Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes.

BACKGROUND: The fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes. METHODS: In this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes. RESULTS: The simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals. CONCLUSIONS: In this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes.

The impact of the scale and hierarchical structure of health human resources on the level of medical services-based on China's four major economic regions.

BACKGROUND: Ensuring that the scale and hierarchical structure of health human resources are rational, and that medical services are efficient and fair, is an important task of practical significance. On this basis, examining the impact of health human resources on the level of medical services presents a new and formidable challenge. This study aims to delve into how the scale and hierarchical structure of health human resources in China's four major economic regions affect the fairness and efficiency of medical services, and to identify optimization strategies. METHODS: This study utilizes provincial panel data from China's four major economic regions spanning the years 2009 to 2021. Initially, it provides a statistical description of the current state of health human resources and the level of medical services. Subsequently, it employs a fixed-effects model to analyze the impact of the scale and hierarchical structure of health human resources, as well as their interactive effects, on the fairness and efficiency of medical services, and discusses the interactive mechanisms between medical service fairness and medical service efficiency. Furthermore, after conducting a comprehensive evaluation of the level of medical services using the entropy weight method, it explores the regional heterogeneity and temporal dynamics in the influence of the scale and hierarchical structure of health human resources on the level of medical services. Finally, the study examines the scientific validity and rationality of the research findings through various robustness checks, including the substitution of research variables and models. RESULTS: The study found that the scale of health human resources has a promoting effect on the equity of medical services (ß ≤ 0.643, p ≤ 0.01), but exhibits an inhibitory effect on the efficiency of medical services (ß ≥ -0.079, p ≤ 0.1); the hierarchical structure of health human resources shows a positive impact on both the equity and efficiency of medical services (ßequity ≤ 0.160, p ≤ 0.01; ßefficiency ≤ 0.341, p ≤ 0.05); at the same time, the results indicate that the interactive effect of the scale and hierarchical structure of health human resources promotes equity in medical services (ß = 0.067, p ≤ 0.01), but restricts the efficiency of medical services (ß ≥ -0.039, p ≤ 0.01); the mechanism by which health human resources affect the level of medical services in China's western and northeastern regions is more pronounced than in the central and eastern regions; after the implementation of the "Healthy China 2030" Planning Outline, the role of health human resources in the level of medical services has been strengthened; in the robustness tests, the model remains robust after replacing the core explanatory variables, with R2 maintained between 0.869 and 0.972, and the dynamic GMM model test shows a significant second-order lag in the level of medical services (ßequity ≤ 0.149, p ≤ 0.01; ßefficiency ≤ 0.461, p ≤ 0.01); the channel test results prove that managerial personnel and other technical personnel are key pathways in regulating the impact of medical staff on the level of medical services. CONCLUSION: This study provides an in-depth analysis of the impact of health human resources on the level of medical services, revealing that both the scale and hierarchical structure of health human resources significantly affect the equity and efficiency of medical services. Furthermore, the influence of health human resources on the level of medical services exhibits regional heterogeneity and temporal characteristics. Robustness tests ensure the scientific validity and robustness of the research conclusions. This provides effective references for optimizing the allocation of health human resources and improving the level of medical services.

Evolution of psychological distress with age and its determinants in later life: evidence from 17-wave social survey data in Japan.

BACKGROUND: Psychological distress (PD) is a major risk factor for mental health among middle-aged and older adults and affects their quality of life and well-being. This study aimed to examine the evolution of PD with age and the relative importance of its determinants, issues that have been insufficiently studied. METHODS: We used longitudinal data obtained from 17-wave social surveys conducted in Japan from 2005 to 2021, to track 34,128 individuals (16,555 men and 17,573 women) born between 1946 and 1955. We defined PD as a Kessler 6 score (range: 0-24) ≥ 5 and estimated fixed-effects regression models to examine the evolution of its proportion with age. We also conducted a mediation analysis to examine the relative importance of specific mediators such as self-rated health (SRH), activities of daily living (ADL), and social participation, in the association between age and PD. RESULTS: Regression model results confirmed an increase in PD with age. Poor SRH, issues with ADL, and no social participation were key mediators of aging on PD, accounting for 34.2% (95% confidence interval [CI]: 21.0-47.3%), 13.7% (95% CI: 8.2-19.3%), and 10.5% (95% CI: 8.0-13.0%), respectively; consequently increasing PD between 50 and 75 years. CONCLUSION: The results suggest the need for policy support to encourage middle-aged and older adults to promote health and increase social participation in order to prevent depression while aging.

Heterogeneity in health funding and disparities in health outcome: a comparison between high focus and non-high focus states in India.

BACKGROUND: The Central Government of India introduced the National Health Mission (NHM) in 2005 to improve health outcomes by enhancing publicly financed (government) health expenditure and health infrastructure at the state level. This study aims to examine the effects of the state-level heterogeneity in publicly financed spending on health services on major health outcomes such as life expectancy, infant mortality rate, child mortality rate, the incidence of malaria, and immunization coverage (i.e., BCG, Polio, Measles, and Tetanus). METHODS: This study investigates the relationships between publicly financed health expenditure and health outcomes by controlling income and infrastructure levels across 28 Indian States from 2005 to 2016. Along with all states, the empirical analysis has also been carried out for high-focus and non-high-focus states as per the NHM fund flow criteria. It has applied panel fixed-effects and random effects model wherever required based on the Hausman test. RESULTS: The empirical results show that publicly financed health expenditure reduces infant mortality, child mortality, and malaria cases. At the same time, it improves life expectancy and immunization coverage in India. It also finds that the relationship between publicly financed health expenditure and health outcomes is weak, especially in the high-focus states. CONCLUSIONS: Given the healthcare need for achieving desirable health outcomes, Indian States should enhance publicly financed expenditure on health services. This study augments essential guidance for implementing public health policies in developing countries.

The impact of differentiated feed-in tariffs on the resource misallocation of wind power: From the perspectives of the intensive and extensive margins.

The widespread implementation of feed-in tariff (FIT) policies has played a crucial role in fostering the development of wind power, with their positive effects firmly established in numerous studies. However, the impact of regionally differentiated FIT policies on the misallocation of wind power resources remains a topic of contention, with limited research dedicated to this area. This paper aims to address this gap by examining the implications of such policies on the intensive and extensive margins of wind power installed capacity in China, shedding light on the underlying mechanisms driving resource misallocation. Empirical findings indicate that, concerning the intensive margin, the policy amplifies the concentration of wind power investments in regions characterized by abundant wind resources but low electricity demand. These regions present favorable conditions for large-scale wind farms with cost advantages, consequently exacerbating the misallocation of wind power resources. However, on the extensive margin, the policy promotes the likelihood of locating small and medium-sized wind farms in regions with poor wind resources but higher tariff rates, thus partially mitigating resource misallocation. In summary, China's policy hampers wind power investments in regions characterized by high electricity demand but limited wind resources. This suggests that the negative impact on the intensive margin outweighs the positive impact on the extensive margin. The findings of this study bear significant implications for the development of renewable energy support policies, particularly in countries grappling with substantial regional disparities in renewable energy resources.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials.

Stepped-wedge cluster randomized trials (SW-CRTs) are typically analyzed using mixed effects models. The fixed effects model is a useful alternative that controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. In principle, all clusters in SW-CRTs are designed to eventually receive the intervention, but in real-world research, some trials can end with unexposed clusters (clusters that never received the intervention), such as when a trial is terminated early based on interim analysis results. Typically, unexposed clusters are expected to contribute no information to the fixed effects intervention effect estimator and are excluded from fixed effects analyses. In this article we mathematically prove that inclusion of unexposed clusters improves the precision of the fixed effects least squares dummy variable (LSDV) intervention effect estimator, re-analyze data from a recent SW-CRT of a novel palliative care intervention containing an unexposed cluster, and evaluate the methods by simulation. We found that including unexposed clusters improves the precision of the fixed effects LSDV intervention effect estimator in both real and simulated datasets. Our simulations also reveal an increase in power and decrease in root mean square error. These improvements are present even if the assumptions of constant residual variance and period effects are violated. In the case that a SW-CRT concludes with unexposed clusters, these unexposed clusters can be included in the fixed effects LSDV analysis to improve precision, power, and root mean square error.

Pre-pandemic social isolation as a predictor of the adverse impact of the pandemic on self-rated health: A longitudinal COVID-19 study in Japan.

Many studies have found adverse effects of the coronavirus disease pandemic on health. Irrespective of being infected by the coronavirus, lockdowns and other measures to restrict mobility have worsened an individual's subjective health assessment. Unlike previous studies, this study examined how pre-pandemic social isolation (in the form of no interaction with others and having no social support) affected the impact of the pandemic on self-rated health in Japan. To this end, we estimated fixed-effects models using 4172 observations of 2086 individuals obtained from a three-wave Internet nationwide survey conducted in January/February 2019 and February 2020 (before the pandemic), in March 2021 (when the pandemic-related state of emergency was effective in four prefectures and just after it was lifted in six prefectures), and in October/November (a full month after the state of emergency was lifted in all prefectures). The state of emergency raised the probability of reporting poor health by 17.8 (95% confidence interval [CI]:1.9-33.8) percentage points among the participants who had not interacted with others before the pandemic, compared with only 0.7 (95% CI: -3.1-4.5) percentage points among other participants. Similar results were obtained in the absence of social support prior to the pandemic. In conclusion, pre-pandemic social isolation was detrimental to health, suggesting that policy measures are needed to avoid social isolation to increase the resilience of public health to external shocks.

Social engagement and physical frailty in later life: does marital status matter?

BACKGROUND: Physical frailty is a common characteristic of older people with the ageing process and has been viewed as a major public health issue. The longitudinal association between different social engagement and physical frailty among older people has not been explored adequately in China. Marital status forms a critical context for the link between social engagement and frailty among older people, which might constitute a moderating process. The purpose of the present study is to investigate the longitudinal association between social engagement and the changes in physical frailty among Chinese older adults, and to examine whether the association between social engagement and frailty differs by marital status. METHODS: The data use in this study were from the data from the China Health and Retirement Longitudinal Study aged 60+ years from 2011 to 2015. A total of 6575 respondents who participated in at least one follow-up wave were included in the analysis. The relationship between social engagement and changes in frailty over time, and the moderating role of marital status were estimated using individual fixed-effects models. Sensitive analyses were conducted to test the robustness of the results. RESULTS: After adjusting the confounders, participants who interact with friends (Coef: -1.309, P < 0.001), engaging in hobby groups (Coef: -1.189, P < 0.001), engaging in sports groups (Coef: -0.945, P = 0.001), and volunteering (Coef: -1.957, P = 0.001) with a frequency of almost daily had a significantly lower frailty risk than participants who never engaging in those activities. The association between frequent engaging in hobby groups and physical frailty was strongest for unmarried than married older adults (Coef: -1.325, P = 0.031). CONCLUSIONS: Frequent social engagement might help to decrease the risk of frailty in the Chinese older population. This finding has important implications for public health policy and encourages the incorporation of a broad range of social engagement into the daily lives of older individuals. Specially, encouraging unmarried older adults to engage in intellectual activities, such as playing chess or Mahjong with others, may be an effective way to reduce physical frailty.

A Comparison of Fixed-Effects and Random-Effects Models for Multivariate Meta-Analysis Using an SEM Approach.

This study compared fixed-effects (FE) and random-effects (RE) models in meta-analysis for synthesizing multivariate effect sizes under the framework of structural equation modeling. Monte Carlo simulations were conducted to examine the performance characteristics of the two models under different data conditions. The results indicated that, for the homogeneous case, there was little difference between the FE model and the RE model applications. But the FE model had better performance in standard error estimation when number of studies is not large and the sample size of primary studies is small. Furthermore, under the heterogeneous case, FE model exhibited biased estimates of population parameters and extreme levels of inflated Type I error in testing the effect size estimates. However, RE model maintained unbiased estimates of the population parameters, and controlled Type I error well under various data conditions investigated. These findings provided empirical evidence that it is likely that RE model application in a meta-analysis would be preferred when the number of primary studies and the sample sizes in the primary studies are reasonably large, and FE model could be favored for situations with smaller numbers of studies and uniformly small sample sizes of primary studies.

Inconsistency and drop-minimum data analysis.

Even though consistency is an important issue in multi-regional clinical trials and inconsistency is often anticipated, solutions for handling inconsistency are rare. If a region's treatment effects are inconsistent with that of the other regions, pooling all the regions to estimate the overall treatment effect may not be reasonable. Unlike the multiple center clinical trials conducted in the USA and Europe, in multi-regional clinical trials, different regional regulatory agencies may have their own ways to interpret data and approve new drugs. It is therefore practical to consider the case in which the data from the region with the minimal observed treatment effect is excluded from the analysis in order to attain the regulatory approval of the study drug. Under such cases, what is the appropriate statistical approach for the remaining regions? We provide a solution first formulated within the fixed effects framework and then extend it to discrete random effects models. Copyright © 2016 John Wiley & Sons, Ltd.

Modeling conditional dependence among multiple diagnostic tests.

When multiple imperfect dichotomous diagnostic tests are applied to an individual, it is possible that some or all of their results remain dependent even after conditioning on the true disease status. The estimates could be biased if this conditional dependence is ignored when using the test results to infer about the prevalence of a disease or the accuracies of the diagnostic tests. However, statistical methods correcting for this bias by modelling higher-order conditional dependence terms between multiple diagnostic tests are not well addressed in the literature. This paper extends a Bayesian fixed effects model for 2 diagnostic tests with pairwise correlation to cases with 3 or more diagnostic tests with higher order correlations. Simulation results show that the proposed fixed effects model works well both in the case when the tests are highly correlated and in the case when the tests are truly conditionally independent, provided adequate external information is available in the form of fixed constraints or prior distributions. A data set on the diagnosis of childhood pulmonary tuberculosis is used to illustrate the proposed model.

It takes a village: Fixed-effects analysis of neighborhood collective efficacy and children's development.

BACKGROUND: Previous studies suggest that neighborhood social capital is associated with children's mental health. The purpose of this study was to examine the association between neighborhood collective efficacy and children's psychosocial development. METHODS: We used data on children and their parents (n = 918) who were part of the Japanese study of Stratification, Health, Income, and Neighborhood (JSHINE) from 2010 to 2013 (wave 1 and wave 2). Households were recruited from the Tokyo metropolitan area through clustered random sampling. Changes in children's psychosocial development (assessed using a child behavioral checklist) between waves 1 and 2 were regressed on parents' perceptions of changes in neighborhood collective efficacy (social cohesion and informal social control). RESULTS: Change in perception of neighborhood social cohesion was inversely associated with change in child total problems (ß = -0.22; 95% confidence interval [CI]: -0.37 to -0.001; effect size d = -0.03). Change in perceptions of neighborhood informal social control was inversely associated with change in children's externalizing problems (ß = -0.16; 95% CI: -0.30 to -0.03; d = -0.02). CONCLUSIONS: The results of these fixed-effects models suggest that strengthening neighborhood collective efficacy is related to improvements in child psychosocial development.

Number of children and later-life mortality among Finns born 1938-50.

We investigated the association between number of offspring and later-life mortality of Finnish men and women born 1938-50, and whether the association was explained by living conditions in own childhood and adulthood, chronic conditions, fertility timing, and unobserved characteristics common to siblings. We used a longitudinal 1950 census sample to estimate mortality at ages 50-72. Relative to parents of two children, all-cause mortality is highest among childless men and women, and elevated among those with one child, independently of observed confounders. Fixed-effect models, which control for unobserved characteristics shared by siblings, clearly support these findings among men. Cardiovascular mortality is higher among men with no, one, or at least four children than among those with two. Living conditions in adulthood contribute to the association between the number of children and mortality to a greater extent than childhood background, and chronic conditions contribute to the excess mortality of the childless.

The evaluation of the scoring systems: the fixed effects model under known variances.

A number of scoring systems for proficiency testing and interlaboratory comparison are in use by the metrology community. The choice of scoring system for a given study is often based on the study coordinator's experience and anecdotal knowledge, perhaps attributable to a historic lack of detailed and formal explanation about the foundation of these systems. This has influenced the development of new scoring systems, some of them departing from the well-established hypothesis testing theory. Often, different scoring systems give different results not because one may be better than the others but because, as they are documented, the user cannot control the confidence level of the test. We present a formal evaluation of seven of these systems under the fixed effects model assuming known variances. Under these sound assumptions, the systems analyzed all have the same statistical properties. Furthermore, these systems are all members of a family of systems based on strictly increasing functions in which the statistical decision problem is invariant. Under the fixed effects model with known variances, no unbiased scoring system can provide greater statistical power than the members of this family of systems. We apply these results to the lead content of water example provided in International Standard ISO 13528:2015 "Statistical methods for use in proficiency testing by interlaboratory comparisons."

An updated method for risk adjustment in outcomes research.

OBJECTIVE: To demonstrate why meta-analytic methods need modification before they can be used to aggregate rates or effect sizes in outcomes research, under the constraint of no common underlying effect or rate. METHODS: Studies are presented that require different types of risk adjustment. First, we demonstrate using rates that external risk adjustment through standardization can be achieved using modified meta-analytic methods, but only with a model that allows input of user-defined weights. Next, we extend these observations to internal risk adjustment of comparative effect sizes. RESULTS: We show that this procedure produces identical results to conventional age standardization if a rate is being standardized for age. We also demonstrate that risk adjustment of effect sizes can be achieved with this modified method but cannot be done using standard meta-analysis. CONCLUSIONS: We conclude that this method allows risk adjustment to be performed in situations in which currently the fixed- or random-effects methods of meta-analysis are inappropriately used. The latter should be avoided when the underlying aim is risk adjustment rather than meta-analysis.

Effect of family background on the educational gradient in lifetime fertility of Finnish women born 1940-50.

An inverse association between education and fertility in women has been found in many societies but the causes of this association remain inadequately understood. We investigated whether observed and unobserved family-background characteristics explained educational differences in lifetime fertility among 35,212 Finnish women born in 1940-50. Poisson and logistic regression models, adjusted for measured socio-demographic family-background characteristics and for unobserved family characteristics shared by siblings, were used to analyse the relationship between education and the number of children, having any children, and fertility beyond the first child. The woman's education and the socio-economic position of the family were negatively associated with fertility. Observed family characteristics moderately (3-28 per cent) explained the association between education and fertility, and results from models including unobserved characteristics supported this interpretation. The remaining association may represent a causal relationship between education and fertility or joint preferences that form independently of our measures of background.

Signal detection of adverse events with imperfect confirmation rates in vaccine safety studies using self-controlled case series design.

The Vaccine Safety Datalink project captures electronic health record data including vaccinations and medically attended adverse events on 8.8 million enrollees annually from participating managed care organizations in the United States. While the automated vaccination data are generally of high quality, a presumptive adverse event based on diagnosis codes in automated health care data may not be true (misclassification). Consequently, analyses using automated health care data can generate false positive results, where an association between the vaccine and outcome is incorrectly identified, as well as false negative findings, where a true association or signal is missed. We developed novel conditional Poisson regression models and fixed effects models that accommodate misclassification of adverse event outcome for self-controlled case series design. We conducted simulation studies to evaluate their performance in signal detection in vaccine safety hypotheses generating (screening) studies. We also reanalyzed four previously identified signals in a recent vaccine safety study using the newly proposed models. Our simulation studies demonstrated that (i) outcome misclassification resulted in both false positive and false negative signals in screening studies; (ii) the newly proposed models reduced both the rates of false positive and false negative signals. In reanalyses of four previously identified signals using the novel statistical models, the incidence rate ratio estimates and statistical significances were similar to those using conventional models and including only medical record review confirmed cases.

How to conduct a meta-analysis in hand surgery. Part I: principles and procedures.

Meta-analysis (MA) is a fundamental statistical tool for combining the results of different studies to obtain potentially high-level evidence that can be implemented in clinical practice. Although its use in clinical research is increasing, MAs are still relatively rare in hand surgery. Therefore, it should be important for every hand surgeon to not only know how to interpret a MA, but also how to perform one. The purpose of this first of a two-part article is to introduce the principles of MA and describe the main models and methods used to pool effect estimates.

Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data.

Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods.

Baiting the hook: Fish scarcity, gendered division of labour, and the fish-for-sex trade.

Transactional sex in sub-Saharan Africa's fishing communities, driven by the highly gendered organisation of production, is widely recognised as a key driver of HIV transmission in lakeshore areas. This longitudinal study investigates the economic drivers of the trade and its impact on sexual health outcomes. Specifically, the impact of regional and district fish market shocks and comparable maize shocks on facility-level sexual health outcomes are examined in Tanzania's shoreline communities. Following unfavourable shocks to the fish market, such as high prices or low amounts of fish captured, this paper finds that new HIV cases, newly pregnant women attending antenatal clinic, and the number of people treated for syphilis increases with proximity to the shoreline, supporting the hypothesis that the fish-for-sex trade intensifies when fish supply is relatively scarce. Further, the observed increase in new HIV cases is driven by new cases in women. Contrasting effects are observed following maize price shocks, where facilities see an increase in both male and female new HIV cases following a favourable price shock. These findings highlight the role that gender-based organisation of production plays in shaping sexual health inequalities following shocks to a good.