Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Identifying county-level factors that influence pre-exposure prophylaxis (PrEP) adherence is critical for ending the HIV epidemic in the United States (US). PrEP primary reversal is a term used to describe patients who do not obtain their prescribed medication from the pharmacy. This study sought to identify factors associated with PrEP reversal at the county level in 2018. Data were collected from Symphony Health Analytics, AIDS Vu, the US Census Bureau, and the Centers for Disease Control and Prevention National Prevention Information Network. Bivariate Choropleth maps were created to identify counties with high and low levels of PrEP reversal and HIV incidence. This was followed by bivariate analysis to determine the association between predictor variables and percent PrEP reversal. Finally multivariable logistic regressions were used to assess the association between percent PrEP reversal and variables that were significant from the bivariate analysis. A total of 308 counties were included in this analysis, where the mean number of PrEP prescriptions for counties was 44, with a median of 14 (Interquartile range 7-34). In the multivariable analysis, counties with higher level of unemployment (aOR: 1.10, 95% CI: 1.05-1.16) and rural counties (1.10: 1.04-1.17) had higher odds of PrEP reversal; while counties with higher household crowding (0.97: 0.95-0.99) had lower odds of PrEP reversal. Findings show the need for expanding and implementing programs as well as policies to improve PrEP services that are tailored to local socioeconomic circumstances.
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Source |
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http://dx.doi.org/10.1007/s10461-024-04585-8 | DOI Listing |
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