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.

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.

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.

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.

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.

BACKGROUND: Active surveillance systems for monitoring vaccine safety among pregnant women address some of the limitations of a current passive surveillance approach utilized in low- and middle-income countries (LMIC). However, few active surveillance systems in LMIC exist. Our study assessed the feasibility of utilizing three existing data collection systems in Kenya for active surveillance of maternal immunization and to assess the applicability of Global Alignment of Immunization Safety Assessment in pregnancy (GAIA) case definitions that were initially developed for clinical trials within these systems. METHODS: We assessed applicability of GAIA case definition for maternal Tetanus Toxoid exposure, stillbirth, low birth weight, small for gestational age, Neonatal Invasive Blood Stream Infection (NIBSI), prematurity and neonatal death in two routine web-based health information systems (Kenya EMR and DHIS-2), and a web-based population-based pregnancy research platform (ANCOV(1)) in Kenya. RESULTS: All three HIS were capable of reporting selected outcomes to varying degrees of GAIA certainty. The ANCOV platform was the most robust in collecting and collating clinical data for effective maternal pharmacovigilance. The utilization of facility- and district-aggregated data limits the usefulness of DHIS-2 in pharmacovigilance as currently operationalized. While the Kenya EMR contained individual level data and meets the key considerations for effective pharmacovigilance, it was used primarily for HIV care and treatment records in a small proportion of health facilities and would require additional resources to expand to all antenatal care facilities and to link maternal and infant records. DISCUSSION: Population-based research studies may offer a responsive short-term option for implementing maternal vaccine pharmacovigilance in LMICs. However, the foundation exists for long-term capacity building within the national health electronic data systems to provide this critical service as well as ensure participation of the country in international studies on maternal vaccine safety.

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.

We urgently need answers to basic epidemiological questions regarding SARS-CoV-2 infection in pregnant and postpartum women and its effect on their newborns. While many national registries, health facilities, and research groups are collecting relevant data, we need a collaborative and methodologically rigorous approach to better combine these data and address knowledge gaps, especially those related to rare outcomes. We propose that using a sequential, prospective meta-analysis (PMA) is the best approach to generate data for policy- and practice-oriented guidelines. As the pandemic evolves, additional studies identified retrospectively by the steering committee or through living systematic reviews will be invited to participate in this PMA. Investigators can contribute to the PMA by either submitting individual patient data or running standardized code to generate aggregate data estimates. For the primary analysis, we will pool data using two-stage meta-analysis methods. The meta-analyses will be updated as additional data accrue in each contributing study and as additional studies meet study-specific time or data accrual thresholds for sharing. At the time of publication, investigators of 25 studies, including more than 76,000 pregnancies, in 41 countries had agreed to share data for this analysis. Among the included studies, 12 have a contemporaneous comparison group of pregnancies without COVID-19, and four studies include a comparison group of non-pregnant women of reproductive age with COVID-19. Protocols and updates will be maintained publicly. Results will be shared with key stakeholders, including the World Health Organization (WHO) Maternal, Newborn, Child, and Adolescent Health (MNCAH) Research Working Group. Data contributors will share results with local stakeholders. Scientific publications will be published in open-access journals on an ongoing basis.Competing Interest StatementClare Whitehead declares a a relationship with the following entities, Ferring Pharmaceuticals COVID19 Investigational, Grant, NHMRC Fellowship (salary support). Alice Panchaud declares the following research grants to institution: H2020-Grant (Consortium member of Innovative medicine initiative call 13 topic 9) (ConcePTION), Efficacy and safety studies on Medicines EMA/2017/09/PE/11, Lot 4, WP 2 lead (CONSIGN: Study on impact of COVID-19 infection and medicines in pregnancy), Safety monitoring of COVID-19 vaccines in the EU Reopening of competition no. 20 under a framework contract following procurement procedure EMA/2017/09/PE (Lot 3) 4. Federal Office of Public Health (207000 CHF). (The COVI-Preg registry). Edward Mullins declares a relationship with the following entities National Institute for Health Research (Project grant for PAN COVID study) Deborah Money declares a relationship with the following entities, Canadian Institutes of Health Research (payments to my institution only), Public Health Agency of Canada (payments to my institution only), BC Womens Foundation (payments to my institution only) and is a Member of the COVID-19 Immunity Task Force sponsored by the Canadian government. Torri D. Metz declares a relationship with the following entities, Pfizer (site Principal Investigator for SARS-CoV-2 vaccination in pregnancy study, money paid to institution and member of Medical Advisory Board for SARS-CoV-2 vaccination in pregnancy study, money paid to me), NICHD (subcommittee Chair for the NICHD Maternal-Fetal Medicine Units Network Gestational Research Assessments of COVID-19 (GRAVID) study), and Society for Maternal-Fetal Medicine (board member). Erica Lokken declares a relationship with the following entity, US NIH (paid institution). Karen L. Kotloff declares a relationship with the following entity, Bill and Melinda Gates Foundation. Siran He declares a relationship with the following entity, Bill and Melinda Gates Foundtion (payments made to my institution). Valerie Flaherman declares a relationship with the following entities, Bill and Melinda Gates Foundation (payments to my institution), Yellow Chair Foundati n (payments to my institution), Robert Woods Johnson Foundation (payments to my institution), CDC Foundation, California Health Care Foundation (payments to my institution), Tara Health Foundation (payments to my institution), UCSF Womens Health Center of Excellence (payments to my institution) and California Department of Health Care Services (payments made to my institution). Jose Sanin-Blair declares a relationship with the following entity, Ferring Pharmaceuticals which give a grant ($10,000) for the expenses of RECOGEST trial and is a part of the Columbian Federation of Perinatology Yalda Afshar declares a relationship with the following entities, Bill and Melinda Gates Foundation (payments made to my institution), CDC Foundation (payments made to my institution), Robert Woods Johnson Foundation (payments made to my institution), and UCLA Deans Office COVID-19 research (payments made to my institution). Rebecca Cliffton declares a relationship with the following entity, NIH HD36801 (MFMU Network DCC).Clinical TrialPROSPERO ID: 188955Funding StatementFunded by the Bill &amp; Melinda Gates Foundation grant to Emily Smith (INV-022057) at George Washington University and a grant to Emily Smith via a grant from the Bill &amp; Melinda Gates Foundation to Stephanie Gaw (INV-017035) at University of California San Francisco.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This is a protocol paper and thus exempt from ethical approval. Ultimately, the meta-analysis study is exempt from human research ethics approval as the study authors will be synthesizing de-identified or aggregate data.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThis is a protocol paper and there is no related data to share.

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.

OBJECTIVE: This sequential, prospective meta-analysis (sPMA) 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, adverse birth outcomes. DATA SOURCES: We prospectively invited study investigators to join the sPMA 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. STUDY APPRAISAL AND SYNTHESIS 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 two-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 (pre-existing diabetes, hypertension, cardiovascular disease) versus those without were at higher risk for COVID-19 severity and pregnancy health outcomes (fetal death, preterm birth, low birthweight). Participants with COVID-19 and HIV were 1.74 times (95% CI: 1.12, 2.71) more likely to be admitted to the ICU. Pregnant women who were underweight before pregnancy were at higher risk of ICU admission (RR 5.53, 95% CI: 2.27, 13.44), ventilation (RR 9.36, 95% CI: 3.87, 22.63), and pregnancy-related death (RR 14.10, 95% CI: 2.83, 70.36). Pre-pregnancy obesity was also a risk factor for severe COVID-19 outcomes including ICU admission (RR 1.81, 95% CI: 1.26,2.60), ventilation (RR 2.05, 95% CI: 1.20,3.51), any critical care (RR 1.89, 95% CI: 1.28,2.77), and pneumonia (RR 1.66, 95% CI: 1.18,2.33). Anemic pregnant women with COVID-19 also had increased risk of ICU admission (RR 1.63, 95% CI: 1.25, 2.11) and death (RR 2.36, 95% CI: 1.15, 4.81). CONCLUSION: We found that pregnant women with comorbidities including diabetes, 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, pre-pregnancy 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.

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.

We urgently need answers to basic epidemiological questions regarding SARS-CoV-2 infection in pregnant and postpartum women and its effect on their newborns. While many national registries, health facilities, and research groups are collecting relevant data, we need a collaborative and methodologically rigorous approach to better combine these data and address knowledge gaps, especially those related to rare outcomes. We propose that using a sequential, prospective meta-analysis (PMA) is the best approach to generate data for policy- and practice-oriented guidelines. As the pandemic evolves, additional studies identified retrospectively by the steering committee or through living systematic reviews will be invited to participate in this PMA. Investigators can contribute to the PMA by either submitting individual patient data or running standardized code to generate aggregate data estimates. For the primary analysis, we will pool data using two-stage meta-analysis methods. The meta-analyses will be updated as additional data accrue in each contributing study and as additional studies meet study-specific time or data accrual thresholds for sharing. At the time of publication, investigators of 25 studies, including more than 76,000 pregnancies, in 41 countries had agreed to share data for this analysis. Among the included studies, 12 have a contemporaneous comparison group of pregnancies without COVID-19, and four studies include a comparison group of non-pregnant women of reproductive age with COVID-19. Protocols and updates will be maintained publicly. Results will be shared with key stakeholders, including the World Health Organization (WHO) Maternal, Newborn, Child, and Adolescent Health (MNCAH) Research Working Group. Data contributors will share results with local stakeholders. Scientific publications will be published in open-access journals on an ongoing basis.

BACKGROUND: Kenya introduced the monovalent G1P [8] Rotarix(R) vaccine into the infant immunization schedule in July 2014. We examined trends in rotavirus group A (RVA) genotype distribution pre- (January 2010-June 2014) and post- (July 2014-December 2018) RVA vaccine introduction. METHODS: Stool samples were collected from children aged < 13 years from four surveillance sites across Kenya: Kilifi County Hospital, Tabitha Clinic Nairobi, Lwak Mission Hospital, and Siaya County Referral Hospital (children aged < 5 years only). Samples were screened for RVA using enzyme linked immunosorbent assay (ELISA) and VP7 and VP4 genes sequenced to infer genotypes. RESULTS: We genotyped 614 samples in pre-vaccine and 261 in post-vaccine introduction periods. During the pre-vaccine introduction period, the most frequent RVA genotypes were G1P [8] (45.8%), G8P [4] (15.8%), G9P [8] (13.2%), G2P [4] (7.0%) and G3P [6] (3.1%). In the post-vaccine introduction period, the most frequent genotypes were G1P [8] (52.1%), G2P [4] (20.7%) and G3P [8] (16.1%). Predominant genotypes varied by year and site in both pre and post-vaccine periods. Temporal genotype patterns showed an increase in prevalence of vaccine heterotypic genotypes, such as the commonly DS-1-like G2P [4] (7.0 to 20.7%, P < .001) and G3P [8] (1.3 to 16.1%, P < .001) genotypes in the post-vaccine introduction period. Additionally, we observed a decline in prevalence of genotypes G8P [4] (15.8 to 0.4%, P < .001) and G9P [8] (13.2 to 5.4%, P < .001) in the post-vaccine introduction period. Phylogenetic analysis of genotype G1P [8], revealed circulation of strains of lineages G1-I, G1-II and P [8]-1, P [8]-III and P [8]-IV. Considerable genetic diversity was observed between the pre and post-vaccine strains, evidenced by distinct clusters. CONCLUSION: Genotype prevalence varied from before to after vaccine introduction. Such observations emphasize the need for long-term surveillance to monitor vaccine impact. These changes may represent natural secular variation or possible immuno-epidemiological changes arising from the introduction of the vaccine. Full genome sequencing could provide insights into post-vaccine evolutionary pressures and antigenic diversity.

INTRODUCTION: Monovalent rotavirus vaccine, RotarixTM, was introduced in Kenya in July 2014, is recommended to infants as oral doses at ages 6 and 10 weeks. A multi-site study was established in two population based surveillance sites to evaluate vaccine impact on the incidence of rotavirus-associated hospitalisations (RVH). METHODS: Hospital-based surveillance was conducted from January 2010 to June 2017 for acute diarrhoea hospitalisations among children aged <5 years in two health facilities in Kenya. A controlled interrupted time series analysis was undertaken to compare RVH pre and post vaccine introduction using rotavirus negative cases as a control series. The change in incidence post vaccine introduction was estimated from a negative binomial model that adjusted for secular trend, seasonality and multiple health worker industrial actions (strikes). RESULTS: Between January 2010 and June 2017 there were 1513 and 1652 diarrhoea hospitalisations in Kilifi and Siaya; among those tested for rotavirus, 28% (315/1142) and 23% (197/877) were positive, respectively. There was a 57% (95% CI: 8 to 80) reduction in RVH observed in the first year post vaccine introduction in Kilifi and a 59% (95% CI: 20 to 79) reduction in Siaya. In the second year, RVH decreased further at both sites, 80% (95% CI: 46 to 93) reduction in Kilifi and 82% reduction in Siaya (95% CI: 61 to 92), and this reduction was sustained at both sites into the third year. CONCLUSIONS: A substantial reduction of RVH and all-cause diarrhoea has been observed in two demographic surveillance sites in Kenya within 3 years of vaccine introduction.

BACKGROUND: Parasite prevalence has been used widely as a measure of malaria transmission, especially in malaria endemic areas. However, its contribution and relationship to malaria mortality across different age groups has not been well investigated. Previous studies in a health and demographic surveillance systems (HDSS) platform in western Kenya quantified the contribution of incidence and entomological inoculation rates (EIR) to mortality. The study assessed the relationship between outcomes of malaria parasitaemia surveys and mortality across age groups. METHODS: Parasitological data from annual cross-sectional surveys from the Kisumu HDSS between 2007 and 2015 were used to determine malaria parasite prevalence (PP) and clinical malaria (parasites plus reported fever within 24 h or temperature above 37.5 degrees C). Household surveys and verbal autopsy (VA) were used to obtain data on all-cause and malaria-specific mortality. Bayesian negative binomial geo-statistical regression models were used to investigate the association of PP/clinical malaria with mortality across different age groups. Estimates based on yearly data were compared with those from aggregated data over 4 to 5-year periods, which is the typical period that mortality data are available from national demographic and health surveys. RESULTS: Using 5-year aggregated data, associations were established between parasite prevalence and malaria-specific mortality in the whole population (RRmalaria = 1.66; 95% Bayesian Credible Intervals: 1.07-2.54) and children 1-4 years (RRmalaria = 2.29; 1.17-4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RRall-cause = 1.32; 1.01-1.74); (RRmalaria = 2.50; 1.27-4.81), children 1-4 years (RRall-cause = 1.89; 1.00-3.51); (RRmalaria = 3.37; 1.23-8.93) and in older children 5-14 years (RRall-cause = 3.94; 1.34-11.10); (RRmalaria = 7.56; 1.20-39.54), no association was found among neonates, adults (15-59 years) and the elderly (60+ years). Distance to health facilities, socioeconomic status, elevation and survey year were important factors for all-cause and malaria-specific mortality. CONCLUSION: Malaria parasitaemia from cross-sectional surveys was associated with mortality across age groups over 4 to 5 year periods with clinical malaria more strongly associated with mortality than parasite prevalence. This effect was stronger in children 5-14 years compared to other age-groups. Further analyses of data from other HDSS sites or similar platforms would be useful in investigating the relationship between malaria and mortality across different endemicity levels.

BACKGROUND: Rotavirus remains a leading cause of diarrheal illness and death among children worldwide. Data on rotavirus vaccine effectiveness in sub-Saharan Africa are limited. Kenya introduced monovalent rotavirus vaccine (RV1) in July 2014. We assessed RV1 effectiveness against rotavirus-associated hospitalization in Kenyan children. METHODS: Between July-2014 and December-2017, we conducted surveillance for acute gastroenteritis (AGE) in three hospitals across Kenya. We analysed data from children age-eligible for >/=1 RV1 dose, with stool tested for rotavirus and confirmed vaccination history. We compared RV1 coverage among those who tested rotavirus-positive (cases) versus rotavirus-negative (controls) using multivariable logistic regression; effectiveness was 1-adjusted odds ratio for vaccination x100%. RESULTS: Among 677 eligible children, 110 (16%) were rotavirus-positive. Vaccination data were available for 91 (83%) cases; 51 (56%) had received 2 RV1 doses and 33 (36%) 0 doses. Among 567 controls, 418 (74%) had vaccination data; 308 (74%) had 2 doses and 69 (16%) 0 doses. Overall 2-dose effectiveness was 64% (95% confidence interval [CI]: 35-80%); for children aged <12 months 67% (95%CI: 30-84%) and children aged >/=12 months 72% (95%CI: 10-91%). Significant effectiveness was seen in children with normal weight-for-age (84% [95%CI: 62-93%]), length/height-for-age (75% [95%CI: 48-88%]) and weight-for-length/height (84% [95%CI: 64-93%]); however, no protection was found among underweight, stunted nor wasted children. CONCLUSIONS: RV1 in the routine Kenyan immunization program provides significant protection against rotavirus AGE hospitalization. Protection was sustained beyond infancy. Malnutrition appears to diminish vaccine effectiveness. Efforts to improve rotavirus vaccine uptake and nutritional status are important to maximize vaccine benefit.

BACKGROUND: Rotavirus vaccine was introduced in Kenya immunization program in July 2014. Pre-vaccine disease burden estimates are important for assessing vaccine impact. METHODS: Children with acute gastroenteritis (AGE) (>/=3 loose stools and/or >/= 1 episode of unexplained vomiting followed by loose stool within a 24-h period), hospitalized in Siaya County Referral Hospital (SCRH) from January 2010 through December 2013 were enrolled. Stool specimens were tested for rotavirus (RV) using an enzyme immunoassay (EIA). Hospitalization rates were calculated using person-years of observation (PYO) from the Health Demographic Surveillance System (HDSS) as a denominator, while adjusting for healthcare utilization at household level and proportion of stool specimen collected from patients who met the case definition at the surveillance hospital. Mortality rates were calculated using PYO as the denominator and number of deaths estimated using total deaths in the HDSS, proportion of deaths attributed to diarrhoea by verbal autopsy (VA) and percent positive for rotavirus AGE (RVAGE) hospitalizations. RESULTS: Of 7760 all-cause hospitalizations among children < 5 years of age, 3793 (49%) were included in the analysis. Of these, 21% (805) had AGE; RV was detected in 143 (26%) of 541 stools tested. Among children < 5 years, the estimated hospitalization rates per 100,000 PYO for AGE and RVAGE were 2413 and 429, respectively. Mortality rate associated with AGE and RVAGE were 176 and 45 per 100,000 PYO, respectively. CONCLUSION: AGE and RVAGE caused substantial health care burden (hospitalizations and deaths) before rotavirus vaccine introduction in Kenya.

BACKGROUND: In order to better understand respiratory syncytial virus (RSV) epidemiology and burden in tropical Africa, optimal case definitions for detection of RSV cases need to be identified. METHODS: We used data collected between September 2009 - August 2013 from children aged <5 years hospitalized with acute respiratory Illness at Siaya County Referral Hospital. We evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of individual signs, symptoms and standard respiratory disease case definitions (severe acute respiratory illness [SARI]; hospitalized influenza-like illness [hILI]; integrated management of childhood illness [IMCI] pneumonia) to detect laboratory-confirmed RSV infection. We also evaluated an alternative case definition of cough or difficulty breathing plus hypoxia, in-drawing, or wheeze. RESULTS: Among 4714 children hospitalized with ARI, 3810 (81 %) were tested for RSV; and 470 (12 %) were positive. Among individual signs and symptoms, cough alone had the highest sensitivity to detect laboratory-confirmed RSV [96 %, 95 % CI (95-98)]. Hypoxia, wheezing, stridor, nasal flaring and chest wall in-drawing had sensitivities ranging from 8 to 31 %, but had specificities >75 %. Of the standard respiratory case definitions, SARI had the highest sensitivity [83 %, 95 % CI (79-86)] whereas IMCI severe pneumonia had the highest specificity [91 %, 95 % CI (90-92)]. The alternative case definition (cough or difficulty breathing plus hypoxia, in-drawing, or wheeze) had a sensitivity of [55 %, 95 % CI (50-59)] and a specificity of [60 %, 95 % CI (59-62)]. The PPV for all case definitions and individual signs/symptoms ranged from 11 to 20 % while the negative predictive values were >87 %. When we stratified by age <1 year and 1- < 5 years, difficulty breathing, severe pneumonia and the alternative case definition were more sensitive in children aged <1 year [70 % vs. 54 %, p < 0.01], [19 % vs. 11 %, p = 0.01] and [66 % vs. 43 %, p < 0.01] respectively, while non-severe pneumonia was more sensitive [14 % vs. 26 %, p < 0.01] among children aged 1- < 5 years. CONCLUSION: The sensitivity and specificity of different commonly used case definitions for detecting laboratory-confirmed RSV cases varied widely, while the positive predictive value was consistently low. Optimal choice of case definition will depend upon study context and research objectives.

Although the severe acute respiratory illness (SARI) case definition is increasingly used for inpatient influenza surveillance, pneumonia is a more familiar term to clinicians and policymakers. We evaluated WHO case definitions for severe acute respiratory illness (SARI) and pneumonia (Integrated Management of Childhood Illnesses (IMCI) for children aged <5 years and Integrated Management of Adolescent and Adult Illnesses (IMAI) for patients aged ≥13 years) for detecting laboratory-confirmed influenza among hospitalized ARI patients. Sensitivities were 84% for SARI and 69% for IMCI pneumonia in children aged <5 years and 60% for SARI and 57% for IMAI pneumonia in patients aged ≥13 years. Clinical pneumonia case definitions may be a useful complement to SARI for inpatient influenza surveillance.

There is a theoretical risk of adverse events following immunization with a preservative-free, 2-dose vial formulation of 10-valent-pneumococcal conjugate vaccine (PCV10). We set out to measure this risk. Four population-based surveillance sites in Kenya (total annual birth cohort of 11,500 infants) were used to conduct a 2-year post-introduction vaccine safety study of PCV10. Injection-site abscesses occurring within 7 days following vaccine administration were clinically diagnosed in all study sites (passive facility-based surveillance) and, also, detected by caregiver-reported symptoms of swelling plus discharge in two sites (active household-based surveillance). Abscess risk was expressed as the number of abscesses per 100,000 injections and was compared for the second vs first vial dose of PCV10 and for PCV10 vs pentavalent vaccine (comparator). A total of 58,288 PCV10 injections were recorded, including 24,054 and 19,702 identified as first and second vial doses, respectively (14,532 unknown vial dose). The risk ratio for abscess following injection with the second (41 per 100,000) vs first (33 per 100,000) vial dose of PCV10 was 1.22 (95% confidence interval [CI] 0.37-4.06). The comparator vaccine was changed from a 2-dose to 10-dose presentation midway through the study. The matched odds ratios for abscess following PCV10 were 1.00 (95% CI 0.12-8.56) and 0.27 (95% CI 0.14-0.54) when compared to the 2-dose and 10-dose pentavalent vaccine presentations, respectively. In Kenya immunization with PCV10 was not associated with an increased risk of injection site abscess, providing confidence that the vaccine may be safely used in Africa. The relatively higher risk of abscess following the 10-dose presentation of pentavalent vaccine merits further study.

BACKGROUND: Non-communicable diseases (NCDs) result in more deaths globally than other causes. Monitoring systems require strengthening to attribute the NCD burden and deaths in low and middle-income countries (LMICs). Data from health and demographic surveillance systems (HDSS) can contribute towards this goal. METHODS AND FINDINGS: Between 2003 and 2010, 15,228 deaths in adults aged 15 years (y) and older were identified retrospectively using the HDSS census and verbal autopsy in rural western Kenya, attributed into broad categories using InterVA-4 computer algorithms; 37% were ascribed to NCDs, 60% to communicable diseases (CDs), 3% to injuries, and <1% maternal causes. Median age at death for NCDs was 66y and 71y for females and males, respectively, with 43% (39% male, 48% female) of NCD deaths occurring prematurely among adults aged below 65y. NCD deaths were mainly attributed to cancers (35%) and cardio-vascular diseases (CVDs; 29%). The proportionate mortality from NCDs rose from 35% in 2003 to 45% in 2010 (chi2 linear trend 93.4; p<0.001). While overall annual mortality rates (MRs) for NCDs fell, cancer-specific MRs rose from 200 to 262 per 100,000 population, mainly due to increasing deaths in adults aged 65y and older, and to respiratory neoplasms in all age groups. The substantial fall in CD MRs resulted in similar MRs for CDs and NCDs among all adult females by 2010. NCD MRs for adults aged 15y to <65y fell from 409 to 183 per 100,000 among females and from 517 to 283 per 100,000 population among males. NCD MRs were higher among males than females aged both below, and at or above, 65y. CONCLUSIONS: NCDs constitute a significant proportion of deaths in rural western Kenya. Evidence of the increasing contribution of NCDs to overall mortality supports international recommendations to introduce or enhance prevention, screening, diagnosis and treatment programmes in LMICs.

BACKGROUND: Assessing the progress in achieving the United Nation's Millennium Development Goals in terms of population health requires consistent and reliable information on cause-specific mortality, which is often rare in resource-constrained countries. Health and demographic surveillance systems (HDSS) have largely used medical personnel to review and assign likely causes of death based on the information gathered from standardized verbal autopsy (VA) forms. However, this approach is expensive and time consuming, and it may lead to biased results based on the knowledge and experience of individual clinicians. We assessed the cause-specific mortality for children under 5 years old (under-5 deaths) in Siaya County, obtained from a computer-based probabilistic model (InterVA-4). DESIGN: Successfully completed VA interviews for under-5 deaths conducted between January 2003 and December 2010 in the Kenya Medical Research Institute/US Centers for Disease Control and Prevention HDSS were extracted from the VA database and processed using the InterVA-4 (version 4.02) model for interpretation. Cause-specific mortality fractions were then generated from the causes of death produced by the model. RESULTS: A total of 84.33% (6,621) childhood deaths had completed VA data during the study period. Children aged 1-4 years constituted 48.53% of all cases, and 42.50% were from infants. A single cause of death was assigned to 89.18% (5,940) of cases, 8.35% (556) of cases were assigned two causes, and 2.10% (140) were assigned 'indeterminate' as cause of death by the InterVA-4 model. Overall, malaria (28.20%) was the leading cause of death, followed by acute respiratory infection including pneumonia (25.10%), in under-5 children over the study period. But in the first 5 years of the study period, acute respiratory infection including pneumonia was the main cause of death, followed by malaria. Similar trends were also reported in infants (29 days-11 months) and children aged 1-4 years. CONCLUSIONS: Under-5 cause-specific mortality obtained using the InterVA-4 model is consistent with existing knowledge on the burden of childhood diseases in rural western Kenya.

BACKGROUND: As the HIV/AIDS pandemic has evolved over recent decades, Africa has been the most affected region, even though a large proportion of HIV/AIDS deaths have not been documented at the individual level. Systematic application of verbal autopsy (VA) methods in defined populations provides an opportunity to assess the mortality burden of the pandemic from individual data. OBJECTIVE: To present standardised comparisons of HIV/AIDS-related mortality at sites across Africa and Asia, including closely related causes of death such as pulmonary tuberculosis (PTB) and pneumonia. DESIGN: Deaths related to HIV/AIDS were extracted from individual demographic and VA data from 22 INDEPTH sites across Africa and Asia. VA data were standardised to WHO 2012 standard causes of death assigned using the InterVA-4 model. Between-site comparisons of mortality rates were standardised using the INDEPTH 2013 standard population. RESULTS: The dataset covered a total of 10,773 deaths attributed to HIV/AIDS, observed over 12,204,043 person-years. HIV/AIDS-related mortality fractions and mortality rates varied widely across Africa and Asia, with highest burdens in eastern and southern Africa, and lowest burdens in Asia. There was evidence of rapidly declining rates at the sites with the heaviest burdens. HIV/AIDS mortality was also strongly related to PTB mortality. On a country basis, there were strong similarities between HIV/AIDS mortality rates at INDEPTH sites and those derived from modelled estimates. CONCLUSIONS: Measuring HIV/AIDS-related mortality continues to be a challenging issue, all the more so as anti-retroviral treatment programmes alleviate mortality risks. The congruence between these results and other estimates adds plausibility to both approaches. These data, covering some of the highest mortality observed during the pandemic, will be an important baseline for understanding the future decline of HIV/AIDS.

Recent global malaria burden modeling efforts have produced significantly different estimates, particularly in adult malaria mortality. To measure malaria control progress, accurate malaria burden estimates across age groups are necessary. We determined age-specific malaria mortality rates in western Kenya to compare with recent global estimates. We collected data from 148,000 persons in a health and demographic surveillance system from 2003-2010. Standardized verbal autopsies were conducted for all deaths; probable cause of death was assigned using the InterVA-4 model. Annual malaria mortality rates per 1,000 person-years were generated by age group. Trends were analyzed using Poisson regression. From 2003-2010, in children <5 years the malaria mortality rate decreased from 13.2 to 3.7 per 1,000 person-years; the declines were greatest in the first three years of life. In children 5-14 years, the malaria mortality rate remained stable at 0.5 per 1,000 person-years. In persons ≥15 years, the malaria mortality rate decreased from 1.5 to 0.4 per 1,000 person-years. The malaria mortality rates in young children and persons aged ≥15 years decreased dramatically from 2003-2010 in western Kenya, but rates in older children have not declined. Sharp declines in some age groups likely reflect the national scale up of malaria control interventions and rapid expansion of HIV prevention services. These data highlight the importance of age-specific malaria mortality ascertainment and support current strategies to include all age groups in malaria control interventions.

BACKGROUND: In Kenya, detailed data on the age-specific burden of influenza and RSV are essential to inform use of limited vaccination and treatment resources. METHODS: We analyzed surveillance data from August 2009 to July 2012 for hospitalized severe acute respiratory illness (SARI) and outpatient influenza-like illness (ILI) at two health facilities in western Kenya to estimate the burden of influenza and respiratory syncytial virus (RSV). Incidence rates were estimated by dividing the number of cases with laboratory-confirmed virus infections by the mid-year population. Rates were adjusted for healthcare-seeking behavior, and to account for patients who met the SARI/ILI case definitions but were not tested. RESULTS: The average annual incidence of influenza-associated SARI hospitalization per 1,000 persons was 2.7 (95% CI 1.8-3.9) among children <5 years and 0.3 (95% CI 0.2-0.4) among persons ≥5 years; for RSV-associated SARI hospitalization, it was 5.2 (95% CI 4.0-6.8) among children <5 years and 0.1 (95% CI 0.0-0.2) among persons ≥5 years. The incidence of influenza-associated medically-attended ILI per 1,000 was 24.0 (95% CI 16.6-34.7) among children <5 years and 3.8 (95% CI 2.6-5.7) among persons ≥5 years. The incidence of RSV-associated medically-attended ILI was 24.6 (95% CI 17.0-35.4) among children <5 years and 0.8 (95% CI 0.3-1.9) among persons ≥5 years. CONCLUSIONS: Influenza and RSV both exact an important burden in children. This highlights the possible value of influenza vaccines, and future RSV vaccines, for Kenyan children.

BACKGROUND: Pediatric respiratory disease is a major cause of morbidity and mortality in the developing world. We evaluated a modified respiratory index of severity in children (mRISC) scoring system as a standard tool to identify children at greater risk of death from respiratory illness in Kenya. MATERIALS AND METHODS: We analyzed data from children <5 years old who were hospitalized with respiratory illness at Siaya District Hospital from 2009-2012. We used a multivariable logistic regression model to identify patient characteristics predictive for in-hospital mortality. Model discrimination was evaluated using the concordance statistic. Using bootstrap samples, we re-estimated the coefficients and the optimism of the model. The mRISC score for each child was developed by adding up the points assigned to each factor associated with mortality based on the coefficients in the multivariable model. RESULTS: We analyzed data from 3,581 children hospitalized with respiratory illness; including 218 (6%) who died. Low weight-for-age [adjusted odds ratio (aOR) = 2.1; 95% CI 1.3-3.2], very low weight-for-age (aOR = 3.8; 95% CI 2.7-5.4), caretaker-reported history of unconsciousness (aOR = 2.3; 95% CI 1.6-3.4), inability to drink or breastfeed (aOR = 1.8; 95% CI 1.2-2.8), chest wall in-drawing (aOR = 2.2; 95% CI 1.5-3.1), and being not fully conscious on physical exam (aOR = 8.0; 95% CI 5.1-12.6) were independently associated with mortality. The positive predictive value for mortality increased with increasing mRISC scores. CONCLUSIONS: A modified RISC scoring system based on a set of easily measurable clinical features at admission was able to identify children at greater risk of death from respiratory illness in Kenya.

BACKGROUND: In many GAVI-eligible countries, effectiveness of new vaccines will be evaluated by case-control methodology. To inform the design and assess selection bias of a future case-control study of rotavirus vaccine effectiveness (VE) in western Kenya, we performed a sham case-control study evaluating VE of pentavalent vaccine (DTP-Hib-HepB) against rotavirus acute gastroenteritis (AGE). METHODS: From ongoing rotavirus surveillance, we defined cases as children 12 weeks to 23 months old with EIA-confirmed rotavirus AGE. We enrolled one community-based and two hospital-based control groups. We collected vaccination status from cards at enrollment, or later in homes, and evaluated VE by logistic regression. RESULTS: We enrolled 91 cases (64 inpatient, 27 outpatient), 252 non-rotavirus AGE facility-based controls (unmatched), 203 non-AGE facility-based controls (age-matched) and 271 community controls (age-matched). Documented receipt of 3 pentavalent doses was 77% among cases and ranged from 81-86% among controls. One percent of cases and 0-2% of controls had no pentavalent doses. The adjusted odds ratio of three versus zero doses for being a case was 3.27 (95% CI 0.01-1010) for community controls and 0.69 (95% CI 0.06-7.75) for non-rotavirus hospital-based AGE controls, translating to VE of -227% and 31%, respectively, with wide confidence intervals. (No facility-based non-AGE controls were unvaccinated.) Similar results were found for ≥2 pentavalent doses and for severe rotavirus AGE. CONCLUSIONS: The study showed that it is feasible to carry out a real case control in the study area, but this needs to be done as soon as the vaccine is introduced to capture the real impact. Sham case-control or pilot studies before vaccine introduction can be useful in designing case-control VE studies.

BACKGROUND: Diarrhea is a leading cause of hospitalization and death in children <5 years of age. OBJECTIVES: To facilitate evaluation of the impact of rotavirus vaccine introduction in western Kenya, we estimated baseline rates of rotavirus-associated hospitalization and mortality among children <5 years of age. METHODS: From January 2010 to December 2011, we collected demographic, clinical and laboratory data for children <5 years of age seeking care at the district hospital and 2 outpatient facilities within a Health and Demographic Surveillance System (HDSS). Children with acute gastroenteritis (AGE), defined as ≥3 loose stools and/or ≥1 episode of unexplained vomiting followed by loose stool within a 24-hour period, were asked to provide a stool sample for rotavirus ELISA testing. Rates of rotavirus-associated hospitalization and mortality were estimated using time of residence in the HDSS to calculate person-years of observation. To estimate the rotavirus-associated mortality rate, we applied the percentage positive for rotavirus among AGE hospitalizations to verbal autopsy estimates of diarrhea deaths in the HDSS. RESULTS: There were 4991 hospitalizations of children <5 years of age; 1134 (23%) were for AGE and stool specimens were obtained from 790 (70%). Rotavirus was detected in 211 (27%) specimens. Among 4951 <5 outpatient sick visits, 608 (12%) were for AGE; 320 (51%) provided specimens and 62 (20%) were positive for rotavirus. Rotavirus AGE accounted for 501 <5 hospitalizations per 100,000 person-years of observation. Rotavirus-associated <5 mortality was 136 deaths per 100,000 person-years of observation. CONCLUSIONS: Continued surveillance of rotavirus AGE will provide timely data on the population-level impact of rotavirus vaccine following its likely introduction in 2014.

BACKGROUND: Knowing the national disease burden of severe influenza in low-income countries can inform policy decisions around influenza treatment and prevention. We present a novel methodology using locally generated data for estimating this burden. METHODS AND FINDINGS: This method begins with calculating the hospitalized severe acute respiratory illness (SARI) incidence for children <5 years old and persons >=5 years old from population-based surveillance in one province. This base rate of SARI is then adjusted for each province based on the prevalence of risk factors and healthcare-seeking behavior. The percentage of SARI with influenza virus detected is determined from provincial-level sentinel surveillance and applied to the adjusted provincial rates of hospitalized SARI. Healthcare-seeking data from healthcare utilization surveys is used to estimate non-hospitalized influenza-associated SARI. Rates of hospitalized and non-hospitalized influenza-associated SARI are applied to census data to calculate the national number of cases. The method was field-tested in Kenya, and validated in Guatemala, using data from August 2009-July 2011. In Kenya (2009 population 38.6 million persons), the annual number of hospitalized influenza-associated SARI cases ranged from 17,129-27,659 for children <5 years old (2.9-4.7 per 1,000 persons) and 6,882-7,836 for persons >=5 years old (0.21-0.24 per 1,000 persons), depending on year and base rate used. In Guatemala (2011 population 14.7 million persons), the annual number of hospitalized cases of influenza-associated pneumonia ranged from 1,065-2,259 (0.5-1.0 per 1,000 persons) among children <5 years old and 779-2,252 cases (0.1-0.2 per 1,000 persons) for persons >=5 years old, depending on year and base rate used. In both countries, the number of non-hospitalized influenza-associated cases was several-fold higher than the hospitalized cases. CONCLUSIONS: Influenza virus was associated with a substantial amount of severe disease in Kenya and Guatemala. This method can be performed in most low and lower-middle income countries.

BACKGROUND: Although children <5 years old in sub-Saharan Africa are vulnerable to both malaria and influenza, little is known about co-infection. METHODS: This retrospective, cross-sectional study in rural western Kenya examined outpatient visits and hospitalizations associated with febrile acute respiratory illness (ARI) during a two-year period (July 2009 - June 2011) in children <5 years old. RESULTS: Across sites, 45% (149/331) of influenza-positive cases were co-infected with malaria, while only 6% (149/2408) of malaria-positive patients were co-infected with influenza. Depending on age, co-infection was present in 4-8% of outpatient visits and 1-3% of inpatient admissions for febrile ARI. Children with influenza were less likely than those without to have malaria (risk ratios [RRs] = 0.57-0.76 across sites and ages), and children with malaria were less likely than those without to have influenza (RRs = 0.36-0.63). Among co-infected children aged 24-59 months hospital length of stay was 2.7 and 2.8 days longer than influenza-only infected children at the two sites, and 1.3 and 3.1 days longer than those with malaria-only (p's < .01). CONCLUSIONS: Co-infection with malaria and influenza was uncommon but associated with longer hospitalization than single infections among children 24-59 months old.