Malaria mortality characterization and the relationship between malaria mortality and climate in Chimoio, Mozambique.

BACKGROUND: The United Nation's sustainable development goal for 2030 is to eradicate the global malaria epidemic, primarily as the disease continues to be one of the major concerns for public health in sub-Saharan Africa. In 2015, the region accounted for 90% of malaria deaths. Mozambique recorded a malaria mortality rate of 42.75 (per 100,000). In Chimoio, Mozambique's fifth largest city, malaria is the fourth leading cause of death (9.4%). Few data on malaria mortality exists in Mozambique, particularly in relation to Chimoio. The objective of this study was to characterize malaria mortality trends and its spatial distribution in Chimoio. METHODS: Malaria mortality data and climate data were extracted from the Chimoio Civil Registration records, and the Regional Weather station, from 2010 to 2014. The malaria crude mortality rate was calculated. ANOVA, Tukey's, Chi square, and time series were carried out and an intervention analysis ARIMA model developed. RESULTS: A total of 944 malaria death cases were registered in Chimoio, 729 of these among Chimoio residents (77%). The average malaria mortality by gender was 44.9% for females and 55.1% for males. The age of death varied from 0 to 96 years, with an average age of 25.9 (SE = 0.79) years old. January presented the highest average of malaria deaths, and urban areas presented a lower crude malaria mortality rate. Rural neighbourhoods with good accessibility present the highest malaria crude mortality rate, over 85 per 100,000. Seasonal ARMA (2,0)(1,0)12 fitted the data although it was not able to capture malaria mortality peaks occurring during malaria outbreaks. Intervention effect properly fit the mortality peaks and reduced ARMA's root mean square error by almost 25%. CONCLUSION: Malaria mortality is increasing in Chimoio; children between 1 and 4 years old represent 13% of Chimoio population, but account for 25% of malaria mortality. Malaria mortality shows seasonal and spatial characteristics. More studies should be carried out for malaria eradication in the municipality.

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique.

BACKGROUND: Mozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication. METHODS: Time series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model. RESULTS: Between 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1)52, and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately. CONCLUSION: The Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.

Malaria mortality characterization and the relationship between malaria mortality and climate in Chimoio, Mozambique

The United Nation's sustainable development goal for 2030 is to eradicate the global malaria epidemic, primarily as the disease continues to be one of the major concerns for public health in sub-Saharan Africa. In 2015, the region accounted for 90% of malaria deaths. Mozambique recorded a malaria mortality rate of 42.75 (per 100,000). In Chimoio, Mozambique's ffth largest city, malaria is the fourth leading cause of death (9.4%). Few data on malaria mortality exists in Mozambique, particularly in relation to Chimoio. The objective of this study was to characterize malaria mortality trends and its spatial distribution in Chimoio. Malaria mortality data and climate data were extracted from the Chimoio Civil Registration records, and the Regional Weather station, from 2010 to 2014. The malaria crude mortality rate was calculated. ANOVA, Tukey's, Chisquare, and time series were carried out and an intervention analysis ARIMA model developed. A total of 944 malaria death cases were registered in Chimoio, 729 of these among Chimoio residents (77%). The average malaria mortality by gender was 44.9% for females and 55.1% for males. The age of death varied from 0 to 96 years, with an average age of 25.9 (SE = 0.79) years old. January presented the highest average of malaria deaths, and urban areas presented a lower crude malaria mortality rate. Rural neighbourhoods with good accessibility present the highest malaria crude mortality rate, over 85 per 100,000. Seasonal ARMA (2,0)(1,0)12 ftted the data although it was not able to capture malaria mortality peaks occurring during malaria outbreaks. Intervention efect properly ft the mortality peaks and reduced ARMA's root mean square error by almost 25%. Malaria mortality is increasing in Chimoio; children between 1 and 4 years old represent 13% of Chimoio population, but account for 25% of malaria mortality. Malaria mortality shows seasonal and spatial characteristics. More studies should be carried out for malaria eradication in the municipality.

Spatio-temporal variation and socio-demographic characters of malaria in Chimoio municipality, Mozambique.

BACKGROUND: In Africa, urban malaria is a major concern, since the towns and especially their suburbs are growing quickly. In Mozambique, malaria represents 45 % of all cases of outpatient visits and 56 % of inpatient visits at paediatric clinics. Malaria is a major public health burden in Chimoio Mozambique and few studies on malaria exist. METHODS: The study was carried out to establish the spatiality and temporality of malaria and describe socio-demographic characteristics of malaria patients in Chimoio. Weekly malaria data for 9 years (2006-2014) were collected from the district Epidemiological Bulletin and incidence by season, age, gender, and residence was calculated. SPSS version 20 was used for statistical analysis and ArcGis 10.1 was used to produce maps. RESULTS: The annual overall average of malaria incidence was 20.1 % and the attributable fraction (AF) of malaria was 16 %. There were differences in weekly and yearly malaria occurrences throughout the period. There was no difference in malaria cases between male and female patients. Children under 5 years of age are three times more prone to malaria than adults (p < 0.05). Three temporal clusters of malaria were identified: cluster 1, weeks 25-47 with average weekly cases of 618 (sd = 251.9), cluster 2, weeks 18-24 and 48-51 with average weekly cases of 1066 (sd = 317.4). cluster 3, weeks 1-17 and 52 with average weekly cases of 1587 (sd = 722.4). Similarly, three different clusters were identified according to residential areas: cluster 1 (10 %) mostly urban, cluster 2 (22 %) mostly suburbs, cluster 3 (28 %) mostly rural areas. CONCLUSION: Malaria is increasing in the suburbs, and rural areas present more cases of malaria compared to urban areas. This article is an initial step to understand the dynamics of malaria in Chimoio. Results suggest that malaria varies in time and space, and that precision public health strategy should be used to control malaria occurrence. Studies on weather factors affecting malaria cases, bed net usage, and others should be undertaken.