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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Child growth failure, as indicated by low height-for-age z-scores (HAZ), is an important metric of health, social inequality, and food insecurity. Understanding the environmental pathways to this outcome can provide insight into how to prevent it. While other studies have examined the environmental determinants of HAZ, there is no agreed upon best-practices approach to measure the environmental context of this outcome. From this literature, we derive a large set of potential environmental predictors and specifications including temperature and precipitation levels, anomalies, and counts as well as vegetation anomalies and trends, which we include using linear, nonlinear, and interactive specifications. We compare these measures and specifications using four rounds of DHS survey data from Burkina Faso and a large set of fixed effects regression models, focusing on exposures from the time of conception through the second year of life and relying on joint hypothesis tests and goodness-of-fit measures to determine which approach best explains HAZ. Our analysis reveals that nonlinear and interactive transformations of climate anomalies, as opposed to climate levels or vegetation indices, provide the best explanation of child growth failure. These results underline the complex and nonlinear pathways through which climate change affects child health and should motivate climate-health researchers to more broadly adopt measures and specifications that capture these pathways.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237046 | PMC |
http://dx.doi.org/10.1007/s11111-023-00414-7 | DOI Listing |
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