Mapping late testers for HIV in Texas

Visualizing the Geography of HIV Observational Cohorts With Density-Adjusted Cartograms

BACKGROUND

Maps are potent tools for describing the spatial distribution of population and disease characteristics and, thereby, for appropriately targeting public health interventions. People with HIV (PWH) tend to live in densely populated and spatially compact areas that may be difficult to visualize on maps using unadjusted geographic or political borders.

SETTING

To illustrate these challenges, we used geographic data from adult PWH at the Vanderbilt Comprehensive Care Clinic (VCCC) in Nashville, Tennessee, and aggregated data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) from 1998 to 2015.

METHODS

We compared choropleth maps that use differential shading of political/geographic boundaries with density-adjusted cartograms that allow for shading and deformed boundaries according to a variable of interest, such as PWH.

RESULTS

Cartograms enlarged high-burden areas and shrank low-burden areas of PWH, improving visual interpretation of where to focus HIV prevention and mitigation efforts, when compared with choropleth maps. Cartograms may also demonstrate cohort representativeness of underlying populations (eg, Tennessee for VCCC or the United States for NA-ACCORD), which can guide efforts to assess external validity and improve generalizability.

CONCLUSION

Choropleth maps and cartograms offer powerful visual evidence of the geographic distribution of HIV disease and cohort representation and should be used to guide targeted public health interventions.

Exploring the Spatial Determinants of Late HIV Diagnosis in Texas

INTRODUCTION

Despite statewide progress and continuous HIV prevention efforts in Texas, HIV diagnosis at a late stage of infection persists. Diagnosis delay differs in magnitude and spatial distribution. We examined the local spatial relationships of late HIV diagnosis with a selection of variables in an area of Texas that includes large metropolises and high HIV morbidity.

METHODS

We compared regression modeling approaches to study the associations between the regional percentage of late HIV diagnosis from 2011 through 2015, regional measures of poverty, lack of health insurance (uninsurance), educational attainment, unemployment, and the average regional distance from residence to an HIV testing site: global ordinary least squares linear regression, spatial error model, geographically weighted regression, and multiscale geographically weighted regression (MGWR). Cartographic representation of the local R², coefficient estimates, and their t values assisted in the interpretation of results.

RESULTS

The MGWR model resulted in a better fit and identified education and uninsurance as globally fixed predictors, whereas the relationships between late HIV diagnosis and poverty, unemployment, and distance varied spatially. The model performed better in rural areas and in suburban areas of the largest cities than in urban areas.

CONCLUSION

The MGWR results provided local estimates of associations. The results highlight the importance of focusing on a local context. Modeling at the local scale is particularly useful for characterizing relationships between explanatory and dependent variables when the relationships vary spatially. In the context of HIV prevention, relationships that are of local relevance can inform local policy and complement routine screening in clinical settings.

How Do Social Capital and HIV/AIDS Outcomes Geographically Cluster and Which Sociocontextual Mechanisms Predict Differences Across Clusters?

BACKGROUND

Place of residence has been associated with HIV transmission risks. Social capital, defined as features of social organization that improve efficiency of society by facilitating coordinated actions, often varies by neighborhood, and hypothesized to have protective effects on HIV care continuum outcomes. We examined whether the association between social capital and 2 HIV care continuum outcomes clustered geographically and whether sociocontextual mechanisms predict differences across clusters.

METHODS

Bivariate Local Moran's I evaluated geographical clustering in the association between social capital (participation in civic and social organizations, 2006, 2008, 2010) and [5-year (2007-2011) prevalence of late HIV diagnosis and linkage to HIV care] across Philadelphia, PA, census tracts (N = 378). Maps documented the clusters and multinomial regression assessed which sociocontextual mechanisms (eg, racial composition) predict differences across clusters.

RESULTS

We identified 4 significant clusters (high social capital-high HIV/AIDS, low social capital-low HIV/AIDS, low social capital-high HIV/AIDS, and high social capital-low HIV/AIDS). Moran's I between social capital and late HIV diagnosis was (I = 0.19, z = 9.54, P < 0.001) and linkage to HIV care (I = 0.06, z = 3.274, P = 0.002). In multivariable analysis, median household income predicted differences across clusters, particularly where social capital was lowest and HIV burden the highest, compared with clusters with high social capital and lowest HIV burden.

DISCUSSION

The association between social participation and HIV care continuum outcomes cluster geographically in Philadelphia, PA. HIV prevention interventions should account for this phenomenon. Reducing geographic disparities will require interventions tailored to each continuum step and that address socioeconomic factors such as neighborhood median income.

An Integrated Service Delivery Model to Identify Persons Living with HIV and to Provide Linkage to HIV Treatment and Care in Prioritized Neighborhoods: A Geotargeted, Program Outcome Study

BACKGROUND

Recent studies have demonstrated that high human immunodeficiency virus (HIV) prevalence (2.1%) rates exist in "high-risk areas" of US cities that are comparable to rates in developing nations. Community-based interventions (CBIs) have demonstrated potential for improving HIV testing in these areas, thereby facilitating early entry and engagement in the HIV continuum of care. By encouraging neighborhood-based community participation through an organized community coalition, Project LINK sought to demonstrate the potential of the CBI concept to improve widespread HIV testing and referral in an area characterized by high poverty and HIV prevalence with few existing HIV-related services.

OBJECTIVE

This study examines the influence of Project LINK to improve linkage-to-care and HIV engagement among residents of its target neighborhoods.

METHODS

Using a venue-based sampling strategy, survey participants were selected from among all adult participants aged 18 years or more at Project LINK community events (n=547). We explored multilevel factors influencing continuum-of-care outcomes (linkage to HIV testing and CBI network referral) through combined geospatial-survey analyses utilizing hierarchical linear model methodologies and random-intercept models that adjusted for baseline effect differences among zip codes. The study specifically examined participant CBI utilization and engagement in relation to individual and psychosocial factors, as well as neighborhood characteristics including the availability of HIV testing services, and the extent of local prevention, education, and clinical support services.

RESULTS

Study participants indicated strong mean intention to test for HIV using CBI agencies (mean 8.66 on 10-point scale [SD 2.51]) and to facilitate referrals to the program (mean 8.81 on 10-point scale [SD 1.86]). Individual-level effects were consistent across simple multiple regression and random-effects models, as well as multilevel models. Participants with lower income expressed greater intentions to obtain HIV tests through LINK (P<.01 across models). HIV testing and CBI referral intention were associated with neighborhood-level factors, including reduced availability of support services (testing P<.001), greater proportion of black/African Americans (testing and referral P<.001), and reduced socioeconomic capital (testing P=.017 and referral P<.001). Across models, participants expressing positive attitudes toward the CBI exhibited greater likelihood of engaging in routine HIV testing (P<.01) and referring others to HIV care (P<.01). Transgender individuals indicated greater intent to refer others to the CBI (P<.05). These outcomes were broadly influenced by distal community-level factors including availability of neighborhood HIV support organizations, population composition socioeconomic status, and high HIV prevalence.

CONCLUSIONS

Project LINK demonstrated its potential as a geotargeted CBI by evidencing greater individual intention to engage in HIV testing, care, and personal referrals to its coalition partner organizations. This study highlights important socioecological effects of US-based CBIs to improve HIV testing and initiate acceptable mechanisms for prompt referral to care among a vulnerable population.

Visualization and analytics tools for infectious disease epidemiology: a systematic review

BACKGROUND

A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools.

METHODS

We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review.

RESULTS

A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use.

DISCUSSION AND CONCLUSION

As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload.

Behind the cascade: analyzing spatial patterns along the HIV care continuum

BACKGROUND

Successful HIV treatment as prevention requires individuals to be tested, aware of their status, linked to and retained in care, and virally suppressed. Spatial analysis may be useful for monitoring HIV care by identifying geographic areas with poor outcomes.

METHODS

Retrospective cohort of 1704 people newly diagnosed with HIV identified from Philadelphia's Enhanced HIV/AIDS Reporting System in 2008-2009, with follow-up to 2011. Outcomes of interest were not linked to care, not linked to care within 90 days, not retained in care, and not virally suppressed. Spatial patterns were analyzed using K-functions to identify "hot spots" for targeted intervention. Geographic components were included in regression analyses along with demographic factors to determine their impact on each outcome.

RESULTS

Overall, 1404 persons (82%) linked to care; 75% (1059/1404) linked within 90 days; 37% (526/1059) were retained in care; and 72% (379/526) achieved viral suppression. Fifty-nine census tracts were in hot spots, with no overlap between outcomes. Persons residing in geographic areas identified by the local K-function analyses were more likely to not link to care [adjusted odds ratio 1.76 (95% confidence interval: 1.30 to 2.40)], not link to care within 90 days (1.49, 1.12-1.99), not be retained in care (1.84, 1.39-2.43), and not be virally suppressed (3.23, 1.87-5.59) than persons not residing in the identified areas.

CONCLUSIONS

This study is the first to identify spatial patterns as a strong independent predictor of linkage to care, retention in care, and viral suppression. Spatial analyses are a valuable tool for characterizing the HIV epidemic and treatment cascade.

Mapping disease at an approximated individual level using aggregate data: a case study of mapping New Hampshire birth defects

Background: Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. Method: We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps. Results: We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots. Conclusions: RCMC is an effective method to disaggregate aggregate data. An RCMC-based disease mapping maximizes the use of available spatial information, and explicitly estimates the spatial uncertainty resulting from aggregation.