It is unclear how the risk of post-covid symptoms evolved during the pandemic, especially before the spread of Severe Acute Respiratory Syndrome Coronavirus 2 variants and the availability of vaccines. We used modified Poisson regressions to compare the risk of six-month post-covid symptoms and their associated risk factors according to the period of first acute covid: during the French first (March-May 2020) or second (September-November 2020) wave. Non-response weights and multiple imputation were used to handle missing data. Among participants aged 15 or more in a national population-based cohort, the risk of post-covid symptoms was 14.6% (95% CI: 13.9%, 15.3%) in March-May 2020, versus 7.0% (95% CI: 6.3%, 7.7%) in September-November 2020 (adjusted RR: 1.36, 95% CI: 1.20, 1.55). For both periods, the risk was higher in the presence of baseline physical condition(s), and it increased with the number of acute symptoms. During the first wave, the risk was also higher for women, in the presence of baseline mental condition(s), and it varied with educational level. In France in 2020, the risk of six-month post-covid symptoms was higher during the first than the second wave. This difference was observed before the spread of variants and the availability of vaccines.

BACKGROUND: Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP.
METHODS: Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall \'envisioned population\' survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census.
RESULTS: Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings.
CONCLUSIONS: Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to handle attrition weights in the propensity score model. Nonetheless, generalized propensity score (GPS) analyses are being used for estimating continuous treatment effects on outcomes when researchers have observational data, and those data sets often have survey or attrition weights that need to be accounted for in the analysis. Here, we extend prior work and show with analytic results that using survey sampling or attrition weights in the GPS estimation stage and the outcome model stage for continuous treatments can result in a more robust estimator than one that does not. Simulation study results show that, although using weights in both estimation stages is sufficient for robust estimation, it is not necessary and unbiased estimation is possible in some cases under various approaches to using weights in estimation. Analysts do not know if the conditions of our simulation studies hold, so use of weights in both estimation stages might provide insurance for reducing potential bias. We discuss the implications of our results in the context of an empirical example.

We analyze repeated cross-sectional survey data collected by the Institute of Global Health Innovation, to characterize the perception and behavior of the Italian population during the Covid-19 pandemic, focusing on the period that spans from April 2020 to July 2021. To accomplish this goal, we propose a Bayesian dynamic latent-class regression model, that accounts for the effect of sampling bias including survey weights into the likelihood function. According to the proposed approach, attitudes towards covid-19 are described via ideal behaviors that are fixed over time, corresponding to different degrees of compliance with spread-preventive measures. The overall tendency toward a specific profile dynamically changes across survey waves via a latent Gaussian process regression, that adjusts for subject-specific covariates. We illustrate the evolution of Italians\' behaviors during the pandemic, providing insights on how the proportion of ideal behaviors has varied during the phases of the lockdown, while measuring the effect of age, sex, region and employment of the respondents on the attitude toward covid-19.

For assessing the HIV epidemic in Kenya, a series of independent HIV indicator household-based surveys of similar design can be used to investigate the trends in key indicators relevant to HIV prevention and control and to describe geographic and sociodemographic disparities, assess the impact of interventions, and develop strategies. We developed methods and tools to facilitate a robust analysis of trends across three national household-based surveys conducted in Kenya in 2007, 2012, and 2018.
We used data from the 2007 and 2012 Kenya AIDS Indicator surveys (KAIS 2007 and KAIS 2012) and the 2018 Kenya Population-based HIV Impact Assessment (KENPHIA 2018). To assess the design and other variables of interest from each study, variables were recoded to ensure that they had equivalent meanings across the three surveys. After assessing weighting procedures for comparability, we used the KAIS 2012 nonresponse weighting procedure to revise normalized KENPHIA weights. Analyses were restricted to geographic areas covered by all three surveys. The revised analysis files were then merged into a single file for pooled analysis. We assessed distributions of age, sex, household wealth, and urban/rural status to identify unexpected changes between surveys. To demonstrate how a trend analysis can be carried out, we used continuous, binary, and time-to-event variables as examples. Specifically, temporal trends in age at first sex and having received an HIV test in the last 12 months were used to demonstrate the proposed analytical approach. These were assessed with respondent-specific variables (age, sex, level of education, and marital status) and household variables (place of residence and wealth index). All analyses were conducted in SAS 9.4, but analysis files were created in Stata and R format to support additional analyses.
This study demonstrates trends in selected indicators to illustrate the approach that can be used in similar settings. The incidence of early sexual debut decreased from 11.63 (95% CI: 10.95-12.34) per 1,000 person-years at risk in 2007 to 10.45 (95% CI: 9.75-11.2) per 1,000 person-years at risk in 2012 and to 9.58 (95% CI: 9.08-10.1) per 1,000 person-years at risk in 2018. HIV-testing rates increased from 12.6% (95% CI: 11.6%-13.6%) in 2007 to 56.1% (95% CI: 54.6%-57.6%) in 2012 but decreased slightly to 55.6% [95% CI: 54.6%-56.6%) in 2018. The decrease in incidence of early sexual debut could be convincingly demonstrated between 2007 and 2012 but not between 2012 and 2018. Similarly, there was virtually no difference between HIV Testing rates in 2012 and 2018.
Our approach can be used to support trend comparisons for variables in HIV surveys in low-income settings. Independent national household surveys can be assessed for comparability, adjusted as appropriate, and used to estimate trends in key indicators. Analyzing trends over time can not only provide insights into Kenya\'s progress toward HIV epidemic control but also identify gaps.

The Health Information National Trends Survey (HINTS) is a probability-based, nationally representative survey conducted routinely to gather information about the American public\'s cancer-related beliefs and behaviors, including the use of cancer-related information. HINTS was created to produce national estimates and has lacked the ability to create accurate and precise state and regional estimates. The motivation for this current work was to create state- and regional-level estimates using a national sample (HINTS) through standard calibration methods. Health estimates at a local level can inform policy decisions that better target the cancer needs within a community. Local-level data allow researchers an opportunity to examine local populations in finer detail without additional costly data collection.
By combining seven cycles of HINTS data from 2012 to 2018 and then raking the previously created person-level weights, we were able to create tables and maps of HINTS subnational survey estimates for key outcomes that have small variances and little potential bias.
This paper describes the methods used to harmonize and aggregate data across cycles, create state- and regional-level estimates from the pooled data, and produce survey weights for the pooled datasets. It demonstrates both the opportunities and the challenges of pooled data analysis.

OBJECTIVE: The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative study of the US population on tobacco use and its effects on health, with 3 waves of data collection between 2013 and 2016. Prior work described the methods of the first wave. In this paper, we describe the methods of the subsequent 2 waves and provide recommendations for how to conduct longitudinal analyses of PATH Study data.
METHODS: We use standard survey quality metrics to evaluate the results of the follow-up waves of the PATH Study. The recommendations and examples of longitudinal and cross-sectional analyses of PATH Study data follow a design-based statistical inference framework.
RESULTS: The quality metrics indicate that the PATH Study sample of approximately 40,000 continuing respondents remains representative of its target population. Depending on the intended analysis, different survey weights may be appropriate.
CONCLUSIONS: The PATH Study data are a valuable resource for regulatory scientists interested in longitudinal analysis of tobacco use and its effects on health. The availability of multiple sets of specialized survey weights enables researchers to target a wide range of tobacco-related analytic questions.

Many studies in psychological and educational research aim to estimate population average treatment effects (PATE) using data from large complex survey samples, and many of these studies use propensity score methods. Recent advances have investigated how to incorporate survey weights with propensity score methods. However, to this point, that work had not been well summarized, and it was not clear how much difference the different PATE estimation methods would make empirically.
The purpose of this study is to systematically summarize the appropriate use of survey weights in propensity score analysis of complex survey data and use a case study to empirically compare the PATE estimates using multiple analysis methods that include ordinary least squares regression, weighted least squares regression, and various propensity score applications.
We first summarize various propensity score methods that handle survey weights. We then demonstrate the performance of various analysis methods using a nationally representative data set, the Early Childhood Longitudinal Study-Kindergarten to estimate the effects of preschool on children\'s academic achievement. The correspondence of the results was evaluated using multiple criteria.
It is important for researchers to think carefully about their estimand of interest and use methods appropriate for that estimand. If interest is in drawing inferences to the survey target population, it is important to take the survey weights into account, particularly in the outcome analysis stage for estimating the PATE. The case study shows, however, not much difference among various analysis methods in one applied example.

This paper describes the methods of the Wave 1 (2018) International Tobacco Control (ITC) Japan Survey. The respondents were adults aged 20 years and older in one of four user groups: (1) cigarette-only smokers who smoked at least monthly and used heated tobacco products (HTPs) not at all or less than weekly, (2) HTP-only users who used HTPs at least weekly and smoked cigarettes not at all or less than monthly, (3) cigarette-HTP dual users who smoked at least monthly and used HTPs at least weekly, and (4) non-users who had never smoked or who smoked less than monthly and used HTPs less than weekly. Eligible respondents were recruited by a commercial survey firm from its online panel. Respondents were allocated proportionally to sample strata based on demographic, geographic, and user type specifications benchmarked to a national reference. Survey weights, accounting for smoking/HTP use status, sex, age, education, and geography, were calibrated to benchmarks from a nationally representative survey in Japan. Response rate was 45.1% and cooperation rate was 96.3%. The total sample size was 4615 (3288 cigarette smokers, 164 exclusive HTP users, 549 cigarette-HTP dual users, and 614 non-users). The 2018 ITC Japan Survey sampling design and survey data collection methods will allow analyses to examine prospectively the use of cigarettes and HTPs in Japan and factors associated with the use of both products and of transitions between them.

To estimate via questionnaire within a population sample of New Zealand (NZ) children aged 6-to-10 years, the prevalence of sleep disordered breathing (SDB) and those struggling academically, and to identify individual and shared risk factors (health and demographic) for parent-reported SDB symptoms and academic difficulties.
In this cross-sectional study, parents/caregivers of children were recruited through schools and social media to complete an online questionnaire covering health and demographic factors, their children\'s SDB symptoms (Pediatric Sleep Questionnaire; PSQ) and parental ratings of academic performance based on teacher feedback relative to expected progress in the national curriculum (well below/below/at/above) in reading, writing, and math.
A total of 1205 children (53% male) aged (mean) eight years two months were included, comprising 79.4% NZ European/other and 15.0% Māori. The survey-weighted prevalence of SDB (based on the PSQ) was 17.5%. This was higher amongst those with academic difficulties rated \'below/well below\' expected progress for reading, writing and math (estimated at 24.0%, 31.0% and 27.5% respectively), with increased odds (adjusted odds ratios) for poor progress of 1.9 (95% CI: 1.2, 3.0), 1.8 (95% CI: 1.2, 2.7) and 2.4 (95% CI: 1.6, 3.7) respectively. There were no shared risk factors common to both SDB and academic difficulties identified from multivariate analyses.
The findings suggest that children with parent-reported SDB symptoms may be at high risk for poor progress in reading, writing, and math. Future research could examine whether treatment of SDB reduces barriers to learning and offsets educational risk.