BMJ medicine最新文献

Case study is a widely used but poorly understood research method, conducted differently in different disciplines. This paper explores philosophical, theoretical and methodological issues in case study research and outlines how to conduct one. It offers preliminary guidance for policy makers on how to select and use case studies for learning and decision making. In social science research, a case study is a detailed, contextualised account of a clearly delineated, real world phenomenon, prepared prospectively using mostly qualitative methods. Social science case studies can be of various kinds (eg, theoretical or naturalistic, single or multiple, typical or extreme). A public health case study is a historical account of a health threat and how it was managed. An implementation science case study evaluates the implementation of an intervention (usually retrospectively), combining quantitative assessment against predefined objectives with a narrative of how the project unfolded. Educational case studies present real world topics as stories illustrated by data and prompt students to discuss these from different angles. Impact case studies summarise the societal impact of a research programme. Many accounts described as case studies are overly brief and superficial. The paper concludes with a call to improve the quality and consistency (and hence the usefulness) of case studies.

Abstract:

Objective: To investigate the magnitude and evolution of inequalities in neonatal mortality rates by using area based socioeconomic indices in France.

Design: National population based study.

Setting: For 2015-20, data from the French National Health Data System (Système National des Données de Santé, SNDS). For 2001-08, neonatal death certificates and aggregate vital statistics data by municipality of residence.

Participants: Live births with a gestational age ≥22 completed weeks to a mother residing in metropolitan France, 2015-20 (4 293 403 live births and 10 869 neonatal deaths), compared with a 2001-08 study (6 202 918 live births and 14 851 neonatal deaths).

Main outcome measures: Differences in neonatal mortality rate (death before day 28 of life) according to the socioeconomic characteristics of the mother's municipality of residence. Comparison with data from a 2001-08 study to assess changes in socioeconomic inequalities and their contribution to the increase in neonatal mortality rate.

Results: The neonatal mortality rate was 2.53 per 1000 live births in 2015-20. Five indicators, previously associated with perinatal mortality, were combined into a perinatal French deprivation index (P-FDep) for the main analysis. P-FDep was categorised into five equal groups (deprivation groups 1-5) for comparison with other research and into 10 equal groups (deprivation groups 1-10) for more granular analyses, with group 1 being the least and group 5 (or group 10) the most deprived group. The rate in the most deprived compared with the least deprived group for P-FDep was 1.71 (95% confidence interval 1.60 to 1.83) times higher, based on the analysis of deprivation groups 1-5. A mortality gradient existed across the groups, translating into 2496 excess deaths (23.3%) when the rate in the least deprived group was applied to all areas. The gradient was more marked when deprivation groups 1-10 were used (relative risk 1.88, 95% CI 1.71 to 2.07 for the highest to the lowest deprived group). Compared with 2001-08 (neonatal mortality rate 2.39 per 1000), the rate remained constant in the least deprived areas, but worsened in the most deprived areas (+10.1% and +11.7% for groups 4 and 5, respectively), increasing the relative risks between the highest and lowest groups, which were 1.54 (95% CI 1.46 to 1.62) for deprivation groups 1-5 and 1.67 (1.55 to 1.79) for deprivation groups 1-10, in 2001-08.

Conclusions: In this study, the socioeconomic level of the mother's place of residence was strongly associated with the neonatal mortality rate. The data showed that inequalities have widened, contributing to the increase in the neonatal mortality rate.

Objective: To evaluate the effectiveness of various de-implementation interventions in primary care, targeting care (treatments or tests) that provides no or limited value for patients (low value care).

Design: Systematic review and meta-analysis.

Data sources: Medline and Scopus databases, from inception to 10 July 2024.

Eligibility criteria for selecting studies: Randomised trials comparing de-implementation interventions with placebo or sham intervention, no intervention, or other de-implementation intervention strategies in primary care. Eligible trials provided information on the use of low value care, total volume of care, appropriate care, and health outcomes.

Data extraction and synthesis: Titles, abstracts, and full texts were screened, data were extracted, and risk of bias was assessed independently and in duplicate. Random effects meta-analyses were conducted, and the certainty of evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.

Results: 13 008 abstracts were screened and 140 were eligible for inclusion in the study. Median follow-up was 287 days (interquartile range 180-365). In 75 (54%) trials the aim was to reduce the use of antibiotics, in 42 (30%) to reduce other drug treatments, in 17 (12%) to reduce imaging, and in 15 (11%) to reduce laboratory testing. The certainty of the evidence was moderate that provider education combined with audit and feedback reduced the use of targeted low value care (odds ratio 0.73, 95% confidence interval (95% CI) 0.63 to 0.84). Provider education (0.86, 95% CI 0.72 to 1.03), audit and feedback (0.82, 0.67 to 1.00), and patient education (0.70, 0.30 to 1.66), and a combination of these strategies (point estimates for odds ratios ranging from 0.57 to 0.64) may reduce the use of targeted low value care (low certainty of evidence for all).

Conclusions: The results suggested with moderate certainty of evidence that provider education combined with audit and feedback reduced the use of targeted low value care. Individual strategies may slightly reduce the use of targeted low value care, but achieving a meaningful impact on low value care may require the use of multiple strategies. The results may be useful for patients, clinicians, policy makers, and guideline developers when deciding on future de-implementation strategies and research priorities.

Systematic review registration: PROSPERO CRD42023411768.

Objectives: To identify and quantify prescriptions after a covid-19 infection compared with other acute respiratory infections in previously healthy patients and those with chronic disease.

Design: Comparative observational study based on German routine data.

Setting: Ambulatory care of all residents in Germany with statutory health insurance (88% of the German population).

Participants: Adults receiving a diagnosis of covid-19 or an acute respiratory infection between the fourth quarter of 2020 and the second quarter of 2021 who had rarely (70 797 and 173 822 with covid-19 and acute respiratory infection, respectively) or frequently (900 593 and 1 755 691, respectively) accessed outpatient medical care in the past.

Main outcome measures: Difference in differences in the proportion of prescriptions of relevant drugs before and one year after infection.

Results: In patients who used the healthcare system less frequently before their covid-19 infection than afterwards, increases in prescription rates for antidiabetics (difference in differences 0.23%, P=0.007), antithrombotics (0.71%, P=0.02), and cardiovascular drugs like beta blockers (0.25%, P=0.03) were observed compared with patients with other acute respiratory infections. One year after infection, the difference in antidiabetic prescription rates was highest. Although a peak in antihypertensive prescription rates was observed six months after infection, antithrombotics were predominantly prescribed during the acute phase. Conversely, patients who had already used the healthcare system on a regular basis before their infection showed no significant long term increases in prescription rates across the drug groups analysed.

Conclusions: This study supports findings that diseases such as diabetes and cardiovascular disease are more prevalent after covid-19 than after other acute respiratory infections. Because the effect is apparent in real world data, future societal implications should be considered, including increased disease burden and growing demand for medical care owing to the increasing need for drugs.

Objective: To determine the importance of comorbidity measures when predicting mortality and revision surgery after elective primary shoulder replacement surgery.

Design: Population based cohort study.

Setting: Linked data from the National Joint Registry and NHS Hospital Episode Statistics were used to identify all elective primary shoulder replacements in England, 6 January 2012 to 30 March 2022.

Participants: 37 176 consenting patients, aged 18-100 years, who had elective primary shoulder replacement surgery.

Main outcome measures: Risk of mortality at 90 and 365 days, and risk of long term revision surgery after the primary surgery.

Results: 37 176 primary shoulder replacement procedures were included; 102 patients died within 90 days and 445 within 365 days of the primary surgery. 1219 patients had revision surgery over a maximum follow-up period of >10 years. The addition of comorbidity measures derived from Hospital Episode Statistics (Charlson comorbidity index with summary hospital mortality index weights, Elixhauser comorbidity index, and hospital frailty risk score) to simpler models resulted in little improvement in predictive performance. Optimism adjusted performance (C index) of the models that included age, sex, American Society of Anesthesiologists (ASA) grade, and main surgical indication was 0.76 (95% confidence interval (CI) 0.72 to 0.81) for 90 day mortality, 0.74 (0.71 to 0.76) for 365 day mortality, and 0.64 (0.63 to 0.66) for revision surgery. The best performing models that included a comorbidity measure had an optimism adjusted C index of 0.77 (95% CI 0.73 to 0.81) for 90 day mortality, 0.76 (0.74 to 0.78) for 365 day mortality, and 0.65 (0.63 to 0.66) for revision surgery. Heterogeneity in model performance across regions of England was low, and decision curve analysis showed minimal improvement in net benefit when including comorbidity measures.

Conclusions: In this study, patient comorbidity scores added little improvement to simpler models that included age, sex, ASA grade, and main surgical indication for predicting mortality and revision surgery after elective primary shoulder replacement surgery. This improvement needs to be balanced against the additional challenges of routine data linkage to obtain these scores.