A Primer on Two-Stage Meta-Analysis with Individual Participant Data Obtained from Complex Sampling Surveys

The increasing availability of individual participant data (IPD) in the social sciences offers new possibilities to synthesize research evidence across primary studies. Two-stage IPD meta-analysis represents a framework that can utilize these possibilities. While most of the methodological research on two-stage IPD meta-analysis focused on its performance compared with other approaches, dealing with the complexities of the primary data has received little attention, particularly when IPD are drawn from complex sampling surveys. Complex sampling surveys often feature clustering, stratification, disproportionate sampling, and multiple stages of sample selection to obtain nationally or internationally representative data from a target population. Furthermore, IPD from these studies are likely to provide more than one effect size. To address the complexities of the primary and meta-analytic data obtained from complex surveys, we propose a two-stage IPD meta-analytic approach and illustrate its utility. To aid meta-analysts who wish to utilize complex survey data, we present a sequence of steps and discuss the methodological decisions and options within. Given its flexibility and ability to deal with the complex nature of the primary data, the proposed two-stage approach opens up new analytical possibilities for synthesizing knowledge meta-analytically.

Prevalence and Correlates of Current Tobacco use and Non-user Susceptibility to using Tobacco Product Among School-going Adolescents in 22 African Countries, 2013-2018

Background Our study examined the prevalence and associated factors of tobacco product use and non-users’ susceptibility to using tobacco products among school-going adolescents in 22 African countries.MethodsWe analysed the cross-sectional 2013-2018 GYTS data from 22 African countries. We conducted complex sampling descriptive and logistic regression analyses. We reported our results using frequencies and proportions for descriptive statistics and adjusted odd ratios and 95% confidence intervals for logistic model.ResultsThe overall prevalence of current use of any tobacco product among adolescents was 19.1%, with more males (23.7%) than females (13.7%) being current users. Zimbabwe and Morocco were the highest (47.1%) and least (12.6%) reported prevalence respectively. Being male (AOR=1.930;95%CI:1.614-2.307), exposure to secondhand smoke within (AOR=2.069;95%CI:1.763-2.429) and outside (AOR=1.364;95%CI:1.138-1.635) the home, not knowledgeable about the harmful effect of secondhand smoke (AOR=1.413;95%CI:1.178-1.693), exposure to tobacco industry promotion (AOR=3.027;95%CI:2.653-3.453) and not in favour of banning smoking in enclosed places (AOR=1.222;95%CI:1.014-1.472) were associated with current use of any tobacco product. The prevalence of the susceptibility to using tobacco products among never users of tobacco products was 12.2%, with no significant gender difference. Mozambique (24.6%) and Algeria (4.5%) had the highest and least prevalence of the susceptibility to using tobacco products among never users, respectively. Exposure to tobacco industry promotion (AOR=1.730;95%CI:1.485-2.015) and those not in favour of banning smoking in enclosed places (AOR= 1.323;95%CI:1.142-1.532) were associated with susceptibility to using any tobacco product among never users of tobacco products.ConclusionOur study reports that tobacco use and non-user susceptibility to using tobacco product among school-going adolescents in the 22 African countries is high. As part of public health efforts, governments and other stakeholders need to fully implement anti-tobacco use campaigns, enforce a complete ban on tobacco promotion and advertising, institute educational programs for families, and anti-tobacco use education for the general public and in schools in line with WHO Framework Convention on Tobacco Control guidelines.

Differences by School Location in Summer and School Monthly Weight Change: Findings from a Nationally Representative Sample

Objectives: To examine changes in accelerations of Body Mass Index (BMI), age-and-sex specific body mass index (zBMI), and 95th percentile of BMI (%BMIp95) during the summer months and school year by school location designation (i.e., urban, suburban, exurban). This study utilized the Early Childhood Longitudinal Study Kindergarten Class of 2010–2011. Methods: Of the 18,174 children in the ECLS-K:2011 dataset, I restricted participants to those with at least two consecutive measures that occurred August/September or April/May. Mixed-effect regression analyses estimated differences in monthly change in BMI, zBMI, and %BMIp95 between the summer and school year while accounting for the ECLS-K complex sampling design. Models also examined differences in the magnitude of BMI, zBMI, and %BMIp95 change between the summer and school year by school location. Post-hoc Benjamini–Hochberg (BH) procedure set at 10% false discovery was incorporated to account for multiple comparisons. Results: A total of 1549 children (48% female, 42% White) had at least two consecutive measures that occurred in August/September or April/May. Among all locale classifications (i.e., urban, suburban, and exurban), children from high-income households comprised the largest proportions for each group (31%, 39%, and 37%), respectively. Among urban and suburban locations, Hispanic children comprised the largest proportions for both groups (43% and 44%), respectively. Among exurban locale classifications, White children comprised the largest proportion of children (60%). Children from suburban and exurban schools experienced significantly less accelerations in monthly zBMI gain when compared to their urban counterparts −0.038 (95CI = −0.071, −0.004) and −0.045 (95CI = −0.083, −0.007), respectively. Children from exurban schools experienced significantly less acceleration in monthly %BMIp95 during the summer months when compared to the school year −0.004 (95CI = −0.007, 0.000). Conclusions: This is one of the first studies to examine summer weight gain by school location. Summer appears to impact children more negatively from urban schools when compared to their suburban and exurban counterparts.

120 Analytical Strategies for Survey Data in Pet Nutrition and Management

Companion animal researchers have been at the forefront of using survey methodologies to study dogs’ and cats’ dietary and health patterns in the general population. The reporting of survey results has increased in recent years, facilitated by the rise in internet access, the modest cost of conducting web surveys, and the capability to target surveys to pet owners through address lists collected by services and social media. Data from population surveys have the potential to garner unique and comprehensive information that complements the understanding offered by designed experiments. Recent developments in survey methodologies and the availability of user-friendly survey tools enable the collection of large-scale or even Big Data sets, not only in the number of survey responses but also in the number and type of variables measured. Irrespective of the sample size, the study of survey data necessitates the consideration of complex sampling designs and analysis approaches that reflect the nature of this data. An overview of the characteristics of complex sampling designs typical of survey data with applications to companion animal nutrition is presented. The fundamentals of the analytical approaches that are suitable for survey data are demonstrated, and procedures available to accommodate clustering, stratification, underrepresentation, and nonresponse are reviewed. Examples of survey data visualization and analysis strategies are presented.

RALSA: the R analyzer for large-scale assessments

This paper presents the R Analyzer for Large-Scale Assessments (), a newly developed package for analyzing data from studies using complex sampling and assessment designs. Such studies are, for example, the IEA’s Trends in International Mathematics and Science Study and the OECD’s Programme for International Student Assessment. The package covers all cycles from a broad range of studies. The paper presents the architecture of the package, the overall workflow and illustrates some basic analyses using it. The package is open-source and free of charge. Other software packages for analyzing large-scale assessment data exist, some of them are proprietary, others are open-source. However, is the first comprehensive package, designed for the user experience and has some distinctive features. One innovation is that the package can convert SPSS data from large scale assessments into native data sets. It can also do so for PISA data from cycles prior to 2015, where the data is provided in tab-delimited text files along with SPSS control syntax files. Another feature is the availability of a graphical user interface, which is also written in and operates in any operating system where a full copy of can be installed. The output from any analysis function is written into an MS Excel workbook with multiple sheets for the estimates, model statistics, analysis information and the calling syntax itself for reproducing the analysis in future. The flexible design of allows for the quick addition of new studies, analysis types and features to the existing ones.

Changes in Dietary Habits and Exercise Pattern of Korean Adolescents from Prior to during the COVID-19 Pandemic

This study aimed to investigate changes in the exercise pattern and dietary habits in adolescents during the COVID-19 pandemic. The 12–18-year-old population in the Korea Youth Risk Behavior Web-Based Survey data of 2019 and 2020 was enrolled. The exercise pattern and dietary habits of 105,600 participants (53,461 in the 2019 group and 52,139 in the 2020 group) were compared. The odds ratios (ORs) for the dietary habits and exercise pattern of the 2020 group compared to the 2019 group were analyzed using multiple logistic regression analysis with complex sampling. The odds of eating fruit, drinking soda, drinking sweet drinks, and consuming fast food were lower in the 2020 group than in the 2019 group (all p < 0.001). The odds of eating breakfast were higher in the 2020 group than in the 2019 group (all p < 0.001). The 2020 group showed lower odds of frequent vigorous and moderate aerobic exercise and higher odds of frequent anaerobic exercise than the 2019 group (all p < 0.001). During the COVID-19 pandemic, adolescents consumed less fruit, soda, and sweet drinks, while they had more breakfast. The frequency of aerobic exercise was lower, while the frequency of anaerobic exercise were higher during the COVID-19 pandemic period.

Cherry Picking

Measures like pre-analysis plans ask researchers to describe planned data collection and justify data exclusions, but they provide little enforceable oversight of primary data collection. We show that a simple algorithm can select large subsets of data that yield economically meaningful and statistically significant treatment effects. The subsets cannot be distinguished from a random sample of the original data, rendering the selection undetectable if peer reviewers are unaware of the size of the original dataset. Our results hold using simulated data and replication data from a well-known study. We show that there are few natural deterrents to dataset manipulation: the results in our selected subset are robust to a range of alternative specifications, our algorithm performs well under complex sampling strategies, and our subset can yield artificially high effects on multiple outcomes. We conclude by proposing a measure to prevent such manipulation in field experiments.

Presence of dogs and proximity to a wildlife reserve increase household level risk of tungiasis in Kwale, Kenya

Introduction Tungiasis is a ectopic skin disease caused by some species of fleas in the Tunga genus, most notably T. penetrans. The disease afflicts poor and marginalized communities in developing countries. Transmission of tungiasis comprises a complex web of factors including domesticated animals and wildlife. This research explores animal and environmental risk factors for tungiasis in an area adjacent to a wildlife reserve in Kwale, Kenya. Methods A two-stage complex sampling strategy was used. Households were selected from three areas in and around Kwale Town, Kenya, an area close to the Kenyan Coast. Households were listed as positive if at least one member had tungiasis. Each household was administered a questionnaire regarding tungiasis behaviors, domesticated animal assets, and wild animal species that frequent the peridomiciliary area. Associations of household tungiasis were tests with household and environmental variables using regression methods. Results The study included 319 households. Of these, 41 (12.85%) were found to have at least one person who had signs of tungiasis. There were 295 (92.48%) households that possessed at least one species of domesticated animal. It was reported that wildlife regularly come into the vicinity of the home 90.59% of households. Presence of dogs around the home (OR 3.85; 95% CI 1.84; 8.11) and proximity to the park were associated with increased risk for tungiasis infestation in humans in a multivariate regression model. Conclusions Human tungiasis is a complex disease associated with domesticated and wild animals. Canines in particular appear to be important determinants of household level risk.