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
The increased prevalence of obesity in many countries in the last decade has resulted in increased morbidity and mortality from hypertension and associated complications. The objective of this work is to analyze the spatial distribution of obesity and hypertension in the state of São Paulo in the period from 2000 to 2010, based on hospital records and admissions from the Hospital Information System of the Unified Health System (HIS - SUS). Coefficients were used for the prevalence of the disease in each municipality averaged out by the empirical Bayesian method, enabling visualization of the spatial pattern of these morbidities in the state. The spatial dependence of these standards was assessed by checking the autocorrelation between the indicators by calculating Moran's Index of Spatial Autocorrelation. Furthermore, the positive correlation (Pearson) between obesity and hypertension was investigated. Data and maps showed clusters of 87 municipalities where there are higher and lower prevalence of hypertension and obesity in the location with marked autocorrelation between neighboring municipalities. The Pearson correlation coefficient found for these municipalities was 0.404 and suggests an association between the morbidities. The spatial analysis techniques proved useful for planning public health actions.
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Source |
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http://dx.doi.org/10.1590/1413-81232014196.15002013 | DOI Listing |
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