ABSTRACT
<p><b>OBJECTIVE</b>To explore the suitable temperature index to establish temperature-mortality model.</p><p><b>METHODS</b>The mortality and meteorological information of Guangzhou between year 2006 and 2010 were collected to explore the association between sendible temperature, heat index and deaths by adopting distributed lag non-linear model to fit the daily maximum, mean and minimum temperature with and without humidity. Q-Q plots based on the standardized residuals of each model were used to qualitatively access the goodness of fitting. The minimum Akaike information criterion (AIC) and residual sum of squares (RSS) value were used to explore the most suitable temperature index for model establishment, and to further analyze the fittest temperature index for different diseases, ages and cold and hot effect.</p><p><b>RESULTS</b>Guangzhou features a subtropical monsoon climate, with an annual average temperature at 22.9°C and daily average relative humidity of 71%. The standardized residuals of all models followed normal distribution. For all death, death from circulation system diseases, the 65-84 years old aging groups and cold effect models, the daily average temperature fit better, whose AIC (RSS) values were the smallest as 11 537 (1897), 9527 (1928), 10 595 (2018) and 11 523 (1899), respectively. However, for death from respiratory system disease, groups aging under 65 years old or over 85 years old and hot effect models, the daily average sendible temperature fit better, whose AIC (RSS) values were the smallest as 8265(1854), 675 (1739), 8550 (1871) and 11 687 (1938), respectively. In comparison with the model controlling both temperature and relative humidity, different diseases, aging groups and cold and hot effect models fitted by sendible temperature index showed smaller AIC (RSS) values. The relative risk (RR) value of the cold effect lagging 0 - 3 days fitting by daily maximal temperature was < 1, and the RR value of it fitting by daily minimum temperature was > 1.04. The RR value of the hot effect lagging 0 - 1 days fitting by daily maximal temperature was < 1.16, and the RR values of it fitting by daily minimum temperature and daily average temperature were > 1.16.</p><p><b>CONCLUSION</b>There were no best temperature indicators for different diseases, ages and cold and hot effect. The model using sendible temperature index better fit the model including relative humidity as a covariable.</p>
Subject(s)
Aged , Aged, 80 and over , Humans , Climate , Mortality , Nonlinear Dynamics , Risk Factors , TemperatureABSTRACT
Objective To study the association between temperature and daily mortality from June 1, 2006 to December 31, 2009 in Guangzhou. Methods Time series approach was used to estimate the impact of temperature on the rates of total and cause-specific daily mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for the long-term time trend, day of week, air pollution and other weather variables. Results A slight sloping U-like relationship between the total mortality and temperature was found, with an optimum average temperature (temperature with lowest mortality risk) value of 19.7 ℃ in Guangzhou. For temperature above the optimum value, the relative risk of total mortality increased by 3.0% (RR=1.030,95%CI: 1.011-1.050) for each increase of degree in Celsius. For average temperature below the optimum value, the relative risk of total mortality and diseases of circulatory system had a 3.3%(RR=0.967,95%CI: 0.936-0.997 ) decrease and a 3.6% ( RR= 0.964,95%CI:0.935-0.994 ) increase,for each degree of Celsius increase, respectively. Conclusion Our findings showed that the temperature had an impact on the daily mortality in Guangzhou. Countermeasures needed to be taken to reduce the temperature related mortality.
ABSTRACT
Objective To understand the prevalence of suicide attempts and its related risk factors in Guangdong province to provide scientific basis information for suicide intervention.Methods Multi-stage stratified random sampling method was used to select the sample.42 sweets or towns were selected from 21 counties or districts through randomly sampling.4 communities were then chosen from every selected town or district,followed by 40 families chosen from every village or community.Questionnaire was used to collect data on suicide attempts and its related risk factors.SPSS 16.0 was used for data analysis.Results 6625 peoples were participated in the study.The 12-month incidence of suicidal attempts was 0.8%.It was higher for females ( 1.1% ) than that for males(0.5%) ,and the 25-35 years(1.6%) age group was among the highest.There was no significant difference between the prevalence rates in urban or rural residents.Results from multivariate analysis showed that factors as:being female (OR=2.1),experienced negative events of life (OR= 15.5),in poor sleeping condition ( OR = 1.6),feeling lonely (OR=1.5 ) and anxiety (OR = 1.8 ) were high risk for suicide attempts.Conclusion Suicide attempts in Guangdong province exhibited a high prevalence with complicated influencing factors.Comprehensive countermeasures are needed to prevent and reduce suicidal behaviors.
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Objective To compare the magnitude of inequities in health-related behaviors among males in Guangdong province, and to investigate the extent of the disparities. Methods Data sets available from the Guangdong Chronic Diseases and Risk Factors Survey 2007 are used.Concentration index (C) and concentration curve are employed to measure the differential of males'health-related behaviors across urban and rural areas in Guangdong. Odds ratios of 6 health-related behaviors among different areas are derived from 4 logistic models, after adjusting for age, married state, educational status, occupation and income. Results Results from Cs reveal that the inequality gradients disadvantageous to men in rural areas are: smoking(C=-0.075, P=0.000), alcohol intake (C=-0.023, P=0.002), blood pressure (C=0.106, P=0.000), blood sugar(C=0.114, P=0.000)and weight (C= 0.107, P= 0.000 ), while lack of physical activity (C= 0.044, P= 0.000) concentrates in the more affluent areas. The magnitudes of these inequalities appear to be higher on health-seeking behaviors than on health-risk behaviors. After adjusting for age and marital status, there is still strong evidence showing the rural-urban differences in the health related behaviors among males in Guangdong province. When educational status, occupation and income are added to the logistic model as control factors, the results have led to a loss of statistical significance on such rural-urban inequalities, indicating that socioeconomic factors play an important role on these health-related behaviors which leads to the inequalities among males in Guangdong province. Conclusion To reduce the gaps in health-related behaviors seen in the rural and urban areas, effective policies should be developed to change the social determinants of rural-urban differences in health and to strengthen the implementation of health-related programs on those vulnerable groups.