A Systematic Review of the Geospatial Barriers to Antiretroviral Initiation, Adherence, and Viral Suppression among People Living with HIV
Drafted Version; Final version available at Sexual Health.
Background: Antiretroviral therapy (ART) has become a cornerstone of not only HIV clinical care, but also of HIV prevention—a strategy referred to as Treatment as Prevention (TasP). However, despite the efficacy of treatment-based programs and policies, structural barriers to ART initiation, adherence, and viral suppression have the potential to reduce TasP effectiveness in key populations. Providing a framework for examining these barriers at a population-level, Geographic Information Systems (GIS) have been used widely to study a variety of HIV-related outcomes. While previous reviews have examined the GIS literature with respect to HIV-testing, – an essential antecedent to clinical care – to date no reviews have summarized the research with respect to other ART-related outcomes. Methods: Therefore, the present review leveraged the PubMed database to identify studies that leveraged GIS to examine the barriers to ART initiation, adherence, and viral suppression with the overall goal of understanding how GIS can be used to improve TasP programs. Joanna Briggs Institute criteria were used for the critical appraisal of included studies. Results: In total, 33 relevant studies were identified, excluding those not utilizing explicit GIS methodology or not examining TasP-related outcomes. Conclusions: Findings highlight geospatial variation in ART success and inequitable distribution of HIV care in racially segregated, economically disadvantaged, and, by some accounts, increasingly rural areas – particularly in North America. Furthermore, this review highlights the utility and current limitations of using GIS to monitor health outcomes related to ART and the need for careful planning of resources with respect to the geospatial movement and location of people living with HIV (PLWH).
The use of antiretroviral therapies (ART) to prevent HIV/AIDS-related morbidity, mortality, and transmission is referred to as Treatment as Prevention (TasP)1–8 and conceptualized as a care continuum in which people living with HIV (PLWH) are (i) diagnosed soon after infection, (ii) initiate ART soon after HIV diagnosis, (iii) remain adherent to ART across their lives, and (iv) achieve viral suppression and/or undetectability.9 Despite the proven efficacy of TasP programs in reducing HIV/AIDS-related morbidity, mortality, and transmission,10–13 HIV continues to be of concern in key demographic and behaviourally-defined populations.14 To understand the drivers underlying these population-specific epidemics, researchers have used a variety of epidemiological approaches, including Geographic Information Systems (GIS).15–17 These methods have shown that TasP-related outcomes (e.g., community viral load) are salient predictors of neighborhood- and area-level HIV incidence.18–20 However, despite the effective use of GIS to evaluate broadly-targeted epidemiological and prevention strategies, such as HIV testing and HIV incidence,26–30 outcomes related to post-diagnosis HIV care represent a uniquely difficult challenge for GIS because they must target specific individuals within communities that are defined by broader and less subtle forces. Therefore, while other reviews have previously examined structural barriers to HIV-testing,26,27 the present literature review aims to systematically sample and summarize available geospatial research examining ART initiation, adherence, and viral suppression among PLWH.
In October 2016, we systematically sampled articles related to geospatial research (i.e., spatial OR geograph* OR geospatial OR neighborhood), HIV (i.e., HIV OR AIDS), and ART (i.e., antiretroviral OR treatment OR care OR viral load OR suppression OR initiation OR adherence). Sampling was conducted using a Boolean keyword search of the PubMed database. Non-English language articles were omitted, and relevant articles were identified from the titles and abstracts of each search result. Each study was then screened twice for inclusion, and articles were excluded if they did not (i) assess treatment-related measures (e.g. ART initiation, adherence, viral suppression, or some proxy measure for these variables), (ii) did not include geospatial methods, or (iii) did not present original research. The sensitivity and specificity of our search strategy was tested a posteriori by searching for “HIV” in four prominent health geography journals: Applied Geography, Health & Place, International Journal for Health Geographics, International Journal of Geographic Information Science. While our search protocol was not indexed, our review was conducted to comply with PRISMA guidelines for systematic reviews and each article was evaluated by our study team based on the Johanna Briggs Institute’s Critical Appraisal Checklist for Observational Studies 28,29
Our PubMed keyword search returned 4,427 studies, 4,209 of which were available in English. Of these, 184 were considered for review based on the relevance of their titles and abstracts. Based on a more thorough review 23 did not present original research findings; 79 did not assess ART initiation, adherence, or viral suppression; 50 did not utilize geospatial approaches. This search strategy resulted in the inclusion of 33 studies – or approximately 0.7% (n = 33/4427) of returned articles (Figure 1). Sensitivity analyses of our search strategy returned 336 search results, 0.6% (n = 2 / 336) of which met our inclusion criteria and 0.3% (n = 1 / 336) of which was not included in our original search results (and was therefore added to our final inclusion list).
Critical Appraisal of Studies
As all studies relied on some level of aggregation or clustering, there was broad risk for ecological bias. Risk for ecological bias was particularly high for risk analysis studies, where ecological-level data was used to represent or modeled alongside person-level data. With that said, there is a fundamental benefit of using ecological data alongside person-level data, as it can characterize environmental factors not necessarily represented by person-level factors. However, due to both day-to-day movements and permanent migration, assigning individuals to specific geographic areas can be problematic. This is because one’s place of residence or the location where they access care may not be representative of where they live and work.30 Similarly, as few studies conducted sensitivity analyses for the level of aggregation (e.g., by country, province, city, zip code, census block) for explanatory factors and outcomes it was difficult to assess the extent to which modifiable units might have biased study results.31
Despite these limitations, most studies were of acceptable quality: Each provided a clear description of their study samples and data sources; Many (but not all) provided sufficient contextual information regarding study settings; Limitations (exempting those noted above) were cautiously outlined; and sources of bias were often well-defined. Statistical analyses, while wide-ranging, were conventional and well-reasoned (though not always described in sufficient detail for lay readers). Outcomes and explanatory factors were generally appropriate and interpreted correctly. Finally, studies protected participant confidentiality by mapping and reporting aggregated and censored data where small counts or detailed geographic features might have raised privacy concerns.
Overview of Findings
Spatial Heterogeneity of TasP Outcomes. As a first step to examining the relationship between geography and TasP outcomes, a number of studies attempted to map and describe the spatial variation of TasP outcomes: Althoff et al. (2016) found that spatial patterns of ART use (range: 8 – 90%), viral suppression (range: 69 – 95%), and retention (range: 21 – 91) varied independently with significant inter- and intra-regional heterogeneity in North, Central, and South America. Likewise, in North America, Hanna et al. (2013) identified significant inter- and intra-regional (i.e., Northeast U.S.A., Western U.S.A., Midwest U.S.A., Southern U.S.A., and Canada) heterogeneity in ART initiation (range: 35 – 94%; p < .001) and viral suppression (range: 45 – 78%; p < .001). Raboud et al. (2010) found that viral load gaps among Canadians living with HIV were more likely among those residing in Quebec (aOR = 1.72, p < 0.0001) and Ontario (aOR = 1.78, p < 0.0001) compared to those living in British Columbia. On a smaller scale, Gordon et al. (2015) found that residence in New York City (compared to elsewhere in New York State) was associated with poorer linkage to care (OR = 0.73, p < 0.01). Sayles et al. (2012) identified heterogeneity in viral suppression within Los Angeles, California (range: 21.9 – 32.3%). Eberhart et al. (2013) found that patients living in “hotspots” associated with attrition, were more likely to not link to care (OR = 1.76, p < 0.05), link to care late (OR = 1.49, p < 0.05), not be retained in care (OR = 1.84, p < 0.05), and not achieve viral suppression (OR = 3.23, p < 0.05). However, the authors noted that the areas associated with each outcome did not overlap geographically. Laraque et al. (2013) found that community viral load undetectability varied by borough of residence (range: 49.8 - 59.2%, p < 0.0001) and public health area (range: 49– 64%, p < 0.0001). Rebeiro et al. (2016) found that retention varied by region in the United States (i.e., Midwest, Northeast, West, and South; range: 7 – 45%).
Rural and Urban Differences in TasP Outcomes. Generalizing these results, several authors compared TasP outcomes by urban-rural residence. Results from these studies have been mixed, but most studies suggest that rural residence is associated with greater attrition from care and poorer viral suppression. For example, Lourenço et al. (2014) found that compared to the urban health authorities, where attrition from the cascade was the lowest (range: 13 - 44%), residents in the rural health authorities were less likely to be linked to care (aOR = 0.54, p < 0.05), retained in care (aOR = 0.55, p < 0.05), on ART (aOR = 0.55, p < 0.05), or virally suppressed (aOR = 0.45, p < 0.05). Similarly, Ohl et al. (2010) found that CD4 count at time of ART initiation was negatively associated with rural residence (68.7 vs. 74.9%, p < 0.01); and Ohl et al. (2011) found that urban residence was associated with faster ART uptake within 180 days (aOR = 1.72, p < 0.05) and 360 days (aOR = 1.63, p < 0.05), but not 720 days (aOR = 1.26, p < 0.05). Likewise, King et al. (2008) found that both urban residence (vs. rural; OR = 1.38, p < 0.001) and state of residence (range: 46.7 – 71.8%, p < 0.01) were associated with receiving highly active antiretroviral therapy. Meanwhile, other studies show that the effect of rural-urban residence varied based on the outcome of interest. For example, Wilson et al. (2011) found that while neither CD4 counts, virologic suppression, nor ART usage differed based on geographic location, rural patients were less likely than urban patients to report >4 annual outpatient visits compared to urban patients (OR = 0.60, p = 0.003). Furthermore, other studies showed that collinearity with demographic variables may neutralize or reverse the observed relationships associated with rural-urban residence. For example. Ohl et al. (2012) found that while residence in micropolitan (OR = 0.75, p < 0.0001) and rural (OR = 0.79, p < 0.0001) areas was associated with lower adherence on the univariate level, this association reversed after adjusting for age, race/ethnicity, substance use, Hep. C. coinfection, and AIDS-status (OR = 1.24, p < 0.05). Similarly, Chakraborty et al. (2015) also found that while rural residence was associated with smaller declines in viral load (Difference: -23,474 copies/mL; p < 0.01), this was not so for CD4 counts (Difference: 5 cells/mm3; p = 0.59). Furthermore, the effect on viral load became non-significant (β = 0.003; SE = 0.014; p = 0.85) after adjusting for age, gender, race, and exposure type (e.g., MSM, IDU). Cooke et al. (2010) found that rural residence was associated with lower ART uptake (OR = 0.65, p = 0.02), but not after accounting for socioeconomic status, education, age, sex, and distance to care (aOR = 0.94, p = 0.77). Finally, several studies simply found non-significant relationships between TasP outcomes and rural-urban residence. For example, Joy et al (2008) found that rural residence was not associated with CD4 counts (OR = 0.89, n.s.). Likewise, Cherutich et al. (2016) found that neither programmatic region (Range: 45 – 69%; p = 0.349) nor urban-rural residence (range: 59 - 63%; p = 0.158) were associated with viral-load.
Area-level Sociodemographic Factors Associated with TasP Outcomes. Irrespective of rurality, authors also sought to identify other neighborhood-level factors that might underlie patterns of spatial heterogeneity. These studies generally show that area-level factors (e.g., racial segregation, economic inequality, neighborhood disorder, crime) were negatively associated with positive TasP outcomes. For example, Arnold et al. (2009) found that racial disparities in mortality disappeared after accounting for neighborhood socio-economic factors, which explained between 19 and 22% of these disparities (p > 0.05). Furthermore, the effect of race and neighborhood-level socioeconomic factors primarily impacted AIDS survival by impacting ART initiation. Similarly, Burke-Miller et al. (2016) found that racial segregation (aOR = 2.45, p = 0.04) and poor-quality of the built environment (aOR = 2.61, p = 0.03) were associated with having a low CD4 count, but not having an unsuppressed viral load (p = 0.87, p = 0.91, respectively). Castel et al., (2012) found that the areas with the highest community viral loads also had the highest poverty rates and the lowest rates of educational achievement. Goswami et al. (2016) found that linkage to care was associated with higher neighborhood-level education (r = 0.12, p = 0.24), lower income inequality (r = -0.19, p – 0.04), lower vacancy (r = -0.20, p < 0.05), higher proportion of telephone serviced homes (r = 0.05, p < 0.001), and fewer HIV/AIDS service providers (r = -0.15, p < 0.01). Furthermore, viral suppression was associated with lower neighborhood-level income inequality (r = 0.13, p = 0.03), higher proportions of telephone serviced homes (r = 0.08, p = 0.006), and more HIV/AIDS service providers (r = 0.02, p = 0.006). However, there was also a moderating effect of income on transportation with car ownership associated with greater linkage to care (p = 0.017) and viral suppression (p = 0.013) in areas with low poverty; and the number of bus stops was negatively associated with viral suppression in areas with high poverty (p = 0.009). Gueler et al. (2015) found that neighborhood socioeconomic status was negatively associated with late presentation (OR = 0.79, p < 0.05), presenting with advanced HIV (OR =0.71, p < 0.05), presenting with AIDS (OR = 0.59, p < 0.05), delayed ART initiation (OR = 0.62), and loss to follow-up (OR = 0.76, p < 0.05); and positively association with achieving viral suppression (OR 1.52, p < 0.05). Hernández-Romieu et al. (2016) found that 42.2% of those entering care in states with low socio-economic marginalization had CD4 counts <200 cells/mm3 compared to 52.8% in states with higher marginalization. Joy et al. (2008) found that higher census-level unemployment predicted delayed ART access (aOR = 1.41, p < 0.05). Rebeiro et al. (2016) found that greater retention in care was associated with older median age (OR = 1.10, p < 0.05) and a lower proportion of African American residents (OR = 0.37, p < 0.05). Shacham et al. (2013) found that higher poverty was associated with higher CD4 cell counts (aOR = 1.56, p < 0.05) and greater unemployment was associated with greater odds or receiving an ART prescription (aOR = 1.47, p < 0.05). Wood et al. (2000) found that ART use was higher in areas with a higher population density (β = 0.02, SE = 0.002, p < 0.01) and more indigenous residents (β = 0.09, SE = 0.12, p < 0.01); and lower in areas with a higher proportion of female residents (β = -0.11, SE = 0.02, p < 0.01). Surratt et al. (2015) found that neighborhood disorder was distally associated with non-adherence among HIV-positive substance users who sold or traded their medications.
Several studies also demonstrated mixed results – representing significant nuance in the relationship between neighborhood level factors and TasP outcomes. For example, Eberhart et al. (2015) found that lower economic deprivation (aOR = 0.92, p < 0.05), increased public transportation (aOR = 1.04, p < 0.05), longer average distance to pharmacies (aOR = 2.41, p < 0.05), and decreasing distance to HIV care (aOR = 0.85, p < 0.05) were associated with residing in poor retention hotspots. Meanwhile, greater economic deprivation (aOR = 1.09, p < 0.05) and shorter average distance to pharmacies (aOR = 0.12, p < 0.05) were, as expected, associated with residence in a poor viral suppression hotspot. Similarly, Kahana et al. (2016) found that socioeconomic disadvantage was negatively associated with ART use (aOR = 0.85, p < 0.05), but was positively associated with ART adherence for at least six months (OR = 1.32, p < 0.05). Ransome et al. (2017) found that higher neighborhood social participation was associated with higher prevalence of late diagnosis (β = 1.37, SE = 0.32, p < 0.001), linkage to care (β = 1.13, SE = 0.20, p < 0.001), and lower prevalence of engagement in care (β = -1.16, SE = 0.30, p < 0.001); higher collective engagement was associated with lower linkage to care (β = -0.62, SE = 0.32, p < 0.05); the percent of residents with more than a 9th grade education was negatively associated with late diagnosis (β = -1.07, SE = 0.22, p < 0.001) and positively associated with engagement in care (β = 0.94, SE = 0.21, p < 0.001); higher unemployment was negatively associated with linkage to care (β = -0.48, SE = 0.17, p < 0.01); and higher assault rates were negatively associated with engagement in care (β = -0.08, SE = 0.02, p < 0.01).
The Impact of Access to Care on TasP Outcomes. Several studies also incorporated spatial measures into their analyses to determine how distance to care impacted TasP outcomes. These studies generally demonstrated that greater distance to care had a negative impact on TasP outcomes. For example, Siedner et al. (2013) found that GPS-tracked distance (β = 0.03, p < 0.001) and Euclidean distance (β = 0.02, p < 0.001) of transportation were associated with delayed HIV care. Likewise, Eberhart et al. (2013) found that greater distance to care was associated with reduced odds of achieving viral suppression (OR = 0.63, p < 0.05). Cooke et al. (2010) found that greater Euclidean distance was associated with lower ART uptake (OR = 0.73, p < 0.01). Relatedly, Wood et al. (2000) found that ART use was associated with residing close to public transit (β = 0.56, SE = 0.93, RR = 1.75, p < 0.01).
However, not all studies have linked distance to care to poorer TasP outcomes. For example, Kloos et al. (2007) found that ART use was association with population size (r = 0.99, p < 0.01), urbanity (r = 0.98, p < 0.01), number of hospitals and health centers nearby (r = 0.95, p < 0.01), but not the actual distance from the nearest health center to an ART hospital (r = 0.22, p = 0.15). Similarly, Johnson et al. (2013) found that distance to clinic was associated with timely ART initiation at one clinic (OR = 0.97, p < 0.01), but not for patients attend a second clinic (OR = 0.99, p > 0.05). Furthermore, several of the studies assessing the impact of access to care show that access can have different effects on different outcomes. For example, Ransome et al. (2017) found that greater distance to care was associated with both late HIV diagnosis (β = 0.96, SE = 0.18, p < 0.01) and greater engagement in care (β = 0.62, SE = 0.27, p < 0.05) – seemingly contradictory trends.
The studies included in our review demonstrate the use of GIS to (i) survey spatial heterogeneity in ART initiation, adherence, and viral suppression;32–45 (ii) link area-level risk factors to health outcomes;46–58 and (iii) describe the impact of healthcare access on ART outcomes.59–64 Overall, our findings suggests that GIS is a useful and necessary tool to monitor the implementation of ART programs and policies worldwide. This is particularly important given the significant challenges posed to urban and rural regional planners, public health officials, and other stakeholders within the context of an epidemic that is increasingly a concern in ethnically segregated neighborhoods, economically depressed regions, and, by some accounts, increasingly rural areas.65 However, while some local findings from these studies are generalizable to other settings and populations, the articles in our review highlighted the use of GIS for examining local epidemics — making the suite of GIS tools especially powerful when used by those with the ability to effect change, inform policy, and plan healthcare delivery.
Considering the increasingly complex and geographic demands placed on HIV prevention systems, future GIS applications might include the development of geographically flexible healthcare delivery and prevention services that can quickly respond to changing spatial patterns of HIV. The utility of such approaches have long been identified within the context of mobile HIV testing,66,67 and therefore the application of these principles to HIV treatment is likely to offer similar benefits for those who experience difficulty initiating or adhering to HIV care.68,69 Indeed, in an epidemic that has in many cases transitioned outside traditional gay communities, traditional brick-and-mortar community-based care must, in some settings, be retooled to meet the changing dynamic of the HIV epidemic—this includes expanding current programs to bring about greater geographic coverage.70 With that said, implementation research on the expansion and delivery of care services remains scant,71 making it difficult to identify which specific strategies result in optimal coverage. Meanwhile, HIV surveillance, monitoring, and clinical services should be centralized — administratively, not geographically72 — to allow for coordinated planning and administration.73 Though limited, investigations of population-based ART management within centralized healthcare settings have shown that central ART programming is increasingly important in an era of micro-epidemics and disease clustering.74–76 As such, centralization is likely to ensure coverage in areas of increasing demand and inadequate supply of ART services.77
With respect to future research, our review highlights the utility of using GIS to engage and monitor ART programs, services, and policies. Further, considering that most studies in our review focused on socioeconomic status (as measured using census data), spatial distance to care (using Euclidian and Network distance), and other social determinants of health (i.e., poverty, race, etc.), we feel that additional analyses are needed to understand how more proximal social factors – such as stigmatization, economic forces, and political geography – might also interrupt TasP implementations in some areas. Indeed, other methodologies suggest that the determinants of TasP success are diverse, making it important to take these factors into consideration when planning geographically-specific interventions aimed at addressing these social determinants of local health.78 Secondly, for the most part, the analyses included in our review were focused primarily on general populations and did not sufficiently delineate across key strata such as GBM, people who inject drugs, and racial/ethnic minorities. Future research should therefore assess the unique spatial geographies of these populations and the syndemic factors which contribute to poorer health outcomes among them.79 Third, our review highlights the importance of combining person-level research with ecological research, to ensure that results derived from clustered data also reflect the real-world phenomenon experienced by individuals. Such combinations might also utilize GPS technology to better understand and articulate the spatial behavior of individuals within the geographies they live. As discussed, geocoding individuals to specific spatial units may not provide sufficient nuance for understanding their movement and migratory patterns – particularly in settings where individuals must traverse between geographic units with some regularity.
Given these research needs, there is a pressing need for open-access and easy-access data across all levels of private and public administration, regardless of industry or focus. Indeed, the success of GIS and other informatics-based studies rests largely on the availability of novel, high quality, and robust data. Therefore, and when possible, research and administrative data should be made available in formats that, while protecting any potential privacy concerns, can be used by informatics researchers. This can be achieved by providing data files that have been pre-aggregated by useful spatial dimensions (e.g., mailing codes, census areas) and by making documentation about license-only data sources more accessible to the public.
As with other literature reviews, the present study has several important limitations. First and foremost, our search terms and database selection may have caused us to miss important studies relevant to this review. As with all literature reviews there is a tradeoff between sensitivity and specificity. With that said, based on our sensitivity analysis and considering the significant overlap in study themes, we believe that our search strategy resulted in a systematic and generally representative sample of GIS studies examining post-diagnosis related outcomes. Second, as we excluded non-English language papers, most papers dealt with North American contexts, particularly focusing on studies from the US and Canada. Third, because non-significant findings are rarely discussed in the abstracts of published papers it may be that some articles with non-significant findings were inappropriately excluded. Fourth, regarding those studies which used geocoding, but not necessarily geostatistical methodology, the selection of articles was somewhat subjective. While geocoding alone is not necessarily considered a geospatial method, inclusion of studies with a significant focus on geography were still judged to be relevant, and therefore included. With respect to these articles, there is a significant risk that the framing, rather than particular features of the analytic approach, merited their inclusion. However, as this was not a meta-analysis, our primary concern was to provide a review of evidence and therefore we felt this was appropriate. Finally, as this review was focused on intermediate TasP-related outcomes, our results are not inclusive of studies assessing mortality or the efficacy of TasP (i.e., using measures such as community viral load to assess the impact of TasP on HIV incidence). While these outcomes are surely of interest, distinct characteristics of these studies have been reviewed elsewhere.20,80 Despite these limitations, we feel that our general findings—especially those regarding the necessity and utility of conducting geospatial analysis to monitor the development of ART programs—likely represent the most salient needs of future HIV research and prevention programming.
In summary, the findings of this review highlight the feasibility and utility of GIS to monitor health outcomes related to ART. Further, in light of the documented shift of the epidemic into socially and geographically segregated and economically disadvantaged communities, our review also highlights the necessity of using GIS studies to track the changing epidemic as it shifts into new populations and conditions. Indeed, understanding the geospatial variations in ART outcomes can help ensure that resources are equitably distributed and accessible to individuals living in areas and neighborhoods where they are most needed. Central planning of these resources, especially when leveraging GIS, can therefore help optimize care for PLWH as well as HIV prevention.
The authors would like to thank Kirk J. Hepburn for editing this manuscript prior to publication and Meghan Winters for here guidance and expertise in assisting with the development of this manuscript. KGC is supported by the Momentum Health Study as part of his doctoral training. Momentum is funded through the National Institute on Drug Abuse (R01DA031055-01A1) and the Canadian Institutes for Health Research, through both project grant and foundation grant awards (MOP-107544, 143342, PJT-153139). JM is supported with grants paid to his institution by the British Columbia Ministry of Health and by the U.S. National Institutes of Health (R01DA036307). He has also received limited unrestricted funding, paid to his institution, from Abbvie, Bristol-Myers Squibb, Gilead Sciences, Janssen, Merck, and ViiV Healthcare. These sponsors had no role in the design and conduct of the study; in the collection, management, analysis and interpretation of the data; or in the preparation, review or approval of the manuscript. We have no conflicts of interest to declare.
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