ABSTRACT
ObjectivesTo analyze the spatial and temporal clustering characteristics and related influencing factors of late diagnosis of HIV/AIDS in Lanzhou， to identify its high-risk areas and time trends in Lanzhou， and to provide a theoretical basis for developing targeted HIV/AIDS prevention and control strategies in Lanzhou. MethodsThe subjects of this study were adult HIV/AIDS cases reported in Lanzhou City between 2011 and 2018. Data used in the study were sourced from the Lanzhou Center for Disease Control and Prevention and the Lanzhou Statistical Yearbook. To analyze the spatial distribution characteristics and influencing factors of the relative risk （RR） of late HIV/AIDS diagnosis， Bayes spatial-temporal model was used. ResultsA total of 1984 new HIV/AIDS cases were reported in Lanzhou from 2011 to 2018， with an mean age of 37.51 years and predominantly male （91.8%）. The number of late diagnosis cases was 982， with an mean age of 39.67 years and a predominance of males （91.8%）. Late diagnosis was more common in older individuals and women with HIV/AIDS. Chengguan District （51.1%）， Anning District （50.3%） and Yuzhong County （51.9%） had an above-average proportion of late diagnosis of HIV/AIDS. The proportion of late diagnosis cases in Lanzhou showed a fluctuating upward trend from 2011 to 2018. The results of Bayes spatial-temporal model showed that the risk of late HIV/AIDS diagnosis in Lanzhou had fluctuated from 2011 to 2015， and then increased rapidly after 2015 ［RR （95% credibility interval， 95%CI） increased from 1.01 （0.84， 1.23） to 1.11 （0.77， 1.97）］； the trends of risk of late diagnosis in Honggu district and three counties were similar to the overall trend in Lanzhou city， while the risk of late diagnosis in Chengguan District and Qilihe District showed a decreasing trend. The regions with the RR for late diagnosis greater than 1 included Yongdeng County （RR=1.07， 95% CI： 0.55， 1.96）， Xigu District （RR=1.04， 95% CI： 0.67， 1.49）， Chengguan District （RR=2.41， 95% CI： 0.85， 6.16）， and Qilihe District （RR=2.03， 95% CI： 1.10， 3.27）. Besides， the heatmap analysis showed that Chengguan District and Qilihe District were the hot spots. The influencing factors analysis showed that the higher GDP per capita （RR=0.65， 95% CI： 0.35， 0.90） and the larger proportion of males with HIV/AIDS cases （RR=0.53， 95% CI： 0.19， 0.92） could lead to the lower the relative risk of late HIV/AIDS diagnosis. However， the higher the population density （RR=1.35， 95% CI： 1.01， 1.81） caused the higher the risk of late diagnosis. ConclusionOur study shows the risk of late diagnosis of HIV/AIDS in Lanzhou was on the rise， and there are significant regional differences. GDP per capita， the proportion of males in HIV/AIDS cases and population density are influencing factors in the late diagnosis of HIV/AIDS. Therefore， for regions with a high risk of late diagnosis or related risk factors， targeted HIV screening and prevention services should be given priority in order to reduce the proportion and risk of late diagnosis of HIV/AIDS.
ABSTRACT
To evaluate the estimation of prevalence ratio (PR) by using bayesian log-binomial regression model and its application,we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software.The results showed that caregivers' recognition of infant's risk signs of diarrhea was associated significantly with a 13％ increase of medical care-seeking.Meanwhile,we compared the differences in PR's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1:not adjusting for the covariates;model 2:adjusting for duration of caregivers' education,model 3:adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model.The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95％CI:1.005-1.265),1.128(95％CI:1.001-1.264)and 1.132(95％CI:1.004-1.267),respectively.Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95％ CI:1.055-1.206) and 1.126(95％ CI:1.051-1.203),respectively,but the model 3 was misconvergence,so COPY method was used to estimate PR,which was 1.125 (95％CI:1.051-1.200).In addition,the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model,but they had a good consistency in estimating PR.Therefore,bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.
ABSTRACT
To evaluate the estimation of prevalence ratio (PR) by using bayesian log-binomial regression model and its application,we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software.The results showed that caregivers' recognition of infant's risk signs of diarrhea was associated significantly with a 13％ increase of medical care-seeking.Meanwhile,we compared the differences in PR's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1:not adjusting for the covariates;model 2:adjusting for duration of caregivers' education,model 3:adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model.The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95％CI:1.005-1.265),1.128(95％CI:1.001-1.264)and 1.132(95％CI:1.004-1.267),respectively.Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95％ CI:1.055-1.206) and 1.126(95％ CI:1.051-1.203),respectively,but the model 3 was misconvergence,so COPY method was used to estimate PR,which was 1.125 (95％CI:1.051-1.200).In addition,the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model,but they had a good consistency in estimating PR.Therefore,bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.
ABSTRACT
Objective To explore the relationship between prevalence of Kashin-Beck disease (KBD) and ecological environment, and to broaden the perspective of KBD etiology. Methods In 37 counties of KBD areas in Gansu Province, information about the ecological environment and implementation situation of control measures (altitude, temperature, rainfall, evapo ration, frost free period, annual sunshine hours, population density, per capita income, the proportion of staple food, returning farmland to forest, to forestry and replant crops) and X-ray detection rate of KBD of 7-12-year-old children in 2012 - 2014 was collected. Using four quantile regression method, the regression model was introduced to analyze the 11 ecological factors which related to the pathogenesis of KBD. The effect of three points on X-ray detection rate of KBD was estimated. Results The X-ray detection rate of KBD was independent of altitude, temperature, evaporation, population density, per capita income and cash crops, and was dependent of rainfall, frost free period, annual sunshine hours, the staple food purchase ratio, and returning farmland to forest and grassland. No matter where in any place numbered, the higher rainfall (measure value:0.003 3 to 0.006 4), the longer frost free period (measure value: 0.029 2 to 0.043 8), the longer annual sunshine hours (measure value:0.001 6 to 0.001 8), and the higher staple food purchase ratio (measure value:0.019 7 to 0.027 6), the higher risk of X-ray detection rate of KBD; the higher returning farmland to forest and to grassland, the lower risk of X-ray detection rate of KBD (measure value: - 0.037 2 to - 0.013 3). Conclusion The X-ray detection rate of KBD is closely related to local ecological environment.