Purpose
Access to health care after pregnancy is especially important for pregnant women with chronic illness. The purpose of our study was to describe the prevalence of chronic illness in pregnant women and factors affecting the receipt of ongoing care.
Methods
We conducted a cross-sectional analysis of 6,294 women between 19 and 45 years of age from the Medical Expenditure Panel Survey (MEPS). Chronic illness was defined using aggregate clinical classification codes in the MEPS. Women were divided into 4 groups: pregnant and currently not pregnant, with and without chronic illness. We analyzed group differences in demographic variables, socioeconomic status, and access to health care. We also estimated inpatient, outpatient, emergency room, and pharmacy expenditures for the 4 study groups. All analyses accounted for the complex survey design of MEPS.
Main Findings
Overall, 27% of pregnant women and 39% of nonpregnant women reported a chronic illness. There were no differences in race/ethnicity, poverty, or health insurance status between pregnant women with and without chronic illness. Women with chronic illness were more likely to have a usual source of care. Among pregnant women, the presence of a chronic condition did increase out-of-pocket expenditures, but did not increase total average health care expenditures, even after adjusting for other characteristics.
Conclusion
Pregnant women with chronic illness were similar to pregnant women without chronic illness in terms of access to care and total health care costs. Further research is needed to determine whether these similarities persist after delivery, given the relatively high prevalence of women with chronic illness who are of childbearing age.
Introduction
Recently, increasing attention has been given to the importance of health care before or between pregnancies—termed “preconception” or “interconception” care—and the role of such care in improving pregnancy and birth outcomes (
American College of Obstetricians and Gynecologists 2005
, Atrash et al 2006
, Johnson et al 2006
, Lu et al 2006
, Posner et al 2006
). Preconception guidelines developed by the Centers for Disease Control and Prevention emphasize management, before pregnancy, of chronic illnesses with adverse effects on perinatal outcomes. Subsequently, recommendations for preconception or interconception care include broadening access to health care for women of childbearing age (Johnson et al 2006
). One facet of such a strategy is continuing care for pregnant women with a chronic illness beyond the postpartum period (Misra et al 2000
). Ongoing care may ameliorate the impact of chronic illness on future pregnancies in addition to contributing to women's health. Little is known, however, about the prevalence of chronic illness in pregnant women or how many women with chronic illness have access to care after delivery—either through a continuous source of insurance or a regular provider of health care. It is also not known how much chronic illness adds to health care costs in women of childbearing age.Pregnant women with a chronic illness may be at increased risk of not receiving needed interconception or internatal care. Diabetes, for example, is a chronic condition 1) associated with poor obstetric and neonatal outcomes that can be prevented by adequate health care before pregnancy (
Fuhrmann et al 1983
, Kitzmiller et al 1991
, Mills et al 1988
) and 2) found disproportionately in minorities (National Institute of Diabetes and Digestive and Kidney Diseases 2005
). Minority women are more likely to be publicly insured, uninsured, or lack a usual source of care in comparison with white women (National Institute of Diabetes and Digestive and Kidney DiseasesNational Diabetes Information Clearinghouse (NDIC)
http://diabetes/niddk.nih.gov
Date: 2005
Altman and Taylor 2001
). Public insurance programs such as Medicaid, which require renewal, can further increase the risk of periods of no insurance coverage (Summer and Mann 2006
). Subsequently, minority women may be at increased risk of not receiving care for potentially harmful perinatal conditions during the interconception period.The purpose of our study was to describe factors associated with chronic illness in pregnancy that could decrease the likelihood of receiving interconception care. We presumed that both chronic illness and decreased access to care during pregnancy would persist postpartum and could be expected to characterize the interpartum as well as perinatal time periods. Using a nationally representative study of households, the Medical Expenditure Panel Survey (MEPS), we extracted information on chronic conditions, sociodemographic variables, insurance status, regular source of care, medical costs, and payer source for pregnant and nonpregnant women, with and without chronic illness.
Our first aim was to describe the prevalence of chronic illness in pregnant women. We examined both physical and mental health conditions common among women in the United States. Our second aim was to describe sociodemographic and health care access differences between pregnant women with and without chronic illness. Our third aim was to describe differences in health care costs between these 2 groups. We used nonpregnant women (with and without chronic illness) for comparison in each analysis, because there is no baseline information on either prevalence of chronic illness or health care costs in this population of relatively young women of childbearing age.
Methods
Data
Data are from the household component of the 2004 MEPS, an annual nationally representative survey of households representative of the U.S. noninstitutionalized civilian population. MEPS contains information on medical conditions, health service utilization, health status, access to care, and charges and source of medical payments (). Details regarding MEPS and the rationale and construction of the MEPS survey can be found online at http://www.meps.ahrq.gov/mepsweb/.
Sample
Our sample was drawn from the 2004 Household Component of the MEPS survey. We defined women as being of childbearing age, between 19 and 45 years (N = 6,294). We excluded women <19 years old because we presumed that the risk of chronic illness in this group would be relatively low and that women <19 could have differential access to care (versus older women) through pediatric health insurance programs.
Identification of Pregnancy and Chronic Illness: Creation of Study Groups
In our study we used both 3-digit International Classification of Diseases—9th edition—Clinical Modification (ICD-9-CM) and aggregate clinical classification codes provided in the medical care event files in MEPS to identify presence of any chronic illness and pregnancy. Pregnancy was broadly defined using codes for pregnancy, delivery, or any pregnancy or delivery-related condition (i.e., “forceps delivery” or “twin gestation”).
The chronic illness categories were selected based on chronic conditions that are prevalent among women in the United States, conditions that may affect perinatal health, and MEPS “priority conditions,” for 2004. The latter conditions are chosen based on their prevalence, expense, and relevance to policy. The list of priority conditions includes long-term “life-threatening” conditions such as cancer or diabetes; “chronic manageable” conditions such as asthma or arthritis; and mental health conditions such as mood disorders (
MEPS HC-087: 2004 Medical Conditions 2006
). We used all MEPS 2004 priority conditions with the exception of gallbladder disease and back problems.MEPS HC-087: 2004 Medical Conditions
Appendix 4: List of priority conditions.
Appendix 4: List of priority conditions.
http://www.meps.ahcpr.gov
Date: 2006
We also reviewed published data from 2 other national surveys (
Misra et al 2000
, Salganicoff et al 2005
) to derive the most common chronic conditions among women. With the exception of headache and allergy-related conditions, the MEPS priority conditions represented the most common chronic conditions in US women. We additionally included chronic conditions such as dementia, cerebrovascular accident (CVA), and osteoporosis to ensure that our comparison group of nonpregnant women with chronic illness did not have a disease profile that was markedly different from pregnant women. Finally, we added 5 conditions that had particular relevance for interconception care and perinatal outcomes. These were thyroid disorders, human immunodeficiency virus (HIV) disease, schizophrenia, substance abuse, and anxiety disorders. The resulting list of physical and mental conditions that we included to define “chronic illness” were asthma, arthritis, cancer (all cancer categories), chronic obstructive pulmonary disease (COPD; includes bronchiectasis and emphysema), CVA, diabetes, heart disease (includes valvular and congenital conditions, coronary artery disease, and ischemia), HIV infection, hypertension, osteoporosis, thyroid disorders, anxiety, dementia, mood disorders (depression and bipolar disorders), schizophrenia, and substance abuse disorders.Based on the presence of pregnancy and chronic illness, we created 4 study groups: 1) pregnant women with a chronic condition; 2) pregnant women without a chronic condition; 3) nonpregnant women with a chronic condition; and 4) nonpregnant women without a chronic condition.
Dependent Variables: Health Care Expenditures
Total and Type of Expenditures
We created variables for 1) type of expenditure and 2) total 2004 annual expenditures for each individual. The MEPS expenditure categories we included in this study were inpatient, outpatient, pharmacy, emergency room, and “other” (e.g., dental care and vision services).
Out-of-pocket Expenditures
MEPS also reports expenditures that were spent by an individual or family on behalf of the individual for health care services. We refer to this spending as out-of-pocket (OOP) expenditures. Our measure of OOP expenditures did not include expenditures for health care insurance premiums.
Out-of-pocket Expenditures Burden
At the individual level, we also measured the burden of OOP expenditures as percent of income spent OOP on health care services. This variable ranged from 0% to 100%. For those who reported zero income and positive expenditures, OOP burden was top-coded at 100%. Similarly, for those with OOP spending exceeding income, OOP burden was top-coded at 100% (
Crystal et al 2000
).Independent Variables
Source of Care
We categorized the MEPS source of care variables as 1) care by a primary care physician; 2) care by a non–primary care physician; 3) care by non-physician provider (other); and 4) no usual source of care.
Other Independent Variables
These consisted of race/ethnicity, age, marital status, region of residence, education, employment status, poverty status, health insurance, and perceived mental and physical health status.
Statistical Techniques
To examine the bivariate group differences in the presence of chronic illness among pregnant women and among all 4 study groups, chi-square tests were used. Multinomial logistic regression on the odds of belonging to 1 of the 3 groups compared with the reference group (pregnant women without chronic illness) was performed to examine group differences in sociodemographic variables and access to care. Parameter estimates from logistic regressions were converted to odds ratios and 95% confidence intervals for these adjusted odds ratios (AOR) are presented for ease of interpretation. To examine health care costs, bivariate group differences in average, total, and OOP expenditures among groups were tested with the t statistic. Again, among the 4 study groups, 3 groups were compared with the reference group (pregnant women without chronic illness). Ordinary least-squares regressions were used to test subgroup differences in annual, total, and components OOP expenditures. For analysis related to expenditures (except OOP spending burden), expenditures were transformed to a logarithmic scale to reduce skewness. Effect estimates for continuous independent variables on the log of annual expenditures can be interpreted as percentage change for each unit of change in the independent variable. The effect of dummy variables in terms of percentage of expenditures can be estimated by exponentiating the regression coefficients of dummy variables and subtracting 1 (i.e., percent change = eƥ − 1;
Halvorsen and Palmquist 1980
).MEPS has a complex sample design, with strata and weights provided for calculation of national estimates. For the present study, all analyses were conducted in SUDAAN 8.0 to appropriately handle study weights and clustering (
Shah et al 1996
).Results
Prevalence of Chronic Conditions
The study sample consisted of 6,294 women ages 19–45. Although the number of pregnant women with chronic illness was relatively small in our study (n = 215), 26.6% of all pregnant women reported a chronic illness (vs. 39.1% of nonpregnant women). For all chronic conditions, the prevalence among pregnant women was lower than in nonpregnant women (Table 1). However, prevalence was only significantly lower (p < .05) for anxiety, hypertension, arthritis, thyroid disorder, diabetes, osteoporosis, CVA, and HIV infection. There were no significant differences among pregnant and nonpregnant women in the prevalence of mood disorder, asthma, chronic lung or heart disease, cancer, substance abuse, schizophrenia, or dementia.
p <.001; .001 < p < .01; .01 < p <.05.
Table 1Prevalence of Chronic Illness in Study Sample (N = 6,294): Medical Expenditure Panel Survey, 2004
| Sample | Nonpregnant Women | Pregnant Women | ||
|---|---|---|---|---|
| n | wt % | n | wt % | |
| 5.439 | 100 | 855 | 100 | |
| Any chronic condition | 2,055 | 39.1 | 215 | 26.6 |
| Mood disorder | 678 | 12.3 | 74 | 9.8 |
| Anxiety | 478 | 9.5 | 47 | 5.9 |
| Arthritis | 339 | 6.3 | 22 | 3.0 |
| Hypertension | 344 | 5.7 | 24 | 2.4 |
| Asthma | 260 | 5.0 | 32 | 4.5 |
| Thyroid disorder | 229 | 4.6 | 17 | 2.2 |
| Chronic obstructive pulmonary disease | 202 | 4.0 | 21 | 3.4 |
| Heart disease | 197 | 3.6 | 21 | 3.0 |
| Diabetes | 180 | 3.0 | 18 | 1.5 |
| Cancer | 141 | 2.7 | 12 | 1.8 |
| Substance abuse | 22 | 0.5 | 2 | 0.3 |
| Osteoporosis | 16 | 0.3 | 0 | 0.0 |
| CVA | 15 | 0.2 | 0 | 0.0 |
| Schizophrenia | 11 | 0.1 | 2 | 0.1 |
| HIV infection | 12 | 0.1 | 0 | 0.0 |
| Dementia | 2 | 0 | 0 | 0.0 |
Note. Table shows actual sample size but weighted percentages. The percentages may not add up to the percent with any chronic illness because the categories are not mutually exclusive.
† Chronic obstructive lung disease includes self-reported diagnoses of emphysema and bronchiectasis.
‡ Includes valvular and congenital conditions, coronary artery disease and ischemia.
§ History of cerebrovascular accident.
Among chronic physical conditions in nonpregnant women, arthritis was the most prevalent, using weighted percentages (6.3%), followed by hypertension (5.7%), asthma (5.0%), and thyroid disorder (4.6%). In pregnant women, the most common physical condition was asthma (4.5%), followed by COPD (3.4%), arthritis (3.0%), and heart disease (3.0%). Mental illness was more common than physical illness in both groups—the prevalence of any mood disorder was 12.3% in nonpregnant women and 9.8% in pregnant women. The prevalence of anxiety was 9.5% in nonpregnant women and 5.9% in pregnant women. We found only a small percentage of women (both pregnant and not pregnant) reporting diagnoses of osteoporosis, HIV disease, or dementia.
Differences in Demographics and Access To Care
Table 2 describes the characteristics of the study sample by the 4 groups of women with and without pregnancy and with and without chronic illness. In the entire sample, 13.3% of women were pregnant. All variables were significant in chi-square analysis, at the 5% level, with the exception of education, region, or likelihood of living in a metropolitan area.
Table 2Profile of Women in the Age Group 19–45 Years By Pregnancy and Chronic Illness: Medical Expenditure Panel Survey, 2004
| Not Pregnant No Chronic Illness | Not Pregnant Chronic Illness | Pregnant No Chronic Illness | Pregnant Chronic Illness | |||||
|---|---|---|---|---|---|---|---|---|
| n | wt % | n | wt % | n | wt % | n | wt % | |
| All | 3,384 | 52.8 | 2,055 | 33.9 | 640 | 9.8 | 215 | 3.5 |
| Age | ||||||||
| 19–24 | 779 | 58.6 | 287 | 24.8 | 207 | 12.7 | 59 | 3.9 |
| 25–29 | 633 | 56.3 | 267 | 22.6 | 171 | 15.3 | 64 | 5.7 |
| 30–34 | 641 | 51.3 | 349 | 29.0 | 164 | 14.9 | 50 | 4.7 |
| 35–45 | 1,331 | 49.2 | 1,152 | 45.2 | 98 | 3.7 | 42 | 1.9 |
| Race/ethnicity | ||||||||
| White | 1,411 | 49.1 | 1,177 | 37.9 | 272 | 9.2 | 113 | 3.7 |
| African American | 579 | 57.8 | 322 | 29.7 | 90 | 9.1 | 35 | 3.4 |
| Latino | 1,116 | 58.1 | 437 | 25.8 | 235 | 12.4 | 60 | 3.6 |
| Other | 278 | 63.9 | 119 | 24.3 | 43 | 9.9 | 7 | 1.9 |
| Marital status | ||||||||
| Married | 1,681 | 50.1 | 977 | 31.9 | 428 | 13.6 | 133 | 4.4 |
| Widowed | 29 | 48.1 | 24 | 46.0 | 1 | 3.6 | 1 | 2.3 |
| Divorced/separated | 376 | 42.1 | 402 | 50.6 | 39 | 4.6 | 21 | 2.7 |
| Never married | 1,298 | 60.8 | 652 | 31.1 | 172 | 5.6 | 60 | 2.5 |
| Metro | ||||||||
| Metro | 2,848 | 53.4 | 1,670 | 33.2 | 544 | 9.8 | 183 | 3.6 |
| Nonmetro | 535 | 49.7 | 385 | 37.5 | 96 | 9.4 | 32 | 3.4 |
| Region | ||||||||
| Northeast | 471 | 53.7 | 328 | 32.9 | 92 | 10.3 | 30 | 3.1 |
| Midwest | 580 | 50.5 | 433 | 36.3 | 123 | 9.3 | 45 | 3.8 |
| South | 1,418 | 54.2 | 813 | 33.7 | 232 | 8.8 | 83 | 3.3 |
| West | 914 | 52.2 | 481 | 32.6 | 193 | 11.2 | 57 | 4.0 |
| Education | ||||||||
| Less than high school | 828 | 51.3 | 463 | 32.8 | 212 | 11.5 | 60 | 4.5 |
| High school | 1,083 | 52.5 | 659 | 35.2 | 170 | 8.6 | 72 | 3.8 |
| Above high school | 1,460 | 53.4 | 928 | 33.6 | 254 | 9.9 | 83 | 3.1 |
| Employed | ||||||||
| Yes | 2,504 | 53.6 | 1,498 | 34.6 | 383 | 8.6 | 136 | 3.1 |
| No | 878 | 49.8 | 557 | 31.4 | 257 | 13.7 | 79 | 5.1 |
| Poverty status | ||||||||
| Poor | 712 | 45.3 | 477 | 37.1 | 186 | 12.4 | 62 | 5.3 |
| Near poor | 901 | 54.5 | 479 | 31.4 | 174 | 10.5 | 51 | 3.6 |
| Middle income | 955 | 53.2 | 593 | 34.7 | 147 | 8.9 | 53 | 3.1 |
| High income | 816 | 54.7 | 506 | 33.1 | 133 | 9.0 | 49 | 3.2 |
| Health insurance | ||||||||
| Private | 1,974 | 52.0 | 1,287 | 34.8 | 357 | 9.7 | 126 | 3.5 |
| Public | 433 | 36.8 | 450 | 40.6 | 205 | 16.5 | 74 | 6.1 |
| Uninsured | 977 | 69.2 | 318 | 24.2 | 78 | 4.9 | 15 | 1.7 |
| Usual source of care | ||||||||
| PCP | 849 | 48.8 | 686 | 38.8 | 160 | 8.9 | 63 | 3.6 |
| Non-PCP | 26 | 35.7 | 33 | 51.2 | 4 | 5.1 | 5 | 8.0 |
| Other | 1,332 | 49.8 | 966 | 36.2 | 290 | 9.9 | 105 | 4.0 |
| No USC | 1,134 | 63.3 | 360 | 23.2 | 183 | 11.2 | 41 | 2.4 |
| Perceived health | ||||||||
| Excellent/very good | 2,224 | 60.2 | 888 | 26.1 | 413 | 10.8 | 107 | 2.9 |
| Good | 959 | 44.6 | 733 | 42.2 | 191 | 9.0 | 74 | 4.2 |
| Fair/poor | 201 | 25.3 | 434 | 64.0 | 36 | 4.5 | 34 | 6.1 |
| Mental health | ||||||||
| Excellent/very good | 2,497 | 58.8 | 1,091 | 27.4 | 474 | 10.8 | 120 | 3.0 |
| Good | 799 | 43.8 | 684 | 44.5 | 147 | 8.1 | 64 | 3.6 |
| Fair/poor | 88 | 19.0 | 280 | 67.6 | 19 | 4.3 | 31 | 9.1 |
Note. Based on 6,294 women aged between 19 and 45 years and alive as of the end of 2004. All variables except education, metro, and region were significant at the 5% level based on chi-square test statistic.
Using multinomial regression (Table 3), we compared the pregnant with no chronic illness group with the other groups of women. We did not observe any significant differences in socioeconomic characteristics between pregnant women with and without chronic illness, except age and marital status. Pregnant women with chronic illness were more likely to be older (age 35–45) than younger (age 19–24; AOR, 2.31; 95% confidence interval [CI], 1.17–4.57) and less likely to be married than pregnant women without a chronic illness (AOR, .52; 95% CI, .30–.91). There were no differences in education, employment status, poverty status, or health insurance between the 2 groups. However, pregnant women with chronic conditions were significantly less likely to have no usual source of care (AOR, 0.50; 95% CI, 0.28–0.90).
p <.001; .001 < p < .01; .01 < p <.05.
Table 3Adjusted Odds Ratios (AOR) and 95% Confidence Interval From Multinomial Regression on Pregnancy and Chronic Illness Categories: Medical Expenditure Panel Survey, 2004
| Not Pregnant No Chronic Illness | Not Pregnant Chronic Illness | Pregnant Chronic Illness | |||||||
|---|---|---|---|---|---|---|---|---|---|
| AOR | 95% CI | Sig | AOR | 95% CI | Sig | AOR | 95% CI | Sig | |
| Age | |||||||||
| 19–24 | |||||||||
| 25–29 | 1.38 | 1.00–1.91 | 1.42 | 0.97–2.08 | 1.70 | 0.89–3.25 | |||
| 30–34 | 1.63 | 1.16–2.29 | 2.31 | 1.57–3.42 | 1.43 | 0.75–2.75 | |||
| 35–45 | 7.21 | 4.88–10.6 | 15.58 | 10.1–23.9 | 2.31 | 1.17–4.57 | |||
| Race/ethnicity | |||||||||
| White | |||||||||
| African American | 0.97 | 0.64–1.47 | 0.54 | 0.35–0.82 | 0.66 | 0.29–1.47 | |||
| Latino | 0.98 | 0.71–1.34 | 0.69 | 0.48–0.99 | 0.78 | 0.44–1.37 | |||
| Other | 1.31 | 0.83–2.05 | 0.63 | 0.40–0.99 | 0.44 | 0.17–1.17 | |||
| Marital status | |||||||||
| Married | 0.17 | 0.13–0.23 | 0.13 | 0.09–0.19 | 0.52 | 0.30–0.91 | |||
| Other | |||||||||
| Metro | |||||||||
| Metro | |||||||||
| Nonmetro | 1.04 | 0.72–1.51 | 1.12 | 0.74–1.71 | 0.88 | 0.44–1.79 | |||
| Region | |||||||||
| Northeast | |||||||||
| Midwest | 1.09 | 0.74–1.61 | 1.23 | 0.83–1.83 | 1.48 | 0.80–2.75 | |||
| South | 1.39 | 0.93–2.10 | 1.70 | 1.13–2.56 | 1.47 | 0.74–2.90 | |||
| West | 0.95 | 0.65–1.39 | 1.18 | 0.77–1.80 | 1.59 | 0.81–3.10 | |||
| Education | |||||||||
| Less than high school | 1.14 | 0.81–1.61 | 1.00 | 0.69–1.45 | 1.19 | 0.65–2.20 | |||
| High school | 1.23 | 0.91–1.66 | 1.08 | 0.80–1.44 | 1.30 | 0.81–2.08 | |||
| Above high school | |||||||||
| Employed | |||||||||
| Yes | 1.30 | 0.98–1.72 | 1.60 | 1.18–2.18 | 1.03 | 0.65–1.65 | |||
| No | |||||||||
| Poverty status | |||||||||
| Poor | 0.49 | 0.31–0.78 | 0.63 | 0.38–1.03 | 1.05 | 0.55–2.01 | |||
| Near poor | 0.73 | 0.52–1.03 | 0.73 | 0.50–1.08 | 0.86 | 0.45–1.64 | |||
| Middle income | 0.90 | 0.65–1.26 | 1.00 | 0.69–1.44 | 0.93 | 0.53–1.65 | |||
| High income | |||||||||
| Health insurance | |||||||||
| Private | |||||||||
| Public | 0.44 | 0.29–0.67 | 0.58 | 0.40–0.85 | 0.66 | 0.37–1.16 | |||
| Uninsured | 3.06 | 1.85–5.06 | 1.63 | 0.97–2.75 | 0.88 | 0.33–2.36 | |||
| Usual source of care | |||||||||
| PCP | 1.03 | 0.74–1.42 | 1.11 | 0.81–1.53 | 0.95 | 0.57–1.60 | |||
| Other | |||||||||
| No USC | 0.96 | 0.70–1.30 | 0.59 | 0.42–0.82 | 0.50 | 0.28–0.90 | |||
| Perceived health | |||||||||
| Excellent/very good | |||||||||
| Good | 0.92 | 0.66–1.29 | 1.64 | 1.17–2.29 | 1.47 | 0.80–2.70 | |||
| Fair/poor | 1.06 | 0.57–1.98 | 4.10 | 2.30–7.30 | 3.15 | 1.34–7.39 | |||
| Mental health | |||||||||
| Excellent/very good | |||||||||
| Good | 1.03 | 0.72–1.46 | 1.54 | 1.05–2.26 | 1.16 | 0.61–2.19 | |||
| Fair/poor | 0.91 | 0.43–1.96 | 3.38 | 1.59–7.16 | 4.64 | 1.60–13.4 | |||
Note. Based on 6,294 women aged between 19 and 45 years and alive as of the end of 2004. Asterisks denote significant group differences compared to the reference group based on multinomial logistic regression on the presence of chronic illness and pregnancy. The regression also includes an intercept term. The reference group for the dependent variable is “Pregnant with no chronic illness.”
Nonpregnant women (with and without chronic illness) were significantly older than pregnant women and less likely to be covered by public insurance. Nonpregnant women without a chronic illness were significantly more likely to be uninsured (AOR, 3.06; 95% CI, 1.85–5.06).
Health Care Expenditures
The top panel of Table 4 reports average, total, and type of expenditures (inpatient, outpatient, prescription drugs, emergency room, and other). The comparison group for average expenditures was pregnant women without chronic illness. Average total expenditures were not significantly different among pregnant women with and without chronic illness ($5,180 vs. $5,914). There were no significant differences in inpatient, emergency, or other medical costs between the 2 groups. However, outpatient and prescription drug expenditures were higher for pregnant women with chronic illness ($2,189 for outpatient vs. $706 for prescription drug expenditures) compared with pregnant women without chronic illness ($1,433 outpatient, $242 prescription drug expenditures). These results remained unchanged even after controlling for all MEPS covariates, including poverty, insurance status, smoking, and exercise (bottom panel of Table 4).
p <.001; .001 < p < .01; .01 < p <.05;
Table 4Average Total, Type, and Out-of-Pocket Expenditures and Out-of-Pocket Spending and Adjusted Parameter Estimates From Separate Ordinary Least-Squares Regressions on Total, Type of Expenditures, Out-of-Pocket Expenditures, and Out-of-Pocket Spending Burden: Medical Expenditure Panel Survey, 2004
| Sample Size with Positive Dollars and Average Dollars Among Those with Positive Dollars by Pregnancy Categories | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Not Pregnant No Chronic Illness | Not Pregnant Chronic Illness | Pregnant No Chronic | Pregnant Chronic Illness | ||||||||
| n | Mean | Sig | n | Mean | Sig | n | Mean | n | Mean | Sig | |
| Expenditures | |||||||||||
| Total | 2,393 | 1,446 | 1,946 | 4,288 | 618 | 5,180 | 215 | 5,914 | |||
| Inpatient | 72 | 9,769 | 173 | 8,838 | 322 | 6,336 | 102 | 6,628 | |||
| Outpatient | 1,823 | 706 | 1,727 | 1,690 | 591 | 1,433 | 209 | 2,189 | |||
| Prescription drugs | 1,554 | 331 | 1,783 | 1,070 | 465 | 242 | 193 | 706 | |||
| Emergency room | 293 | 791 | 407 | 890 | 142 | 846 | 78 | 632 | |||
| Other | 1,426 | 472 | 1,123 | 1,380 | 248 | 469 | 115 | 503 | |||
| Out-of-pocket | |||||||||||
| Spending | 2,150 | 421 | 1,876 | 883 | 537 | 598 | 197 | 697 | |||
| Percent income | 2,150 | 13.01 | 1,876 | 15.45 | 537 | 20.63 | 197 | 20.38 | |||
| Adjusted parameter estimates from ordinary least squares regressions | |||||||||||
| Beta | SE | Sig | Beta | SE | Sig | Beta | SE | Sig | |||
| Expenditures | |||||||||||
| Total | −2.38 | 0.14 | −0.74 | 0.13 | Reference group | 0.16 | 0.17 | ||||
| Inpatient | −4.05 | 0.3 | −3.65 | 0.32 | Reference group | −1.12 | 0.57 | ||||
| Outpatient | −2.71 | 0.14 | −1.00 | 0.15 | Reference group | 0.52 | 0.17 | ||||
| Prescription Drugs | −0.37 | 0.18 | 2.13 | 0.17 | Reference group | 1.8 | 0.27 | ||||
| Emergency Room | −0.47 | 0.16 | −0.05 | 0.20 | Reference group | 0.54 | 0.29 | ||||
| Other | 0.46 | 0.23 | 0.84 | 0.25 | Reference group | 0.68 | 0.32 | ||||
| Out-of-pocket | |||||||||||
| Spending | −0.69 | 0.14 | 0.43 | 0.14 | Reference group | 0.56 | 0.18 | ||||
| Percent income | −3.56 | 2.01 | 1.44 | 2.00 | Reference group | 2.73 | 3.05 | ||||
† .05 < p < .1.
Unadjusted OOP expenditures and OOP expenditures as a percentage of income did not differ significantly between the 2 groups of pregnant women. The average OOP spending for health care was $697 for pregnant women with a chronic illness compared with $598 for pregnant women without a chronic illness. In the adjusted analysis, OOP spending was significantly higher among pregnant women with chronic illness versus those without, but spending as percent of income was not significantly different between the 2 groups. In both groups of pregnant women, OOP expenditures as a percentage of income were higher than in the 2 groups of nonpregnant women.
Nonpregnant women with chronic illness were not significantly different from pregnant women with chronic illness in terms of total, emergency room, and other expenditures. In the adjusted analysis, however, nonpregnant women with chronic illness had lower inpatient, outpatient, and total expenditures than pregnant women with chronic illness, but greater prescription drug expenditures.
Discussion
To our knowledge, this is the first study using a national database to describe the prevalence of chronic illness in pregnant women and its relationship to health care access and expenditures. Although we found that the prevalence of chronic illness overall was lower in pregnant women than in nonpregnant women age 19–45 years, the differences in prevalence rates were very narrow, suggesting that chronic illness is not uncommon in pregnancy. We did not find that the presence of chronic illness in this age group was associated with either decreased access to care or significantly higher health care costs.
Our results, although somewhat consistent with existing literature, are not directly comparable with prior studies of chronic illness in the interconception period, which have used a variety of distinct methodologies to estimate prevalence. For example, in the Central Pennsylvania Women's Health Study,
Weisman et al 2006
found 11% of a study sample age 18–45 had a diagnosis of hypertension in the last 5 years and 28% had a diagnosis of depression or anxiety. This pattern is consistent with our study, in that we also found a greater number of women reporting mental versus physical conditions.Among women in our sample, the prevalence of mood disorders was especially striking. More than 12% of nonpregnant women and 9.8% of pregnant women reported a mood disorder—the highest of any chronic illness. The MEPS category for mood disorders contains ICD-9-CM codes for both depression and bipolar disease. In a study of MEPS data from 2000,
Harman et al 2004
found that the majority of respondents in this category had an ICD-9-CM code for depression (“311”) rather than other disorders.Subsequently, the overall percentage of depression in our sample is consistent with prior studies of primary care and prenatal populations (
Dietz et al 2007
, Gaynes et al 2005
). The prevalence of depression in relation to the other study disorders, however, is concerning, because depression is a possible risk factor for preterm birth (Conway and Kennedy 2004
, Orr et al 2002
), and has been linked to nicotine dependence in pregnancy (Goodwin and Simuro 2007
). Depression during pregnancy is also linked to postpartum depression and depression in future pregnancies. Moreover, ongoing maternal depression and anxiety have significant effects on child and family well-being (Murray et al 1996
, Stowe and Nemeroff 1995
).With regard to the demographic and health care access differences between pregnant women with and without chronic illness, we found few differences between these 2 groups. Perceived health and perceived mental health did differ between women with and without chronic illness, with lower perceived health corresponding, as expected, to the presence of chronic disease. We also found that pregnant women with a chronic illness were more likely to have a usual source of care, which was reassuring.
Overall, our findings suggest that chronic illness in pregnancy may not be associated with decreased access to care. It is not clear though, for women funded by public insurance plans, whether access to care during pregnancy can be equated with access before and after pregnancy. For example, a study of data reported from 8 states using the Pregnancy Risk Assessment Monitoring System found that 50%–69% of women with Medicaid at delivery were uninsured before pregnancy (
Adams et al 2003
). An earlier study of Medicaid data in California also found that up to 50% of women delivering with Medicaid were uninsured before pregnancy (Egerter et al 2002
). Our findings show a similar pattern. The percentage of women using private insurance was relatively consistent across all 4 groups. In both pregnant groups, the percentage of public insurance coverage was higher, possibly representing cross-over during pregnancy from uninsured to publicly insured status. Further studies are needed to document the proportion of pregnant women with chronic illness who lose publicly funded insurance after pregnancy. Moreover, in our analysis, these women were less likely to be married than pregnant women without chronic illness, and after pregnancy, may be less likely to have access to insurance through a spouse.A significant number of women of childbearing age in our study were uninsured. Moreover, 24% of uninsured women and 23% of women without a usual source of care had a chronic illness. Public policy trends, such as the expansion of Medicaid eligibility for pregnant women, although limiting eligibility for young women who are not pregnant or disabled, may be especially problematic for the delivery of preconception health care to women with chronic illnesses The continued bias toward coverage of pregnant women, to the exclusion of low-income women with chronic illness who could potentially become pregnant, may not lead to the cost-effective pregnancy outcomes for which public coverage for maternity care is currently mandated (
Hughes and Runyan 1995
).Privately insured women may have a separate set of risks limiting access to care in the interconception period, in the form of OOP expenditures. Although total expenditures were not different, pregnant women with chronic illness spent more on OOP expenditures than pregnant women without chronic illness. We saw a similar pattern in nonpregnant women with chronic illness in whom total expenditures were lower than in pregnant women with chronic illness, but OOP spending was higher. Prior analysis has shown that privately insured women have higher OOP expenses during pregnancy than women covered by public programs (
Machlin and Rohde 2007
). Despite health care coverage, the presence of a chronic illness may create an additional financial burden on families, especially those families relying on private insurance, who comprised the majority of respondents in MEPS.- Machlin S.
- Rohde F.
Health care expenses for uncomplicated pregnancies.
http://www.meps.ahrq.gov
Date: 2007
Further research is needed to clarify reasons for higher OOP spending in women with chronic illness, which may be related to a lack of comprehensive insurance coverage for expenses such as prescription drugs. It is currently not clear what the sources of OOP spending are for pregnant women. Previous studies of health care costs in pregnancy have analyzed only those costs related to “standard” prenatal care and excluded complicated pregnancies or medication related to conditions such as hypertension (
Machlin and Rohde 2007
). Further research is needed on the types of health care expenses born by women with chronic illness in private versus public plans, while taking into account state variations in coverage for maternity care.- Machlin S.
- Rohde F.
Health care expenses for uncomplicated pregnancies.
http://www.meps.ahrq.gov
Date: 2007
As in the analysis of access to care variables, our cost analysis also demonstrated few differences between pregnant women with and without a chronic illness. A noteworthy finding is that the average total expenditures and inpatient expenditures were not significantly different between the 2 groups. Additionally, we found that nonpregnant women with chronic illness had lower total, inpatient, and outpatient expenditures compared with pregnant women with chronic illness, although pharmacy and other medical expenditures were higher. This was an expected finding, given the relatively high hospital costs associated with delivery of a child (
Machlin and Rohde 2007
).- Machlin S.
- Rohde F.
Health care expenses for uncomplicated pregnancies.
http://www.meps.ahrq.gov
Date: 2007
There are several limitations to our analysis. First, to obtain a sufficient number of women for our sample we grouped a diverse set of chronic illnesses with widely varying effects on maternal health and future pregnancies. Moreover, we did not include all chronic illnesses in our study; we excluded chronic neurologic, renal, hematologic, and liver diseases. Rather than compile a list of all conditions that could adversely affect pregnancy, we used only common conditions that we thought would be relevant to an initial descriptive study. Second, women reporting a diagnosis of a chronic illness, will, at some point, have received sufficient health care for a diagnosis to be made, making chronic illness a marker for receipt of health care. Women who do not have regular health care or access to a regular provider may not be receiving these diagnoses. Third, our data were cross-sectional, capturing women at various points in their pregnancy. Therefore, we may have underestimated some costs associated with delivery. Finally, nonpregnant women are a diverse group—including women who are recently postpartum, have never been pregnant, or may not become pregnant.
Despite these limitations, our findings have significance in the context of priorities to provide preconception and interconception care, especially for high-risk women. We found that chronic illnesses are relatively common in pregnancy and in childbearing women in general. This is important given the widely held assumption that pregnant women are relatively young, healthy, and have pregnancy-limited problems that can be resolved during the prenatal time period only. Because we found few significant cost differences between pregnant women with and without chronic illness, it is interesting to speculate that extension of regular insurance coverage to women postpartum could be a potentially reasonable and cost-effective solution to preventing complications in future pregnancies. However, further studies are needed to examine the association between interconception health care access and pregnancy outcomes in women with chronic conditions.
In the United States, increased age at first pregnancy, increasing ethnic diversity, and a changing pattern of illness (
Strobino et al 2002
) all demand an emphasis on the appropriate internatal care of chronic illness. Moreover, many studies emphasizing the importance of women's health before childbearing, including the persistent intergenerational effect of low birth weight, lend greater importance to a life-course model of health for women versus a more narrow focus on prenatal health and prenatal health care utilization (Halfon and Hochstein 2002
, Lu and Halfon 2003
, Misra et al 2003
). Focusing on the health of women with chronic illness throughout their lives, versus during pregnancy only, allows us to combine the goals of maternal child health and women's health in a way that benefits both women and their families.References
- Transitions in insurance coverage from before pregnancy through delivery in nine states, 1996–1999.Health Affairs. 2003; 22: 219-229
- Women in the health care system: Health status, insurance, and access to care.(MEPS Research Findings No. 17. AHRQ Pub. No. 02-0004) Agency for Healthcare Research and Quality, Rockville, MD2001
- The importance of preconception care in the continuum of women's health care (ACOG Committee Opinion No. 313).Obstetrics & Gynecology. 2005; 106: 665-666
- Preconception care for improving perinatal outcomes: the time to act.Maternal Child Health Journal. 2006; 10: 3-11
- Maternal depression and the production of infant health.Southern Economic Journal. 2004; 71: 260-286
- Out-of-pocket health care costs among older Americans.Series B Psychological Sciences and Social Sciences55. 2000; : S51-S62
- Clinically identified maternal depression before, during, and after pregnancies ending in live births.American Journal of Psychiatry. 2007; 164: 1515-1520
- Timing of insurance coverage and use of prenatal care among low-income women.American Journal of Public Health. 2002; 92: 423-427
- Prevention of congenital malformations in infants of insulin-dependent diabetic mothers.Diabetes Care. 1983; 6: 219-223
- Perinatal depression: Prevalence, screening accuracy, and screening outcomes.(Evidence Report/Technology Assessment No. 119. AHRQ Publication No. 05-E006-2) Agency for Healthcare Research and Quality, Rockville, MD2005
- Mental disorders and nicotine dependence among pregnant women in the United States.Obstetrics & Gynecology. 2007; 109: 875-883
- Life course health development: An integrated framework for developing health, policy, and research.Milbank Quarterly. 2002; 80: 433-479
- The interpretation of dummy variables in semilogarithmic equations.American Economic Review. 1980; 70: 474-475
- Disparities in the adequacy of depression treatment in the United States.Psychiatric Services. 2004; 55: 1379-1385
- Prenatal care and public policy: Lessons for promoting women's health.Journal of the American Medical Womens Association. 1995; 50: 156-160
- Recommendations to improve preconception health and health care—United States.Morbidity and Mortality Weekly Report. 2006; 55: 1-23
- Preconception care of diabetes.JAMA. 1991; 265: 731-736
- Racial and ethnic disparities in birth outcomes: A life-course perspective.Maternal Child Health Journal. 2003; 7: 13-30
- Preconception care between pregnancies: The content of internatal care.Maternal Child Health Journal. 2006; 10: 107-122
- Health care expenses for uncomplicated pregnancies.http://www.meps.ahrq.govDate: 2007
- Survey background.http://www.meps.ahrq.gov(Accessed August 29, 2007)Date: 2006
- Appendix 4: List of priority conditions.http://www.meps.ahcpr.gov(Accessed February 29, 2008)Date: 2006
- Lack of relation of increased malformation rates in infants of diabetic mothers to glycemic control during organogenesis.New England Journal of Medicine. 1988; 318: 671-676
- An intersection of women's and perinatal health: The role of chronic conditions.Women's Health Issues. 2000; 10: 256-267
- Integrated perinatal health framework: a multiple determinants model with a lifespan approach.American Journal of Preventive Medicine. 2003; 25: 65-75
- The impact of postnatal depression and associated adversity on early mother-infant interactions and later infant outcome.Child Development. 1996; 67: 2512-2526
- http://diabetes/niddk.nih.gov(August 29, 2007)Date: 2005
- Maternal prenatal depressive symptoms and spontaneous preterm births among African-American women in Baltimore, Maryland.American Journal of Epidemiology. 2002; 156: 797-802
- The national summit on preconception care: A summary of concepts and recommendations.Maternal Child Health Journal. 2006; 10: 199-207
- Women and health care: A national profile.Kaiser Family Foundation, Menlo Park, CA2005
- SUDAAN user's manual, release 7.0.Research Triangle Institute, Research Triangle Park, NC1996
- Women at risk for postpartum-onset major depression.American Journal of Obstetrics and Gynecology. 1995; 173: 639-645
- Charting a course for the future of women's health in the United States: Concepts, findings and recommendations.Social Science and Medicine. 2002; 54: 839-848
- Instability of public health insurance coverage for children and their families: Causes, consequences, and remedies.The Commonwealth Fund, New York2006
- Preconceptual health: Risks of adverse pregnancy outcomes by reproductive life stage in the Central Pennsylvania Women's Health Study (CePAWHS).Women's Health Issues. 2006; 16: 216-224
Biography
Sharmila Chatterjee MD, MPH, is a research instructor in the Department of Health Policy and Management, Boston University School of Public Health and the Department of Family Medicine, Boston University Medical School. She is currently finishing a health services research fellowship at the Center for Health Quality, Outcomes and Economic Research (CHQOER) based at the Bedford VA Hospital, Bedford, Massachusetts.
Usha Sambamoorthi, PhD, is a Professor and Director of Women's Health and Population-based Mental Health Disparities in the Department of Psychiatry, University of Massachusetts Medical School. She is also an affiliated research investigator with the Center for Healthcare Knowledge Management at the VA New Jersey Healthcare System.
Dr. Sambamoorthi is an economist and her current interests are in the areas of disparities by gender, race/ethnicity, age, and disability, and care of individuals with multiple chronic conditions.
Milton Kotelchuck, PhD, MPH, is Professor and Chair Emeritus in the Department of Maternal and Child Health, Boston University School of Public Health (and Professor in the Departments of Obstetrics and Gynecology and Pediatrics, Boston University Medical School). His research has focused on the adequacy and content of prenatal care, and its relation to maternal and child health outcomes. His current work addresses women's health in the interconception period and the structure and content of internatal care.
Article info
Publication history
Published online: October 27, 2008
Accepted:
June 24,
2008
Received in revised form:
May 6,
2008
Received:
January 4,
2008
Footnotes
The authors have no direct financial interests that might pose a conflict of interest in connection with the submitted manuscript.
Identification
Copyright
© 2008 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.
