Item selection, scaling and construct validation of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) measurement tool in adults.

PURPOSE: To select and scale items for the seven domains of the Patient-Reported Inventory of Self-Management of Chronic Conditions (PRISM-CC) and assess its construct validity. METHODS: Using an online survey, data on 100 potential items, and other variables for assessing construct validity, were collected from 1055 adults with one or more chronic health conditions. Based on a validated conceptual model, confirmatory factor analysis (CFA) and item response models (IRT) were used to select and scale potential items and assess the internal consistency and structural validity of the PRISM-CC. To further assess construct validity, hypothesis testing of known relationships was conducted using structural equation models. RESULTS: Of 100 potential items, 36 (4-8 per domain) were selected, providing excellent fit to our hypothesized correlated factors model and demonstrating internal consistency and structural validity of the PRISM-CC. Hypothesized associations between PRISM-CC domains and other measures and variables were confirmed, providing further evidence of construct validity. CONCLUSION: The PRISM-CC overcomes limitations of assessment tools currently available to measure patient self-management of chronic health conditions. This study provides strong evidence for the internal consistency and construct validity of the PRISM-CC as an instrument to assess patient-reported difficulty in self-managing different aspects of daily life with one or more chronic conditions. Further research is needed to assess its measurement equivalence across patient attributes, ability to measure clinically important change, and utility to inform self-management support.

Self-reported injuries among Canadian adolescents: rates and key correlates. / Blessures autodéclarées chez les adolescents canadiens : taux et principaux corrélats.

INTRODUCTION: Injuries sustained by adolescents in Canada represent a costly public health issue. Much of the limited research in this area uses administrative data, which underestimate injury prevalence by ignoring injuries that are not treated by the health care system. Self-reported data provide population-based estimates and include contextual information that can be used to identify injury correlates and possible targets for public health interventions aimed at decreased injury burden. METHODS: The 2017 wave of the Canadian Community Health Survey was used to calculate the prevalence of self-reported total, intentional and unintentional injuries. We compared injury prevalence according to age, sex, employment status, presence of a mood disorder, presence of an anxiety disorder, smoking and binge drinking. Analyses were performed using logistic regression to identify significantly different injury prevalence estimates across key correlates. RESULTS: Overall past-12-month injury prevalence among adolescents living in Canada was 31.4% (95% CI: 29.4%-33.5%). Most injuries were unintentional. All provinces had estimates within a few percentage points, except Saskatchewan, which had substantially higher prevalence for both overall and unintentional injury. Smoking and binge drinking were significantly associated with higher injury prevalence in most jurisdictions. Remaining correlates exhibited nonsignificant or inconsistent associations with injury prevalence. CONCLUSION: The data suggest that injury prevention interventions aimed at reducing alcohol consumption, particularly binge drinking, may be effective in reducing adolescent injury across Canada. Future research is needed to determine how provincial context (such as mental health support for adolescents or programs and policies aimed at reducing substance use) impacts injury rates.

Potential of community-based risk estimates for improving hospital performance measures and discharge planning.

BACKGROUND: Risk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients' residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates. METHODS: Using hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence. RESULTS: Community of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management. CONCLUSION: Contextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve.

Health Care Provider Utilization of Prescription Monitoring Programs: A Systematic Review and Meta-Analysis.

OBJECTIVE: To synthesize the literature on the proportion of health care providers who access and use prescription monitoring program data in their practice, as well as associated barriers to the use of such data. DESIGN: We performed a systematic review using a standard systematic review method with meta-analysis and qualitative meta-summary. We included full-published peer-reviewed reports of study data, as well as theses and dissertations. METHODS: We identified relevant quantitative and qualitative studies. We synthesized outcomes related to prescription monitoring program data use (i.e., ever used, frequency of use). We pooled the proportion of health care providers who had ever used prescription monitoring program data by using random effects models, and we used meta-summary methodology to identify prescription monitoring program use barriers. RESULTS: Fifty-three studies were included in our review, all from the United States. Of these, 46 reported on prescription monitoring program use and 32 reported on barriers. The pooled proportion of health care providers who had ever used prescription monitoring program data was 0.57 (95% confidence interval: 0.48-0.66). Common barriers to prescription monitoring program data use included time constraints and administrative burdens, low perceived value of prescription monitoring program data, and problems with prescription monitoring program system usability. CONCLUSIONS: Our study found that health care providers underutilize prescription monitoring program data and that many barriers exist to prescription monitoring program data use.

The effectiveness of prescription drug monitoring programs at reducing opioid-related harms and consequences: a systematic review.

BACKGROUND: In order to address the opioid crisis in North America, many regions have adopted preventative strategies, such as prescription drug monitoring programs (PDMPs). PDMPs aim to increase patient safety by certifying that opioids are prescribed in appropriate quantities. We aimed to synthesize the literature on changes in opioid-related harms and consequences, an important measure of PDMP effectiveness. METHODS: We completed a systematic review. We conducted a narrative synthesis of opioid-related harms and consequences from PDMP implementation. Outcomes were grouped into categories by theme: opioid dependence, opioid-related care outcomes, opioid-related adverse events, and opioid-related legal and crime outcomes. RESULTS: We included a total of 22 studies (49 PDMPs) in our review. Two studies reported on illicit and problematic use but found no significant associations with PDMP status. Eight studies examined the association between PDMP status and opioid-related care outcomes, of which two found that treatment admissions for prescriptions opioids were lower in states with PDMP programs (p < 0.05). Of the thirteen studies that reported on opioid-related adverse events, two found significant (p < 0.001 and p < 0.05) but conflicting results with one finding a decrease in opioid-related overdose deaths after PDMP implementation and the other an increase. Lastly, two studies found no statistically significant association between PDMP status and opioid-related legal and crime outcomes (crime rates, identification of potential dealers, and diversion). CONCLUSION: Our study found limited evidence to support overall associations between PDMPs and reductions in opioid-related consequences. However, this should not detract from the value of PDMPs' larger role of improving opioid prescribing.

Effectiveness of Prescription Monitoring Programs in Reducing Opioid Prescribing, Dispensing, and Use Outcomes: A Systematic Review.

Prescription monitoring programs (PMPs) house and monitor data about the prescribing practices of health care providers, as well as medications received by patients. PMPs aim to promote the appropriate use of prescription opioids by providing this information to prescribers and dispensers. Our objective in this systematic review was to comprehensively identify and assess the available evidence about the impact of PMPs on opioid prescribing and dispensing, multiple provider use for obtaining opioids, inappropriate opioid prescribing, and the extent of nonmedical prescription opioid use. We used a comprehensive search strategy and included study designs that could determine changes in outcomes with the implementation of a PMP. We included 24 studies; 75% of studies were conducted in the United States, and studies encompassed data years from 1993 to 2014. Overall, we did not find evidence to support an association between PMPs and decreased opioid prescribing and dispensing. We found limited, but inconsistent, evidence that PMPs were associated with reduced schedule II opioid prescribing and dispensing, as well as multiple provider use. Covariate adjustment was often inadequate in analyses, as was the timing of outcome and PMP measurement. Future studies should broaden their geographic scope to other countries and use more recent data with standard measurement. PERSPECTIVE: This systematic review aimed to determine the effectiveness of PMPs in changing prescribing practices and prescription opioid use. The findings from this review will inform policymakers and PMP administrators about the current state of the evidence on program effectiveness.