Clinical Epidemiology最新文献

Background: The impact of the coronavirus disease 2019 (COVID-19) pandemic on asthma medication trajectories, including changes in medication type or dosage, remains unclear. This study compared dispensing patterns among adults who initiated asthma inhalers before pandemic (cohorts 2014 and 2017) and during pandemic (cohort 2020).

Methods: We performed retrospective inception cohort study using University of Groningen IADB.nl community pharmacy dispensing database. Individuals aged 16-44 years who initiated inhaled asthma treatment in 2014, 2017, or 2020 were followed for 2 years. Treatment steps (1-5) were based on the Global Initiative for Asthma (GINA) guideline. Primary outcomes included time to treatment step switch (step-up or step-down) and time to first oral prednisolone/prednisone, and were compared between cohorts using Cox regression models.

Results: In 2014, 2017 and 2020, 1193, 960 and 730 patients initiated asthma inhalers, respectively. In all cohorts, more than half of the patients initiated treatment at the lowest step. During 2-year follow-up, fewer patients switched their treatment steps in 2020 when compared with 2014 (adjusted hazard ratio (aHR): 0.86 (95% confidence interval (CI): 0.76-0.99). From 2014 to 2020, the likelihood of treatment stepping-down decreased over time, with a 21% in 2017 (aHR: 0.79 (0.68-0.92)) and 24% in 2020 (aHR: 0.76 (0.64-0.90)) compared to 2014, while the likelihood of stepping-up did not change significantly. The risk of taking oral prednisolone/prednisone was also significantly lower in 2020 (aHR: 0.76 (0.61-0.94)).

Conclusion: During the pandemic, fewer asthma patients switched treatment steps and took oral prednisolone/prednisone. Since 2014, fewer individuals stepping down medication, with a decrease of 21% in 2017 and 24% in 2020. Possible drivers include improved adherence, better asthma control, and increased telemedicine use-trends that predate and have been accelerated by the pandemic. Research incorporating clinical data is necessary to confirm these hypotheses.

Purpose: To date, complete and long-term registrations of diseases and events are not available in every situation. As a useful proxy, medication usage data is very promising. For instance, real-world dispensing data from pharmacies are attractive because of the high validity of drug dispensing data, large sample sizes, and long-term registration. However, before application as a proxy, validity must be assessed. Therefore, in this study, we aim to assess the validity of various medicines used as a proxy for major adverse cardio-cerebrovascular events (MACCE), that is, to identify an incident or previous hospitalization for a MACCE.

Patients and methods: Using the claims database of a large Dutch healthcare insurer, we estimated the concordance between hospitalization claims for MACCE and specific claims for dispensings to treat MACCE in a cohort of patients on primary preventive antihypertensive and/or antihyperlipidemic therapy between 2013 and 2020.

Results: In a cohort of more than 110,000 patients, a dispensing of either vitamin K antagonists, platelet aggregation inhibitors, or nitrates was predictive of an incident hospitalization for a MACCE between 2013 and 2020, with a sensitivity of 71.5% (95% CI: 70.4-72.5%) and specificity of 93.2% (95% CI 91.1-93.4%), and any history of hospitalization for a MACCE (prevalence) with a sensitivity of 86.9% (95% CI: 86.5-87.3%) and specificity of 81.9 (956% CI: 81.6-82.1%), while positive predicted value remains low. Sensitivity analyses across age, sex, and patients with asthma/COPD or diabetes showed a similarly good performance.

Conclusion: Claims for the dispensings of vitamin K antagonists, platelet aggregation inhibitors, and/or nitrates can be validly used as a proxy for new and previous hospitalization for MACCE in patients on primary preventive therapy. Further study is required to assess the validity of such dispensing claims for non-hospitalized cerebro-cardiovascular events and whether the results are generalizable in non-Dutch cohorts.

Background: Mental disorders such as depression and anxiety are common for women of reproductive age and impact pregnancy and parenting. Invasive Group B Streptococcus disease (iGBS) is a leading cause of neonatal morbidity and mortality worldwide. Little is known about the short and long-term risk of common mental disorders in birthing parents whose infants had iGBS in the first 89 days after birth. We aimed to examine the risk of depression and anxiety in birthing parents with iGBS-affected infants in a cohort study with prospectively collected data from Danish registries.

Materials and methods: Using Danish healthcare registries from 1997 to 2018, we obtained data on iGBS-affected children and their birthing parents. A comparison cohort was randomly sampled (1:50) through risk-set sampling, and matched on persons' age, year of child´s birth, and parity. The risk of using antidepressant medicines and depression or anxiety diagnosis was analyzed with cumulative incidence function and in Cox proportional hazards regression models.

Results: During the study period, we identified 1,552 women with iGBS-affected child and 76,879 matched comparators. During a median follow-up of 9∙9 years, the cumulative incidence of antidepressants use among birthing parents with iGBS-affected children was 31% (95% confidence interval, CI: 28-34%), as compared with 29% (95% CI: 28-30%) among members of the comparison cohort (hazard ratio 1∙12 [95% CI: 1∙01-1∙25]). A 16% increase in the rate of diagnosed depression or anxiety was observed in the overall follow-up period.

Conclusion: Our findings provide evidence of a slightly increased risk of antidepressant use and diagnosed depression or anxiety in parents who gave birth to children with a history of iGBS compared to a matched cohort of birthing parents whose infants did not develop iGBS. Our findings highlight the importance of addressing the mental health needs of birthing parents affected by their children' iGBS.

Purpose: We aimed to analyse the characteristics related to non-attendance at general outpatient hospital clinics in patients aged ≥18 years. An increased focus has been directed towards patient non-attendance at hospital appointments as it is related to patient risk and waste of resources in the healthcare system.

Patients and methods: In this cohort study, we retrieved data from the entire Region of Southern Denmark on i) non-attendance from the electronic medical journals from January 1, 2021 until December 31, 2022, and ii) data on all attended appointments from Danish health registries in the same period. We analysed the overall proportion of non-attendance, and characteristics of patients with non-attendance, relative to those who attended an appointment. We examined hospital type and patient demographics (age, sex, comorbidity, socioeconomic factors).

Results: Included were 614,157 patients, of which 12,244 were patients with a first non-attendance and 601,913 patients with a first attended appointment. The overall non-attendance proportion was 2.0%. The most prevalent type of underlying disease among non-attendant patients was musculoskeletal/connective tissue diseases (10%), which was also the most prevalent group of diseases among patients who attended hospital appointments (11%). In the regression model, compared to those who attended, the two strongest associations for non-attendance were patients aged 18-34 years, aOR=2.69 (95% CI 2.52-2.85), and patients diagnosed with mental/behavioral disorders, aOR=2.60 (95% CI 2.39-2.82). Other sociodemographic factors were associated with non-attendance including male sex (aOR=1.90 (95% CI 1.82-1.96)), patients aged 35-54 years (aOR=1.89 (95% CI 1.78-2.01)), living alone (aOR=1.72 (95% CI 1.65-1.79)), and not Danish nationality (aOR=1.65 (95% CI 1.57-1.74)).

Conclusion: Based on data from the Region of Southern Denmark (corresponding to 20% of the Danish population), the non-attendance proportion was low (2.0%). More research is needed, including other data-sets validating our findings, validation of registration practices, and qualitative research aspects of non-attendance.

Purpose: We aimed to identify the association between obesity and nonalcoholic fatty liver disease (NAFLD) and to quantify the mediating effects of insulin resistance (IR) and chronic inflammation through observational studies and Mendelian randomization (MR).

Patients and methods: In the current study, three IR-related indicators and three indicators of inflammation were included. The individual and combined mediated effects of IR and inflammation in the association between obesity and NAFLD were investigated in two cross-sectional studies, the Fuqing Cohort from China and the National Health and Nutrition Examination Survey (NHANES). Total, direct, and indirect effects were estimated through direct counterfactual imputation estimation, and the proportion of mediating effects was calculated. We applied a two-step MR to determine the causal mediating role of IR and chronic inflammation in the pathway between obesity and NAFLD by using single nucleotide polymorphisms as instrumental variables to predict obesity, IR, and inflammation genetically.

Results: In the Fuqing Cohort, all obese phenotypes were associated with an elevated NAFLD risk. Moreover, indicators of IR such as homeostatic model assessment of insulin resistance (HOMA-IR) and indicators of inflammation such as C-reactive protein (CRP) were significantly and positively associated with NAFLD risk. Individuals with obesity had significantly higher levels of IR and inflammation indicators compared to non-obese individuals. The indirect proportions of insulin and HOMA-IR accounted for 50.97-66.72% in the associations between obese phenotypes and NAFLD risk, while the proportions of inflammation indicators were < 14%. Similar results were observed in the NHANES analysis. In the MR analysis, the indirect effects of HOMA-IR and CRP were statistically significant with a greater mediated proportion explained by HOMA-IR than CRP.

Conclusion: Through two population-based studies and MR, we found the causal mediation roles of IR and inflammation in the association between obesity and NAFLD, in which HOMA-IR and CRP showed stable, significant mediation effects. Furthermore, HOMA-IR showed a higher mediation effect than CRP. We emphasize the vital role of HOMA-IR in NAFLD monitoring.

Background: The Danish National Patient Registry (DNPR) is a central source of information on hospital contacts for the Danish population and is a key data source for health-related Danish registry studies. The data structure of DNPR was updated from DNPR2 to DNPR3 in 2019, where a key patient-type variable for classification of inpatient, outpatient, or emergency wards was removed. This affects how hospital contacts can be defined and compared across different calendar years.

Aim: To present and compare different algorithms to determine the type of hospital visit (inpatient, outpatient, or emergency) for all hospital visits in Denmark from 2006 to 2021 across DNPR2 and DNPR3.

Methods: The monthly number of hospital visits per 1000 citizens was presented for four different algorithms: 1) a validated approach suggested by Skjøth et al, 2) an approach suggested by the Danish Ministry of Health and Elderly, 3) the latter combined with patient type variables available in DNPR2 only, and 4) a consensus-driven algorithm introduced by Gregersen et al.

Results: Using the same algorithm for DNPR2 and DNPR3 yielded the most similar results across calendar years. The least variation across calendar years was observed for the approach suggested by the Danish Ministry of Health and Elderly, whereas the validated approach suggested by Skjøth et al was more in line with the patient-type variable previously used in DNPR2. When comparing the algorithms, the main difference in the number of hospital visits was observed for inpatient and emergency visits.

Conclusion: We recommend using the same algorithm across DNPR2 and DNPR3. The choice of algorithm should be based on the disease or patient group being studied and by considering how the approaches reflect reality and need in the actual study. We recommend the algorithm suggested by Skjøth et al for the specific clinical situations presented in this study.

Purpose: The g-formula offers a promising approach to analyze long-term dynamic asthma treatment trajectories. This study investigates whether the g-formula can simulate real-world asthma treatment trajectories and predicts subgroup differences in switching behavior.

Patients and methods: This retrospective cohort study identified individuals aged 16- to 45 years who initiated inhaled asthma medication in the Netherlands between 1994 and 2021, from the IADB.nl pharmacy dispensing database. We used the g-formula combined with logistic regression to predict treatment trajectories and their associations with various patient characteristics, such as age, sex, chronic drug treatment for atopic diseases (ATD), cardiovascular diseases (CVD), thyroid diseases, arthritis, diabetes, gastroesophageal reflux disease (GERD), mental health problems (MHP), and immunosuppressants.

Results: The simulations predicted 76% of individuals to switch treatment, on average 2.3 times, with the first switch occurring on average after 8.3 months, which agrees with the real-world observations (77%, 2.3 times and 7.9 months, respectively). Fewer 45-year-olds switched treatment compared to 16-year-olds (74% vs 78%, p < 0.001), but they switched earlier (8.1 vs 8.6 months, p < 0.001) and more frequently (2.4 vs 2.3 times, p < 0.001). Women were more likely to switch compared to men. Patients with ATD, CVD, MHP, or GERD switched significantly less often (p < 0.05).

Conclusion: The g-formula effectively simulates asthma treatment trajectories and found higher age, male sex, ATD, CVD, MHP, and GERD to decrease overall switching behavior. These patients might benefit from earlier intervention or closer monitoring to reduce delays in treatment progression.

Objective: Although the 5-item modified frailty index (mFI-5) has been found to be associated postoperative outcomes, there are limited studies examining its utility in urologic surgery. Our purpose is to evaluate the association between the mFI-5 and postoperative mortality and complications among patients undergoing urologic surgery.

Methods: This retrospective cohort study used the American College of Surgeons National Surgical Quality Improvement Program database from 2015 to 2020. All adult patients who underwent urologic procedures were included. The mFI-5 includes five items: hypertension, diabetes, congestive heart failure, chronic obstructive pulmonary disease, and physical function status. Each item is assigned one point, and an mFI-5 score of 2 or greater indicates frailty. The primary outcome was postoperative mortality, while secondary outcomes were postoperative complications. Propensity score analysis was employed to control for confounders.

Results: After propensity score matching, each group contained 55,322 surgical patients. The patients in the frailty group were at risks of in-hospital mortality (absolute risk increase [ARI] 0.29%) and higher postoperative complications, including acute myocardial infarction (ARI 0.25%), pneumonia (ARI 0.42%), sepsis (ARI 0.41%), and septic shock (0.2%). Compared to the non-frailty group, the length of hospital stay was higher in the frailty group.

Conclusion: Patients with an mFI-5 score of 2 or greater were associated with an increased risk of postoperative mortality and complications, including myocardial infarction, pneumonia, sepsis, and septic shock. The mFI-5 is a simple index that quickly identifies frail patients. This allows for the implementation of prehabilitation and nutritional strategies targeted at enhancing their physiological reserve and optimizing their surgical outcomes.

Background: Basic science evidence reveals interactions between the immune and bone systems. However, population studies linking infectious diseases and musculoskeletal (MSK) disorders are limited and inconsistent. We aimed to examine the risk of six main MSK disorders (osteoarthritis, rheumatoid arthritis, osteoporosis, gout, low back pain, and neck pain) following hospital-treated infections in a large cohort with long follow-up periods.

Methods: We analysed data from 502,409 UK Biobank participants. Participants free of specific MSK disorders at baseline were included in each analysis. Hospital-treated infections before baseline were identified using national inpatient data, while incident MSK outcomes were ascertained from inpatient records, primary care, and death registers. Participants with prior infections were propensity score matched (1:5) with those without. Hazard ratios (HRs) and absolute rate differences (ARDs) with 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. To assess potential reverse causality due to delayed diagnosis of preexisting illness, analyses were repeated excluding MSK disorder cases that occurred within the first 5 and 10 years post-baseline.

Results: A hospital-treated infection was associated with increased risks of all six MSK disorders, with particularly strong associations for osteoporosis (HR, 1.55 [1.48-1.63]; ARD, 1.48 [95% CI 1.29-1.68] per 1000 person-years) and rheumatoid arthritis (HR, 1.53 [1.41-1.65]; ARD, 0.58 [0.46-0.71] per 1000 person-years), while other disorders showed HRs of 1.28-1.32. Bacterial and viral infections showed similar associations, with MSK infections (generally stronger risk) and other locations both linked to increased risks. Associations remained significant even for incident cases that occurred more than 10 years post-baseline.

Conclusion: Hospital-treated infections are associated with long-term MSK disorder risks, regardless of pathogen type or disorder nature (inflammatory or degenerative). Long-term monitoring and care for MSK health in patients with prior hospital-treated infections are recommended.

Purpose: This study applied machine learning (ML) and explainable artificial intelligence (XAI) to predict changes in HbA1c levels, a critical biomarker for monitoring glycemic control, within 12 months of initiating a new antidiabetic drug in patients diagnosed with type 2 diabetes. It also aimed to identify the predictors associated with these changes.

Patients and methods: Electronic health records (EHR) from 10,139 type 2 diabetes patients in North Karelia, Finland, were used to train models integrating randomized controlled trial (RCT)-derived HbA1c change values as predictors, creating offset models that integrate RCT insights with real-world data. Various ML models-including linear regression (LR), multi-layer perceptron (MLP), ridge regression (RR), random forest (RF), and XGBoost (XGB)-were evaluated using R² and RMSE metrics. Baseline models used data at or before drug initiation, while follow-up models included the first post-drug HbA1c measurement, improving performance by incorporating dynamic patient data. Model performance was also compared to expected HbA1c changes from clinical trials.

Results: Results showed that ML models outperform RCT model, while LR, MLP, and RR models had comparable performance, RF and XGB models exhibited overfitting. The follow-up MLP model outperformed the baseline MLP model, with higher R² scores (0.74, 0.65) and lower RMSE values (6.94, 7.62), compared to the baseline model (R²: 0.52, 0.54; RMSE: 9.27, 9.50). Key predictors of HbA1c change included baseline and post-drug initiation HbA1c values, fasting plasma glucose, and HDL cholesterol.

Conclusion: Using EHR and ML models allows for the development of more realistic and individualized predictions of HbA1c changes, accounting for more diverse patient populations and their heterogeneous nature, offering more tailored and effective treatment strategies for managing T2D. The use of XAI provided insights into the influence of specific predictors, enhancing model interpretability and clinical relevance. Future research will explore treatment selection models.