Using GIS mapping to improve access to prescription medications in rural Minnesota

Area deprivation index as a predictor of survival and treatment disparities in older adults with Acute Myeloid Leukemia: A population-based Study

67-OR: Measures of Social Determinants of Health and Risk of Incident Type 2 Diabetes

Area deprivation index and social vulnerability index in Milwaukee County: Impact on hospital outcomes after traumatic injuries

We suggest using the ADI as the geographically based SDoH index in orthopaedic surgery in the United States. Further, we caution against comparing findings in one study that use one geographically based SDoH index to another study's findings that incorporates another geographically based SDoH index. Although the general findings may be the same, the strength of association and clinical relevance could differ and have policy ramifications that are not otherwise appreciated; however, the degree to which this may be true is an area for future inquiry.

Disparities in Emergency Versus Elective Surgery: Comparing Measures of Neighborhood Social Vulnerability

Delays in hospital presentation limit access to acute stroke treatments. While prior research has focused on patient-level factors, broader ecological and social determinants have not been well studied. We aimed to create a geospatial map of prehospital delay and examine the role of community-level social vulnerability. We studied patients with ischemic stroke who arrived by emergency medical services in 2015 to 2017 from the American Heart Association Get With The Guidelines-Stroke registry. The primary outcome was time to hospital arrival after stroke (in minutes), beginning at last known well in most cases. Using Geographic Information System mapping, we displayed the geography of delay. We then used Cox proportional hazard models to study the relationship between community-level factors and arrival time (adjusted hazard ratios [aHR] <1.0 indicate delay). The primary exposure was the social vulnerability index (SVI), a metric of social vulnerability for every ZIP Code Tabulation Area ranging from 0.0 to 1.0. Of 750 336 patients, 149 145 met inclusion criteria. The mean age was 73 years, and 51% were female. The median time to hospital arrival was 140 minutes (Q1: 60 minutes, Q3: 458 minutes). The geospatial map revealed that many zones of delay overlapped with socially vulnerable areas (https://harvard-cga.maps.arcgis.com/apps/webappviewer/index.html?id=08f6e885c71b457f83cefc71013bcaa7). Cox models (aHR, 95% CI) confirmed that higher SVI, including quartiles 3 (aHR, 0.96 [95% CI, 0.93-0.98]) and 4 (aHR, 0.93 [95% CI, 0.91-0.95]), was associated with delay. Patients from SVI quartile 4 neighborhoods arrived 15.6 minutes [15-16.2] slower than patients from SVI quartile 1. Specific SVI themes associated with delay were a community's socioeconomic status (aHR, 0.80 [95% CI, 0.74-0.85]) and housing type and transportation (aHR, 0.89 [95% CI, 0.84-0.94]). This map of acute stroke presentation times shows areas with a high incidence of delay. Increased social vulnerability characterizes these areas. Such places should be systematically targeted to improve population-level stroke presentation times.

Current respiratory virus surveillance relies primarily on medically attended, symptomatic cases, which may distort the true patterns of respiratory virus infection. Estimating the underlying respiratory viral infection patterns, regardless of the symptomatic status, may help us to better intervene and address issues of racial and ethnic health equity. We tested decedents from the Wayne County Medical Examiner's Office (Wayne & Monroe Counties, Michigan including Detroit) regardless of the cause of death. Nasopharyngeal samples were collected from decedents at the Wayne County Medical Examiner's Office between October 2020 and September 2022 and tested for a panel of respiratory viruses. We identified 3430 decedents with catchment addresses, which we linked to the social vulnerability index (SVI) and area deprivation index (ADI) through US Census tract/block. We evaluated non-linear associations between each of adenovirus (AdV), SARS-CoV-2, parainfluenza virus 2 (PIV2), rhinovirus (RV), and respiratory syncytial virus (RSV) prevalence and ADI, SVI, and SVI subthemes using splines in log-binomial regression models. There were few statistically significant associations observed between overall SVI or ADI and respiratory virus infection prevalence. However, RV was significantly associated with ADI, SVI, and most SVI subthemes. The SVI Minority Status and Language (MSL) subtheme was statistically significantly associated with the prevalence of AdV and RV (p < 0.05) and associations with SARS-CoV-2 and RSV approached significance (p < 0.10). Our results suggest that the MSL subtheme of SVI may be the most informative community-level predictor of respiratory virus infections and could be used to prioritize health-equity-focused distribution of public health resources.

Introduction: Stark disparities exist in the prevalence of Alzheimer’s disease (AD) and related dementias (ADRD) by social determinants of health (SDoH). Limited research is available on associations of SDoH with AD/ADRD biomarkers. This study aims to assess the impact of SDoH measures on ADRD-associated neuroimaging and plasma biomarkers. Hypothesis: SDoH measures of area deprivation index (ADI), social vulnerability index (SVI), and environmental justice index (EJI) will be positively associated with poorer ADRD-associated neuroimaging and plasma biomarkers, and cardiometabolic health will partially account for this association. Methods: This study includes participant data from the Wake Forest Alzheimer’s Disease Research Center from baseline visits. Independent SDoH variables include ADI, SVI, and EJI. Dependent variables include cortical thickness and white matter hyperintensity (WMH) volume, adjusted for intracranial volume, from MRI, and plasma glial fibrillary acidic protein (GFAP) and pTau-181. We conducted bivariate and hierarchical multivariable linear regressions, with demographics (age, sex, education,&amp;cognitive diagnosis) [Model 1], additional adjustment for cardiometabolic health with cardiometabolic index (CMI) [Model 2], and estimated glomerular filtration rate (eGFR) [Model 3; for plasma biomarkers] as covariates. Results: In bivariate analyses, a significant negative association was observed between ADI and cortical thickness (β=-0.08, p=0.047). No other significant associations were observed. In multivariable analyses (Table 1) , EJI was negatively associated with cortical thickness (β=-0.04, p=0.04), and both ADI and SVI were negatively associated with GFAP (β=-0.36, p&lt;0.01&amp;β=-0.52, p&lt;0.01, respectively), in Model 1. Additionally adjusting for CMI in Model 2, the negative associations of ADI and SVI with GFAP were preserved (β=-0.33, p=0.02&amp;β=-0.50, p&lt;0.01, respectively). In Model 3, further adjusting for eGFR, ADI remained negatively associated with GFAP (β=-0.37, p=0.03). Conclusions: Place-based environmental injustice was associated with lower cortical thickness, while neighborhood disadvantage and social vulnerability were associated with higher GFAP levels in adjusted models, suggesting place-based SDoH may be associated with structural brain changes and neuroinflammation. The impact of place-based SDoH needs to be further studied in association with ADRD biomarkers to better understand how we may tackle SDoH among older adults.

The COVID-19 disaster has socio-economic impacts in various regions, including Central Java. COVID-19 has the potential to exacerbate social vulnerability due to the limited internal conditions of individuals to survive. This study tries to analyze social vulnerability based on indicators of individual conditions demographically, socio-economically, and the ability to access service facilities which can be exacerbated by the potential danger of COVID-19. This study aims to analyze the level of social vulnerability and the spatial and temporal distribution of the COVID-19 disaster in Central Java. This study uses a quantitative analysis method by calculating Principle Component Analysis (PCA) and Social Vulnerability Index (SoVI) to identify the level of social vulnerability in Central Java. Analysis of the level of the COVID-19 disaster uses cumulative positive case data which is analyzed spatially and temporally using ArcGIS. This study shows that the higher the SoVI value in a region, the higher the level of individual social vulnerability in that region. The results showed that the highest level of social vulnerability to the COVID-19 disaster in Central Java was the cities of Semarang, Cilacap and Banyumas which were influenced by the conditions of individual vulnerability and the number of positive cases of COVID-19.

The social vulnerability index (SVI) is a place-based index used to stratify community risk. We evaluated the impact of SVI on pregnancy and infant outcomes in patients with surgically treatable congenital anomalies.This is a retrospective study of pregnant patients and infant dyads diagnosed from 2014 to 2022 with congenital anomalies amenable to surgical treatment. Dyads were grouped into SVI quartiles. Primary outcomes were infant morbidity and mortality, and secondary outcomes included prenatal care services, pregnancy course characteristics, and pregnant person co-morbidities. The area deprivation index and child opportunity index were also collected. Bivariate comparisons of patient characteristics and unadjusted odds ratios for death or morbidity stratified by SVI quartile were performed.Two hundred and ninety-five dyads met the inclusion criteria. Ten point two percent had low SVI, 23.7% low-medium, 35.3% medium-high, and 30.9% high. The only prenatal care service associated with SVI quartile was fetal MRI (p = 0.038), but no directional trend was observed. Infant diagnoses included 11.5% congenital diaphragmatic hernia, 27.5% gastroschisis, 18.6% intestinal atresia, 9.2% lower urinary tract obstruction, 20.3% myelomeningocele, 9.2% omphalocele, 0.3% sacrococcygeal teratoma, 8.5% tracheoesophageal fistula. The odds ratio of poor infant outcomes by SVI quartile showed a nonsignificant elevated odds ratio in the highest quartile SVI (low-medium SVI OR: 0.66 [95% CI: 0.14, 2.35], medium-high SVI OR: 0.78 [95% CI: 0.17, 2.63], and high SVI OR: 1.57 [95% CI: 0.32, 6.4]).SVI quartile was not associated with infant outcomes in patients with surgically treatable congenital anomalies. Future studies should examine the impact of SVI or other indices of social vulnerability on perinatal and long-term postnatal outcomes in these high-risk patients. · Healthcare inequities warrant exploration in congenital surgical pathologies.. · Explored SVI quartile association with infant outcomes.. · Primary outcomes were not associated with SVI quartile.. · Nonsignificantly higher odds of poor outcome in patients with high SVI quartile..

The Impact of Socio-Economic Determinants of Health on PedsQL and Pain Outcomes Among Individuals with Sickle Cell Disease

6534 Background: Social determinants of health (SDOH) can predispose underserved communities to poor cancer outcomes. The CDC has created a Social Vulnerability Index (SVI) score for US counties that integrates four SDOH: socioeconomic status, household composition &amp; disability, minority status &amp; language, and housing type &amp; transportation. Scores range from 0 to 1, with higher values signifying more vulnerability. SVI is a significant determinant of overall mortality, but its association with cancer mortality is unclear. This study aimed to investigate if there is a relationship between SVI and cancer mortality. Methods: CDC WONDER (Wide-Ranging Online Data for Epidemiological Research) was used to estimate age-adjusted mortality rates per 100,000 person-years with 95% CIs for adults &gt; 18 years of age from 3,030 (96%) US counties between 2014-2018 for a composite of three cancers (lung, breast, and colon cancer), individual cancer subtypes, and demographic groups (sex, ethnicity/race, urban/rural classification). Age-adjusted mortality rates were compared across SVI quartiles: 1st (least vulnerable) to 4th (most vulnerable). Linear regression was used to identify the association between the 4th vs. 1st SVI quartile and the odds of being above the median mortality rate for composite cancers, individual cancer subtypes, and demographic groups. Results: Overall, age-adjusted composite cancer mortality rate per 100,000 person-years was 122.9 (lung cancer 82.8, breast cancer 38.1, colon cancer 21.9). The largest concentration of most vulnerable US counties and composite cancer mortality was in the southeastern US. Age-adjusted composite cancer mortality rates increased from 1st to 4th SVI quartiles. Counties in the 4th SVI quartile vs. 1st SVI quartile were significantly more likely to be above the median mortality rate for composite cancer (OR 6.46 [95% CI, 5.16 - 8.08]), lung cancer (6.88 [5.46 - 8.66]), breast cancer (2.77 [2.17 - 3.54]), and colon cancer (6.20 [4.82 - 7.97]). Among all races, non-Hispanic Black adults in the 4th SVI quartile vs. 1st SVI quartile were significantly more likely to be above the median mortality rate for composite cancer (OR 9.46 [95% CI, 6.19 - 14.4]), lung cancer (13.8 [7.87 - 24.1]), breast cancer (5.53 [3.16 - 9.68]), and colon cancer (6.34 [3.69 - 10.9]). Moreover, rural counties in the 4th SVI quartile vs. 1st SVI quartile were between 2- to 8-times more likely to be above the median mortality rate for composite cancer and individual cancer subtypes. Conclusions: This study highlights the most socially vulnerable US counties have higher cancer mortality rates than the least vulnerable US counties. Furthermore, non-Hispanic Black adults and rural counties in the most socially vulnerable category have higher cancer mortality rates than those in the least socially vulnerable category. Additional work is needed to understand how SVI can be used for better resource allocation to help mitigate cancer mortality.

Introduction House fires result in devastating injuries that are costly to individuals and their communities. Low socioeconomic communities experience a disproportionate share of house fires. We noticed an increasing trend in admissions related to house fires at our burn center. Applying Geographic information system (GIS) and spatial analysis, we examined the relationship between residential fires and socioeconomic variables included in well-established vulnerability indices, the area deprivation index (ADI) and social vulnerability index (SVI). Methods This was a retrospective cohort study. Addresses of house fires occurring in our state and resulting in admissions to our burn center were obtained from our burn registry. Socioeconomic and built environment datasets were collected from the 2020 Census Bureau data and transformed into quartiles. The residential addresses of fires were geocoded and spatially merged at the census tract level with the collected Census data. Spatial binary logistic regression models were used to estimate the association between social vulnerability factors and multiple serious house fires. Distance to the burn center was included as a covariate. Results The ADI was more closely associated with residential fires than the SVI. Separate logistic regression models for each predictor suggest that most social vulnerability factors included in ADI and SVI were associated with multiple residential fires: family poverty, unemployment, low education, household income, single parent ratio, house values, Black resident ratio, Hispanic resident ratio, monthly gross rent, no vehicle ratio, home ownership, and overcrowded homes. A spatial multivariable logistic regression model showed that the odds of experiencing multiple serious residential fires were higher in the 4th quartiles of the ADI (higher deprivation; OR = 5.9), the Black resident ratio (OR = 4.9), and the mobile home ratio (OR 2.5) compared to the 1st quartiles (p &amp;lt; 0.05). Conclusions Serious house fires are associated with increasing social disparities. Our results suggest that identifying census tracts with higher ADI, Black resident ratios, and mobile home ratios can improve focused multi-stakeholder community interventions in our state. Further studies are warranted to build a more precise model for house fire prevention. Applicability of Research to Practice GIS and spatial analysis can be used to explore the relationship between socioeconomic and physical environment variables and serious housefires. This knowledge could be used to inform a community intervention to decrease these deadly and expensive fires. Funding for the Study Study was partially funded by Injury Prevention Center

The authors utilized the area deprivation index (ADI), a validated composite measure of socioeconomic disadvantage, and the social vulnerability index (SVI) to examine whether differences in neighborhood deprivation impact interventions and outcomes among patients with craniosynostosis. Patients who underwent craniosynostosis repair between 2012 and 2017 were included. The authors collected data about demographic characteristics, comorbidities, follow-up visits, interventions, complications, desire for revision, and speech, developmental, and behavioral outcomes. National percentiles for ADI and SVI were determined using zip and Federal Information Processing Standard (FIPS) codes. ADI and SVI were analyzed by tertile. Firth logistic regressions and Spearman correlations were used to assess associations between ADI/SVI tertile and outcomes/interventions that differed on univariate analysis. Subgroup analysis was performed to examine these associations in patients with nonsyndromic craniosynostosis. Differences in length of follow-up among the nonsyndromic patients in the different deprivation groups were assessed with multivariate Cox regressions. In total, 195 patients were included, with 37% of patients in the most disadvantaged ADI tertile and 20% of patients in the most vulnerable SVI tertile. Patients in more disadvantaged ADI tertiles were less likely to have physician-reported desire (OR 0.17, 95% CI 0.04-0.61, p < 0.01) or parent-reported desire (OR 0.16, 95% CI 0.04-0.52, p < 0.01) for revision, independent of sex and insurance status. In the nonsyndromic subgroup, inclusion in a more disadvantaged ADI tertile was associated with increased odds of speech/language concerns (OR 4.42, 95% CI 1.41-22.62, p < 0.01). There were no differences in interventions received or outcomes among SVI tertiles (p ≥ 0.24). Neither ADI nor SVI tertile was associated with risk of loss to follow-up among nonsyndromic patients (p ≥ 0.38). Patients from the most disadvantaged neighborhoods may be at risk for poor speech outcomes and different standards of assessment for revision. Neighborhood measures of disadvantage represent a valuable tool to improve patient-centered care by allowing for modification of treatment protocols to meet the unique needs of patients and their families.

Background:Anterior cruciate ligament (ACL) reconstruction typically yields excellent outcomes, but recent attention has turned to how social determinants of health affect recovery. This study evaluates the association of the Area Deprivation Index (ADI) and Social Vulnerability Index (SVI) with scores on the Patient-Reported Outcomes Measurement Information System (PROMIS) and the likelihood of patients undergoing ACL reconstruction surgery achieving the minimal clinically important difference (MCID) postoperatively.Hypothesis:Greater social deprivation and vulnerability correlate with worse outcomes and lower odds of achieving the MCID after ACL reconstruction surgery.Study Design:Cohort study; Level of evidence, 3.Methods:The authors retrospectively reviewed patients aged ≥13 years who underwent arthroscopic ACL reconstruction between 2015 and 2023 and completed preoperative and ≥6-month postoperative PROMIS surveys. ADI and SVI scores were based on geocoded addresses and zip codes, respectively, and divided into quartiles based on percentile rank (0-25th, 25th-50th, 50th-75th, and 75th-100th). The least deprived quartile was quartile 1 and the most was quartile 4. Statistical analyses included analysis of variance, chi-square test, and logistic regression to assess associations with PROMIS scores and MCID achievement.Results:A total of 576 patients met inclusion criteria (mean ± SD age, 27.9 ± 11.7 years). ADI and SVI quartiles were evenly distributed. Significant differences were found in race, body mass index, insurance status, and smoking history across quartiles. Patients in the highest ADI/SVI quartiles had higher body mass index, more Black representation, and greater use of public insurance. Preoperative PROMIS scores were mostly similar, except for higher pain interference in the most deprived quartile. Postoperative improvements were significant across all domains after surgery (P < .05). ADI quartile 4 had worse pain interference and depression scores as compared with the less deprived quartiles. MCID achievement was generally high, but logistic regression showed lower odds of achieving the MCID for pain interference in ADI quartile 4 after ACL reconstruction surgery (odds ratio, 0.55; P = .018).Conclusion:Despite socioeconomic disparities, ACL reconstruction surgery led to meaningful improvements in function and mental health outcomes. Although patients in higher ADI quartiles demonstrated worse postoperative pain interference and depression scores, overall MCID achievement was not associated with ADI or SVI, suggesting that ACL reconstruction benefits patients across socioeconomic backgrounds. These findings suggest that social determinants of health may influence the magnitude of postoperative outcomes, but patients of varying socioeconomic backgrounds benefit from ACL reconstruction surgery.