Geographic location may be related to the receipt of quality HIV health care services. Clinical outcomes and health care utilization were evaluated in rural, urban, and peri-urban patients seen at high-volume US urban-based HIV care sites.
Zip codes for 8773 HIV patients followed in 2005 at seven HIV Research Network sites were categorized as rural (population <10,000), peri-urban (10,000-100,000), and urban (>100,000). Clinical and demographic characteristics, inpatient and outpatient (OP) utilization, AIDS-defining illness rates, receipt of highly active antiretroviral therapy (HAART), opportunistic infection (OI) prophylaxis usage, and virologic suppression were compared among patients, using χ(2) tests for categorical variables, t-tests for means, and logistic regression for HAART utilization.
HIV-infected rural (n=170) and peri-urban (n=215) patients were less likely to be Black or Hispanic than urban HIV patients. Peri-urban subjects were more likely to report MSM as their HIV risk factor than rural or urban subjects. Age, gender, CD4 or HIV-RNA distribution, virologic suppression, HAART usage, or OI prophylaxis did not differ by geographic location. In multivariate analysis, rural and peri-urban patients were less likely to have four or more annual outpatient visits than urban patients. Rural patients were less likely to receive HAART if they were Black. Overall, geographic location (as defined by home zip code) did not affect receipt of HAART or OI prophylaxis.
Although demographic and health care utilization differences were seen among rural, peri-urban, and urban HIV patients, most HIV outcomes and medication use were comparable across geographic areas. As with HIV care for urban-dwelling patients, areas for improvement for non-urban HIV patients include access to HAART among minorities and injection drug users.
"Studies show that rural communities may not be well equipped to meet the needs of underserved individuals, resulting in higher mental and physical health disparities for persons residing in less populated areas compared to those in urban communities (Chu & Selwyn, 2008; Leira, Hess, Torner, & Adams, 2008; Thorpe, Van Houtven, Sleath, & Thorpe, 2010). A study of HIV service utilization by people living with HIV in different-sized communities showed that rural residents were less likely to attend four or more healthcare appointments per year and less likely to receive antiretroviral medication for HIV infection if they were Black (Wilson et al., 2011). "
[Show abstract][Hide abstract] ABSTRACT: Abstract The aim of this study was to compare the mental health, substance use, and sexual risk behaviors of rural and non-rural transgender persons. Online banner advertisements were used to recruit 1,229 self-identified rural and non-rural transgender adults (18+ years) residing in the US. Primary findings include: significant differences in mental health between rural and non-rural transmen; relatively low levels of binge drinking across groups, although high levels of marijuana use; and high levels of unprotected sex among transwomen. The results confirm that mental and physical health services for transgender persons residing in rural areas are urgently needed.
Journal of Homosexuality 12/2013; 61(8). DOI:10.1080/00918369.2014.872502 · 0.78 Impact Factor
"Differences in land use, transportation networks, population density and distribution among different regions, such as rural vs urban areas also influence spatial access to the pharmacies and thus to the medications and health information provided by them. For example, one study showed that access to Human Immunodeficiency Virus-related retroviral medications, information and related health care services differed significantly for rural and urban residents
[Show abstract][Hide abstract] ABSTRACT: Background
Only a small amount of research has focused on the relationship between socio-economic status (SES) and geographic access to prescription medications at community pharmacies in North America and Europe. To examine the relationship between a community’s socio-economic context and its residents’ geographic access to common medications in pharmacies, we hypothesized that differences are present in access to pharmacies across communities with different socio-economic environments, and in availability of commonly prescribed medications within pharmacies located in communities with different socio-economic status.
We visited 408 pharmacies located in 168 socio-economically diverse communities to assess the availability of commonly prescribed medications. We collected the following information at each pharmacy visited: hours of operation, pharmacy type, in-store medication availability, and the cash price of the 13 most commonly prescribed medications. We calculated descriptive statistics for the sample and fitted a series of hierarchical linear models to test our hypothesis that the in-stock availability of medications differs by the socio-economic conditions of the community. This was accomplished by modeling medication availability in pharmacies on the socio-economic factors operating at the community level in a socio-economically devise urban area.
Pharmacies in poor communities had significantly higher odds of medications being out of stock, OR=1.24, 95% CI [1.02, 1.52]. There was also a significant difference in density of smaller, independent pharmacies with very limited stock and hours of operation, and larger, chain pharmacies in poor communities as compared to the middle and low-poverty communities.
The findings suggest that geographic access to a neighborhood pharmacy, the type of pharmacy, and availability of commonly prescribed medications varies significantly across communities. In extreme cases, entire communities could be deemed “medication deserts” because geographic access to pharmacies and the availability of the most prescribed medications within them were very poor. To our knowledge, this study is first to report on the relationship between SES and geographic access to medications using small area econometric analysis techniques. Our findings should be reasonably generalizable to other urban areas in North America and Europe and suggest that more research is required to better understand the relationship of socio-economic environments and access to medications to develop strategies to achieve equitable medication access.
International Journal of Health Geographics 11/2012; 11(1):48. DOI:10.1186/1476-072X-11-48 · 2.62 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To gain a better understanding of the HIV epidemic in rural South Carolina (SC) by contrasting 3 definitions of rural and urban areas.
The sample included newly diagnosed HIV cases aged ≥18 years in SC between January 1, 2005, and December 31, 2011. Each individual was assigned a rural or urban status as defined by the Office of Management and Budget (OMB), Census Bureau (CB), and Rural Urban Commuting Area (RUCA) classifications. Descriptive statistics were conducted to compare sociodemographic characteristics, CD4 counts, viral loads, and time to AIDS diagnosis between rural and urban populations. Kappa statistics measured the agreement between the 3 definitions of rurality.
Depending on the definition used, the proportion of newly diagnosed HIV cases in rural areas varied from 23.3% to 32.0%. Based on the OMB and RUCA definitions, rural residents with HIV were more likely to be older, women, black, and non-Hispanic, report heterosexual contact, and have an AIDS diagnosis within 1 year of their HIV diagnosis. The OMB and RUCA definitions had a nearly perfect agreement (kappa = 0.8614; 95% CI = 0.8457, 0.8772), while poor agreements were noted between the OMB and CB or the RUCA and CB definitions.
When examining the rural HIV epidemic, how "rural" is defined matters. Using 3 definitions of rurality, statistically significant differences were found in demographic characteristics, timing of HIV diagnosis and the proportion of rural residents diagnosed with HIV in SC. The findings suggest possible misclassification biases that may adversely influence services and resource distribution.
The Journal of Rural Health 12/2013; 30(3). DOI:10.1111/jrh.12057 · 1.45 Impact Factor
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