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Malaria transmission is spatially heterogeneous. This reduces the efficacy of control strategies, but focusing control strategies on clusters or ‘hotspots’ of transmission may be highly effective. Among 1500 homesteads in coastal Kenya we calculated (a) the fraction of febrile children with positive malaria smears per homestead, and (b) the mean age of children with malaria per homestead. These two measures were inversely correlated, indicating that children in homesteads at higher transmission acquire immunity more rapidly. This inverse correlation increased gradually with increasing spatial scale of analysis, and hotspots of febrile malaria were identified at every scale. We found hotspots within hotspots, down to the level of an individual homestead. Febrile malaria hotspots were temporally unstable, but 4 km radius hotspots could be targeted for 1 month following 1 month periods of surveillance. DOI: http://dx.doi.org/10.7554/eLife.02130.001, eLife digest Malaria remains a formidable threat to public health in tropical regions. The parasite that causes the disease is transmitted to humans by bites from infected mosquitoes, and the complicated lifecycle of the parasite makes developing vaccines difficult. However, preventive strategies are effective at reducing the spread of malaria. The two most widely used and effective strategies are the use of pesticide-treated bed nets to create a barrier between sleeping families and biting mosquitoes, and indoor residual spraying to reduce the numbers of mosquitoes biting sleeping families in homesteads. Other potential preventive strategies include killing mosquito larvae in breeding sites and mass anti-malarial drug treatment for infected humans. Targeting preventive efforts to malaria hotspots—the areas where the risk of malaria transmission is greatest—may help to eliminate malaria more efficiently. Unfortunately, identifying hotspots is complicated as there are many different factors that affect how malaria spreads. These factors range from ecological conditions such as rainfall and soil type, to human effects like population density and migration. Bejon et al. have examined the patterns of malaria transmission in Kenya over 9 years. Over this period, 54% of children who went to health clinics with a fever tested positive for the parasite that causes malaria. Infected children from areas with the highest rate of malaria infection were, on average, younger than those from less infected regions. This makes sense as in highly affected areas children have a greater chance of encountering the parasite at an early age. They are therefore more likely to get malaria when younger and, as exposure to the parasite can provide some immunity to a child, they are also less likely to get infected again when older. In addition, mapping the spread of malaria reveals hotspots at different geographical scales. Bejon et al. could see hotspots within hotspots, and in some cases could go as far as identifying the individual homesteads most at risk of malaria. Public health workers could potentially use these analyses to identify areas that are likely to be hotspots and then target preventive measures there for the next month. However, the constantly changing locations of the hotspots means workers would have to reanalyse the data and retarget their interventions at the end of each month. DOI: http://dx.doi.org/10.7554/eLife.02130.002

We estimated premature mortality and identified causes of death and associated factors in people with active convulsive epilepsy (ACE) in rural Kenya.In this prospective population-based study, people with ACE were identified in a cross-sectional survey and followed up regularly for 3 years, during which information on deaths and associated factors was collected. We used a validated verbal autopsy tool to establish putative causes of death. Age-specific rate ratios and standardized mortality ratios were estimated. Poisson regression was used to identify mortality risk factors.There were 61 deaths among 754 people with ACE, yielding a rate of 33.3/1,000 persons/year. Overall standardized mortality ratio was 6.5. Mortality was higher across all ACE age groups. Nonadherence to antiepileptic drugs (adjusted rate ratio [aRR] 3.37), cognitive impairment (aRR 4.55), and age (50+ years) (rate ratio 4.56) were risk factors for premature mortality. Most deaths (56%) were directly related to epilepsy, with prolonged seizures/possible status epilepticus (38%) most frequently associated with death; some of these may have been due to sudden unexpected death in epilepsy (SUDEP). Possible SUDEP was the likely cause in another 7%.Mortality in people with ACE was more than 6-fold greater than expected. This may be reduced by improving treatment adherence and prompt management of prolonged seizures and supporting those with cognitive impairment.

Background Sickle cell disease (SCD) is common in many parts of sub-Saharan Africa (SSA), where it is associated with high early mortality. In the absence of newborn screening, most deaths among children with SCD go unrecognized and unrecorded. As a result, SCD does not receive the attention it deserves as a leading cause of death among children in SSA. In the current study, we explored the potential utility of verbal autopsy (VA) as a tool for attributing underlying cause of death (COD) in children to SCD. Methods We used the 2007 WHO Sample Vital Registration with Verbal Autopsy (SAVVY) VA tool to determine COD among child residents of the Kilifi Health and Demographic Surveillance System (KHDSS), Kenya, who died between January 2008 and April 2011. VAs were coded both by physician review (physician coded verbal autopsy, PCVA) using COD categories based on the WHO International Classification of Diseases 10th Edition (ICD-10) and by using the InterVA-4 probabilistic model after extracting data according to the 2012 WHO VA standard. Both of these methods were validated against one of two gold standards: hospital ICD-10 physician-assigned COD for children who died in Kilifi District Hospital (KDH) and, where available, laboratory confirmed SCD status for those who died in the community. Results Overall, 6% and 5% of deaths were attributed to SCD on the basis of PCVA and the InterVA-4 model, respectively. Of the total deaths, 22% occurred in hospital, where the agreement coefficient (AC1) for SCD between PCVA and hospital physician diagnosis was 95.5%, and agreement between InterVA-4 and hospital physician diagnosis was 96.9%. Confirmatory laboratory evidence of SCD status was available for 15% of deaths, in which the AC1 against PCVA was 87.5%. Conclusions Other recent studies and provisional data from this study, outlining the importance of SCD as a cause of death in children in many parts of the developing world, contributed to the inclusion of specific SCD questions in the 2012 version of the WHO VA instruments, and a specific code for SCD has now been included in the WHO and InterVA-4 COD listings. With these modifications, VA may provide a useful approach to quantifying the contribution of SCD to childhood mortality in rural African communities. Further studies will be needed to evaluate the generalizability of our findings beyond our local context.

Background : Mortality from external causes, of all kinds, is an important component of overall mortality on a global basis. However, these deaths, like others in Africa and Asia, are often not counted or documented on an individual basis. Overviews of the state of external cause mortality in Africa and Asia are therefore based on uncertain information. The INDEPTH Network maintains longitudinal surveillance, including cause of death, at population sites across Africa and Asia, which offers important opportunities to document external cause mortality at the population level across a range of settings. Objective : To describe patterns of mortality from external causes at INDEPTH Network sites across Africa and Asia, according to the WHO 2012 verbal autopsy (VA) cause categories. Design : All deaths at INDEPTH sites are routinely registered and followed up with VA interviews. For this study, VA archives were transformed into the WHO 2012 VA standard format and processed using the InterVA-4 model to assign cause of death. Routine surveillance data also provide person-time denominators for mortality rates. Results : A total of 5,884 deaths due to external causes were documented over 11,828,253 person-years. Approximately one-quarter of those deaths were to children younger than 15 years. Causes of death were dominated by childhood drowning in Bangladesh, and by transport-related deaths and intentional injuries elsewhere. Detailed mortality rates are presented by cause of death, age group, and sex. Conclusions : The patterns of external cause mortality found here generally corresponded with expectations and other sources of information, but they fill some important gaps in population-based mortality data. They provide an important source of information to inform potentially preventive intervention designs. Keywords: external causes; accidents; suicide; assault; transport; drowning; Africa; Asia; mortality; INDEPTH Network; verbal autopsy; InterVA (Published: 29 October 2014) Citation: Glob Health Action 2014, 7 : 25366 - http://dx.doi.org/10.3402/gha.v7.25366 SPECIAL ISSUE : This paper is part of the Special Issue: INDEPTH Network Cause-Specific Mortality . More papers from this issue can be found at http://www.globalhealthaction.net

Purpose There are only a few studies of incidence of epilepsy in low and middle income countries (LMICs). These are often small and conducted in specific age groups or areas where the prevalence of risk factors is high; therefore, these studies are not representative of the wider populations. We determined the incidence of convulsive epilepsy (CE) in a large rural population in Kenya. Methods We conducted two cross-sectional surveys 5 years apart within a health and demographic surveillance system. Initially we identified residents without epilepsy who were then reexamined in the follow-up survey to determine incidence. We estimated the overall incidence of CE and incidence by age-group, sex, and by administrative location. Estimates were adjusted for attrition during case identification and for the sensitivity of the screening method. Key Findings In a cohort of 151,408 people, 194 developed CE over the 5 years. The minimum crude incidence rate was 37.6/100,000 persons per year (95% confidence interval (CI) 32.7-43.3) and adjusted for loss to follow-up, and the sensitivity of the survey methodology was 77.0/100,000 persons per year (95% CI 67.7-87.4). Incidence was highest in children 6-12 years (96.1/100,000 persons per year; 95% CI 78.4-117.9), and was lowest in the 29-49 year age group (37.4/100,000 persons per year; 95% CI 25.7-54.7). Significance We estimated a high incidence of convulsive epilepsy in this population. Incidence was highest early and late in life, suggesting that preventive interventions should target exposures that occur in these age groups. Incidence of focal epilepsy was more than twice that of generalized epilepsy, suggesting that etiologies that are amenable to intervention were most important in this population. It is likely that incidence is underestimated because of the early mortality of incident cases. © 2013 The Authors. Epilepsia published by Wiley Periodicals, Inc.

The Kilifi Vaccine Monitoring Study (KiVMS) is a long-term continuous cohort study set up to investigate effectiveness, impact, coverage, safety and indirect vaccine effects by recruiting birth cohorts and, where applicable, cohorts of older and adults. It is based in the area covered by the Kilifi Health and Demographic Surveillance System, Kilifi, Kenya, and currently has records of 33 962 children in the birth cohort database. A major strength of KiVMS is its unique integration of a vaccine registry, a morbidity surveillance system and the largest health and demographic surveillance system (HDSS) in Africa.

Summary The Kilifi Health and Demographic Surveillance System (KHDSS), located on the Indian Ocean coast of Kenya, was established in 2000 as a record of births, pregnancies, migration events and deaths and is maintained by 4-monthly household visits. The study area was selected to capture the majority of patients admitted to Kilifi District Hospital. The KHDSS has 260 000 residents and the hospital admits 4400 paediatric patients and 3400 adult patients per year. At the hospital, morbidity events are linked in real time by a computer search of the population register. Linked surveillance was extended to KHDSS vaccine clinics in 2008. KHDSS data have been used to define the incidence of hospital presentation with childhood infectious diseases (e.g. rotavirus diarrhoea, pneumococcal disease), to test the association between genetic risk factors (e.g. thalassaemia and sickle cell disease) and infectious diseases, to define the community prevalence of chronic diseases (e.g. epilepsy), to evaluate access to health care and to calculate the operational effectiveness of major public health interventions (e.g. conjugate Haemophilus influenzae type b vaccine). Rapport with residents is maintained through an active programme of community engagement. A system of collaborative engagement exists for sharing data on survival, morbidity, socio-economic status and vaccine coverage.

Summary Background Many investigators have suggested that malaria infection predisposes individuals to bacteraemia. We tested this hypothesis with mendelian randomisation studies of children with the malaria-protective phenotype of sickle-cell trait (HbAS). Methods This study was done in a defined area around Kilifi District Hospital, Kilifi, Kenya. We did a matched case-control study to identify risk factors for invasive bacterial disease, in which cases were children aged 3 months to 13 years who were admitted to hospital with bacteraemia between Sept 16, 1999, and July 31, 2002. We aimed to match two controls, by age, sex, location, and time of recruitment, for every case. We then did a longitudinal case-control study to assess the relation between HbAS and invasive bacterial disease as malaria incidence decreased. Cases were children aged 0–13 years who were admitted to hospital with bacteraemia between Jan 1, 1999, and Dec 31, 2007. Controls were born in the study area between Jan 1, 2006, and June 23, 2009. Finally, we modelled the annual incidence of bacteraemia against the community prevalence of malaria during 9 years with Poisson regression. Results In the matched case-control study, we recruited 292 cases—we recruited two controls for 236, and one for the remaining 56. Sickle-cell disease, HIV, leucocyte haemozoin pigment, and undernutrition were positively associated with bacteraemia and HbAS was strongly negatively associated with bacteraemia (odds ratio 0·36; 95% CI 0·20–0·65). In the longitudinal case-control study, we assessed data from 1454 cases and 10 749 controls. During the study period, the incidence of admission to hospital with malaria per 1000 child-years decreased from 28·5 to 3·45, with a reduction in protection afforded by HbAS against bacteraemia occurring in parallel (p=0·0008). The incidence of hospital admissions for bacteraemia per 1000 child-years also decreased from 2·59 to 1·45. The bacteraemia incidence rate ratio associated with malaria parasitaemia was 6·69 (95% CI 1·31–34·3) and, at a community parasite prevalence of 29% in 1999, 62% (8·2–91) of bacteraemia cases were attributable to malaria. Interpretation Malaria infection strongly predisposes individuals to bacteraemia and can account for more than half of all cases of bacteraemia in malaria-endemic areas. Interventions to control malaria will have a major additional benefit by reducing the burden of invasive bacterial disease. Funding Wellcome Trust.

OBJECTIVE: To explore excess paediatric mortality after discharge from Kilifi District Hospital, Kenya, and its duration and risk factors. METHODS: Hospital and demographic data were used to describe post-discharge mortality and survival probability in children aged -4 but 13 days (HR: 1.8). Older age was protective (reference

Background The most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya. Methods Between March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (κ) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD. Results HCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (κ = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (κ) value of 0.32 (95% CI: 0.30, 0.38). Overall, (κ) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD. Conclusion Both the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.

OBJECTIVE: To explore the relationship between homestead distance to hospital and access to care and to estimate the sensitivity of hospital-based surveillance in Kilifi district, Kenya. METHODS: In 2002-2006, clinical information was obtained from all children admitted to Kilifi District Hospital and linked to demographic surveillance data. Travel times to the hospital were calculated using geographic information systems and regression models were constructed to examine the relationships between travel time, cause-specific hospitalization rates and probability of death in hospital. Access to care ratios relating hospitalization rates to community mortality rates were computed and used to estimate surveillance sensitivity. FINDINGS: The analysis included 7200 admissions (64 per 1000 child-years). Median pedestrian and vehicular travel times to hospital were 237 and 61 minutes, respectively. Hospitalization rates decreased by 21% per hour of travel by foot and 28% per half hour of travel by vehicle. Distance decay was steeper for meningitis than for pneumonia, for females than for males, and for areas where mothers had less education on average. Distance was positively associated with the probability of dying in hospital. Overall access to care ratios, which represent the probability that a child in need of hospitalization will have access to care at the hospital, were 51-58% for pneumonia and 66-70% for meningitis. CONCLUSION: In this setting, hospital utilization rates decreased and the severity of cases admitted to hospital increased as distance between homestead and hospital increased. Access to hospital care for children living in remote areas was low, particularly for those with less severe conditions. Distance decay was attenuated by increased levels of maternal education. Hospital-based surveillance underestimated pneumonia and meningitis incidence by more than 45% and 30%, respectively.

BACKGROUND: Febrile malaria is the most common clinical manifestation of P. falciparum infection, and is often the primary endpoint in clinical trials and epidemiological studies. Subjective and objective fevers are both used to define the endpoint, but have not been carefully compared, and the relative incidence of clinical malaria by active and passive case detection is unknown. METHODS: We analyzed data from cohorts under active and passive surveillance, including 19,462 presentations with fever and 5,551 blood tests for asymptomatic parasitaemia. A logistic regression model was used to calculate Malaria Attributable Fractions (MAFs) for various case definitions. Incidences of febrile malaria by active and passive surveillance were compared in a subset of children matched for age and location. RESULTS: Active surveillance identified three times the incidence of clinical malaria as passive surveillance in a subset of children matched for age and location. Objective fever (temperature≥37.5°C) gave consistently higher MAFs than case definitions based on subjective fever. CONCLUSION: The endpoints from active and passive surveillance have high specificity, but the incidence of endpoints is lower on passive surveillance. Subjective fever had low specificity and should not be used in primary endpoint. Passive surveillance will reduce the power of clinical trials but may cost-effectively deliver acceptable sensitivity in studies of large populations.

Background Acute seizures are common among children admitted to hospitals in resource poor countries. However, there is little data on the burden, causes and outcome of neonatal seizures in sub-Saharan Africa. We determined the minimum incidence, aetiology and immediate outcome of seizures among neonates admitted to a rural district hospital in Kenya. Methods From 1st January 2003 to 31st December 2007, we assessed for seizures all neonates (age 0-28 days) admitted to the Kilifi District Hospital, who were resident in a defined, regularly enumerated study area. The population denominator, the number of live births in the community on 1 July 2005 (the study midpoint) was modelled from the census data. Results Seizures were reported in 142/1572 (9.0%) of neonatal admissions. The incidence was 39.5 [95% confidence interval (CI) 26.4-56.7] per 1000 live-births and incidence increased with birth weight. The main diagnoses in neonates with seizures were sepsis in 85 (60%), neonatal encephalopathy in 30 (21%) and meningitis in 21 (15%), but only neonatal encephalopathy and bacterial meningitis were independently associated with seizures. Neonates with seizures had a longer hospitalization [median period 7 days - interquartile range (IQR) 4 to10] -compared to 5 days [IQR 3 to 8] for those without seizures, P = 0.02). Overall, there was no difference in inpatient case fatality between neonates with and without seizures but, when this outcome was stratified by birth weight, it was significantly higher in neonates ≥ 2.5 kg compared to low birth weight neonates [odds ratio 1.59 (95%CI 1.02 to 2.46), P = 0.037]. Up to 13% of the surviving newborn with seizures had neurological abnormalities at discharge. Conclusion There is a high incidence of neonatal seizures in this area of Kenya and the most important causes are neonatal encephalopathy and meningitis. The high incidence of neonatal seizures may be a reflection of the quality of the perinatal and postnatal care available to the neonates.

Background Rotavirus, predominantly of group A, is a major cause of severe diarrhoea worldwide, with the greatest burden falling on young children living in less-developed countries. Vaccines directed against this virus have shown promise in recent trials, and are undergoing effectiveness evaluation in sub-Saharan Africa. In this region limited childhood data are available on the incidence and clinical characteristics of severe group A rotavirus disease. Advocacy for vaccine intervention and interpretation of effectiveness following implementation will benefit from accurate base-line estimates of the incidence and severity of rotavirus paediatric admissions in relevant populations. The study objective was to accurately define the incidence and severity of group A rotavirus disease in a resource-poor setting necessary to make informed decisions on the need for vaccine prevention. Methods and Findings Between 2002 and 2004 we conducted prospective surveillance for group A rotavirus infection at Kilifi District Hospital in coastal Kenya. Children < 13 y of age were eligible as “cases” if admitted with diarrhoea, and “controls” if admitted without diarrhoea. We calculated the incidence of hospital admission with group A rotavirus using data from a demographic surveillance study of 220,000 people in Kilifi District. Of 15,347 childhood admissions 3,296 (22%) had diarrhoea, 2,039 were tested for group A rotavirus antigen and, of these, 588 (29%) were positive. 372 (63%) rotavirus-positive cases were infants. Of 620 controls 19 (3.1%, 95% confidence interval [CI] 1.9–4.7) were rotavirus positive. The annual incidence (per 100,000 children) of rotavirus-positive admissions was 1,431 (95% CI 1,275–1,600) in infants and 478 (437–521) in under-5-y-olds, and highest proximal to the hospital. Compared to children with rotavirus-negative diarrhoea, rotavirus-positive cases were less likely to have coexisting illnesses and more likely to have acidosis (46% versus 17%) and severe electrolyte imbalance except hyponatraemia. In-hospital case fatality was 2% among rotavirus-positive and 9% among rotavirus-negative children. Conclusions In Kilifi > 2% of children are admitted to hospital with group A rotavirus diarrhoea in the first 5 y of life. This translates into over 28,000 vaccine-preventable hospitalisations per year across Kenya, and is likely to be a considerable underestimate. Group A rotavirus diarrhoea is associated with acute life-threatening metabolic derangement in otherwise healthy children. Although mortality is low in this clinical research setting this may not be generally true in African hospitals lacking rapid and appropriate management., Combining prospective hospital-based surveillance with demographic data in Kilifi, Kenya, James Nokes and colleagues assess the burden of rotavirus diarrhea in young children., Editors' Summary Background. Rotavirus is a leading global cause of diarrhea in babies and young children. Indeed, most children become infected at least once with this virus before their fifth birthday. Rotavirus is usually spread by children or their caregivers failing to wash their hands properly after going to the toilet and then contaminating food or drink. The symptoms of rotavirus infection—diarrhea, vomiting, and fever—are usually mild, but if the diarrhea is severe it can quickly lead to dehydration. Mild to moderate dehydration can be treated at home by providing the patient with plenty of fluids or with a special rehydration drink that replaces lost water and salts. However, for infants or toddlers who become severely dehydrated, rehydration with intravenous fluids (fluids injected directly into a vein) in hospital may be essential. Unfortunately, in developing countries in sub-Saharan Africa and elsewhere, this treatment is not widely available and every year more than half a million young children die from rotavirus infections. Why Was This Study Done? Two rotavirus vaccines that could reduce this burden of disease are currently undergoing clinical trials to determine their effectiveness in sub-Saharan Africa. However, very little is known about the incidence of severe rotavirus infections among children living in this region (that is, how many children develop severe disease every year) or about the clinical characteristics of the disease here. Public-health officials need this baseline information before they can make informed decisions about the mass introduction of rotavirus vaccination and to help them judge whether the intervention has been successful if it is introduced. In this study, the researchers examine the incidence and clinical characteristics of rotavirus infections (specifically, group A rotavirus [GARV] infections; there are several different rotaviruses but GARV causes most human infections) among children admitted to the district hospital in Kilifi, Kenya. What Did the Researchers Do and Find? During the 3-year study, more than 15,000 children under the age of 13 years were admitted to Kilifi District Hospital, a little under a quarter of whom had severe diarrhea. Nearly a third of the patients admitted with diarrhea who were tested had a GARV-specific protein in their stools (faeces); by contrast, only three in 100 children admitted without diarrhea showed any evidence of GARV infection. Two-thirds of the GARV-positive children were infants (under 1 year old). Using these figures and health surveillance data (records of births, deaths, and causes of death) collected in the area around the hospital, the researchers calculated that the annual incidence (per 100,000 children) of GARV-positive hospital admissions in the region was 1,431 for infants and 478 for children under age 5 years. Children with GARV-positive diarrhea were less likely to have other illnesses (for example, malnutrition) than those admitted with GARV-negative diarrhea, the researchers report, but were more likely to have life-threatening complications such as severe dehydration and salt imbalances in their blood. However, despite being more ill on admission, only 1 in 50 children with GARV-positive diarrhea died, compared to nearly 1 in 10 of the children with GARV-negative diarrhea; the GARV-positive children also left hospital quicker than those who were GARV-negative. What Do These Findings Mean? These findings indicate that severe GARV-positive diarrhea is a major cause of hospital admission among otherwise healthy young children in the Kilifi region of Kenya. By the time they are 5 years old, the researchers estimate that 1 in 50 of the children living in this region will have been admitted to hospital with severe GARV-positive diarrhea. Because rotavirus vaccines prevent virtually all severe rotavirus-associated disease (at least in developed countries where their effectiveness has been extensively tested), the researchers estimate that vaccination might prevent more than 28,000 hospitalizations annually across Kenya; however, this prediction assumes that it is valid to extrapolate from the data obtained from this one district hospital to the entire country. Additional Information. Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050153. The US Centers for Disease Control and Prevention provides information about rotavirus infections, surveillance, and vaccination (in English and Spanish) The UK National Health Service Direct health encyclopedia provides information on rotavirus infections MedlinePlus also provides links to information on rotavirus (in English and Spanish) The African Rotavirus Surveillance Network is working to improve knowledge about rotavirus infections in Africa The Rotavirus Vaccine Program aims to reduce child illness and death from diarrhea by increasing the availability of rotavirus vaccines in developing countries (in English and Spanish) PATH, a nonprofit international organization that aims to create sustainable, culturally relevant solutions to global health problems, also provides detailed information on rotavirus surveillance and disease burden

BACKGROUND: Few large-scale studies of epilepsy have been done in sub-Saharan Africa. We aimed to estimate the prevalence of, treatment gap in, and possible risk factors for active convulsive epilepsy in Kenyan people aged 6 years or older living in a rural area. METHODS: We undertook a three-phase screening survey of 151,408 individuals followed by a nested community case-control study. Treatment gap was defined as the proportion of cases of active convulsive epilepsy without detectable amounts of antiepileptic drugs in blood. FINDINGS: Overall prevalence of active convulsive epilepsy was 2.9 per 1000 (95% CI 2.6-3.2); after adjustment for non-response and sensitivity, prevalence was 4.5 per 1000 (4.1-4.9). Substantial heterogeneity was noted in prevalence, with evidence of clustering. Treatment gap was 70.3% (65.9-74.5), with weak evidence of a difference by sex and area. Adjusted odds of active convulsive epilepsy for all individuals were increased with a family history of non-febrile convulsions (odds ratio 3.3, 95% CI 2.4-4.7; p

CONTEXT: Plasmodium falciparum appears to have a particular propensity to involve the brain but the burden, risk factors, and full extent of neurological involvement have not been systematically described. OBJECTIVES: To determine the incidence and describe the clinical phenotypes and outcomes of neurological involvement in African children with acute falciparum malaria. DESIGN, SETTING, AND PATIENTS: A review of records of all children younger than 14 years admitted to a Kenyan district hospital with malaria from January 1992 through December 2004. Neurological involvement was defined as convulsive seizures, agitation, prostration, or impaired consciousness or coma. MAIN OUTCOME MEASURES: The incidence, pattern, and outcome of neurological involvement. RESULTS: Of 58,239 children admitted, 19,560 (33.6%) had malaria as the primary clinical diagnosis. Neurological involvement was observed in 9313 children (47.6%) and manifested as seizures (6563/17,517 [37.5%]), agitation (316/11,193 [2.8%]), prostration (3223/15,643 [20.6%]), and impaired consciousness or coma (2129/16,080 [13.2%]). In children younger than 5 years, the mean annual incidence of admissions with malaria was 2694 per 100,000 persons and the incidence of malaria with neurological involvement was 1156 per 100,000 persons. However, readmissions may have led to a 10% overestimate in incidence. Children with neurological involvement were older (median, 26 [interquartile range {IQR}, 15-41] vs 21 [IQR, 10-40] months; P

Background There are few studies on the epidemiology of epilepsy in large populations in Low and Middle Income Countries (LMIC). Most studies in these regions use two-stage population-based screening surveys, which are time-consuming and costly to implement in large populations required to generate accurate estimates. We examined the sensitivity and specificity of a three-stage cross-sectional screening methodology in detecting active convulsive epilepsy (ACE), which can be embedded within on-going census of demographic surveillance systems. We validated a three-stage cross-sectional screening methodology on a randomly selected sample of participants of a three-stage prevalence survey of epilepsy. Diagnosis of ACE by an experienced clinician was used as ‘gold standard’. We further compared the expenditure of this method with the standard two-stage methodology. Results We screened 4442 subjects in the validation and identified 35 cases of ACE. Of these, 18 were identified as false negatives, most of whom (15/18) were missed in the first stage and a few (3/18) in the second stage of the three-stage screening. Overall, this methodology had a sensitivity of 48.6% and a specificity of 100%. It was 37% cheaper than a two-stage survey. Conclusion This was the first study to evaluate the performance of a multi-stage screening methodology used to detect epilepsy in demographic surveillance sites. This method had poor sensitivity attributed mainly to stigma-related non-response in the first stage. This method needs to take into consideration the poor sensitivity and the savings in expenditure and time as well as validation in target populations. Our findings suggest the need for continued efforts to develop and improve case-ascertainment methods in population-based epidemiological studies of epilepsy in LMIC.

Background Policy-makers evaluating country progress towards the Millennium Development Goals also examine trends in health inequities. Distance to health facilities is a known determinant of health care utilization and may drive inequalities in health outcomes; we aimed to investigate its effects on childhood mortality. Methods The Epidemiological and Demographic Surveillance System in Kilifi District, Kenya, collects data on vital events and migrations in a population of 220,000 people. We used Geographic Information Systems to estimate pedestrian and vehicular travel times to hospitals and vaccine clinics and developed proportional-hazards models to evaluate the effects of travel time on mortality hazard in children less than 5 years of age, accounting for sex, ethnic group, maternal education, migrant status, rainfall and calendar time. Results In 2004-6, under-5 and under-1 mortality ratios were 65 and 46 per 1,000 live-births, respectively. Median pedestrian and vehicular travel times to hospital were 193 min (inter-quartile range: 125-267) and 49 min (32-72); analogous values for vaccine clinics were 47 (25-73) and 26 min (13-40). Infant and under-5 mortality varied two-fold across geographic locations, ranging from 34.5 to 61.9 per 1000 child-years and 8.8 to 18.1 per 1000, respectively. However, distance to health facilities was not associated with mortality. Hazard Ratios (HR) were 0.99 (95% CI 0.95-1.04) per hour and 1.01 (95% CI 0.95-1.08) per half-hour of pedestrian and vehicular travel to hospital, respectively, and 1.00 (95% CI 0.99-1.04) and 0.97 (95% CI 0.92-1.05) per quarter-hour of pedestrian and vehicular travel to vaccine clinics in children Conclusions Significant spatial variations in mortality were observed across the area, but were not correlated with distance to health facilities. We conclude that given the present density of health facilities in Kenya, geographic access to curative services does not influence population-level mortality.

Search terms for papers on post-discharge mortality in children following diarrhea admission. Table S1. Discharge diagnosis of children admitted with diarrhea. Table S2. Distribution of inpatient and post-discharge mortality by age groups amongst children admitted with diarrhea. Table S3. Association between nutritional status and inpatient mortality amongst children admitted with diarrhea. Table S4. Univariable and multivariable analysis of laboratory variables, not systematically tested, associated with inpatient deaths amongst children admitted with diarrhea. Table S5. Post-discharge mortality amongst children discharged alive and residents of KHDSS by admission diagnosis. Table S6. Association between nutritional status and post-discharge mortality amongst children admitted with diarrhea and residents of KHDSS. Table S7. Univariable and multivariable analysis of laboratory variables, not tested systematically, associated with post-discharge mortality amongst children admitted with diarrhea and residents of KHDSS. Box S1. Models for inpatient and post-discharge mortality prediction. Figure S1. Kernel-smoothed baseline hazard of the Post-discharge deaths model. (DOCX 119 kb)

Search terms for papers on post-discharge mortality in children following diarrhea admission. Table S1. Discharge diagnosis of children admitted with diarrhea. Table S2. Distribution of inpatient and post-discharge mortality by age groups amongst children admitted with diarrhea. Table S3. Association between nutritional status and inpatient mortality amongst children admitted with diarrhea. Table S4. Univariable and multivariable analysis of laboratory variables, not systematically tested, associated with inpatient deaths amongst children admitted with diarrhea. Table S5. Post-discharge mortality amongst children discharged alive and residents of KHDSS by admission diagnosis. Table S6. Association between nutritional status and post-discharge mortality amongst children admitted with diarrhea and residents of KHDSS. Table S7. Univariable and multivariable analysis of laboratory variables, not tested systematically, associated with post-discharge mortality amongst children admitted with diarrhea and residents of KHDSS. Box S1. Models for inpatient and post-discharge mortality prediction. Figure S1. Kernel-smoothed baseline hazard of the Post-discharge deaths model. (DOCX 119 kb)

Timeline for the KHDSS bednet survey, sero-survey and the mass distribution. (DOCX 260Â kb)