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. 2022 Jan;38(1):194-206.
doi: 10.1111/jrh.12519. Epub 2020 Sep 23.

Selecting Health Need Indicators for Spatial Equity Analysis in the New Zealand Primary Care Context

Jesse Whitehead  1 , Amber L Pearson  2 , Ross Lawrenson  3 , Polly Atatoa Carr  1
Affiliations

Selecting Health Need Indicators for Spatial Equity Analysis in the New Zealand Primary Care Context

Jesse Whitehead et al. J Rural Health. 2022 Jan.

Abstract

Purpose: To examine potential indicators of health need for primary care in spatial equity research, and evidence of the Inverse Care Law in the Waikato region of New Zealand.

Methods: A cross-sectional analysis of 7 health need indicators (ambulatory sensitive hospitalizations; cancer rate; mortality rate; New Zealand index of multiple deprivation-health domain; age; New Zealand index of deprivation; smoking rate) that were identified through a systematic review was carried out. Values of indicators were mapped and analyzed using geographic information systems (GIS). Spearman's correlations were calculated between indicators, and clusters of high need were identified through spatial autocorrelation. The impact of incorporating indicator-based weightings into an accessibility model was tested using analysis of variance and Spearman's correlations. General practice service spatial equity was assessed by comparing clusters of high access versus need, and quantified through the Gini coefficient.

Findings: Ambulatory sensitive hospitalization (ASH) rates were significantly correlated with all indicators. Health needs were significantly clustered, but incorporating indicator weightings into the spatial accessibility analysis did not impact accessibility scores. A misalignment of access and need, and a Gini coefficient of 0.281 suggest that services are not equitably distributed.

Conclusion: ASH rates seem a robust indicator of health need. However, data access issues may restrict their use. Area-level socioeconomic deprivation measures incorporate some social determinants of health, and they have potential for wider use. High need clusters vary spatially according to the indicator used. GIS techniques can identify "hot-spots" of need, but these can be masked in accessibility models.

Keywords: access to care; demography; epidemiology; geography; health services research.

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References

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