Literature Collection

BACKGROUND: Breastfeeding among lactating people with opioid use disorder taking buprenorphine monotherapy is generally accepted, as low concentrations of buprenorphine and metabolites in human milk have been well-established. The use of buprenorphine-naloxone for pregnant and lactating people with opioid use disorder is expanding and there is no information available regarding the concentrations of naloxone and their metabolites in human milk to recommend the use of this combination medication during lactation. RESEARCH AIMS: To determine the concentrations of buprenorphine and naloxone and their primary metabolites in human milk, maternal plasma, and infant plasma, among lactating buprenorphine-naloxone maintained people and their infants. METHODS: Four lactating buprenorphine-naloxone maintained people provided plasma and human milk samples on Days 2, 3, 4, 14, and 30 postpartum. Infant plasma was obtained on Day 14. RESULTS: Concentrations of buprenorphine, norbuprenorphine and their glucuronide metabolites were present in maternal plasma and human milk at low concentrations, consistent with previous research in lactating buprenorphine monotherapy participants. Naloxone was not detected, or was detected at concentrations below the limit of quantification, in maternal plasma and in all except one human milk sample at Day 30. Naloxone was not detected or detected at concentrations below the limit of quantification in all infant plasma samples. CONCLUSION: Results support the use of buprenorphine-naloxone by lactating people who meet appropriate criteria for breastfeeding.

BACKGROUND: The health conditions of elderly people who have multiple chronic illnesses and geriatric syndromes lead to severe health problems, which result in decreased ability to function and increased need for medical services. METHODS: The study used a retrospective cohort design with propensity score matching and multivariable modeling to examine the impact of different care models on outcomes over a 24-month period. Five outcomes including hospitalization rates and functional decline rates through ADL and IADL assessments, cognitive decline rates, mortality rates, and service usage rates. RESULTS: The matching process achieved balanced baseline characteristics between groups because SMD measurements stayed below 0.05. Geriatric syndromes had high prevalence rates with 40.0% cognitive impairment, 32.0% frailty, 32.5% depression, 49.0% polypharmacy, and 68.5% multimorbidity. The health service utilization showed substantial levels, which included 92 hospital admissions per 100 person-years and 134 emergency visits per 100 person-years. 31.4% of participants experienced functional decline in activities of daily living ADL and 42.7% suffered a similar decline in instrumental activities of daily living IADL while EQ-5D showed quality-of-life deterioration of -0.04. Integrated care provided benefits which decreased hospital admissions (IRR 0.78, 95% CI 0.71-0.87) and 30-day readmissions (OR 0.81) and reduced length of stay (-1.2 days). Cognitive decline underwent partial protection (MoCA change +0.86; dementia conversion HR 0.71), and mortality decreased (HR 0.76). Multivariable models show that frailty (OR 2.21) and cognitive impairment (OR 1.42) served as strong predictors of functional decline. The protective effects of integrated care continued to exist (OR 0.81). Mediation analysis suggested that approximately 25% of the observed advantage came from reduced frailty. CONCLUSION: Older adults exhibit high burdens of geriatric syndromes and healthcare utilization, which leads to their functional and cognitive decline. Integrated care models reduce hospital admissions and delay cognitive decline while increasing patient survival because they help manage frailty and provide unified treatment.

AIMS: To estimate and compare the economic burden of obesity across three Integrated Care Boards in England, with a specific focus on the contribution of Type 2 diabetes and related complications. METHODS: An adapted burden of obesity model integrating estimated national health costs data with local population metrics was applied to the integrated care boards to estimate health care, social care, productivity and economic and quality of life associated costs. Data were stratified by sector, age, sex, BMI and prevalence of obesity-related complications. RESULTS: Across the regions, 599,248 adults were living with obesity. Total economic burden reflected obesity prevalence, with the highest costs in Leicester, Leicestershire and Rutland, followed by Bedfordshire, Luton and Milton Keynes and then Northamptonshire. Despite this, Bedfordshire, Luton and Milton Keynes had the highest per-patient costs across all cost categories. Social care and productivity losses each contributed approximately 30% of total costs, with informal care comprising 96% of social care expenditure. Per-patient healthcare costs increased with BMI. Type 2 diabetes and hypertension were among the most prevalent and costly complications, while stroke and coronary heart disease had the highest per-patient costs. Coexisting coronary heart disease with Type 2 diabetes significantly increased treatment costs. Obesity was more prevalent among working-age adults, with males incurring higher per-patient complication costs across all conditions and integrated care boards. CONCLUSIONS: This study demonstrates the significant and variable local economic burden of obesity. Disparities in per-patient costs, sex and complication profiles highlight the need for stratified, data-driven commissioning. Targeted prevention in high-burden areas can help ICBs reduce system pressures and guide effective local strategies.

Background/local problem : An integrated healthcare system conducted surveys to assess overall well-being to describe perceptions of work environment and to inform meaningful intervention. Burnout and high turnover are compromising workplace safety and quality of care. METHODS: Surveys were sent to all eligible employees in the organization and participation was voluntary and confidential. The 2022 surveys were conducted, and 6,913 nurses participated. INTERVENTIONS: Statistical testing was conducted to identify demographic differences in burnout for tenure, age, manager/non-manager, and care setting. Regressions were conducted to determine top predictors of burnout. RESULTS: Significant differences were found in tenure ( P < .001), age ( P < .001), and manager/ non-manager ( P = .009). Mental health, retention, and workload significantly predicted burnout, F (3,4609) = 1028.33, P < .001, R2 adj = 0.40. CONCLUSION: Nursing burnout ultimately affects turnover and retention in a healthcare system. Monitoring by age, tenure, manager status, and care setting are vital for addressing burnout for nurses.