Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 143
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Objective: To examine geographic variation in preventable hospitalizations among Medicaid/CHIP-enrolled children and to test the association between preventable hospitalizations and a novel measure of racialized economic segregation, which captures residential segregation within ZIP codes based on race and income simultaneously.
Data Sources: We supplement claims and enrollment data from the Transformed Medicaid Statistical Information System (T-MSIS) representing over 12 million Medicaid/CHIP enrollees in 24 states with data from the Public Health Disparities Geocoding Project measuring racialized economic segregation.
Study Design: We measure preventable hospitalizations by ZIP code among children. We use logistic regression to estimate the association between ZIP code-level measures of racialized economic segregation and preventable hospitalizations, controlling for sex, age, rurality, eligibility group, managed care plan type, and state.
Data Extraction Methods: We include children ages 0-17 continuously enrolled in Medicaid/CHIP throughout 2018. We use validated algorithms to identify preventable hospitalizations, which account for characteristics of the pediatric population and exclude children with certain underlying conditions.
Principal Findings: Preventable hospitalizations vary substantially across ZIP codes, and a quarter of ZIP codes have rates exceeding 150 hospitalizations per 100,000 Medicaid-enrolled children per year. Preventable hospitalization rates vary significantly by level of racialized economic segregation: children living in the ZIP codes that have the highest concentration of low-income, non-Hispanic Black residents have adjusted rates of 181 per 100,000 children, compared to 110 per 100,000 for children in ZIP codes that have the highest concentration of high-income, non-Hispanic white residents (p < 0.01). This pattern is driven by asthma-related preventable hospitalizations.
Conclusions: Medicaid-enrolled children's risk of preventable hospitalizations depends on where they live, and children in economically and racially segregated neighborhoods-specifically those with higher concentrations of low-income, non-Hispanic Black residents-are at particularly high risk. It will be important to identify and implement Medicaid/CHIP and other policies that increase access to high-quality preventive care and that address structural drivers of children's health inequities.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154153 | PMC |
http://dx.doi.org/10.1111/1475-6773.14120 | DOI Listing |
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