School of Science, Mathematical Sciences Discipline, RMIT University, Melbourne, 3000, Australia.
Int J Equity Health. 2022 Aug 27;21(1):118. doi: 10.1186/s12939-022-01713-5.
Measuring health inequality is essential to ensure that everyone has equal accessibility to health care. Studies in the past have continuously presented and showed areas or groups of people affected by various inequality in accessing the health resources and services to help improve this matter. Alongside, disease prevention is as important to minimise the disease burden and improve health and quality of life. These aspects are interlinked and greatly contributes to one's health.
In this study, the Gini coefficient and Lorenz curve are used to give an indication of the overall health inequality. The impact of this inequality in granular level is demonstrated using Bayesian estimation for disease detection. The Bayesian estimation used a two-component modelling approach that separates the case detection process and incidence rate using a mixed Poisson distribution while capturing underlying spatio-temporal characteristics. Bayesian model averaging is used in conjunction with the two-component modelling approach to improve the accuracy of estimates by incorporating many candidate models into the analysis instead of using fixed component models. This method is applied to an infectious disease, influenza, in Victoria, Australia between 2013 and 2016 and the corresponding primary health care of the state.
There is a relatively equal distribution of health resources and services pertaining to general practitioners (GP) and GP clinics in Victoria, Australia. Roughly 80 percent of the population shares 70 percent of the number of GPs and GP clinics. The Bayesian estimation with model averaging revealed that access difficulty to health services impacts both case detection probability and incidence rate. Minimal differences are recorded in the observed and estimated incidence of influenza cases considering social deprivation factors. In most years, areas in Victoria's southwest and eastern parts have potential under-reported cases consistent with their relatively lower number of GP or GP clinics.
The Bayesian model estimated a slight discrepancy between the estimated incidence and the observed cases of influenza in Victoria, Australia in 2013-2016 period. This is consistent with the relatively equal health resources and services in the state. This finding is beneficial in determining areas with potential under-reported cases and under-served health care. The proposed approach in this study provides insight into the impact of health inequality in disease detection without requiring costly and time-extensive surveys and relying mainly on the data at hand. Furthermore, the application of Bayesian model averaging provided a flexible modelling framework that allows covariates to move between case detection and incidence models.
衡量健康不平等对于确保每个人都能平等获得医疗保健至关重要。过去的研究不断提出并展示了在获得健康资源和服务方面受到各种不平等影响的地区或人群，以帮助改善这一问题。同时，疾病预防对于减轻疾病负担、改善健康和生活质量同样重要。这些方面相互关联，对人们的健康有重大影响。
在这项研究中，基尼系数和洛伦兹曲线用于表示整体健康不平等。使用贝叶斯估计来检测疾病，以展示这种不平等在细粒度水平上的影响。贝叶斯估计使用了一种两组件建模方法，该方法使用混合泊松分布将病例检测过程和发病率分开，同时捕捉潜在的时空特征。贝叶斯模型平均与两组件建模方法结合使用，通过将许多候选模型纳入分析而不是使用固定组件模型，来提高估计的准确性。该方法应用于澳大利亚维多利亚州 2013 年至 2016 年间的传染病流感以及该州的相应初级保健。
澳大利亚维多利亚州的普通医生（GP）和 GP 诊所的卫生资源和服务分配相对均衡。大约 80%的人口拥有 70%的 GP 和 GP 诊所数量。具有模型平均的贝叶斯估计表明，获得卫生服务的难度会影响病例检测概率和发病率。考虑到社会贫困因素，流感病例的观察到的和估计的发病率记录的差异极小。在大多数年份中，维多利亚州西南部和东部地区的病例报告可能不足，这与该地区相对较少的 GP 或 GP 诊所数量一致。
贝叶斯模型估计了澳大利亚维多利亚州 2013-2016 年期间流感的估计发病率和观察到的病例之间存在轻微差异。这与该州相对均衡的卫生资源和服务相一致。这一发现有助于确定潜在报告不足的病例和服务不足的卫生保健地区。本研究中提出的方法提供了一种无需进行昂贵和耗时的调查，主要依靠手头数据，了解健康不平等对疾病检测影响的见解。此外，贝叶斯模型平均的应用提供了一个灵活的建模框架，允许协变量在病例检测和发病率模型之间移动。
