Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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South Africa has one of the highest child mortality and stunting rates in the world. Flexible geoadditive models were used to investigate the geospatial variations in child mortality and stunting in South Africa. We used consecutive rounds of national surveys (2008-2017). The child mortality declined from 31 % to 24 % over time. Lack of medical insurance, black ethnicity, low-socioeconomic conditions, and poor housing conditions were identified as the most significant correlates of child mortality. The model predicted degrees of freedom which was estimated as 19.55 (p < 0.001), provided compelling evidence for sub-geographical level variations in child mortality which ranged from 6 % to 35 % across the country. Population level impact of the distal characteristics on child mortality and stunting exceeded that of other risk factors. Geospatial analysis can help in monitoring trends in child mortality over time and in evaluating the impact of health interventions.
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http://dx.doi.org/10.1016/j.sste.2024.100653 | DOI Listing |