County-level predictors of retention in care status among people living with HIV in South Carolina from 2010 to 2016: a data-driven approach

Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study

Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims to identify the best machine learning model that predicts the RIC transition over time. Extracting from the enhanced HIV/AIDS reporting system, this study included 9765 PLWH from 2005 to 2020 in South Carolina. Transition of RIC was defined as the change of RIC status in each two-year time window. We applied seven classifiers, such as Random Forest, Support Vector Machine, eXtreme Gradient Boosting and Long-short-term memory, for each lagged response to predict the subsequent year's RIC transition. Classification performance was assessed using balanced prediction accuracy, the area under the curve (AUC), recall, precision and F1 scores. The proportion of the four categories of RIC transition was 13.59%, 29.78%, 9.06% and 47.57%, respectively. Support Vector Machine was the best approach for every lag model based on both the F1 score (0.713, 0.717 and 0.719) and AUC (0.920, 0.925 and 0.928). The findings could facilitate the risk augment of PLWH who are prone to follow-up so that clinicians and policymakers could come up with more specific strategies and relocate resources for intervention to keep them sustained in HIV care.

Place Visitation Data Reveals the Geographic and Racial Disparities of COVID-19 Impact on HIV Service Utilization in the Deep South

BACKGROUND

The COVID-19 pandemic has posed unprecedented pressure to health care systems, and interrupted health care delivery and access including HIV care in the United States' Deep South, which endures a double epidemic of HIV and COVID-19. Ryan White programs cover HIV care services for over half of PLWH in the Deep South. Given the important role of Ryan White programs, examining the visitation changes to Ryan White facilities during the pandemic offers insights into the impact of the pandemic on HIV healthcare utilization.

OBJECTIVES

Analyze the geographic distribution of HIV facility visitors at the county level before and during the pandemic in the nine US states of Deep South (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas) to reveal the geographic and racial disparity in visitation disruption caused by the pandemic.

METHODS

We first extracted mobile device-based visitation data for Ryan White HIV facilities in the Deep South during 2019 and 2020. To quantify the disruption in visitations during 2020, we calculated the visitation reduction rate (VRR) for each county, using 2019 data as the baseline. Next, we conducted a spatial analysis of the VRR values to uncover geographical disparities in visitation interruptions. To investigate racial disparities, we performed spatial regression analyses with VRR as the dependent variable, and the percentages of Black, Hispanic, and Asian populations as the independent variables. In this analysis, we controlled for potential confounders.

RESULTS

Geographic disparities in visitation reduction were observed, with all nine Deep South states experiencing significant drops. Georgia experienced the highest visitation loss (VRR = -0.58), followed by Texas (-0.47), Alabama (0.47), and Tennessee (-0.46), while South Carolina had the smallest reductions (-0.11). All the regression models consistently revealed racial disparities in visitation interruption. That is, counties with a higher proportion of Black population tended to have higher RW facility visitation reductions.

CONCLUSIONS

Our analysis revealed distinct geographic disparities in visitation interruptions at Ryan White HIV facilities in the Deep South during the COVID-19 pandemic in 2020. Furthermore, we found that the Black/African American population experienced a greater disruption at the county level in the Deep South during this period.

Residential Segregation and County-Level COVID-19 Booster Coverage in the Deep South: Surveillance Report and Ecological Study

BACKGROUND

COVID-19 had a greater impact in the Deep South compared with other regions in the United States. While vaccination remains a top priority for all eligible individuals, data regarding the progress of booster coverage in the Deep South and how the coverage varies by county and age are sparse. Despite existing evidence of racial and ethnic disparities in COVID-19 vaccinations at the individual level, there is an urgent need for evidence at the population level. Such information could highlight vulnerable communities and guide future health care policy-making and resource allocation.

OBJECTIVE

We aimed to evaluate county-level COVID-19 booster coverage by age group in the Deep South and explore its association with residential segregation.

METHODS

An ecological study was conducted at the population level by integrating COVID-19 vaccine surveillance data, residential segregation index, and county-level factors across the 418 counties of 5 Deep South states from December 15, 2021, to October 19, 2022. We analyzed the cumulative percentages of county-level COVID-19 booster uptake by age group (eg, 12 to 17 years, 18 to 64 years, and at least 65 years) by the end of the study period. The longitudinal relationships were examined between residential segregation, the interaction of time and residential segregation, and COVID-19 booster coverage using the Poisson model.

RESULTS

As of October 19, 2022, among the 418 counties, the median of booster uptake was 40% (IQR 37.8%-43%). Compared with older adults (ie, at least 65 years; median 63.1%, IQR 59.5%-66.5%), youth (ie, 12 to 17 years; median 14.1%, IQR 11.3%-17.4%) and adults (ie, 18 to 64 years; median 33.4%, IQR 30.5%-36.5%) had lower percentages of booster uptake. There was geospatial heterogeneity in the county-level COVID-19 booster coverage. We found that higher segregated counties had lower percentages of booster coverage. Such relationships attenuated as time increased. The findings were consistent across the age groups.

CONCLUSIONS

The progress of county-level COVID-19 booster coverage in the Deep South was slow and varied by age group. Residential segregation precluded the county-level COVID-19 booster coverage across age groups. Future efforts regarding vaccination strategies should focus on youth and adults. Health care facilities and resources are needed in racial and ethnic minority communities.

County-level variations in linkage to care among people newly diagnosed with HIV in South Carolina: A longitudinal analysis from 2010 to 2018

BACKGROUND

Timely linkage to care (LTC) is key in the HIV care continuum, as it enables people newly diagnosed with HIV (PNWH) to benefit from HIV treatment at the earliest stage. Previous studies have found LTC disparities by individual factors, but data are limited beyond the individual level, especially at the county level. This study examined the temporal and geographic variations of county-level LTC status across 46 counties in South Carolina (SC) from 2010 to 2018 and the association of county-level characteristics with LTC status.

METHODS

All adults newly diagnosed with HIV from 2010 to 2018 in SC were included in this study. County-level LTC status was defined as 1 = "high LTC (≥ yearly national LTC percentage)" and 0 = "low LTC (< yearly national LTC percentage)". A generalized estimating equation model with stepwise selection was employed to examine the relationship between 29 county-level characteristics and LTC status.

RESULTS

The number of counties with high LTC in SC decreased from 34 to 21 from 2010 to 2018. In the generalized estimating equation model, six out of 29 factors were significantly associated with LTC status. Counties with a higher percentage of males (OR = 0.07, 95%CI: 0.02~0.29) and persons with at least four years of college (OR = 0.07, 95%CI: 0.02~0.34) were less likely to have high LTC. However, counties with more mental health centers per PNWH (OR = 45.09, 95%CI: 6.81~298.55) were more likely to have high LTC.

CONCLUSIONS

Factors associated with demographic characteristics and healthcare resources contributed to the variations of LTC status at the county level. Interventions targeting increasing the accessibility to mental health facilities could help improve LTC.

Exploring the distribution of risk factors for drop-out from Ponseti treatment for clubfoot across Bangladesh using geospatial cluster analysis

Clubfoot is a congenital anomaly affecting 1/1,000 live births. Ponseti casting is an effective and affordable treatment. About 75% of affected children have access to Ponseti treatment in Bangladesh, but 20% are at risk of drop-out. We aimed to identify the areas in Bangladesh where patients are at high or low risk for drop-out. This study used a cross-sectional design based on publicly available data. The nationwide clubfoot program: 'Walk for Life' identified five risk factors for drop-out from the Ponseti treatment, specific to the Bangladeshi setting: household poverty, household size, population working in agriculture, educational attainment and travel time to the clinic. We explored the spatial distribution and clustering of these five risk factors. The spatial distribution of children <5 years with clubfoot and the population density differ widely across the different sub-districts of Bangladesh. Analysis of risk factor distribution and cluster analysis showed areas at high risk for dropout in the Northeast and the Southwest, with poverty, educational attainment and working in agriculture as the most prevalent driving risk factor. Across the entire country, twenty-one multivariate high-risk clusters were identified. As the risk factors for drop-out from clubfoot care are not equally distributed across Bangladesh, there is a need in regional prioritization and diversification of treatment and enrolment policies. Local stakeholders and policy makers can identify high-risk areas and allocate resources effectively.

Power of Big Data in ending HIV

The articles in this special issue of AIDS focus on the application of the so-called Big Data science (BDS) as applied to a variety of HIV-applied research questions in the sphere of health services and epidemiology. Recent advances in technology means that a critical mass of HIV-related health data with actionable intelligence is available for optimizing health outcomes, improving and informing surveillance. Data science will play a key but complementary role in supporting current efforts in prevention, diagnosis, treatment, and response needed to end the HIV epidemic. This collection provides a glimpse of the promise inherent in leveraging the digital age and improved methods in Big Data science to reimagine HIV treatment and prevention in a digital age.