Cervical cancer incidence and mortality rates in the United States have decreased 67% over the past 3 decades, a reduction mainly attributed to widespread use of the Papanicolaou (Pap) test for cervical cancer screening. In the general population, receipt of cervical cancer screening is positively associated with having health insurance. Less is known about the role insurance plays among women seeking care in community health centers, where screening services are available regardless of insurance status. The objective of our study was to assess the association between cervical cancer screening and insurance status in Oregon and California community health centers by using data from electronic health records.
We used bilevel log-binomial regression models to estimate prevalence ratios and 95% confidence intervals for receipt of a Pap test by insurance status, adjusted for patient-level demographic factors and a clinic-level random effect.
Insurance status was a significant predictor of cervical cancer screening, but the effect varied by race/ethnicity and age. In our study uninsured non-Hispanic white women were less likely to receive a Pap test than were uninsured women of other races. Young, uninsured Hispanic women were more likely to receive a Pap test than were young, fully insured Hispanic women, a finding not previously reported.
Electronic health records enable population-level surveillance in community health centers and can reveal factors influencing use of preventive services. Although community health centers provide cervical cancer screening regardless of insurance status, disparities persist in the association between insurance status and receipt of Pap tests. In our study, after adjusting for demographic factors, being continuously insured throughout the study period improved the likelihood of receiving a Pap test for many women.
Cervical cancer is a significant public health challenge in the United States. Approximately 12,000 women were expected to receive a diagnosis of cervical cancer in 2012, and 4,220 were expected to die of the disease (
Community health centers (CHCs) play an important role in addressing disparities in cervical cancer screening. CHCs serve the primary health care needs of over 20 million people in the United States, 38% of whom are uninsured (
We used data from EHRs to assess the association between cervical cancer screening and insurance status in a population of CHC patients. Our objective was to acquire a better understanding of factors affecting use of preventive health care services by CHC patients to inform efforts to reduce health disparities in underserved communities.
This cross-sectional study involved secondary analysis of EHR data for a clinic-based population sample. Data were supplied by OCHIN, a nonprofit organization that hosts networked EHRs for CHCs in 13 states. Patient records for women attending 17 clinics located in Oregon and California were retrieved by electronic query from the OCHIN EHR database. These clinics, operated by 5 CHC organizations, were selected because they had both administrative and clinical records available for the entire study period, 2008 through 2010. The study protocol was approved both by the research review committee of OCHIN’s Practice-Based Research Network, which includes CHC representatives, and by the Oregon Health and Science University Institutional Review Board.
Eligible subjects were women who were aged 24 to 64 years in 2010, who had made 1 or more medical visits at a study clinic during 2010, and, to control for the effects of having a usual source of care (
The outcome of interest was receipt of cervical cancer screening from 2008 through 2010. Receipt of screening was defined by evidence of a completed order for a Pap test in the patients’ EHR. Pap tests performed within 9 months of a prior Pap smear abnormality or related diagnosis of a cervical abnormality were considered diagnostic rather than screening tests, and excluded from the analysis (
Covariates included age, race/ethnicity, and household income as a percentage of the federal poverty level (FPL). Selection and categorization of covariates were based on availability of data and informed by previous studies examining use of cervical cancer prevention services (
Analysis was restricted to subjects with complete information for all covariates. Descriptive statistics were generated and χ2 tests performed to examine differences in distribution of sociodemographic covariates by insurance group. A series of univariable and multivariable bilevel log-binomial regression models was used to estimate unadjusted prevalence ratios (PRs) and adjusted prevalence ratios (APRs) and 95% confidence intervals (CIs) for receipt of cervical cancer screening by insurance status. Log-binomial models were preferred over logistic models, as the latter can strongly overestimate relative risk when the outcome of interest is relatively common (prevalence ≥10%) (
Variables associated with the outcome at the
Our final study sample included 11,560 women from the base population of 24,382. We excluded a total of 12,822 women, 1,680 who had a history of hysterectomy, 7,943 who had not had an office visit both in 2010 and during or before 2008, 2,683 who were pregnant during the study period, and 516 whose race/ethnicity or FPL data were missing. Six percent (n = 1,217) of Pap tests identified in the EHR were ordered for diagnostic rather than screening purposes and excluded from the analysis.
Within the study sample, 22.9% of women had no insurance coverage from 2008 through 2010, 33.4% had partial coverage, and 44.8% were continuously covered (
| Demographic Characteristic | Total Population, n (%) | No Coverage, n (%) | Partial Coverage, n (%) | Continuous Coverage, n (%) |
|
|---|---|---|---|---|---|
|
| 11,560 (100.0) | 2,642 (22.9) | 3,856 (33.4) | 5,062 (43.8) | NA |
|
| 7,346 (63.5) | 1,787 (67.6) | 2,322 (60.2) | 3,237 (63.9) | <.001 |
|
| |||||
| 21–39 | 5,324 (46.1) | 1,479 (56.0) | 1,985 (51.5) | 1,860 (36.7) | <.001 |
| 40–64 | 6,236 (53.9) | 1,163 (44.0) | 1,871 (48.5) | 3,202 (63.3) | |
|
| |||||
| Non-Hispanic white | 5,426 (46.9) | 928 (35.1) | 2,246 (58.2) | 2,252 (44.5) | <.001 |
| Hispanic | 4,424 (38.3) | 1,523 (57.6) | 880 (22.8) | 2,021 (39.9) | |
| Non-Hispanic other | 1,710 (14.8) | 191(7.2) | 730 (18.9) | 789 (15.6) | |
|
| |||||
| ≥ 100% of FPL | 3,551 (30.7) | 1,286 (48.7) | 1,232 (32.0) | 1,033 (20.4) | <.001 |
| 0–99% of FPL | 8,009 (69.3) | 1,356 (51.3) | 2,624 (68.0) | 4,029 (79.6) | |
Abbreviations: FPL, federal poverty level.
Each independent variable was significantly associated with cervical cancer screening in univariable regression models (
| Demographic Characteristic | N | Women Receiving Pap Test, n (% ) | Prevalence Ratio (95% Confidence Interval) |
|---|---|---|---|
|
| 11,560 | 7,346 (63.5) | NA |
|
| |||
| Continuous coverage | 5,062 | 3,237 (63.9) | 1 [Reference] |
| Partial coverage | 3,856 | 2,322 (60.2) | 0.98 (0.95–1.01) |
| No coverage | 2,642 | 1,787 (67.6) | 1.08 (1.04–1.12) |
|
| |||
| 21–39 | 5,324 | 3,531 (66.3) | 1 [Reference] |
| 40–64 | 6,236 | 3,815 (61.2) | 0.93 (0.91–0.96) |
|
| |||
| Non-Hispanic white | 5,426 | 2,899 (53.4) | 1 [Reference] |
| Hispanic | 4,424 | 3,337 (75.4) | 1.39 (1.34–1.44) |
| Non-Hispanic other | 1,710 | 1,110 (64.9) | 1.16 (1.11–1.22) |
|
| |||
| ≥ 100% of FPL | 3,551 | 2,335 (65.8) | 1 [Reference] |
| 0-99% of FPL | 8,009 | 5,011 (62.6) | 0.95 (0.92–0.98) |
Abbreviations: FPL, federal poverty level.
Regression models included patient-level factors as fixed effects at level 1 and a clinic-level random intercept at level 2.
Prevalence ratios are significant at the α = .05 level if the 95% confidence interval does not contain 1.00.
In addition to main effects, the final multivariable model for estimating prevalence of cervical cancer screening included 3 pairwise interactions: insurance coverage by age (
Adjusted prevalence ratios (APRs) and 95% confidence intervals (CIs) for receipt of cervical cancer screening, by insurance status and stratified by race/ethnicity and age, for women in selected Oregon and California OCHIN-affiliated community health centers, from 2008 through 2010 (N = 11,560). Adjusted prevalence ratios are significant at the α = .05 level if the 95% confidence interval does not contain 1.00.
Insurance Comparison and Race/Ethnicity Age, y Adjusted Prevalence Ratio (95% Confidence Interval) for Receipt of Papanicolaou Test
Non-Hispanic white
40–64
1.00 (0.93–1.07)
21–39
0.91 (0.84–0.99)
Hispanic
40–64
0.94 (0.88–1.00)
21–39
0.91 (0.84–0.99)
Non-Hispanic other
40–64
1.05 (0.95–1.17)
21–39
1.09 (0.99–1.20)
Non-Hispanic white
40–64
0.88 (0.79–0.97)
21–39
0.80 (0.71–0.91)
Hispanic
40–64
0.80 (0.75–0.86)
21–39
1.11 (1.05–1.18)
Non-Hispanic other 40–64
0.86 (0.72–1.02)
21–39 0.98 (0.83–1.17)
Our study demonstrates how EHRs enable population-level surveillance among the uninsured and underinsured without resorting to cost- and time-intensive patient surveys. Over 2,600 women who had no health insurance from 2008 through 2010 were included in this study, along with 3,856 women sporadically insured during the same period. The analyses performed here could not have been conducted by using claims data, which misses services received during periods without insurance coverage. Other methods of medical chart abstraction would be impractical given the size of the analytic samples.
Overall, 64% of the women in the study population received a Pap test, a lower proportion than reported in the 2010 National Health Interview Survey (83%) (
In adjusted models, being uninsured lowered the likelihood of receiving a Pap test more for non-Hispanic white women than for Hispanic women and those of other races/ethnicities. Similar results have been reported previously (
The most intriguing finding in this analysis was that uninsured Latinas aged 21 to 39 had a higher likelihood of cervical cancer screening than their peers who were continuously insured throughout this study. While the effect was modest (APR, 1.11; 95% CI, 1.05–1.18), no other examples were found in the literature that document higher Pap test rates for the uninsured than for the fully insured, when controlling for other demographic factors. The result is unlikely to be explained by NBCCEDP participation, because the program is aimed almost exclusively at women aged 40 years or older. It is possible, however, that the study clinics may have concurrently participated in state, county, or CHC-led programs that emphasized cervical cancer screening for younger uninsured Hispanic women, thus contributing to the observed findings. A more detailed analysis of screening rates by clinic may provide useful insight into factors underlying this result. Alternatively, the elevated screening rate among uninsured younger Latinas may be linked to the women’s country of birth. Rodríguez et al (
This study has several limitations. First, data were limited to selected CHCs in Oregon and California, so the results may not be generalizable. Second, receipt of Pap tests was identified via search algorithms with commonly used procedure and diagnostic codes, and a small percentage of services may have been missed if coded differently. The potential for this type of error is probably minor because search algorithms were based on validated scripts developed for federal reporting to the Uniform Data System (
Third, no information was available regarding Pap tests received outside the OCHIN CHC network. Consequently, Pap test screening in the study population may be underestimated. The results may also be biased if patients more likely to seek care outside the study clinics were disproportionally distributed among the insurance groups. This could explain some of the differences observed. To minimize the potential for underreporting, the study sample was restricted to established patients. Prior research indicates that women having a usual source of care may be less likely to seek routine cervical and breast cancer screenings elsewhere (
Fourth, because of inconsistencies in data collection at clinic sites, duration of coverage by private insurance may have been over- or underestimated, potentially resulting in misclassification of patients by insurance category. To test this possibility, the data were reanalyzed limiting insured patients to Medicaid and Medicare recipients only. Resulting APRs for receipt of Pap tests by insurance coverage were essentially the same, suggesting that misclassification by insurance status is not a major concern. The use of a single category to define partial insurance may also be considered a limitation, potentially masking dose–response associations between time covered and receipt of cervical cancer screening among partially insured subjects. Although beyond the scope of this study, a subanalysis of partially insured subjects using narrower bands of coverage would allow assessment of such trends.
Fifth, regression models did not explicitly account for organizational differences among the CHCs that could influence receipt of cervical cancer screening, such as provider demographics, clinic participation in prevention programs, and scope of services offered. Finally, this report does not address overuse of Pap tests, a topic of emerging interest as the US health care system struggles to contain costs. EHR data have been successfully used to identify Pap tests performed sooner than recommended for women at low risk of cervical cancer (
Despite these limitations, this study highlights the utility of EHRs for population-level surveillance among the uninsured and underinsured, demonstrating how EHRs can be leveraged to reveal patterns of preventive health care service use in CHC patients. The results indicate that in this population insurance-related disparities in receipt of cervical cancer screening persist even though Pap tests are available regardless of insurance status. After adjusting for patient demographics, having continuous insurance improved the likelihood of cervical cancer screening for many women studied here. Additional research is warranted to further understand the role of insurance in preventive service uptake among CHC patients.
The authors acknowledge the support of OCHIN, Inc, and its member organizations that generously allowed access to the data that made this study possible.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.