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: 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
Background: Since 2011 human leptospirosis incidence in China has remained steadily low with persistent pockets of notifications reported in communities within the Upper Yangtze River Basin (UYRB) and Pearl River Basin (PRB). To help guide health authorities within these residual areas to identify communities where interventions should be targeted, this study quantified the local effect of socioeconomic and environmental factors on the spatial distribution of leptospirosis incidence and developed predictive maps of leptospirosis incidence for UYRB and PRB.
Methods: Data on all human leptospirosis cases reported during 2005-2016 across the UYRB and PRB regions were geolocated at the county-level and included in the analysis. Bayesian conditional autoregressive (CAR) models with zero-inflated Poisson link for leptospirosis incidence were developed after adjustment of environmental and socioeconomic factors such as precipitation, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), land surface temperature (LST), elevation, slope, land cover, crop production, livestock density, gross domestic product and population density.
Results: The relationship of environmental and socioeconomic variables with human leptospirosis incidence varied between both regions. While across UYRB incidence of human leptospirosis was associated with MNDWI and elevation, in PRB human leptospirosis incidence was significantly associated with NDVI, livestock density and land cover. Precipitation was significantly and positively associated with the spatial variation of incidence of leptospirosis in both regions. After accounting for the effect of environmental and socioeconomic factors, the predicted distribution of residual high-incidence county is potentially more widespread both in the UYRB and PRB compared to the observed distribution. In the UYRB, the highest predicted incidence was found along the border of Chongqing and Guizhou towards Sichuan basin and northwest Yunnan. The highest predicted incidence was also identified in counties in the central and lower reaches of the PRB.
Conclusions: This study demonstrated significant geographical heterogeneity in leptospirosis incidence within UYRB and PRB, providing an evidence base for prioritising targeted interventions in counties identified with the highest predicted incidence. Furthermore, environmental drivers of leptospirosis incidence were highly specific to each of the regions, emphasizing the importance of localized control measures. The findings also suggested the need to expand interventional coverage and to support surveillance and diagnostic capacity on the predicted high-risk areas.
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http://dx.doi.org/10.1016/j.scitotenv.2020.138251 | DOI Listing |
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