Modelling Deprivation Level and Multimorbidity in a Health District

Investigating the relationship between social deprivation and health outcomes in China: using spatial regression analysis approach

BACKGROUND

Although significant progress has been made in the health status of Chinese citizens, disparities are still strikingly evident. This paper reveals the interconnection between social deprivation and the health of the Chinese population using the latest census data, and delves into the impact of social deprivation on health outcomes.

METHODS

To assess social deprivation, this study selected 14 indicators from six domains: income, employment, education, housing condition, housing area, and demographic structure. The social deprivation value was calculated using entropy method, variation coefficient method, CRITIC method, and principal component analysis method, and its spatial distribution was compared. Meanwhile, the best models are selected from ordinary least squares regression models, spatial lag models and spatial error models to analyze the effect of social deprivation on health outcomes according to the performances of these models.

RESULTS

The spatial distribution of social deprivation in China displays notable heterogeneity. The best models indicates that social deprivation is negatively correlated with mortality rate of Class A and B infectious diseases, average life expectancy and proportion of healthy elderly, but positively correlated with incidence rate of Class A and B infectious diseases, maternal mortality rate, and prevalence rate of low-weight children. The part of regression models for analyzing the relationship between social deprivation and metrics like incidence rate of infectious diseases, maternal mortality rate, average life expectancy, and proportion of healthy elderly are in the form of spatial lag. The part of regression models for analyzing the relationship between social deprivation with mortality rate of Class A and B infectious diseases and prevalence rate of low weight children are in the form of spatial error.

CONCLUSION

Social deprivation impacts the health of different populations, and this influence exhibits correlation and interaction across various regions. Therefore, it is necessary for governments to develop policies, particularly those aimed at enhancing the equality of public health services, to address the imbalance in regional development, allocate resources scientifically, and narrow the gap in economic, social, and healthcare development across regions.

[Homelessness as a determinant of health and its impact on quality of life]

OBJECTIVE

To determine the health status of the homeless population who spend the night on the streets and in shelters (extreme residential exclusion), with reference to their quality of life, self-perceived health, comorbidity and access to health resources.

METHOD

A cross-sectional descriptive study was carried out on a sample of 263 homeless people in the city of Valencia by means of a nocturnal count and using the EQ-5D-3L Health Related Quality of Life questionnaire and questions from the European Quality of Life Survey for Spain.

RESULTS

Of the people who participated in the study, 129 of them slept outdoors, 49% (62.79% men, 19.37% women and 17.84% unidentified), while 134 slept in hostels, i.e. 51% (67.16% men and 32.84% women) with an estimated average age of 41.53 years and 55% had been living on the street for less than 12 months. People staying overnight on the streets presented worse quality of life and more than a quarter of the sample lacked a health card.

CONCLUSIONS

There is a relationship between homelessness and poor health conditions in terms of quality of life, perceived health, comorbidity and accessibility to health to resources. The inverse care law is evidenced.