scholarly journals 4536 Identification of the most salient risk factors of preterm birth in the US using geospatial mapping

2020 ◽  
Vol 4 (s1) ◽  
pp. 31-32
Author(s):  
Alexander J Layden ◽  
Janet Catov
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Health Insurance ◽  
Preterm Birth ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
County Level ◽  
Obesity Prevalence ◽  
The Us ◽  
Geospatial Mapping

OBJECTIVES/GOALS: Preterm birth is the most common birth complication in the United States. To date, there are no effective public health strategies to reduce the burden of prematurity. Using geospatial information system (GIS) mapping, we identified the most salient risk factors of preterm birth across US counties targetable for future interventions. METHODS/STUDY POPULATION: Risk factors of preterm birth were identified from the perinatal health nonprofit organization, March of Dimes, and included factors such as obesity, smoking, insurance coverage and poverty. US 2013 county-level data on sociodemographic characteristics, behavioral risk factors and preterm birth were extracted and combined from the American Census, Center for Disease Control, and US Health Resources and Services Administration. Spatial autocorrelation and multivariate spatial regression were used to determine the risk factors most strongly associated with preterm birth. These models were adjusted for race, given well-documented race disparities for preterm birth. As a case-study comparison, we mapped risk factors in the two states with the highest and lowest proportion of preterm births in 2013. RESULTS/ANTICIPATED RESULTS: In our preliminary analysis, obesity was the factor most strongly associated with preterm birth (ß = 7.32, SE: 1.13, p<0.001) at the US county-level. Surprisingly, smoking was not found to be significantly associated with preterm birth. In 2013, Vermont had the lowest prevalence of preterm birth at 7.6% and Mississippi had the highest prevalence of preterm birth at 13.1%. Health insurance coverage and obesity were the two risk factors that differed between Vermont and Mississippi. The median proportion of uninsured individuals in Mississippi counties was four times higher than that of Vermont counties (26.3% vs 10.9%, p<0.01). Similarly, the median obesity prevalence in Mississippi counties was significantly higher than the median obesity prevalence in Vermont counties (38.8% vs. 25.2%). DISCUSSION/SIGNIFICANCE OF IMPACT: Public health efforts aimed at reducing obesity and increasing health insurance coverage may have the greatest impact at addressing the US burden of preterm birth. Further, geospatial mapping is a powerful analytic tool to identify regions in the US where preterm birth interventions would be most beneficial.

scholarly journals The convergence of racial and income disparities in health insurance coverage in the United States

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
De-Chih Lee ◽  
Hailun Liang ◽  
Leiyu Shi
Keyword(s):  
Health Insurance ◽  
Low Income ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Combined Effect ◽  
Medical Expenditure ◽  
The Us ◽  
Race Ethnicity ◽  
Vulnerability Measure

Abstract Objective This study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US. Data source The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study. Study design Logistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively. Data collection/extraction methods We constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findings While income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health. Conclusion Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

Drivers of potentially avoidable emergency admissions in Ireland: an ecological analysis

2018 ◽  
Vol 28 (6) ◽  
pp. 438-448 ◽  
Author(s):  
Brenda Lynch ◽  
Anthony P Fitzgerald ◽  
Paul Corcoran ◽  
Claire Buckley ◽  
Orla Healy ◽  
...  
Keyword(s):  
Health Insurance ◽  
Health System ◽  
Private Health Insurance ◽  
Insurance Coverage ◽  
Emergency Admission ◽  
Health Insurance Coverage ◽  
County Level ◽  
Emergency Admissions ◽  
Admission Rates ◽  
System Factors

BackgroundMany emergency admissions are deemed to be potentially avoidable in a well-performing health system.ObjectiveTo measure the impact of population and health system factors on county-level variation in potentially avoidable emergency admissions in Ireland over the period 2014–2016.MethodsAdmissions data were used to calculate 2014–2016 age-adjusted emergency admission rates for selected conditions by county of residence. Negative binomial regression was used to identify which a priori factors were significantly associated with emergency admissions for these conditions and whether these factors were also associated with total/other emergency admissions. Standardised incidence rate ratios (IRRs) associated with a 1 SD change in risk factors were reported.ResultsNationally, potentially avoidable emergency admissions for the period 2014–2016 (266 395) accounted for 22% of all emergency admissions. Of the population factors, a 1 SD change in the county-level unemployment rate was associated with a 24% higher rate of potentially avoidable emergency admissions (IRR: 1.24; 95% CI 1.04 to 1.41). Significant health system factors included emergency admissions with length of stay equal to 1 day (IRR: 1.20; 95% CI 1.11 to 1.30) and private health insurance coverage (IRR: 0.92; 95% CI 0.89 to 0.96). The full model accounted for 50% of unexplained variation in potentially avoidable emergency admissions in each county. Similar results were found across total/other emergency admissions.ConclusionThe results suggest potentially avoidable emergency admissions and total/other emergency admissions are primarily driven by socioeconomic conditions, hospital admission policy and private health insurance coverage. The distinction between potentially avoidable and all other emergency admissions may not be as useful as previously believed when attempting to identify the causes of regional variation in emergency admission rates.

scholarly journals Geographic variation in health insurance coverage : United States, 2019

2021 ◽  
Author(s):  
Robin A. Cohen ◽  
◽  
Emily P. Terlizzi ◽  
Amy E. Cha ◽  
Michael E. Martinez ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Health Insurance ◽  
Geographic Variation ◽  
Private Health Insurance ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
Public Health Insurance ◽  
National Estimates ◽  
State Regional

This report presents state, regional, and national estimates of the percentage of persons who were uninsured, had private health insurance coverage, and had public health insurance coverage in 2019.

scholarly journals The Convergence of Racial and Income Disparities in Health Insurance Coverage in the United States

2020 ◽  
Author(s):  
De-Chih Lee ◽  
Hailun Liang ◽  
Leiyu Shi
Keyword(s):  
Health Insurance ◽  
Low Income ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Combined Effect ◽  
Medical Expenditure ◽  
The Us ◽  
Race Ethnicity ◽  
Vulnerability Measure

Abstract ObjectiveThis study applies the vulnerability framework and examines the combined effect of race and income on health insurance coverage in the US. Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage.Data sourcesThe household component of the US Medical Expenditure Panel Survey (MEPS-HC) in 2017 was used for the study.Study designLogistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or partially insured, partially insured only, and uninsured only, respectively.Data collection/extraction methodsWe constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findingsWhile income was a significant predictor of health insurance coverage (a difference of 6.1%-7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 66% less odds of being insured instead of uninsured or partially insured than high-income Whites with good health.ConclusionsPolicymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and are racial/ethnic minorities.

scholarly journals The Public Health Insurance Coverage of Novel Targeted Anticancer Medicines in China—In Favor of Whom? A Retrospective Analysis of the Insurance Claim Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingshuang Li ◽  
Yifan Diao ◽  
Jianchun Ye ◽  
Jing Sun ◽  
Yu Jiang
Keyword(s):  
Public Health ◽  
Lung Cancer ◽  
Health Insurance ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
Claim Data ◽  
Public Health Insurance ◽  
The Public ◽  
Supplementary Health Insurance ◽  
Insurance Claim Data

Objectives: This study took Fuzhou city as a case, described how the public health insurance coverage policy in 2016 of novel anti-lung cancer medicines benefited patients, and who benefited the most from the policy in China.Methods: This was a retrospective study based on health insurance claim data with a longitudinal analysis of the level and trend changes of the monthly number of patients to initiate treatment with the novel targeted anti-lung cancer medicines gefitinib and icotinib before and after health insurance coverage. The study also conducted a multivariate linear regression analysis to predict the potential determinants of the share of patient out-of-pocket (OOP) expenditure for lung cancer treatment with the study medicines.Results: The monthly number of the insured patients in Fuzhou who initiated the treatment with the studied novel targeted anti-lung cancer medication abruptly increased by 26 in the month of the health insurance coverage (95% CI: 14–37, p &lt; 0.01) and kept at an increasing level afterward (p &lt; 0.01). By controlling the other factors, the shares of OOP expenditure for lung cancer treatment of the patients who were formal employee program enrollees not entitled to government-funded supplementary health insurance coverage and resident program enrollees were 18.3% (95% CI: 14.1–22.6) and 26.7% (95% CI: 21.0–32.4) higher than that of the patients who were formal employee program enrollees with government-funded supplementary health insurance coverage.Conclusion: The public health insurance coverage of novel anti-lung cancer medicines benefited patients generally. To enable that patients benefit from this policy more equally and thoroughly, in order to achieve the policy goal of not to leave anyone behind, it is necessary to strengthen the benefits package of the resident program and to optimize the current financing mechanism of the public health insurance system.

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