Search

Rationale: Accurately identifying use of life support in hospital administrative data enhances the data's value for quality improvement and research in critical illness.Objectives: To assess the accuracy of administrative hospital data for identifying invasive mechanical ventilation (IMV), acute renal replacement therapy (RRT), and intravenous vasoactive drugs in unselected adult intensive care unit (ICU) patients.Methods: We employed the administrative dataset of the Discharge Abstract Database from the Province of Manitoba during 2007-2012, using nationally standardized diagnosis and procedure codes to identify the three types of life support. The criterion standard was the Winnipeg ICU Database, which contains daily clinical information about all admissions to all 11 adult ICUs within the Winnipeg Regional Health Authority. For all individuals aged 40 years or older at ICU admission, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value of the administrative data for identifying life support. We also assessed the ability of the administrative data to identify overlapping use of the forms of life support.Results: Over the study period, there were 20,764 eligible ICU admissions; 52.6% (10,914) involved IMV, 46.8% (9,724) involved vasoactive agents, and 4.4% (907) involved acute RRT. Identification of IMV from administrative data procedure codes was good, with all four parameters exceeding 90%. The procedure code for use of selected vasoactive drugs had a sensitivity of zero; addition of diagnosis codes for shock raised the sensitivity to only 23% (95% confidence interval [CI], 22-24%). Both the sensitivity and specificity for acute RRT procedure codes exceeded 92%, but owing to low prevalence of RRT, the PPV was only 55% (95% CI, 53-58%). Addition of diagnosis codes for acute renal failure did not appreciably improve performance. Overlapping use of the three types of life support was substantial. Among those receiving any one of the types of life support, 68-76% received at least one of the two other types assessed. Considering use of any one or more of the three forms of life support, the administrative data had a PPV of 97% (95% CI, 96-97%) and a negative predictive value of 69% (95% CI, 68-70%).Conclusions: Administrative data accurately identify IMV but not use of vasoactive drugs or acute RRT. [ABSTRACT FROM AUTHOR]

Background: Concerns have been raised over the practice of transferring nursing home residents to hospital at their end of life. Objective: To examine the family and facility factors that may influence the decision to transfer nursing home residents to hospital in the last month of life. Research design: Secondary data analysis includes a sample of 119 bereaved family members from 21 nursing homes located in Central Canada. Method: A binary logistic regression analysis was conducted to explore the predictors for hospital transfers. Results: Terminal hospital transfers were common: 70% of nursing home residents were sent to hospitals in the last month of their life, and the likelihood of terminal hospital transfers increased by having an adult child as decision-maker (odds ratio (OR) = 5.03; 95% confidence interval (CI) = 1.6, 16; significance level/probability value (p) = 0.007) or having a lower family income (OR = 2.9; 95% CI =1.1, 2.9; p = 0.027). Discussion and implications: The identified predictors for terminal hospital transfers are helpful in targeting and developing interventions to improve end-of-life care. Particular emphasis should therefore be placed on targeting families with low income and children of the nursing home residents for educational initiatives such as advance care planning awareness, in order to prevent terminal hospital transfers. It is hoped that policy-makers and practitioners can start addressing the findings of this study to reduce terminal hospital transfers at end of life and promote quality end-of-life care in nursing homes. [ABSTRACT FROM AUTHOR]

Introduction: International medical graduates (IMGs) seeking licensure in Canada have been recruited to practise in medically underserviced areas, but retention of these physicians remains a concern. This study explored retention of IMG family physicians in Manitoba and its predictors. Methods: We used data from the University of Manitoba, provincial registries and Manitoba Health. Inclusion criteria were IMGs who completed University of Manitoba IMG training or assessment programs, and their return-of-service. Practice location, certification and licensure status were examined. We used logistic regression to consider the effects of a mentorship program, Manitoba residency at application, IMG program and years since program graduation on retention. Results: A total of 197 IMGs met the inclusion criteria. Most IMGs (63.5%) remained in Manitoba, and 59.2% of this group practised outside of Winnipeg. Of those remaining in Manitoba, most (69.6%) held full provincial licensure and national certification. The regression model -was significant (χ²4 = 13.94, p = 0.007), explaining 10% of the variance in retention. Two predictors were significant: years since program graduation and Manitoba residency at the time of application. Conclusion: Long-term retention of IMG physicians remains a concern. Potential interventions likely to increase retention, such as Manitoba residency at application and a focus on mentorship programs, should be further explored. [ABSTRACT FROM AUTHOR]

Background Registered midwives, obstetricians/gynecologists, and general or family practice physicians (GPs) provide maternity care across Canada. Few North American studies have assessed whether maternity outcomes differ across these three groups. This study compared maternal and neonatal outcomes of low-risk pregnant women whose birth was attended by registered midwives, obstetricians/gynecologists, and family practice physicians in Winnipeg, Manitoba from 2001/02 to 2012/13. Methods Descriptive statistics and logistic regression were used to examine differences in types of intervention, mode of delivery, and outcomes by provider type among low-risk women. Logistic regression models controlled for socio-demographic and birth-related covariates. Results Low-risk births comprised 83,774 (48.7%) of total births ( n = 171,910). The adjusted odds ratio ( aOR), (95% confidence interval) for midwife vs OB/ GYN showed women who had a midwife attend the birth had reduced odds of having an episiotomy 0.47 (0.40-0.54), epidural 0.25 (0.23-0.27), and cesarean delivery 0.13 (0.10-0.16) and their infants had less Neonatal Intensive Care Unit admissions 0.28 (0.18-0.43). The aOR for GP versus OB/ GYN showed women who had a GP had reduced odds of having an epidural/spinal 0.83 (0.79-0.88) and cesarean delivery 0.44 (0.40-0.48). Conclusions The effectiveness of Manitoba maternity services can be improved with increased use of integrated midwifery services. Future research should examine how midwifery and physician-led models of care differ, and the influence of these differences on birth outcomes and cost-effectiveness to the health care system. Improvement of data tracking systems is also needed. [ABSTRACT FROM AUTHOR]

A qualitative study was undertaken to explore the perceptions and experiences of nurse leaders associated with their participation in the pilot testing of a national nursing database. Interviews with 18 participants were conducted and analyzed using a direct content analysis approach. Three themes emerged including selecting, accessing, and uploading indicators; using indicators and monitoring tools for improvement; and perceiving involvement as a catalyst. Study findings may inform quality improvement efforts in health care organizations. [ABSTRACT FROM AUTHOR]

Background: Electronic Medical Records (EMRs) are increasingly used in the provision of primary care and have been compiled into databases which can be utilized for surveillance, research and informing practice. The primary purpose of these records is for the provision of individual patient care; validation and examination of underlying limitations is crucial for use for research and data quality improvement. This study examines and describes the validity of chronic disease case definition algorithms and factors affecting data quality in a primary care EMR database. Methods: A retrospective chart audit of an age stratified random sample was used to validate and examine diagnostic algorithms applied to EMR data from the Manitoba Primary Care Research Network (MaPCReN), part of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The presence of diabetes, hypertension, depression, osteoarthritis and chronic obstructive pulmonary disease (COPD) was determined by review of the medical record and compared to algorithm identified cases to identify discrepancies and describe the underlying contributing factors. Results: The algorithm for diabetes had high sensitivity, specificity and positive predictive value (PPV) with all scores being over 90%. Specificities of the algorithms were greater than 90% for all conditions except for hypertension at 79.2%. The largest deficits in algorithm performance included poor PPV for COPD at 36.7% and limited sensitivity for COPD, depression and osteoarthritis at 72.0%, 73.3% and 63.2% respectively. Main sources of discrepancy included missing coding, alternative coding, inappropriate diagnosis detection based on medications used for alternate indications, inappropriate exclusion due to comorbidity and loss of data. Conclusions: Comparison to medical chart review shows that at MaPCReN the CPCSSN case finding algorithms are valid with a few limitations. This study provides the basis for the validated data to be utilized for research and informs users of its limitations. Analysis of underlying discrepancies provides the ability to improve algorithm performance and facilitate improved data quality. [ABSTRACT FROM AUTHOR]

A newborn screen designed to predict family risk was examined to: (a) determine whether all families with newborns were screened; (b) evaluate its predictive validity for identifying risk of out-of-home placement, as a proxy for maltreatment; (c) determine which items were most predictive of out-of-home placement. All infants born in Manitoba, Canada from 2000 to 2002 were followed until March 31, 2004 (N = 40,886) by linking four population-based data sets: (a) newborn screening data on biological, psychological, and social risks; (b) population registry data on demographics; (c) hospital discharge data on newborn birth records; (d) data on children entering out-of-home care. Of the study population, 18.4% were not screened and 3.0% were placed in out-of-home care at least once during the study period. Infants not screened were twice as likely to enter care compared to those screened (4.9% vs. 2.5%). Infants screening at risk were 15 times more likely to enter care than those screening “not at risk.” Sensitivity and specificity of the screen were 77.6% and 83.3%, respectively. Screening efforts to identify vulnerable families missed a substantial portion of families needing support. The screening tool demonstrated moderate predictive validity for identifying children at risk of entering care in the first years of life. [ABSTRACT FROM PUBLISHER]

Information-rich environments, with access and funding provided by government, make it possible to organize longitudinal administrative data to support analyses of policy-relevant questions. This paper describes insights into children's well-being and social equity obtained from data available in Manitoba, Canada, and highlights findings that have engaged policymakers. Analyses draw on Manitoba-linked data providing information over time (going back to 1970 in some files) and across space (with residential location documented every six months) for each provincial resident. Routinely collected data from the Ministries of Health, Education, and Family Services and Consumer Affairs have been integrated with a population registry. Identifying risk factors and presenting outcomes by social groups and by local communities capture the attention of policymakers. Linking an individual's area of residence to census and health data has led to developing measures of population health status and socioeconomic status. These measures focus on whether delivery patterns track health and educational needs, and a population registry makes it possible to describe who is (and is not) served by each program. The nature of health and social research has been changed by the development of information-rich environments. Many findings in Manitoba could not be replicated without a population registry. Engaging decision makers through effective presentations can ensure continuing support for diverse efforts based on these environments, and this article suggests ways of better communicating with policymakers. [ABSTRACT FROM AUTHOR]