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

Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda

BACKGROUND

Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey.

METHODS

The results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model's ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated.

RESULTS

Models fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier.

CONCLUSIONS

These results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to transmission regularly occurring outside of the home. Additionally, they suggest that when predicting malaria risk the benefit may not outweigh the high costs of attaining detailed information on household predictors. Instead, using remotely-sensed data provides an equally effective, cost-efficient alternative.

Community case management of malaria in Western Kenya: performance of community health volunteers in active malaria case surveillance

BACKGROUND

In western Kenya, not all malaria cases are reported as stipulated in the community case management of malaria (CCMm) strategy. This underreporting affects the equity distribution of malaria commodities and the evaluation of interventions. The current study aimed to evaluate the effectiveness of community health volunteers' active case detection and management of malaria in western Kenya.

METHODS

Cross-sectional active case detection (ACD) of malaria survey was carried out between May and August 2021 in three eco-epidemiologically distinct zones in Kisumu, western Kenya: Kano Plains, Lowland lakeshore and Highland Plateau. The CHVs conducted biweekly ACD of malaria household visits to interview and examine residents for febrile illness. The Community Health Volunteers (CHVs) performance during the ACD of malaria was observed and interviews done using structured questionnaires.

RESULTS

Of the total 28,800 surveyed, 2597 (9%) had fever and associated malaria symptoms. Eco-epidemiological zones, gender, age group, axillary body temperature, bed net use, travel history, and survey month all had a significant association with malaria febrile illness (p < 0.05). The qualification of the CHV had a significant influence on the quality of their service. The number of health trainings received by the CHVs was significantly related to the correctness of using job aid (χ2 = 6.261, df = 1, p = 0.012) and safety procedures during the ACD activity (χ2 = 4.114, df = 1, p = 0.043). Male CHVs were more likely than female CHVs to correctly refer RDT-negative febrile residents to a health facility for further treatment (OR = 3.94, 95% CI = 1.85-5.44, p < 0.0001). Most of RDT-negative febrile residents who were correctly referred to the health facility came from the clusters with a CHV having 10 years of experience or more (OR = 1.29, 95% CI = 1.05-1.57, p = 0.016). Febrile residents in clusters managed by CHVs with more than 10 years of experience (OR = 1.82, 95% CI = 1.43-2.31, p < 0.0001), who had a secondary education (OR = 1.53, 95% CI = 1.27-1.85, p < 0.0001), and were over the age of 50 (OR = 1.44, 95% CI = 1.18-1.76, p < 0.0001), were more likely to seek malaria treatment in public hospitals. All RDT positive febrile residents were given anti-malarial by the CHVs, and RDT negatives were referred to the nearest health facility for further treatment.

CONCLUSIONS

The CHV's years of experience, education level, and age had a significant influence on their service quality. Understanding the qualifications of CHVs can assist healthcare systems and policymakers in designing effective interventions that assist CHVs in providing high-quality services to their communities.

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.