National Institute of Demographic and Economic Analysis, University of Waikato, Hamilton, New Zealand.
Department of Geography, Environment and Spatial Sciences, Michigan State University, East Lansing, Michigan.
J Rural Health. 2022 Jan;38(1):194-206. doi: 10.1111/jrh.12519. Epub 2020 Sep 23.
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.
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.
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.
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.
在初级保健的空间公平性研究中,检验潜在的健康需求指标,并在新西兰怀卡托地区寻找“反医疗照顾定律”的证据。
通过系统评价确定了 7 个健康需求指标(门诊敏感住院率;癌症发病率;死亡率;新西兰多种剥夺指数-健康领域;年龄;新西兰剥夺指数;吸烟率),对其进行了横断面分析。利用地理信息系统(GIS)对指标值进行了映射和分析。计算了指标之间的斯皮尔曼相关性,并通过空间自相关识别了高需求集群。通过方差分析和斯皮尔曼相关性检验了在可达性模型中纳入指标加权的影响。通过比较高可达性和高需求集群,评估了普通实践服务的空间公平性,并通过基尼系数进行了量化。
门诊敏感住院率(ASH)与所有指标均显著相关。健康需求显著聚集,但将指标加权纳入空间可达性分析并未影响可达性评分。可达性和需求之间的错位以及基尼系数为 0.281 表明服务分配不均。
ASH 率似乎是健康需求的一个可靠指标。但是,数据访问问题可能会限制其使用。基于区域的社会经济剥夺指标包含了一些健康的社会决定因素,具有更广泛的应用潜力。高需求集群根据所使用的指标而具有空间差异。GIS 技术可以识别需求的“热点”,但这些热点可能会在可达性模型中被掩盖。