Distributional Regression Techniques in Socioeconomic Research on the Inequality of Health with an Application on the Relationship between Mental Health and Income

How does air pollution threaten mental health? Protocol for a machine-learning enhanced systematic map

INTRODUCTION

Air pollution exposure has influenced a broad range of mental health conditions. It has attracted research from multiple disciplines such as biomedical sciences, epidemiology, neurological science, and social science due to its importance for public health, with implications for environmental policies. Establishing and identifying the causal and moderator effects is challenging and is particularly concerning considering the different mental health measurements, study designs and data collection strategies (eg, surveys, interviews) in different disciplines. This has created a fragmented research landscape which hinders efforts to integrate key insights from different niches, and makes it difficult to identify current research trends and gaps.

METHOD AND ANALYSIS

This systematic map will follow the Collaboration for Environmental Evidence's guidelines and standards and Reporting Standards for Systematic Evidence Syntheses guidelines. Different databases and relevant web-based search engines will be used to collect the relevant literature. The time period of search strategies is conducted from the inception of the database until November 2022. Citation tracing and backward references snowballing will be used to identify additional studies. Data will be extracted by combining of literature mining and manual correction. Data coding for each article will be completed by two independent reviewers and conflicts will be reconciled between them. Machine learning technology will be applied throughout the systematic mapping process. Literature mining will rapidly screen and code the numerous available articles, enabling the breadth and diversity of the expanding literature base to be considered. The systematic map output will be provided as a publicly available database.

ETHICS AND DISSEMINATION

Primary data will not be collected and ethical approval is not required in this study. The findings of this study will be disseminated through a peer-reviewed scientific journal and academic conference presentations.

Occupational Differences in Psychological Distress Between Chinese Dentists and Dental Nurses

Background

Doctors and allied health professionals are facing serious mental health issues, which have received widespread attention. This study aimed to explore the occupational differences in psychological distress between Chinese dentists and dental nurses.

Materials and Methods

The data was collected from a cross-sectional study conducted by the Chongqing Stomatological Association. Medical personnel involved in this survey were invited to complete a battery of self-administrated questionnaires, specifically the General Health Questionnaire-12, Maslach Burnout Inventory, and career choice regret scale. Data on demographic characteristics and working conditions were also collected. The results of these questionnaires were analyzed with SPSS (version 23.0). Univariate and multivariable analyzes were conducted to explore the influencing factors.

Results

A total of 3,020 valid questionnaires, including 1,855 dentists and 1,165 dental nurses, were collected from 11 provinces of China. In general, 23.8% of responders exhibited psychological distress. The rate of dentists was 25.7%, and that of dental nurses was 20.8%. The prevalence was 4.9% higher in dentists than in dental nurses (P < 0.05). The multivariable analysis showed that factors associated with psychological distress for dentists were lower income, burnout, high job stress, career-choice regret, and lack of sufficient personal time, and that for dental nurses were age, lower income, longer working hours per week, burnout, high job stress, low job satisfaction, lack of sufficient personal time, and poor medical environment.

Conclusion

The prevalence of psychological distress was relatively high among dental medical staff, and dentists showed a higher prevalence than dental nurses. Nurses have more risk factors for psychological distress than dentists. These results indicate that it is necessary to monitor the mental health status of dental medical staff and implement accurate strategies for dentists and dental nurses to promote their physical and mental health.

Risk factors relate to the variability of health outcomes as well as the mean: A GAMLSS tutorial

Background:

Risk factors or interventions may affect the variability as well as the mean of health outcomes. Understanding this can aid aetiological understanding and public health translation, in that interventions which shift the outcome mean and reduce variability are typically preferable to those which affect only the mean. However, most commonly used statistical tools do not test for differences in variability. Tools that do have few epidemiological applications to date, and fewer applications still have attempted to explain their resulting findings. We thus provide a tutorial for investigating this using GAMLSS (Generalised Additive Models for Location, Scale and Shape).

Methods:

The 1970 British birth cohort study was used, with body mass index (BMI; N = 6007) and mental wellbeing (Warwick-Edinburgh Mental Wellbeing Scale; N = 7104) measured in midlife (42–46 years) as outcomes. We used GAMLSS to investigate how multiple risk factors (sex, childhood social class, and midlife physical inactivity) related to differences in health outcome mean and variability.

Results:

Risk factors were related to sizable differences in outcome variability—for example males had marginally higher mean BMI yet 28% lower variability; lower social class and physical inactivity were each associated with higher mean and higher variability (6.1% and 13.5% higher variability, respectively). For mental wellbeing, gender was not associated with the mean while males had lower variability (–3.9%); lower social class and physical inactivity were each associated with lower mean yet higher variability (7.2% and 10.9% higher variability, respectively).

Conclusions:

The results highlight how GAMLSS can be used to investigate how risk factors or interventions may influence the variability in health outcomes. This underutilised approach to the analysis of continuously distributed outcomes may have broader utility in epidemiologic, medical, and psychological sciences. A tutorial and replication syntax is provided online to facilitate this (https://osf.io/5tvz6/).

Funding:

DB is supported by the Economic and Social Research Council (grant number ES/M001660/1), The Academy of Medical Sciences / Wellcome Trust (“Springboard Health of the Public in 2040” award: HOP001/1025); DB and LW are supported by the Medical Research Council (MR/V002147/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Gender differences in the mental health impact of the COVID-19 lockdown: Longitudinal evidence from the Netherlands

Recent contributions highlighted gender differences in the mental health consequences of COVID-19 lockdowns. However, their cross-sectional designs cannot differentiate between pre-existing gender differences and differences induced by lockdowns. Estimating fixed-effects models using longitudinal data from the Lifelines biobank and cohort study with repeated mental health measurements throughout the lockdown, we overcome this caveat. Significant gender differences in mental health during the lockdown were found, where women experienced more depression symptoms and disorders and men experienced more anxiety symptoms and disorders stemming from the lockdown. Policymakers need to keep in mind that the COVID-19 lockdowns have different effects on mental health for men and women.

The Effect of Business Cycles on Health Expenditure: A Story of Income Inequality in China

Using the panel data of 31 regions in China from 2002 to 2018, this study aims to investigate the effect of business cycles on health expenditure from the role of income inequality. We find that health expenditure experiences a change from pro-cyclical to counter-cyclical with business cycles. Specifically, business cycles have a different influence on health expenditure before and after the financial crisis in 2008. Our findings also show that income inequality can moderate the impact of business cycles on health expenditure in China. More importantly, the role of income inequality in the above issue varies from different regions. We conclude that the government should try to take active steps to control health expenditure by decreasing income inequality.

A distributional regression approach to income-related inequality of health in Australia

Background

Several studies have confirmed the existence of a significant positive relationship between income and health. Conventional regression techniques such as Ordinary Least Squares only help identify the effect of the covariates on the mean of the health variable. In this way, important information of the income-health relationship could be overlooked. As an alternative, we apply and compare unconventional regression techniques.

Methods

We adopt a distributional approach because we want to allow the effect of income on health to vary according to people’s health status. We start by analysing the income-health relationship using a distributional regression model that falls into the GAMLSS (Generalized Additive Models for Location, Scale and Shape) framework. We assume a gamma distribution to model the health variable and specify the parameters of this distribution as linear functions of a set of explanatory variables. For comparison, we also adopt a quantile regression analysis. Based on predicted health quantiles, we use both a parametric and a non-parametric approach to estimate the lower tail of the health distribution.

Results

Our data come from Wave 13 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, collected in 2013-2014. According to GAMLSS, we find that the risk of ending up in poor, fair or average health is lower for those who have relatively high incomes ($80,000) than for those who have relatively low incomes ($20,000), for both smokers and non-smokers. In relative terms, the risk-lowering effect of income appears to be the largest for those who are in poor health, again for both smokers and non-smokers. The results obtained on the basis of quantile regression are to a large extent comparable to those obtained by means of GAMLSS regression.

Conclusions

Both distributional regression techniques point in the direction of a non-uniform effect of income on health, and are therefore promising complements to conventional regression techniques as far as the analysis of the income-health relationship is concerned.