Can the Social Vulnerability Index Be Used for More Than Emergency Preparedness? An Examination Using Youth Physical Fitness Data

Robb, Sara Wagner; Benson, Kelsey M; White, Ayla O.

doi:10.1123/jpah.2015-0042

Cited by 38 publications

(19 citation statements)

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“…The distribution of sickle genotype and gender is similar among age cohorts, although treatment exposure and the proportion of uninsured subjects increase with age (Table ). Approximately 40% of participants live within a census tract defined as high vulnerability using the CDC's Social Vulnerability Index . There were no significant differences in sex, age distribution, sickle genotype, or treatment exposure between subjects who agreed (N = 1,044) and those who declined (N = 30) to participate in SCCRIP.…”

Section: Resultsmentioning

confidence: 97%

Sickle Cell Clinical Research and Intervention Program (SCCRIP): A lifespan cohort study for sickle cell disease progression from the pediatric stage into adulthood

Hankins

Estepp

Hodges

et al. 2018

Pediatric Blood & Cancer

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Section: Resultsmentioning

confidence: 97%

Sickle Cell Clinical Research and Intervention Program (SCCRIP): A lifespan cohort study for sickle cell disease progression from the pediatric stage into adulthood

Hankins

Estepp

Hodges

et al. 2018

Pediatric Blood & Cancer

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“…Thirdly, while the CDC/ATSDR SVI was developed to help public health officials and local planners to better prepare for and respond to emergency events including disease outbreaks and natural disasters ( Flanagan et al, 2011 , 2018 ), our study supports the use of this index to identify locations that may be at higher risk of non-adherence to medication, a key T2DM self-care behaviour. Other studies have also reported that it may be useful for understanding other diabetes self-care behaviours including physical activity and nutrition ( Gay et al, 2016 ; Yu, Woo, Emrich, & Wang, 2020 ). Our research contribution validates a valuable public resource in the context of a significant health issue that has rarely been considered from a social and contextual viewpoint, as opposed to a medical, individual level perspective.…”

Section: Discussionmentioning

confidence: 99%

“…Examples of variables included are poverty, single parent households, English language proficiency and vehicle access. This index has been primarily used for planning for and responding to natural disasters ( Flanagan, Gregory, Hallisey, Heitgerd, & Lewis, 2011 ); however, it also has been recognised as a way to understand social conditions that can impact health behaviours including physical activity ( Gay, Robb, Benson, & White, 2016 ). The 2018 version of the SVI was used for this study, with the specific variable representing the overall percentile ranking of vulnerability across all four themes for each census tract in Ohio.…”

Section: Methodsmentioning

confidence: 99%

Geographic variation in and contextual factors related to biguanide adherence amongst medicaid enrolees with type 2 Diabetes Mellitus

Learnihan

Schroers

Coote

et al. 2022

SSM - Population Health

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Much is known about the adverse impacts on diabetes outcomes of non-adherence to diabetes medication. Less is known about how adherence to diabetes medication varies geographically, and the correspondence of this variation to social and contextual factors. Using pharmacy claims data over a two-year period, this study analysed non-adherence to biguanide medication for N=24,387 adult Medicaid enrolees diagnosed with Type 2 Diabetes Mellitus (T2DM) and residing in Ohio. Spatial analysis was used to detect clusters of census tract level rates of non-adherence, defined as the proportion of patients below the Proportion Days Covered (PDC) threshold of 80%, the level at which patients have a reasonable likelihood of achieving most clinical benefit from their medication. Multilevel models were used to understand associations between medication non-adherence and contextual factors including social vulnerability, urbanicity and distance to utilised pharmacy, with adjustment for individual-level covariates. These findings indicate that contextual factors are associated with medication non-adherence in Medicaid clients with T2DM. They suggest a need for spatially specific, multifaceted intervention programmes that target and/or account for the features of residential settings beyond individual and health system-level factors alone. While “environmental” considerations are often acknowledged, few intervention initiatives are predicated on explicit knowledge of spatially variable influences that can be targeted to enable and support medication adherence.

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“…Moreover, the resulting data were aggregated to the census-tract level. Applications have attempted to extend these aggregate measures to characterizing subpopulations that might need special healthcare services [ 6 ]. Our approach shares the intent to screen populations using survey data and to capitalize on the consensus that social determinants are critical to health.…”

Section: Methodsmentioning

confidence: 99%

A Population-Based Approach to Mapping Vulnerability to Diabetes

Linder

Marko

Tian

et al. 2018

IJERPH

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Of the 382 million people worldwide with diabetes, and if current trends continue, nearly half a billion people worldwide will have diabetes by 2035. Two-thirds of current diabetics are living in urban centers and the urban concentration of individuals with diabetes is on the rise. The problem is that in the absence of widespread clinical testing, there is no reliable way to predict which segments of the population are the most vulnerable to the onset of diabetes. Knowing who the most vulnerable are, and where they live, can guide the efficient allocation of prevention resources. Toward this end, we introduce the concept of composite vulnerability, which includes both group and individual-level attributes, and we provide a demonstration of its application to a large urban setting. The components of composite vulnerability are estimated using a novel, population-based, procedure that relies on sample survey data and nonparametric statistical techniques. First, cluster analysis identified three multivariate profiles of adult residents with type 2 diabetes, based on 35 socioeconomic indicators. Second, the undiagnosed population was screened for vulnerability based on their resemblance or fit to these multivariate profiles. Geographic neighborhoods with high concentrations of “vulnerables” could then be identified. In parallel, recursive partitioning found the best predictors of type 2 diabetes in this urban population, combined them with indicators of disadvantage, and applied them to residents in the selected neighborhoods to establish relative levels of composite vulnerability. Neighborhoods with high concentrations of residents manifesting composite vulnerability can be easily identified for targeting community-based prevention measures.

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Can the Social Vulnerability Index Be Used for More Than Emergency Preparedness? An Examination Using Youth Physical Fitness Data

Abstract: Youth fitness in Georgia was related to socioeconomic, demographic, and geographic themes. The SVI may be a useful needs assessment tool for health officials and researchers examining multilevel influences on health behaviors or identifying communities for prevention efforts.

Cited by 38 publications

References 0 publications

Sickle Cell Clinical Research and Intervention Program (SCCRIP): A lifespan cohort study for sickle cell disease progression from the pediatric stage into adulthood

Sickle Cell Clinical Research and Intervention Program (SCCRIP): A lifespan cohort study for sickle cell disease progression from the pediatric stage into adulthood

Geographic variation in and contextual factors related to biguanide adherence amongst medicaid enrolees with type 2 Diabetes Mellitus

A Population-Based Approach to Mapping Vulnerability to Diabetes

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