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.

A study on the potential of digital economy in reducing agricultural carbon emissions.

Agriculture is a significant source of carbon emissions, which have a substantial environmental impact. The digital economy plays a vital role in mitigating these emissions through innovative digital solutions. As a leading agricultural nation, China faces substantial pressure to reduce its agricultural carbon emissions(ACE). This paper aims to thoroughly examine the relationship between the growth of the rural digital economy and ACE. To achieve this, we utilize an extensive panel dataset covering China's provinces from 2011 to 2020, analyzing the dynamic and spatial effects of digital economy development on ACE. The key findings of this research are as follows: (1) The rapid expansion of the digital economy significantly reduces ACE. (2) The impact of digital economic development on lowering ACE varies spatially, with a clear progression from eastern to western regions. (3) The digital economy helps reduce ACE through three specific channels: fostering technological innovation, enhancing scale efficiency management, and providing agricultural financial incentives. Based on these findings, this study proposes policy recommendations to improve digital infrastructure, promote balanced regional development in the digital economy, and optimize the management of agricultural science and technology. These policy insights aim to transform agriculture and achieve the goal of reducing ACE, thereby contributing to broader environmental sustainability.

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.

The impact of hydrogen fuel cell heavy-duty trucks purchase subsidies on air quality.

The pollutant emissions of diesel-powered heavy-duty trucks (HDTs) seriously damage the air quality. The promotion of hydrogen fuel cell HDTs through purchase subsidy policy to reduce emissions has become an important approach to control air pollution. This study focuses on the impact of hydrogen fuel cell HDT purchase subsidies on air quality in the context of China, covering the panel data of 31 Chinese cities from 2014 to 2021 and applying a two-way fixed effects model to analyze the contribution of purchase subsidies and hydrogen refueling station construction subsidies to air quality. Results show that (1) the increase in purchase subsidies could improve the air quality by around 6.1% and there is a lag effect. (2) Purchase subsidies make a larger contribution to air quality compared with construction subsidies. (3) Purchase subsidies can improve air quality by reducing carbon emissions in transport industry. In sight of these results, policy makers should emphasize the implementation of purchase subsidies and hydrogen refueling station construction subsidies and stimulate manufacturers to improve the performance of hydrogen fuel cell so as to contribute more to the environment.

A new title to an old song: some observations on the conduct of meta-analyses.

The purpose of this commentary is to draw attention to some unsatisfactory features in the way that meta-analyses are presented in assisted reproduction journals. These features will be illustrated with reference to recently published papers. An appeal will be made for a more fastidious and rigorous approach to this statistical technique that has become a ubiquitous part of the research effort in assisted reproduction treatment.

A scan statistic for identifying optimal risk windows in vaccine safety studies using self-controlled case series design.

In the examination of the association between vaccines and rare adverse events after vaccination in postlicensure observational studies, it is challenging to define appropriate risk windows because prelicensure RCTs provide little insight on the timing of specific adverse events. Past vaccine safety studies have often used prespecified risk windows based on prior publications, biological understanding of the vaccine, and expert opinion. Recently, a data-driven approach was developed to identify appropriate risk windows for vaccine safety studies that use the self-controlled case series design. This approach employs both the maximum incidence rate ratio and the linear relation between the estimated incidence rate ratio and the inverse of average person time at risk, given a specified risk window. In this paper, we present a scan statistic that can identify appropriate risk windows in vaccine safety studies using the self-controlled case series design while taking into account the dependence of time intervals within an individual and while adjusting for time-varying covariates such as age and seasonality. This approach uses the maximum likelihood ratio test based on fixed-effects models, which has been used for analyzing data from self-controlled case series design in addition to conditional Poisson models.