Drive time to care and retention in HIV care: Rural–urban differences among Medicaid enrollees in the United States South

Abstract

Purpose

Less than 50% of people with HIV (PWH) in the United States are retained in care, a key step along the HIV care continuum. We examined the impact of geographic access to care on retention in care for urban and rural PWH.

Methods

We used Medicaid claims and clinician data (Medicaid Analytic eXtract and MAX Provider Characteristics, 2009–2012) for 13 Southern states plus the District of Columbia. We calculated drive time from the enrollees’ ZIP Code Tabulation Area to their usual source of care. We used generalized estimating equations to examine the association between drive time to care >30 min (versus ≤30 min) and retention in care, overall and stratified by rurality. In sensitivity analysis, we examined the definition of retention in care, states included in the analysis, and enrollee‐ and care‐related characteristics.

Findings

The sample included 49,596 PWH. Overall, the association between drive time >30 min and retention was significant, but small (adjusted odds ratio [aOR] 1.01, 95% confidence interval [CI] 1.00, 1.01) and was not significant in urban areas; however, the significance and direction of the association differed in sensitivity analysis. In rural areas, driving >30 min to care was associated with 7% higher odds of retention in care (aOR 1.07, 95% CI 1.05, 1.08) and this association remained significant and positive in nearly all sensitivity analyses.

Conclusions

For PWH in rural areas, greater drive time is consistently associated with greater retention in care. Disentangling the mechanisms of this relationship is a future research priority.

INTRODUCTION

Only half of people diagnosed with HIV in the US are retained in care, ¹ which has tempered optimism about the positive downstream effects associated with retention, including achieving HIV viral suppression, reducing new infections and ending the HIV epidemic. ² , ³ , ⁴ Lower retention in HIV care overall and among specific populations, such as those living in rural communities, ⁵ may be due in part to structural factors, including when, where, how and by whom care is delivered. Geography has been raised as a potential structural barrier to care, although evidence is mixed on its association with HIV outcomes. Studies suggest that greater distance or drive time to HIV care is associated with poorer linkage to care, retention in care and HIV viral suppression. ⁶ , ⁷ Conversely, other work suggests a relationship between greater proximity to care and worse linkage to or retention in care ⁸ and, similarly, a relationship between shorter distances to care and clusters of neighborhoods with poor retention. ⁹ These studies have overwhelmingly examined these relationships in urban communities. However, findings may not generalize to rural areas, where people with HIV (PWH) have longer drive times to care, ¹⁰ , ¹¹ , ¹² as well as lower retention in care and viral suppression relative to those in the most urban areas. ⁵ The current study examines geographic access to care and retention in care among PWH across both urban and rural locales.

METHODS

Data

We used patient‐level demographic information, including ZIP code of residence, and administrative and prescription claims from the 2009−2012 Medicaid Analytic eXtract for 13 Southern states plus the District of Columbia (DC) to identify enrollees with HIV and their clinicians and assess retention in care. States included Alabama, Arkansas, Delaware, Georgia, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, Tennessee, and West Virginia. These data remain a relevant source for understanding the present‐day relationship between geographic access and retention in HIV care: Medicaid has been the largest single source of insurance for this population over nearly a decade, consistently covering 43−47% of diagnosed PWH. ¹³ , ¹⁴ , ¹⁵ Household vehicle access ¹⁶ and rurality ¹⁷ have remained similarly stable since 2010, so drive time to health care facilities is likely consistent over time, particularly in rural areas where transportation infrastructure changes may be slower. The data capture those from urban and, importantly, rural locales, which comprise a substantial burden of HIV in current priority jurisdictions nationally. These data have been used in other recent analyses of geographic accessibility to care for PWH, ¹² , ¹⁸ which resulted in policy conclusions that aligned with the consensus in the field beyond the years of our data. ¹⁹ , ²⁰ Finally, the data share similar distributions of enrollee demographics, basis of Medicaid eligibility, and retention in HIV care as current Medicaid enrollees with HIV. ²¹ , ²²

We also used county‐level data on rurality (National Center for Health Statistics, 2013), HIV prevalence (AIDSVu, 2015), health care supply and social determinants of health (Area Health Resources File, 2010). Additional geographic data, used to calculate drive time to usual care, ¹² came from the US Census Bureau, ²³ UDS Mapper, ²⁴ and ArcGIS (Esri, Redlands, CA, 2017).

Analytic sample

Following prior work, we selected from the claims data nonelderly adult enrollees with HIV ¹² , ¹⁸ , ²⁵ and their most frequent clinician. ¹² We excluded claims, patients and clinicians with missing or incorrect data needed to estimate drive time to care, as well as clinicians practicing in medical specialties unlikely to provide HIV management (e.g., surgery). ¹² HIV diagnosis was defined by having, across the entire sample period, ≥2 HIV diagnosis codes on separate claims ≥30 days apart and ≥1 HIV‐related verification code (i.e., procedure code for an HIV‐related laboratory test or filled prescription for ART other than for pre‐exposure prophylaxis), with the first diagnosis code preceding the first verification code.

Variables

Drive time to usual care

The primary independent variable was an indicator of whether the one‐way drive time to the usual source of care exceeds 30 min, which is a common threshold for assessing geographic accessibility of care. ²⁶ One‐way drive time from an enrollee's residence to their usual source of care was calculated using ArcGIS version 10.5 (Esri, Redlands, California). ¹² Enrollee residence was approximated as the geocoordinates for the population‐weighted centroid of the enrollee's ZIP Code Tabulation Area, a geographic unit based on postal codes. Usual source of care for each enrollee living with HIV was the geocoded location of the most frequent servicing clinician for that enrollee in a given calendar‐year. ¹²

Retention in care

The dependent variable was retention in care, defined as ≥2 HIV care markers (claim(s) for HIV‐related medical visit, HIV‐related laboratory test or antiretroviral prescription) at least 90 days apart within a given calendar‐year. ²⁵ This aligns with current definitions of annual retention in care from the Health Resources and Services Administration (≥2 HIV medical visits or HIV viral load tests ≥90 days apart in a 12‐month period) ²⁷ and Centers for Disease Control and Prevention (≥2 HIV‐related laboratory tests ≥3 months apart in a calendar year), ²⁸ as well as the frequency of HIV laboratory monitoring recommended at the time of our data.

Control variables

At the individual‐level, we controlled for demographic factors that influence retention in care, including enrollee gender, age group and race and ethnicity. ²⁹ , ³⁰ We also controlled for enrollee health status, including history of hepatitis B or C co‐infection, ³¹ , ³² common non‐AIDS comorbidities, ³³ and AIDS‐defining illnesses over the observation period. ³⁴ At the county‐level, we controlled for characteristics that shape the context of care, ³⁵ including primary care provider and hospital supply, educational attainment, nonelderly adult unemployment rate, and median household income, as well as HIV prevalence. ³⁶ “Urban” enrollees resided in metropolitan statistical areas with population centers ≥50,000 inhabitants, while “rural” enrollees resided in micropolitan and noncore areas, with population centers <50,000 inhabitants. ³⁷ This county‐level measure of rurality is contemporaneous with the claims data and consistent with other area‐level data used here, appropriate for our multi‐year dataset given stability in county boundaries over time, relevant to HIV surveillance and policy, and suitable for our research question regarding access to care.

Data analysis

We summarized sample characteristics at the enrollee‐year level using univariate and bivariate statistics. To assess the association between drive time >30 min and retention in care, we used generalized estimating equations, which capture correlated outcomes within patients over time, with state and year fixed effects. ²⁵ Models were first pooled, then stratified by rurality.

In sensitivity analysis, we varied the definition of retention in care, examining retention using medical visits only and HIV‐related laboratory tests only. We also examined the association between drive time and retention in different subsamples: We restricted the sample to enrollees from all states except Florida, the state with the most enrollees and the highest estimated state‐level HIV prevalence ³⁸ in our sample, and, separately, to enrollees from states in the Deep South (i.e., excluding DC, Delaware, Maryland, Oklahoma, West Virginia), given higher HIV diagnosis rates in the Deep South relative to other Southern states. ³⁹ , ⁴⁰ Additionally, the association between drive time and retention may be influenced by transportation access. While all states are required to provide nonemergency medical transportation (NEMT) to adult Medicaid enrollees, flexibility in state implementation of these benefits means that access to Medicaid‐supported NEMT may vary (e.g., by eligibility category). ⁴¹ We therefore restricted the sample to calendar‐years in which enrollees received any Medicaid NEMT benefits. ¹² Next, we restricted the sample to those enrolled in comprehensive managed care (versus fee‐for‐service) for ≥6 months in a given calendar‐year. Practice patterns differ for those in managed care, ⁴² and fewer rural Medicaid enrollees participate in comprehensive managed care than urban enrollees, likely due to more limited provider networks in rural areas which may necessitate use of out‐of‐network care ⁴³ or longer travel to reach in‐network providers, particularly HIV‐experienced clinicians. ¹² , ¹⁸ Finally, we restricted the sample to those with medical conditions requiring closer HIV management, defined by evidence of pregnancy, HIV‐related nephropathy, any AIDS‐defining illness, or pneumocystis jirovecii pneumonia within a calendar‐year. ²⁵

Analyses were conducted in SAS (version 9.4; SAS Inc., Cary, NC, USA).

RESULTS

Sample characteristics

Our analytic sample comprised 49,596 enrollees with HIV (representing 127,369 enrollee‐years). Enrollees lived in urban areas during 88% of their total enrollment‐years and were retained in care for 73% of total enrollment‐years; retention in care was significantly different between urban (74%) and rural (72%) enrollee‐years (p < 0.0001; Table 1). Driving >30 min to care (p < 0.0001) and living in an urban area (p < 0.0001) were each independently associated with retention.

TABLE 1.

Sample characteristics (N [%] or median [interquartile range]).

		Retained in care †
	Overall	No	Yes	p
Total enrollee‐years	127,369	34,031	93,338	–
Drive time ‡				<0.0001
≤30 min	93,690	25,416 (27.1%)	68,274 (72.9%)
>30 min	33,679	8615 (25.6%)	25,064 (74.4%)
Rurality §				<0.0001
Rural	14,991	4233 (28.2%)	10,758 (71.8%)
Urban	112,378	29,798 (26.5%)	82,580 (73.5%)
Race and ethnicity				<0.0001
Non‐Hispanic White	20,069	5101 (25.4%)	14,968 (74.6%)
Non‐Hispanic Black	70,947	19,501 (27.5%)	51,446 (72.5%)
Non‐Hispanic other race	959	267 (27.8%)	692 (72.2%)
Hispanic (any race)	7973	1601 (20.1%)	6372 (79.9%)
Missing	27,421	7561 (27.6%)	19,860 (72.4%)
Age group (years)				<0.0001
19−24	5177	2009 (38.8%)	3168 (61.2%)
25−34	19,064	7107 (37.3%)	11,957 (62.7%)
35−44	30,825	8952 (29%)	21,873 (71%)
45−54	45,463	10,256 (22.6%)	35,207 (77.4%)
55−64	26,840	5707 (21.3%)	21,133 (78.7%)
Gender				<0.0001
Male	58,851	14,856 (25.2%)	43,995 (74.8%)
Female	67,442	18,842 (27.9%)	48,600 (72.1%)
Missing	1076	333 (31%)	743 (69.1%)
Number of co‐infections ¶				<0.0001
None	114,523	32,328 (28.2%)	82,195 (71.8%)
1 or more	12,846	1703 (13.3%)	11,143 (86.7%)
Number of comorbidities ††				<0.0001
None	87,518	25,702 (29.4%)	61,816 (70.6%)
1 or more	39,851	8329 (20.9%)	31,522 (79.1%)
Number of AIDS‐defining illnesses ‡‡				<0.0001
None	108,552	28,691 (26.4%)	79,861 (73.6%)
1 or more	18,817	5340 (28.4%)	13,477 (71.6%)
County HIV prevalence (diagnosed cases/100,000 population)	632 (770)	632 (758)	623 (776)	0.0934
County health care supply
Number of primary care physicians/100,000 population	78.6 (24)	78.0 (24)	78.6 (26.8)	0.1280
Number of hospital beds	1992 (3089)	1992 (3215)	1992 (3089)	0.7901
County social determinants of health
Percentage of population with less than high school diploma	0.09 (0.04)	0.09 (0.04)	0.09 (0.04)	0.4683
Percentage of population currently unemployed	0.27 (0.06)	0.27 (0.06)	0.27 (0.06)	0.0281
Median household income ($1000)	46.4 (9.07)	46.75 (8.9)	46.22 (9.66)	0.0006

^† Retention in care: ≥2 HIV care markers (HIV‐related medical visit, HIV‐related laboratory test, or antiretroviral prescription) ≥90 days apart within a given calendar‐year. ²⁵

^‡ Drive time: One‐way drive time from enrollee residence to usual care >30 min vs. ≤30 min. ¹²

^§ Rurality: based on enrollee county of residence, with urban counties including those in metropolitan statistical areas (i.e., population centers ≥50,000) and rural counties including those in micropolitan or noncore areas (i.e., population centers <50,000). ³⁷

^¶ Co‐infections: diagnosis of hepatitis B and/or C infection within the calendar‐year. ²⁵

^†† Comorbidities: diagnosis of ≥1 common comorbidities from the Charlson comorbidity index (e.g., myocardial infarction, diabetes, dementia), ³³ excluding HIV and AIDS‐defining illnesses, within the calendar‐year.

^‡‡ AIDS‐defining illnesses: diagnosis of ≥1 AIDS‐defining illness (e.g., candidiasis, cervical cancer, Kaposi sarcoma) within the calendar‐year. ²⁵ , ³⁴

Association between drive time and retention in care

Overall, driving >30 min to care was associated with 1% higher odds of retention in care (Table 2). In urban areas, the association was not significant; however, in rural areas, driving >30 min to care was associated with 7% higher odds of retention in care.

TABLE 2.

Adjusted association between geographic accessibility and retention in HIV care, overall and by rurality.

	Overall		Urban		Rural
	aOR (95% CI)	p	aOR (95% CI)	p	aOR (95% CI)	p
Drive time †
≤30 min	ref	–	ref	–	ref	–
>30 min	1.01 (1.00, 1.01)	<0.0001	0.99 (0.99, 1.00)	0.9989	1.07 (1.05, 1.08)	<0.0001
Rurality ‡
Urban	ref	–	–	–	–	–
Rural	0.98 (0.97, 0.99)	0.0037	–	–	–	–
Race and ethnicity
Non‐Hispanic White	ref	–	ref	–	ref	–
Non‐Hispanic Black	0.98 (0.97, 0.98)	<0.0001	0.98 (0.97, 0.99)	<0.0001	0.97 (0.95, 0.99)	0.0040
Non‐Hispanic other race	0.98 (0.96, 1.01)	0.3550	1.00 (0.97, 1.03)	0.7603	0.96 (0.91, 1.01)	0.1902
Hispanic (any race)	1.01 (1.00, 1.02)	0.0281	1.01 (1.00, 1.02)	0.0308	1.03 (0.97, 1.09)	0.2631
Missing	1.01 (1.00, 1.02)	0.0010	1.01 (1.00, 1.02)	0.0015	1.01 (0.98, 1.04)	0.3917
Age group (years)
19−24	ref	–	ref	–	ref	–
25−34	1.01 (0.99, 1.02)	0.0638	1.01 (0.99, 1.02)	0.1481	1.02 (0.98, 1.06)	0.1962
35−44	1.08 (1.07, 1.10)	<0.0001	1.07 (1.06, 1.09)	<0.0001	1.14 (1.10, 1.19)	<0.0001
45−54	1.13 (1.12, 1.15)	<0.0001	1.13 (1.11, 1.14)	<0.0001	1.17 (1.13, 1.22)	<0.0001
55−64	1.13 (1.12, 1.15)	<0.0001	1.13 (1.11, 1.14)	<0.0001	1.19 (1.14, 1.23)	<0.0001
Gender
Male	ref	–	ref	–	ref	–
Female	1.00 (0.99, 1.00)	0.6757	1.00 (0.99, 1.00)	0.3174	0.99 (0.97, 1.00)	0.2741
Missing	1.00 (0.81, 1.23)	0.9572	1.00 (0.81, 1.23)	0.9851	1.01 (0.44, 2.34)	0.9639
Number of co‐infections §
None	ref	–	ref	–	ref	–
1 or more	1.11 (1.10, 1.12)	<0.0001	1.11 (1.10, 1.12)	<0.0001	1.10 (1.07, 1.13)	<0.0001
Number of comorbidities ¶
None	ref	–	ref	–	ref	–
1 or more	1.04 (1.03, 1.04)	<0.0001	1.04 (1.03, 1.05)	<0.0001	1.02 (1.01, 1.04)	0.0004
Number of AIDS‐defining illnesses ††
None	ref	–	ref	–	ref	–
1 or more	0.94 (0.94, 0.95)	<0.0001	0.94 (0.94, 0.95)	<0.0001	0.95 (0.93, 0.97)	<0.0001
County HIV prevalence (diagnosed cases/100,000 pop.)	0.99 (0.99, 1.00)	0.1395	0.99 (0.99, 1.00)	0.0594	0.99 (0.99, 1.00)	0.8422
County health care supply
Number of primary care physicians/100,000 pop.	1.00 (0.99, 1.00)	0.4251	1.00 (0.99, 1.00)	0.2935	1.00 (0.99, 1.00)	0.4680
Number of hospital beds	1.00 (0.99, 1.00)	0.4954	1.00 (0.99, 1.00)	0.3271	0.99 (0.99, 0.99)	0.0090
County social determinants of health
Percentage of pop. with less than high school diploma	1.09 (0.93, 1.28)	0.2479	1.06 (0.88, 1.28)	0.5080	0.90 (0.66, 1.23)	0.5260
Percentage of pop. currently unemployed	1.11 (1.01, 1.22)	0.0305	1.29 (1.14, 1.47)	<0.0001	0.97 (0.81, 1.17)	0.8123
Median household income ($1000)	1.00 (0.99, 1.00)	0.1587	1.00 (1.00, 1.00)	0.0237	1.00 (0.99, 1.00)	0.5367

Abbreviations: aOR = adjusted odds ratio. CI = confidence interval. N = number of enrollees. Ny = number of enrollee‐years. pop = population.

^† Drive time: One‐way drive time from enrollee residence to usual care >30 min vs. ≤30 min. ¹²

^‡ Rurality: based on enrollee county of residence, with urban counties including those in metropolitan statistical areas (i.e., population centers ≥50,000) and rural counties including those in micropolitan or noncore areas (i.e., population centers <50,000). ³⁷

^§ Co‐infections: diagnosis of hepatitis B and/or C infection within the calendar‐year. ²⁵

^¶ Comorbidities: diagnosis of ≥1 common comorbidities from the Charlson comorbidity index (e.g., myocardial infarction, diabetes, dementia), ²⁵ , ³³ excluding HIV and AIDS‐defining illnesses, within the calendar‐year.

^†† AIDS‐defining illnesses: diagnosis of ≥1 AIDS‐defining illness (e.g., candidiasis, cervical cancer, Kaposi sarcoma) within the calendar‐year. ²⁵ , ³⁴

The significance and direction of the association differed in sensitivity and subsample analyses (Figure 1). Overall and in urban areas, driving >30 min was associated with significantly lower odds of retention for both alternative definitions of retention in care. In rural areas, the association remained positive and significant when defining retention by receipt of HIV medical visits (aOR 1.08 [95% confidence interval [CI] 1.06, 1.10]) or HIV‐related laboratory tests only (1.08 [1.06, 1.10]). When excluding Florida, restricting the sample to those who received Medicaid transportation benefits, and among those living in the Deep South, driving longer to access care was associated with higher odds of retention in the overall sample and/or among rural residents, but not in urban areas. For those in managed care, driving longer to the usual source of care was associated with lower odds of retention overall (0.98 [0.98, 0.99]) and in urban areas (0.97 [0.96, 0.98]), but was associated with higher odds of retention in rural areas (1.07 [1.06, 1.09]). Finally, for those in the intensive HIV management subgroup, drive time >30 min was not associated with retention in care overall or in urban or rural areas.

FIGURE 1.

Adjusted association between drive time to care >30 min and retention in HIV care: sensitivity and subsample analyses. The three panels show results of logistic regressions of drive time to usual care on retention in HIV care among the full sample and stratified by urban versus rural residence. Circles with horizontal bars represent adjusted odds ratios and confidence intervals; filled circles indicate statistically significant differences in odds of retention between drive time >30 min and drive time ≤30 min. “Rows” represent baseline, sensitivity, and subsample analyses. The “any care marker” definition of retention is the baseline definition (i.e., ≥2 HIV care markers [HIV‐related medical visit, HIV‐related laboratory test, or antiretroviral prescription] ≥90 days apart within a given calendar‐year). The “labs only” definition of retention is similar but only includes HIV‐related laboratory tests as a care marker; the “visits only” definition of retention is again similar but only includes HIV‐related medical visits as a care marker. Subsample analyses were: enrollees in all states except Florida; enrollees who received nonemergency medical transportation assistance within the calendar‐year; enrollees in the “Deep South” only (i.e., excluding District of Columbia, Delaware, Maryland, Oklahoma, and West Virginia); enrollees in managed care programs for ≥6 months within a calendar year; and enrollees eligible for intensive HIV management due to pregnancy, HIV‐related nephropathy, AIDS‐defining illnesses, or pneumocystis jirovecii pneumonia within the calendar‐year.

DISCUSSION

These findings add to an existing body of literature suggesting a complex relationship between geographic accessibility and outcomes along the HIV care continuum. ⁶ , ⁷ , ⁸ , ⁹ , ⁴⁴ , ⁴⁵ The current study builds on previous work by including a large rural sample and examining the association separately for urban and rural areas, finding different relationships in each. For those living in urban areas, longer drive times may represent a structural barrier to care. This may be particularly true for urban areas with limited public transportation, given the null association between geographic accessibility and retention for enrollees with HIV in urban areas who received Medicaid nonemergency transportation assistance. For rural areas, the opposite relationship—poorer geographic accessibility associated with greater odds of retention—may be due to several factors. Enrollees in rural areas who travel longer may seek care at HIV specialty clinics, which are disproportionately located in urban areas ¹⁰ , ¹¹ , ¹⁸ and may provide more comprehensive, coordinated care than local non‐HIV‐specialist, primary care clinics. This is supported by qualitative evidence from the US South suggesting that PWH value connections with their HIV clinician and are willing to drive long distances to continue receiving care from their preferred HIV clinician. ⁴⁶ Alternatively, PWH in rural areas who are retained in care may opt for more distant care to avoid disclosure and stigma, ⁴⁷ , ⁴⁸ , ⁴⁹ which is more common among PWH in rural and smaller urban areas. ⁵⁰ Cognitive dissonance may also explain this relationship; when more effort is expended (e.g., drive time), commitment to behavior (e.g., care) is stronger. ⁵¹ Future work should disentangle the reasons for this association in order to provide solutions that minimize travel burden for PWH in rural areas while supporting retention in care.

Limitations

Our use of Medicaid administrative claims to identify enrollees with HIV, as well as assess geographic accessibility to care and retention, represented “revealed” spatial accessibility (i.e., actual use of services), ⁵² versus potential spatial accessibility (i.e., possible use of services). Our sample was selected based on evidence of an HIV‐related care marker(s)—suggesting at least some engagement in health care—and therefore excluded enrollees with HIV who received no HIV care across the entire sample period or were not yet diagnosed. Accordingly, we may have overestimated retention and therefore underestimated the effect size of the association between geographic accessibility and retention. Our use of a binary, county‐level measure of rurality may not capture differences in rurality that may be apparent at more granular units of geographic analysis. Enrollees who received NEMT may represent different subgroups across states; for example, some states restricted this benefit to those eligible for Medicaid under national mandatory coverage rules (i.e., categorically needy). ⁴¹ However, we believe this variation is captured in the specified model through use of state‐level fixed effects and thus unlikely to affect our results.

In addition, it is possible that our data may not fully reflect current national trends in geographic accessibility, particularly given patchwork state Medicaid expansion and the rise in telemedicine due to the COVID‐19 public health emergency. However, our findings likely reflect current practice patterns in HIV care, quality of care definitions and outcomes including retention in care, ²² , ⁵³ and Medicaid enrollees with HIV. ²¹ The mixed evidence on the association between telemedicine use and HIV care continuum outcomes ⁵⁴ as well as continued patient‐level barriers to telemedicine in both rural and urban areas ⁵⁵ , ⁵⁶ suggest that despite rapid implementation of telemedicine in some clinical areas and patient populations, the relationships identified in this study remain relevant. In sensitivity analysis, the relationship between drive time and retention was largely consistent across subsamples of states, suggesting that findings may be generalizable beyond the state‐years included in our sample. Finally, ongoing challenges in HIV workforce capacity ¹⁸ , ²⁰ , ⁵⁷ reinforce the continued relevance of geographic accessibility as a potential determinant of HIV care outcomes.

CONCLUSIONS

For PWH in the rural US South, longer drive time to usual care is consistently associated with greater retention in care, while for those in urban areas, longer drive time to care has a null or negative association with retention. Understanding the mechanisms underlying these diverging relationships, examining additional outcomes such as viral suppression, and identifying the clinical and policy solutions that best support retention in care for both urban and rural PWH remain key priorities. Doing so is a critical step toward reducing urban–rural disparities in HIV care continuum outcomes and ending the US HIV epidemic.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Kimmel AD, Bono RS, Pan Z, et al. Drive time to care and retention in HIV care: Rural–urban differences among Medicaid enrollees in the United States South. J Rural Health. 2025;41:e12877. 10.1111/jrh.12877