Environmental factors and population at risk of malaria in Nkomazi municipality, South Africa

Evaluating the Prevalence and Risk Factors of Schistosomiasis Amongst School-Aged Children in Low- and Middle-Income Communities: Ehlanzeni District Municipality, South Africa, 2015-2021

This study aimed to assess the prevalence and identify risk factors of schistosomiasis among school-aged children in low- and middle-income communities. A retrospective cross-sectional study was conducted to review patient records of school-age children. Data on gender, age, sub-district, area residing in, patient status, history of bilharzia, presence of blood in the urine, and schistosomiasis diagnoses were collected. The data were analyzed using IBM Statistical Package for the Social Sciences (SPSS) version 27. Logistic regression was employed to determine the factors associated with schistosomiasis. The overall prevalence of schistosomiasis in the study population was 75%, with higher prevalence observed among male children (89%), children aged between 10 and 14 years (59%), urban areas (51%), and rural-dominated districts, particularly Bushbuckridge (42%) and City of Mbombela (51%). Age, especially 10-14 years old (p ˂ 0.01; 95%CI: 1.98-2.29), a history of bilharzia (p = 0.01; 95%CI: 1.15-1.96), and the presence of blood in urine (p ˂ 0.01; 95%CI: 2.02-2.40) were significantly associated with schistosomiasis while being a female child was found to be a protective factor (AOR: 0.35; CI 0.35-0.41). This study underscores the importance of implementing robust screening procedures and the necessity for health education to mitigate the high prevalence of schistosomiasis and prevent its further spread.

Malaria among under-five children in Ethiopia: a systematic review and meta-analysis

Background

Globally, malaria is among the leading cause of under-five mortality and morbidity. Despite various malaria elimination strategies being implemented in the last decades, malaria remains a major public health concern, particularly in tropical and sub-tropical regions. Furthermore, there have been limited and inconclusive studies in Ethiopia to generate information for action towards malaria in under-five children. Additionally, there is a considerable disparity between the results of the existing studies. Therefore, the pooled estimate from this study will provide a more conclusive result to take evidence-based interventional measures against under-five malaria.

Methods

The protocol of this review is registered at PROSPERO with registration number CRD42020157886. All appropriate databases and grey literature were searched to find relevant articles. Studies reporting the prevalence or risk factors of malaria among under-five children were included. The quality of each study was assessed using the Newcastle–Ottawa Quality Assessment Scale (NOS). Data was extracted using Microsoft Excel 2016 and analysis was done using STATA 16.0 statistical software. The pooled prevalence and its associated factors of malaria were determined using a random effect model. Heterogeneity between studies was assessed using the Cochrane Q-test statistics and I² test. Furthermore, publication bias was checked by the visual inspection of the funnel plot and using Egger’s and Begg’s statistical tests.

Results

Twelve studies with 34,842 under-five children were included. The pooled prevalence of under-five malaria was 22.03% (95% CI 12.25%, 31.80%). Lack of insecticide-treated mosquito net utilization (AOR: 5.67, 95% CI 3.6, 7.74), poor knowledge of child caretakers towards malaria transmission (AOR: 2.79, 95% CI 1.70, 3.89), and living near mosquito breeding sites (AOR: 5.05, 95% CI 2.92, 7.19) were risk factors of under-five malaria.

Conclusion

More than one in five children aged under five years were infected with malaria. This suggests the rate of under-five malaria is far off the 2030 national malaria elimination programme of Ethiopia. The Government should strengthen malaria control strategies such as disseminating insecticide-treated mosquito nets (ITNs), advocating the utilization of ITNs, and raising community awareness regarding malaria transmission.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04370-9.

Assessing the Correlation between Malaria Case Mortality Rates and Access to Health Facilities in the Malaria Region of Vhembe District, South Africa

Background

Local villages in the Vhembe district of South Africa have experienced high malaria infection rates and a high variability of malaria case mortality rates over the past 20 years. This research project sets out to determine if specific socioeconomic factors have influence on the varying malaria case mortality rates.

Methods

The study used existing malaria records of all reported malaria cases in the Vhembe district between 1998 and 2017. The data set was sampled using maximum variation sampling combined with a stratified sampling approach to select the source locations with the highest reported variations in malaria case mortality. The number of medical facilities used, distances to the medical facilities, and proximity to significant water sources were subsequently spatially and statistically analysed for potential correlations between these factors and the malaria case fatality rates of the source locations.

Results

Within the period of study, a total of 57,974 malaria infections were reported from 850 source locations across the villages and neighbourhoods. The result of the sampling methods gave 30 source locations with highest reported variations in malaria case mortality. The statistical analysis indicated a significant negative correlation between the case mortality rates and the number of medical facilities used, the number of infections reported, and the maximum and mean distances travelled to the medical facilities used. In addition, the analysis indicated a positive correlation between the minimum distances travelled to the medical facilities used and the case mortality rates. The spatial analysis supported the majority of the findings from the statistical analysis. Proximity to significant water bodies was not found to have any significant impact on case mortality rates.

Conclusion

The results suggested that malaria patients from larger communities, those who had financial or other means to consult more advanced facilities, or those with a larger variety of services had a significantly lower risk of mortality. The findings of this study could assist societies and authorities in mitigating the negative effects of malaria infections on human life expectancies through improved socioeconomic development.

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.

Impact of an Irrigation Dam on the Transmission and Diversity of Plasmodium falciparum in a Seasonal Malaria Transmission Area of Northern Ghana

Water bodies such as dams are known to alter the local transmission patterns of a number of infectious diseases, especially those transmitted by insects and other arthropod vectors. The impact of an irrigation dam on submicroscopic asexual parasite carriage in individuals living in a seasonal malaria transmission area of northern Ghana was investigated. A total of 288 archived DNA samples from two cross-sectional surveys in two communities in the Bongo District of Northern Ghana were analysed. Parasite density was determined by light microscopy and PCR, and parasite diversity was assessed by genotyping of the polymorphic Plasmodium falciparum msp2 block-3 region. Submicroscopic parasitaemia was estimated as the proportional difference between positive samples identified by PCR and microscopy. Dry season submicroscopic parasite prevalence was significantly higher (71.0%, p=0.013) at the dam site compared with the nondam site (49.2%). Similarly, wet season submicroscopic parasite prevalence was significantly higher at the dam site (54.5%, p=0.008) compared with the nondam site (33.0%). There was no difference in parasite density between sites in the dry season (p=0.90) and in the wet season (p=0.85). Multiplicity of infection (MOI) based on PCR data was significantly higher at the dam site compared with the nondam site during the dry season (p < 0.0001) but similar between sites during the wet season. MOI at the nondam site was significantly higher in the wet season than in the dry season (2.49, 1.26, p < 0.0001) but similar between seasons at the dam site. Multivariate analysis showed higher odds of carrying submicroscopic parasites at the dam site in both dry season (OR = 7.46, 95% CI = 3.07–18.15) and in wet season (OR = 1.73, 95% CI = 1.04–2.86). The study findings suggest that large water bodies impact year-round carriage of submicroscopic parasites and sustain Plasmodium transmission.

Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman's correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer-December, January, and February (DJF)-can be explained by spring-September, October, and November (SON)-rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.

Study of the epidemiological behavior of malaria in the Darien Region, Panama. 2015-2017

BACKGROUND

Malaria is endemic in Darién and an assessment of the different factors affecting its epidemiology is crucial for the development of adequate strategies of surveillance, prevention, and disease control. The objective of this study was to determine the main characteristics of the epidemiological behavior of malaria in the Darien region.

METHODS

This research was comprised of a retrospective analysis to determine the incidence and malaria distribution in the Darien region from 2015 to 2017. We evaluated malaria indicators, disease distribution, incidence (by age group and sex), diagnostic methods, treatment, and control measures. In addition, we examined the cross-border migration activity and its possible contribution to the maintenance and distribution of malaria.

RESULTS

During the period of 2015-2017, we examined 41,141 thick blood smear samples, out of which 501 tested positive for malaria. Plasmodium vivax was responsible for 92.2% of those infections. Males comprised 62.7% of the total diagnosed cases. Meanwhile, a similar percentage, 62.7%, of the total cases were registered in economically active ages. The more frequent symptoms included fever (99.4%) and chills (97.4%), with 53.1% of cases registering between 2,000 and 6,000 parasites/μl of blood. The annual parasitic incidence (API) average was 3.0/1,000 inhabitants, while the slide positivity rate (SPR) was 1.2% and the annual blood examination rate (ABER) 22.5%. In Darién there is a constant internal and cross-border migration movement between Panama and Colombia. Malaria control measures consisted of the active and passive search of suspected cases and of the application of vector control measures.

CONCLUSION

This study provides an additional perspective on malaria epidemiology in Darién. Additional efforts are required to intensify malaria surveillance and to achieve an effective control, eventually moving closer to the objective of malaria elimination. At the same time, there is a need for more eco-epidemiological, entomological and migratory studies to determine how these factors contribute to the patterns of maintenance and dissemination of malaria.

Social determinants of malaria in an endemic area of Indonesia

Background

Malaria is an increasing concern in Indonesia. Socio-demographic factors were found to strongly influence malaria prevalence. This research aimed to explore the associations between socio-demographic factors and malaria prevalence in Indonesia.

Methods

The study used a cross-sectional design and analysed relationships among the explanatory variables of malaria prevalence in five endemic provinces using multivariable logistic regression.

Results

The analysis of baseline socio-demographic data revealed the following independent risk variables related to malaria prevalence: gender, age, occupation, knowledge of the availability of healthcare services, measures taken to protect from mosquito bites, and housing condition of study participants. Multivariable analysis showed that participants who were unaware of the availability of health facilities were 4.2 times more likely to have malaria than those who were aware of the health facilities (adjusted odds ratio = 4.18; 95% CI 1.52–11.45; P = 0.005).

Conclusions

Factors that can be managed and would favour malaria elimination include a range of prevention behaviours at the individual level and using the networks at the community level of primary healthcare centres. This study suggests that improving the availability of a variety of health facilities in endemic areas, information about their services, and access to these is essential.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2760-8) contains supplementary material, which is available to authorized users.

Spatial overlaps in the distribution of HIV/AIDS and malaria in Zimbabwe

BACKGROUND

In most developing economies particularly in Africa, more people are likely to die of HIV/AIDS and malaria compared to other diseases. HIV/AIDS tends to be superimposed on the long standing malaria burden particularly in sub-Saharan Africa. The detection and understanding of spatial overlaps in disease occurrence is important for integrated and targeted disease control. Integrated disease control can enhance efficiency and cost-effectiveness through the development of drugs targeting multiple infections in the same geographic space.

METHODS

Using Zimbabwe as a case study, this study tests the hypothesis that malaria clusters coincide with HIV/AIDS clusters in space. Case data for the two diseases were obtained from the Ministry of Health and Child Care in Zimbabwe at district level via the District Health Information System (DHIS). Kulldorff's spatial scan statistic was used to test for spatial overlaps in clusters of high cases of HIV/AIDS and malaria at district level. The spatial scan test was used to identify areas with higher cases of HIV/AIDS and malaria than would be expected under spatial randomness.

RESULTS

Results of this study indicate that primary clusters of HIV/AIDS and malaria were not spatially coincident in Zimbabwe. While no spatial overlaps were detected between primary clusters of the two diseases, spatial overlaps were detected among statistically significant secondary clusters of HIV/AIDS and malaria. Spatial overlaps between HIV/AIDS and malaria occurred in five districts in the northern and eastern regions of Zimbabwe. In addition, findings of this study indicate that HIV/AIDS is more widespread in Zimbabwe compared to malaria.

CONCLUSIONS

The results of this study may therefore be used as a basis for spatially-targeted control of HIV/AIDS and malaria particularly in high disease burden areas. This is important as previous interventions have targeted the two diseases separately. Thus, targeted control could assist in resource allocation through prioritising areas in greatest need hence maximising the impact of disease control.

Exploring the Influence of Daily Climate Variables on Malaria Transmission and Abundance of Anopheles arabiensis over Nkomazi Local Municipality, Mpumalanga Province, South Africa

The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998-2002, 2004-2006, and 2010-2013 and a noticeable periodicity value of 256-512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.

Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5-20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D²) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a₁ index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.

Micro-spatial distribution of malaria cases and control strategies at ward level in Gwanda district, Matabeleland South, Zimbabwe

Background

Although there has been a decline in the number of malaria cases in Zimbabwe since 2010, the disease remains the biggest public health threat in the country. Gwanda district, located in Matabeleland South Province of Zimbabwe has progressed to the malaria pre-elimination phase. The aim of this study was to determine the spatial distribution of malaria incidence at ward level for improving the planning and implementation of malaria elimination in the district.

Methods

The Poisson purely spatial model was used to detect malaria clusters and their properties, including relative risk and significance levels at ward level. The geographically weighted Poisson regression (GWPR) model was used to explore the potential role and significance of environmental variables [rainfall, minimum and maximum temperature, altitude, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rural/urban] and malaria control strategies [indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs)] on the spatial patterns of malaria incidence at ward level.

Results

Two significant clusters (p < 0.05) of malaria cases were identified: (1) ward 24 south of Gwanda district and (2) ward 9 in the urban municipality, with relative risks of 5.583 and 4.316, respectively. The semiparametric-GWPR model with both local and global variables had higher performance based on AICc (70.882) compared to global regression (74.390) and GWPR which assumed that all variables varied locally (73.364). The semiparametric-GWPR captured the spatially non-stationary relationship between malaria cases and minimum temperature, NDVI, NDWI, and altitude at the ward level. The influence of LLINs, IRS and rural or urban did not vary and remained in the model as global terms. NDWI (positive coefficients) and NDVI (range from negative to positive coefficients) showed significant association with malaria cases in some of the wards. The IRS had a protection effect on malaria incidence as expected.

Conclusions

Malaria incidence is heterogeneous even in low-transmission zones including those in pre-elimination phase. The relationship between malaria cases and NDWI, NDVI, altitude, and minimum temperature may vary at local level. The results of this study can be used in planning and implementation of malaria control strategies at district and ward levels.

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.