% confidence intervals (CI) where appropriate.Bivariate analyses were used to show the distribution of individuals in the3 prescription drug insurance categories stratified by demographic charac-teristics, socioeconomic status, and employment characteristics. Hierarchi-cal multinomial logistic models were used to assess the associations ofdemographic factors, socioeconomic factors, and employment status withprescription drug insurance status. Demographic variables were added tothe model first, followed by socioeconomic factors and finally by employ-ment characteristics.



3. Results

The 2000 MEPS data contained responses from 14,298 persons betweenthe ages of 18 and 64 in the first interview round of 2000. Seventy-five ofthese individuals lacked sufficient insurance information for us to determinetheir drug coverage status throughout the year. This left responses from14,223 respondents (representing 168.7 million working-age adults) for anal-ysis. Our descriptive analysis suggests that during the year 2000, 12.5% (21.1million) of the working-age adults in the United States had unstable pre-scription drug insurance (Table 1). Two-thirds of the sample members(66.1%) had continuous prescription drug insurance coverage for the entireyear, whereas 21.5% had no prescription drug insurance coverage over theentire year of 2000. As reported in Table 2, roughly half (51.2%) of the re-spondents in our sample were female. A majority of the sample was white(72.2%), followed by Hispanics (12.1%), blacks (11.6%), and adults of‘‘other’’ ethnicity (4.1%). Half of the sample (50.7%) had 12 years or lessof education. The typical sample member was from a high-income family(44.5%) and stably employed (43.7%) in a firm with less than 100employees.

Table 2 compares characteristics of those with continuous prescriptiondrug insurance, no prescription drug insurance, and unstable prescriptiondrug insurance during 2000. There was considerable variation across differ-ent prescription drug insurance categories with respect to many variables.Although the proportions of people in the 18-24 and 55-64 years age groupswere similar (15.6% and 13.9%), the prevalence of instability in the pre-scription drug coverage was more than twice as high among 18-24 year oldswhen compared with those aged 55-64 (27% vs 12%) (Table 2). Geograph-ically, more people from the South go without prescription drug insurance(41%) or have unstable drug insurance status (37%) compared with thosefrom the other regions. Marital status is also associated with prescriptiondrug insurance status, with the largest proportion of people having no pre-scription drug insurance (57%) or unstable drug insurance status (52%) be-ing unmarried during 2000. A change in marital status during 2000 wastwice as common (8%) among those with unstable prescription drug insur-ance compared with those who had a change in marital status during 2000and continuous drug coverage (4%).

Table 1

Drug insurance status among working-age adults in the year 2000 (N¼ 168.7 million)

Drug insurance status N in millions (%)

Continuous drug insurance during 2000 111.6 (66.1)

No drug insurance during 2000 36.2 (21.5)

Unstable drug insurance status during 2000 21.1 (12.5)



Table 2

Comparisons of demographic, socioeconomic and employment characteristics

across drug insurance status (N¼ 168.7 million)

Total

Continuous

drug insurance

during 2000

No drug

insurance

during 2000

Unstable drug

insurance status

during 2000

N in millions (%)

Demographics

Age) 18-24 years 26.4 (15.6) 13.4 (12) 7.4 (20) 5.6 (27)

25-34 years 37.4 (22.2) 24.2 (22) 8.0 (22) 5.2 (25)

35-54 years 81.6 (48.4) 58.5 (52) 15.3 (42) 7.8 (37)

55-64 years 23.5 (13.9) 15.5 (14) 5.5 (15) 2.5 (12)

Gender) Female 86.5 (51.2) 59.5 (53) 16.3 (45) 10.7 (51)

Male 82.3 (48.8) 52.1 (47) 19.8 (55) 10.4 (49)

Race) Whites 121.9 (72.2) 86.3 (77) 20.9 (58) 14.7 (70)

Blacks 19.5 (11.6) 8.8 (8) 7.8 (22) 2.9 (14)

Hispanics 20.4 (12.1) 12.3 (11) 5.6 (16) 2.5 (12)

Othersa 6.9 (4.1) 4.3 (4) 1.7 (5) 0.9 (4)

Region) Northeast 31.9 (18.9) 22.2 (20) 6.1 (17) 3.6 (17)

Midwest 38.9 (23.0) 27.5 (25) 6.9 (19) 4.5 (21)

South 59.8 (35.4) 37.4 (33) 14.7 (41) 7.7 (37)

West 38.3 (22.7) 24.6 (22) 8.4 (23) 5.3 (25)

Marital

status)Married during

2000

91.2 (54.1) 68.9 (62) 13.9 (39) 8.4 (40)

Unmarried

during 2000b70.0 (41.5) 38.6 (35) 20.4 (57) 11.0 (52)

Unstable marital

statusc7.4 (4.4) 4.1 (4) 1.7 (5) 1.6 (8)

Health

status)Excellent/very

good/good

151.2 (89.6) 101.3 (91) 31.2 (86) 18.7 (89)

Fair/poor 17.5 (10.4) 10.3 (9) 4.9 (14) 2.3 (11)

Socioeconomic status

Education) � 12 years 85.5 (50.7) 48.5 (43) 23.9 (66) 13.1 (62)

>12 and � 16

years

65.7 (38.9) 49.0 (44) 10.0 (28) 6.7 (32)

5þ College years 17.6 (10.4) 14.1 (13) 2.2 (6) 1.3 (6)

Income) Low/near poor/

poord40.4 (23.9) 17.3 (15) 15.2 (42) 7.9 (37)

Middlee 53.4 (31.6) 34.0 (30) 12.2 (34) 7.2 (34)

Highf 75.2 (44.5) 60.5 (54) 8.7 (24) 6.0 (28)

Employment characteristics

Employment

status and

firm size)

Unemployed

during 2000

24.8 (14.7) 14.3 (13) 7.6 (21) 2.9 (14)

Stably employed

in small firmg73.8 (43.7) 46.9 (42) 18.1 (50) 8.8 (42)

(Continued)



Indicators of socioeconomic status were also associated with prescriptiondrug insurance status. Instability in prescription drug insurancewasmos com-mon among persons with 12 or fewer years of education (62%) (Table 2). Thisis in stark contrast to those with 5 or more years of college education (6%).Similarly, 66% of respondents with no drug insurance throughout the yearhad 12 or fewer years of education.Household incomewas also related to druginsurance status,with those living in higher income households less likely to gowithout prescription drug insurance or experience instability in prescriptiondrug insurance.

Table 2

(Continued)

Total

Continuous

drug insurance

during 2000

No drug

insurance

during 2000

Unstable drug

insurance status

during 2000

N in millions (%)

Stably employed

in medium

firmh

22.5 (13.3) 18.5 (17) 2.0 (6) 2.0 (9)

Stably employed

in large firm

sizei

20.5 (12.1) 17.6 (16) 1.4 (4) 1.5 (7)

Stably employed

with unknown

firm sizej

12.3 (7.3) 7.5 (7) 2.6 (7) 2.2 (11)

Unstably

employed in

small firm

7.8 (4.6) 3.7 (3) 2.2 (6) 1.9 (9)

Unstably

employed in

medium firm

1.7 (1.0) 0.8 (1) 0.4 (1) 0.5 (3)

Unstably

employed in

large firm

1.5 (0.9) 0.7 (1) 0.4 (1) 0.4 (2)

Unstably

employed with

unknown firm

size

3.8 (2.3) 1.6 (1) 1.4 (4) 0.8 (4)

Unweighted n¼ 14,223. Values in parentheses are percentages; *c2 statistic significant at

P< 0.01.a Includes American Indian, Aleut/Eskimo, and Asian/Pacific Islander.b Includes widowed, divorced, separated, and never married.c Marital status changed anytime during 2000.d <200% Federal Poverty Level.e 200-400% Federal Poverty Level.f �400% Federal Poverty Level.g <100 employees.h 100-499 employees.i �500 employees.j Firm size either varied or was unknown in any of the 3 rounds.



Employment characteristics were a strong predictor of prescription druginsurance status, also. Stably employed people were more likely to have con-tinuous drug coverage irrespective of firm size when compared with the un-stably employed group. Among the stably employed group, instability inprescription drug insurance was more prevalent in small firms (42%) com-pared with large firms (7%). Unstable employment was associated with un-stable drug coverage regardless of firm size.

Adjusted odds ratio estimates from the multinomial logistic regressionmodel displayed in Table 3 suggest that many demographic characteristicsremain significantly associated with prescription drug insurance status whencontrolling for the effects of other demographics concomitantly. Those of orolder than 24 years were less likely to experience unstable prescription druginsurance during 2000 compared with those aged 18-24. For example, per-sons in the 55-64 age group were 46% less likely to have unstable drug cov-erage in comparison with the those in the 18-24 age group. Males were 13%more likely to have gaps in drug coverage than females.

Compared with whites, blacks were 3.67 (95% CI 3.01-4.48) times morelikely to have no prescription drug insurance and 76% more likely to haveunstable prescription drug insurance compared with whites. Persons report-ing fair or poor health status in the first interview of the year were 1.45 timesas likely (95% CI 1.26, 1.67) to have no drug insurance throughout 2000 or30% more likely to experience drug insurance instability compared withthose reporting excellent, good, or very good health status.

The addition of socioeconomic characteristics to the model containingjust demographics affected the relationship between some variables and pre-scription drug insurance status minimally (Table 4). The relationship be-tween age and drug insurance instability remained substantively the same.However, other estimates were affected more dramatically. To illustrate,adjustment for education and income reduced estimates of drug insuranceinstability for Hispanics to 27% less likely [AOR 0.95 (95% CI 0.77-1.17)vs AOR 0.73 (95% CI 0.59-0.91)] than whites. Similarly, inclusion of socio-economic status variables reduced estimates of drug insurance instability forblacks from 76% more likely than whites to 25% more likely [AOR 1.76(95% CI 1.42-2.19) vs AOR 1.25 times as likely (95% CI 1.00-1.56)]. Healthstatus was no longer a predictor of drug insurance status in this model.

In the final model including adjustments for employment status and firmsize (Table 5), the adjusted odds of having unstable prescription drug insur-ance in the South remain significantly higher [AOR 1.25 (95% CI 1.02-1.54)]when compared to that in the Northeast. Those unstably employed in a firmof any size experience a significant increase in the risk of drug insurance in-stability compared with the unemployed group. Persons stably employed insmall firms are 27% more likely to possess unstable drug coverage comparedwith the unemployed group, whereas those stably employed in firms withmore than 500 employees (large firms) are 33% less likely to report instabil-ity in their drug coverage. With the exception of individuals aged 35-54,


membership in other age categories is no longer a significant predictor ofdrug coverage stability, while living in the South becomes a significant pre-dictor of this phenomenon in the final model.

4. Discussion

One goal of this study was to document the prevalence of prescriptiondrug insurance instability among working-age adults. Our analyses of the2000 MEPS suggest that prescription drug insurance instability was

Table 3

Multinomial logit model predicting prescription drug insurance status

(demographics only) (N¼ 168.7 million)

Variable

No drug insurance during

2000 vs continuous drug

insurance during 2000

Unstable drug insurance

status during 2000 vs

continuous drug insurance

during 2000

Odds ratios (95% CI)

Age 18-24 years 1.00 1.00

25-34 years 0.80 (0.67, 0.95)) 0.62 (0.51, 0.76)))

35-54 years 0.78 (0.64, 0.94))) 0.45 (0.37, 0.54)))

55-64 years 1.13 (0.92, 1.38) 0.54 (0.42, 0.71)))

Gender Female 1.00 1.00

Male 1.46 (1.35, 1.59))) 1.13 (1.03, 1.25)))

Race Whites 1.00 1.00

Blacks 3.67 (3.01, 4.48))) 1.76 (1.42, 2.19)))

Hispanics 1.46 (1.17, 1.81))) 0.95 (0.77, 1.17)

Othersa 1.81 (1.28, 2.55))) 1.23 (0.86, 1.77)

Region Northeast 1.00 1.00

Midwest 1.04 (0.85, 1.28) 1.05 (0.82, 1.35)

South 1.43 (1.19, 1.72))) 1.30 (1.03, 1.65))

West 1.07 (0.87, 1.32) 1.24 (0.99, 1.56)

Marital status Married during

2000

1.00 1.00

Unmarried

during 2000b2.53 (2.21, 2.91))) 1.91 (1.60, 2.29)))

Unstable marital

statusc1.99 (1.50, 2.64))) 2.71 (2.04, 3.61)))

Health status Excellent/very

good/good

1.00 1.00

Fair/poor 1.45 (1.26, 1.67))) 1.30 (1.05, 1.61))

Model F statistic¼ 25.77 (26, 163 df), P< 0.01. )P< 0.05; ))P< 0.01.a Includes American Indian, Aleut/Eskimo, and Asian/Pacific Islander.b Includes widowed, divorced, separated, and never married.c Marital status changed anytime during 2000.




Table 4

Multinomial logit model predicting prescription drug insurance status (demographics

and socioeconomic status) (N¼ 168.7 million)

Variable

No drug insurance during

2000 vs continuous drug

insurance during 2000



continuous drug insurance

during 2000


Demographics

Age 18-24 years 1.00 1.00

25-34 years 0.89 (0.76, 1.05) 0.70 (0.57, 0.85)))

35-54 years 0.93 (0.78, 1.13) 0.54 (0.45, 0.65)))

55-64 years 1.32 (1.08, 1.61))) 0.64 (0.49, 0.84)))


Male 1.59 (1.45, 1.75))) 1.22 (1.11, 1.35)))


Blacks 2.50 (2.08, 3.02))) 1.25 (1.00, 1.56))

Hispanics 1.08 (0.86, 1.36) 0.73 (0.59, 0.91)))

Othersa 1.73 (1.21, 2.47))) 1.19 (0.83, 1.72)


Midwest 1.01 (0.82, 1.25) 1.01 (0.80, 1.29)

South 1.35 (1.14, 1.60))) 1.23 (1.00, 1.51)

West 1.05 (0.85, 1.30) 1.21 (0.99, 1.50)

Marital status Married during

2000

1.00 1.00

Unmarried

during 2000b2.28 (1.99, 2.62))) 1.76 (1.47, 2.10)))

Unstable marital

statusc1.59 (1.20, 2.10))) 2.23 (1.68, 2.95)))

Health status Excellent/very

good/good

1.00 1.00

Fair/poor 0.96 (0.83, 1.11) 0.90 (0.73, 1.12)


Education � 12 years 1.00 1.00

>12 and � 16

years

0.63 (0.56, 0.71))) 0.68 (0.60, 0.78)))

5þCollege years 0.60 (0.47, 0.76))) 0.59 (0.45, 0.78)))

Income Low/near poor/

poord1.00 1.00

Middlee 0.48 (0.41, 0.57))) 0.53 (0.45, 0.63)))

Highf 0.25 (0.20, 0.30))) 0.30 (0.25, 0.35)))

Model F statistic¼ 36.16 (34, 155 df), P< 0.01. )P< 0.05; ))P< 0.01.a Includes American Indian, Aleut/Eskimo, and Asian/Pacific Islander.b Includes widowed, divorced, separated, and never married.c Marital status changed anytime during 2000.d <200% Federal Poverty Level.e 200-400% Federal Poverty Level.f
�400% Federal Poverty Level.



Table 5

Multinomial logit model predicting prescription drug insurance status (demographics,

socioeconomic status, and employment characteristics) (N¼ 168.7 million)

Variable

No drug insurance

during 2000 vs

continuous drug

insurance

during 2000



continuous drug

insurance

during 2000


Demographics

Age 18-24 years 1.00 1.00

25-34 years 1.04 (0.88, 1.22) 0.82 (0.66, 1.00)

35-54 years 1.12 (0.92, 1.36) 0.66 (0.54, 0.80)))

55-64 years 1.45 (1.17, 1.79))) 0.78 (0.60, 1.02)


Male 1.64 (1.49, 1.80))) 1.25 (1.13, 1.39)))


Blacks 2.62 (2.17, 3.15))) 1.26 (1.00, 1.59)

Hispanics 1.20 (0.97, 1.50) 0.75 (0.61, 0.94))

Othersa 1.84 (1.30, 2.62))) 1.21 (0.84, 1.72)


Midwest 1.02 (0.83, 1.27) 1.04 (0.82, 1.31)

South 1.36 (1.14, 1.62))) 1.25 (1.02, 1.54))

West 1.01 (0.81, 1.25) 1.21 (0.98, 1.49)

Marital status Married during 2000 1.00 1.00

Unmarried during

2000b2.33 (2.02, 2.68))) 1.75 (1.47, 2.09)))

Unstable marital statusc 1.65 (1.25, 2.19))) 2.23 (1.68, 2.96)))

Health status Excellent/very good/

good

1.00 1.00

Fair/poor 0.89 (0.76, 1.05) 0.93 (0.74, 1.16)


Education � 12 years 1.00 1.00

>12 and � 16 years 0.64 (0.57, 0.73))) 0.69 (0.60, 0.79)))

5þCollege years 0.65 (0.52, 0.81))) 0.61 (0.46, 0.80)))

Income Low/near poor/

poord1.00 1.00

Middlee 0.54 (0.47, 0.64))) 0.55 (0.47, 0.66)))

Highf 0.29 (0.24, 0.35))) 0.32 (0.26, 0.38)))

Employment characteristics

Employment

Status &

Firm Size

Unemployed during

2000

1.00 1.00

Stably employed in

small firmg1.09 (0.91, 1.31) 1.27 (1.01, 1.59))

Stably employed in

medium firmh0.32 (0.25, 0.41))) 0.79 (0.62, 1.01)

(Continued)



relatively common among working-age adults, with 21.1 million persons or12.5% of those aged 18-64 experiencing this phenomenon. It is unknownwhether the proportion of individuals with unstable prescription drug cov-erage is increasing or decreasing as this is the first study of this kind to theauthors’ knowledge. However, given that the proportion of employers offer-ing health benefits of any kind declined from 69% in 2000 to 60% in 2005, itseems likely that the proportion of working-age adults with unstable drugcoverage will increase.26

A second objective of this study was to describe the association betweenprescription drug insurance status and demographic, socioeconomic, andemployment characteristics. Our results suggest that demographics such asage, gender, race, census region, and marital status were all significant pre-dictors of prescription drug insurance status. These findings were consistentin both bivariate and multivariate analyses.

We found that young adults (those aged 18-24) had a significant chanceof having gaps in prescription drug coverage. These findings are similar to

Table 5

(Continued)

Variable

No drug insurance

during 2000 vs

continuous drug

insurance

during 2000



continuous drug

insurance

during 2000

Stably employed in

large firm sizei0.25 (0.19, 0.34))) 0.67 (0.49, 0.92))

Stably employed with

unknown firm sizej0.84 (0.64, 1.09) 1.86 (1.41, 2.44)))

Unstably employed in

small firmj1.26 (0.99, 1.62) 2.42 (1.81, 3.24)))


medium firm

1.19 (0.70, 2.01) 3.28 (1.88, 5.74)))


large firm

1.21 (0.72, 2.03) 2.76 (1.64, 4.65)))

Unstably employed

with unknown firm

size

1.69 (1.15, 2.49))) 2.30 (1.42, 3.75)))

Model F statistic¼ 39.61 (50, 139 df), P< 0.01. )P< 0.05; ))P< 0.01.a Includes American Indian, Aleut/Eskimo, and Asian/Pacific Islander.b Includes widowed, divorced, separated, and never married.c Marital status changed anytime during 2000.d <200% Federal Poverty Level.e 200-400% Federal Poverty Level.f �400% Federal Poverty Level.g <100 employees.h 100-499 employees.i �500 employees.j Firm size either varied or was unknown in any of the 3 rounds.



uninsured estimates in the United States obtained using the household com-ponent of the MEPS for 2000 and 2001.27 Age may play a significant role inwhether a person experiences prescription drug coverage instability for avariety of reasons. A study by Collins et al28 suggested that age 19 is a criticalturning point in insurance instability among both privately and publicly in-sured young adults because employers who offer health benefits stop cover-ing dependent children at age 18 or 19 if they do not attend college. A studyfrom the Commonwealth Fund reported that more than half of high-schoolgraduates who do not go on to college experience some time uninsured inthe year after graduation and that among those who do go on to college,graduation often marks a break in their health coverage.29 In addition, jobsavailable to members of this age group often are low wage or temporary anddo not offer health benefits.

Our analyses show that gaps in prescription drug coverage are more prev-alent among men, even after controlling for other variables. Studies have of-ten shown that women use more health care services than men.30 This couldbe attributed to the differences in health perceptions, frequency of reportingsymptoms, and incidence of certain illnesses. Women are also more likely touse prescription drugs than men, which may lead them to search for employ-ment situations offering drug coverage.31 However, the number of uninsuredwomen between the ages of 18 and 64 is growing rapidly and may soon out-number uninsured men.32

At least one prior study reported that Hispanics under 65 years were morelikely than white, black, or Asian or Pacific Islander non-Hispanics to lackhealth insurance during 2001 to 2002.27 Although our unadjusted analysis(Table 2) confirmed this finding, our fully specified model (Table 5) impliedthat Hispanics actually were 25% less likely than whites to experienceprescription drug coverage instability. This may suggest that when other rele-vant factors (eg, socioeconomic status, employment status) are held constant,Hispanics value and seek out jobs offering generous health benefit packagesmore consistently than whites. Alternately, this finding may result from thefact that Hispanics are more likely than whites to receive health insurancefrom Medicaid programs,33 which nearly always offer drug coverage.

Comparedwith otherCensus regions, respondents from theSouth reportedmore instability in drug coverage than those residing in other regions. Thisfinding accords with a U.S. Census Bureau report based on data from the2005 Current Population Survey Annual Social and Economic Supplement.33

The South has the lowest median household income of all 4 regions and thus,has the highest poverty rate. The low-household income and relatively high-poverty rate in the area suggest that there are a large number of ‘‘workingpoor’’ individuals in the area. These persons often earn too much to qualifyfor Medicaid or other public assistance programs, but not enough to affordhealth insurance.34 Not surprisingly, the South also had the highest uninsuredrate (18.3%).13 In addition, because many of the jobs available in the area arelow-wage positions, they are less likely to offer health benefits.


Marital status is also a strong predictor of drug insurance status. Marriedadults were less likely to have gaps in drug coverage than single adults(Table 2). This is consistent with the findings obtained from 3 surveys con-ducted during 1995-1997.19One possible explanation for this is that bothmar-riage partners often are employed, providing 2 possible sources for health carecoverage.13 Related to this is the finding that instability inmarriage status wasassociated with unstable drug coverage. Having dependent coverage exposesan individual to losing insurance if they become widowed or divorced.

Our results show that respondents with higher education have fewer gapsin drug coverage. This can be explained by reference to the Grossman modelof demand for health.35 With even some college education, an individual ismuch more likely to obtain a relatively high-paying job. According toGrossman’s model of demand for health, individuals are not passive con-sumers of health but active producers who spend time and money on theproduction of health. This theory posits that persons earning higher wageshave more incentive to maintain their health because their stock of humancapital is more productive (in the sense of yielding greater pecuniary returns)than persons earning relatively low wages. As such, those with higher levelsof education have more incentive to maintain continuous insurance cover-age because it permits access to health-restoring inputs such as prescriptionmedications.

Adults stably employed in firms with less than 100 employees experiencedgaps in prescription drug coverage more often than did adults stably em-ployed in larger firms. This could be due to the nature of jobs in these firms.These employers are less likely to offer health benefits to their employeesand to pay lower shares of the premiums when they do.36 One reason forthis is that small employers have fewer employees over which to spreadthe administrative costs of the health benefit plan. Another reason is thathealth insurance premiums often are experience rated at the firm level.The risk pools upon which premiums are based in small firms are quitesmall, which can lead to large premium increases for all employees in sub-sequent years if just a few workers experience expensive illnesses.

Our analyses show that employment stability is strongly related to main-taining continuous drug coverage. Temporary job loss due to layoffs, jobelimination, termination, or worker choice may underlie employment insta-bility. It is not unusual to have a gap of time between jobs in today’s workworld; these gaps often leave workers and their families with no insurancecoverage. Given that most employment-based health coverage requires somewaiting period before new employees can enroll, even workers who begina new job immediately upon leaving another are likely to experience druginsurance instability upon a job change. One implication of this is that asthe workforce becomes increasingly mobile, more and more workers arelikely to experience a temporary loss of drug insurance.

The results of this study suggest several policy initiatives that may de-crease gaps in drug coverage. For example, expansion of premium assistance




programs may be useful for this purpose.37 Premium assistance programsprovide subsidies to small firms’ low-income employees. The goals of theseprograms are to support existing employer-provided coverage for the work-ing poor. This assistance could be extended to periods between jobs so thatthe unemployed could better afford coverage provided under COBRA (Con-solidated Omnibus Budget Reconciliation Act) (P.L. 99-272). Another op-tion might be to allow small employers to join together in purchasinginsurance benefits, thereby increasing the size of risk pools and increasingthe number of persons over which administrative costs could be spread.

However, other findings suggest that these policies may be less than sat-isfactory. Even after controlling for employment status, educational attain-ment and household income still were significant predictors of insuranceinstability. These results imply that only long-term social change, such asimprovements in the U.S. educational system, will result in significant reduc-tions in the number of working-age adults without access to drug coverage.Better education allows workers the access to better jobs, which, in turn, aremore likely to offer health benefits and increases incentives for workers toaccept health insurance offers.35

4.1. Future research

One area of research suggested by the current study is that of trackingchanges in the number and proportion of individuals with unstable drug in-surance from year to year. Whether this number has changed and by howmuch is not known at this time. These data could provide an importantmetric for monitoring inequities in the United States’ health care system.Second, numerous studies have investigated the effects of unstable insuranceon access to care.5,19,38-42 Health insurance instability affects health careutilization in various areas such as physician visits, prescription drug use,and preventive care use such as mammography, pap tests, cholesterol tests,influenza vaccinations, and prostate examinations. However, none of thesestudies have examined the effect of drug insurance instability on the use ofmedications for chronic illness. Future research could examine the associa-tion between prescription drug insurance status and access to medicationsfor these conditions.

4.2. Limitations

The results of this study should be viewed in light of several limitations. Aproblem common to all surveys involves the reliance on self-reported data,which may not be accurate due to recall problems. AlthoughMEPS uses mul-tiple rounds of interviewing of the same respondents in an effort to mitigatesuch problems, some errors are likely to remain. Second, our analysis classi-fied individuals with and without drug insurance for a 12-month period,which may not be a sufficient period to study drug insurance instability.



However, we still documented a significant proportion of respondents whoexperienced prescription drug insurance instability during 2000. Finally, wecreated the drug insurance instability variable in this study using data fromseveral items referring to insurance programs typically offering drug cover-age. This method may be prone to error for at least 2 reasons. First, respon-dents may have received coverage from other sources not tapped by theseitems (eg, charities). Second, individual drug benefits offered under Medicaidcan be exhausted in some states before medical benefits. Our assumption ofdrug coverage for persons reporting health insurance coverage from aMedic-aid program would not be valid in these cases.

5. Conclusions

The goals of this study were to document the prevalence of prescriptiondrug insurance instability and to enumerate the factors associated with thisphenomenon. We found that prescription drug insurance instability wasa prevalent phenomenon among working-age adults in the United Statesduring 2000. The results of this study suggest that demographics, socioeco-nomic status, and employment characteristics are important predictors ofprescription drug insurance status. Life changing events, such as changesin marital status or employment, were associated with increased vulnerabil-ity to drug insurance instability.

More than 156 million people under the age of 65, or 61.9% of thenonelderly population, had employer-provided health insurance in 2003.43

Employers benefit from having insured employees by enhancing their abilityto employ and maintain employees, increasing their health, productivity,and morale, and reducing the costs of absenteeism. As such, the develop-ment and evaluation of both short and long-term policies and interventionsthat decrease drug coverage instability are in the interest of individuals,employers, and, through their link to economic productivity, the UnitedStates.37

Acknowledgment

The authors would like to acknowledge Dr. Beth Virnig (School of PublicHealth, University of Minnesota), for helpful comments on previous draftsof this manuscript.

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Appendix A

Variables used from MEPS, 2000

Independent variables

Demographicsa

age31x: age of each RU member in round 3/1

sex: gender of each RU member

racex: race

hispanx: Hispanic ethnicity of each RU member

region00: census region for the RU as of 12/31/00

marry31x, marry42x, marry53x: marital status of each RU member in rounds 3/1, 4/2, and 5/3,

respectively.

rthlth31: perceived health status as of round 3/1

Socioeconomic statusa

educyear: years of education when first entered MEPS

povcat00: family income as percent of poverty line

Employment characteristicsa

empst31, empst42, empst53: employment status of each RU member in rounds 3/1, 4/2, and 5/3,

respectively.

numemp31, numemp42, numemp53: number of employees at the location of the person’s current

main job in rounds 3/1, 4/2, and 5/3, respectively.

Dependent variables

Prescription drug insurance statusa

pmedin31, pmedin42, pmedin53: indicate whether the respondent was covered by a private health

insurance plan that included at least some prescription drug insurance coverage for each

round (3/1, 4/2, 5/3) of 2000, respectively.

triev00: ever have Tricare during year 2000

triat00x: any time coverage Tricare as of 12/31/2000

mcdev00: ever have Medicaid during year 2000

mcaid00x: coverage by Medicaid/Schip as of 2/31/2000

mcdat00x: at anytime coverage by Medicaid/Schip as of 12/31/2000

Weighting variables

perwt00f: final person weight, 2000

varpsu00: variance estimation primary sampling unit on the 2000 MEPS

varstr00: variance estimation stratum on the 2000 MEPS

RU: reporting unit; 3/1: data obtained in round 3 of Panel 4 and round 1 of Panel 5; 4/2: data

obtained in round 4 of Panel 4 and round 2 of Panel 5; 5/3: data obtained in round 5 of Panel 4

and round 3 of Panel 5.a Detailed information about the imputed or the edited values of the variables could be ob-

tained from the Codebook and Documentation of the MEPS HC-050: 2000 Full Year Consol-

idated Data File.

drug insurance instability and its correlates: results from the 2000 medical expenditure panel...

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