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

Malaria in under-five children: prevalence and multi-factor analysis of high-risk African countries

BACKGROUND

Malaria remains a significant public health challenge in Sub-Saharan Africa (SSA), particularly affecting under-five (UN5) children. Despite global efforts to control the disease, its prevalence in high-risk African countries continues to be alarming, with records of substantial morbidity and mortality rates. Understanding the association of multiple childhood, maternal, and household factors with malaria prevalence, especially among vulnerable young populations, is crucial for effective intervention strategies.

OBJECTIVE

This study examines the prevalence of malaria among UN5 children in selected high-risk SSA countries and analyzes its association with various childhood, maternal, and household factors.

METHODS

Data from the Malaria Indicator Surveys (MIS) spanning from 2010 to 2023 were analyzed. A weighted sample of 35,624 UN5 children from seven countries in sub-Saharan Africa (SSA) known for high malaria prevalence was considered in the analyses. Descriptive statistics and modified Poisson regression analysis were used to assess the association of multiple factors with malaria prevalence. Stata version 15 software was used in analyzing the data and statistical significance was set at a 5% significance level.

RESULTS

The overall pooled prevalence of malaria among the studied population was 26.2%, with substantial country-specific variations observed. In terms of child factors, a child's age was significantly associated with malaria prevalence (APR = 1.010, 95% CI: 1.007-1.012). Children of mothers with higher education levels (APR for higher education = 0.586, 95% CI: 0.425-0.806) and Fansidar uptake during pregnancy (APR = 0.731, 95% CI: 0.666-0.802) were associated with lower malaria risk. Children from middle-wealth (APR = 0.783, 95% CI: 0.706-0.869) and rich (APR = 0.499, 95% CI: 0.426-0.584) households had considerably lower malaria prevalence compared to those from poor households. Additionally, rural residency was associated with a higher risk of malaria compared to urban residency (APR = 1.545, 95% CI: 1.255-1.903).

CONCLUSION

The study highlights a notable malaria prevalence among under-five (UN5) children in high-risk SSA countries, influenced significantly by factors such as maternal education, Fansidar uptake during pregnancy, socioeconomic status, and residency. These findings underscore the importance of targeted malaria prevention strategies that address these key determinants to effectively reduce the malaria burden in this vulnerable population.

Modelling sociodemographic factors that affect malaria prevalence in Sussundenga, Mozambique: a cross-sectional study

Background: Malaria is still one of the leading causes of mortality and morbidity in Mozambique with little progress in malaria control over the past 20 years. Sussundenga is one of most affected areas. Malaria transmission has a strong association with environmental and sociodemographic factors. The knowledge of sociodemographic factors that affects malaria, may be used to improve the strategic planning for its control. Currently such studies have not been performed in Sussundenga. Thus, the objective of this study is to model the relationship between malaria and sociodemographic factors in Sussundenga, Mozambique.

Methods: Houses in the study area were digitalized and enumerated using Google Earth Pro version 7.3. In this study 100 houses were randomly selected to conduct a community survey of Plasmodiumfalciparum parasite prevalence using rapid diagnostic test (RDT). During the survey, a questionnaire was conducted to assess the sociodemographic factors of the participants. Descriptive statistics were analyzed and backward stepwise logistic regression was performed establishing a relationship between positive cases and the factors. The analysis was carried out using SPSS version 20 package.

Results: The overall P. falciparum prevalence was 31.6%. Half of the malaria positive cases occurred in age group 5 to 14 years. Previous malaria treatment, population density and age group were significant predictors for the model. The model explained 13.5% of the variance in malaria positive cases and sensitivity of the final model was 73.3%.

Conclusion: In this area the highest burden of P. falciparum infection was among those aged 5–14 years old. Malaria infection was related to sociodemographic factors. Targeting malaria control at community level can combat the disease more effectively than waiting for cases at health centers. These finding can be used to guide more effective interventions in this region.

Multilevel modelling of the risk of malaria among children aged under five years in Nigeria

BACKGROUND

Malaria is still a major cause of morbidity and mortality among children aged <5 y (U5s). This study assessed individual, household and community risk factors for malaria in Nigerian U5s.

METHODS

Data from the Nigerian Malaria Health Indicator Survey 2015 were pooled for analyses. This comprised a national survey of 329 clusters. Children aged 6-59 mo who were tested for malaria using microscopy were retained. Multilevel logit model accounting for sampling design was used to assess individual, household and community factors associated with malaria parasitaemia.

RESULTS

A total of 5742 children were assessed for malaria parasitaemia with an overall prevalence of 27% (95% CI 26 to 28%). Plasmodium falciparum constituted 98% of the Plasmodium species. There was no significant difference in parasitaemia between older children and those aged ≤12 mo. In adjusted analyses, rural living, northwest region, a household size of >7, dependence on river and rainwater as primary water source were associated with higher odds of parasitaemia, while higher wealth index, all U5s who slept under a bed net and dependence on packaged water were associated with lower odds of parasitaemia.

CONCLUSION

Despite sustained investment in malaria control and prevention, a quarter of the overall study population of U5s have malaria. Across the six geopolitical zones, the highest burden was in children living in the poorest rural households.

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Malaria is one of the leading causes of morbidity and mortality in Mozambique, which has the fifth highest prevalence in the world. Sussundenga District in Manica Province has documented high P. falciparum incidence at the local rural health center (RHC). This study's objective was to analyze the P. falciparum temporal variation and model its pattern in Sussundenga District, Mozambique. Data from weekly epidemiological bulletins (BES) was collected from 2015 to 2019 and a time-series analysis was applied. For temporal modeling, a Box-Jenkins method was used with an autoregressive integrated moving average (ARIMA). Over the study period, 372,498 cases of P. falciparum were recorded in Sussundenga. There were weekly and yearly variations in incidence overall (p < 0.001). Children under five years had decreased malaria tendency, while patients over five years had an increased tendency. The ARIMA (2,2,1) (1,1,1) 52 model presented the least Root Mean Square being the most appropriate for forecasting. The goodness of fit was 68.15% for malaria patients less than five years old and 73.2% for malaria patients over five years old. The findings indicate that cases are decreasing among individuals less than five years and are increasing slightly in those older than five years. The P. falciparum case occurrence has a weekly temporal pattern peaking during the wet season. Based on the spatial and temporal distribution using ARIMA modelling, more efficient strategies that target this seasonality can be implemented to reduce the overall malaria burden in both Sussundenga District and regionally.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.

A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations

BACKGROUND

The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.

METHODS

We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression.

RESULTS

Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = - 0.989; 95% CI - 0.998, - 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations.

CONCLUSIONS

In many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions.

The use of spatial and genetic tools to assess Plasmodium falciparum transmission in Lusaka, Zambia between 2011 and 2015

BACKGROUND

Zambia has set itself the ambitious target of eliminating malaria by 2021. To continue tracking transmission to zero, new interventions, tools and approaches are required.

METHODS

Urban reactive case detection (RCD) was performed in Lusaka city from 2011 to 2015 to better understand the location and drivers of malaria transmission. Briefly, index cases were followed to their home and all consenting individuals living in the index house and nine proximal houses were tested with a malaria rapid diagnostic test and treated if positive. A brief survey was performed and for certain responses, a dried blood spot sample collected for genetic analysis. Aggregate health facility data, individual RCD response data and genetic results were analysed spatially and against environmental correlates.

RESULTS

Total number of malaria cases remained relatively constant, while the average age of incident cases and the proportion of incident cases reporting recent travel both increased. The estimated R₀ in Lusaka was < 1 throughout the study period. RCD responses performed within 250 m of uninhabited/vacant land were associated with a higher probability of identifying additional infections.

CONCLUSIONS

Evidence suggests that the majority of malaria infections are imported from outside Lusaka. However there remains some level of local transmission occurring on the periphery of urban settlements, namely in the wet season. Unfortunately, due to the higher-than-expected complexity of infections and the small number of samples tested, genetic analysis was unable to identify any meaningful trends in the data.

Trends of Plasmodium falciparum prevalence in two communities of Muheza district North-eastern Tanzania: correlation between parasite prevalence, malaria interventions and rainfall in the context of re-emergence of malaria after two decades of progressively declining transmission

Background

Although the recent decline of malaria burden in some African countries has been attributed to a scale-up of interventions, such as bed nets (insecticide-treated bed nets, ITNs/long-lasting insecticidal nets, LLINs), the contribution of other factors to these changes has not been rigorously assessed. This study assessed the trends of Plasmodium falciparum prevalence in Magoda (1992–2017) and Mpapayu (1998–2017) villages of Muheza district, North-eastern Tanzania, in relation to changes in the levels of different interventions and rainfall patterns.

Methods

Individuals aged 0–19 years were recruited in cross-sectional surveys to determine the prevalence of P. falciparum infections in relation to different malaria interventions deployed, particularly bed nets and anti-malarial drugs. Trends and patterns of rainfall in Muheza for 35 years (from 1981 to 2016) were assessed to determine changes in the amount and pattern of rainfall and their possible impacts on P. falciparum prevalence besides of those ascribed to interventions.

Results

High prevalence (84–54%) was reported between 1992 and 2000 in Magoda, and 1998 and 2000 in Mpapayu, but it declined sharply from 2001 to 2004 (from 52.0 to 25.0%), followed by a progressive decline between 2008 and 2012 (to ≤ 7% in both villages). However, the prevalence increased significantly from 2013 to 2016 reaching ≥ 20.0% in 2016 (both villages), but declined in the two villages to ≤ 13% in 2017. Overall and age specific P. falciparum prevalence decreased in both villages over the years but with a peak prevalence shifting from children aged 5–9 years to those aged 10–19 years from 2008 onwards. Bed net coverage increased from < 4% in 1998 to > 98% in 2001 and was ≥ 85.0% in 2004 in both villages; followed by fluctuations with coverage ranging from 35.0 to ≤ 98% between 2008 and 2017. The 12-month weighted anomaly standardized precipitation index showed a marked rainfall deficit in 1990–1996 and 1999–2010 coinciding with declining prevalence and despite relatively high bed net coverage from 2000. From 1992, the risk of infection decreased steadily up to 2013 when the lowest risk was observed (RR = 0.07; 95% CI 0.06–0.08, P < 0.001), but it was significantly higher during periods with positive rainfall anomalies (RR = 2.79; 95% CI 2.23–3.50, P < 0.001). The risk was lower among individuals not owning bed nets compared to those with nets (RR = 1.35; 95% CI 1.22–1.49, P < 0.001).

Conclusions

A decline in prevalence up to 2012 and resurgence thereafter was likely associated with changes in monthly rainfall, offset against changing malaria interventions. A sustained surveillance covering multiple factors needs to be undertaken and climate must be taken into consideration when relating control interventions to malaria prevalence.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2395-1) contains supplementary material, which is available to authorized users.

Mapping and Modelling Malaria Risk Areas Using Climate, Socio-Demographic and Clinical Variables in Chimoio, Mozambique

Background: Malaria continues to be a major public health concern in Africa. Approximately 3.2 billion people worldwide are still at risk of contracting malaria, and 80% of deaths caused by malaria are concentrated in only 15 countries, most of which are in Africa. These high-burden countries have achieved a lower than average reduction of malaria incidence and mortality, and Mozambique is among these countries. Malaria eradication is therefore one of Mozambique’s main priorities. Few studies on malaria have been carried out in Chimoio, and there is no malaria map risk of the area. This map is important to identify areas at risk for application of Public Precision Health approaches. By using GIS-based spatial modelling techniques, the research goal of this article was to map and model malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Methods: A 30 m × 30 m Landsat image, ArcGIS 10.2 and BioclimData were used. A conceptual model for spatial problems was used to create the final risk map. The risks factors used were: the mean temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, NDVI, land use and land cover, malaria prevalence and population density. Layers were created in a raster dataset. For class value comparisons between layers, numeric values were assigned to classes within each map layer, giving them the same importance. The input dataset were ranked, with different weights according to their suitability. The reclassified outputs of the data were combined. Results: Chimoio presented 96% moderate risk and 4% high-risk areas. The map showed that the central and south-west “Residential areas”, namely, Centro Hipico, Trangapsso, Bairro 5 and 1° de Maio, had a high risk of malaria, while the rest of the residential areas had a moderate risk. Conclusions: The entire Chimoio population is at risk of contracting malaria, and the precise estimation of malaria risk, therefore, has important precision public health implications and for the planning of effective control measures, such as the proper time and place to spray to combat vectors, distribution of bed nets and other control measures.

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R² = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-017-2205-6) contains supplementary material, which is available to authorized users.

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.

Assessment of the utility of a symptom-based algorithm for identifying febrile patients for malaria diagnostic testing in Senegal

BACKGROUND

Malaria rapid diagnostic tests (RDTs) enable point-of-care testing to be nearly as sensitive and specific as reference microscopy. The Senegal National Malaria Control Programme introduced RDTs in 2007, along with a case management algorithm for uncomplicated febrile illness, in which the first step stipulates that if a febrile patient of any age has symptoms indicative of febrile illness other than malaria (e.g., cough or rash), they would not be tested for malaria, but treated for the apparent illness and receive an RDT for malaria only if they returned in 48 h without improvement.

METHODS

A year-long study in 16 health posts was conducted to determine the algorithm's capacity to identify patients with Plasmodium falciparum infection identifiable by RDT. Health post personnel enrolled patients of all ages with fever (≥37.5 °C) or history of fever in the previous 2 days. After clinical assessment, a nurse staffing the health post determined whether a patient should receive an RDT according to the diagnostic algorithm, but performed an RDT for all enrolled patients.

RESULTS

Over 1 year, 6039 patients were enrolled and 58% (3483) were determined to require an RDT according to the algorithm. Overall, 23% (1373/6039) had a positive RDT, 34% (1130/3376) during rainy season and 9% (243/2661) during dry season. The first step of the algorithm identified only 78% of patients with a positive RDT, varying by transmission season (rainy 80%, dry 70%), malaria transmission zone (high 75%, low 95%), and age group (under 5 years 68%, 5 years and older 84%).

CONCLUSIONS

In all but the lowest malaria transmission zone, use of the algorithm excludes an unacceptably large proportion of patients with malaria from receiving an RDT at their first visit, denying them timely diagnosis and treatment. While the algorithm was adopted within a context of malaria control and scarce resources, with the goal of treating patients with symptomatic malaria, Senegal has now adopted a policy of universal diagnosis of patients with fever or history of fever. In addition, in the current context of malaria elimination, the paradigm of case management needs to shift towards the identification and treatment of all patients with malaria infection.