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BACKGROUND Caregivers of children with health problems (CHPs; usually mothers) experience more physical and psychological health problems than those of children without health problems (non-CHPs). Primarily cross-sectional and survey-driven, this literature has not yet explored whether these health differences existed before the birth of the CHPs, or are exacerbated postbirth. METHODS Using linked administrative health data on all mother-child dyads for children born in the year 2000 in British Columbia, Canada, we examined maternal health before, during, and after the birth of CHPs, and compared it between mothers of CHPs and non-CHPs with piecewise growth curve modeling. RESULTS Compared with mothers of non-CHPs, mothers of CHPs had more physician visits (8.09 vs. 11.07), more medication types (1.81 vs. 2.60), and were more likely to be diagnosed with selected health conditions (30.9% vs. 42.5%) 4 years before the birth of the child. Over the 7-year postbirth period, the health of the 2 groups of mothers further diverged: while mothers of CHPs showed increases on physician visits and types of medication, mothers of non-CHPs did not experience any changes in physician visits and had less steep increases for types of medication. CONCLUSIONS Health issues associated with having a child with a health problem may begin well before the birth of the child, but also appear to be exacerbated postbirth. The health challenges of caregivers of CHPs may be multifactorial, involving both preexisting conditions and the stresses associated with caring for a child with health problems.

BACKGROUND In 2002 British Columbia, Canada began redistributing its hospital services. OBJECTIVE AND DESIGN We used administrative data and interrupted time series analyses to determine how recent hospital closures affected patient outcomes. SUBJECTS All adult acute myocardial infarction (AMI), stroke, and trauma events in British Columbia between fiscal years 1999 and 2013. Cases were patients whose closest hospital closed. Controls were matched by condition, year of event, and condition-specific hospital volume where treatment was received. MEASURES Thirty-day mortality and hospital bypass rates. RESULTS We matched 3267 AMI, 2852 stroke, and 6318 trauma cases to 1996, 1604, and 3640 controls, respectively. The 30-day mortality rate at baseline was 7.0% [95% confidence interval (CI), 4.0%-10.1%] for AMI, 5.3% (95% CI, 2.4%-8.1%) for stroke, and 1.2% (95% CI, 0.3%-2.1%) for trauma controls. The 30-day mortality rate for cases was 14.3% (95% CI, 7.1%-21.7%) for AMI, 12.0% (95% CI, 5.1%-18.9%) for stroke, and 3.1% for trauma (95% CI, 0.9%-5.2%) cases. There was no significant change in 30-day mortality for cases, and no significant difference in change in mortality rates between cases and controls following the intervention. The difference in hospital bypass rates between cases and controls was 50.1% (95% CI, 42.3%-57.9%) for AMI, 36.2% (95% CI, 27.4%-44.9%) for stroke, and 32.2% (95% CI, 27.7%-36.8%) for trauma cases preintervention. Following the intervention, the difference in bypass rates dropped by 15.5% (95% CI, 3.5%-27.5%) for AMI, 25.3% (95% CI, 11.7%-38.8%) for stroke, and 22.7% (95% CI, 15.7%-29.6%) for trauma cases. CONCLUSIONS Hospital closures did not affect patient mortality.

Background Case-mix systems and comorbidity indices aggregate clinical information about patients over time and are used to characterize need for health care services. These tools were validated for their original purpose, but those purposes are varied, and they have not been compared directly in the context of predicting costs of health care services. Objective To compare predictions of next-year health care service costs across 4 tools, including: the Johns Hopkins Adjusted Clinical Groups (ACG), the Elixhauser Comorbidity Index, Charlson-Deyo Comorbidity Index, and the Canadian Institute for Health Information (CIHI) population grouper. Methods British Columbia administrative data from fiscal years 2012-2013 were used to generate case-mix variables and the comorbidity indices. Outcome variables include next-year (2013-2014) total, physician, acute care, and pharmaceutical costs, Outcomes were modeled using 2-part models. Performance was compared using adjusted R, root mean squared error, and mean absolute error using the predicted and the actual next-year cost. Results Models including the CIHI grouper (239 conditions) and ACG system had similar performance in most cost categories and slightly better fit than Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). Adding a dummy variable for nonusers in the models for CCI and ECI increased R values slightly. Conclusions All these systems have empirical support for use in predicting health care costs, despite in some cases being developed for other purposes. No system is particularly effective at predicting next-year acute care cost, likely because acute events are often by definition unexpected. The freely available ECI and CCI comorbidity indices implemented using the highest-performing methods developed here may be a good choice in many circumstances.

BACKGROUND Primary medical care is changing-more female providers, desire for better work-life balance, and increasing availability of walk-in clinics have altered service delivery. There is no uniform physician practice style, and understanding service availability and delivery requires analysis of family physicians' practice patterns, rather than just physician counts. METHODS This paper offers a new approach for describing the practice habits of primary care physicians. We use administrative data to identify activities associated with acting as "most responsible" physicians. We used British Columbia's administrative health care data from 2007/2008 to 2011/2012 to derive information regarding physicians, patients, and service delivery. We developed 5 variables to describe practice style: referrals, oversight, screening, initial prescribing for long-term medications, and repeat visits. Cluster analysis revealed 3 distinct groups of physicians. RESULTS Only 24% of the primary care physicians were assigned to the high-responsibility group, whereas 36% and 39% were in the low-responsibility and mixed-practice groups, respectively. All cluster variables follow a similar pattern, with the high-responsibility and low-responsibility physicians many multiples apart on the means and the mixed group falling in between. Several forms of sensitivity analysis confirmed the robustness of these results. CONCLUSIONS Physician practice patterns influence the effective supply of primary care. The fact that more than one third of British Columbia physicians are identified as "low responsibility" has implications for the delivery of primary care, both in ensuring that people have access to regular care and in insuring high-quality and comprehensive care.

This study investigated whether for-profit (FP) versus not-for-profit (NP) ownership of long-term care facilities resulted in a difference in hospital admission and mortality rates among facility residents in British Columbia, Canada.This retrospective cohort study used administrative data on all residents of British Columbia long-term care facilities between April 1, 1996, and August 1, 1999 (n = 43,065). Hospitalizations were examined for 6 diagnoses (falls, pneumonia, anemia, dehydration, urinary tract infection, and decubitus ulcers and/or gangrene), which are considered to be reflective of facility quality of care. In addition to FP versus NP status, facilities were divided into ownership subgroups to investigate outcomes by differences in governance and operational structures.We found that, overall, FP facilities demonstrated higher adjusted hospitalization rates for pneumonia, anemia, and dehydration and no difference for falls, urinary tract infections, or DCU/gangrene. FP facilities demonstrated higher adjusted hospitalization rates compared with NP facilities attached to a hospital, amalgamated to a regional health authority, or that were multisite. This effect was not present when comparing FP facilities to NP single-site facilities. There was no difference in mortality rates in FP versus NP facilities.The higher adjusted hospitalization rates in FP versus NP facilities is consistent with previous research from U.S. authors. However, the superior performance by the NP sector is driven by NP-owned facilities connected to a hospital or health authority, or that had more than one site of operation.