PurposeTo estimate the burden of lifetime epilepsy (LTE) and active epilepsy (AE) and examine the influence of study characteristics on prevalence estimates.MethodsWe searched online databases and identified articles using prespecified criteria. Random-effects meta-analyses were used to estimate the median prevalence in developed countries and in urban and rural settings in developing countries. The impact of study characteristics on prevalence estimates was determined using meta-regression models.ResultsThe median LTE prevalence for developed countries was 5.8 per 1,000 (5th–95th percentile range 2.7–12.4) compared to 15.4 per 1,000 (4.8–49.6) for rural and 10.3 (2.8–37.7) for urban studies in developing countries. The median prevalence of AE was 4.9 per 1,000 (2.3–10.3) for developed countries and 12.7 per 1,000 (3.5–45.5) and 5.9 (3.4–10.2) in rural and urban studies in developing countries. The estimates of burden for LTE and AE in developed countries were 6.8 million (5th–95th percentile range 3.2–14.7) and 5.7 million (2.7–12.2), respectively. In developing countries these were 45 (14–145) million LTE and 17 (10–133) million AE in rural areas and 17 (5–61) million LTE and 10 (5–17) million AE in urban areas. Studies involving all ages or only adults showed higher estimates than pediatric studies. Higher prevalence estimates were also associated with rural location and small study size.ConclusionsThis study estimates the global burden of epilepsy and the proportions with AE, which may benefit from treatment. There are systematic differences in reported prevalence estimates, which are only partially explained by study characteristics.

SummaryBackgroundThe prevalence of epilepsy in sub-Saharan Africa seems to be higher than in other parts of the world, but estimates vary substantially for unknown reasons. We assessed the prevalence and risk factors of active convulsive epilepsy across five centres in this region.MethodsWe did large population-based cross-sectional and case-control studies in five Health and Demographic Surveillance System centres: Kilifi, Kenya (Dec 3, 2007–July 31, 2008); Agincourt, South Africa (Aug 4, 2008–Feb 27, 2009); Iganga-Mayuge, Uganda (Feb 2, 2009–Oct 30, 2009); Ifakara, Tanzania (May 4, 2009–Dec 31, 2009); and Kintampo, Ghana (Aug 2, 2010–April 29, 2011). We used a three-stage screening process to identify people with active convulsive epilepsy. Prevalence was estimated as the ratio of confirmed cases to the population screened and was adjusted for sensitivity and attrition between stages. For each case, an age-matched control individual was randomly selected from the relevant centre's census database. Fieldworkers masked to the status of the person they were interviewing administered questionnaires to individuals with active convulsive epilepsy and control individuals to assess sociodemographic variables and historical risk factors (perinatal events, head injuries, and diet). Blood samples were taken from a randomly selected subgroup of 300 participants with epilepsy and 300 control individuals from each centre and were screened for antibodies to Toxocara canis, Toxoplasma gondii, Onchocerca volvulus, Plasmodium falciparum, Taenia solium, and HIV. We estimated odds ratios (ORs) with logistic regression, adjusted for age, sex, education, employment, and marital status.Results586 607 residents in the study areas were screened in stage one, of whom 1711 were diagnosed as having active convulsive epilepsy. Prevalence adjusted for attrition and sensitivity varied between sites: 7·8 per 1000 people (95% CI 7·5–8·2) in Kilifi, 7·0 (6·2–7·4) in Agincourt, 10·3 (9·5–11·1) in Iganga-Mayuge, 14·8 (13·8–15·4) in Ifakara, and 10·1 (9·5–10·7) in Kintampo. The 1711 individuals with the disorder and 2032 control individuals were given questionnaires. In children (aged <18 years), the greatest relative increases in prevalence were associated with difficulties feeding, crying, or breathing after birth (OR 10·23, 95% CI 5·85–17·88; p<0·0001); abnormal antenatal periods (2·15, 1·53–3·02; p<0·0001); and head injury (1·97, 1·28–3·03; p=0·002). In adults (aged ≥18 years), the disorder was significantly associated with admission to hospital with malaria or fever (2·28, 1·06–4·92; p=0·036), exposure to T canis (1·74, 1·27–2·40; p=0·0006), exposure to T gondii (1·39, 1·05–1·84; p=0·021), and exposure to O volvulus (2·23, 1·56–3·19; p<0·0001). Hypertension (2·13, 1·08–4·20; p=0·029) and exposure to T solium (7·03, 2·06–24·00; p=0·002) were risk factors for adult-onset disease.InterpretationThe prevalence of active convulsive epilepsy varies in sub-Saharan Africa and that the variation is probably a result of differences in risk factors. Prog...

SummaryBackground-House screening should protect people against malaria. We assessed whether two types of house screening, full screening of windows, doors and closing eaves or installing netting ceilings in local houses, could reduce malaria vector house entry and anaemia in children, in an area of seasonal transmission.

Objective: To estimate the pooled incidence of epilepsy from published studies and investigate sources of heterogeneity in the estimates. Methods:We searched online databases for incidence studies and used meta-analytic methods to analyze the data.Results: Thirty-three articles met the entry criteria. The median incidence of epilepsy was 50.4/100,000/year (interquartile range [IQR] 33.6-75.6), while it was 45.0 (IQR 30.3-66.7) for high-income countries and 81.7 (IQR 28.0-239.5) for low-and middle-income countries. Population-based studies had higher incidence estimates than hospital-based studies (p ϭ 0.02) while retrospective study design was associated with lower estimates than prospective studies (p ϭ 0.04). Conclusion:We provide data that could potentially be used to assess the burden and analyze the trends in incidence of epilepsy. Our results support the need for large population-based incidence studies of epilepsy. Neurology Epilepsy is one of the most prevalent noncommunicable neurologic conditions and an important cause of disability and mortality.1 It is estimated to affect almost 70 million people worldwide. 2 The prevalence of epilepsy in low-and middle-income countries (LMIC) is about twice that of high-income countries (HIC).2 Since mortality is high early in the course of epilepsy and spontaneous remission may occur, [3][4][5][6] prevalence data may significantly underestimate the burden of epilepsy. Thus, incidence of epilepsy, which is not diminished by disease-specific mortality, could be useful in enriching prevalence data in the assessment of the burden of epilepsy.While many prevalence studies have been reported, 2,7-9 there are only a few studies of incidence. Existing studies suggest a higher incidence of epilepsy in LMIC than in HIC, although it is not clear if this difference is real or due to methodologic differences.10 These estimates are diverse, limiting their utility in informing public health policy and resource allocation for prevention. Reasons for this variability are not clear.One published review of the incidence of epilepsy did not utilize meta-analytic methods. 11 It did not provide confidence intervals for the aggregate estimates, quantify heterogeneity in incidence rates, or identify the reasons for the observed variation.

No abstract

The spread of SARS-CoV-2 in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on March 12, 2020 and an overwhelming number of cases and deaths were expected but by July 31, 2020 there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti–SARS-CoV-2 IgG among blood donors in Kenya in April-June 2020. Crude seroprevalence was 5.6% (174/3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% CI 2.9–5.8%) and was highest in urban counties, Mombasa (8.0%), Nairobi (7.3%) and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance and these results will help guide the pandemic response in Kenya, and across Africa.

Objectives To estimate survival after a diagnosis of dementia in primary care, compared with people without dementia, and to determine incidence of dementia.Design Cohort study using data from The Health Improvement Network (THIN), a primary care database.Setting 353 general practices in the United Kingdom providing data to THIN.Participants All adults aged 60 years or over with a first ever code for dementia from 1990 to 2007 (n=22 529); random sample of five participants without dementia for every participant with dementia matched on practice and time period (n=112 645).Main outcome measures Median survival by age and sex; mortality rates; incidence of dementia by age, sex, and deprivation.Results The median survival of people with dementia diagnosed at age 60-69 was 6.7 (interquartile range 3.1-10.8) years, falling to 1.9 (0.7-3.6) years for those diagnosed at age 90 or over. Adjusted mortality rates were highest in the first year after diagnosis (relative risk 3.68, 95% confidence interval 3.44 to 3.94). This dropped to 2.49 (2.29 to 2.71) in the second year. The incidence of recorded dementia remained stable over time (3-4/1000 person years at risk). The incidence was higher in women and in younger age groups (60-79 years) living in deprived areas.Conclusions Median survival was much lower than in screened populations. These clinically relevant estimates can assist patients and carers, clinicians, and policy makers when planning support for this population. The high risk of death in the first year after diagnosis may reflect diagnoses made at times of crisis or late in the disease trajectory. Late recording of diagnoses of dementia in primary care may result in missed opportunities for potential early interventions.

Background The effect of the COVID-19 pandemic on HIV outcomes in low-income and middle-income countries is poorly described. We aimed to measure the impact of the 2020 national COVID-19 lockdown on HIV testing and treatment in KwaZulu-Natal, South Africa, where 1•7 million people are living with HIV.Methods In this interrupted time series analysis, we analysed anonymised programmatic data from 65 primary care clinics in KwaZulu-Natal province, South Africa. We included data from people testing for HIV, initiating antiretroviral therapy (ART), and collecting ART at participating clinics during the study period, with no age restrictions. We used descriptive statistics to summarise demographic and clinical data, and present crude summaries of the main outcomes of numbers of HIV tests per month, ART initiations per week, and ART collection visits per week, before and after the national lockdown that began on March 27, 2020. We used Poisson segmented regression models to estimate the immediate impact of the lockdown on these outcomes, as well as post-lockdown trends.