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de Oliveira Costa J, Pearson SA, Brieger D, Lujic S, Shawon MSR, Jorm LR, van Gool K, Falster MO. In-hospital outcomes by insurance type among patients undergoing percutaneous coronary interventions for acute myocardial infarction in New South Wales public hospitals. Int J Equity Health 2023; 22:226. [PMID: 37872627 PMCID: PMC10594777 DOI: 10.1186/s12939-023-02030-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/03/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND International evidence suggests patients receiving cardiac interventions experience differential outcomes by their insurance status. We investigated outcomes of in-hospital care according to insurance status among patients admitted in public hospitals with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI). METHODS We conducted a cohort study within the Australian universal health care system with supplemental private insurance. Using linked hospital and mortality data, we included patients aged 18 + years admitted to New South Wales public hospitals with AMI and undergoing their first PCI from 2017-2020. We measured hospital-acquired complications (HACs), length of stay (LOS) and in-hospital mortality among propensity score-matched private and publicly funded patients. Matching was based on socio-demographic, clinical, admission and hospital-related factors. RESULTS Of 18,237 inpatients, 30.0% were privately funded. In the propensity-matched cohort (n = 10,630), private patients had lower rates of in-hospital mortality than public patients (odds ratio: 0.59, 95% CI: 0.45-0.77; approximately 11 deaths avoided per 1,000 people undergoing PCI procedures). Mortality differences were mostly driven by STEMI patients and those from major cities. There were no significant differences in rates of HACs or average LOS in private, compared to public, patients. CONCLUSION Our findings suggest patients undergoing PCI in Australian public hospitals with private health insurance experience lower in-hospital mortality compared with their publicly insured counterparts, but in-hospital complications are not related to patient health insurance status. Our findings are likely due to unmeasured confounding of broader patient selection, socioeconomic differences and pathways of care (e.g. access to emergency and ambulatory care; delays in treatment) that should be investigated to improve equity in health outcomes.
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Affiliation(s)
- Juliana de Oliveira Costa
- Medicines Intelligence Research Program, School of Population Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia.
- Centre for Big Data Research in Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia.
| | - Sallie-Anne Pearson
- Medicines Intelligence Research Program, School of Population Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - David Brieger
- Concord Clinical School - The University of Sydney, Sydney, Australia
| | - Sanja Lujic
- Centre for Big Data Research in Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - Md Shajedur Rahman Shawon
- Centre for Big Data Research in Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - Louisa R Jorm
- Centre for Big Data Research in Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
| | - Kees van Gool
- Centre for Health Economics Research and Evaluation - University of Technology Sydney, Sydney, Australia
| | - Michael O Falster
- Medicines Intelligence Research Program, School of Population Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
- Centre for Big Data Research in Health - Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia
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Morisod K, Luta X, Marti J, Spycher J, Malebranche M, Bodenmann P. Measuring Health Equity in Emergency Care Using Routinely Collected Data: A Systematic Review. Health Equity 2022; 5:801-817. [PMID: 35018313 PMCID: PMC8742300 DOI: 10.1089/heq.2021.0035] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Achieving equity in health care remains a challenge for health care systems worldwide and marked inequities in access and quality of care persist. Identifying health care equity indicators is an important first step in integrating the concept of equity into assessments of health care system performance, particularly in emergency care. Methods: We conducted a systematic review of administrative data-derived health care equity indicators and their association with socioeconomic determinants of health (SEDH) in emergency care settings. Following PRISMA-Equity reporting guidelines, Ovid MEDLINE, EMBASE, PubMed, and Web of Science were searched for relevant studies. The outcomes of interest were indicators of health care equity and the associated SEDH they examine. Results: Among 29 studies identified, 14 equity indicators were identified and grouped into four categories that reflect the patient emergency care pathway. Total emergency department (ED) visits and ambulatory care-sensitive condition-related ED visits were the two most frequently used equity indicators. The studies analyzed equity based on seven SEDH: social deprivation, income, education level, social class, insurance coverage, health literacy, and financial and nonfinancial barriers. Despite some conflicting results, all identified SEDH are associated with inequalities in access to and use of emergency care. Conclusion: The use of administrative data-derived indicators in combination with identified SEDH could improve the measurement of health care equity in emergency care settings across health care systems worldwide. Using a combination of indicators is likely to lead to a more comprehensive, well-rounded measurement of health care equity than using any one indicator in isolation. Although studies analyzed focused on emergency care settings, it seems possible to extrapolate these indicators to measure equity in other areas of the health care system. Further studies elucidating root causes of health inequities in and outside the health care system are needed.
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Affiliation(s)
- Kevin Morisod
- Department of Vulnerabilities and Social Medicine, Centre for Primary Care and Public Health (Unisanté), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Xhyljeta Luta
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Department of Epidemiology and Health Systems, Centre for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Joachim Marti
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Department of Epidemiology and Health Systems, Centre for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Jacques Spycher
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Department of Epidemiology and Health Systems, Centre for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Mary Malebranche
- Department of Vulnerabilities and Social Medicine, Centre for Primary Care and Public Health (Unisanté), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Department of Medicine, University of Calgary, Calgary, Canada
| | - Patrick Bodenmann
- Department of Vulnerabilities and Social Medicine, Centre for Primary Care and Public Health (Unisanté), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
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Gillam MH, Pratt NL, Inacio MCS, Shakib S, Sanders P, Lau DH, Roughead EE. Rehospitalizations for complications and mortality following pacemaker implantation: A retrospective cohort study in an older population. Clin Cardiol 2018; 41:1480-1486. [PMID: 30294784 DOI: 10.1002/clc.23091] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 09/26/2018] [Accepted: 10/02/2018] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION A large number of older people receive pacemakers each year but broad population-based studies that describe complications following pacemaker implantation in this population are lacking. METHODS We conducted a retrospective cohort study using data from the Australian Government Department of Veterans' Affairs database. The cohort consisted of patients who received a pacemaker from 2005 to 2014. The outcomes were subsequent rehospitalizations for infections, procedure-related complications, thromboembolism, cardiovascular events (heart failure, myocardial infarction, and atrial fibrillation), and reoperation of pacemaker, and mortality. RESULTS There were 10 883 pacemakers recipients, the median age was 86 years (interquartile range 83-89), 61% were males, and 74% received a dual-chamber pacemaker. Within 90 days postdischarge, rehospitalizations were occasioned by pacemaker infection in 0.5%, device-related complications in 1.5%, cerebral infarction in 0.7%, and heart failure in 6% of single-chamber pacemaker recipients. In dual-chamber pacemaker recipients rehospitalizations were occasioned by pacemaker infection in 0.4%, septicemia in 0.4%, device-related complications in 1.2%, cerebral infarction in 0.3%, and heart failure in 3%. Rehospitalizations for pacemaker adjustment occurred in 1.5% of patients. The 90-day postdischarge mortality was 5% and 3% in patients with single- and dual-chamber pacemaker, respectively. CONCLUSION Rehospitalizations for infection, procedure-related complications, or thromboembolism occurred in 1% to 2% of patients within 90 days postdischarge, while 10% of single chamber and 7% of dual-chamber recipients experienced a rehospitalization for a cardiovascular event.
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Affiliation(s)
- Marianne H Gillam
- School of Pharmacy and Medical Sciences, The Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia
| | - Nicole L Pratt
- School of Pharmacy and Medical Sciences, The Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia
| | - Maria C S Inacio
- School of Pharmacy and Medical Sciences, The Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia
| | - Sepehr Shakib
- Department of Clinical Pharmacology, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
| | - Prashanthan Sanders
- Centre for Heart Rhythm Disorders, South Australia Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Dennis H Lau
- Centre for Heart Rhythm Disorders, South Australia Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Elizabeth E Roughead
- School of Pharmacy and Medical Sciences, The Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia
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Guha A, Xiang X, Haddad D, Buck B, Gao X, Dunleavy M, Liu E, Patel D, Fedorov VV, Daoud EG. Eleven-year trends of inpatient pacemaker implantation in patients diagnosed with sick sinus syndrome. J Cardiovasc Electrophysiol 2017; 28:933-943. [PMID: 28471545 DOI: 10.1111/jce.13248] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 04/26/2017] [Accepted: 04/28/2017] [Indexed: 10/19/2022]
Abstract
BACKGROUND Pacemakers (PM) are used for managing sick sinus syndrome (SSS). This study evaluates predictors and trends of PM implantation for SSS. METHODS Patients were identified from the National Inpatient Sample dataset (2003-2013). Included patients were ≥18 years old, had a diagnosis of sinus node dysfunction and atrial arrhythmia (i.e., SSS). Patients who died, transferred out, who had prior device, or had a defibrillator or resynchronization therapy device implanted were excluded. Included patients were then stratified by if a PM was implanted. Data regarding SSS, trends of PM utilization, and multivariable models of factors associated with PM implantation are presented. RESULTS Note that 328,670 patients satisfied study criteria. This study compared patients who underwent (87.4%) PM implantation to those who did not undergo (12.6%) PM implantation. The annual trends for hospitalization with SSS and PM placement have been decreasing (P <0.001). Variables associated with lower likelihood for PM implantation include young age, female sex, non-Caucasian race, chronic heart failure, Charlson Comorbidity Score ≥1, emergency room and weekend admission, hospital stay ≤3 days, and high cardiology inpatient volume. Greater likelihood for PM implantation was associated with hyperlipidemia, hypertension, and hospitals that were either private, large, Northeastern location, or with high cardiac procedural volume. CONCLUSIONS Analyzing 11-year data from a national inpatient database demonstrate a number of relevant variables that impact PM utilization that include not only clinical but also nonclinical variables such as socioeconomic status, gender, and hospital features. Racial and gender bias toward PM implantation are unchanged and persist through 2013.
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Affiliation(s)
- Avirup Guha
- Ohio State University Division of Cardiovascular Medicine, Columbus, Ohio, USA
| | - Xiao Xiang
- Ohio State University Division of Epidemiology, College of Public Health, Columbus, Ohio, USA
| | - Devin Haddad
- Ohio State University Division of Internal Medicine, Columbus, Ohio, USA
| | - Benjamin Buck
- Ohio State University Division of Internal Medicine, Columbus, Ohio, USA
| | - Xu Gao
- Ohio State University Division of Internal Medicine, Columbus, Ohio, USA
| | - Michael Dunleavy
- Ohio State University Division of Internal Medicine, Columbus, Ohio, USA
| | - Ellen Liu
- Ohio State University Division of Internal Medicine, Columbus, Ohio, USA
| | - Dilesh Patel
- Ohio State University Division of Cardiovascular Medicine, Columbus, Ohio, USA
| | - Vadim V Fedorov
- Ohio State University Department of Physiology and Cellular Biology, Columbus, Ohio, USA
| | - Emile G Daoud
- Ohio State University Division of Cardiovascular Medicine, Columbus, Ohio, USA
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