Suchergebnisse

BackgroundThe shifting landscape of abortion care from a hospital-only to a distributed service including primary care has implications for how to identify abortion cohorts for research and surveillance. The objectives of this study were to 1) create an improved approach to define abortion cohorts using linked administrative data sets and 2) evaluate the performance of this approach for abortion surveillance compared with standard approaches.
MethodsWe applied four principles to identify induced abortion cohorts when some services are delivered beyond hospital settings; 1) exclude early pregnancy losses and postpartum procedures; 2) use multiple data sources; 3) define episodes of care; 4) apply a hierarchical algorithm to determine abortion date to a population-based cohort of all abortion events in Ontario (Canada) from January 1, 2018-March 15, 2020. We calculated risk differences (RD, with 95% confidence intervals) comparing the proportion of medication vs. surgical, first vs. second trimester, and complication incidence applying these principles vs. standard approaches.
ResultsHospital-only data (versus multiple data sources) underestimated the frequency of medication abortion (16.1% vs. 31.4%; RD -15.3% [-14.3, -16.3]) and first-trimester abortion (82.1% vs. 94.5%; RD -12.8 [-11.4, 13.4]) and overestimated incidence of abortion complication (2.9% vs. 0.69%; RD 2.2% [1.8, 2.7]). An unlinked (versus linked) approach underestimated the frequency of abortion complications (0.19% vs 0.69%, -RD 0.50% [-0.44 - -0.56]). Including (versus excluding) abortions following early pregnancy loss or delivery events increased the estimated incidence of abortion complications (1.29% vs. 0.69%, RD 0.60% [0.51-0.69].
ConclusionNew methods are required to accurately identify abortion cohorts for surveillance or research. When legal or regulatory approaches to medication abortion evolve to enable abortion in primary care or office-based settings, hospital-based surveillance systems will become incomplete and biased; to continue valid and complete abortion surveillance, methods must be adjusted to ensure complete capture of procedures across all settings.

ObjectivesThe objective of this study was to describe the receipt of potentially non-beneficial interventions in the last 100 days of life of cancer patients and to examine variations in these interventions according to patient characteristics and cancer site.
ApproachWe conducted a population-based retrospective cohort study of all adults age 18+ who died of cancer in Ontario, Canada between January 1, 2013 and December 31, 2017 using linked administrative health data held at ICES. Potentially non-beneficial interventions were captured via hospital discharge records and included chemotherapy, major surgery, intensive care unit admission, cardiopulmonary resuscitation, defibrillation, dialysis, percutaneous coronary intervention, mechanical ventilation, feeding tube placement, blood transfusion and bronchoscopy. We used bivariate analyses and multivariable Poisson regression to examine associations between the receipt of interventions and decedent age, sex, rurality, area-level income, and cancer site.
ResultsAmong the 125,755 decedents, the most common intervention was blood transfusion (18.1%) and major surgery (12.8%); 23.8% received no interventions, while 14% of decedents received 3+ interventions. Lower intervention rates were observed in older patients (adjusted rate ratio (RR) 0.46, 95% confidence interval (CI) 0.44-0.49 for age 95+ vs. 19-44), females (RR 0.93, 95% CI 0.92-0.94), and individuals living in higher income areas (RR 0.96, 95% CI 0.95-0.98 for highest vs. lowest income quintile). Higher intervention rates were observed in rural patients (RR 1.13, 95% CI 1.11-1.14). Patients with pancreatic cancer had the highest intervention rate (RR 1.13, 95% CI 1.10-1.16), while breast cancer patients had the lowest intervention rate (RR 0.86, 95% CI 0.84-0.89) compared to colorectal cancer patients.
ConclusionPotentially non-beneficial interventions were common in the last 100 days of life of patients with cancer. Variations in interventions across patient demographics and cancer site may reflect differences in healthcare access, end-of-life care preferences, patients' prognostic awareness, and disease factors.