Widening the lens of population-based health research to climate change impacts and adaptation: the climate change and health evaluation and response system (CHEERS).

Background: Climate change significantly impacts health in low-and middle-income countries (LMICs), exacerbating vulnerabilities. Comprehensive data for evidence-based research and decision-making is crucial but scarce. Health and Demographic Surveillance Sites (HDSSs) in Africa and Asia provide a robust infrastructure with longitudinal population cohort data, yet they lack climate-health specific data. Acquiring this information is essential for understanding the burden of climate-sensitive diseases on populations and guiding targeted policies and interventions in LMICs to enhance mitigation and adaptation capacities. Objective: The objective of this research is to develop and implement the Change and Health Evaluation and Response System (CHEERS) as a methodological framework, designed to facilitate the generation and ongoing monitoring of climate change and health-related data within existing Health and Demographic Surveillance Sites (HDSSs) and comparable research infrastructures. Methods: CHEERS uses a multi-tiered approach to assess health and environmental exposures at the individual, household, and community levels, utilizing digital tools such as wearable devices, indoor temperature and humidity measurements, remotely sensed satellite data, and 3D-printed weather stations. The CHEERS framework utilizes a graph database to efficiently manage and analyze diverse data types, leveraging graph algorithms to understand the complex interplay between health and environmental exposures. Results: The Nouna CHEERS site, established in 2022, has yielded significant preliminary findings. By using remotely-sensed data, the site has been able to predict crop yield at a household level in Nouna and explore the relationships between yield, socioeconomic factors, and health outcomes. The feasibility and acceptability of wearable technology have been confirmed in rural Burkina Faso for obtaining individual-level data, despite the presence of technical challenges. The use of wearables to study the impact of extreme weather on health has shown significant effects of heat exposure on sleep and daily activity, highlighting the urgent need for interventions to mitigate adverse health consequences. Conclusion: Implementing the CHEERS in research infrastructures can advance climate change and health research, as large and longitudinal datasets have been scarce for LMICs. This data can inform health priorities, guide resource allocation to address climate change and health exposures, and protect vulnerable communities in LMICs from these exposures.

Malaria control and elimination in Kenya: economy-wide benefits and regional disparities.

BACKGROUND: Malaria remains a public health problem in Kenya despite several concerted control efforts. Empirical evidence regarding malaria effects in Kenya suggests that the disease imposes substantial economic costs, jeopardizing the achievement of sustainable development goals. The Kenya Malaria Strategy (2019-2023), which is currently being implemented, is one of several sequential malaria control and elimination strategies. The strategy targets reducing malaria incidences and deaths by 75% of the 2016 levels by 2023 through spending around Kenyan Shillings 61.9 billion over 5 years. This paper assesses the economy-wide implications of implementing this strategy. METHODS: An economy-wide simulation model is calibrated to a comprehensive 2019 database for Kenya, considering different epidemiological zones. Two scenarios are simulated with the model. The first scenario (GOVT) simulates the annual costs of implementing the Kenya Malaria Strategy by increasing government expenditure on malaria control and elimination programmes. The second scenario (LABOR) reduces malaria incidences by 75% in all epidemiological malaria zones without accounting for the changes in government expenditure, which translates into rising the household labour endowment (benefits of the strategy). RESULTS: Implementing the Kenya Malaria Strategy (2019-2023) enhances gross domestic product at the end of the strategy implementation period due to more available labour. In the short term, government health expenditure (direct malaria costs) increases significantly, which is critical in controlling and eliminating malaria. Expanding the health sector raises the demand for production factors, such as labour and capital. The prices for these factors rise, boosting producer and consumer prices of non-health-related products. Consequently, household welfare decreases during the strategy implementation period. In the long run, household labour endowment increases due to reduced malaria incidences and deaths (indirect malaria costs). However, the size of the effects varies across malaria epidemiological and agroecological zones depending on malaria prevalence and factor ownership. CONCLUSIONS: This paper provides policymakers with an ex-ante assessment of the implications of malaria control and elimination on household welfare across various malaria epidemiological zones. These insights assist in developing and implementing related policy measures that reduce the undesirable effects in the short run. Besides, the paper supports an economically beneficial long-term malaria control and elimination effect.

Real world external validation of metabolic gestational age assessment in Kenya.

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.

Assessment of malaria infection among pregnant women and children below five years of age attending rural health facilities of Kenya: A cross-sectional survey in two counties of Kenya.

BACKGROUND: In Kenya, health service delivery and access to health care remains a challenge for vulnerable populations, particularly pregnant women and children below five years. The aim of this study, therefore, was to determine the positivity rate of Plasmodium falciparum parasites in pregnant women and children below five years of age seeking healthcare services at the rural health facilities of Kwale and Siaya counties as well as their access and uptake of malaria control integrated services, like antenatal care (ANC), offered in those facilities. METHODS: Cluster random sampling method was used to select pregnant women and children below five years receiving maternal and child health services using two cross-sectional surveys conducted in eleven rural health facilities in two malaria endemic counties in western and coastal regions of Kenya. Each consenting participant provided single blood sample for determining malaria parasitaemia using microscopy and polymerase chain reaction (PCR) techniques. RESULTS: Using PCR technique, the overall malaria positivity rate was 27.9% (95%CI: 20.9-37.2), and was 34.1% (95%CI: 27.1-42.9) and 22.0% (95%CI: 13.3-36.3) in children below five years and pregnant women respectively. Additionally, using microscopy, the overall positivity rate was 39.0% (95%CI: 29.5-51.6), and was 50.4% (95%CI: 39.4-64.5) and 30.6% (95%CI: 22.4-41.7) in children below five years and pregnant women respectively. Siaya County in western Kenya showed higher malaria positivity rates for both children (36.4% and 54.9%) and pregnant women (27.8% and 38.5%) using both PCR and microscopy diagnosis techniques respectively, compared to Kwale County that showed positivity rates of 27.2% and 37.9% for children and 5.2% and 8.6% for pregnant women similarly using both PCR and microscopy techniques respectively. Pregnant women presenting themselves for their first ANC visit were up to five times at risk of malaria infection, (adjusted odds ratio = 5.40, 95%CI: 0.96-30.50, p = 0.046). CONCLUSION: Despite evidence of ANC attendance and administration of intermittent preventive treatment with sulfadoxine-pyrimethamine (IPTp-SP) dosage during these visits, malaria positivity rate was still high among pregnant women and children below five years in these two rural counties. These findings are important to the Kenyan National Malaria Control Programme and will help contribute to improvement of policies on integration of malaria control approaches in rural health facilities.

Insecticide resistance exerts significant fitness costs in immature stages of Anopheles gambiae in western Kenya.

BACKGROUND: Despite increasing documentation of insecticide resistance in malaria vectors against public health insecticides in sub-Saharan Africa, there is a paucity of information on the potential fitness costs of pyrethroid resistance in malaria vectors, which is important in improving the current resistant management strategies. This study aimed to assess the fitness cost effects of insecticide resistance on the development and survival of immature Anopheles gambiae from western Kenya. METHODS: Two-hour old, first instar larvae (L1) were introduced and raised in basins containing soil and rainwater in a semi-field set-up. Each day the number of surviving individuals per larval stage was counted and their stage of development were recorded until they emerged as adults. The larval life-history trait parameters measured include mean larval development time, daily survival and pupal emergence. Pyrethroid-resistant colony of An. gambiae sensu stricto and susceptible colony originating from the same site and with the same genetic background were used. Kisumu laboratory susceptible colony was used as a reference. RESULTS: The resistant colony had a significantly longer larval development time through the developmental stages than the susceptible colony. The resistant colony took an average of 2 days longer to develop from first instar (L1) to fourth instar (L4) (8.8 ± 0.2 days) compared to the susceptible colony (6.6 ± 0.2 days). The development time from first instar to pupa formation was significantly longer by 3 days in the resistant colony (10.28 ± 0.3 days) than in susceptible colony (7.5 ± 0.2 days). The time from egg hatching to adult emergence was significantly longer for the resistant colony (12.1 ± 0.3 days) than the susceptible colony (9.6 ± 0.2 days). The pupation rate (80%; 95% (CI: 77.5-83.6) vs 83.5%; 95% (CI: 80.6-86.3)) and adult emergence rate (86.3% vs 92.8%) did not differ between the resistant and susceptible colonies, respectively. The sex ratio of the females to males for the resistant (1:1.2) and susceptible colonies (1:1.07) was significantly different. CONCLUSION: The study showed that pyrethroid resistance in An. gambiae had a fitness cost on their pre-imaginal development time and survival. Insecticide resistance delayed the development and reduced the survivorship of An. gambiae larvae. The study findings are important in understanding the fitness cost of insecticide resistance vectors that could contribute to shaping resistant management strategies.

Metabolic gestational age assessment in low resource settings: a validation protocol.

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.

Assessment of mosquito larval productivity among different land use types for targeted malaria vector control in the western Kenya highlands.

BACKGROUND: Mosquito larval source management (LSM) is likely to be more effective when adequate information such as dominant species, seasonal abundance, type of productive habitat, and land use type are available for targeted sites. LSM has been an effective strategy for reducing malaria morbidity in both urban and rural areas in Africa where sufficient proportions of larval habitats can be targeted. In this study, we conducted longitudinal larval source surveillance in the western Kenya highlands, generating data which can be used to establish cost-effective targeted intervention tools. METHODS: One hundred and twenty-four (124) positive larval habitats were monitored weekly and sampled for mosquito larvae over the 85-week period from 28 July 2009 to 3 March 2011. Two villages in the western Kenya highlands, Mbale and Iguhu, were included in the study. After preliminary sampling, habitats were classified into four types: hoof prints (n = 21; 17 % of total), swamps (n = 32; 26%), abandoned goldmines (n = 35; 28%) and drainage ditches (n = 36; 29%). Positive habitats occurred in two land use types: farmland (66) and pasture (58). No positive larval habitats occurred in shrub land or forest. RESULTS: A total of 46,846 larvae were sampled, of which 44.1% (20,907) were from abandoned goldmines, 30.9% (14,469) from drainage ditches, 22.4% (10,499) from swamps and 2.1% (971) from hoof prints. In terms of land use types, 57.2% (26,799) of the sampled larvae were from pasture and 42.8% (20,047) were from farmland. Of the specimens identified morphologically, 24,583 (52.5%) were Anopheles gambiae s.l., 11,901 (25.4%) were Culex quinquefasciatus, 5628 (12%) were An. funestus s.l. and 4734 (10.1%) were other anopheline species (An. coustani, An. squamosus, An. ziemanni or An. implexus). Malaria vector dynamics varied seasonally, with An.gambiae s.s. dominating during wet season and An.arabiensis during dry season. An increased proportion of An. arabiensis was observed compared to previous studies. CONCLUSION: These results suggest that long-term monitoring of larval habitats can establish effective surveillance systems and tools. Additionally, the results suggest that larval control is most effective in the dry season due to habitat restriction, with abandoned goldmines, drainage ditches and swamps being the best habitats to target. Both farmland and pasture should be targeted for effective larval control. An increased proportion of An. arabiensis in the An. gambiae complex was noticed in this study for the very first time in the western Kenya highlands; hence, further control tools should be in place for effective control of An. arabiensis.