Examining the association of rurality with opioid-related morbidity and mortality in Georgia: A geospatial analysis.

INTRODUCTION: The US opioid epidemic continues to escalate, with overdose deaths being the most-used metric to quantify its burden. There is significant geographic variation in opioid-related outcomes. Rural areas experience unique challenges, yet many studies oversimplify rurality characterizations. Contextual factors, such as area deprivation, are also important to consider when understanding a community's need for treatment services and prevention programming. This study aims to provide a geospatial snapshot of the opioid epidemic in Georgia using several metrics of opioid-related morbidity and mortality and explore differences by rurality across counties. METHODS: This was a spatial ecologic study. Negative binominal regression was used to model the relationship of county rurality with four opioid-related outcomes - overdose mortality, emergency department visits, inpatient hospitalizations, and overdose reversals - adjusting for county-level sex, racial/ethnic, and age distributions. Area Deprivation Index was also included. RESULTS: There was significant geographic variation across the state for all four opioid-related outcomes. Counts remained highest among the metro areas. For rates, counties in the top quartile of rates varied by outcome and were often rural areas. In the final models, rurality designation was largely unrelated to opioid outcomes, with the exception of medium metro areas (inversely related to hospitalizations and overdose reversals) and non-core areas (inversely related to hospitalizations), as compared to large central metro areas. Higher deprivation was significantly related to increased ED visits and hospitalizations, but not overdose mortality and reversals. CONCLUSIONS: When quantifying the burden of the opioid epidemic in a community, it is essential to consider multiple outcomes of morbidity and mortality. Understanding what outcomes are problematic for specific communities, in combination with their demographic and socioeconomic context, can provide insight into gaps in the treatment continuum and potential areas for intervention. Additionally, compared to demographic and socioeconomic factors, rurality may no longer be a salient predictor of the severity of the opioid epidemic in an area.

The relationship between neighborhood typologies and self-rated health in Maryland: A latent class analysis.

Despite widespread evidence that neighborhood conditions impact health, few studies apply theory to clarify the physical and social factors in communities that drive health outcomes. Latent class analysis (LCA) addresses such gaps by identifying distinct neighborhood typologies and the joint influence that neighborhood-level factors play in health promotion. In the current study, we conducted a theory-driven investigation to describe Maryland neighborhood typologies and examined differences in area-level self-rated poor mental and physical health across typologies. We conducted an LCA of Maryland census tracts (n = 1384) using 21 indicators of physical and social characteristics. We estimated differences in tract-level self-rated physical and mental health across neighborhood typologies using global Wald tests and pairwise comparisons. Five neighborhood classes emerged: Suburban Resourced (n = 410, 29.6%), Rural Resourced (n = 313, 22.6%), Urban Underserved (n = 283, 20.4%), Urban Transient (n = 226, 16.3%), Rural Health Shortage (n = 152, 11.0%). Prevalence of self-rated poor physical and mental health varied significantly (p < 0.0001) by neighborhood typology, with the Suburban Resourced neighborhood class demonstrating the lowest prevalence of poor health and the Urban Underserved neighborhoods demonstrating the poorest health. Our results highlight the complexity of defining "healthy" neighborhoods and areas of focus to mitigate community-level health disparities to achieve health equity.

Prenatal subjective social status and birth weight.

PURPOSE: Subjective social status (SSS), perceived social standing relative to others, has been associated with health status, independent of objective socioeconomic status (SES). Few studies have examined the relationship of prenatal maternal SSS with birth outcomes. We evaluated the association of SSS in pregnancy with low birth weight (LBW) and high birth weight (HBW). METHODS: A total of 378 pregnant women rated their SSS from 1 (low) to 10 (high) compared to others in the United States (SSS-US) and compared to their community (SSS-Comm). Multivariable logistic regression was used to examine the relationship between SSS and odds of LBW or HBW. RESULTS: Higher SSS-US was associated with lower odds of HBW in unadjusted models (OR 0.76, 95% CI 0.60-0.96; p < 0.05); this relationship persisted after controlling for objective SES, health, and demographic factors (OR 0.73, 95% CI 0.53-0.99; p < 0.05). Neither SSS measure was associated with LBW. CONCLUSIONS: Pregnant women who view themselves as having lower status than others in the US have greater odds of HBW, over and above the influence of factors known to be associated with birth weight. SSS, a brief and non-stigmatizing measure, might help identify women at elevated social risk for adverse birth outcomes.