Unmet healthcare needs and health inequalities in people with spinal cord injury: a direct regression inequality decomposition

International Journal for Equity in Health, 2023 · DOI: https://doi.org/10.1186/s12939-023-01848-z · Published: January 1, 2023

Spinal Cord Injury Healthcare Public Health

Simple Explanation

This study examines health inequalities in people with spinal cord injury (SCI) across different income levels. It investigates how unmet healthcare needs and other factors contribute to these inequalities. The study uses data from the International Spinal Cord Injury Survey (InSCI) across 22 countries.

Study Duration

Not specified

Participants

11,529 participants with spinal cord injury across 22 countries

Evidence Level

Cohort study using a cross-sectional survey

Key Findings

1
Better health outcomes (more years living with the injury) are more likely observed among high-income groups.
2
For years living with the injury, inequality is mostly explained by non-modifiable factors like age at the time of injury.
3
For the comorbidity index, inequality is mostly explained by unmet healthcare needs and the cause of the injury, which are modifiable factors.

Research Summary

The study found that better health status is evident among high-income groups, reflected in pro-rich inequalities. Age at the time of injury is the most important factor explaining inequalities in years living with the injury. Unmet health care needs are the most important factor explaining inequalities in comorbidities.

Practical Implications

Address Health System Issues

The efficiency of the health system is crucial for people with SCI, who require frequent access to various services.

Reduce Inequity

Addressing problems from public health, inequalities of opportunities, risks, and income is important to reduce inequity.

Targeted Policy Design

Understanding the differences between countries generates evidence to better design national policies aiming at targeting health inequalities.

Study Limitations

1
Potential selection bias related to nonrandom data collection in some countries.
2
Use of self-reported data could generate bias and underestimation of results.
3
The data are not representative of the entire SCI population.

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