Spatial analysis of factors impacting lower limb major amputation rates in Hungary

Abstract

Summary: Background: Lower limb major amputations represent a substantial public health burden in Hungary, where previous research revealed markedly high rates with significant spatial variations. Therefore, we aimed to assess to what extent healthcare and socio-economic factors in the local environment explain the regional disparity. Patients and methods: In a retrospective cohort analysis, based on the healthcare administrative data of the Hungarian population, lower limb major amputations were identified from 1st of January 2017 to 31st of December 2019. The permanent residence of the amputees on the local administrative level (197 geographic units) was used to identify potential healthcare (outpatient care, revascularisation activity) and socio-economic (educational attainment, local infrastructure and services, income and employment) determinants of amputations. Spatial effects were modelled using the spatial Durbin error regression model. Results: 10,209 patients underwent 11,649 lower limb major amputations in the observational period. In our spatial analysis, outpatient care was not associated with local amputation rates. However, revascularisation activity in a geographic unit entailed an increased rate of amputations, while revascularisations in the neighbouring areas were associated with a lower rate of amputations, resulting in an overall neutral effect (β=−0.002, 95% CI: −0.05 – 0.04, p=0.96). The local socio-economic environment had a significant direct inverse association with amputations (β=−7.45, 95% CI: −10.50 – −4.42, p<0.0001) . Our spatial model showed better performance than the traditional statistical modelling (ordinary least squares regression), explaining 37% of the variation in amputations rates. Conclusions: Regional environmental factors explain a substantial portion of spatial disparities in amputation practice. While the socio-economic environment shows a significant inverse relationship with the regional amputation rates, the impact of the local healthcare-related factors (outpatient care, revascularisation activity) is not straightforward. Unravelling the impact of the location on amputation practice requires complex spatial modelling, which may guide efficient healthcare policy decisions.

The importance of socio-economic determinants of health in the care of patients with peripheral artery disease: A narrative review from VAS

“…[83][84][85] Low nSES also increased the odds of having major lower-limb amputations. 34,86 Although iSES seems to play the more decisive role, a geostatistical model considering only the neighborhood revealed that nSES represented the predominant impact on amputation rate; the effect of healthcare service disparities was less influential, and 37% of the variability in amputation practice could be attributed to the living environment of amputees. 34 The socio-economic gradients in amputation rates may also exist when comparing different countries.…”

“…34,86 Although iSES seems to play the more decisive role, a geostatistical model considering only the neighborhood revealed that nSES represented the predominant impact on amputation rate; the effect of healthcare service disparities was less influential, and 37% of the variability in amputation practice could be attributed to the living environment of amputees. 34 The socio-economic gradients in amputation rates may also exist when comparing different countries. A striking difference in amputation rates across Europe is an example of the East/West divide in health 87 that may be partly accounted for by the complex disparities found in socio-economic characteristics of Western European and Central and Eastern European countries.…”

“…In this sense, the researcher is forced to work out this problem, as delineated above. 34 The next analytical problem is how to tackle the various interactions between the different SDoH, not forgetting about critical covariates (Figure 1). As an example, SDoH may be interpreted at the level of the individuals at risk (the individual SES, or iSES) and the neighborhood (the neighborhood SES, or nSES) where affected people live.…”

Perifériás verőérbetegek szűrése és ellátása Magyarországon

“…Statisz tikai elemzéseink szerint egy adott járás incidenciaértéke lokális, szomszédsági, megyei, az eddigiekben részleteiben nem feltárt tényezôkre vezethetô vissza. Legújabb komplex térelemzésünk szerint a járásokra vonatkoztatott amputációs aktivitás még csak nem is a járóbeteg-ellátás (háziorvosi, érsebészeti, angiológiai) strukturális mutatóitól, vagy az adott járásra vonatkoztatott revascularisatiós aktivitásától függ, hanem sokkal inkább az egészségügyi ellátáshoz csak közvetetten kapcsolható, annál sokkal szélesebb értelmû szocioökonómiai háttértôl (16). Mindez nagyon komplex tényezôk együtthatását sejteti, ami nem meglepô, tekintettel az amputációk distalis ki -meneteli indikátor természetére.…”