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Neighborhood Health Care Access and Sexually Transmitted Infections Among Women in the Southern United States

A Cross-Sectional Multilevel Analysis

Haley, Danielle F. PhD*; Edmonds, Andrew PhD; Belenky, Nadya PhD; Hickson, DeMarc A. PhD§; Ramirez, Catalina MPH, MPA; Wingood, Gina M. ScD; Bolivar, Hector MD; Golub, Elizabeth PhD∥**; Adimora, Adaora A. MD†‡††

Sexually Transmitted Diseases: January 2018 - Volume 45 - Issue 1 - p 19–24
doi: 10.1097/OLQ.0000000000000685
Original Studies
Free

Introduction The United States has experienced an increase in reportable sexually transmitted infections (STIs) while simultaneously experiencing a decline in safety net services for STI testing and treatment. This multilevel study assessed relationships between neighborhood-level access to health care and STIs among a predominantly Human Immunodeficiency Virus (HIV)-seropositive cohort of women living in the south.

Methods This cross-sectional multilevel analysis included baseline data from HIV-seropositive and HIV-seronegative women enrolled in the Women’s Interagency HIV Study sites in Alabama, Florida, Georgia, Mississippi, and North Carolina between 2013 and 2015 (N = 666). Administrative data (eg, United States Census) described health care access (eg, percentage of residents with a primary care provider, percentage of residents with health insurance) in the census tracts where women lived. Sexually transmitted infections (chlamydia, gonorrhea, trichomoniasis, or early syphilis) were diagnosed using laboratory testing. Generalized estimating equations were used to determine relationships between tract-level characteristics and STIs. Analyses were conducted using SAS 9.4.

Results Seventy percent of participants were HIV-seropositive. Eleven percent of participants had an STI. A 4-unit increase in the percentage of residents with a primary care provider was associated with 39% lower STI risk (risk ratio, 0.61, 95% confidence interval, 0.38–0.99). The percentage of tract residents with health insurance was not associated with STIs (risk ratio, 0.98, 95% confidence interval, 0.91–1.05). Relationships did not vary by HIV status.

Conclusions Greater neighborhood health care access was associated with fewer STIs. Research should establish the causality of this relationship and pathways through which neighborhood health care access influences STIs. Structural interventions and programs increasing linkage to care may reduce STIs.

In a predominantly human immunodeficiency virus–seropositive cohort of women living in the southern United States, women living in neighborhoods with better health care access were less likely to have sexually transmitted infections.

From the *Institute for Global Health and Infectious Diseases, School of Medicine, †Department of Epidemiology, Gillings School of Global Public Health, ‡Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC; §Department of Epidemiology and Biostatistics, Jackson State University School of Public Health, Jackson, MS; ¶Department of Sociomedical Sciences, Lerner Center for Public Health Promotion, Mailman School of Public Health at Columbia University, New York, NY; ∥Division of Infectious Diseases, University of Miami Miller School of Medicine, Miami, FL; **Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and ††Department of Medicine, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC

Acknowledgements: The authors thank the Women’s Interagency HIV Study participants for sharing their time and experiences. The authors also acknowledge the efforts and dedication of WIHS study staff, with special thanks to Ighovwerha Ofotokun, Sarah Sanford, Deja Er, Rachael Farah-Abraham, Carrigan Parrish, Zenoria Causey, Venetra McKinney, Lisa Rohn, Jess Donohue, and Christine Alden.

Conflict of interest: None declared.

The Women’s Interagency HIV Study (WIHS) is funded primarily by the National Institute of Allergy and Infectious Diseases, with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute on Drug Abuse, and the National Institute on Mental Health. Participant data in this manuscript were collected by the UAB-MS WIHS (PIs: Michael Saag, Mirjam-Colette Kempf, and Deborah Konkle-Parker), U01-AI-103401; Atlanta WIHS (PIs: Ighovwerha Ofotokun and Gina Wingood), U01-AI-103408; Miami WIHS (PIs: Margaret Fischl and Lisa Metsch), U01-AI-103397; UNC WIHS (PI: Adaora Adimora) U01-AI-103390; WIHS Data Management and Analysis Center (PIs: Stephen Gange and Elizabeth Golub) U01-AI-042590. Targeted supplemental funding for specific projects is also provided by the National Institute of Dental and Craniofacial Research, the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Deafness and other Communication Disorders, and the NIH Office of Research on Women’s Health. WIHS data collection is also supported by UL1-TR000454 (Atlanta CTSA). DAH’s time was supported by the Centers for Disease Control and Prevention under Cooperative Agreement U01PS003315 as part of the Minority HIV/AIDS Research Initiative. The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health.

Author Contributions: D.F.H. conceptualized the study, analyzed and interpreted data, and led article development. A.E., N.B., D.A.H., C.R., G.M.W., H.B., E.G., and A.A.A. contributed meaningfully to the interpretation of data and revision of the article. All authors approved the final version.

Correspondence: Danielle F. Haley, PhD, University of North Carolina at Chapel Hill School of Medicine, Institute for Global Health and Infectious Diseases, 130 Mason Farm Road, Chapel Hill, NC, 27599. E-mail: danielle_haley@med.unc.edu.

Received for publication April 17, 2017, and accepted June 20, 2017.

The United States (US) has experienced a dramatic increase in the rate of reportable sexually transmitted infections (STIs) in the past decade.1 Sexually transmitted infections cost the US health care system nearly US $16 billion a year, contribute to comorbidities (eg, pelvic inflammatory disease), ongoing transmission of STIs including HIV, and may result in congenital or neonatal transmission of STIs.1,2 There are marked racial and geographic disparities in the distribution of STIs in the US.1 In 2015, the rate of newly identified infections (per 100,000 female adults/adolescents) among black/African American women, as compared with white women, was nearly 10-fold greater for gonorrhea (371.9 vs 38.2 cases), 9-fold greater for syphilis (5.3 vs 0.6 cases), and 5-fold greater for chlamydia (1384.8 vs 256.7 cases).1 The South consistently reports high rates of STIs; over 70% of states reporting the highest rates of chlamydia and gonorrhea in 2015 were in this region.1

The US has also simultaneously experienced a decline in safety net services for STI testing and treatment.1 In 2012, more than half of the state and local STI programs underwent budget cuts which resulted in clinic closures and reductions in clinic hours, contact tracing, and screening for common STIs.1 People using STI clinic services are more likely to be poor, uninsured, non-white, and represent some of the most financially and medically vulnerable populations in the US.3–5 Natural experiments suggest that divestment in local STI testing services results in underidentification and treatment of active STI cases.6,7 In this changing landscape, access to health care may be an important determinant of STI testing and treatment. Notably, in 2015, over 72% of reported chlamydia, gonorrhea, and syphilis cases were detected in venues other than STI clinics (eg, private physician offices).1

Although the implementation of the Patient Protection and Affordable Care Act (ACA) has the potential to increase access to health care, including STI screening and treatment, by decreasing the number of low-income people who lack health insurance, implementation of the ACA, especially in the South, has been limited by the lack of Medicaid expansion.8,9 Furthermore, it is unclear how current legislation seeking to “repeal and replace” the ACA would impact insurance coverage and access to care for millions of individuals.10 People without insurance are more likely to report poor access to health care (eg, not having a primary care provider) and unmet needs for health care.8,11 Poor health care access may promote ongoing STI transmission and endemicity by increasing the proportion of the population with untreated STIs, thereby increasing the likelihood that a woman will be exposed to an infected sexual partner.5 People with or at increased risk for HIV and other STIs tend to live closer to their sexual partners than do people from lower-risk populations,12 and a growing body of research suggests that features of the social (eg, neighborhood socioeconomic advantage) and built (eg, health care infrastructure) environment are associated with STIs as well as unmet needs for health care.11,13–15 To date, no multilevel studies have explored relationships between neighborhood-level health care access and STIs. An understanding of whether and how neighborhood health care access is associated with having an STI can inform local STI prevention programming and structural interventions, including health care policy.

This multilevel study explored relationships between neighborhood-level access to health care and having an STI among a predominantly African American, low-income cohort of women living in the South. The present analysis sought to evaluate whether:

  • (1) neighborhood health care access (defined as the percentage of census tract residents with a primary care provider and the percentage of census tract residents with health insurance) was associated with having an STI; and
  • (2) relationships between neighborhood health care access and having an STI varied by HIV status. Neighborhood factors may be less influential for women with HIV, who may be more likely to receive STI screening and treatment as part of their HIV-related clinical care.16

This analysis was guided by the Gelberg-Andersen Model for Vulnerable Populations (G-AMVP).17 The G-AMVP has been used successfully to describe predisposing, enabling, and need predictors of infectious disease and health care utilization among vulnerable populations (eg, minorities, people living in poverty) in the United States.11,18,19Predisposing characteristics include individual-level factors existing before the perception of illness (eg, sociodemographics) and variables that reflect vulnerability (eg, substance use). Enabling characteristics include facilitators or barriers to care (eg, neighborhood health care access). Need includes factors that may initiate health care seeking (eg, HIV infection).

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MATERIALS AND METHODS

The Women’s Interagency HIV Study (WIHS) is a multisite, prospective cohort study designed to characterize the impact and progression of HIV among US women.20,21 Between October 2013 and September 2015, clinical research sites in Alabama, Florida, Georgia, Mississippi, and North Carolina enrolled women with HIV, as well as HIV-seronegative women. Women’s Interagency HIV Study eligibility criteria included being a woman between 25 and 60 years old. Women with HIV were antiretroviral therapy-naive or started highly active antiretroviral therapy after December 31, 2004; had never used didanosine, zalcitabine, or stavudine (unless during pregnancy or for preexposure or postexposure HIV prophylaxis); and had documented pre-highly active antiretroviral therapy CD4 counts and HIV viral load. The HIV-seronegative women reported that either she or her sexual partner met at least 1 factor associated with increased risk of HIV acquisition in the last 5 years (eg, illicit drug use, STI diagnosis).

Participants were recruited by WIHS using several strategies, including clinic- and community-based organization referrals. Institutional review board approval was obtained at each of the collaborating institutions, and written informed consent was obtained before initiation of study procedures. Methods are described in detail elsewhere.20,21 This secondary analysis, approved by the University of North Carolina at Chapel Hill Institutional Review Board, is restricted to WIHS participants who provided written informed consent to collect and geocode their residential address.

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Measures

Outcome: Current STI

The binary outcome, having a current STI, was defined as a laboratory-confirmed diagnosis of chlamydia, gonorrhea, trichomoniasis, or early syphilis (titer and confirmatory test results consistent with primary, secondary, or early latent [<1 year duration] infection) at baseline. Participants with an STI were referred to medical providers for treatment.

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Census Tract Enabling Characteristics

Participant residential addresses were geocoded (ie, latitude–longitude coordinates) and linked to census tracts. Measures capturing health care access and socioeconomic advantage in the census tracts where women lived were created using existing data sources (eg, US Census) and reflect G-AMVP enabling factors (eg, community resources).17

Census tract estimates capturing the percentage of census tract residents who reported they had at least one person who they considered to be their personal doctor or health care provider were created by PolicyMap using nationally representative Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance (BRFSS) data for 2013.22 Multilevel models with poststratification (including state-level estimates of residents having a primary care provider and tract-level estimates of income, age, and race/ethnicity) were used to create tract estimates.22

We created 3 measures of census tract health insurance coverage using American Community Survey 5-year estimates (2009–2013) aligning with US Census categorizations of health insurance coverage.23 Any health insurance coverage was defined as the percentage of tract residents aged 18 to 64 years reporting any health insurance (ie, employer-based, direct-purchase, Medicare, Medicaid/Means-tested public coverage, Tricare/Military health care, or VA health care). Private coverage was defined as the percentage of tract residents aged 18 to 64 years reporting employer-based, direct-purchase, Tricare or other military health insurance. Public coverage was defined as the percentage of tract residents aged 18 to 64 years reporting coverage through Medicaid, Medicare, VA Health Care, or individual state health plans.

Measures of census tract socioeconomic advantage were created using American Community Survey 5-year estimates (2009–2013): percentage of residents living above the poverty level, percentage of residents 25 years or older with a high school degree or greater, percentage of residents older than 16 years who were employed. Because these measures were correlated, we used principal component analysis with orthogonal rotation (varimax) to capture underlying constructs and avoid multicollinearity. The principal component analysis produced 1 component with eigenvalue greater than 1 which accounted for 75% of the variability explained by these factors. Continuous, standardized component scores were extracted for each participant and included in multivariable models.

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Participant Characteristics

The WIHS collected all demographic and behavioral data using interviewer-administered questionnaires. Participant-level characteristics that might confound or modify relationships between tract-level health care access and having an STI aligning with the G-AMVP were determined a priori via a literature review.2,11,17–19 Behaviors reflected the past 6 months, and covariates were binary unless otherwise noted.

Participant predisposing characteristics included: age in years (continuous, mean-centered), non-Hispanic African American race/ethnicity, less than high school education or equivalent, being a sexual minority (gay or bisexual), and alcohol or substance use (>7 drinks in the past week or any use of crack, cocaine, heroin, marijuana, hallucinogens, club drugs, methamphetamines, or recreational prescription drug use in the last 6 months).

Participant-enabling characteristics included having health insurance (any public, veteran, private, or student health insurance) and competing needs for medical care (delaying or not getting needed health care due to cost).

Need was measured as being HIV-seropositive, defined as a reactive serologic enzyme-linked immunosorbent assay test and a confirmed positive Western blot or a detectable plasma HIV-1 ribonucleic acid. We treated HIV status as an effect modifier to evaluate whether HIV status modified the magnitude or direction of the relationship between neighborhood health care access and having an STI.

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Analysis

t tests and χ2 tests were used to compare distributions of census tract and participant characteristics by HIV status. We modeled bivariate and multivariable relationships using generalized estimating equations with a binomial distribution and log link (to estimate risk ratios [RRs]) and an exchangeable correlation structure. In each model, participants were nested within census tracts, which were nested within sites. Tract-level insurance variables were correlated (Pearson r ≥ 0.7) and as a result, modeled separately. We ran 3 multivariable models: each model included the mean-centered tract-level percentage of residents with a primary care provider, one of the tract-level mean-centered insurance variables (ie, percentage of residents with any insurance, private insurance, or public insurance), and all individual-level variables.

Because the aim of this study was to determine whether the magnitudes or directions of relationships between tract-level characteristics and having a current STI vary by HIV status, we tested statistically for multiplicative interactions between neighborhood health care access and having an STI by HIV status by entering cross-level interaction terms for HIV status and each tract-level health care access measure stepwise (eg, HIV status*percentage of tract residents with a primary care provider, HIV status*percentage of tract residents with any insurance), retaining interaction terms with P values less than 0.05 in the final multivariable model.

Because past research suggests that neighborhood socioeconomic characteristics are associated with STIs as well as health care access, we also evaluated whether the relationships between census tract health care access and having an STI were independent of census tract socioeconomic advantage.11,12,14,15 As a sensitivity analysis, we reran each of the 3 final multivariable models controlling for census tract socioeconomic advantage, comparing RR estimates for tract health care access variables in models controlling for and without tract-level socioeconomic advantage. We determined a priori that differences in magnitudes less than 10% suggested that relationships between census tract health care access and having an STI were independent of census tract socioeconomic advantage. All analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC).

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RESULTS

A total of 845 women were enrolled in the Southern US. One hundred seventy-nine women were excluded from these analyses because they (1) did not have geocoded address data (n = 116; the majority of these women did not consent for geocoding [n = 65, 56.0%]) or (2) were missing 1 or more STI laboratory test results (n = 63). A comparison of all participant-level characteristics included in the multivariable models for participants excluded from the analyses, as compared with the analytic sample, indicated that participants excluded from these analyses because of missing geocoded address data were more likely to report alcohol or substance use (48.1% vs 37.9%, P < 0.05). Participants enrolled at study sites other than Georgia had higher rates of missing STI data, as did HIV-seropositive participants (P < 0.05). We included these variables in all multivariable models to minimize potential confounding.

In the final analytic sample (N = 666), participants lived in census tracts, where, on average, 68.6% of residents had health insurance and 74.0% had a primary care provider (Table 1). Eleven percent of the participants (n = 76) tested positive for at least 1 STI at baseline. The mean age of participants was 43.5 years (SD, 9.4), 70.7% were HIV-seropositive, and 84.6% identified as non-Hispanic African American. Participants with HIV were less likely to report being a sexual minority (6.4% vs 16.4%), alcohol or illicit substance use (34.5% vs 47.2%), and unmet needs for medical care (22.5% vs 45.1%) in the past 6 months, but were more likely to have health insurance (63.8% vs 50.3%) than HIV-seronegative participants (P > 0.05). There were no differences by HIV status (P > 0.05) in tract characteristics, nor in current STI status.

TABLE 1

TABLE 1

We ran 3 different multivariable models; each included the percentage of tract residents with a primary care provider and 1 of the 3 variables capturing tract health insurance coverage (ie, any insurance, private insurance, public insurance). The effect estimates and corresponding confidence intervals (CIs) for tract-level percentage of residents having a primary care provider and tract-level insurance coverage were comparable (ie, within 3%), regardless of the operationalization of tract-level health insurance coverage used (results not presented). For brevity, models including the percentage of residents with a primary care provider and the percentage of tract residents with any insurance are presented (Table 2).

TABLE 2

TABLE 2

In bivariate analyses, the tract percentage of residents with a primary care provider was inversely associated with having an STI (RR, 0.61; 95% CI, 0.38–0.97). In the multivariable model controlling for participant-level characteristics, a 4-unit increase in the percentage of tract residents with a primary care provider (eg, from 74% to 78%) was associated with a 39% lower risk of having an STI (RR, 0.61; 95% CI, 0.38–0.99). The percentage of tract residents with health insurance was not associated with STIs in bivariate (RR, 0.96; 95% CI, 0.90–1.02) nor multivariable models (RR, 0.98; 95% CI, 0.91–1.05). Relationships between neighborhood-level health care access and STIs did not vary by HIV status (all interaction terms P > 0.05). Tract socioeconomic advantage was not associated with STIs in bivariate (RR, 0.85; 95% CI, 0.69–1.04) nor multivariable models (RR, 0.87; 95% CI, 0.65–1.15). Risk ratios for tract-level health care access characteristics in models with and without tract-level socioeconomic advantage were within 3% for all comparisons, suggesting that relationships between tract-level health care access and STIs were independent of socioeconomic advantage.

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DISCUSSION

In this multilevel analysis controlling for participant-level characteristics, we found that residing in census tracts where a greater percentage of residents have a primary care provider was associated with lower risk of having an STI among women living in the south. This relationship did not vary by HIV status.

Individuals living in areas with more health care infrastructure are more likely to have a primary care provider, and in turn, use preventative health care services.24 Residents living in neighborhoods with greater linkages to primary care providers are more likely to receive STI testing and treatment as part of their regular care and may face fewer barriers to accessing STI testing and treatment.13,24–26 We hypothesized that neighborhood health care access would be less influential for women with HIV, who may be more likely to receive STI screening and treatment as part of their HIV-related clinical care. However, the relationship between tract percentage of residents having a primary care provider and having an STI did not vary by HIV status. Recent research indicates that testing for STIs among persons with HIV is low, suggesting that missed opportunities for STI screening and treatment persist regardless of HIV status.16,27

In 2014, the year in which the majority of participants were enrolled, nearly 90% of Americans reported having health insurance.23 Study participants on average lived in tracts where only 67% of residents reported any health insurance coverage. Living in neighborhoods with high percentages of uninsured residents is associated with lower health care access and more unmet health needs, even among the insured.28 Areas with low insurance coverage may face challenges attracting and maintaining health care resources, such as clinics and health care providers.9,10 Surprisingly, tract insurance coverage was not associated with STIs, regardless of HIV status. It is likely that we did not have the power in our sample to detect relationships between neighborhood insurance coverage and STIs because 90% of the study participants lived in tracts with health insurance coverage below the national average. This research captures neighborhood health care access before implementation of the ACA. Future studies could explore relationships of changes in tract-level insurance coverage to women's sexual health over time.9

These findings are subject to limitations. Although WIHS provides a high-quality sample of women with or at increased risk of HIV infection in the Southern US, study participants agree to long-term follow-up and may not be representative of the general population. A history of STI diagnosis (within 5 years) was one possible WIHS eligibility criteria for HIV-seronegative women. Participants excluded from this analysis due to lack of geocoded address data may have lived in qualitatively different neighborhoods. However, participants with and without geocoded address data were not statistically different with respect to STI status (P > 0.05). We did not have sufficient prevalence to assess relationships between neighborhood health care access and each STI type (eg, chlamydia alone). However, past research supports that the geographic distributions of STIs share common spatial cores.29 In addition, we are unable to quantify the underlying STI prevalence in the participant’s sexual networks. Census tract estimates capturing the percentage of residents having a primary care provider were constructed using BRFSS data, which rely predominantly on landlines to conduct sampling frameworks and collect data; findings may not extend to cell phone only households.30 However, systematic review suggests that BRFSS estimates of self-reported behaviors and conditions are comparable to other national surveys.30 Due to the cross-sectional nature of our study, we are unable to draw conclusions regarding the causality of relationships between tract characteristics and STIs.

Collectively, our findings underscore the importance of neighborhood health care access in women’s sexual health. Additional research is needed to establish the causal direction of relationships between neighborhood factors and STI risk and to elucidate the pathways through which neighborhood health care access reduces vulnerability to STIs among women living in the south. If future research supports our findings, neighborhood-level programs increasing access and linkage to health care may reduce STIs and improve women’s sexual health.

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REFERENCES

1. Centers for Disease Control and Prevention. Sexually Transmitted Disease Surveillance 2015. Atlanta: 2016.
2. Watts DH, Springer G, Minkoff H, et al. The occurrence of vaginal infections among HIV-infected and high-risk HIV-uninfected women: Longitudinal findings of the women's interagency HIV study. J Acquir Immune Defic Syndr 2006; 43:161–168.
3. Pathela P, Klingler EJ, Guerry SL, et al. Sexually transmitted infection clinics as safety net providers: Exploring the role of categorical sexually transmitted infection clinics in an era of health care reform. Sex Transm Dis 2015; 42:286–293.
4. Hoover KW, Parsell BW, Leichliter JS, et al. Continuing need for sexually transmitted disease clinics after the Affordable Care Act. Am J Public Health 2015; 105 Suppl 5:S690–S695.
5. Hogben M, Leichliter JS. Social determinants and sexually transmitted disease disparities. Sex Transm Dis 2008; 35(12 Suppl):S13–S18.
6. Chesson HW, Harrison P, Scotton CR, et al. Does funding for HIV and sexually transmitted disease prevention matter? Evidence from panel data. Eval Rev 2005; 29:3–23.
7. Kimball AM, Lafferty WE, Kassler WJ, et al. The impact of health care market changes on local decision making and STD care: Experience in three counties. Am J Prev Med 1997; 13(6 Suppl):75–84.
8. United States. Agency for Healthcare Research and Quality. National healthcare disparities report. In: AHRQ publication. Rockville, MD: U.S. Dept. of Health and Human Services, 2014. p. v.
9. Jones RK, Sonfield A. Health insurance coverage among women of reproductive age before and after implementation of the affordable care act. Contraception 2016; 93:386–391.
10. Rosenbaum S, Rothenberg S, Schmucker S, et al. How Will Repealing the ACA Affect Medicaid? Impact on Health Care Coverage, Delivery, and Payment. In: The Commonwealth Fund. http://www.commonwealthfund.org/publications/issue-briefs/2017/mar/repeal-aca-medicaid; March 2017.
11. Haley DF, Linton S, Luo R, et al. Public housing relocations and relationships of changes in neighborhood disadvantage and transportation access to unmet need for medical care. J Health Care Poor Underserved 2017; 28:315–328.
12. Cooper HL, Bonney L, Luo R, et al. Public housing relocations and partnership dynamics in areas with high prevalences of sexually transmitted infections. Sex Transm Dis 2016; 43:222–230.
13. Rodriguez HP, Chen J, Owusu-Edusei K, et al. Local public health systems and the incidence of sexually transmitted diseases. Am J Public Health 2012; 102:1773–1781.
14. Peterson LE, Litaker DG. County-level poverty is equally associated with unmet health care needs in rural and urban settings. J Rural Health 2010; 26:373–382.
15. Haley DF, Kramer MR, Adimora AA, et al. Relationships between neighbourhood characteristics and current STI status among HIV-infected and HIV-uninfected women living in the Southern USA: A cross-sectional multilevel analysis. Sex Transm Infect 2017; 10.1136/sextrans-2016-052889.
16. Pearson WS, Davis AD, Hoover KW, et al. Demographic and health services characteristics associated with testing for sexually transmitted infections among a commercially insured population of HIV-positive patients. J Acquir Immune Defic Syndr 2015; 70:269–274.
17. Gelberg L, Andersen RM, Leake BD. The behavioral model for vulnerable populations: Application to medical care use and outcomes for homeless people. Health Serv Res 2000; 34:1273–1302.
18. Doran KM, Shumway M, Hoff RA, et al. Correlates of hospital use in homeless and unstably housed women: the role of physical health and pain. Womens Health Issues 2014; 24:535–541.
19. Stein JA, Andersen R, Gelberg L. Applying the Gelberg-Andersen behavioral model for vulnerable populations to health services utilization in homeless women. J Health Psychol 2007; 12:791–804.
20. Hessol NA, Weber KM, Holman S, et al. Retention and attendance of women enrolled in a large prospective study of HIV-1 in the United States. J Womens Health (Larchmt) 2009; 18:1627–1637.
21. Bacon MC, von Wyl V, Alden C, et al. The Women's Interagency HIV Study: An observational cohort brings clinical sciences to the bench. Clin Diagn Lab Immunol 2005; 12:1013–1019.
22. PolicyMap. Data Sources:Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System. In: https://www.policymap.com/data/our-data-directory/-CDC%20Behavioral%20Risk%20Factor%20Surveillance%20System.
23. U.S. Census Bureau. Health insurance coverage in the United States. In: Current population reports P60-250. Washington, DC: U.S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau, 2014.
24. Continelli T, McGinnis S, Holmes T. The effect of local primary care physician supply on the utilization of preventive health services in the United States. Health Place 2010; 16:942–951.
25. Hendryx MS, Ahern MM, Lovrich NP, et al. Access to health care and community social capital. Health Serv Res 2002; 37:87–103.
26. Ahern MM, Hendryx MS. Social capital and trust in providers. Soc Sci Med 2003; 57:1195–1203.
27. Flagg EW, Weinstock HS, Frazier EL, et al. Bacterial sexually transmitted infections among HIV-infected patients in the United States: estimates from the Medical Monitoring Project. Sex Transm Dis 2015; 42:171–179.
28. Pagán JA, Pauly MV. Community-level uninsurance and the unmet medical needs of insured and uninsured adults. Health Serv Res 2006; 41(3 Pt 1):788–803.
29. Law DC, Serre ML, Christakos G, et al. Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies. Sex Transm Infect 2004; 80:294–299.
30. Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004-2011. BMC Med Res Methodol 2013; 13:49.
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