Background <p>Environmental factors such as air pollution and access to greenspace are increasingly recognised as important determinants of population health and healthcare expenditure. This study examined the relationship between ambient PM₂.₅, urban greenspace, and household healthcare expenditure in China, and assessed how healthcare expenditure was concentrated across these gradients of environmental exposure.</p> Methods <p>We conducted a cross-sectional analysis using data from the 6th Health Services Survey in Shandong Province, linking household healthcare expenditure to residential greenspace (NDVI) and ambient PM₂.₅ at the village level. We used Generalised Linear Mixed Models with village-level random effects to estimate the relationship between household healthcare expenditure and environmental exposures. To examine inequalities in healthcare expenditure, we calculated concentration indices (<i>CI</i>s), quantifying the distribution of healthcare expenditure across levels of greenspace and air pollution.</p> Results <p>A total of 27,603 individuals were included in the analysis. Higher NDVI exposure was associated with lower household healthcare expenditure (Q2: <i>β</i> = −0.21, <i>P</i> &lt; 0.01; Q3: <i>β</i> = −0.21, <i>P</i> &lt; 0.01; Q4: <i>β</i> = −0.26, <i>P</i> &lt; 0.01), whereas higher ambient PM₂.₅ concentrations were linked to increased expenditure (Q2: <i>β</i> = 0.13, <i>P</i> &lt; 0.01; Q3: <i>β</i> = 0.17, <i>P</i> &lt; 0.01; Q4: <i>β</i> = 0.30, <i>P</i> &lt; 0.01). In both models, older age (45–65 and ≥ 65 years) and underweight status were associated with higher healthcare expenditure (NDVI: <i>β</i> = 0.13–0.32, <i>P</i> &lt; 0.01; PM₂.₅: <i>β</i> = 0.15–0.35, <i>P</i> &lt; 0.01), as were households earning above 60% of median income (NDVI: <i>β</i> = 0.10, <i>P</i> &lt; 0.01; PM₂.₅: <i>β</i> = 0.11, <i>P</i> &lt; 0.01). Health insurance was linked to lower expenditure in the NDVI model (<i>β</i> = −0.10, <i>P</i> &lt; 0.001) but not in the PM₂.₅ model. Inequality analyses indicated that household healthcare expenditure was disproportionately concentrated among residents with lower greenspace (NDVI: <i>PCI</i> = − 0.04, 95% <i>CI</i>: −0.05 to − 0.03) and higher PM₂.₅ exposure (<i>PCI</i> = 0.06, 95% <i>CI</i>: 0.05 to 0.07).</p> Conclusion <p>Our findings highlight the importance of integrating environmental equity into public health and policy interventions to reduce healthcare expenditure and inequalities.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Ambient PM₂.₅, residential greenspace, and household healthcare expenditure in Shandong, China

  • Siyuan Wang,
  • Zhiwei Xu,
  • Gian Luca Di Tanna,
  • Raksha Pandya-Wood,
  • Mingsheng Chen,
  • Laura Downey,
  • Stephen Jan,
  • Lei Si

摘要

Background

Environmental factors such as air pollution and access to greenspace are increasingly recognised as important determinants of population health and healthcare expenditure. This study examined the relationship between ambient PM₂.₅, urban greenspace, and household healthcare expenditure in China, and assessed how healthcare expenditure was concentrated across these gradients of environmental exposure.

Methods

We conducted a cross-sectional analysis using data from the 6th Health Services Survey in Shandong Province, linking household healthcare expenditure to residential greenspace (NDVI) and ambient PM₂.₅ at the village level. We used Generalised Linear Mixed Models with village-level random effects to estimate the relationship between household healthcare expenditure and environmental exposures. To examine inequalities in healthcare expenditure, we calculated concentration indices (CIs), quantifying the distribution of healthcare expenditure across levels of greenspace and air pollution.

Results

A total of 27,603 individuals were included in the analysis. Higher NDVI exposure was associated with lower household healthcare expenditure (Q2: β = −0.21, P < 0.01; Q3: β = −0.21, P < 0.01; Q4: β = −0.26, P < 0.01), whereas higher ambient PM₂.₅ concentrations were linked to increased expenditure (Q2: β = 0.13, P < 0.01; Q3: β = 0.17, P < 0.01; Q4: β = 0.30, P < 0.01). In both models, older age (45–65 and ≥ 65 years) and underweight status were associated with higher healthcare expenditure (NDVI: β = 0.13–0.32, P < 0.01; PM₂.₅: β = 0.15–0.35, P < 0.01), as were households earning above 60% of median income (NDVI: β = 0.10, P < 0.01; PM₂.₅: β = 0.11, P < 0.01). Health insurance was linked to lower expenditure in the NDVI model (β = −0.10, P < 0.001) but not in the PM₂.₅ model. Inequality analyses indicated that household healthcare expenditure was disproportionately concentrated among residents with lower greenspace (NDVI: PCI = − 0.04, 95% CI: −0.05 to − 0.03) and higher PM₂.₅ exposure (PCI = 0.06, 95% CI: 0.05 to 0.07).

Conclusion

Our findings highlight the importance of integrating environmental equity into public health and policy interventions to reduce healthcare expenditure and inequalities.