Spatial-temporal Analysis Of Malaria And The Factors Associated With Its Infection In Hainan, China

ObsjectivesThe study explored the spatio-temporal characters of malaria and the factors effected the incidence of malaria in Hainan province since1990to2010in order to provide the evidence for the prevention and control policy of malaria in Hainan and other similar districts. It also provided a methodological reference for similar studies by the comprehensive application of time and space methods in the field of malaria research.Results1. The incidence of malaria was decreased in the past few years in Hainan Province. Overall, the highest peak of malaria infection was July and August was the minimum peak. Hainan was February. The southwest region in Hainan is the area of highest incidence of malaria disease. Population distribution,30-50-year-old male resident was reported to be high-risk population. We found that peasants, workers and migrant workers accounting for85.11%of the total number of reports.2. We applied space-time permutation scan statistic to analyze malaria incidence data and found that from2005to2010, there was seven malaria gathering area in Hainan (P<0.05). For malaria incidence data in2010, we found three gathering area (P<0.05). using data from January2005to December2009to establish ARIMA model to predict the incidence of the disease in January2010, the predictive value was0.15/10million,95%confidence interval was [-1.04,1.33]. The actual detection incidence rate of this month was0.17/10million, and the actual monthly incidence value falls within the95%confidence interval of the predicted value, the relative prediction error was11.8%. We re-fitting the model after the actual incidence of malaria in January2010included in the time series model to predict the incidence of malaria from February to December in2010, the actual incidence of malaria was85.75%lower than the theoretical incidence of malaria.3. We obtained the principal component from15temperature indicators and13precipitation indicators by using principal component analysis. The dependent variable of multiple linear regression model was the incidence of malaria in2010, and the outcome variables were the male population, agricultural population, minority ethnic population, GDP, first industry, secondary industry and tertiary industry, GDP per capita, the total number of health institutions, the number of practicing physicians, net income of rural households per capita, net income of urban households per capita, the main component of the temperature, the main component of the rainfall, humidity and other explanatory variables. The risk factors including net income of urban households per capita, the main component of the precipitation and humidity, and the protective factors include the male population, agriculture population, per capita GDP, the number of medical institutions, the number of practicing physicians, net income of rural households per capita.ConclusionThis study focused on the malaria epidemiological characteristics and spatio-temporal aggregation since1990in Hainan Province, and we also studied the main factors associated with different incidents of malaria among different cities and counties in Hainan. The malaria in Hainan was infected with a certain periodicity, in a certain regional, and with a higher incidence in certain populations. Time series model can be good fit to the trend of the incidence of malaria in the time series. It can be used to predict changes in the incidence of malaria under the premise of prevention and control measures, the population of immune status and population movements did not significantly changed. The policy and instruction implemented since2010to eliminate malaria program in Hainan is obvious, the forecast result of time series model in2010also proved this point. In addition, precipitation and humidity will affect the incidence of malaria in different cities and counties. And economic, health and people’s living standards are also important factors associated with the incidence of malaria. The results of this study will help the local relevant departments further strengthen the prevention and control of malaria epidemics.Innovation This study integrated a number of areas of information and knowledge. The integration of time and space for the malaria epidemic data broke the limitations of time and space research. It not only analyzed the data since direct reporting network system developed, but also study the malaria incidence data from1990. In space, our study broke the boundaries of the counties of Hainan Province. We use space scanning scaned the epidemic situation of the whole province to provide new ideas for early warning of malaria disease. The integration of demography, economics, and health knowledge made malaria epidemic and its influencing factors analysis more dimensional, more fit the actual. The analysis method which combined natural factors and social factors in the field of malaria research is rare at home and abroad.