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: Malaria transmission reduction is a goal of many malaria control programmes. Little is known of how much mortality can be reduced by specific reductions in transmission. Verbal autopsy (VA) is widely used for estimating malaria specific mortality rates, but does not reliably distinguish malaria from other febrile illnesses. Overall malaria attributable mortality includes both direct and indirect deaths. It is unclear what proportion of the deaths averted by reducing malaria transmission are classified as malaria in VA. METHODS: Both all-cause, and cause-specific mortality reported by VA for children under 5 years of age, were assembled from the KEMRI/CDC health and demographic surveillance system in Siaya county, rural Western Kenya for the years 2002-2004. These were linked to household-specific estimates of the Plasmodium falciparum entomological inoculation rate (EIR) based on high resolution spatio-temporal geostatistical modelling of entomological data. All-cause and malaria specific mortality (by VA), were analysed in relation to EIR, insecticide-treated net use (ITN), socioeconomic status (SES) and parameters describing space-time correlation. Time at risk for each child was analysed using Bayesian geostatistical Cox proportional hazard models, with time-dependent covariates. The outputs were used to estimate the diagnostic performance of VA in measuring mortality that can be attributed to malaria exposure. RESULTS: The overall under-five mortality rate was 80 per 1000 person-years during the study period. Eighty-one percent of the total deaths were assigned causes of death by VA, with malaria assigned as the main cause of death except in the neonatal period. Although no trend was observed in malaria-specific mortality assessed by VA, ITN use was associated with reduced all-cause mortality in infants (hazard ratio 0.15, 95% CI 0.02, 0.63) and the EIR was strongly associated with both all-cause and malaria-specific mortality. 48.2% of the deaths could be attributed to malaria by analysing the exposure-response relationship, though only 20.5% of VAs assigned malaria as the cause and the sensitivity of VAs was estimated to be only 26%. Although VAs assigned some deaths to malaria even in areas where there was estimated to be no exposure, the specificity of the VAs was estimated to be 85%. CONCLUSION: Interventions that reduce P. falciparum transmission intensity will not only significantly reduce malaria-diagnosed mortality, but also mortality assigned to other causes in under-5 year old children in endemic areas. In this setting, the VA tool based on clinician review substantially underestimates the number of deaths that could be averted by reducing malaria exposure in childhood, but has a reasonably high specificity. This suggests that malaria transmission-reducing interventions such as ITNs can potentially reduce overall child mortality by as much as twice the total direct malaria burden estimated from VAs.

Introduction: Despite the rollout of antiretroviral therapy (ART), challenges remain in ensuring timely access to care and treatment for people living with HIV. As part of a multi-country study to investigate HIV mortality, we conducted health facility surveys within 10 health and demographic surveillance system sites across six countries in Eastern and Southern Africa to investigate clinic-level factors influencing (i) use of HIV testing services, (ii) use of HIV care and treatment and (iii) patient retention on ART. Methods: Health facilities (n = 156) were sampled within 10 surveillance sites: Nairobi and Kisumu (Kenya), Karonga (Malawi), Agincourt and uMkhanyakude (South Africa), Ifakara and Kisesa (Tanzania), Kyamulibwa and Rakai (Uganda) and Manicaland (Zimbabwe). Structured questionnaires were administered to in-charge staff members of HIV testing, prevention of mother-to-child transmission (PMTCT) and ART units within the facilities. Forty-one indicators influencing uptake and patient retention along the continuum of HIV care were compared across sites using descriptive statistics. Results: The number of facilities surveyed ranged from six in Malawi to 36 in Zimbabwe. Eighty percent were governmentrun; 73% were lower-level facilities and 17% were district/referral hospitals. Client load varied widely, from less than one up to 65 HIV testing clients per provider per week. Most facilities (>80%) delivered services or interventions that would support patient retention in care such as delivering free services, offering PMTCT within antenatal care, pre-ART monitoring and adherence counselling. Many facilities under-delivered in several areas, however, such as targeted testing for high-risk groups (21%) and mobile testing (36%). There were also intra-site and inter-site differences, including in the delivery of Option B+ (ranging from 6% in Kisumu to 93% in Kyamulibwa), and nurse-led ART initiation (ranging from 50% in Kisesa to 100% in Karonga and Agincourt). Only facilities in Malawi did not require additional lab tests for ART initiation. Stock-outs of HIV test kits and antiretroviral drugs were particularly common in Tanzania. Conclusions: We identified a high standard of health facility performance in delivering strategies that may support progression through the continuum of HIV care. HIV testing policy and practice was particularly weak. Inter- and intracountry differences in quality and coverage represent opportunities to improve the delivery of comprehensive services to people living with HIV.

BACKGROUND: Females in low and middle income countries (LMICs) have difficulty coping with menstrual needs, but few studies have examined the social or health implications of these needs. METHODS: Responses from 3418 menstruating females aged 13-29 years were extracted from an HIV and behavioral risks cross-sectional survey conducted in rural western Kenya. We examined sanitary products used, provision of products from sexual partners or from transactional sex, and demographic and sexual exposures. RESULTS: Overall, 75% of females reported using commercial pads and 25% used traditional materials such as cloth or items like paper or tissue, with 10% of girls <15 years old depending on makeshift items. Two-thirds of females with no education relied on traditional items. Having attended secondary school increased the odds of using commercial pads among married (adjusted odds ratios [AOR] 4.8, 95% confidence interval [CI] 3.25-7.12) and single females (AOR 2.17, 95% CI 1.04-4.55). Married females had lower odds of pad use if they reported early (<12 years of age) compared with later (≥18 years) sexual debut (64% vs. 78%, AOR 0.45, 95% CI 0.21-0.97). Two-thirds of pad users received them from sexual partners. Receipt was lower among married females if partners were violent (AOR 0.67, 95% CI 0.53-0.85). Receipt among single females was higher if they had two or more sexual partners in the past year (AOR 2.11, 95% CI 1.04-4.29). Prevalence of engaging in sex for money to buy pads was low (1.3%); however, 10% of 15-year-olds reported this, with girls ≤15 having significantly higher odds compared with females over 15 (AOR 2.84, 95% CI 0.89-9.11). The odds of having transactional sex for pads was higher among females having two or more partners in the past 12 months (AOR 4.86, 95% CI 2.06-11.43). CONCLUSIONS: Menstrual needs of impoverished females in rural LMICs settings likely leads to increased physical and sexual harms. Studies are required to strengthen knowledge and to evaluate interventions to reduce these harms.

BACKGROUND: Coverage of civil registration and vital statistics varies globally, with most deaths in Africa and Asia remaining either unregistered or registered without cause of death. One important constraint has been a lack of fit-for-purpose tools for registering deaths and assigning causes in situations where no doctor is involved. Verbal autopsy (interviewing care-givers and witnesses to deaths and interpreting their information into causes of death) is the only available solution. Automated interpretation of verbal autopsy data into cause of death information is essential for rapid, consistent and affordable processing. METHODS: Verbal autopsy archives covering 54 182 deaths from five African and Asian countries were sourced on the basis of their geographical, epidemiological and methodological diversity, with existing physician-coded causes of death attributed. These data were unified into the WHO 2012 verbal autopsy standard format, and processed using the InterVA-4 model. Cause-specific mortality fractions from InterVA-4 and physician codes were calculated for each of 60 WHO 2012 cause categories, by age group, sex and source. Results from the two approaches were assessed for concordance and ratios of fractions by cause category. As an alternative metric, the Wilcoxon matched-pairs signed ranks test with two one-sided tests for stochastic equivalence was used. FINDINGS: The overall concordance correlation coefficient between InterVA-4 and physician codes was 0.83 (95% CI 0.75 to 0.91) and this increased to 0.97 (95% CI 0.96 to 0.99) when HIV/AIDS and pulmonary TB deaths were combined into a single category. Over half (53%) of the cause category ratios between InterVA-4 and physician codes by source were not significantly different from unity at the 99% level, increasing to 62% by age group. Wilcoxon tests for stochastic equivalence also demonstrated equivalence. CONCLUSIONS: These findings show strong concordance between InterVA-4 and physician-coded findings over this large and diverse data set. Although these analyses cannot prove that either approach constitutes absolute truth, there was high public health equivalence between the findings. Given the urgent need for adequate cause of death data from settings where deaths currently pass unregistered, and since the WHO 2012 verbal autopsy standard and InterVA-4 tools represent relatively simple, cheap and available methods for determining cause of death on a large scale, they should be used as current tools of choice to fill gaps in cause of death data.

BACKGROUND: Continuous monitoring in Health and Demographic Surveillance sites (HDSS) allows for collection of longitudinal demographic data, health related and socio-economic indicators of the site population. We sought to use household survey data collected between 2002-2006 in the Kenya Medical Research Institute in collaboration with Centers for Disease Control and prevention (KEMRI/CDC) HDSS site in Asembo and Gem western Kenya to estimate socio-economic status (SES) and assess changes of SES over time and space. METHODS: Data on household assets and characteristics, mainly source of drinking water, cooking fuel and occupation of household head was annually collected from 44,313 unique households during the study period. An SES index was calculated as a weighted average of assets using weights generated via Principal Component Analysis (PCA), Polychoric PCA and Multiple Correspondence Analysis (MCA) methods applied to the pooled data. The index from the best method was used to rank households into SES quintiles and assess their transition over time across SES categories. Kriging was employed to produce SES maps at the start and the end of the study period. RESULTS: First component of PCA, Polychoric PCA and MCA accounted for 13.7%, 31.8% and 47.3% respectively of the total variance of all variables. The gap between the poorest and the least poor increased from 1% at the start to 6% at the end of the study period. Spatial analysis revealed that the increase in least poor households was centered in the lower part of study area (Asembo) over time. No significant changes were observed in Gem. CONCLUSION: The HDSS sites can provide a platform to assess spatial-temporal changes in the SES status of the population. Evidence on how SES varied over time and space within the same geographical area may provide a useful tool to design interventions in health and other areas that have a close bearing to the SES of the population.

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.

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.

Water, sanitation and hygiene (WASH) programs in African schools have received increased attention, particularly around the potential impact of poor menstrual hygiene management (MHM) on equity for girls' education. This study was conducted prior to a menstrual feasibility study in rural Kenya, to examine current WASH in primary schools and the resources available for menstruating schoolgirls. Cross-sectional surveys were performed in 62 primary schools during unannounced visits. Of these, 60% had handwashing water, 13% had washing water in latrines for menstruating girls, and 2% had soap. Latrines were structurally sound and 16% were clean. Most schools (84%) had separate latrines for girls, but the majority (77%) had no lock. Non-governmental organizations (NGOs) supported WASH in 76% of schools. Schools receiving WASH interventions were more likely to have: cleaner latrines (Risk Ratio (RR) 1.5; 95% Confidence Intervals [CI] 1.0, 2.1), handwashing facilities (RR 1.6, CI 1.1, 2.5), handwashing water (RR 2.7; CI 1.4, 5.2), and water in girls' latrines (RR 4.0; CI 1.4, 11.6). Schools continue to lack essential WASH facilities for menstruating girls. While external support for school WASH interventions improved MHM quality, the impact of these contributions remains insufficient. Further support is required to meet international recommendations for healthy, gender-equitable schools.

The KEMRI/Centers for Disease Control and Prevention (CDC) Health and Demographic Surveillance System (HDSS) is located in Rarieda, Siaya and Gem Districts (Siaya County), lying northeast of Lake Victoria in Nyanza Province, western Kenya. The KEMRI/CDC HDSS, with approximately 220 000 inhabitants, has been the foundation for a variety of studies, including evaluations of insecticide-treated bed nets, burden of diarrhoeal disease and tuberculosis, malaria parasitaemia and anaemia, treatment strategies and immunological correlates of malaria infection, and numerous HIV, tuberculosis, malaria and diarrhoeal disease treatment and vaccine efficacy and effectiveness trials for more than a decade. Current studies include operations research to measure the uptake and effectiveness of the programmatic implementation of integrated malaria control strategies, HIV services, newly introduced vaccines and clinical trials. The HDSS provides general demographic and health information (such as population age structure and density, fertility rates, birth and death rates, in- and out-migrations, patterns of health care access and utilization and the local economics of health care) as well as disease- or intervention-specific information. The HDSS also collects verbal autopsy information on all deaths. Studies take advantage of the sampling frame inherent in the HDSS, whether at individual, household/compound or neighbourhood level.

BACKGROUND: Understanding the impact of reducing Plasmodium falciparum malaria transmission requires estimates of the relationship between health outcomes and exposure to infectious mosquitoes. However, measures of exposure such as mosquito density and entomological inoculation rate (EIR) are generally aggregated over large areas and time periods, biasing the outcome-exposure relationship. There are few studies examining the extent and drivers of local variation in malaria exposure in endemic areas. METHODS: We describe the spatio-temporal dynamics of malaria transmission intensity measured by mosquito density and EIR in the KEMRI/CDC health and demographic surveillance system using entomological data collected during 2002-2004. Geostatistical zero inflated binomial and negative binomial models were applied to obtain location specific (house) estimates of sporozoite rates and mosquito densities respectively. Model-based predictions were multiplied to estimate the spatial pattern of annual entomological inoculation rate, a measure of the number of infective bites a person receive per unit of time. The models included environmental and climatic predictors extracted from satellite data, harmonic seasonal trends and parameters describing space-time correlation. RESULTS: Anopheles gambiae s.l was the main vector species accounting for 86% (n=2309) of the total collected mosquitoes with the remainder being Anopheles funestus. Sixty eight percent (757/1110) of the surveyed houses had no mosquitoes. Distance to water bodies, vegetation and day temperature were significantly associated with mosquito density. Overall annual point estimates of EIR were 6.7, 9.3 and 9.6 infectious bites per annum for 2002, 2003 and 2004 respectively. Monthly mosquito density and EIR varied over the study period peaking in May during the wet season. The predicted and observed densities and EIR showed a strong seasonal and spatial pattern over the study area. CONCLUSIONS: Spatio-temporal maps of malaria transmission intensity obtained in this study are not only useful in understanding variability in malaria epidemiology over small areas but also provides a high resolution exposure surface that can be used to analyse the impact of malaria exposure on mortality.

The proportion of malaria vectors harboring the infectious stage of the parasite (the sporozoite rates) is an important component of measures of malaria transmission. Variation in time and/or space in sporozoite rates contribute substantially to spatio-temporal variation in transmission. However, because most vectors test negative for sporozoites, sporozoite rate data are sparse with large number of observed zeros across locations or over time in the case of longitudinal data. Rarely are appropriate methods and models used in analyzing such data. In this study, Bayesian zero inflated binomial (ZIB) geostatistical models were developed and compared with standard binomial analogues to analyze sporozoite data obtained from the KEMRI/CDC health and demographic surveillance system (HDSS) site in rural Western Kenya during 2002-2004. ZIB models showed a better predictive ability, identified more significant covariates and obtained narrower credible intervals for all parameters compared to standard geostatistical binomial model. (2011 Elsevier Ltd.)