Bayesian spatio-temporal modeling of mortality in relation to malaria incidence in Western Kenya

Plasmodium falciparum msp-1 and msp-2 genetic diversity and multiplicity of infection in isolates from Congolese patients in the Republic of Congo

With limited up to date data from the Republic of Congo, the aim of this study was to investigate allelic polymorphism of merozoite surface protein-1 (msp-1) and merozoite surface protein-2 (msp-2). This will help assess the genetic diversity and multiplicity of Plasmodium falciparum infection (MOI), from uncomplicated malaria individuals living in Brazzaville. Between March and October 2021, a cross-sectional study was carried out at a health center in Madibou District located in the south of Brazzaville. Plasmodium infection was diagnosed in human blood by microscopy and the block 2 of P. falciparum msp-1 and block 3 of msp-2 genes were genotyped by nested PCR. Overall, 57 genotypes with fragment sizes ranging from 110 to 410 bp were recorded for msp-1, among which 25, 21, and 11 genotypes identified for K1, MAD20, and RO33 allelic families respectively. RO33 (34.3%) and MAD20 (34.3%) allelic families were more frequent compared to K1 (31.4%) although the difference was not statistically significant. Also, 47 msp-2 genotypes were identified, including 26 FC27 genotypes type, and 21 genotypes belonging to the 3D7 allelic family. FC27 was more frequent (52.3%) compared to 3D7 (47.7%). The prevalence of the polyclonal infection was 90.0% while the MOI was 2.90 ± 1.0. The MOI and polyclonal infection were not significantly associated with the parasitaemia and anaemia. This study reveals a high genetic diversity and the trend of increasing MOI of P. falciparum isolates from the south of Brazzaville, compared to the reports from the same setting before the COVID-19 pandemic.

Public health determinants of child malaria mortality: a surveillance study within Siaya County, Western Kenya

BACKGROUND

Malaria deaths among children have been declining worldwide during the last two decades. Despite preventive, epidemiologic and therapy-development work, mortality rate decline has stagnated in western Kenya resulting in persistently high child malaria morbidity and mortality. The aim of this study was to identify public health determinants influencing the high burden of malaria deaths among children in this region.

METHODS

A total of 221,929 children, 111,488 females and 110,441 males, under the age of 5 years were enrolled in the Kenya Medical Research Institute/Center for Disease Control Health and Demographic Surveillance System (KEMRI/CDC HDSS) study area in Siaya County during the period 2003-2013. Cause of death was determined by use of verbal autopsy. Age-specific mortality rates were computed, and cox proportional hazard regression was used to model time to malaria death controlling for the socio-demographic factors. A variety of demographic, social and epidemiologic factors were examined.

RESULTS

In total 8,696 (3.9%) children died during the study period. Malaria was the most prevalent cause of death and constituted 33.2% of all causes of death, followed by acute respiratory infections (26.7%) and HIV/AIDS related deaths (18.6%). There was a marked decrease in overall mortality rate from 2003 to 2013, except for a spike in the rates in 2008. The hazard of death differed between age groups with the youngest having the highest hazard of death HR 6.07 (95% CI 5.10-7.22). Overall, the risk attenuated with age and mortality risks were limited beyond 4 years of age. Longer distance to healthcare HR of 1.44 (95% CI 1.29-1.60), l ow maternal education HR 3.91 (95% CI 1.86-8.22), and low socioeconomic status HR 1.44 (95% CI 1.26-1.64) were all significantly associated with increased hazard of malaria death among children.

CONCLUSIONS

While child mortality due to malaria in the study area in Western Kenya, has been decreasing, a final step toward significant risk reduction is yet to be accomplished. This study highlights residual proximal determinants of risk which can further inform preventive actions.

Geostatistical analysis and mapping of malaria risk in children of Mozambique

Malaria remains one of the most prevalent infectious diseases in the tropics and subtropics, and Mozambique is not an exception. To design geographically targeted and effective intervention mechanisms of malaria, an up-to-date map that shows the spatial distribution of malaria is needed. This study analyzed 2018 Mozambique Malaria Indicator Survey using geostatistical methods to: i) explore individual, household, and community-level determinants of malaria in under-five children, ii) prepare a malaria prevalence map in Mozambique, and iii) produce prediction prevalence maps and exceedence probability across the country. The results show the overall weighted prevalence of malaria was 38.9% (N = 4347, with 95% CI: 36.9%–40.8%). Across different provinces of Mozambique, the prevalence of malaria ranges from 1% in Maputo city to 57.3% in Cabo Delgado province. Malaria prevalence was found to be higher in rural areas, increased with child’s age, and decreased with household wealth index and mother’s level of education. Given the high prevalence of childhood malaria observed in Mozambique there is an urgent need for effective public health interventions in malaria hot spot areas. The household determinants of malaria infection that are identified in this study as well as the maps of parasitaemia risk could be used by malaria control program implementers to define priority intervention areas.

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

Malaria-anemia comorbidity prevalence as a measure of malaria-related deaths in sub-Saharan Africa

Different methods and data sources have been utilized to determine the relationship between malaria and mortality in endemic countries. Most of these efforts have focused on deaths directly attributed to malaria, while they overlooked causes of mortality that might be indirectly related to the disease, for instance anemia. We estimated the association of malaria parasitaemia, anemia, and malaria-anemia comorbidity with all-cause under-five mortality and evaluated the potential of malaria-anemia comorbidity prevalence to quantify malaria-related deaths in sub-Saharan Africa. We analysed data from Demographic and Health Surveys (DHS) and employed Bayesian geostatistical models. Mortality hazard obtained from malaria-anemia comorbidity prevalence was up to 3·5 times higher compared to the hazard related to Plasmodium parasitaemia only. Malaria parasite prevalence alone could not always capture a statistically important association with under-five mortality. Geographical variation of the malaria-anemia comorbidity effect was observed in most, but not all, countries. We concluded that the malaria burden in sub-Saharan Africa is considerably underestimated when anemia in not taken into account and that the malaria-anemia comorbidity prevalence provides a useful measure of the malaria-related deaths.

Modelling the relationship between malaria prevalence as a measure of transmission and mortality across age groups

Background

Parasite prevalence has been used widely as a measure of malaria transmission, especially in malaria endemic areas. However, its contribution and relationship to malaria mortality across different age groups has not been well investigated. Previous studies in a health and demographic surveillance systems (HDSS) platform in western Kenya quantified the contribution of incidence and entomological inoculation rates (EIR) to mortality. The study assessed the relationship between outcomes of malaria parasitaemia surveys and mortality across age groups.

Methods

Parasitological data from annual cross-sectional surveys from the Kisumu HDSS between 2007 and 2015 were used to determine malaria parasite prevalence (PP) and clinical malaria (parasites plus reported fever within 24 h or temperature above 37.5 °C). Household surveys and verbal autopsy (VA) were used to obtain data on all-cause and malaria-specific mortality. Bayesian negative binomial geo-statistical regression models were used to investigate the association of PP/clinical malaria with mortality across different age groups. Estimates based on yearly data were compared with those from aggregated data over 4 to 5-year periods, which is the typical period that mortality data are available from national demographic and health surveys.

Results

Using 5-year aggregated data, associations were established between parasite prevalence and malaria-specific mortality in the whole population (RR_malaria = 1.66; 95% Bayesian Credible Intervals: 1.07–2.54) and children 1–4 years (RR_malaria = 2.29; 1.17–4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RR_all-cause = 1.32; 1.01–1.74); (RR_malaria = 2.50; 1.27–4.81), children 1–4 years (RR_all-cause = 1.89; 1.00–3.51); (RR_malaria = 3.37; 1.23–8.93) and in older children 5–14 years (RR_all-cause = 3.94; 1.34–11.10); (RR_malaria = 7.56; 1.20–39.54), no association was found among neonates, adults (15–59 years) and the elderly (60+ years). Distance to health facilities, socioeconomic status, elevation and survey year were important factors for all-cause and malaria-specific mortality.

Conclusion

Malaria parasitaemia from cross-sectional surveys was associated with mortality across age groups over 4 to 5 year periods with clinical malaria more strongly associated with mortality than parasite prevalence. This effect was stronger in children 5–14 years compared to other age-groups. Further analyses of data from other HDSS sites or similar platforms would be useful in investigating the relationship between malaria and mortality across different endemicity levels.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2869-9) contains supplementary material, which is available to authorized users.

Malaria smear positivity among Kenyan children peaks at intermediate temperatures as predicted by ecological models

Background

Ambient temperature is an important determinant of malaria transmission and suitability, affecting the life-cycle of the Plasmodium parasite and Anopheles vector. Early models predicted a thermal malaria transmission optimum of 31 °C, later revised to 25 °C using experimental data from mosquito and parasite biology. However, the link between ambient temperature and human malaria incidence remains poorly resolved.

Methods

To evaluate the relationship between ambient temperature and malaria risk, 5833 febrile children (<18 years-old) with an acute, non-localizing febrile illness were enrolled from four heterogenous outpatient clinic sites in Kenya (Chulaimbo, Kisumu, Msambweni and Ukunda). Thick and thin blood smears were evaluated for the presence of malaria parasites. Daily temperature estimates were obtained from land logger data, and rainfall from National Oceanic and Atmospheric Administration (NOAA)’s Africa Rainfall Climatology (ARC) data. Thirty-day mean temperature and 30-day cumulative rainfall were estimated and each lagged by 30 days, relative to the febrile visit. A generalized linear mixed model was used to assess relationships between malaria smear positivity and predictors including temperature, rainfall, age, sex, mosquito exposure and socioeconomic status.

Results

Malaria smear positivity varied between 42–83% across four clinic sites in western and coastal Kenya, with highest smear positivity in the rural, western site. The temperature ranges were cooler in the western sites and warmer in the coastal sites. In multivariate analysis controlling for socioeconomic status, age, sex, rainfall and bednet use, malaria smear positivity peaked near 25 °C at all four sites, as predicted a priori from an ecological model.

Conclusions

This study provides direct field evidence of a unimodal relationship between ambient temperature and human malaria incidence with a peak in malaria transmission occurring at lower temperatures than previously recognized clinically. This nonlinear relationship with an intermediate optimal temperature implies that future climate warming could expand malaria incidence in cooler, highland regions while decreasing incidence in already warm regions with average temperatures above 25 °C. These findings support efforts to further understand the nonlinear association between ambient temperature and vector-borne diseases to better allocate resources and respond to disease threats in a future, warmer world.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3547-z) contains supplementary material, which is available to authorized users.