Florida neighborhood analysis of social determinants and their relationship to life expectancy

Abstract

Background Social determinants of health (SDOH) contribute to unequal life expectancy (LE). Only a handful of papers have analyzed these relationships at the neighborhood level as opposed to the county level. This study draws on both the SDOH and social vulnerability literature to identify relevant factors affecting LE. Methods LE was calculated from mortality records for Florida from 2009 to 2013 for 3640 census tracts with reliable estimates. A spatial Durbin error model (SDEM) quantified the direction and magnitude of the factors to LE. The SDEM contains a spatial error term and jointly estimates both local and neighborhood associations. This methodology controls for non-independence between census tracts to provide unbiased statistical estimates. Results Factors significantly related to an increase in LE, include percentage (%) of the population who identify as Hispanic (beta coefficient [β]: 0.06, p-value [P] < 0.001) and % of age dependent populations (% population < 5 years old and % population > 65) (β: 0.13, P < 0.001). Conversely, the following factors exhibited significant negative LE associations, % of households with no automobile (β: -0.05, P < 0.001), % of mobile homes (β: -0.02, P < 0.001), and % of female headed households (β: -0.11, P < 0.001). Conclusions Results from the SDEM demonstrate social vulnerability indicators account for additional geographic LE variability beyond commonly studied SDOH. Empirical findings from this analysis can help local health departments identify drivers of spatial health disparities at the local level.

A Vulnerability Assessment for a Future HIV Outbreak Associated With Injection Drug Use in Illinois, 2017–2018

“…In studies adjusted by race, homelessness and unstable housing has been shown to be associated with sharing used syringes among people who inject drugs, and higher physical health and mental health morbidity and mortality overall (Maness and Khan, 2014 ; Auerswald et al, 2016 ; Rezaei et al, 2020 ). Unstable housing for renting families can result in poorer health outcomes for caregivers and their children, including increased adult depression and childhood hospitalizations, and in one study the percent of mobile homes has been inversely associated with life expectancy at the census tract level (Sandel et al, 2018 ; Melix et al, 2020 ).…”

Spatial variations and social determinants of life expectancy in China, 2005–2020: A population-based spatial panel modelling study

“…Family structure and household composition could have both positive and negative impacts on health. 6 , 8 In this component, we summarized that sex, race/ethnicity, family structure and household composition might influence LE disparities. Since most of individuals in China are Han population, we thus excluded race/ethnicity and defined “population characteristics” as “sex-age population structure and population mortality”.…”

“… 32 , 33 It is a typical feature of spatial data that attributes of adjacent spatial neighbors are correlated, and those correlations violate the assumptions of statistical independence in conventional (nonspatial) statistical methods. 6 Hence, the estimates of conventional methods may be inefficient and biased which might lead to incorrect inference on the relationship between attributes. In contrast, in the consideration of spatial interactions, spatial models explicitly model non-independence by using spatially weighted dependent variable, independent variables and an error term, which account for “spatially embedded social processes” and the flow of people, resources, ideas, goods and services within and between neighboring places.…”

“…2 , 3 , 4 It is not only an index reflects mortality level, but also a comprehensive measure widely used in the evaluation of population health status, quality-of-life and allows for tracking geographically health disparities and assessing factors related to these disparities. 5 , 6 Social determinants of health (SDOH) are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks, and they embody the consequences of multifaceted societal processes and norms that shape living conditions and produce a broad range of health disparities. 7 , 8 Therefore, describing the spatial variations of LE and identifying SDOH of LE would be of great necessity to examine the effects of SDOH on LE to unveil disparate health outcomes and interpret how SDOH influence LE disparities in China.…”

“… 8 , 11 , 12 Besides, in the consideration of spatial autocorrelation which refers to degree to which one province is similar to other nearby provinces in LE and SDOH, some studies have also found that SDOH were not only affected local LE directly but also exerted indirect effects towards LE of nearby areas when incorporated with spatial perspectives. 6 , 13 , 14 In China, Luo and Xie, 15 Wu et al., 16 Wang et al. 17 , 18 and Jiang et al.…”