Little is known about socioeconomic equity in access to healthcare among people with eating disorders in Australia. This study aims to measure the extent of inequity in eating disorder-related healthcare utilization, analyze trends, and explore the sources of inequalities using New South Wales (NSW) administrative linked health data for 2005 to 2020.

Socioeconomic inequities were measured using concentration index approach, and decomposition analysis was conducted to explain the factors accounting for inequality. Healthcare utilization included: public inpatient admissions, private inpatient admissions, visits to public mental health outpatient clinics and emergency department visits, with three different measures (probability of visit, total and conditional number of visits) for each outcome.

Private hospital admissions due to eating disorders were concentrated among individuals from higher socioeconomic status (SES) from 2005 to 2020. There was no significant inequity in the probability of public hospital admissions for the same period. Public outpatient visits were utilized more by people from lower SES from 2008 to 2020. Emergency department visits were equitable, but more utilized by those from lower SES in 2020.

Public hospital and emergency department services were equitably used by people with eating disorders in NSW, but individuals from high SES were more likely to be admitted to private hospitals for eating disorder care. Use of public hospital outpatient services was higher for those from lower SES. These findings can assist policymakers in understanding the equity of the healthcare system and developing programs to improve fairness in eating disorder-related healthcare in NSW.

The aim of this study was to investigate the factors influencing urban–rural differences in depressive symptoms among old people in China and to measure the contribution of relevant influencing factors.

A cross-sectional research. The 2018 data from The Chinese Longitudinal Health Longevity Survey (CLHLS).

Twenty-three provinces in China.

From the 8th CLHLS, 11,245 elderly participants were selected who met the requirements of the study.

We established binary logistic regression models to explore the main influencing factors of their depressive symptoms and used Fairlie models to analyze the influencing factors of the differences in depressive symptoms between the urban and rural elderly and their contribution.

The percentage of depressive symptoms among Chinese older adults was 11.72%, and the results showed that rural older adults (12.41%) had higher rates of depressive symptoms than urban (10.13%). The Fairlie decomposition analysis revealed that 73.96% of the difference in depressive symptoms could be explained, which was primarily associated with differences in annual income (31.51%), education level (28.05%), sleep time ( − 25.67%), self-reported health (24.18%), instrumental activities of daily living dysfunction (20.73%), exercise (17.72%), living status ( − 8.31%), age ( − 3.84%), activities of daily living dysfunction ( − 3.29%), and social activity (2.44%).

The prevalence of depressive symptoms was higher in rural than in urban older adults, which was primarily associated with differences in socioeconomic status, personal lifestyle, and health status factors between the urban and rural residents. If these factors were addressed, we could make targeted and precise intervention strategies to improve the mental health of high-risk elderly.

The primary purpose of this study is to examine changes in socio-economic inequality in nutritional status (stunting and underweight) among children in Empowered Action Group (EAG) states.

The study is based on the most recent two wave’s cross-sectional data from the National Family Health Survey (NFHS) conducted in 2005–2006 (NFHS-3) and 2015–2016 (NFHS-4). The study used height-for-age (stunting) and weight-for-age (underweight) of children as anthropometric indicators.

EAG states including Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, Uttarakhand and Uttar Pradesh in India.

The study includes a total of 11 858 (NFHS-3) and 92 630 (NFHS-4) children under 5 years of age.

The socio-economic inequality in stunting remained unchanged in all EAG states. At the same time, the inequality in underweight decreased during 2005–2016. On decomposing, the factors contributing to socio-economic inequality revealed that household wealth index, mother’s education and mother’s nutritional status were the largest contributors to stunting (47 %, 24 % and 8 %) and underweight (51 %, 21 % and 16 %), respectively, in 2015–2016.

The study concluded the socio-economic inequality in underweight among children under 5 years of age increased over the years in EAG states in India. Altogether, household wealth index, mother’s education and mother’s nutritional status contributed to nearly 80 % to inequality in stunting and 90 % to inequality in underweight in 2015–2016. Hence, efforts should be made to minimise the socio-economic inequality in the nutritional status of children, particularly in EAG states in India.

To obtain projections of the prevalence of childhood malnutrition indicators up to 2030 and to analyse the changes of wealth-based inequality in malnutrition indicators and the degree of contribution of socio-economic determinants to the inequities in malnutrition indicators in Bangladesh. Additionally, to identify the risk factors of childhood malnutrition.

Cross-sectional study. A Bayesian linear regression model was used to estimate trends and projections of malnutrition. For equity analysis, slope index, relative index and decomposition in concentration index were used. Multilevel logistic models were used to identify risk factors of malnutrition.

Household surveys in Bangladesh from 1996 to 2014.

Children under the age of 5 years.

A decreasing trend was observed for all malnutrition indices. In 1990, predicted prevalence of stunting, wasting and underweight was 55·0, 15·9 and 61·8 %, respectively. By 2030, prevalence is projected to reduce to 28·8 % for stunting, 12·3 % for wasting and 17·4 % for underweight. Prevalence of stunting, wasting and underweight were 34·3, 6·9 and 32·8 percentage points lower in the richest households than the poorest households. Contribution of the wealth index to child malnutrition increased over time and the largest contribution of pro-poor inequity was explained by wealth index. Being an underweight mother, parents with a lower level of education and poorer households were the key risk factors for stunting and underweight.

Our findings show an evidence-based need for targeted interventions to improve education and household income-generating activities among poor households to reduce inequalities and reduce the burden of child malnutrition in Bangladesh.