Social determinants of health as upstream risk factors for Salmonella and Campylobacter infections in Pennsylvania

2021 ◽  
Author(s):  
Erica E. Smith
Keyword(s):  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Campylobacter Infections

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Vol 28 (1) ◽  
pp. e100439
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
Data Framework ◽  
The Impact ◽  
Machine Learning Models

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities.MethodsWe combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance.ResultsOur results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased.ConclusionIncorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals SOCIAL DETERMINANTS OF HEALTH DRIVE EMERGENCY ROOM AND HOSPITAL USE BY DUAL ELIGIBLE AND DISABLED PENNSYLVANIANS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S964-S964
Author(s):  
Sih-Ting Cai ◽  
Howard Degenholtz ◽  
Hayley Germack
Keyword(s):  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Physical Dependence ◽  
Health Hazards ◽  
Determinants Of Health ◽  
Economic Insecurity ◽  
Potential Health ◽  
Icd 10 ◽  
Dual Eligible

Abstract The study examined correlates and consequences of social determinants of health risk factors (SDoH) among dual eligible aged and disabled individuals; Pennsylvania is transitioning this population into a managed care plan with responsibility for care coordination and incentives to prevent hospitalization and nursing home placement. Medicaid and Medicare claims were used to identify people with SDoH based on ICD-10 codes in 2016 in four domains: economic insecurity, life stressors, physical dependence, and potential health hazards. Of 281,918 people, 38.6% had one or more SDoH. Among people with severe mental illnesses (SMI; schizophrenia, psychosis, major depressive disorder, or bipolar disorder), the prevalence of SDoH was 57.9%. Of people with one or more SDoH, 42% visited the ED, compared to only 32% of people with no SDoH. Economic insecurity (OR 1.68; CI 1.59-1.78), life stressors (OR 1.39; CI 1.29-1.48), physical dependence, (OR 2.01; CI 1.97-2.06), and potential health hazards (OR 1.52; CI 1.47-1.56) were independently associated with risk of hospitalization, controlling for age, gender, race, SMI, chronic conditions and disability. The introduction of diagnosis codes for SDoH under ICD-10 has facilitated identifying individuals with deficits that might increase health care use above and beyond their underlying health status. Although the prevalence of these risk factors as captured in diagnosis data is likely an underestimate, the strong association with subsequent ED use and hospitalization lends credence to these indicators. Medicare and Medicaid claims data can be used to identify people with SDoH and target interventions to prevent downstream health services use.

scholarly journals Childhood obesity in Mexico: social determinants of health and other risk factors

2018 ◽  
pp. bcr-2017-223862
Author(s):  
David Avelar Rodriguez ◽  
Erick Manuel Toro Monjaraz ◽  
Karen Rubi Ignorosa Arellano ◽  
Jaime Ramirez Mayans
Keyword(s):  
Risk Factors ◽  
Childhood Obesity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health

scholarly journals Social determinants of health associated with hemodialysis non-adherence and emergency department utilization: a pilot observational study

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Kamna S. Balhara ◽  
Lori Fisher ◽  
Naya El Hage ◽  
Rosemarie G. Ramos ◽  
Bernard G. Jaar
Keyword(s):  
Risk Factors ◽  
Health Literacy ◽  
Observational Study ◽  
Social Determinants Of Health ◽  
Resource Utilization ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Emergency Department Utilization ◽  
Convenience Sample ◽  
Physical Limitations

Abstract Background Dialysis patients who miss treatments are twice as likely to visit emergency departments (EDs) compared to adherent patients; however, prospective studies assessing ED use after missed treatments are limited. This interdisciplinary pilot study aimed to identify social determinants of health (SDOH) associated with missing hemodialysis (HD) and presenting to the ED, and describe resource utilization associated with such visits. Methods We conducted a prospective observational study with a convenience sample of patients presenting to the ED after missing HD (cases); patients at local dialysis centers identified as HD-compliant by their nephrologists served as matched controls. Patients were interviewed with validated instruments capturing associated risk factors, including SDOH. ED resource utilization by cases was determined by chart review. Chi-square tests and ANOVA were used to detect statistically significant group differences. Results All cases visiting the ED had laboratory and radiographic studies; 40% needed physician-performed procedures. Mean ED length of stay (LOS) for cases was 17 h; 76% of patients were admitted with average LOS of 6 days. Comparing 25 cases and 24 controls, we found no difference in economic stability, educational attainment, health literacy, family support, or satisfaction with nephrology care. However, cases were more dependent on public transport for dialysis (p = 0.03). Despite comparable comorbidity burdens, cases were more likely to have impaired mobility, physical limitations, and higher severity of pain and depression. (p < 0.05). Conclusions ED visits after missed HD resulted in elevated LOS and admission rates. Frequently-cited SDOH such as health literacy did not confer significant risk for missing HD. However, pain, physical limitations, and depression were higher among cases. Community-specific collaborations between EDs and dialysis centers would be valuable in identifying risk factors specific to missed HD and ED use, to develop strategies to improve treatment adherence and reduce unnecessary ED utilization.

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
The United States ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
The Impact ◽  
Machine Learning Models

The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the United States, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities. We combined county-level COVID-19 testing data, COVID-19 vaccination rates, and SDOH information in Tennessee. Between February-May 2021, we trained machine learning models on a semi-monthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance. Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race, and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased. Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policymakers with additional data resources to improve health equity and resilience to future public health emergencies.

Age-standardized prevalence of social determinants of health and associated race-ethnic disparities in stroke survivors

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
T Parekh
Keyword(s):  
Risk Factors ◽  
Food Insecurity ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Stroke Risk ◽  
Healthcare Access ◽  
Determinants Of Health ◽  
Stroke Survivors ◽  
Stroke Risk Factors ◽  
Marginal Effects

Abstract Funding Acknowledgements Type of funding sources: None. Background Stroke is the third leading cause of death in the United States, with evident differences in health outcomes by race and socioeconomic factors. We aim to focus on social determinants of health by race/ethnicity and education level that greatly influences the health-related quality of life in stroke survivors. Method Using the 2017 Behavior Risk Factor Surveillance System (BRFSS) survey data, the direct age-adjusted prevalence was standardized to the 2000 projected US population. Multivariable weighted logistic regression models were post-estimated to calculate marginal effects of age, gender, education, and race on social determinants of health (housing insecurity, food insecurity, healthcare access hardship) at mean values of other predictors for stroke survivors. Models were adjusted for demographics, socioeconomic position, and stroke risk factors. Marginal effects (ME) reported as predicted probabilities. Result Among stroke survivors, nearly 27% reported housing insecurity and healthcare access hardship, and 48% reported food insecurity. The prevalence of housing insecurity was significantly higher among female (31.69%) than male (21.98%) survivors, and of race, highest among Non-Hispanic-Black (37.49%), lower among Non-Hispanic-Whites (23.83%), and lowest among Hispanics (17.20%) stroke survivors. In contrast, food insecurity was highest among Hispanics (63.71%). Healthcare access hardship was similar across the group with a comparatively lower prevalence in Non-Hispanic-White stroke survivors (25.32%). The predicted probability of housing insecurity was significantly higher among young adults compared to older adults aged 65 or above [ME 26.8 (95CI: 14.5-39.1 vs. ME 1.4 (95CI: 0.9-2.0)]. Of race, Black, NH stroke survivors showed a higher probability of housing insecurity [ME 12.4 (95CI: 6.3-18.3)], while the probability of food insecurity [ME 39.3 (95CI: 11.1-67.6)] and healthcare access was higher among other Non-Hispanic groups. The probability of any insecurities was similar among male and female stroke survivors. Stroke survivors with less than high school education showed a significantly higher probability of housing and food insecurity, in addition to healthcare access. Conclusion Social inequalities along with racial disparities in stroke survivors necessitate tailored intervention to reduce the burden of stroke. It is crucial to address socioeconomic factors such as housing, food, and healthcare access that promote the development of stroke risk factors. Abstract Figure.

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