Geographical socioeconomic disadvantage is associated with adverse outcomes following major amputation in diabetic patients

Abstract

Objective: Socioeconomic disadvantage is a known predictor of adverse outcomes and amputation in patients with diabetes. However, its association with outcomes after major amputation has not been described. Here, we aimed to determine the association of geographic socioeconomic disadvantage with 30-day readmission and 1-year reamputation rates among patients with diabetes undergoing major amputation.Methods: Patients from the Maryland Health Services Cost Review Commission Database who underwent major lower extremity amputation with a concurrent diagnosis of diabetes mellitus between 2015 and 2017 were stratified by socioeconomic disadvantage as determined by the area deprivation index (ADI) (ADI1 [least deprived] to ADI4 [most deprived]). The primary outcomes were rates of 30-day readmission and 1-year reamputation, evaluated using multivariable logistic regression models and Kaplan-Meier survival analyses.Results: A total of 910 patients were evaluated (66.0% male, 49.2% Black), including 30.9% ADI1 (least deprived), 28.6% ADI2, 19.1% ADI3, and 21.2% ADI4 (most deprived). After adjusting for differences in baseline demographic and clinical factors, the odds of 30-day readmission was similar among ADI groups (P > .05 for all). Independent predictors of 30-day readmission included female sex (odds ratio [OR], 1.45), Medicare insurance (vs private insurance; OR, 1.76), and peripheral artery disease (OR, 1.49) (P < .05 for all). The odds of 1-year reamputation was significantly greater among ADI4 (vs ADI1; OR, 1.74), those with a readmission for stump complication or infection/sepsis (OR, 2.65), and those with CHF (OR, 1.53) or PAD (OR, 1.59) (P < .05 for all).Conclusions: Geographic socioeconomic disadvantage is independently associated with 1-year reamputation, but not 30-day readmission, among Maryland patients undergoing a major amputation for diabetes. A directed approach at improving postoperative management of chronic disease progression in socioeconomically deprived patients may be beneficial to reducing long-term morbidity in this high-risk group.

Association of Neighborhood Socioeconomic Deprivation with Utilization and Costs of Anterior Cervical Discectomy and Fusion

“…41 We also summarize ADI into broader groups of ADI tertiles as previously described. 22 There are open questions about the validity of averaging deciles without population-level weighting, as this may particularly impact zip codes with greater variability in geographic deprivation. Though our approach may decrease granularity, the simultaneous generalization of ADI data to five-digit zip code and ADI decile scale to tertile works in the spirit of the remarks of the UW Neighborhood Atlas team regarding the use of ADI at noncensus level tracts.…”

“…For the purposes of this study, ADI deciles for the state of Maryland were averaged for five-digit ZCTA. The Missouri Census Data Center’s Census geographic crosswalk was utilized to match ZCTA to ADI percentiles and to determine geographic rurality, as described in prior literature 22,25 . Neighborhoods were then categorized into three tertiles of ADI based on categorically defined cutoffs, consistent with the methodology of previous studies 22 .…”

“…22,25 Neighborhoods were then categorized into three tertiles of ADI based on categorically defined cutoffs, consistent with the methodology of previous studies. 22 Population data were obtained from the US Census. 26…”

“…metric to predict poor outcomes in a variety of settings, including cardiac surgery, limb amputations, management of chronic lower back pain, and cancer treatment. [21][22][23][24] The aim of this study was to analyze the association of ADI with the utilization and costs of ACDF surgery. We hypothesized that patients with higher ADI (i.e.…”

“…ADI was developed as measure of neighborhood disadvantage and incorporates 17 measures of deprivation, including measures of poverty, housing, employment, and education level (Table 1). 20 It has proven to be a robust metric to predict poor outcomes in a variety of settings, including cardiac surgery, limb amputations, management of chronic lower back pain, and cancer treatment 21–24 …”