Are hospital process quality indicators influenced by socio-demographic health determinants

Buja, Alessandra; Oliveira, Daniel Canavese de; Furlan, Patrizia; Lago, Laura Pérez; Saïa, Mario; Baldo, Vincenzo

doi:10.1093/eurpub/cku253

Cited by 6 publications

(12 citation statements)

References 25 publications

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“…Multivariate analysis nonetheless showed that gender disparities in STEMI management persisted after the network’s implementation. This is consistent with the gender disparities seen in our previous studies [ 42 , 43 , 44 ]. This inequity in the administration of the evidence-based best treatment for STEMI might be partly attributable to a greater delay before women access hospital care, which would prevent the adoption of a PCI approach.…”

Section: Discussionsupporting

confidence: 94%

Impact of the Regional Network for AMI in the Management of STEMI on Care Processes, Outcomes and Health Inequities in the Veneto Region, Italy

Saïa

Mantoan

Fonzo

et al. 2018

IJERPH

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Cardiovascular diseases are a leading cause of death in Europe. Outcomes in terms of mortality and health equity in the management of patients with ST-Elevation Myocardial Infarction (STEMI) are influenced by health care service organization. The main aim of the present study was to examine the impact of the new organizational model of the Veneto Region’s network for Acute Myocardial Infarction (AMI) to facilitate primary percutaneous coronary intervention (PCI) on STEMI, and its efficacy in reducing health inequities. A retrospective cohort study was conducted on HDRs in the Veneto Region for the period 2007–2016, analyzing 65,261 hospitalizations for AMI. The proportion of patients with STEMI treated with PCI within 24 h increased significantly for men and women, and was statistically much higher for patients over 75 years of age (APC, 75–84: 9.8; >85: 12.5) than for younger patients (APC, <45: 3.3; 45–64: 4.9), with no difference relating to citizenship. The reduction in in-hospital, STEMI-related mortality was only statistically significant for patients aged 75–84 (APC: −3.0 [−4.5;−1.6]), and for Italians (APC: −1.9 [−3.2;−0.6]). Multivariate analyses confirmed a reduction in the disparities between socio-demographic categories. Although the new network improved the care process and reduced health care disparities in all subgroups, these efforts did not result in the expected survival benefit in all patient subgroups.

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Section: Discussionsupporting

confidence: 94%

Impact of the Regional Network for AMI in the Management of STEMI on Care Processes, Outcomes and Health Inequities in the Veneto Region, Italy

Saïa

Mantoan

Fonzo

et al. 2018

IJERPH

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“…Aging rate was expected to be related to IHD mortality. However, illiteracy and accessibility indexes that are often discussed in scientific literature but not specifying its real importance and weight [3,7,8] were important for index formation and can be considered in public policies to reduce mortality. Examples are increasing the number of ambulances as well as the differential management of patients who are more than 120 minutes from the reference hemodynamics centers.…”

Section: Plos Onementioning

confidence: 99%

“…However, models based on individual data do not represent cultural and socioeconomic disease determinants, which are essential for public health policy and population well-being. The relationship between municipality indicators with socioeconomic and demographic factors, health coverage, and high mortality IHD rates is well documented in the scientific literature [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Mapping risk of ischemic heart disease using machine learning in a Brazilian state

et al. 2020

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Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran’s I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities’ guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná.

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Section: Introductionmentioning

confidence: 99%

“…8 9 The impacts of these factors weigh heavily on individual health outcomes and often are cumulatively responsible, along with chronic conditions, for disparities in disabilities, increased hospitalisations and more frequent hospital readmissions. [10][11][12][13] In comparison with other high-income and middleincome countries, the USA consistently scores poorly in overall health outcomes and access to healthcareassociated with socioeconomic factors. 14 In Washington state, the effects of health disparities have been linked with a 10-year difference in life expectancy between urban and rural counties and a 12-year gap in longevity for particular racial and ethnic groups.…”

Section: Introductionmentioning

confidence: 99%

Implementing screening for social determinants of health using the Core 5 screening tool

et al. 2021

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Social determinants of health (SDOH) have been documented to underpin 80% of overall health and are being increasingly recognised as key factors in addressing tertiary health outcomes. Yet, despite the widespread acceptance of the association of SDOH with health outcomes, more than two-thirds of hospitals do not screen for social risk factors that indicate individual-level adverse SDOH. Such screening for social risk factors represents the first step in connecting patients with resources and documents the prevalence of social needs. The aim of this project was to implement the Core 5 social risk screening tool and evaluate its efficacy and usability in identifying social risk factors in a presurgical spine population. Prior to this implementation, screening for social risk had not been performed. The Model for Improvement provided a framework for implementing and evaluating the Core 5 social risk screening tool. Methods included implementation of a patient self-report social risk screening tool, referral workflow to connect patients with needed resources and evaluation of staff feasibility in using the Core 5 tool. The results indicated that the screening tool identified patients with social risk factors and staff reported perceptions of efficacy and usability in clinical workflow. Overall, 52 of 88 (59%) of subjects in the presurgical spine population were effectively screened. Of these, five patients (10%) had identified social needs that needed to be addressed prior to surgery. The staff usability survey for the Core 5 tool demonstrated high acceptance and usability, with an average score of 4.4 (out of 5). Future work should evaluate the efficacy of the screening tool in other ambulatory and tertiary settings.

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Are hospital process quality indicators influenced by socio-demographic health determinants

Abstract: Monitoring the evidence-based hospital health care process indicators reveals undesirable disparities. Administrative data sets are of considerable practical value in broad-based quality assessments and as a screening tool, also in the health disparities domain.

Cited by 6 publications

References 25 publications

Impact of the Regional Network for AMI in the Management of STEMI on Care Processes, Outcomes and Health Inequities in the Veneto Region, Italy

Impact of the Regional Network for AMI in the Management of STEMI on Care Processes, Outcomes and Health Inequities in the Veneto Region, Italy

Mapping risk of ischemic heart disease using machine learning in a Brazilian state

Implementing screening for social determinants of health using the Core 5 screening tool

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