Risk adjustment for hospital use using social security data: cross sectional small area analysis

Area of Science:

• Health Services Research
• Public Health Policy
• Socioeconomic Determinants of Health

Background:

• Understanding socioeconomic factors influencing healthcare access is crucial for equitable resource allocation.
• Poverty and social deprivation are linked to increased demand for acute hospital services.
• Existing hospital funding models may not adequately account for population needs.

Purpose of the Study:

• To identify demographic and socioeconomic determinants of acute hospital treatment needs at a small area level.
• To investigate the relationship between poverty and the utilization of inpatient services.
• To create a risk adjustment formula for hospital funding using annually updatable variables.

Main Methods:

• Cross-sectional analysis incorporating spatial interactive modeling to assess population proximity to health facilities.
• Two-stage weighted least squares regression to model inpatient service use against service supply and needs drivers.
• Inclusion of socioeconomic census variables, income support, family credit uptake, and mortality data.

Main Results:

• A predictive statistical model for inpatient service use was developed, incorporating income support, family credit, elderly individuals living alone, standardized mortality ratio, and low birth weight.
• The derived risk adjustment formula primarily reallocates hospital resources from urban to rural areas.
• Socioeconomic data, particularly social security information, significantly impacts the model's outcomes.

Conclusions:

• A population risk adjustment formula for acute hospital treatment has been developed.
• Four of the five key variables in the formula can be updated annually, improving its timeliness.
• The inclusion of social security data substantially enhances the model and the resulting funding distribution.