A Geospatial Analysis of Social and Structural Determinants of Health and High HIV Prevalence in Alabama, USA.

OBJECTIVE: This study utilizes geospatial analytic techniques to examine HIV hotspots in Alabama leveraging Medicaid utilization data. METHODS: This cross-sectional study leveraged Medicaid utilization data from Alabama's 67 counties, averaging 9,861 Medicaid recipients aged > 18 years old per county. We used Alabama Medicaid administrative claims data from January 1, 2016, to December 31, 2020, to identify individuals with HIV. Using Microsoft SQL Server, we obtained the average annual count of HIV Medicaid claims in each of the 67 Alabama counties (numerator) and the number of adult Medicaid recipients in each county (denominator), and standardized with a multiplier of 100,000. We also examined several other area-level summary variables (e.g., non-high school completion, income greater than four times the federal poverty level, social associations, urbanicity/rurality) as social and structural determinants of health. County-boundary choropleth maps were created representing the geographic distribution of HIV rates per 100,000 adult Medicaid recipients in Alabama. Leveraging ESRI ArcGIS and local indicators of spatial association (LISA), results were examined using local Moran's I to identify geographic hotspots. RESULTS: Eleven counties had HIV rates higher than 100 per 100,000. Three were hotspots. Being an HIV hotspot was significantly associated with relatively low educational attainment and less severe poverty than other areas in the state. CONCLUSIONS: Findings suggesting that the HIV clusters in Alabama were categorized by significantly less severe poverty and lower educational attainment can aid ongoing efforts to strategically target resources and end the HIV epidemic in U.S.' Deep South.

Medication-assisted treatment and self-help group participation among military veterans with opioid or alcohol use disorder.

INTRODUCTION: Medication-assisted treatment (MAT) is a combination of behavioural therapy and medications to assist with recovery and has been administered to individuals with alcohol and opioid withdrawal symptoms. Military veterans seeking MAT could have barriers preventing them from receiving the care they desire. The present study sought to compare outcomes in individuals who received MAT or those who participated in self-help groups for opioid or alcohol use disorder. In addition, the present study sought to compare outcomes between veterans and non-military-connected individuals. METHODS: We used the 2015-2017 United States Treatment Episode Data Set Discharges data from the Substance Abuse and Mental Health Services Administration. The data set included 138 594 unique discharges. A multinomial logistic regression model was used to examine differences in substance use outcomes for veterans/non-veterans in MAT and a self-help group. RESULTS: Fewer veterans (2.58%) than non-veterans (4.28%) reported usage of MAT. Fewer veterans (38.94%) than non-veterans (40.17%) reported signing up for a self-help group. Finally, those who participated in MAT and a self-help group had a better outcome (66.64%)-defined as no substance use at discharge-than those who only received MAT (43.02%) and those who did not participate in MAT or self-help groups (34.84%). CONCLUSIONS: Recommendations for future research on MAT and implementation for the veteran population would benefit the literature base.

A Fay-Herriot Model for Estimating Subjective Cognitive Decline among Military Veterans.

BACKGROUND: Although studies have examined the geographic distribution of dementia among the general population in order to develop geographically targeted interventions, no studies have examined the geographic distribution of subjective cognitive decline (SCD) among military veterans specifically. OBJECTIVES: To map the geographic distribution of subjective cognitive decline from 2011-2019 in the United States among military veterans. DESIGN: Cross-sectional. SETTING: United States. PARTICIPANTS: Individuals reporting previous service in the United States Armed Forces. MEASUREMENTS: Using 2011 Behavioral Risk Factor Surveillance System (BRFSS) data, which is last year for which geocoded SCD data is publicly available, we estimated the survey-weighted county-level prevalence of veteran SCD for counties with >30 veterans (43 counties in 7 states). We then developed a Fay-Herriot small area estimation linear model using auxiliary data from the Census, with county-level veteran-specific covariates including % >65 years old, % female, % college educated, and median income. Following model validation, we created beta-weighted predictions of veteran SCD for all USA counties for 2011-2019 using relevant time-specific Census auxiliary data. We provide choropleth maps of our predictions. RESULTS: Results of our model on 43 counties showed that county-level rates of SCD were significantly associated with all auxiliary variables except annual income (F = 1.49, df = 4, 38). Direct survey-weighted rates were correlated with model-predicted rates in 43 counties (Pearson r = 0.32). Regarding predicted rates for the entire USA, the average county-level prevalence rate of veteran SCD in 2011 was 13.83% (SD = 7.35), but 29.13% in 2019 (SD = 14.71) - although variation in these rates were evident across counties. CONCLUSIONS: SCD has increased since 2011 among veterans. Veterans Affairs hospitals should implement plans that include cognitive assessments, referral to resources, and monitoring patient progress, especially in rural areas.

Small area estimation of human papillomavirus vaccination coverage among school-age children in Alabama counties.

OBJECTIVES: This study estimated county-level human papillomavirus (HPV) vaccination initiation rates in Alabama and determined whether disparities existed between counties in the Mississippi Delta region (MDR) and Appalachian region (AR). STUDY DESIGN: This study used an observational cross-sectional design. METHODS: We used small area estimation methodology to estimate rates of medical provider-verified HPV vaccine initiation among school-age children in Alabama. Data for the study were retrieved from the 2015 National Immunization Survey (n = 22,205) and the US Census Bureau. RESULTS: The predictive model results showed that older age (odds ratio [OR] = 1.22, 95% confidence interval [CI] = 1.16, 1.29) was positively associated with vaccination initiation and black (OR = 0.79, 95% CI = 0.71, 0.87), white (OR = 0.56, 95% CI = 0.52, 0.60), and 'other' race/ethnicities (OR = 0.78, 95% CI = 0.70, 0.86), compared with Hispanics, and was negatively associated with vaccination initiation. The median (x̃)-modeled HPV vaccination initiation rate for all Alabama counties was 50.83% (interquartile range = 5.00%). Modeled HPV vaccination initiation rates were lowest in AR counties (x̃ = 49.81%), followed by counties not in the AR or MDR (x̃ = 53.26%) and MDR counties (x̃ = 54.90%). CONCLUSIONS: Culturally sensitive school-based HPV vaccine delivery programs are needed for children living in AR counties in Alabama.

Prevalence of chronic obstructive pulmonary disease: county-level risk factors based on the Social Ecological Model.

AIMS: Chronic obstructive pulmonary disease (COPD) was the third leading cause of death among Americans in 2014. Multiple factors, ranging from proximal (i.e. biological characteristics and behaviors) to distal (i.e. environmental characteristics and policies), have been shown to predict COPD outcomes. The Social Ecological Model (SEM), which specifies that five levels of influence (i.e. intrapersonal, interpersonal, institutional, community, and public policy factors) are implicated in the development of ill health, has not been used in epidemiological studies of COPD prevalence. A better understanding of the community-level correlates of COPD prevalence may improve community health practice. METHODS: Using several sources of county-level secondary data ( n = 646), changes in COPD prevalence from 2012 to 2014 were predicted from the five levels of the SEM using ordinary least squares (OLS) hierarchical linear regression. The geographic distribution of county-level changes in COPD prevalence was analyzed with a choropleth map. RESULTS: County-level COPD prevalence increased by 4.76% between 2012 and 2014 overall; however, the greatest increases were observed among counties within the Appalachian region (i.e. counties in West Virginia). Results showed that the SEM, with five levels of influence, was a statistically significant framework for examining changes in county-level COPD prevalence, F = 10.21, p < .001, R2 = 0.28. Statistically significant county-level predictors of changes in COPD prevalence included racial composition, ethnic composition, poverty, altitude, air pollution, and smoking policy. CONCLUSION: COPD prevention may be assisted by the implementation of community-based programs rooted in a multilevel approach.