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      Prevalence of active convulsive epilepsy in sub-Saharan Africa and associated risk factors: cross-sectional and case-control studies

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          Summary

          Background

          The 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.

          Methods

          We 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.

          Results

          586 607 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.

          Interpretation

          The prevalence of active convulsive epilepsy varies in sub-Saharan Africa and that the variation is probably a result of differences in risk factors. Programmes to control parasitic diseases and interventions to improve antenatal and perinatal care could substantially reduce the prevalence of epilepsy in this region.

          Funding

          Wellcome Trust, University of the Witwatersrand, and South African Medical Research Council.

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          Most cited references73

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          No Adjustments Are Needed for Multiple Comparisons

          Adjustments for making multiple comparisons in large bodies of data are recommended to avoid rejecting the null hypothesis too readily. Unfortunately, reducing the type I error for null associations increases the type II error for those associations that are not null. The theoretical basis for advocating a routine adjustment for multiple comparisons is the "universal null hypothesis" that "chance" serves as the first-order explanation for observed phenomena. This hypothesis undermines the basic premises of empirical research, which holds that nature follows regular laws that may be studied through observations. A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature. Furthermore, scientists should not be so reluctant to explore leads that may turn out to be wrong that they penalize themselves by missing possibly important findings.
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            Evidence-based, cost-effective interventions: how many newborn babies can we save?

            In this second article of the neonatal survival series, we identify 16 interventions with proven efficacy (implementation under ideal conditions) for neonatal survival and combine them into packages for scaling up in health systems, according to three service delivery modes (outreach, family-community, and facility-based clinical care). All the packages of care are cost effective compared with single interventions. Universal (99%) coverage of these interventions could avert an estimated 41-72% of neonatal deaths worldwide. At 90% coverage, intrapartum and postnatal packages have similar effects on neonatal mortality--two-fold to three-fold greater than that of antenatal care. However, running costs are two-fold higher for intrapartum than for postnatal care. A combination of universal--ie, for all settings--outreach and family-community care at 90% coverage averts 18-37% of neonatal deaths. Most of this benefit is derived from family-community care, and greater effect is seen in settings with very high neonatal mortality. Reductions in neonatal mortality that exceed 50% can be achieved with an integrated, high-coverage programme of universal outreach and family-community care, consisting of 12% and 26%, respectively, of total running costs, plus universal facility-based clinical services, which make up 62% of the total cost. Early success in averting neonatal deaths is possible in settings with high mortality and weak health systems through outreach and family-community care, including health education to improve home-care practices, to create demand for skilled care, and to improve care seeking. Simultaneous expansion of clinical care for babies and mothers is essential to achieve the reduction in neonatal deaths needed to meet the Millennium Development Goal for child survival.
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              Multiple imputation of discrete and continuous data by fully conditional specification.

              The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in the cases studied. The present paper reviews and compares the approaches. JM and FCS were applied to pubertal development data of 3801 Dutch girls that had missing data on menarche (two categories), breast development (five categories) and pubic hair development (six stages). Imputations for these data were created under two models: a multivariate normal model with rounding and a conditionally specified discrete model. The JM approach introduced biases in the reference curves, whereas FCS did not. The paper concludes that FCS is a useful and easily applied flexible alternative to JM when no convenient and realistic joint distribution can be specified.
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                Author and article information

                Journal
                Lancet Neurol
                Lancet Neurol
                Lancet Neurology
                Lancet Pub. Group
                1474-4422
                1474-4465
                13 March 2013
                13 March 2013
                : 12
                : 3
                : 253-263
                Affiliations
                [a ]Studies of Epidemiology of Epilepsy in Demographic Surveillance Systems, International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH), Accra, Ghana
                [b ]Kenya Medical Research Institute/Wellcome Trust Research Programme, Centre for Geographic Medicine Research–Coast, Kilifi, Kenya
                [c ]Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
                [d ]MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK
                [e ]Faculty of Epidemiology and Population Health, and Clinical Research Unit, London School of Hygiene and Tropical Medicine, London, UK
                [f ]MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [g ]Epidemiology and Public Health, Department of Public Health and Clinical Medicine and Department of Pharmacology and Clinical Neuroscience, Umeå University, Umeå, Sweden
                [h ]Iganga-Mayuge Health and Demographic Surveillance System, Iganga, Uganda
                [i ]Department of Paediatrics and Child Health, Makerere University College of Health Sciences, Kampala, Uganda
                [j ]Kintampo Health Research Centre, Kintampo, Ghana
                [k ]Ifakara Health Institute, Ifakara, Tanzania
                [l ]Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK
                [m ]Neurosciences Unit, UCL Institute of Child Health, University College London, London, UK
                [n ]Stichting Epilepsie Instellingen Nederland, Heemstede, Netherlands
                [o ]Department of Psychiatry, University of Oxford, Oxford, UK
                Author notes
                [* ]Correspondence to: Dr Anthony K Ngugi, Kenya Medical Research Institute/Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, PO Box 230, Kilifi 80108, Kenya kngugi26@ 123456gmail.com
                [†]

                Joint first authors

                Article
                LANEUR70003
                10.1016/S1474-4422(13)70003-6
                3581814
                23375964
                3f6b45b8-8565-4ef7-9a18-b71ece8170d3
                © 2013 Elsevier Ltd. All rights reserved.

                This document may be redistributed and reused, subject to certain conditions.

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