scholarly journals Neighborhood Disadvantage Measures and COVID-19 Cases in Boston, 2020

2021 ◽  
pp. 003335492110028
Author(s):  
Margaret E. Samuels-Kalow ◽  
Stephen Dorner ◽  
Rebecca E. Cash ◽  
Sayon Dutta ◽  
Benjamin White ◽  
...  
Keyword(s):  
Health Insurance ◽  
Negative Binomial ◽  
Assistance Program ◽  
Neighborhood Factors ◽  
Policy Makers ◽  
Crude Incidence ◽  
Health Insurance Status ◽  
Nutrition Assistance ◽  
Adjusted Incidence ◽  
Binomial Models

Objective Understanding the pattern of population risk for coronavirus disease 2019 (COVID-19) is critically important for health systems and policy makers. The objective of this study was to describe the association between neighborhood factors and number of COVID-19 cases. We hypothesized an association between disadvantaged neighborhoods and clusters of COVID-19 cases. Methods We analyzed data on patients presenting to a large health care system in Boston during February 5–May 4, 2020. We used a bivariate local join-count procedure to determine colocation between census tracts with high rates of neighborhood demographic characteristics (eg, Hispanic race/ethnicity) and measures of disadvantage (eg, health insurance status) and COVID-19 cases. We used negative binomial models to assess independent associations between neighborhood factors and the incidence of COVID-19. Results A total of 9898 COVID-19 patients were in the cohort. The overall crude incidence in the study area was 32 cases per 10 000 population, and the adjusted incidence per census tract ranged from 2 to 405 per 10 000 population. We found significant colocation of several neighborhood factors and the top quintile of cases: percentage of population that was Hispanic, non-Hispanic Black, without health insurance, receiving Supplemental Nutrition Assistance Program benefits, and living in poverty. Factors associated with increased incidence of COVID-19 included percentage of population that is Hispanic (incidence rate ratio [IRR] = 1.25; 95% CI, 1.23-1.28) and percentage of households living in poverty (IRR = 1.25; 95% CI, 1.19-1.32). Conclusions We found a significant association between neighborhoods with high rates of disadvantage and COVID-19. Policy makers need to consider these health inequities when responding to the pandemic and planning for subsequent health needs.

Health Insurance Status as a Predictor of Mode of Colon Cancer Detection but Not Stage at Diagnosis: Implications for Early Detection

2021 ◽  
pp. 003335492199917
Author(s):  
Lindsey A. Jones ◽  
Katherine C. Brewer ◽  
Leslie R. Carnahan ◽  
Jennifer A. Parsons ◽  
Blase N. Polite ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Health Insurance ◽  
Early Detection ◽  
Late Stage ◽  
Early Stage ◽  
Insurance Status ◽  
Stage At Diagnosis ◽  
Health Insurance Status ◽  
Percentage Point Increase ◽  
Point Increase

Objective For colon cancer patients, one goal of health insurance is to improve access to screening that leads to early detection, early-stage diagnosis, and polyp removal, all of which results in easier treatment and better outcomes. We examined associations among health insurance status, mode of detection (screen detection vs symptomatic presentation), and stage at diagnosis (early vs late) in a diverse sample of patients recently diagnosed with colon cancer from the Chicago metropolitan area. Methods Data came from the Colon Cancer Patterns of Care in Chicago study of racial and socioeconomic disparities in colon cancer screening, diagnosis, and care. We collected data from the medical records of non-Hispanic Black and non-Hispanic White patients aged ≥50 and diagnosed with colon cancer from October 2010 through January 2014 (N = 348). We used logistic regression with marginal standardization to model associations between health insurance status and study outcomes. Results After adjusting for age, race, sex, and socioeconomic status, being continuously insured 5 years before diagnosis and through diagnosis was associated with a 20 (95% CI, 8-33) percentage-point increase in prevalence of screen detection. Screen detection in turn was associated with a 15 (95% CI, 3-27) percentage-point increase in early-stage diagnosis; however, nearly half (47%; n = 54) of the 114 screen-detected patients were still diagnosed at late stage (stage 3 or 4). Health insurance status was not associated with earlier stage at diagnosis. Conclusions For health insurance to effectively shift stage at diagnosis, stronger associations are needed between health insurance and screening-related detection; between screening-related detection and early stage at diagnosis; or both. Findings also highlight the need to better understand factors contributing to late-stage colon cancer diagnosis despite screen detection.

scholarly journals Health Insurance Coverage before and after the Affordable Care Act in the USA

Sci ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 25
Author(s):  
Jesse Patrick ◽  
Philip Q. Yang
Keyword(s):  
Health Insurance ◽  
National Health ◽  
Affordable Care Act ◽  
The United States ◽  
Insurance Status ◽  
Health Insurance Status ◽  
Full Implementation ◽  
Before And After ◽  
The Affordable Care Act ◽  
Partial Implementation

The Affordable Care Act (ACA) is at the crossroads. It is important to evaluate the effectiveness of the ACA in order to make rational decisions about the ongoing healthcare reform, but existing research into its effect on health insurance status in the United States is insufficient and descriptive. Using data from the National Health Interview Surveys from 2009 to 2015, this study examines changes in health insurance status and its determinants before the ACA in 2009, during its partial implementation in 2010–2013, and after its full implementation in 2014 and 2015. The results of trend analysis indicate a significant increase in national health insurance rate from 82.2% in 2009 to 89.4% in 2015. Logistic regression analyses confirm the similar impact of age, gender, race, marital status, nativity, citizenship, education, and poverty on health insurance status before and after the ACA. Despite similar effects across years, controlling for other variables, youth aged 26 or below, the foreign-born, Asians, and other races had a greater probability of gaining health insurance after the ACA than before the ACA; however, the odds of obtaining health insurance for Hispanics and the impoverished rose slightly during the partial implementation of the ACA, but somewhat declined after the full implementation of the ACA starting in 2014. These findings should be taken into account by the U.S. Government in deciding the fate of the ACA.

scholarly journals Association of Health Insurance Status with Outcomes of Sepsis in Adult Patients: A Retrospective Cohort Study

2021 ◽  
Vol 18 (11) ◽  
pp. 5777
Author(s):  
Gaon-Sorae Wang ◽  
Kyoung-Min You ◽  
You-Hwan Jo ◽  
Hui-Jai Lee ◽  
Jong-Hwan Shin ◽  
...  
Keyword(s):  
Health Insurance ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Cohort Analysis ◽  
Adult Patients ◽  
Multivariate Logistic Regression Model ◽  
Insurance Status ◽  
Matched Cohort ◽  
Health Insurance Status ◽  
Significant Difference

(1) Background: Sepsis is a life-threatening disease, and various demographic and socioeconomic factors affect outcomes in sepsis. However, little is known regarding the potential association between health insurance status and outcomes of sepsis in Korea. We evaluated the association of health insurance and clinical outcomes in patients with sepsis. (2) Methods: Prospective cohort data of adult patients with sepsis and septic shock from March 2016 to December 2018 in three hospitals were retrospectively analyzed. We categorized patients into two groups according to their health insurance status: National Health Insurance (NHI) and Medical Aid (MA). The primary end point was in-hospital mortality. The multivariate logistic regression model and propensity score matching were used. (3) Results: Of a total of 2526 eligible patients, 2329 (92.2%) were covered by NHI, and 197 (7.8%) were covered by MA. The MA group had fewer males, more chronic kidney disease, more multiple sources of infection, and more patients with initial lactate > 2 mmol/L. In-hospital, 28-day, and 90-day mortality were not significantly different between the two groups and in-hospital mortality was not different in the subgroup analysis. Furthermore, health insurance status was not independently associated with in-hospital mortality in multivariate analysis and was not associated with survival outcomes in the propensity score-matched cohort. (4) Conclusion: Our propensity score-matched cohort analysis demonstrated that there was no significant difference in in-hospital mortality by health insurance status in patients with sepsis.

scholarly journals Does Supplemental Nutrition Assistance Program Reduce Food Insecurity among Households with Children? Evidence from the Current Population Survey

2021 ◽  
Vol 18 (6) ◽  
pp. 3178
Author(s):  
Jun Zhang ◽  
Yanghao Wang ◽  
Steven T. Yen
Keyword(s):  
Food Security ◽  
Food Insecurity ◽  
Current Population Survey ◽  
Population Survey ◽  
Supplemental Nutrition Assistance Program ◽  
Supplemental Nutrition ◽  
Assistance Program ◽  
Current Population ◽  
Households With Children ◽  
Nutrition Assistance

The Supplemental Nutrition Assistance Program (SNAP) is designed to improve household diet and food security—a pressing problem confronting low-income families in the United States. Previous studies on the issue often ignored the methodological issue of endogenous program participation. We revisit this important issue by estimating a simultaneous equation system with ordinal household food insecurity. Data are drawn from the 2009–2011 Current Population Survey Food Security Supplement (CPS-FSS), restricted to SNAP-eligible households with children. Our results add to the stocks of empirical findings that SNAP participation ameliorates food insecurity among adults only, but increases the probabilities of low and very low food security among children. These contradictory results indicate that our selection approach with a single cross section is only partially successful, and that additional efforts are needed in further analyses of this complicated issue, perhaps with longitudinal data. Socio-demographic variables are found to affect food-secure households and food-insecure households differently, but affect SNAP nonparticipants and participants in the same direction. The state policy tools, such as broad-based categorical eligibility (BBCE) and simplified reporting, can encourage SNAP participation and thus ameliorate food insecurity. Our findings can inform policy deliberations.

scholarly journals The Provision of Assistance Does Not Substantially Impact the Accuracy of 24-Hour Dietary Recalls Completed Using the Automated Self-Administered 24-H Dietary Assessment Tool among Women with Low Incomes

2019 ◽  
Vol 149 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Sharon I Kirkpatrick ◽  
Patricia M Guenther ◽  
Deirdre Douglass ◽  
Thea Zimmerman ◽  
Lisa L Kahle ◽  
...  
Keyword(s):  
Low Income ◽  
Assessment Tool ◽  
Dietary Assessment ◽  
Food Group ◽  
Supplemental Nutrition Assistance Program ◽  
Assistance Program ◽  
Plate Waste ◽  
Low Incomes ◽  
Portion Sizes ◽  
Nutrition Assistance

ABSTRACT Background Evidence is lacking informing the use of the Automated Self-Administered 24-h Dietary Assessment Tool (ASA24) with populations characterized by low income. Objective This study was conducted among women with low incomes to evaluate the accuracy of ASA24 recalls completed independently and with assistance. Methods Three hundred and two women, aged ≥18 y and with incomes below the Supplemental Nutrition Assistance Program thresholds, served themselves from a buffet; amounts taken as well as plate waste were unobtrusively weighed to enable calculation of true intake for 3 meals. The following day, women completed ASA24-2016 independently (n = 148) or with assistance from a trained paraprofessional in a small group (n = 154). Regression modeling examined differences by condition in agreement between true and reported foods; energy, nutrient, and food group intakes; and portion sizes. Results Participants who completed ASA24 independently and those who received assistance reported matches for 71.9% and 73.5% (P = 0.56) of items truly consumed, respectively. Exclusions (consumed but not reported) were highest for lunch (at which participants consumed approximately 2 times the number of distinct foods and beverages compared with breakfast and dinner). Commonly excluded foods were additions to main dishes (e.g., tomatoes in salad). On average, excluded foods contributed 43.6 g (46.2 kcal) and 40.1 g (43.2 kcal) among those in the independent and assisted conditions, respectively. Gaps between true and reported intake were different between conditions for folate and iron. Within conditions, significant gaps were observed for protein, vitamin D, and meat (both conditions); vitamin A, iron, and magnesium (independent); and folate, calcium, and vegetables (assisted). For foods and beverages for which matches were reported, no difference in the gap between true and reported portion sizes was observed by condition (P = 0.22). Conclusions ASA24 performed relatively well among women with low incomes; however, accuracy was somewhat lower than previously observed among adults with a range of incomes. The provision of assistance did not significantly impact accuracy.

scholarly journals Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
Author(s):  
Lee ◽  
Guldmann ◽  
Choi
Keyword(s):  
Negative Binomial ◽  
Age Groups ◽  
Urban Environments ◽  
Vehicle Miles Traveled ◽  
Crash Frequency ◽  
Rural Environments ◽  
Lower Likelihood ◽  
Conditional Autoregressive ◽  
The Relationship ◽  
Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

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