BACKGROUND 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. METHODS 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. RESULTS 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). CONCLUSION 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.

中文翻译：

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一种使用集中指数来衡量对社会经济不平等的空间影响的方法。

背景技术尽管空间效应加剧了中低收入国家在医疗服务利用和其他健康结果方面的不平等，但尚未尝试将邻里效应的影响纳入基于集中度指数的公平分析中。这项研究旨在分解和估计空间影响对马拉维接受HIV检测的不平等的影响。方法我们开发了一种使用空间权重矩阵在浓度指数内反映空间效应的新方法。使用空间滞后模型表示空间自相关。我们使用来自马拉维人口健康调查（n = 24,562）的数据来说明新方法。需要变量，例如“过去12个月的任何性传播感染”，“生殖器溃疡/溃疡”，“生殖器排出” 在集中度指数中使用了诸如教育，识字，财富，婚姻和教育等非必要变量。使用结合空间影响的修正浓度指数，我们估算了2015-2016年居住在马拉维的男女两性在艾滋病检测方面的不平等状况，控制了需求和非需求变量。结果对于女性，使用概率估计值和新的空间概率估计值分别将因需要变量引起的不平等估计为-0.001和-0.0009（按穷人估计），而将因不需要变量引起的不平等估计为0.01和0.0068（pro-poor）。丰富）使用概率和新的空间概率估计器。结果表明，空间效应增加了女性中艾滋病毒吸收的估计不平等。水平不平等几乎相同（0.0103对0。0102）应用空间滞后模型。对于男性，由于使用概率和新的空间概率估计器，需要变量导致的不平等估计为-0.0002；但是，对于概率模型和新的空间概率模型，由于非必要变量导致的不平等估计为-0.006和-0.0074。两种模型的水平不平等相同（-0.0057）。结论我们的研究结果表明，社会经济地位较低的男性在调整空间影响后更有可能接受HIV检测。这项研究开发了一种新颖的方法论方法，该方法将空间效应的估计纳入了公平分析的通用方法中。我们发现，由非必要因素驱动的马拉维艾滋病毒吸收不平等的重要组成部分可以通过空间效应来解释。当应用空间模型时，男性由于不需要利隆圭人而导致的不平等，以及女性因萨利马地区的水平不平等而改变了这一迹象。该方法可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使用或其他卫生结果的影响，从而影响服务提供的建议。