Emerging Themes in Epidemiology最新文献

{"title":"A distributional approach to obtain adjusted comparisons of proportions of a population at risk.","authors":"Odile Sauzet,&nbsp;Jürgen Breckenkamp,&nbsp;Theda Borde,&nbsp;Silke Brenne,&nbsp;Matthias David,&nbsp;Oliver Razum,&nbsp;Janet L Peacock","doi":"10.1186/s12982-016-0050-2","DOIUrl":"https://doi.org/10.1186/s12982-016-0050-2","url":null,"abstract":"<p><strong>Background: </strong>Dichotomisation of continuous data has statistical drawbacks such as loss of power but may be useful in epidemiological research to define high risk individuals.</p><p><strong>Methods: </strong>We extend a methodology for the presentation of comparison of proportions derived from a comparison of means for a continuous outcome to reflect the relationship between a continuous outcome and covariates in a linear (mixed) model without losing statistical power. The so called \"distributional method\" is described and using perinatal data for illustration, results from the distributional method are compared to those of logistic regression and to quantile regression for three different outcomes.</p><p><strong>Results: </strong>Estimates obtained using the distributional method for the comparison of proportions are consistently more precise than those obtained using logistic regression. For one of the three outcomes the estimates obtained from the distributional method and from logistic regression disagreed highlighting that the relationships between outcome and covariate differ conceptually between the two models.</p><p><strong>Conclusion: </strong>When an outcome follows the required condition of distribution shift between exposure groups, the results of a linear regression model can be followed by the corresponding comparison of proportions at risk. This dual approach provides more precise estimates than logistic regression thus avoiding the drawback of the usual dichotomisation of continuous outcomes.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-016-0050-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34561507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is Middle-Upper Arm Circumference \"normally\" distributed? Secondary data analysis of 852 nutrition surveys.","authors":"Severine Frison,&nbsp;Francesco Checchi,&nbsp;Marko Kerac,&nbsp;Jennifer Nicholas","doi":"10.1186/s12982-016-0048-9","DOIUrl":"https://doi.org/10.1186/s12982-016-0048-9","url":null,"abstract":"<p><strong>Background: </strong>Wasting is a major public health issue throughout the developing world. Out of the 6.9 million estimated deaths among children under five annually, over 800,000 deaths (11.6 %) are attributed to wasting. Wasting is quantified as low Weight-For-Height (WFH) and/or low Mid-Upper Arm Circumference (MUAC) (since 2005). Many statistical procedures are based on the assumption that the data used are normally distributed. Analyses have been conducted on the distribution of WFH but there are no equivalent studies on the distribution of MUAC.</p><p><strong>Methods: </strong>This secondary data analysis assesses the normality of the MUAC distributions of 852 nutrition cross-sectional survey datasets of children from 6 to 59 months old and examines different approaches to normalise \"non-normal\" distributions.</p><p><strong>Results: </strong>The distribution of MUAC showed no departure from a normal distribution in 319 (37.7 %) distributions using the Shapiro-Wilk test. Out of the 533 surveys showing departure from a normal distribution, 183 (34.3 %) were skewed (D'Agostino test) and 196 (36.8 %) had a kurtosis different to the one observed in the normal distribution (Anscombe-Glynn test). Testing for normality can be sensitive to data quality, design effect and sample size. Out of the 533 surveys showing departure from a normal distribution, 294 (55.2 %) showed high digit preference, 164 (30.8 %) had a large design effect, and 204 (38.3 %) a large sample size. Spline and LOESS smoothing techniques were explored and both techniques work well. After Spline smoothing, 56.7 % of the MUAC distributions showing departure from normality were \"normalised\" and 59.7 % after LOESS. Box-Cox power transformation had similar results on distributions showing departure from normality with 57 % of distributions approximating \"normal\" after transformation. Applying Box-Cox transformation after Spline or Loess smoothing techniques increased that proportion to 82.4 and 82.7 % respectively.</p><p><strong>Conclusion: </strong>This suggests that statistical approaches relying on the normal distribution assumption can be successfully applied to MUAC. In light of this promising finding, further research is ongoing to evaluate the performance of a normal distribution based approach to estimating the prevalence of wasting using MUAC.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-016-0048-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34458281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maximising follow-up participation rates in a large scale 45 and Up Study in Australia.","authors":"Adrian Bauman,&nbsp;Philayrath Phongsavan,&nbsp;Alison Cowle,&nbsp;Emily Banks,&nbsp;Louisa Jorm,&nbsp;Kris Rogers,&nbsp;Bin Jalaludin,&nbsp;Anne Grunseit","doi":"10.1186/s12982-016-0046-y","DOIUrl":"https://doi.org/10.1186/s12982-016-0046-y","url":null,"abstract":"<p><strong>Background: </strong>The issue of poor response rates to population surveys has existed for some decades, but few studies have explored methods to improve the response rate in follow-up population cohort studies.</p><p><strong>Methods: </strong>A sample of 100,000 adults from the 45 and Up Study, a large population cohort in Australia, were followed up 3.5 years after the baseline cohort was assembled. A pilot mail-out of 5000 surveys produced a response rate of only 41.7 %. This study tested methods of enhancing response rate, with three groups of 1000 each allocated to (1) receiving an advance notice postcard followed by a questionnaire, (2) receiving a questionnaire and then follow-up reminder letter, and (3) both these strategies.</p><p><strong>Results: </strong>The enhanced strategies all produced an improved response rate compared to the pilot, with a resulting mean response rate of 53.7 %. Highest response was found when both the postcard and questionnaire reminder were used (56.4 %) but this was only significantly higher when compared to postcard alone (50.5 %) but not reminder alone (54.1 %). The combined approach was used for recruitment among the remaining 92,000 participants, with a resultant further increased response rate of 61.6 %.</p><p><strong>Conclusions: </strong>Survey prompting with a postcard and a reminder follow-up questionnaire, applied separately or combined can enhance follow-up rates in large scale survey-based epidemiological studies.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-016-0046-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34411019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data","authors":"H. Kumamaru, S. Schneeweiss, R. Glynn, S. Setoguchi, J. Gagne","doi":"10.1186/s12982-016-0047-x","DOIUrl":"https://doi.org/10.1186/s12982-016-0047-x","url":null,"abstract":"","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-016-0047-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65723476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods of nutrition surveillance in low-income countries.","authors":"Veronica Tuffrey, Andrew Hall","doi":"10.1186/s12982-016-0045-z","DOIUrl":"10.1186/s12982-016-0045-z","url":null,"abstract":"<p><strong>Background: </strong>In 1974 a joint FAO/UNICEF/WHO Expert Committee met to develop methods for nutrition surveillance. There has been much interest and activity in this topic since then, however there is a lack of guidance for practitioners and confusion exists around the terminology of nutrition surveillance. In this paper we propose a classification of data collection activities, consider the technical issues for each category, and examine the potential applications and challenges related to information and communication technology.</p><p><strong>Analysis: </strong>There are three major approaches used to collect primary data for nutrition surveillance: repeated cross-sectional surveys; community-based sentinel monitoring; and the collection of data in schools. There are three major sources of secondary data for surveillance: from feeding centres, health facilities, and community-based data collection, including mass screening for malnutrition in children. Surveillance systems involving repeated surveys are suitable for monitoring and comparing national trends and for planning and policy development. To plan at a local level, surveys at district level or in programme implementation areas are ideal, but given the usually high cost of primary data collection, data obtained from health systems are more appropriate provided they are interpreted with caution and with contextual information. For early warning, data from health systems and sentinel site assessments may be valuable, if consistent in their methods of collection and any systematic bias is deemed to be steady. For evaluation purposes, surveillance systems can only give plausible evidence of whether a programme is effective. However the implementation of programmes can be monitored as long as data are collected on process indicators such as access to, and use of, services. Surveillance systems also have an important role to provide information that can be used for advocacy and for promoting accountability for actions or lack of actions, including service delivery.</p><p><strong>Conclusion: </strong>This paper identifies issues that affect the collection of nutrition surveillance data, and proposes definitions of terms to differentiate between diverse sources of data of variable accuracy and validity. Increased interest in nutrition globally has resulted in high level commitments to reduce and prevent undernutrition. This review helps to address the need for accurate and regular data to convert these commitments into practice.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4797352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65723440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hypertension and diabetes in Africa: design and implementation of a large population-based study of burden and risk factors in rural and urban Malawi","authors":"A. Crampin, N. Kayuni, A. Amberbir, C. Musicha, O. Koole, Terence Tafatatha, K. Branson, Jacqueline Saul, Elenaus Mwaiyeghele, Lawrence Nkhwazi, A. Phiri, A. Price, B. Mwagomba, C. Mwansambo, S. Jaffar, M. Nyirenda","doi":"10.1186/s12982-015-0039-2","DOIUrl":"https://doi.org/10.1186/s12982-015-0039-2","url":null,"abstract":"","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-015-0039-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65723175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The obese gut microbiome across the epidemiologic transition","authors":"L. Dugas, Miles Fuller, J. Gilbert, B. Layden","doi":"10.1186/s12982-015-0044-5","DOIUrl":"https://doi.org/10.1186/s12982-015-0044-5","url":null,"abstract":"","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-015-0044-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65723426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines","authors":"Laura M. Grajeda, A. Ivanescu, M. Saito, C. Crainiceanu, D. Jaganath, R. Gilman, J. Crabtree, D. Kelleher, L. Cabrera, V. Cama, W. Checkley","doi":"10.1186/s12982-015-0038-3","DOIUrl":"https://doi.org/10.1186/s12982-015-0038-3","url":null,"abstract":"","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2016-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-015-0038-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65723164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-aided survey sampling and implementation in low- and middle-income contexts: a low-cost/low-tech alternative","authors":"Marco J. Haenssgen","doi":"10.1186/s12982-015-0041-8","DOIUrl":"https://doi.org/10.1186/s12982-015-0041-8","url":null,"abstract":"","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2015-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-015-0041-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65723861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control","authors":"S. Grabich, W. Robinson, S. Engel, C. Konrad, D. Richardson, J. Horney","doi":"10.1186/s12982-015-0042-7","DOIUrl":"https://doi.org/10.1186/s12982-015-0042-7","url":null,"abstract":"","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2015-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-015-0042-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65723873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}