Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Institute for Global Health, University College London, London, UK.
Int J Equity Health. 2020 Jan 14;19(1):9. doi: 10.1186/s12939-019-1080-5.
Although spatial effects contribute to inequalities in health care service utilisation and other health outcomes in low and middle income countries, there have been no attempts to incorporate the impact of neighbourhood effects into equity analyses based on concentration indices. This study aimed to decompose and estimate the contribution of spatial effects on inequalities in uptake of HIV tests in Malawi.
We developed a new method of reflecting spatial effects within the concentration index using a spatial weight matrix. Spatial autocorrelation is presented using a spatial lag model. We use data from the Malawi Demographic Health Survey (n = 24,562) to illustrate the new methodology. Need variables such as 'Any STI last 12 month', 'Genital sore/ulcer', 'Genital discharge' and non need variables such as Education, Literacy, Wealth, Marriage, and education were used in the concentration index. Using our modified concentration index that incorporates spatial effects, we estimate inequalities in uptake of HIV testing amongst both women and men living in Malawi in 2015-2016, controlling for need and non-need variables.
For women, inequalities due to need variables were estimated at - 0.001 and - 0.0009 (pro-poor) using the probit and new spatial probit estimators, respectively, whereas inequalities due to non-need variables were estimated at 0.01 and 0.0068 (pro-rich) using the probit and new spatial probit estimators. The results suggest that spatial effects increase estimated inequalities in HIV uptake amongst women. Horizontal inequity was almost identical (0.0103 vs 0.0102) after applying the spatial lag model. For men, inequalities due to need variables were estimated at - 0.0002 using both the probit and new spatial probit estimators; however, inequalities due to non-need variables were estimated at - 0.006 and - 0.0074 for the probit and new spatial probit models. Horizontal inequity was the same for both models (- 0.0057).
Our findings suggest that men from lower socioeconomic groups are more likely to receive an HIV test after adjustment for spatial effects. This study develops a novel methodological approach that incorporates estimation of spatial effects into a common approach to equity analysis. We find that a significant component of inequalities in HIV uptake in Malawi driven by non-need factors can be explained by spatial effects. When the spatial model was applied, the inequality due to non need in Lilongwe for men and horizontal inequity in Salima for women changed the sign. This approach can be used to explore inequalities in other contexts and settings to better understand the impact of spatial effects on health service use or other health outcomes, impacting on recommendations for service delivery.
尽管空间效应导致了中低收入国家医疗服务利用和其他健康结果的不平等，但尚未有人尝试根据集中指数将邻里效应的影响纳入公平性分析中。本研究旨在分解并估计空间效应对马拉维艾滋病毒检测使用率不平等的贡献。
我们开发了一种新方法，通过空间权重矩阵在集中指数中反映空间效应。使用空间滞后模型呈现空间自相关。我们使用来自马拉维人口与健康调查（n=24562）的数据来说明新方法。在集中指数中使用需要变量，如“过去 12 个月的任何性传播感染”、“生殖器疮/溃疡”、“生殖器分泌物”和不需要的变量，如教育、识字、财富、婚姻和教育。使用我们修改后的集中指数，该指数纳入了空间效应，我们估计了 2015-2016 年生活在马拉维的妇女和男子接受艾滋病毒检测的不平等程度，同时控制了需要和不需要的变量。
对于女性，使用概率单位和新的空间概率单位估计器，分别估计由需要变量引起的不平等为-0.001 和-0.0009（有利于穷人），而由不需要变量引起的不平等为 0.01 和 0.0068（有利于富人）。结果表明，空间效应对女性接受艾滋病毒检测的不平等估计值产生了影响。应用空间滞后模型后，水平不公平几乎相同（0.0103 对 0.0102）。对于男性，使用概率单位和新的空间概率单位估计器，由需要变量引起的不平等分别估计为-0.0002；然而，由不需要变量引起的不平等分别估计为概率单位模型中的-0.006 和-0.0074，以及新的空间概率单位模型。两个模型的水平不公平性相同（-0.0057）。
我们的研究结果表明，在调整空间效应后，来自较低社会经济群体的男性更有可能接受艾滋病毒检测。本研究开发了一种新的方法学方法，将空间效应的估计纳入公平性分析的常用方法中。我们发现，马拉维艾滋病毒检测使用率不平等的一个重要组成部分是由非需要因素驱动的，可以用空间效应来解释。当应用空间模型时，男性在利隆圭的不平等和女性在萨利马的水平不公平性的符号发生了变化。这种方法可以用于探索其他背景和环境下的不平等现象，以更好地了解空间效应对卫生服务利用或其他健康结果的影响，从而为服务提供提出建议。
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