Differences of Rainfall-Malaria Associations in Lowland and Highland in Western Kenya

Forecasting malaria dynamics based on causal relations between control interventions, climatic factors, and disease incidence in western Kenya

Background

Malaria remains one of the deadliest diseases worldwide, especially among young children in sub-Saharan Africa. Predictive models are necessary for effective planning and resource allocation; however, statistical models suffer from association pitfalls. In this study, we used empirical dynamic modelling (EDM) to investigate causal links between climatic factors and intervention coverage with malaria for short-term forecasting.

Methods

Based on data spanning the period from 2008 to 2022, we used convergent cross-mapping (CCM) to identify suitable lags for climatic drivers and investigate their effects, interaction strength, and suitability ranges on malaria incidence. Monthly malaria cases were collected at St. Elizabeth Lwak Mission Hospital. Intervention coverage and population movement data were obtained from household surveys in Asembo, western Kenya. Daytime land surface temperature (LSTD), rainfall, relative humidity (RH), wind speed, solar radiation, crop cover, and surface water coverage were extracted from remote sensing sources. Short-term forecasting of malaria incidence was performed using state-space reconstruction.

Results

We observed causal links between climatic drivers, bed net use, and malaria incidence. LSTD lagged over the previous month; rainfall and RH lagged over the previous two months; and wind speed in the current month had the highest predictive skills. Increases in LSTD, wind speed, and bed net use negatively affected incidence, while increases in rainfall and humidity had positive effects. Interaction strengths were more pronounced at temperature, rainfall, RH, wind speed, and bed net coverage ranges of 30-35°C, 30-120 mm, 67-80%, 0.5-0.7 m/s, and above 90%, respectively. Temperature and rainfall exceeding 35°C and 180 mm, respectively, had a greater negative effect. We also observed good short-term predictive performance using the multivariable forecasting model (Pearson correlation coefficient = 0.85, root mean square error = 0.15).

Conclusions

Our findings demonstrate the utility of CCM in establishing causal linkages between malaria incidence and both climatic and non-climatic drivers. By identifying these causal links and suitability ranges, we provide valuable information for modelling the impact of future climate scenarios.

Pollen effects in a changing climate: Ragweed pollen exposure and sleepiness in immunotherapy patients of a Southeastern Michigan allergy clinic

Allergic rhino-conjunctivitis (AR) is a globally relevant health disorder characterized by sneezing, rhinorrhea and sleep disturbance. Ragweed (Ambrosia artemisiifolia) is a plant common to North America and an important allergen. Coarse methods of measuring airborne pollen counts are used to predict seasonal allergy symptoms. This research used a longitudinal study design with a novel, model-based raster of predicted pollen counts to test associations with self-reported symptoms of AR collected from patients receiving immunotherapy for pollen allergies at an allergy clinic. Researchers visited a clinic six times over three weeks. Immunotherapy patients were asked to fill out a brief intake survey on allergic and symptomatic profiles, daytime sleepiness, housing quality, and demographics. Participants responded to a daily, emailed survey on sleepiness and asthma symptoms for 21 days. Using the date and location of responses, ragweed pollen counts were extracted from a prognostic, model based raster (25km pixels). Lag associations of pollen counts with sleepiness were tested using a logistic regression model , adjusted for housing and demographic characteristics, in a distributed lag non-linear model (DLNM) framework. 49 people participated in the study. 26 (52%) were female. The mean age was 37.9 years. Asthma/allergy symptoms were not associated with ragweed pollen but sleepiness was highest two days after exposure (Estimate: 0.33 [0.04,0.62]). Subjects traveled widely during the study period. Intense exposures to ragweed pollen may be associated with daytime sleepiness within small exposure windows. Model-based predicted pollen counts could be used to study health impacts of pollen in people with disease severe enough to receive immunotherapy. Daytime sleepiness can affect productivity and injury risk, and pollen season length and allergenicity may be increasing with climate change. Thus our results may have important implications for population health.

Malaria prevalence and associated population and ecological risk factors among women and children under 5 years in Rwanda

Background

Malaria continues to pose a substantial public health concern in Rwanda, despite substantial progress in recent years. Little is known about effect of ecological factors and their interaction in malaria transmission. Understanding the prevalence and identifying risk factors, both population-based and ecological such as zooprophylaxis and irrigation are crucial for targeted intervention strategies.

Methods

This study analyzed the 6th iteration of the Demographic and Health Survey conducted in Rwanda between 2019 and 2020. The study employed a nationally representative sample, utilizing rapid diagnostic tests and blood smear microscopy to determine malaria prevalence among women and under 5 years old children. Logistic regression analysis was used in R version 4.3.1 to evaluate population and ecological risk factors that are associated with malaria prevalence. Additionally, interactive effects of ecological factors on malaria were evaluated.

Results

The analysis revealed a notable malaria prevalence in Rwanda, emphasizing the continued significance of malaria control efforts. Approximately 1.79 % of the population tested positive for malaria. Proximity to irrigation sites and lowland were identified as significant risk factors to malaria with adjusted odds ratio (AOR) 1.47(1.00-2.15) and AOR 5.44(4.01-8.61) respectively however cattle ownership exhibited a protective effect AOR 0.41(0.23-0.72). Interactive effects of livestock and irrigation on malaria prevalence were revealed. Additionally, population-based risk factors, including age, household wealth, utilization of Insecticide Treated Nets, were associated with varying malaria risks.

Conclusion

This study underscores the persistent challenge of malaria in Rwanda and the importance of tailored intervention strategies. To effectively combat malaria, efforts must consider the interplay of ecological factors, such as high cattle density, and demographic factors, targeting high-risk populations, especially those living in proximity to lowlands and irrigation areas. These findings provide critical insights for advancing malaria elimination efforts in Rwanda and serve as a basis for comprehensive public health planning and action.

Association between environmental factors and dengue incidence in Lao People's Democratic Republic: a nationwide time-series study

BACKGROUND

Dengue fever is a vector-borne disease of global public health concern, with an increasing number of cases and a widening area of endemicity in recent years. Meteorological factors influence dengue transmission. This study aimed to estimate the association between meteorological factors (i.e., temperature and rainfall) and dengue incidence and the effect of altitude on this association in the Lao People's Democratic Republic (Lao PDR).

METHODS

We used weekly dengue incidence and meteorological data, including temperature and rainfall, from 18 jurisdictions in Lao PDR from 2015 to 2019. A two-stage distributed lag nonlinear model with a quasi-Poisson distribution was used to account for the nonlinear and delayed associations between dengue incidence and meteorological variables, adjusting for long-term time trends and autocorrelation.

RESULTS

A total of 55,561 cases were reported in Lao PDR from 2015 to 2019. The cumulative relative risk for the 90th percentile of weekly mean temperature (29 °C) over 22 weeks was estimated at 4.21 (95% confidence interval: 2.00-8.84), relative to the 25th percentile (24 °C). The cumulative relative risk for the weekly total rainfall over 12 weeks peaked at 82 mm (relative risk = 1.76, 95% confidence interval: 0.91-3.40) relative to no rain. However, the risk decreased significantly when heavy rain exceeded 200 mm. We found no evidence that altitude modified these associations.

CONCLUSIONS

We found a lagged nonlinear relationship between meteorological factors and dengue incidence in Lao PDR. These findings can be used to develop climate-based early warning systems and provide insights for improving vector control in the country.

Self-Reported Symptoms at Last Febrile Illness as a Predictor of Treatment-Seeking in Western Kenya: A Cross-Sectional Study

Timely treatment-seeking behavior can reduce morbidity and mortality due to infectious diseases. Patterns of treatment-seeking behavior can differ by access to health care, and perceptions of disease severity and symptoms. We evaluated the association between self-reported symptoms at last illness and the level of treatment-seeking behaviors. We analyzed cross-sectional data from 1,037 participants from the lowlands and highlands of Western Kenya from 2015 using logistic regression models. There was considerable heterogeneity in the symptoms and treatment-seeking behaviors reported among individuals who were febrile at their last illness. A greater number of self-reported categories of symptoms tended to be associated with a higher likelihood of treatment-seeking in both sites. Participants were significantly more likely to seek treatment if they reported fever, aches, and digestive symptoms at last illness than just fever and aches or fever alone, but the frequency of treatment-seeking for fever in combination with aches and respiratory symptoms did not follow a consistent pattern. Among those who sought treatment, most used a formal source, but the patterns were inconsistent across sites and by the number of symptoms categories. Understanding the drivers of treatment-seeking behavior after febrile illness is important to control and treat infectious diseases in Kenya.

Climate Change Drives the Transmission and Spread of Vector-Borne Diseases: An Ecological Perspective

Climate change affects ecosystems and human health in multiple dimensions. With the acceleration of climate change, climate-sensitive vector-borne diseases (VBDs) pose an increasing threat to public health. This paper summaries 10 publications on the impacts of climate change on ecosystems and human health; then it synthesizes the other existing literature to more broadly explain how climate change drives the transmission and spread of VBDs through an ecological perspective. We highlight the multi-dimensional nature of climate change, its interaction with other factors, and the impact of the COVID-19 pandemic on transmission and spread of VBDs, specifically including: (1) the generally nonlinear relationship of local climate (temperature, precipitation and wind) and VBD transmission, with temperature especially exhibiting an n-shape relation; (2) the time-lagged effect of regional climate phenomena (the El Niño-Southern Oscillation and North Atlantic Oscillation) on VBD transmission; (3) the u-shaped effect of extreme climate (heat waves, cold waves, floods, and droughts) on VBD spread; (4) how interactions between non-climatic (land use and human mobility) and climatic factors increase VBD transmission and spread; and (5) that the impact of the COVID-19 pandemic on climate change is debatable, and its impact on VBDs remains uncertain. By exploring the influence of climate change and non-climatic factors on VBD transmission and spread, this paper provides scientific understanding and guidance for their effective prevention and control.

Clinical malaria incidence and health seeking pattern in geographically heterogeneous landscape of western Kenya

Background

Malaria remains a public health problem in Kenya despite sustained interventions deployed by the government. One of the major impediments to effective malaria control is a lack of accurate diagnosis and effective treatment. This study was conducted to assess clinical malaria incidence and treatment seeking profiles of febrile cases in western Kenya.

Methods

Active case detection of malaria was carried out in three eco-epidemiologically distinct zones topologically characterized as lakeshore, hillside, and highland plateau in Kisumu County, western Kenya, from March 2020 to March 2021. Community Health Volunteers (CHVs) conducted biweekly visits to residents in their households to interview and examine for febrile illness. A febrile case was defined as an individual having fever (axillary temperature ≥ 37.5 °C) during examination or complaints of fever and other nonspecific malaria related symptoms 1–2 days before examination. Prior to the biweekly malaria testing by the CHVs, the participants' treatment seeking methods were based on their behaviors in response to febrile illness. In suspected malaria cases, finger-prick blood samples were taken and tested for malaria parasites with ultra-sensitive Alere^® malaria rapid diagnostic tests (RDT) and subjected to real-time polymerase chain reaction (RT-PCR) for quality control examination.

Results

Of the total 5838 residents interviewed, 2205 residents had high temperature or reported febrile illness in the previous two days before the visit. Clinical malaria incidence (cases/1000people/month) was highest in the lakeshore zone (24.3), followed by the hillside (18.7) and the highland plateau zone (10.3). Clinical malaria incidence showed significant difference across gender (χ² = 7.57; df = 2, p = 0.0227) and age group (χ² = 58.34; df = 4, p < 0.0001). Treatment seeking patterns of malaria febrile cases showed significant difference with doing nothing (48.7%) and purchasing antimalarials from drug shops (38.1%) being the most common health-seeking pattern among the 2205 febrile residents (χ² = 21.875; df = 4, p < 0.0001). Caregivers of 802 school-aged children aged 5–14 years with fever primarily sought treatment from drug shops (28.9%) and public hospitals (14.0%), with significant lower proportions of children receiving treatment from traditional medication (2.9%) and private hospital (4.4%) (p < 0.0001). There was no significant difference in care givers' treatment seeking patterns for feverish children under the age of five (p = 0.086). Residents with clinical malaria cases in the lakeshore and hillside zones sought treatment primarily from public hospitals (61.9%, 60/97) traditional medication (51.1%, 23/45) respectively (p < 0.0001). However, there was no significant difference in the treatment seeking patterns of highland plateau residents with clinical malaria (p = 0.431).The main factors associated with the decision to seek treatment were the travel distance to the health facility, the severity of the disease, confidence in the treatment, and affordability.

Conclusion

Clinical malaria incidence remains highest in the Lakeshore (24.3cases/1000 people/month) despite high LLINs coverage (90%). The travel distance to the health facility, severity of disease and affordability were mainly associated with 80% of residents either self-medicating or doing nothing to alleviate their illness. The findings of this study suggest that the Ministry of Health should strengthen community case management of malaria by providing supportive supervision of community health volunteers to advocate for community awareness, early diagnosis, and treatment of malaria.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07757-w.

Influence of landscape heterogeneity on entomological and parasitological indices of malaria in Kisumu, Western Kenya

Background

Identification and characterization of larval habitats, documentation of Anopheles spp. composition and abundance, and Plasmodium spp. infection burden are critical components of integrated vector management. The present study aimed to investigate the effect of landscape heterogeneity on entomological and parasitological indices of malaria in western Kenya.

Methods

A cross-sectional entomological and parasitological survey was conducted along an altitudinal transect in three eco-epidemiological zones: lakeshore along the lakeside, hillside, and highland plateau during the wet and dry seasons in 2020 in Kisumu County, Kenya. Larval habitats for Anopheles mosquitoes were identified and characterized. Adult mosquitoes were sampled using pyrethrum spray catches (PSC). Finger prick blood samples were taken from residents and examined for malaria parasites by real-time PCR (RT-PCR).

Results

Increased risk of Plasmodium falciparum infection was associated with residency in the lakeshore zone, school-age children, rainy season, and no ITNs (χ² = 41.201, df = 9, P < 0.0001). Similarly, lakeshore zone and the rainy season significantly increased Anopheles spp. abundance. However, house structures such as wall type and whether the eave spaces were closed or open, as well as the use of ITNs, did not affect Anopheles spp. densities in the homes (χ² = 38.695, df = 7, P < 0.0001). Anopheles funestus (41.8%) and An. arabiensis (29.1%) were the most abundant vectors in all zones. Sporozoite prevalence was 5.6% and 3.2% in the two species respectively. The lakeshore zone had the highest sporozoite prevalence (4.4%, 7/160) and inoculation rates (135.2 infective bites/person/year). High larval densities were significantly associated with lakeshore zone and hillside zones, animal hoof prints and tire truck larval habitats, wetland and pasture land, and the wet season. The larval habitat types differed significantly across the landscape zones and seasonality (χ² = 1453.044, df = 298, P < 0.0001).

Conclusion

The empirical evidence on the impact of landscape heterogeneity and seasonality on vector densities, parasite transmission, and Plasmodium infections in humans emphasizes the importance of tailoring specific adaptive environmental management interventions to specific landscape attributes to have a significant impact on transmission reduction.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05447-9.

Risk associations of submicroscopic malaria infection in lakeshore, plateau and highland areas of Kisumu County in western Kenya

BACKGROUND

Persons with submicroscopic malaria infection are a major reservoir of gametocytes that sustain malaria transmission in sub-Saharan Africa. Despite recent decreases in the national malaria burden in Kenya due to vector control interventions, malaria transmission continues to be high in western regions of the country bordering Lake Victoria. The objective of this study was to advance knowledge of the topographical, demographic and behavioral risk factors associated with submicroscopic malaria infection in the Lake Victoria basin in Kisumu County.

METHODS

Cross-sectional community surveys for malaria infection were undertaken in three eco-epidemiologically distinct zones in Nyakach sub-County, Kisumu. Adjacent regions were topologically characterized as lakeshore, hillside and highland plateau. Surveys were conducted during the 2019 and 2020 wet and dry seasons. Finger prick blood smears and dry blood spots (DBS) on filter paper were collected from 1,777 healthy volunteers for microscopic inspection and real time-PCR (RT-PCR) diagnosis of Plasmodium infection. Persons who were PCR positive but blood smear negative were considered to harbor submicroscopic infections. Topographical, demographic and behavioral risk factors were correlated with community prevalence of submicroscopic infections.

RESULTS

Out of a total of 1,777 blood samples collected, 14.2% (253/1,777) were diagnosed as submicroscopic infections. Blood smear microscopy and RT-PCR, respectively, detected 3.7% (66/1,777) and 18% (319/1,777) infections. Blood smears results were exclusively positive for P. falciparum, whereas RT-PCR also detected P. malariae and P. ovale mono- and co-infections. Submicroscopic infection prevalence was associated with topographical variation (χ2 = 39.344, df = 2, p<0.0001). The highest prevalence was observed in the lakeshore zone (20.6%, n = 622) followed by the hillside (13.6%, n = 595) and highland plateau zones (7.9%, n = 560). Infection prevalence varied significantly according to season (χ2 = 17.374, df = 3, p<0.0001). The highest prevalence was observed in residents of the lakeshore zone in the 2019 dry season (29.9%, n = 167) and 2020 and 2019 rainy seasons (21.5%, n = 144 and 18.1%, n = 155, respectively). In both the rainy and dry seasons the likelihood of submicroscopic infection was higher in the lakeshore (AOR: 2.71, 95% CI = 1.85-3.95; p<0.0001) and hillside (AOR: 1.74, 95% CI = 1.17-2.61, p = 0.007) than in the highland plateau zones. Residence in the lakeshore zone (p<0.0001), male sex (p = 0.025), school age (p = 0.002), and living in mud houses (p = 0.044) increased the risk of submicroscopic malaria infection. Bed net use (p = 0.112) and occupation (p = 0.116) were not associated with submicroscopic infection prevalence.

CONCLUSION

Topographic features of the local landscape and seasonality are major correlates of submicroscopic malaria infection in the Lake Victoria area of western Kenya. Diagnostic tests more sensitive than blood smear microscopy will allow for monitoring and targeting geographic sites where additional vector interventions are needed to reduce malaria transmission.

Long-Term PM2.5 Exposure Is Associated with Symptoms of Acute Respiratory Infections among Children under Five Years of Age in Kenya, 2014

Introduction: Short-term exposures to air pollutants such as particulate matter (PM) have been associated with increased risk for symptoms of acute respiratory infections (ARIs). Less well understood is how long-term exposures to fine PM (PM2.5) might increase risk of ARIs and their symptoms. This research uses georeferenced Demographic Health Survey (DHS) data from Kenya (2014) along with a remote sensing based raster of PM2.5 concentrations to test associations between PM2.5 exposure and ARI symptoms in children for up to 12 monthly lags. Methods: Predicted PM2.5 concentrations were extracted from raster of monthly averages for latitude/longitude locations of survey clusters. These data and other environmental and demographic data were used in a logistic regression model of ARI symptoms within a distributed lag nonlinear modeling framework (DLNM) to test lag associations of PM2.5 exposure with binary presence/absence of ARI symptoms in the previous two weeks. Results: Out of 7036 children under five for whom data were available, 46.8% reported ARI symptoms in the previous two weeks. Exposure to PM2.5 within the same month and as an average for the previous 12 months was 18.31 and 22.1 µg/m3, respectively, far in excess of guidelines set by the World Health Organization. One-year average PM2.5 exposure was higher for children who experienced ARI symptoms compared with children who did not (22.4 vs. 21.8 µg/m3, p < 0.0001.) Logistic regression models using the DLNM framework indicated that while PM exposure was not significantly associated with ARI symptoms for early lags, exposure to high concentrations of PM2.5 (90th percentile) was associated with elevated odds for ARI symptoms along a gradient of lag exposure time even when controlling for age, sex, types of cooking fuels, and precipitation. Conclusions: Long-term exposure to high concentrations of PM2.5 may increase risk for acute respiratory problems in small children. However, more work should be carried out to increase capacity to accurately measure air pollutants in emerging economies such as Kenya.

Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

BACKGROUND

Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda.

METHODS

This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs' catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence.

RESULTS

Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4.

CONCLUSIONS

In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

Long-Lasting Insecticidal Nets Incorporating Piperonyl Butoxide Reduce the Risk of Malaria in Children in Western Kenya: A Cluster Randomized Controlled Trial

Malaria vectors have acquired an enzyme that metabolizes pyrethroids. To tackle this problem, we evaluated long-lasting insecticidal nets incorporating piperonyl butoxide (PBO-LLINs) with a community-based cluster randomized control trial in western Kenya. The primary endpoints were anopheline density and Plasmodium falciparum polymerase chain reaction (PCR)-positive prevalence (PCRpfPR) of children aged 7 months to 10 years. Four clusters were randomly selected for each of the treatment and control arms (eight clusters in total) from 12 clusters, and PBO-LLINs and standard LLINs were distributed in February 2011 to 982 and 1,028 houses for treatment and control arms, respectively. Entomological surveys targeted 20 houses in each cluster, and epidemiological surveys targeted 150 children. Cluster-level permutation tests evaluated the effectiveness using the fitted values from individual level regression models adjusted for baseline. Bootstrapping estimated 95% confidence intervals (CIs). The medians of anophelines per house were 1.4 (interquartile range [IQR]: 2.3) and 3.4 (IQR: 3.7) in the intervention and control arms after 3 months, and 0.4 (IQR: 0.2) and 1.6 (IQR: 0.5) after 10 months, respectively. The differences were -2.5 (95% CI: -6.4 to -0.6) and -1.3 (95% CI: -2.0 to -0.7), respectively. The datasets of 861 and 775 children were analyzed in two epidemiological surveys. The median PCRpfPRs were 25% (IQR: 11%) in the intervention arm and 52% (IQR: 11%) in the control arm after 5 months and 33% (IQR: 11%) and 45% (IQR: 5%) after 12 months. The PCRpfPR ratios were 0.67 (95% CI: 0.38, 0.91) and 0.74 (95% CI: 0.53, 0.90), respectively. We confirmed the superiority of PBO-LLINs.

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.