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.

The influence of malaria control interventions and climate variability on changes in the geographical distribution of parasite prevalence in Kenya between 2015 and 2020.

BACKGROUND: The burden of malaria in Kenya was showing a declining trend, but appears to have reached a plateau in recent years. This study estimated changes in the geographical distribution of malaria parasite risk in the country between the years 2015 and 2020, and quantified the contribution of malaria control interventions and climatic/ environmental factors to these changes. METHODS: Bayesian geostatistical models were used to analyse the Kenyan 2015 and 2020 Malaria Indicator Survey (MIS) data. Bivariate models were fitted to identify the most important control intervention indicators and climatic/environmental predictors of parasitaemia risk by age groups (6-59 months and 5-14 years). Parasitaemia risk and the number of infected children were predicted over a 1 × 1 km2 grid. The probability of the decline in parasitaemia risk in 2020 compared to 2015 was also evaluated over the gridded surface and factors associated with changes in parasitaemia risk between the two surveys were evaluated. RESULTS: There was a significant decline in the coverage of most malaria indicators related to Insecticide Treated Nets (ITN) and Artemisinin Combination Therapies (ACT) interventions. Overall, there was a 31% and 26% reduction in malaria prevalence among children aged < 5 and 5-14 years, respectively. Among younger children, the highest reduction (50%) and increase (41%) were in the low-risk and semi-arid epi zones, respectively; while among older children there was increased risk in both the low-risk (83%) and semi-arid (100%) epi zones. Increase in nightlights and the proportion of individuals using ITNs in 2020 were associated with reduced parasitaemia risk. CONCLUSION: Increased nightlights and ITN use could have led to the reduction in parasitaemia risk. However, the reduction is heterogeneous and there was increased risk in northern Kenya. Taken together, these results suggest that constant surveillance and re-evaluation of parasite and vector control measures in areas with increased transmission is necessary. The methods used in this analysis can be employed in other settings.

The effects of climatic and non-climatic factors on malaria mortality at different spatial scales in western Kenya, 2008-2019.

BACKGROUND: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya. METHODS: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality. RESULTS: Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level. CONCLUSION: Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.

Health care-seeking behavior for childhood illnesses in western Kenya: Qualitative findings from the Child Health and Mortality Prevention Surveillance (CHAMPS) Study.

Background: Child mortality in Kenya is 41 per 1,000 live births, despite extensive investment in maternal, newborn, and child health interventions. Caregivers' health-seeking for childhood illness is an important determinant of child survival, and delayed healthcare is associated with high child mortality. We explore determinants of health-seeking decisions for childhood illnesses among caregivers in western Kenya. Methods: We conducted a qualitative study of 88 community members between April 2017 and February 2018 using purposive sampling in an informal urban settlement in Kisumu County, and in rural Siaya County. Key informant interviews, semi-structured interviews and focus group discussions were performed. We adopted the Partners for Applied Social Sciences model focusing on factors that influence the decision-making process to seek healthcare for sick infants and children. The discussions were audio-recorded and transcribed. Data management was completed on Nvivo® software. Iterative analysis process was utilized and themes were identified and collated. Results: Our findings reveal four thematic areas: Illness interpretation, the role of social relationship on illness recognition and response, medical pluralism and healthcare access. Participants reported some illnesses are caused by supernatural powers and some by biological factors, and that the illness etiology would determine the health-seeking pathway. It was common to seek consensus from respected community members on the diagnosis and therefore presumed cause and necessary treatment for a child's illness. Medical pluralism was commonly practiced and caregivers would alternate between biomedicine and traditional medicine. Accessibility of healthcare may determine the health seeking pathway. Caregivers unable to afford biomedical care may choose traditional medicine as a cheaper alternative. Conclusion: Health seeking behavior was driven by illness interpretation, financial cost associated with healthcare and advice from extended family and community. These findings enrich the perspectives of health education programs to develop health messages that address factors that hinder prompt health care seeking.

Impact of DREAMS interventions on attitudes towards gender norms among adolescent girls and young women: Findings from a prospective cohort in Kenya.

The DREAMS partnership aims to deliver a comprehensive package to reduce HIV incidence among adolescent girls and young women (AGYW), including through shifting gender norms. We evaluate DREAMS' effect on attitudes towards gender norms in two Kenyan settings. AGYW aged 15-22 in Nairobi (n = 852) and Gem (n = 761) were randomly selected for cohort enrolment in 2017-18 and followed-up to 2019. We described the proportion of AGYW and their male peers with equitable attitudes towards gender norms, using an adapted version of the GEM scale. We estimated the association between self-reported invitation to DREAMS (in 2017-18) and AGYW's attitudes towards two dimensions of gender norms, and then applied a causal inference framework to estimate the difference in the proportion of AGYW with equitable attitudes under the counterfactual scenarios that all versus none were DREAMS beneficiaries. We estimated that overall, 90.2% versus 87.1% of AGYW would have equitable norms around sexual and reproductive health decision-making in Nairobi if all versus none were DREAMS beneficiaries (+3.1; 95%CI:-2.5, +9.0). In Gem, we estimated a risk difference of +1.0 (89.6% vs 88.6%, 95%CI: -3.6,+5.6). There was no evidence for an effect of DREAMS on attitudes towards violence-related norms (Nairobi: 82.7% vs 82.2%, +0.5; 95%CI: -5.3,+6.5; Gem: 44.3% vs 48.2%, -3.9; 95%CI: -11.7,+3.0). We found no evidence of an impact of DREAMS invitation on individual attitudes towards gender norms. In some cases, equitable attitudes at enrolment left limited scope for improvement, and additional effort may be required to shift inequitable violence attitudes among both AGYW and their male peers.

Prevalence and risk factors of sexually transmitted infections in the setting of a generalized HIV epidemic-a population-based study, western Kenya.

BACKGROUND: Sexually transmitted infections (STIs) cause adverse health outcomes, including increasing HIV acquisition/transmission risk. We analyzed data from an HIV biomarker and behavioral survey to estimate STI prevalence, and explore associated factors in the setting of a generalized HIV epidemic in Siaya County, western Kenya. METHODS: Data were collected in March-September 2022 through face-to-face interviews using structured questionnaires; records from 9643 sexually active participants aged 13+ years were included in the analysis. We calculated weighted self-reported STI prevalence, by sex, age, and HIV status and explored associated factors using multivariable logistic regression. RESULTS: Median age was 37 years and 59.9% were female; HIV prevalence was 18.0%. Overall STI prevalence was 1.8%; 1.5-fold higher among males vs. females, and 2.6-fold higher among participants living with HIV vs. those without. HIV status and multiple sexual partners were independently associated with STI in both sexes. Mind-altering substance use and being circumcised were associated with STI among males. CONCLUSIONS: This study estimates STI prevalence in the setting of high HIV prevalence. Findings underscore the importance of: effective STI screening in HIV clinics and HIV testing and counseling in STI clinics; screening and counseling on substance use, and HIV pre-exposure prophylaxis; and intensive sexual health counseling in male circumcision programmes.

Pregnancy-related mortality up to 1 year postpartum in sub-Saharan Africa: an analysis of verbal autopsy data from six countries.

OBJECTIVE: To compare the causes of death for women who died during pregnancy and within the first 42 days postpartum with those of women who died between >42 days and within 1 year postpartum. DESIGN: Open population cohort (Health and Demographic Surveillance Systems). SETTING: Ten Health and Demographic Surveillance Systems (HDSS) in The Gambia, Kenya, Malawi, Tanzania, Ethiopia and South Africa. POPULATION: 2114 deaths which occurred within 1 year of the end of pregnancy where a verbal autopsy interview was conducted from 2000 to 2019. METHODS: InterVA5 and InSilicoVA verbal autopsy algorithms were used to attribute the most likely underlying cause of death, which were grouped according to adapted International Classification of Diseases-Maternal Mortality categories. Multinomial regression was used to compare differences in causes of deaths within 42 days versus 43-365 days postpartum adjusting for HDSS and time period (2000-2009 and 2010-2019). MAIN OUTCOME MEASURES: Cause of death and the verbal autopsy Circumstances of Mortality Categories (COMCATs). RESULTS: Of 2114 deaths, 1212 deaths occurred within 42 days postpartum and 902 between 43 and 365 days postpartum. Compared with deaths within 42 days, deaths from HIV and TB, other infectious diseases, and non-communicable diseases constituted a significantly larger proportion of late pregnancy-related deaths beyond 42 days postpartum, and health system failures were important in the circumstances of those deaths. The contribution of HIV and TB to deaths beyond 42 days postpartum was greatest in Southern Africa. The causes of pregnancy-related mortality within and beyond 42 days postpartum did not change significantly between 2000-2009 and 2010-2019. CONCLUSIONS: Cause of death data from the extended postpartum period are critical to inform prevention. The dominance of HIV and TB, other infectious and non-communicable diseases to (late) pregnancy-related mortality highlights the need for better integration of non-obstetric care with ante-, intra- and postpartum care in high-burden settings.

Prevalence and Missed Cases of Respiratory Distress Syndrome Disease Amongst Neonatal Deaths Enrolled in the Kenya Child Health and Mortality Prevention Surveillance Network (CHAMPS) Program Between 2017 and 2021.

Objectives. To describe RDS in neonatal deaths at the CHAMPS-Kenya site between 2017 and 2021. Methods. We included 165 neonatal deaths whose their Causes of death (COD) were determined by a panel of experts using data from post-mortem conducted through minimally invasive tissue specimen testing, clinical records, and verbal autopsy. Results. Twenty-six percent (43/165) of neonatal deaths were attributable to RDS. Most cases occurred in low birthweight and preterm neonates. From these cases, less than half of the hospitalizations were diagnosed with RDS before death, and essential diagnostic tests were not performed in most cases. Most cases received suboptimal levels of supplemental oxygen, and critical interventions like surfactant replacement therapy and mechanical ventilation were not adequately utilized when available. Conclusion. The study highlights the urgent need for improved diagnosis and management of RDS, emphasizing the importance of increasing clinical suspicion and enhancing training in its clinical management to reduce mortality rates.

Acceptability of minimally invasive autopsy by community members and healthcare workers in Siaya and Kisumu counties, western Kenya, 2017-2018.

Worldwide, nearly six million children under the age of five (<5s) die annually, a substantial proportion of which are due to preventable and treatable diseases. Efforts to reduce child mortality indicators in the most affected regions are often undermined by a lack of accurate cause of death data. To generate timely and more accurate causes of death data for <5s, the Child Health and Mortality Prevention Surveillance (CHAMPS) Network established mortality surveillance in multiple countries using Minimally Invasive Tissue Sampling (MITS) in <5 deaths. Here we present acceptability of MITS by community members and healthcare workers in Siaya and Kisumu counties, western Kenya. From April 2017 to February 2018, we conducted 40 in-depth interviews and five focus group discussions with healthcare workers and community members, before and during CHAMPS implementation. Participants were purposively selected. Field observations to understand traditional death-related practices were also performed. Interviews were transcribed into Nvivo 11.0 for data organization and management. Analysis was guided by the grounded theory approach. Facilitators of acceptability were desire to understand why death occurred, timely performance of MITS procedures, potential for MITS results in improving clinical practice and specific assistance provided to families by the CHAMPS program. However, cultural and religious beliefs highlighted important challenges to acceptability, including CHAMPS teams recruiting after a child's death, rumours and myths, unmet expectations from families, and fear by healthcare workers that some families could use MITS results to sue for negligence. Increasing MITS uptake requires sustained strategies to strengthen the identified facilitators of acceptability and simultaneously address the barriers. MITS acceptance will contribute to better characterization of causes of death and support the development of improved interventions aimed at reducing <5 mortality.

Post-Discharge Risk of Mortality in Children under 5 Years of Age in Western Kenya: A Retrospective Cohort Study.

Limited evidence suggests that children in sub-Saharan Africa hospitalized with all-cause severe anemia or severe acute malnutrition (SAM) are at high risk of dying in the first few months after discharge. We aimed to compare the risks of post-discharge mortality by health condition among hospitalized children in an area with high malaria transmission in western Kenya. We conducted a retrospective cohort study among recently discharged children aged < 5 years using mortality data from a health and demographic surveillance system that included household and pediatric in-hospital surveillance. Cox regression was used to compare post-discharge mortality. Between 2008 and 2013, overall in-hospital mortality was 2.8% (101/3,639). The mortality by 6 months after discharge (primary outcome) was 6.2% (159/2,556) and was highest in children with SAM (21.6%), followed by severe anemia (15.5%), severe pneumonia (5.6%), "other conditions" (5.6%), and severe malaria (0.7%). Overall, the 6-month post-discharge mortality in children hospitalized with SAM (hazard ratio [HR] = 3.95, 2.60-6.00, P < 0.001) or severe anemia (HR = 2.55, 1.74-3.71, P < 0.001) was significantly higher than that in children without these conditions. Severe malaria was associated with lower 6-month post-discharge mortality than children without severe malaria (HR = 0.33, 0.21-0.53, P < 0.001). The odds of dying by 6 months after discharge tended to be higher than during the in-hospital period for all children, except for those admitted with severe malaria. The first 6 months after discharge is a high-risk period for mortality among children admitted with severe anemia and SAM in western Kenya. Strategies to address this risk period are urgently needed.

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population.

Introduction: Postpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort. Method: Four machine learning models - logistic regression, naïve Bayes, decision tree, and random forest - were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve. Result: The naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population. Discussion: This study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.

Comparison of programmatic data from antenatal clinics with population-based HIV prevalence estimates in the era of universal test and treat in western Kenya.

OBJECTIVE: To compare HIV prevalence estimates from routine programme data in antenatal care (ANC) clinics in western Kenya with HIV prevalence estimates in a general population sample in the era of universal test and treat (UTT). METHODS: The study was conducted in the area covered by the Siaya Health Demographic Surveillance System (Siaya HDSS) in western Kenya and used data from ANC clinics and the general population. ANC data (n = 1,724) were collected in 2018 from 13 clinics located within the HDSS. The general population was a random sample of women of reproductive age (15-49) who reside in the Siaya HDSS and participated in an HIV sero-prevalence survey in 2018 (n = 2,019). Total and age-specific HIV prevalence estimates were produced from both datasets and demographic decomposition methods were used to quantify the contribution of the differences in age distributions and age-specific HIV prevalence to the total HIV prevalence estimates. RESULTS: Total HIV prevalence was 18.0% (95% CI 16.3-19.9%) in the ANC population compared with 18.4% (95% CI 16.8-20.2%) in the general population sample. At most ages, HIV prevalence was higher in the ANC population than in the general population. The age distribution of the ANC population was younger than that of the general population, and because HIV prevalence increases with age, this reduced the total HIV prevalence among ANC attendees relative to prevalence standardised to the general population age distribution. CONCLUSION: In the era of UTT, total HIV prevalence among ANC attendees and the general population were comparable, but age-specific HIV prevalence was higher in the ANC population in most age groups. The expansion of treatment may have led to changes in both the fertility of women living with HIV and their use of ANC services, and our results lend support to the assertion that the relationship between ANC and general population HIV prevalence estimates are highly dynamic.

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.

The long-term impact of HIV/AIDS on socio-economic status: a comparative analysis of households headed by HIV-positive and HIV-negative individuals in Western Kenya.

HIV/AIDS is known to have adverse effects on individual and family socio-economic status due to the loss of productive time and over-expenditure in treatment. However, empirical data on how HIV/AIDS affects households' socio-economic status are insufficient. We linked socio-economic data from a Health and Demographic Surveillance System (HDSS) that implements an HIV/AIDS Longitudinal bio-behavioural survey (LBBS) to understand the long-term impact of HIV/AIDS on households' socio-economic status between 2010 and 2018. We compared changes in socio-economic status between households headed by HIV-negative and -positive individuals. A logistic regression was used to assess factors that influence socio-economic status. The level of education and household size were not significant predictors of households' socio-economic status. Households headed by HIV-positive individuals could maintain their baseline socio-economic status (unadjusted RRR = 1.17, 95% CI: 1.01, 1.36) but improvement chances were reduced despite a non-significant association (unadjusted RRR = 0.98, 95% CI: 0.80, 1.20). While HIV/AIDS is known to disrupt economic growth, in this setting, being a male household head, old and widowed reduces chances of improved socio-economic status. The elderly people, widows and widowers are disadvantaged. Consequently, there is a need for special programmes, which seek to empower the identified vulnerable groups economically. .

Malaria, climate variability, and interventions: modelling transmission dynamics.

Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data. The demographic surveillance systems in Africa offer unique platforms for quantifying the relative effects of weather variability on the burden of malaria. Here, using a process-based stochastic transmission model, we show that in the lowlands of malaria endemic western Kenya, variations in climatic factors played a key role in driving malaria incidence during 2008-2019, despite high bed net coverage and use among the population. The model captures some of the main mechanisms of human, parasite, and vector dynamics, and opens the possibility to forecast malaria in endemic regions, taking into account the interaction between future climatic conditions and intervention scenarios.

The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: A time-series analysis of monthly incidence data from 2008 to 2019.

Background: Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya. Methods: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence. Results: Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR] = 0.70, 95% Bayesian credible interval [BCI]: 0.59-0.82), while rainfall was associated with increased incidence (IRR = 1.27, 95% BCI: 1.10-1.44). Bed net use was associated with a decline in malaria incidence in children aged 6-59 months (IRR = 0.78, 95% BCI: 0.70-0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population. Conclusions: Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6-59 months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.

Adverse maternal, fetal, and newborn outcomes among pregnant women with SARS-CoV-2 infection: an individual participant data meta-analysis.

INTRODUCTION: Despite a growing body of research on the risks of SARS-CoV-2 infection during pregnancy, there is continued controversy given heterogeneity in the quality and design of published studies. METHODS: We screened ongoing studies in our sequential, prospective meta-analysis. We pooled individual participant data to estimate the absolute and relative risk (RR) of adverse outcomes among pregnant women with SARS-CoV-2 infection, compared with confirmed negative pregnancies. We evaluated the risk of bias using a modified Newcastle-Ottawa Scale. RESULTS: We screened 137 studies and included 12 studies in 12 countries involving 13 136 pregnant women.Pregnant women with SARS-CoV-2 infection-as compared with uninfected pregnant women-were at significantly increased risk of maternal mortality (10 studies; n=1490; RR 7.68, 95% CI 1.70 to 34.61); admission to intensive care unit (8 studies; n=6660; RR 3.81, 95% CI 2.03 to 7.17); receiving mechanical ventilation (7 studies; n=4887; RR 15.23, 95% CI 4.32 to 53.71); receiving any critical care (7 studies; n=4735; RR 5.48, 95% CI 2.57 to 11.72); and being diagnosed with pneumonia (6 studies; n=4573; RR 23.46, 95% CI 3.03 to 181.39) and thromboembolic disease (8 studies; n=5146; RR 5.50, 95% CI 1.12 to 27.12).Neonates born to women with SARS-CoV-2 infection were more likely to be admitted to a neonatal care unit after birth (7 studies; n=7637; RR 1.86, 95% CI 1.12 to 3.08); be born preterm (7 studies; n=6233; RR 1.71, 95% CI 1.28 to 2.29) or moderately preterm (7 studies; n=6071; RR 2.92, 95% CI 1.88 to 4.54); and to be born low birth weight (12 studies; n=11 930; RR 1.19, 95% CI 1.02 to 1.40). Infection was not linked to stillbirth. Studies were generally at low or moderate risk of bias. CONCLUSIONS: This analysis indicates that SARS-CoV-2 infection at any time during pregnancy increases the risk of maternal death, severe maternal morbidities and neonatal morbidity, but not stillbirth or intrauterine growth restriction. As more data become available, we will update these findings per the published protocol.

Clinical risk factors of adverse outcomes among women with COVID-19 in the pregnancy and postpartum period: a sequential, prospective meta-analysis.

OBJECTIVE: This sequential, prospective meta-analysis sought to identify risk factors among pregnant and postpartum women with COVID-19 for adverse outcomes related to disease severity, maternal morbidities, neonatal mortality and morbidity, and adverse birth outcomes. DATA SOURCES: We prospectively invited study investigators to join the sequential, prospective meta-analysis via professional research networks beginning in March 2020. STUDY ELIGIBILITY CRITERIA: Eligible studies included those recruiting at least 25 consecutive cases of COVID-19 in pregnancy within a defined catchment area. METHODS: We included individual patient data from 21 participating studies. Data quality was assessed, and harmonized variables for risk factors and outcomes were constructed. Duplicate cases were removed. Pooled estimates for the absolute and relative risk of adverse outcomes comparing those with and without each risk factor were generated using a 2-stage meta-analysis. RESULTS: We collected data from 33 countries and territories, including 21,977 cases of SARS-CoV-2 infection in pregnancy or postpartum. We found that women with comorbidities (preexisting diabetes mellitus, hypertension, cardiovascular disease) vs those without were at higher risk for COVID-19 severity and adverse pregnancy outcomes (fetal death, preterm birth, low birthweight). Participants with COVID-19 and HIV were 1.74 times (95% confidence interval, 1.12-2.71) more likely to be admitted to the intensive care unit. Pregnant women who were underweight before pregnancy were at higher risk of intensive care unit admission (relative risk, 5.53; 95% confidence interval, 2.27-13.44), ventilation (relative risk, 9.36; 95% confidence interval, 3.87-22.63), and pregnancy-related death (relative risk, 14.10; 95% confidence interval, 2.83-70.36). Prepregnancy obesity was also a risk factor for severe COVID-19 outcomes including intensive care unit admission (relative risk, 1.81; 95% confidence interval, 1.26-2.60), ventilation (relative risk, 2.05; 95% confidence interval, 1.20-3.51), any critical care (relative risk, 1.89; 95% confidence interval, 1.28-2.77), and pneumonia (relative risk, 1.66; 95% confidence interval, 1.18-2.33). Anemic pregnant women with COVID-19 also had increased risk of intensive care unit admission (relative risk, 1.63; 95% confidence interval, 1.25-2.11) and death (relative risk, 2.36; 95% confidence interval, 1.15-4.81). CONCLUSION: We found that pregnant women with comorbidities including diabetes mellitus, hypertension, and cardiovascular disease were at increased risk for severe COVID-19-related outcomes, maternal morbidities, and adverse birth outcomes. We also identified several less commonly known risk factors, including HIV infection, prepregnancy underweight, and anemia. Although pregnant women are already considered a high-risk population, special priority for prevention and treatment should be given to pregnant women with these additional risk factors.

Women's risk of death beyond 42 days post partum: a pooled analysis of longitudinal Health and Demographic Surveillance System data in sub-Saharan Africa.

BACKGROUND: WHO's standard definitions of pregnancy-related and maternal deaths only include deaths that occur within 42 days of delivery, termination, or abortion, with major implications for post-partum care and maternal mortality surveillance. We therefore estimated post-partum survival from childbirth up to 1 year post partum to evaluate the empirical justification for the 42-day post-partum threshold. METHODS: We used prospective, longitudinal Health and Demographic Surveillance System (HDSS) data from 30 sites across 12 sub-Saharan African countries to estimate women's risk of death from childbirth until 1 year post partum from all causes. Observations were included if the childbirth occurred from 1991 onwards in the HDSS site and maternal age was 10-54 years. We calculated person-years as the time between childbirth and next birth, outmigration, death, or the end of the first year post partum, whichever occurred first. For six post-partum risk intervals (0-1 days, 2-6 days, 7-13 days, 14-41 days, 42-122 days, and 4-11 months), we calculated the adjusted rate ratios of death relative to a baseline risk of 12-17 months post partum. FINDINGS: Between Jan 1, 1991, and Feb 24, 2020, 647 104 births occurred in the HDSS sites, contributing to 602 170 person-years of exposure time and 1967 deaths within 1 year of delivery. After adjustment for confounding, mortality was 38·82 (95% CI 33·21-45·29) times higher than baseline on days 0-1 after childbirth, 4·97 (3·94-6·21) times higher for days 2-6, 3·35 (2·64-4·20) times higher for days 7-13, and 2·06 (1·74-2·44) times higher for days 14-41. From 42 days to 4 months post partum, mortality was still 1·20 (1·03-1·39) times higher (ie, a 20% higher risk), but deaths in this interval would be excluded from measurement of pregnancy-related mortality. Extending the WHO 42-day post-partum threshold up to 4 months would increase the post-partum pregnancy-related mortality ratio by 40%. INTERPRETATION: This multicountry study has implications for measurement and clinical practice. It makes the case for WHO to extend the 42-day post-partum threshold to capture the full duration of risk of pregnancy-related deaths. There is a need for a new indicator to track late pregnancy-related deaths that occur beyond 42 days, which are otherwise excluded from global maternal health surveillance efforts. Our results also emphasise the need for international agencies to disaggregate estimates by antepartum, intrapartum, postpartum, and extended post-partum periods. Additionally, the schedule and content of postnatal care packages should reflect the extended duration of post-partum risk. FUNDING: The UK Economic and Social Research Council.

HIV Risk Factors and Risk Perception Among Adolescent Girls and Young Women: Results From a Population-Based Survey in Western Kenya, 2018.

BACKGROUND: In sub-Saharan Africa, HIV prevalence in adolescent girls and young women (AGYW) is 2-fold to 3-fold higher than that in adolescent boys and young men. Understanding AGYW's perception of HIV risk is essential for HIV prevention efforts. METHODS: We analyzed data from a HIV biobehavioral survey conducted in western Kenya in 2018. Data from AGYW aged 15-24 years who had a documented HIV status were included. We calculated weighted prevalence and evaluated factors associated with outcomes of interest (HIV infection and high risk perception) using generalized linear models to calculate prevalence ratios. RESULTS: A total of 3828 AGYW were included; 63% were aged 15-19 years. HIV prevalence was 4.5% and 14.5% of sexually active AGYW had high risk perception. Over 70% of participants had accessed HIV testing and counseling in the past 12 months. Factors associated with both HIV infection and high risk perception included having an HIV-positive partner or partner with unknown status and having a sexually transmitted infection in the past 12 months. Having an older (by ≥10 years) partner was associated with HIV infection, but not high risk perception. Less than 30% of sexually active AGYW with 3 or more HIV risk factors had high perception of HIV risk. CONCLUSION: Gaps in perceived HIV risk persist among AGYW in Kenya. High access to HIV testing and prevention services in this population highlights platforms through which AGYW may be reached with improved risk counseling, and to increase uptake of HIV prevention strategies.