Transverse Incision for Pancreatoduodenectomy Reduces Wound Complications: A Single-Center Analysis of 399 Patients.

BACKGROUND: Even if the minimally invasive approach is advancing in pancreatic surgery, the open approach is still the standard for a pancreatoduodenectomy. There are two types of incisions used: the midline incision (MI) and transverse incision (TI). The aim of this study was to compare these two incision types, especially regarding wound complications. METHODS: A retrospective review of 399 patients who underwent a pancreatoduodenectomy at the University Hospital Erlangen between 2012 and 2021 was performed. A total of 169 patients with MIs were compared with 230 patients with TIs, with a focus on postoperative fascial dehiscence, postoperative superficial surgical site infection (SSSI) and the occurrence of incisional hernias during follow-up. RESULTS: Postoperative fascial dehiscence, postoperative SSSI and incisional hernias occurred in 3%, 8% and 5% of patients, respectively. Postoperative SSSI and incisional hernias were significantly less frequent in the TI group (SSI: 5% vs. 12%, p = 0.024; incisional hernia: 2% vs. 8%, p = 0.041). A multivariate analysis confirmed the TI type as an independent protective factor for the occurrence of SSSI and incisional hernias (HR 0.45 (95% CI = 0.20-0.99), p = 0.046 and HR 0.18 (95% CI = 0.04-0.92), p = 0.039, respectively). CONCLUSION: Our data suggest that the transverse incision for pancreatoduodenectomy is associated with reduced wound complications. This finding should be confirmed by a randomized controlled trial.

Design, Implementation, and Evolution of the Medicaid Outcomes Distributed Research Network (MODRN).

BACKGROUND: In the US, Medicaid covers over 80 million Americans. Comparing access, quality, and costs across Medicaid programs can provide policymakers with much-needed information. As each Medicaid agency collects its member data, multiple barriers prevent sharing Medicaid data between states. To address this gap, the Medicaid Outcomes Distributed Research Network (MODRN) developed a research network of states to conduct rapid multi-state analyses without sharing individual-level data across states. OBJECTIVE: To describe goals, design, implementation, and evolution of MODRN to inform other research networks. METHODS: MODRN implemented a distributed research network using a common data model, with each state analyzing its own data; developed standardized measure specifications and statistical software code to conduct analyses; and disseminated findings to state and federal Medicaid policymakers. Based on feedback on Medicaid agency priorities, MODRN first sought to inform Medicaid policy to improve opioid use disorder treatment, particularly medication treatment. RESULTS: Since its 2017 inception, MODRN created 21 opioid use disorder quality measures in 13 states. MODRN modified its common data model over time to include additional elements. Initial barriers included harmonizing utilization data from Medicaid billing codes across states and adapting statistical methods to combine state-level results. The network demonstrated its utility and addressed barriers to conducting multi-state analyses of Medicaid administrative data. CONCLUSIONS: MODRN created a new, scalable, successful model for conducting policy research while complying with federal and state regulations to protect beneficiary health information. Platforms like MODRN may prove useful for emerging health challenges to facilitate evidence-based policymaking in Medicaid programs.

Use of Medications for Treatment of Opioid Use Disorder Among US Medicaid Enrollees in 11 States, 2014-2018.

Importance: There is limited information about trends in the treatment of opioid use disorder (OUD) among Medicaid enrollees. Objective: To examine the use of medications for OUD and potential indicators of quality of care in multiple states. Design, Setting, and Participants: Exploratory serial cross-sectional study of 1 024 301 Medicaid enrollees in 11 states aged 12 through 64 years (not eligible for Medicare) with International Classification of Diseases, Ninth Revision (ICD-9 or ICD-10) codes for OUD from 2014 through 2018. Each state used generalized estimating equations to estimate associations between enrollee characteristics and outcome measure prevalence, subsequently pooled to generate global estimates using random effects meta-analyses. Exposures: Calendar year, demographic characteristics, eligibility groups, and comorbidities. Main Outcomes and Measures: Use of medications for OUD (buprenorphine, methadone, or naltrexone); potential indicators of good quality (OUD medication continuity for 180 days, behavioral health counseling, urine drug tests); potential indicators of poor quality (prescribing of opioid analgesics and benzodiazepines). Results: In 2018, 41.7% of Medicaid enrollees with OUD were aged 21 through 34 years, 51.2% were female, 76.1% were non-Hispanic White, 50.7% were eligible through Medicaid expansion, and 50.6% had other substance use disorders. Prevalence of OUD increased in these 11 states from 3.3% (290 628 of 8 737 082) in 2014 to 5.0% (527 983 of 10 585 790) in 2018. The pooled prevalence of enrollees with OUD receiving medication treatment increased from 47.8% in 2014 (range across states, 35.3% to 74.5%) to 57.1% in 2018 (range, 45.7% to 71.7%). The overall prevalence of enrollees receiving 180 days of continuous medications for OUD did not significantly change from the 2014-2015 to 2017-2018 periods (-0.01 prevalence difference, 95% CI, -0.03 to 0.02) with state variability in trend (90% prediction interval, -0.08 to 0.06). Non-Hispanic Black enrollees had lower OUD medication use than White enrollees (prevalence ratio [PR], 0.72; 95% CI, 0.64 to 0.81; P < .001; 90% prediction interval, 0.52 to 1.00). Pregnant women had higher use of OUD medications (PR, 1.18; 95% CI, 1.11-1.25; P < .001; 90% prediction interval, 1.01-1.38) and medication continuity (PR, 1.14; 95% CI, 1.10-1.17, P < .001; 90% prediction interval, 1.06-1.22) than did other eligibility groups. Conclusions and Relevance: Among US Medicaid enrollees in 11 states, the prevalence of medication use for treatment of opioid use disorder increased from 2014 through 2018. The pattern in other states requires further research.

Influence of peer networks on physician adoption of new drugs.

Although physicians learn about new medical technologies from their peers, the magnitude and source of peer influence is unknown. We estimate the effect of peer adoption of three first-in-class medications (dabigatran, sitigliptin, and aliskiren) on physicians' own adoption of those medications. We included 11,958 physicians in Pennsylvania prescribing anticoagulant, antidiabetic, and antihypertensive medications. We constructed 4 types of peer networks based on shared Medicare and Medicaid patients, medical group affiliation, hospital affiliation, and medical school/residency training. Instrumental variables analysis was used to estimate the causal effect of peer adoption (fraction of peers in each network adopting the new drug) on physician adoption (prescribing at least the median number prescriptions within 15 months of the new drug's introduction). We illustrate how physician network position can inform targeting of interventions to physicians by computing a social multiplier. Dabigatran was adopted by 25.2%, sitagliptin by 24.5% and aliskiren by 8.3% of physicians. A 10-percentage point increase in peer adoption in the patient-sharing network led to a 5.90% (SE = 1.50%, p<0.001) increase in physician adoption of dabigatran, 8.32% (SE = 1.51%, p<0.001) increase in sitagliptin, and 7.84% increase in aliskiren adoption (SE = 2.93%, p<0.001). Peer effects through shared hospital affiliation were positive but not significant, and medical group and training network effects were not reliably estimated. Physicians in the top decile of patient-sharing network peers were estimated to have nearly 2-fold stronger influence on their peers' adoption compared to physicians in the top decile of prescribing volume. Limitations include lack of detailed clinical information and pharmaceutical promotion, variables which may influence physician adoption but which are unlikely to bias our peer effect estimates. Peer adoption, especially by those with whom physicians share patients, strongly influenced physician adoption of new drugs. Our study shows the potential for using information on physician peer networks to improve technology diffusion.