[Use of health insurance data to analyse pain management practices in the nursing-home setting]

Relationship between social interaction and health of the floating elderly population in China: an analysis based on interaction type, mode and frequency

BACKGROUND

Although the health of the floating elderly population is an important part of active and healthy ageing, it is neglected in current Chinese society. Based on the general consensus that social interaction can affect the health of the floating population, this study explored whether the interaction type, mode and frequency influenced the health of the floating elderly population in China and investigated the variability of these influential effects.

METHODS

This study used the China Migrant Population Dynamic Survey Data 2017 and selected 5239 floating elderly individuals over 60 years old. Self-rated health was used to assess the comprehensive health status of respondents. Social interaction was measured by the interaction type, mode and frequency. Descriptive statistical analysis was used to analyse the health and social interaction status. An ordinal probit model was used to estimate the influential effects and differences on health caused by social interaction. The 2SLS model was used to examine the mutual causality relationship between interaction frequency and health, and a robustness test was conducted.

RESULTS

A total of 44.6% interacted with local residents, 14.2% participated in interaction activities based on geographical relations, and only 4.3% and 7% participated in interactions based on business and interest, respectively. Interacting with natives improved individuals' self-rated health by 18.5%; specifically, geographical interaction increased self-rated health by 40.9%, occupational interaction increased it by 25.2%, interest-based interaction increased it by 41.2%, and interaction frequency improved the self-rated health (β = 0.128). In addition, sex, education level, personal income, and floating into the eastern region had a positive effect on individuals' health. However, age, spouse and hukou exerted a negative effect.

CONCLUSIONS

This study demonstrated that interacting with local residents could improve the health of floating elderly population, and revealed that interest-based interaction and their frequency had a positive impact on health. The government should speed up the construction of the voluntary service system and encourage the floating elderly population to realize their personal value in social interaction. In addition, the reform of the hukou system should be further promoted, so as to remove institutional barriers to the social interaction.

How does age affect the care dependency risk one year after stroke? A study based on claims data from a German health insurance fund

BACKGROUND

The objective of this study is to investigate the effect of age on care dependency risk 1 year after stroke. Two research questions are addressed: (1) How strong is the association between age and care dependency risk 1 year after stroke and (2) can this association be explained by burden of disease?

METHODS

The study is based on claims data from a German statutory health insurance fund. The study population was drawn from all continuously insured members with principal diagnoses of ischaemic stroke, hemorrhagic stroke, or transient ischaemic attack in 2007 who survived for 1 year after stroke and who were not dependent on care before their first stroke (n = 2864). Data were collected over a 1-year period. People are considered to be dependent on care if they, due to a physical, mental or psychological illness or disability, require substantial assistance in carrying out activities of daily living for a period of at least 6 months. Burden of disease was assessed by stroke subtype, history of stroke, comorbidities as well as geriatric multimorbidity. Regression models were used for data analysis.

RESULTS

21.6 % of patients became care dependent during the observation period. Post-stroke care dependency risk was significantly associated with age. Relative to the reference group (0-65 years), the odds ratio of care dependency was 11.30 (95 % CI: 7.82-16.34) in patients aged 86+ years and 5.10 (95 % CI: 3.88-6.71) in patients aged 76-85 years. These associations were not explained by burden of disease. On the contrary, age effects became stronger when burden of disease was included in the regression model (by between 1.1 and 28 %).

CONCLUSIONS

Our results show that age has an effect on care dependency risk that cannot be explained by burden of disease. Thus, there must be other underlying age-dependent factors that account for the remaining age effects (e.g., social conditions). Further studies are needed to explore the causes of the strong age effects observed.