Background

In response to the opioid crisis, states and health systems are encouraging clinicians to use risk mitigation strategies aimed at assessing a patient's risk for opioid misuse or abuse: opioid agreements, prescription drug monitoring programs (PDMPs), and urine drug tests (UDT). Objective: The objective of this qualitative study was to understand how clinicians perceived and used risk mitigation strategies for opioid abuse/misuse and identify barriers to implementation. Methods: We interviewed clinicians who prescribe opioid medications in the outpatient setting from 2016-2018 and analyzed the data using Constructivist Grounded Theory methodology. Results: We interviewed 21 primary care clinicians and 12 specialists. Nearly all clinicians reported using the PDMP. Some clinicians (adopters) found the opioid agreement and UDTs to be valuable, but most (non-adopters) did not. Adopters found the agreements and UDTs helpful in treating patients equitably, setting limits, and having objective evidence of misuse; protocols and workflows facilitated the use of the strategies. Non-adopters perceived the strategies as awkward, disruptive to the clinician-patient relationship, and introducing a power differential; they also cited lack of time and resources as barriers to use. Conclusions: Our study demonstrates that clinicians in certain settings have found effective ways to implement and use the PDMP, opioid agreements, and UDT but that other clinicians are less comfortable with their use. Administrators and policymakers should ensure that the strategies are designed in a way that strengthens the clinician-patient relationship while maximizing safety for patients and that clinicians are adequately trained and supported when introducing the strategies.

Objective

Given the changing political and social climate around opioids, we examined how clinicians in the outpatient setting made decisions about managing opioid prescriptions for new patients already on long-term opioid therapy.

Methods

We conducted in-depth interviews with 32 clinicians in Southern California who prescribed opioid medications in the outpatient setting for chronic pain. The study design, interview guides, and coding for this qualitative study were guided by constructivist grounded theory methodology.

Results

We identified three approaches to assuming a new patient's opioid prescriptions. Staunch Opposers, mostly clinicians with specialized training in pain medicine, were averse to continuing opioid prescriptions for new patients and often screened outpatients seeking opioids. Cautious and Conflicted Prescribers were wary about prescribing opioids but were willing to refill prescriptions if they perceived the patient as trustworthy and the medication fell within their comfort zone. Clinicians in the first two groups felt resentful about other clinicians "dumping" patients on opioids on them. Rapport Builders, mostly primary care physicians, were the most willing to assume opioid prescriptions and were strategic in their approach to transitioning patients to safer doses.

Conclusions

Clinicians with the most training in pain management were the least willing to assume responsibility for opioid prescriptions for patients already on long-term opioid therapy. In contrast, primary care clinicians were the most willing to assume this responsibility. However, primary care clinicians face barriers to providing high-quality care for patients with complex pain conditions, such as short visit times and less specialized training.

Background

Stable, affordable housing is an established determinant of health. As affordable housing shortages across the USA threaten to displace people from their homes, it is important to understand the implications of cost-related residential moves for healthcare access.

Objective

To examine the relationship between cost-related moves and unmet medical needs.

Design

We performed a cross-sectional analysis of 7 waves (2011-2017) of the California Health Interview Survey.

Participants

We included all respondents ages 18 and older.

Main measures

The primary predictor variable was residential move history in the past 5 years (cost-related move, non-cost-related move, or no move). The primary outcome was unmet medical needs in the past year (necessary medications and/or medical care that were delayed or not received).

Key results

Our sample included 146,417 adults (42-47% response rate), representing a weighted population of 28,518,590. Overall, 20.3% of the sample reported unmet medical needs in the past year, and 4.9% reported a cost-related move in the past 5 years. In multivariable logistic regression models, adjusted risk of unmet medical needs increased for adults with both cost-related moves (aOR 1.38; 95% CI 1.19-1.59) and non-cost-related moves (aOR 1.17; 95% CI 1.09-1.26) compared to those with no moves. Among people who had moved, those with cost-related moves were more likely to report unmet medical needs compared to people with non-cost-related moves (p = 0.03).

Conclusions

People who have moved due to unaffordable housing represent a population at increased risk for unmet medical needs. Policy makers seeking to improve population health should consider strategies to limit cost-related moves and to mitigate their adverse effects on healthcare access.

Background

Computerized provider order entry (CPOE) with clinical decision support is a basic criterion for hospitals' meaningful use of electronic health record systems. A study was conducted to evaluate from the societal perspective the cost-utility of implementing CPOE in acute care hospitals in the United States.

Methods

A decision-analytical model compared CPOE with paper ordering among patients admitted to acute care hospitals with >25 beds. Parameters included start-up and maintenance costs, as well as costs for provider time use, medication and laboratory test ordering, and preventable adverse drug events. Probabilistic analyses produced incremental costs, effectiveness, and cost-effectiveness ratios for hospitals in four bed-size categories (25-72, 72-141, 141-267, 267-2,249).

Results

Relative to paper ordering and using typical estimates of implementation costs, CPOE had, on average, >99% probability of yielding savings to society and improving health. Per hospital in each size category, mean life-time savings -in millions-were $11.6 (standard deviation, $9.30), $34.4 ($21.2), $71.8 ($43.8), and $170 ($119) (2012 dollars), respectively, and quality-adjusted life-years (QALYs) gained were 19.9 (16.9), 53.7 (38.7), 109 (79.6), and 249 (205). Incremental effectiveness and costs were less favorable in certain circumstances, such as high implementation costs. Nationwide, anticipated increases in CPOE implementation from 2009 through 2015 could save $133 billion and 201,000 QALYs.

Conclusions

In addition to improving health, implementing CPOE with clinical decision support could yield substantial long-term savings to society in the United States, although results for individual hospitals are likely to vary.

Background

Given the risks of opioids, clinicians are under growing pressure to treat pain with non-opioid medications. Yet non-opioid analgesics such as non-steroidal anti-inflammatory drugs (NSAIDs) have their own risks: patients with kidney disease or gastrointestinal diseases can experience serious adverse events. We examined the likelihood that patients with back pain diagnoses and contraindications to NSAIDs and opioids received an opioid prescription in primary care.

Methods

We identified office visits for back pain from 2012 to 2017 and sampled the first office visit per patient per year (N = 24,543 visits). We created indicators reflecting contraindications for NSAIDs (kidney, liver, cardiovascular/cerebrovascular, and gastrointestinal diseases; concurrent or chronic use of anticoagulants/antiplatelets, chronic corticosteroid use) and opioids (depression, anxiety, substance use (SUD) and bipolar disorders, and concurrent benzodiazepines) and estimated four logistic regression models, with the one model including all patient visits and models 2-4 stratifying for previous opioid use. We estimated the population attributable risk for each contraindication.

Results

In our model with all patients-visits, patients received an opioid prescription at 4% of visits. The predicted probability (PP) of receiving an opioid was 4% without kidney disease vs. 7% with kidney disease; marginal effect (ME): 3%; 95%CI: 1-4%). For chronic or concurrent anticoagulant/antiplatelet prescriptions, the PPs were 4% vs. 6% (ME: 2%; 95%CI: 1-3%). For concurrent benzodiazepines, the PPs were 4% vs. 11% (ME: 7%, 95%CI: 5-9%) and for SUD, the PPs were 4% vs. 5% (ME: 1%, 95%CI: 0-3%). For the model including patients with previous long-term opioid use, the PPs for concurrent benzodiazepines were 25% vs. 24% (ME: -1%; 95%CI: - 18-16%). The population attributable risk (PAR) for NSAID and opioid contraindications was small. For kidney disease, the PAR was 0.16% (95%CI: 0.08-0.23%), 0.44% (95%CI: 0.30-0.58%) for anticoagulants and antiplatelets, 0.13% for substance use (95%CI: 0.03-0.22%) and 0.20% for concurrent benzodiazepine use (95%CI: 0.13-0.26%).

Conclusions

Patients with diagnoses of kidney disease and concurrent use of anticoagulants/antiplatelet medications had a higher probability of receiving an opioid prescription at a primary care visit for low back pain, but these conditions do not explain a large proportion of the opioid prescriptions.

Objective

Quality improvement (QI) interventions can improve glycemic control, but little is known about their value. We systematically reviewed economic evaluations of QI interventions for glycemic control among adults with type 1 or type 2 diabetes.

Research design and methods

We used English-language studies from high-income countries that evaluated organizational changes and reported program and utilization-related costs, chosen from PubMed, EconLit, Centre for Reviews and Dissemination, New York Academy of Medicine's Grey Literature Report, and WorldCat (January 2004 to August 2016). We extracted data regarding intervention, study design, change in HbA_1c, time horizon, perspective, incremental net cost (studies lasting ≤3 years), incremental cost-effectiveness ratio (ICER) (studies lasting ≥20 years), and study quality. Weighted least-squares regression analysis was used to estimate mean changes in HbA_1c and incremental net cost.

Results

Of 3,646 records, 46 unique studies were eligible. Across 19 randomized controlled trials (RCTs), HbA_1c declined by 0.26% (95% CI 0.17-0.35) or 3 mmol/mol (2 to 4) relative to usual care. In 8 RCTs lasting ≤3 years, incremental net costs were $116 (95% CI -$612 to $843) per patient annually. Long-term ICERs were $100,000-$115,000/quality-adjusted life year (QALY) in 3 RCTs, $50,000-$99,999/QALY in 1 RCT, $0-$49,999/QALY in 4 RCTs, and dominant in 1 RCT. Results were more favorable in non-RCTs. Our limitations include the fact that the studies had diverse designs and involved moderate risk of bias.

Conclusions

Diverse multifaceted QI interventions that lower HbA_1c appear to be a fair-to-good value relative to usual care, depending on society's willingness to pay for improvements in health.

Background and objective

Early detection methods for pancreatic cancer are lacking. We aimed to develop a prediction model for pancreatic cancer based on changes in health captured by healthcare claims data.

Methods

We conducted a case-control study on 29,646 Medicare-enrolled patients aged 68 years and above with pancreatic ductal adenocarcinoma (PDAC) reported to the Surveillance Epidemiology an End Results (SEER) tumor registries program in 2004-2011 and 88,938 age and sex-matched controls. We developed a prediction model using multivariable logistic regression on Medicare claims for 16 risk factors and pre-diagnostic symptoms of PDAC present within 15 months prior to PDAC diagnosis. Claims within 3 months of PDAC diagnosis were excluded in sensitivity analyses. We evaluated the discriminatory power of the model with the area under the receiver operating curve (AUC) and performed cross-validation by bootstrapping.

Results

The prediction model on all cases and controls reached AUC of 0.68. Excluding the final 3 months of claims lowered the AUC to 0.58. Among new-onset diabetes patients, the prediction model reached AUC of 0.73, which decreased to 0.63 when claims from the final 3 months were excluded. Performance measures of the prediction models was confirmed by internal validation using the bootstrap method.

Conclusion

Models based on healthcare claims for clinical risk factors, symptoms and signs of pancreatic cancer are limited in classifying those who go on to diagnosis of pancreatic cancer and those who do not, especially when excluding claims that immediately precede the diagnosis of PDAC.

Background

The Health Information Technology for Economic and Clinical Health (HITECH) Act subsidizes implementation by hospitals of electronic health records with computerized provider order entry (CPOE), which may reduce patient injuries caused by medication errors (preventable adverse drug events, pADEs). Effects on pADEs have not been rigorously quantified, and effects on medication errors have been variable. The objectives of this analysis were to assess the effectiveness of CPOE at reducing pADEs in hospital-related settings, and examine reasons for heterogeneous effects on medication errors.

Methods

Articles were identified using MEDLINE, Cochrane Library, Econlit, web-based databases, and bibliographies of previous systematic reviews (September 2013). Eligible studies compared CPOE with paper-order entry in acute care hospitals, and examined diverse pADEs or medication errors. Studies on children or with limited event-detection methods were excluded. Two investigators extracted data on events and factors potentially associated with effectiveness. We used random effects models to pool data.

Results

Sixteen studies addressing medication errors met pooling criteria; six also addressed pADEs. Thirteen studies used pre-post designs. Compared with paper-order entry, CPOE was associated with half as many pADEs (pooled risk ratio (RR) = 0.47, 95% CI 0.31 to 0.71) and medication errors (RR = 0.46, 95% CI 0.35 to 0.60). Regarding reasons for heterogeneous effects on medication errors, five intervention factors and two contextual factors were sufficiently reported to support subgroup analyses or meta-regression. Differences between commercial versus homegrown systems, presence and sophistication of clinical decision support, hospital-wide versus limited implementation, and US versus non-US studies were not significant, nor was timing of publication. Higher baseline rates of medication errors predicted greater reductions (P < 0.001). Other context and implementation variables were seldom reported.

Conclusions

In hospital-related settings, implementing CPOE is associated with a greater than 50% decline in pADEs, although the studies used weak designs. Decreases in medication errors are similar and robust to variations in important aspects of intervention design and context. This suggests that CPOE implementation, as subsidized under the HITECH Act, may benefit public health. More detailed reporting of the context and process of implementation could shed light on factors associated with greater effectiveness.

Background

Worsening glycemic control indicates elevated risk of pancreatic ductal adenocarcinoma (PDAC). We developed prediction models for PDAC among those with worsening glycemic control after diabetes diagnosis.

Methods

In 2000-2016 records within the Veterans Affairs Health System (VA), we identified three cohorts with progression of diabetes: (i) insulin initiation (n = 449,685), (ii) initiation of combination oral hypoglycemic medication (n = 414,460), and (iii) hemoglobin A1c (HbA1c) ≥8% with ≥Δ1% within 15 months (n = 593,401). We computed 12-, 36-, and 60-month incidence of PDAC and developed prediction models separately for males and females, with consideration of >30 demographic, behavioral, clinical, and laboratory variables. Models were selected to optimize Akaike's Information Criterion, and performance for predicting 12-, 36-, and 60-month incident PDAC was evaluated by bootstrap.

Results

Incidence of PDAC was highest for insulin initiators and greater in males than in females. Optimism-corrected c-indices of the models for predicting 36-month incidence of PDAC in the male population were: (i) 0.72, (ii) 0.70, and (iii) 0.71, respectively. Models performed better for predicting 12-month incident PDAC [c-index (i) 0.78, (ii) 0.73, (iii) 0.76 for males], and worse for predicting 60-month incident PDAC [c-index (i) 0.69, (ii) 0.67, (iii) 0.68 for males]. Model performance was lower among females. For subjects whose model-predicted 36-month PDAC risks were ≥1%, the observed incidences were (i) 1.9%, (ii) 2.2%, and (iii) 1.8%.

Conclusions

Sex-specific models for PDAC can estimate risk of PDAC at the time of progression of diabetes.

Impact

Our models can identify diabetes patients who would benefit from PDAC screening.