Emerging Themes in Epidemiology最新文献

{"title":"The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria.","authors":"Stephen Nash,&nbsp;Victoria Tittle,&nbsp;Andrew Abaasa,&nbsp;Richard E Sanya,&nbsp;Gershim Asiki,&nbsp;Christian Holm Hansen,&nbsp;Heiner Grosskurth,&nbsp;Saidi Kapiga,&nbsp;Chris Grundy","doi":"10.1186/s12982-018-0079-5","DOIUrl":"https://doi.org/10.1186/s12982-018-0079-5","url":null,"abstract":"<p><strong>Background: </strong>Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda. We split the dataset into two sections; one to obtain parameters and one to test the validity of the models.</p><p><strong>Results: </strong>Out of 66 FC, we were able to estimate populations for 47. There were 16 FC in the test set. The estimates for total population from all three methods were similar, with errors less than 2.2%. Estimates of individual FC populations were more widely discrepant.</p><p><strong>Conclusions: </strong>In our rural Ugandan setting, it was possible to use a simple area based model to get reasonable estimates of total population. However, there were often large errors in estimates for individual villages.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-018-0079-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36410259","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":"Clarifying questions about \"risk factors\": predictors versus explanation.","authors":"C Mary Schooling,&nbsp;Heidi E Jones","doi":"10.1186/s12982-018-0080-z","DOIUrl":"10.1186/s12982-018-0080-z","url":null,"abstract":"<p><strong>Background: </strong>In biomedical research much effort is thought to be wasted. Recommendations for improvement have largely focused on processes and procedures. Here, we additionally suggest less ambiguity concerning the questions addressed.</p><p><strong>Methods: </strong>We clarify the distinction between two conflated concepts, prediction and explanation, both encompassed by the term \"risk factor\", and give methods and presentation appropriate for each.</p><p><strong>Results: </strong>Risk prediction studies use statistical techniques to generate contextually specific data-driven models requiring a representative sample that identify people at risk of health conditions efficiently (target populations for interventions). Risk prediction studies do not necessarily include causes (targets of intervention), but may include cheap and easy to measure surrogates or biomarkers of causes. Explanatory studies, ideally embedded within an informative model of reality, assess the role of causal factors which if targeted for interventions, are likely to improve outcomes. Predictive models allow identification of people or populations at elevated disease risk enabling targeting of proven interventions acting on causal factors. Explanatory models allow identification of causal factors to target across populations to prevent disease.</p><p><strong>Conclusion: </strong>Ensuring a clear match of question to methods and interpretation will reduce research waste due to misinterpretation.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-018-0080-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36403840","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":"Cannons and sparrows: an exact maximum likelihood non-parametric test for meta-analysis of k 2 × 2 tables.","authors":"Lawrence M Paul","doi":"10.1186/s12982-018-0077-7","DOIUrl":"10.1186/s12982-018-0077-7","url":null,"abstract":"<p><strong>Background: </strong>The use of meta-analysis to aggregate multiple studies has increased dramatically over the last 30 years. For meta-analysis of homogeneous data where the effect sizes for the studies contributing to the meta-analysis differ only by statistical error, the Mantel-Haenszel technique has typically been utilized. If homogeneity cannot be assumed or established, the most popular technique is the inverse-variance DerSimonian-Laird technique. However, both of these techniques are based on large sample, asymptotic assumptions and are, at best, an approximation especially when the number of cases observed in any cell of the corresponding contingency tables is small.</p><p><strong>Results: </strong>This paper develops an exact, non-parametric test based on a maximum likelihood test statistic as an alternative to the asymptotic techniques. Further, the test can be used across a wide range of heterogeneity. Monte Carlo simulations show that for the homogeneous case, the ML-NP-EXACT technique to be generally more powerful than the DerSimonian-Laird inverse-variance technique for realistic, smaller values of disease probability, and across a large range of odds ratios, number of contributing studies, and sample size. Possibly most important, for large values of heterogeneity, the pre-specified level of Type I Error is much better maintained by the ML-NP-EXACT technique relative to the DerSimonian-Laird technique. A fully tested implementation in the R statistical language is freely available from the author.</p><p><strong>Conclusions: </strong>This research has developed an exact test for the meta-analysis of dichotomous data. The ML-NP-EXACT technique was strongly superior to the DerSimonian-Laird technique in maintaining a pre-specified level of Type I Error. As shown, the DerSimonian-Laird technique demonstrated many large violations of this level. Given the various biases towards finding statistical significance prevalent in epidemiology today, a strong focus on maintaining a pre-specified level of Type I Error would seem critical.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-018-0077-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36293961","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":"The contributions and future direction of Program Science in HIV/STI prevention.","authors":"Marissa Becker,&nbsp;Sharmistha Mishra,&nbsp;Sevgi Aral,&nbsp;Parinita Bhattacharjee,&nbsp;Rob Lorway,&nbsp;Kalada Green,&nbsp;John Anthony,&nbsp;Shajy Isac,&nbsp;Faran Emmanuel,&nbsp;Helgar Musyoki,&nbsp;Lisa Lazarus,&nbsp;Laura H Thompson,&nbsp;Eve Cheuk,&nbsp;James F Blanchard","doi":"10.1186/s12982-018-0076-8","DOIUrl":"https://doi.org/10.1186/s12982-018-0076-8","url":null,"abstract":"<p><strong>Background: </strong>Program Science is an iterative, multi-phase research and program framework where programs drive the scientific inquiry, and both program and science are aligned towards a collective goal of improving population health.</p><p><strong>Discussion: </strong>To achieve this, Program Science involves the systematic application of theoretical and empirical knowledge to optimize the scale, quality and impact of public health programs. Program Science tools and approaches developed for strategic planning, program implementation, and program management and evaluation have been incorporated into HIV and sexually transmitted infection prevention programs in Kenya, Nigeria, India, and the United States.</p><p><strong>Conclusion: </strong>In this paper, we highlight key scientific contributions that emerged from the growing application of Program Science in the field of HIV and STI prevention, and conclude by proposing future directions for Program Science.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-018-0076-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36196389","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":"Change in quality of malnutrition surveys between 1986 and 2015.","authors":"Emmanuel Grellety, Michael H Golden","doi":"10.1186/s12982-018-0075-9","DOIUrl":"10.1186/s12982-018-0075-9","url":null,"abstract":"<p><strong>Background: </strong>Representative surveys collecting weight, height and MUAC are used to estimate the prevalence of acute malnutrition. The results are then used to assess the scale of malnutrition in a population and type of nutritional intervention required. There have been changes in methodology over recent decades; the objective of this study was to determine if these have resulted in higher quality surveys.</p><p><strong>Methods: </strong>In order to examine the change in reliability of such surveys we have analysed the statistical distributions of the derived anthropometric parameters from 1843 surveys conducted by 19 agencies between 1986 and 2015.</p><p><strong>Results: </strong>With the introduction of standardised guidelines and software by 2003 and their more general application from 2007 the mean standard deviation, kurtosis and skewness of the parameters used to assess nutritional status have each moved to now approximate the distribution of the WHO standards when the exclusion of outliers from analysis is based upon SMART flagging procedure. Where WHO flags, that only exclude data incompatible with life, are used the quality of anthropometric surveys has improved and the results now approach those seen with SMART flags and the WHO standards distribution. Agencies vary in their uptake and adherence to standard guidelines. Those agencies that fully implement the guidelines achieve the most consistently reliable results.</p><p><strong>Conclusions: </strong>Standard methods should be universally used to produce reliable data and tests of data quality and SMART type flagging procedures should be applied and reported to ensure that the data are credible and therefore inform appropriate intervention. Use of SMART guidelines has coincided with reliable anthropometric data since 2007.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5972441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36196390","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":"Role of survey response rates on valid inference: an application to HIV prevalence estimates.","authors":"Miguel Marino, Marcello Pagano","doi":"10.1186/s12982-018-0074-x","DOIUrl":"10.1186/s12982-018-0074-x","url":null,"abstract":"<p><strong>Background: </strong>Nationally-representative surveys suggest that females have a higher prevalence of HIV than males in most African countries. Unfortunately, these results are made on the basis of surveys with non-ignorable missing data. This study evaluates the impact that differential survey nonresponse rates between males and females can have on the point estimate of the HIV prevalence ratio of these two classifiers.</p><p><strong>Methods: </strong>We study 29 Demographic and Health Surveys (DHS) from 2001 to 2010. Instead of employing often used multiple imputation models with a Missing at Random assumption that may not hold in this setting, we assess the effect of ignoring the information contained in the missing HIV information for males and females through three proposed statistical measures. These measures can be used in settings where the interest is comparing the prevalence of a disease between two groups. The proposed measures do not utilize parametric models and can be implemented by researchers of any level. They are: (1) an upper bound on the potential bias of the usual practise of using reported HIV prevalence estimates that ignore subjects who have missing HIV outcomes. (2) Plausible range intervals to account for nonresponses, without any additional parametric modeling assumptions. (3) Prevalence ratio inflation factors to correct the point estimate of the HIV prevalence ratio, if estimates of nonresponders' HIV prevalences were known.</p><p><strong>Results: </strong>In 86% of countries, males have higher upper bounds of HIV prevalence than females, this is consonant with males possibly having higher infection rates than females. Additionally, 74% of surveys have a <i>plausible</i> range that crosses 1.0, suggesting a plausible equivalence between male and female HIV prevalences.</p><p><strong>Conclusions: </strong>It is quite reasonable to conclude that there is so much DHS nonresponse in evaluating the HIV status question, that existing data is plausibly generated by the situation where the virus is equally distributed between the sexes.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2018-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5839032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35903247","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":"Modelling fertility in rural South Africa with combined nonlinear parametric and semi-parametric methods.","authors":"Robert W Eyre,&nbsp;Thomas House,&nbsp;F Xavier Gómez-Olivé,&nbsp;Frances E Griffiths","doi":"10.1186/s12982-018-0073-y","DOIUrl":"https://doi.org/10.1186/s12982-018-0073-y","url":null,"abstract":"<p><strong>Background: </strong>Central to the study of populations, and therefore to the analysis of the development of countries undergoing major transitions, is the calculation of fertility patterns and their dependence on different variables such as age, education, and socio-economic status. Most epidemiological research on these matters rely on the often unjustified assumption of (generalised) linearity, or alternatively makes a parametric assumption (e.g. for age-patterns).</p><p><strong>Methods: </strong>We consider nonlinearity of fertility in the covariates by combining an established nonlinear parametric model for fertility over age with nonlinear modelling of fertility over other covariates. For the latter, we use the semi-parametric method of Gaussian process regression which is a popular methodology in many fields including machine learning, computer science, and systems biology. We applied the method to data from the Agincourt Health and Socio-Demographic Surveillance System, annual census rounds performed on a poor rural region of South Africa since 1992, to analyse fertility patterns over age and socio-economic status.</p><p><strong>Results: </strong>We capture a previously established age-pattern of fertility, whilst being able to more robustly model the relationship between fertility and socio-economic status without unjustified a priori assumptions of linearity. Peak fertility over age is shown to be increasing over time, as well as for adolescents but not for those later in life for whom fertility is generally decreasing over time.</p><p><strong>Conclusions: </strong>Combining Gaussian process regression with nonlinear parametric modelling of fertility over age allowed for the incorporation of further covariates into the analysis without needing to assume a linear relationship. This enabled us to provide further insights into the fertility patterns of the Agincourt study area, in particular the interaction between age and socio-economic status.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-018-0073-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35885842","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":"Novel metrics for growth model selection.","authors":"Matthew R Grigsby, Junrui Di, Andrew Leroux, Vadim Zipunnikov, Luo Xiao, Ciprian Crainiceanu, William Checkley","doi":"10.1186/s12982-018-0072-z","DOIUrl":"10.1186/s12982-018-0072-z","url":null,"abstract":"<p><strong>Background: </strong>Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predictive accuracy based on modifications of the mean squared error criteria.</p><p><strong>Methods: </strong>Three metrics were created: normalized, age-adjusted, and weighted mean squared error (MSE). Predictive performance metrics were used to compare linear mixed effects models and functional regression models. Prediction accuracy was assessed by partitioning the observed data into training and test datasets. This partitioning was constructed to assess prediction accuracy for backward (i.e., early growth), forward (i.e., late growth), in-range, and on new-individuals. Analyses were done with height measurements from 215 Peruvian children with data spanning from near birth to 2 years of age.</p><p><strong>Results: </strong>Functional models outperformed linear mixed effects models in all scenarios tested. In particular, prediction errors for functional concurrent regression (FCR) and functional principal component analysis models were approximately 6% lower when compared to linear mixed effects models. When we weighted subject-specific MSEs according to subject-specific growth rates during infancy, we found that FCR was the best performer in all scenarios.</p><p><strong>Conclusion: </strong>With this novel approach, we can quantitatively compare non-nested models and weight subgroups of interest to select the best performing growth model for a particular application or problem at hand.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5824542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35865435","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":"Effect of correcting for gestational age at birth on population prevalence of early childhood undernutrition.","authors":"Nandita Perumal, Daniel E Roth, Johnna Perdrizet, Aluísio J D Barros, Iná S Santos, Alicia Matijasevich, Diego G Bassani","doi":"10.1186/s12982-018-0070-1","DOIUrl":"10.1186/s12982-018-0070-1","url":null,"abstract":"<p><strong>Background: </strong>Postmenstrual and/or gestational age-corrected age (CA) is required to apply child growth standards to children born preterm (< 37 weeks gestational age). Yet, CA is rarely used in epidemiologic studies in low- and middle-income countries (LMICs), which may bias population estimates of childhood undernutrition. To evaluate the effect of accounting for GA in the application of growth standards, we used GA-specific standards at birth (INTERGROWTH-21st newborn size standards) in conjunction with CA for preterm-born children in the application of World Health Organization Child Growth Standards postnatally (referred to as 'CA' strategy) versus postnatal age for all children, to estimate mean length-for-age (LAZ) and weight-for-age (WAZ) <i>z</i> scores at 0, 3, 12, 24, and 48-months of age in the 2004 Pelotas (Brazil) Birth Cohort.</p><p><strong>Results: </strong>At birth (n = 4066), mean LAZ was higher and the prevalence of stunting (LAZ < -2) was lower using CA versus postnatal age (mean ± SD): - 0.36 ± 1.19 versus - 0.67 ± 1.32; and 8.3 versus 11.6%, respectively. Odds ratio (OR) and population attributable risk (PAR) of stunting due to preterm birth were attenuated and changed inferences using CA versus postnatal age at birth [OR, 95% confidence interval (CI): 1.32 (95% CI 0.95, 1.82) vs 14.7 (95% CI 11.7, 18.4); PAR 3.1 vs 42.9%]; differences in inferences persisted at 3-months. At 12, 24, and 48-months, preterm birth was associated with stunting, but ORs/PARs remained attenuated using CA compared to postnatal age. Findings were similar for weight-for-age <i>z</i> scores.</p><p><strong>Conclusions: </strong>Population-based epidemiologic studies in LMICs in which GA is unused or unavailable may overestimate the prevalence of early childhood undernutrition and inflate the fraction of undernutrition attributable to preterm birth.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35830088","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":"Contextual factors in maternal and newborn health evaluation: a protocol applied in Nigeria, India and Ethiopia.","authors":"Kate Sabot,&nbsp;Tanya Marchant,&nbsp;Neil Spicer,&nbsp;Della Berhanu,&nbsp;Meenakshi Gautham,&nbsp;Nasir Umar,&nbsp;Joanna Schellenberg","doi":"10.1186/s12982-018-0071-0","DOIUrl":"https://doi.org/10.1186/s12982-018-0071-0","url":null,"abstract":"<p><strong>Background: </strong>Understanding the context of a health programme is important in interpreting evaluation findings and in considering the external validity for other settings. Public health researchers can be imprecise and inconsistent in their usage of the word \"context\" and its application to their work. This paper presents an approach to defining context, to capturing relevant contextual information and to using such information to help interpret findings from the perspective of a research group evaluating the effect of diverse innovations on coverage of evidence-based, life-saving interventions for maternal and newborn health in Ethiopia, Nigeria, and India.</p><p><strong>Methods: </strong>We define \"context\" as the background environment or setting of any program, and \"contextual factors\" as those elements of context that could affect implementation of a programme. Through a structured, consultative process, contextual factors were identified while trying to strike a balance between comprehensiveness and feasibility. Thematic areas included demographics and socio-economics, epidemiological profile, health systems and service uptake, infrastructure, education, environment, politics, policy and governance. We outline an approach for capturing and using contextual factors while maximizing use of existing data. Methods include desk reviews, secondary data extraction and key informant interviews. Outputs include databases of contextual factors and summaries of existing maternal and newborn health policies and their implementation. Use of contextual data will be qualitative in nature and may assist in interpreting findings in both quantitative and qualitative aspects of programme evaluation.</p><p><strong>Discussion: </strong>Applying this approach was more resource intensive than expected, in part because routinely available information was not consistently available across settings and more primary data collection was required than anticipated. Data was used only minimally, partly due to a lack of evaluation results that needed further explanation, but also because contextual data was not available for the precise units of analysis or time periods of interest. We would advise others to consider integrating contextual factors within other data collection activities, and to conduct regular reviews of maternal and newborn health policies. This approach and the learnings from its application could help inform the development of guidelines for the collection and use of contextual factors in public health evaluation.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2018-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-018-0071-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35830087","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}