Counterfactual Framework Research Articles

Evaluating the effect of individualized treatment rules using observational data

Individualized treatment rules (ITR) recommend different treatments to individuals based on their observed characteristics. Approaches range from the use of conventional risk prediction models in a counterfactual framework to causal machine learning methods. Randomized controlled trials comparing the use of ITRs to usual care are rare. Objective: we wished to develop a method to emulate such trials from observational data. In the ITR arm, clinicians would not always use the ITR or follow its recommendations. We, therefore, introduce a stochastic component for ITRs’ implementation. Setup: Using Rubin's causal model, we rely on the assumptions of consistency, ignorability and overlap. Estimand of interest: Average Implementation Effect (AIE). The AIE represents the effect the stochastic implementation of a deterministic ITR would have on a given population. Modeling stochastic implementation functions: We propose to consider: - A cognitive bias scenario, i.e., the ITR is implemented more often when the ITR's recommendation is similar to usual care. - A confidence level scenario, i.e., the ITR is implemented only when the (1-α)% confidence interval provided along ITR's recommendation does not cross the critical value. Inference: we developed a procedure to numerically study how the AIE would vary under different stochastic implementation scenarios. Application: we used the MIMIC-III electronic health record to evaluate the population-level impact on 60-day mortality of a new ITR that recommends initiating dialysis only in specific patients based on a combination of six biomarkers. Inclusion criteria included admission to an intensive care unit with acute kidney injury preceded by either mechanical ventilation or vasopressor infusion. From the 53,423 individuals contained in the MIMIC-III database, we included 3,748 adult patients. Results are depicted in the Figure: Panels depict the AIE as a function of the proportion of patients implementing the new ITR under 1/ the cognitive bias scenario (Panel A) and 2/ the confidence level scenario (Panel B). More negative values of the AIE indicate greater benefit from ITR implementation. Ninety-five percent confidence intervals are from the bootstrap. We have developed a new method for observational data that allows emulating randomized trials that compare the stochastic implementation of a new ITR to usual care. Our formal setup delineates the assumption needed for our methodology while the example we provide demonstrates applicability in practice. Causal inference; Precision medicine; Heterogenous treatment effects; Machine learning; External validation Les auteurs n'ont pas précisé leurs éventuels liens d'intérêts.

Complex pain phenotypes: Suicidal ideation and attempt through latent multimorbidity.

Given the relatively high rates of suicidal ideation and attempt among people with chronic pain, there is a need to understand the underlying factors to target suicide prevention efforts. To date, no study has examined the association between pain phenotypes and suicide related behaviors among those with mild traumatic brain injuries. To determine if pain phenotypes were independently associated with suicidal ideation / attempt or if comorbidities within the pain phenotypes account for the association between pain phenotypes and suicide related behaviors. This is a longitudinal retrospective cohort study of suicide ideation/attempts among pain phenotypes previously derived using general mixture latent variable models of the joint distribution of repeated measures of pain scores and pain medications/treatment. We used national VA inpatient, outpatient, and pharmacy data files for Post-9/11 Veterans with mild traumatic injury who entered VA care between fiscal years (FY) 2007 and 2009. We considered a counterfactual causal modeling framework to assess the extent that the pain phenotypes during years 1-5 of VA care were predictive of suicide ideation/attempt during years 6-8 of VA care conditioned on covariates being balanced between pain phenotypes. Without adjustment, pain phenotypes were significant predictors of suicide related behaviors. When we used propensity scores to balance the comorbidities present in the pain phenotypes, the pain phenotypes were no longer significantly associated with suicide related behaviors. These findings suggest that suicide ideation/attempt is associated with pain trajectories primarily through latent multimorbidity. Therefore, it is critical to identify and manage comorbidities (e.g., depression, post-traumatic stress disorder) to prevent tragic outcomes associated with suicide related behaviors throughout the course of chronic pain and mild traumatic brain injury management.

Is It Necessary to Launch a School-Based Financial Literacy Curriculum? Evidence From China.

As starting university is a critical independence milestone for many young people, it would also be the best time to provide them with some financial education (FE). Although there have been many initiatives aimed at enhancing individual financial literacy (FL) and/or financial decision-making, meta-analyses have shown that the effectiveness of FE has been mixed. This study examined the driving forces behind the decision by college students to enroll in a targeted financial literacy curriculum (FLC) and the impact of this attendance on their FL. An endogenous switching model (ESM) was employed to account for the heterogeneity in the decision to attend or not attend the FLC and to counteract any unobservable characteristics. It was found that students with higher self-perceived FL did not prefer to attend the FLC; however, for others, FLC attendance was found to significantly boost their FL in areas such as financial knowledge (FK), financial attitude (FA), and financial behavior (FB), especially for the non-attendees under the counterfactual framework. These “non-attendees” were observed to have some characteristics (e.g., prior knowledge) that made them more financially literate regardless of attendance; however, if they had attended the FLC, they would have gained a greater FL than the attendees. As the FL of the attendees would have been much lower if they had not attended, the FLC appeared to be particularly important for the attendees, which strengthened the case for making the FLC a compulsory part of a general college education.

Transnational Social Stratification? Legal Status of Immigrant Parents and the Educational Achievements of Mexican Children

Although there is evidence documenting the impacts of Mexican parents’ migration to the United States on the educational attainment of the children they leave behind, the potential role of parents’ legal status in stratifying their children's educational achievement is poorly understood. Using data from the Mexican Migration Project, I estimate the educational effects of parents’ documentation status for the children left behind in Mexico. I utilize coarsened exact matching and entropy balancing, alongside community fixed effects, in a counterfactual regression framework to address the endogeneity of parental migration decisions. I find that parental migration's effectiveness as a mechanism for securing educational gains among children left in Mexico differs by parents’ legal status. Documentation allows migrant parents to translate their experiences in the United States into relatively greater educational achievement for their children in Mexico. In the post-1986 period, the non-immigrant children of undocumented parents experienced a significant education penalty. These findings elucidate the effect of US immigration policy on social stratification in Mexican society.

Effectiveness of a community health worker-led case management programme to improve outcomes for people with psychotic disorders in Thailand: a one-year prospective cohort study

BackgroundIntensive case management (ICM) programmes for psychotic patients are effective in improving outcomes, but often unfeasible in resource-poor settings, as they typically require extensive human resources and expertise. We developed and evaluated the effectiveness of a less intensive case management program (LICM), led by community health workers, on one-year social functioning and service use.MethodsA prospective cohort study was conducted on patients aged 18 and above residing in a hospital catchment area. Outcomes were compared between LICM (n = 64) and non-LICM participants (n = 485). A counterfactual framework approach was applied to assess causal effects of the LICM on outcomes. The programme effectiveness was analyzed by augmented-inverse probability of treatment weighting (AIPW) to estimate potential outcome mean (POM) and average treatment effect (ATE). Outcomes were employment status and use of emergency, inpatient and outpatient services. Analyses were stratified by the number of previous psychotic relapse (≤ 1, > 1) to assess heterogeneity of treatment effect on those in an early and later stages of psychotic illness.ResultsIn the early-stage cohort, the likelihood of being employed at one year post-baseline was significantly greater in LICM participants than non-LICM participants (ATE 0.10, 95%CI 0.05–0.14, p < 0.001), whereas service use of all types, except outpatient, was not significantly different between the two groups. In the later-stage cohort, the likelihoods of employment between the two groups at post-baseline were similar (ATE -0.02, 95%CI -0.19–0.15, p = 0.826), whereas service use of all types was significantly higher in LICM participants.ConclusionLICM in a setting where community mental services are scarce may benefit those at an early stage of psychotic illness, by leading to better social functioning and no higher use of unscheduled services at the end of the programme, possibly through their better prognosis and medication adherence. A more intensive case management model may be appropriate for those in a later stage of the illness.

Early-life exposure to Chinese famine and stroke risk in mid- to late life: the mediating roles of cognitive function and depression

BackgroundLimited research has examined the role that famine exposure plays in adulthood stroke risk. We aim to explore the causal implications of early exposure to the Great Chinese Famine on stroke risk and determine whether these associations were mediated by cognitive function, and depression.MethodsWe sampled 12,681 individuals aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) and divided them into fetally exposed, childhood-exposed, adolescence/adulthood-exposed and unexposed groups. Stroke was defined by self- or proxy-reported physician diagnosis. Based on a counterfactual framework, marginal structural models were used to estimate the natural direct effect and the natural indirect effects through cognitive function and depression for causal inference.ResultsWe found that early-life exposure to Chinese famine was directly related to increased stroke risk in mid- to late life. Cognitive function and depression accounted for a greater part of the effect for childhood famine exposure, mediating 36.35% (95%CI: 14.19, 96.19%) of the overall association between famine exposure and incident stroke, than for the fetal, adolescence/adulthood famine exposure groups. However, the natural indirect effect through depression was not significant in the fetally exposed group. The results were robust in the sensitivity analysis of model specification and unobserved confounding.ConclusionsOur findings are consistent with the latency, pathway, and accumulation models, supporting the life-course theory. Early stages of life exposed to the Chinese Famine were associated with higher risk of stroke in mid- to late life. Enhanced cognitive and depression interventions may reduce stroke risk in middle-aged and older Chinese adults who exposure to famine in early life.

Impact of Social Participation Types on Depression in the Elderly in China: An Analysis Based on Counterfactual Causal Inference.

ObjectivesDepression is the leading cause of ill health and disability worldwide, and has become one of the key issues in the public health sector. Social participation is one of the most important measures to support the rapidly aging population and can reduce the risk of depression in the elderly. This study aims to explore the causal relationship between different types of social participation and depression in the elderly, and reduce the level of depression in the elderly by promoting social participation.MethodsIn order to accurately evaluate the causal relationship between participation in different types of social activities and depression in the elderly, this paper uses propensity score matching (PSM) for analysis based on counterfactual framework. The specific matching methods used are: k-nearest neighbor matching method, kernel matching method and radius matching method.ResultsIn this study, 31.4% of the elderly have depression, and the proportion of female elderly is significantly higher. Participation in different social activities has different effects on depression in the elderly. Taking into account sample selection bias, participation in friend-making, exercise, and recreational activities can effectively reduce the risk of depression in the elderly. Compared with other social activities, participation in exercise and recreational activities are more helpful to reduce the risk of depression in the elderly. Participating in exercise activities only reduces the risk of depression in 60–69-year-olds, while participating in economic activities increases the risk of depression in the elderly aged 70 and over. Compared with the male elderly, participation in friend-making, exercise, and recreational activities results in the female elderly having stronger resistance to the risk of depression.ConclusionsDepression was prevalent among the elderly. Through PSM analysis, different types of social participation have different effects on depression in the elderly. In order to maximize the positive effects of different types of social participation on depression in the elderly, it is necessary to provide differentiated social support environment for the elderly. Expanding the research on the relationship between social participation and depression of the elderly will help to formulate more reasonable public health policies and improve the mental health of the elderly.

Does vertical environmental protection pressure promote convergence of urban air pollution?

Recent research shows that the annual average concentration and winter average concentration of PM2.5 in Chinese cities have exhibited a convergence trend since 2013. However, the urban air situation in China remains far from ideal. For an authoritative government, vertical environmental protection pressure (VEPP) may be an important mechanism to motivate cities with high PM2.5 concentration levels to reduce haze pollution more rapidly compared to cities with low PM2.5 concentrations. Thus, VEPP could be considered an institutional advantage in China's vertical air pollution allocation strategy. To investigate the impact of the VEPP of Chinese local governments on the convergence of PM2.5 concentrations, this study uses the counterfactual distribution dynamics analysis framework, which combines parametric regression analysis with the nonparametric distribution dynamics technique. The results show that the Chinese government can promote the convergence of air pollution in local cities to a “favorable” level by increasing the pressure of environmental protection. However, merely increasing VEPP will not completely solve the problem of air pollution in Chinese cities during winter months. Compared with the central and western regions, the VEPP of local governments in the eastern region can promote the convergence of the average annual and winter PM2.5 concentrations.

Efficient estimation of indirect effects in case-control studies using a unified likelihood framework.

Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.

Effects of Motherwort Injection Versus Intramuscular Oxytocin for Preventing Postpartum Hemorrhage Among Women Who Underwent Cesarean Section.

Objectives: Subject to ethical constraints, real-world data are an important resource for evaluating treatment effects of medication use during pregnancy and the postpartum period. This study investigated whether motherwort injection, a traditional Chinese medicine preparation, was more effective than intramuscular (IM) oxytocin for preventing postpartum hemorrhage (PPH) in a real-world setting when intravenous (IV) oxytocin is administered. Methods: We conducted an active-controlled, propensity-score matched cohort study using an established pregnancy registry database. Women who underwent cesarean section and received IV oxytocin at the third stage of labor were included. We used an active-comparator design to minimize indication bias, in which we compared IM motherwort injection in the uterus versus IM oxytocin, both on top of IV oxytocin use. We applied 1:1 propensity-score matching (PSM) to balance patient baseline characteristics and used a logistic regression model to estimate treatment effect (i.e., risk difference (RD) and odds ratio (OR)) by using the counterfactual framework. The outcomes of interest were blood loss over 500 ml within 2 h after delivery (PPH, primary) and blood loss over 1,000 ml (severe PPH, secondary). We conducted four sensitivity analyses to examine the robustness of the results. Results: A total of 22,519 pregnant women underwent cesarean sections, among which 4,081 (18.12%) PPH and 480 (2.13%) severe PPH occurred. Among included women, 586 (2.60%) were administrated with IM motherwort injection, and 21,933 (97.40%) used IM oxytocin. After PSM, patient baseline characteristics were well balanced. Compared with IM oxytocin, the use of IM motherwort injection was associated with significantly lower risk of PPH (RD −25.26%, 95% CI −30.04% to −20.47%, p < 0.001; OR 0.25, 95% CI 0.18 to 0.32, p < 0.001) and severe PPH (RD −3.58%, 95% CI −5.87% to −1.30%, p < 0.001; OR 0.39, 95% CI 0.20 to 0.71, p < 0.002). Sensitivity analyses showed that the results were similar. Conclusion: With the use of data from a real-world setting, the findings consistently showed that among women undergoing cesarean section who had received IV oxytocin, the additional use of IM motherwort injection could achieve a lower risk of PPH as compared to the additional use of IM oxytocin. Our study suggested a paradigm for investigating the treatment effect of Chinese herbal medicine in the real-world practice setting.

Decomposition of the total effect for two mediators: A natural mediated interaction effect framework.

Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect (TE) of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural mediated interaction (MI) effect that captures the two-way and three-way interactions for both scenarios and extends the two-way MIs in the literature. We develop a unified approach for decomposing the TE into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we compare our proposed decomposition to an existing method in a non-sequential two-mediator scenario using simulated data, and illustrate the proposed decomposition for a sequential two-mediator scenario using a real data analysis.

Has environmental policy improved the job quality of migrant workers? A quasi-natural experiment on China's Clean Air Action

Whether China's Clean Air Action can be imperative to job quality improvement of labour force while improving environmental quality is still an ongoing research topic. It is a critical area to understand the economic and social effect of environmental policy under the strategic goal of high-quality economic development. We use China's “Clean Air Action” policy as a quasi-natural experiment to identify the causal effect and potential long-run impact of the environmental policy on internal migrant workers' job quality. Based on the difference-in-difference estimation and a series of robustness tests, such as PSM-DID, counterfactual framework, placebo test, and event study, we find that China's Clean Air Action does have a positive effect on the job quality of migrant workers, and the job quality of the migrants in the Clean Air Action sampling cities increased by 0.31 on an average compared with cities that did not implement Clean Air Action. This positive effect will increase over time, and with the level of regional economic development and urban scale rise. Moreover, the job quality improvement could be driven by the productivity effect and the positive adjustment of labor force employment structure from action. This study provides an important empirical evidence for directions of this new action. Our results suggest that further policy measures with a clear goal together with strong-oriented and sub-regional joint governance mechanisms over different levels of government are called for.

Does long-term exposure to air pollution impair physical and mental health in the middle-aged and older adults? — A causal empirical analysis based on a longitudinal nationwide cohort in China

The world is aging, posing a challenge to public health. Air pollution is increasingly recognized as an important environmental risk factor, with effects on both physical and mental health. Considering the vulnerability of older adults, they tend to have more prevalent comorbidities that may lead to broader consequences. However, evidence to comprehensively assess the causal effects of long-term air pollution exposure on the physical and mental health of older adults remains limited and inconsistent, especially in developing countries.The longitudinal data from the Chinese Family Panel Study (a representative Chinese national cohort study) for 2012, 2014, 2016, and 2018 were included in this study. The Correlated Random Effects Control Function method (CRE-CF) in a counterfactual causal inference framework was employed to explore the causal relationship between long-term exposure to air pollution and physical and mental health and self-rated health status in middle-aged and older adults, considering the ordered categorical nature of health outcomes. The appropriate instrumental variable was selected and validated.This study included 5846 participants aged >45 years in 2012. In the CRE-CF model for activities of daily living (ADLs, positively associated with physical health), subjective memory impairment (SMI, negatively associated with memory health) and self-rated health status in middle-age and older adults, the coefficient of PM2.5 is −0.069, 0.102, and 0.106 respectively, and all statistically significant at 5% level, which suggests that chronic exposure to air pollutants had significant negative effects on ADLs, SMI and self-rated health in middle-aged and older adults.The findings suggest that long-term exposure to air pollutants can impair the health of middle-aged and older adults across the board, including physical and mental health. In the context of an aging society, the findings of this study will provide tremendous implications for the authority to protect them from damage caused by long-term exposure to air pollutants.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science.

Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience. To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends. Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased. Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Mortality disparities between Black and White Americans mediated by income and health behaviors

BackgroundRace disparities in health outcomes including mortality risk are well known, but mediating mechanisms that link race to mortality risk have rarely been formally tested. MethodsWe analyzed public NHANES III data from 1988 to 1994 linked to mortality outcomes prospectively through 2015. Participants included 10,460 non-Hispanic Black (40.5%, n = 4233) and non-Hispanic White (59.5%, n = 6227) adults. Proportional hazards regression models examined mortality risk in association with race, demographics, income, and an index of risky health behaviors including smoking, poor diet and low physical activity. A mediation approach under the counterfactual framework was used to test effects of income and risky health behaviors as mediators between race and mortality risk. ResultsConsidering only race, age and sex, Black participants had significantly higher mortality risk than Whites (HR = 1.46, 95% CI 1.35–1.58). When income and education were added, the race effect was lower but remained significant (HR = 1.15, 95% CI 1.02–1.30). In the subsequent model that also included risky behaviors the association between race and mortality was no longer significant (HR = 1.05, 95% CI 0.92–1.20); both higher income and healthier behaviors contributed to lower mortality risk. There was a significant indirect effect of race on mortality mediated through income, and the direct effect of race on mortality was not significant when the mediating effect of income was considered. Likewise, the risky behavior score significantly mediated the association between race with mortality, and the direct effect of race was not significant. In the separate models, income mediated 62% of the association between race and mortality and lifestyle mediated 61% of the relationship. ConclusionsEfforts to reduce race-based mortality disparities may focus on policies to reduce income-based disparities and promote positive health behaviors that consider variations in socioeconomic resources and personal preferences.

Ultra-processed food intake and all-cause and cause-specific mortality in individuals with cardiovascular disease: the Moli-sani Study.

To evaluate the association of ultra-processed food (UPF) intake and mortality among individuals with history of cardiovascular disease (CVD) and analyse some biological pathways possibly relating UPF intake to death. Longitudinal analysis on 1171 men and women (mean age: 67 ± 10 years) with history of CVD, recruited in the Moli-sani Study (2005-10, Italy) and followed for 10.6 years (median). Food intake was assessed using a food frequency questionnaire. UPF was defined using the NOVA classification according to degree of processing and categorized as quartiles of the ratio (%) between UPF (g/day) and total food consumed (g/day). The mediating effects of 18 inflammatory, metabolic, cardiovascular, and renal biomarkers were evaluated using a logistic regression model within a counterfactual framework. In multivariable-adjusted Cox analyses, higher intake of UPF (Q4, ≥11.3% of total food), as opposed to the lowest (Q1, UPF <4.7%), was associated with higher hazards of all-cause (hazard ratio [HR]: 1.38; 95% confidence interval (CI): 1.00-1.91) and CVD mortality (HR: 1.65; 95% CI: 1.07-2.55). A linear dose-response relationship of 1% increment in UPF intake with all-cause and CVD mortality was also observed. Altered levels of cystatin C explained 18.3% and 16.6% of the relation between UPF (1% increment in the diet) with all-cause and CVD mortality, respectively. A diet rich in UPF is associated with increased hazards of all-cause and CVD mortality among individuals with prior cardiovascular events, possibly through an altered renal function. Elevated UPF intake represents a major public health concern in secondary CVD prevention.

Mediation analysis of social isolation and mortality by health behaviors

Social isolation associated with greater risk for mortality is well documented, however, it is unclear whether and the extent to which social isolation influences mortality through health behaviors. We therefore assessed the mediation between social isolation and mortality by health behaviors. 15,000 adults ages 18–89 years in the Third National Health and Nutrition Examination Survey in the US (NHANES III) (1988–1994) were followed through 2015. The outcomes of overall and cause-specific mortality were assessed by linking with the National Death Index. Social isolation was based on a modified Social Network Index. Mediation analyses were conducted under the counterfactual framework. Compared with participants who were not socially isolated, those who were isolated had significantly increased risk for overall mortality (HR = 1.33, 95% CI: 1.15–1.54), cardiovascular disease (CVD)-specific mortality (HR = 1.28, 95% CI: 1.01–1.62) and mortality from causes other than CVD or cancer (HR = 1.53, 95% CI: 1.22–1.90). Social isolation was not independently associated with risk for cancer-specific mortality. The association between social isolation and cancer-specific mortality was largely (74%) mediated by health behaviors, including smoking, physical activity and quality of diet. The associations between social isolation and CVD or other specific mortality were partly (about one-fifth) explained by unhealthy behaviors. In conclusion, the extent to which social isolation and mortality was mediated by healthy behaviors varies by causes of death. Given mounting evidence that social isolation is increasing in society, it is important to add social isolation to the list of public health concerns in addition to conventional risk factors.

Explaining "What for" in Motion Analysis Research: A Proposal for a Counterfactual Framework That Is Slightly Different From the Theory of Causation.

In motion analysis research, the methodology for estimating the physical processes of human movement is highly developed, but the methodology for interpreting such data is relatively undeveloped. One of the aims of this paper is to demonstrate the importance of developing a conceptual basis for interpreting data about the physical processes of body movement. In this conceptual study, one topic was discussed as a central question: what it means to answer the question what a certain movement technique is aimed for. We first introduced the distinction between explanations from the perspective of causes and explanations from the perspective of purposes as a mode of explaining events, and pointed out the importance of explanations from the perspective of purposes. We next argued that by taking the perspective of whether a given movement technique leads to a desired outcome in comparison to other movement techniques, we can expect to interpret what a given movement technique is for based on objectively observable information rather than the subjective intentions of the athlete. In addition, we discussed how the criterion movement patterns should be defined when assessing the fitness for purpose of a given movement technique in terms of its consequences. In this regard, our argument is that it is necessary to take into account that the exact same movement pattern cannot be performed every time, even for the same motor task, and that there are multiple options for how to define the set of possible movement patterns that can be performed. Our discussion reveals the peculiarity of grasping the meaning of movement techniques, and therefore suggests that there is a substantial need for motion analysis researchers to deepen their conceptual analysis to understand the nature of this issue.

Can agricultural insurance encourage farmers to apply biological pesticides? Evidence from rural China

Based on the survey data from farmers in Hubei, Jiangxi, and Zhejiang provinces, this paper constructs a theoretical model of the impact of agricultural insurance on farmers’ biopesticide application behavior. Furthermore, it adopts the endogenous switching probit model to empirically analyze the effect of agricultural insurance on farmers’ biopesticide application behavior. We also estimate the probability that farmers will buy agricultural insurance and apply biological pesticides under the counterfactual framework. The results show that, first, 59.12% of the farmers in the sample area have purchased agricultural insurance and that 62.45% of the farmers have used biological pesticides. Second, the overall average treatment effect is 0.082. This indicates that, if all farmers buy agricultural insurance, the probability that farmers will apply biological pesticides increases by 8.2%. It can be seen that purchasing agricultural insurance encourages farmers to apply biological pesticides. Third, purchasing agricultural insurance can encourage farmers to increase the use of biological pesticides and reduce the use of chemical pesticides. In addition, purchasing agricultural insurance increases the ratio of the amount of biological pesticides to chemical pesticides. This finding shows that agricultural insurance can increase the relative importance of biological pesticides.

Mediation analysis methods used in observational research: a scoping review and recommendations

BackgroundMediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.MethodsWe searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method.ResultsWe included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction.ConclusionsTo ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.