Development of an outcome indicator framework for a universal health visiting programme using routinely collected data.

BACKGROUND: Universal health visiting has been a cornerstone of preventative healthcare for children in the United Kingdom (UK) for over 100 years. In 2016, Scotland introduced a new Universal Health Visiting Pathway (UHVP), involving a greater number of contacts with a particular emphasis on the first year, visits within the home setting, and rigorous developmental assessment conducted by a qualified Health Visitor. To evaluate the UHVP, an outcome indicator framework was developed using routine administrative data. This paper sets out the development of these indicators. METHODS: A logic model was produced with stakeholders to define the group of outcomes, before further refining and aligning of the measures through discussions with stakeholders and inspection of data. Power calculations were carried out and initial data described for the chosen indicators. RESULTS: Eighteen indicators were selected across eight outcome areas: parental smoking, breastfeeding, immunisations, dental health, developmental concerns, obesity, accidents and injuries, and child protection interventions. Data quality was mixed. Coverage of reviews was high; over 90% of children received key reviews. Individual item completion was more variable: 92.2% had breastfeeding data at 6-8 weeks, whilst 63.2% had BMI recorded at 27-30 months. Prevalence also varied greatly, from 1.3% of children's names being on the Child Protection register for over six months by age three, to 93.6% having received all immunisations by age two. CONCLUSIONS: Home visiting services play a key role in ensuring children and families have the right support to enable the best start in life. As these programmes evolve, it is crucial to understand whether changes lead to improvements in child outcomes. This paper describes a set of indicators using routinely-collected data, lessening additional burden on participants, and reducing response bias which may be apparent in other forms of evaluation. Further research is needed to explore the transferability of this indicator framework to other settings.

Predicting patients with dementia most at risk of needing psychiatric in-patient or enhanced community care using routinely collected clinical data: retrospective multi-site cohort study.

BACKGROUND: Dementia is a common and progressive condition whose prevalence is growing worldwide. It is challenging for healthcare systems to provide continuity in clinical services for all patients from diagnosis to death. AIMS: To test whether individuals who are most likely to need enhanced care later in the disease course can be identified at the point of diagnosis, thus allowing the targeted intervention. METHOD: We used clinical information collected routinely in de-identified electronic patient records from two UK National Health Service (NHS) trusts to identify at diagnosis which individuals were at increased risk of needing enhanced care (psychiatric in-patient or intensive (crisis) community care). RESULTS: We examined the records of a total of 25 326 patients with dementia. A minority (16% in the Cambridgeshire trust and 2.4% in the London trust) needed enhanced care. Patients who needed enhanced care differed from those who did not in age, cognitive test scores and Health of the Nation Outcome Scale scores. Logistic regression discriminated risk, with an area under the receiver operating characteristic curve (AUROC) of up to 0.78 after 1 year and 0.74 after 4 years. We were able to confirm the validity of the approach in two trusts that differed widely in the populations they serve. CONCLUSIONS: It is possible to identify, at the time of diagnosis of dementia, individuals most likely to need enhanced care later in the disease course. This permits the development of targeted clinical interventions for this high-risk group.

Iran's research prioritization: Are we meeting the goals? A study based on clinical trial registry data.

The prioritization of research topics in the health domain is a critical step toward channelling efforts and resources into areas that have received less attention. The objective of this study is to evaluate the implementation of research priorities determined at the national level within Iran for the period spanning five years between 2009 and 2013. We extracted the required data from the Iranian Registry of Clinical Trials (IRCT) website. Then we conducted a matching process between the titles of trials registered in the IRCT until December 3rd, 2013, and the list of national health research priorities in the domains of communicable and non-communicable diseases. The latter was compiled and regulated by the Research and Technology Deputy of the Ministry of Health since 2008. Out of the total 5,049 clinical trials registered in IRCT, 92.3% were carried out within the domain of non-communicable diseases, while 6.1% pertained to the field of communicable diseases and the remaining 1.3% in other fields. 56.4% of the clinical trials conducted in the field of communicable diseases and 32.8% of those conducted in the field of non-communicable diseases were consistent with the research priorities determined in these two fields. During the five-year period of the prioritization goal, there was no significant improvement in adherence to the list of priorities compared to the previous five-year period. Furthermore, certain priorities were neglected within both areas during these periods. It is possible to evaluate the effectiveness of research prioritization using the data obtained from the registration centers of clinical trials. Our study has revealed that the list of priorities has not garnered adequate attention from the research community within the country. Hence, remedial measures are imperative to ensure the priorities are given more attention after publication.

New opioid prescription claims and their clinical indications: results from health administrative data in Quebec, Canada, over 14 years.

OBJECTIVES: Describe new opioid prescription claims, their clinical indications and annual trends among opioid naïve adults covered by the Quebec's public drug insurance plan (QPDIP) for the fiscal years 2006/2007-2019/2020. DESIGN AND SETTING: A retrospective observational study was conducted using data collected between 2006/2007 and 2019/2020 within the Quebec Integrated Chronic Disease Surveillance System, a linkage administrative data. PARTICIPANTS: A cohort of opioid naïve adults and new opioid users was created for each study year (median number=2 263 380 and 168 183, respectively, over study period). INTERVENTION: No. MAIN OUTCOME MEASURE AND ANALYSES: A new opioid prescription was defined as the first opioid prescription claimed by an opioid naïve adult during a given fiscal year. The annual incidence proportion for each year was then calculated and standardised for age. A hierarchical algorithm was built to identify the most likely clinical indication for this prescription. Descriptive and trend analyses were performed. RESULTS: There was a 1.7% decrease of age-standardised annual incidence proportion during the study period, from 7.5% in 2006/2007 to 5.8% in 2019/2020. The decrease was highest after 2016/2017, reaching 5.5% annual percentage change. Median daily dose and days' supply decreased from 27 to 25 morphine milligram equivalent/day and from 5 to 4 days between 2006/2007 and 2019/2020, respectively. Between 2006/2007 and 2019/2020, these prescriptions' most likely clinical indications increased for cancer pain from 34% to 48%, for surgical pain from 31% to 36% and for dental pain from 9% to 11%. Inversely, the musculoskeletal pain decreased from 13% to 2%. There was good consistency between the clinical indications identified by the algorithm and prescriber's specialty or user's characteristics. CONCLUSIONS: New opioid prescription claims (incidence, dose and days' supply) decreased slightly over the last 14 years among QPDIP enrollees, especially after 2016/2017. Non-surgical and non-cancer pain became less common as their clinical indication.

Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning.

BACKGROUND: Personalized medicine offers targeted therapy options for cancer treatment. However, the decision whether to include a patient into next-generation sequencing (NGS) testing is not standardized. This may result in some patients receiving unnecessary testing while others who could benefit from it are not tested. Typically, patients who have exhausted conventional treatment options are of interest for consideration in molecularly targeted therapy. To assist clinicians in decision-making, we developed a decision support tool using routine data from a precision oncology program. METHODS: We trained a machine learning model on clinical data to determine whether molecular profiling should be performed for a patient. To validate the model, the model's predictions were compared with decisions made by a molecular tumor board (MTB) using multiple patient case vignettes with their characteristics. RESULTS: The prediction model included 440 patients with molecular profiling and 13,587 patients without testing. High area under the curve (AUC) scores indicated the importance of engineered features in deciding on molecular profiling. Patient age, physical condition, tumor type, metastases, and previous therapies were the most important features. During the validation MTB experts made the same decision of recommending a patient for molecular profiling only in 10 out of 15 of their previous cases but there was agreement between the experts and the model in 9 out of 15 cases. CONCLUSION: Based on a historical cohort, our predictive model has the potential to assist clinicians in deciding whether to perform molecular profiling.

Patients' experience on pain outcomes after hip arthroplasty: insights from an information tool based on registry data.

BACKGROUND: Arthroplasty registries are rarely used to inform encounters between clinician and patient. This study is part of a larger one which aimed to develop an information tool allowing both to benefit from previous patients' experience after total hip arthroplasty (THA). This study focuses on generating the information tool specifically for pain outcomes. METHODS: Data from the Geneva Arthroplasty Registry (GAR) about patients receiving a primary elective THA between 1996 and 2019 was used. Selected outcomes were identified from patient and surgeon surveys: pain walking, climbing stairs, night pain, pain interference, and pain medication. Clusters of patients with homogeneous outcomes at 1, 5, and 10 years postoperatively were generated based on selected predictors evaluated preoperatively using conditional inference trees (CITs). RESULTS: Data from 6,836 THAs were analysed and 14 CITs generated with 17 predictors found significant (p < 0.05). Baseline WOMAC pain score, SF-12 self-rated health (SRH), number of comorbidities, SF-12 mental component score, and body mass index (BMI) were the most common predictors. Outcome levels varied markedly by clusters whilst predictors changed at different time points for the same outcome. For example, 79% of patients with good to excellent SRH and less than moderate preoperative night pain reported absence of night pain at 1 year after THA; in contrast, for those with fair/poor SHR this figure was 50%. Also, clusters of patients with homogeneous levels of night pain at 1 year were generated based on SRH, Charnley, WOMAC night and pain scores, whilst those at 10 years were based on BMI alone. CONCLUSIONS: The information tool generated under this study can provide prospective patients and clinicians with valuable and understandable information about the experiences of "patients like them" regarding their pain outcomes.

Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models' output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.

Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data.

Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors.

Disparities in Tuberculosis Incidence by Race and Ethnicity Among the U.S.-Born Population in the United States, 2011 to 2021 : An Analysis of National Disease Registry Data.

BACKGROUND: Elevated tuberculosis (TB) incidence rates have recently been reported for racial/ethnic minority populations in the United States. Tracking such disparities is important for assessing progress toward national health equity goals and implementing change. OBJECTIVE: To quantify trends in racial/ethnic disparities in TB incidence among U.S.-born persons. DESIGN: Time-series analysis of national TB registry data for 2011 to 2021. SETTING: United States. PARTICIPANTS: U.S.-born persons stratified by race/ethnicity. MEASUREMENTS: TB incidence rates, incidence rate differences, and incidence rate ratios compared with non-Hispanic White persons; excess TB cases (calculated from incidence rate differences); and the index of disparity. Analyses were stratified by sex and by attribution of TB disease to recent transmission and were adjusted for age, year, and state of residence. RESULTS: In analyses of TB incidence rates for each racial/ethnic population compared with non-Hispanic White persons, incidence rate ratios were as high as 14.2 (95% CI, 13.0 to 15.5) among American Indian or Alaska Native (AI/AN) females. Relative disparities were greater for females, younger persons, and TB attributed to recent transmission. Absolute disparities were greater for males. Excess TB cases in 2011 to 2021 represented 69% (CI, 66% to 71%) and 62% (CI, 60% to 64%) of total cases for females and males, respectively. No evidence was found to indicate that incidence rate ratios decreased over time, and most relative disparity measures showed small, statistically nonsignificant increases. LIMITATION: Analyses assumed complete TB case diagnosis and self-report of race/ethnicity and were not adjusted for medical comorbidities or social determinants of health. CONCLUSION: There are persistent disparities in TB incidence by race/ethnicity. Relative disparities were greater for AI/AN persons, females, and younger persons, and absolute disparities were greater for males. Eliminating these disparities could reduce overall TB incidence by more than 60% among the U.S.-born population. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention.

Two-Year Clinical Outcome of MitraClip Transcatheter Edge-to-Edge Repair From the J-MITRA Registry Data.

BACKGROUND: The introduction of transcatheter edge-to-edge repair for moderate-to-severe or severe mitral regurgitation (MR) utilizing the MitraClip system became reimbursed and clinically accessible in Japan in April 2018. This study presents the 2-year clinical outcomes of all consecutively treated patients who underwent MitraClip implantation in Japan and were prospectively enrolled in the Japanese Circulation Society-oriented J-MITRA registry.Methods and Results: Analysis encompassed 2,739 consecutive patients enrolled in the J-MITRA registry with informed consent (mean age: 78.3±9.6 years, 1,550 males, STS risk score 11.7±8.9), comprising 1,999 cases of functional MR, 644 of degenerative MR and 96 in a mixed group (DMR and FMR). The acute procedure success rate was 88.9%. After MitraClip implantation, >80% exhibited an MR grade ≤2+ and the trend was sustained over the 2 years. Within this observation period, the mortality rate was 19.3% and the rate of heart failure readmissions was 20.6%. The primary composite endpoint, inclusive of cardiovascular death and heart failure readmission, was significantly higher in patients with functional MR than in with degenerative MR (32.0% vs. 17.5%, P<0.001). CONCLUSIONS: The 2-year clinical outcomes after MitraClip implantation were deduced from comprehensive data within an all-Japan registry.

The use of routinely collected healthcare records for outcome assessment in clinical trials: a UK perspective.

The use of routinely collected electronic healthcare records (EHR) for outcome assessment in clinical trials has been described as a 'disruptive' new technique more than a decade ago. Despite this potential, significant methodological issues and regulatory barriers have hampered the progress in this area. This article discusses the key considerations that trialists should take into account when incorporating EHR into their trials. These include considerations of the clinical relevance of the outcome, data timeliness and quality, ethical and regulatory issues, and some practical considerations for clinical trials units. In addition, this article describes the benefits of using EHR which include cost, reduced trial burden for participants and staff, follow up efficiencies, and improved health economic evaluation procedures. We also describe the major regulatory and start up costs of using EHR in clinical trials. This article focuses on the UK specific EHR landscape in clinical trials and would help researchers and trials units considering the use of this method of outcome data collection in their next trial. If the issues described are mitigated, this method will be a formidable tool for conducting pragmatic clinical trials.

Five-year results from a prospective, single-arm European trial on decellularized allografts for aortic valve replacement-the ARISE Study and ARISE Registry Data.

OBJECTIVES: Decellularized aortic homografts (DAH) were introduced as a new option for aortic valve replacement for young patients. METHODS: A prospective, EU-funded, single-arm, multicentre study in 8 centres evaluating non-cryopreserved DAH for aortic valve replacement. RESULTS: A total of 144 patients (99 male) were prospectively enrolled in the ARISE Trial between October 2015 and October 2018 with a median age of 30.4 years [interquartile range (IQR) 15.9-55.1]; 45% had undergone previous cardiac operations, with 19% having 2 or more previous procedures. The mean implanted DAH diameter was 22.6 mm (standard deviation 2.4). The median operation duration was 312 min (IQR 234-417), the median cardiopulmonary bypass time was 154 min (IQR 118-212) and the median cross-clamp time 121 min (IQR 93-150). No postoperative bypass grafting or renal replacement therapy were required. Two early deaths occurred, 1 due to a LCA thrombus on day 3 and 1 due ventricular arrhythmia 5 h postoperation. There were 3 late deaths, 1 death due to endocarditis 4 months postoperatively and 2 unrelated deaths after 5 and 7 years due to cancer and Morbus Wegener resulting in a total mortality of 3.47%. After a median follow-up of 5.9 years [IQR 5.1-6.4, mean 5.5 years. (standard deviation 1.3) max. 7.6 years], the primary efficacy end-points peak gradient with median 11.0 mmHg (IQR 7.8-17.6) and regurgitation of median 0.5 (IQR 0-0.5) of grade 0-3 were excellent. At 5 years, freedom from death/reoperation/endocarditis/bleeding/thromboembolism were 97.9%/93.5%/96.4%/99.2%/99.3%, respectively. CONCLUSIONS: The 5-year results of the prospective multicentre ARISE trial continue to show DAH to be safe for aortic valve replacement with excellent haemodynamics.

The association between midwifery staffing and reported harmful incidents: a cross-sectional analysis of routinely collected data.

BACKGROUND: Independent inquiries have identified that appropriate staffing in maternity units is key to enabling quality care and minimising harm, but optimal staffing levels can be difficult to achieve when there is a shortage of midwives. The services provided and how they are staffed (total staffing, skill-mix and deployment) have been changing, and the effects of workforce changes on care quality and outcomes have not been assessed. This study aims to explore the association between daily midwifery staffing levels and the rate of reported harmful incidents affecting mothers and babies. METHODS: We conducted a cross-sectional analysis of daily reports of clinical incidents in maternity inpatient areas matched with inpatient staffing levels for three maternity services in England, using data from April 2015 to February 2020. Incidents resulting in harm to mothers or babies was the primary outcome measure. Staffing levels were calculated from daily staffing rosters, quantified in Hours Per Patient Day (HPPD) for midwives and maternity assistants. Understaffing was defined as staffing below the mean for the service. A negative binomial hierarchical model was used to assess the relationship between exposure to low staffing and reported incidents involving harm. RESULTS: The sample covered 106,904 maternal admissions over 46 months. The rate of harmful incidents in each of the three services ranged from 2.1 to 3.0 per 100 admissions across the study period. Understaffing by registered midwives was associated with an 11% increase in harmful incidents (adjusted IRR 1.110, 95% CI 1.002,1.229). Understaffing by maternity assistants was not associated with an increase in harmful incidents (adjusted IRR 0.919, 95% 0.813,1.039). Analysis of specific types of incidents showed no statistically significant associations, but most of the point estimates were in the direction of increased incidents when services were understaffed. CONCLUSION: When there is understaffing by registered midwives, more harmful incidents are reported but understaffing by maternity assistants is not associated with higher risk of harms. Adequate registered midwife staffing levels are crucial for maintaining safety. Changes in the profile of maternity service workforces need to be carefully scrutinised to prevent mothers and babies being put at risk of avoidable harm.

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach.

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review.

BACKGROUND: International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses. OBJECTIVE: This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses. METHODS: A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach. RESULTS: Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level. CONCLUSIONS: There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.

Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis.

BACKGROUND: Ascertainment of heart failure (HF) hospitalizations in cardiovascular trials is costly and complex, involving processes that could be streamlined by using routinely collected healthcare data (RCD). The utility of coded RCD for HF outcome ascertainment in randomized trials requires assessment. We systematically reviewed studies assessing RCD-based HF outcome ascertainment against "gold standard" (GS) methods to study the feasibility of using such methods in clinical trials. METHODS: Studies assessing International Classification of Disease (ICD) coded RCD-based HF outcome ascertainment against GS methods and reporting at least one agreement statistic were identified by searching MEDLINE and Embase from inception to May 2021. Data on study characteristics, details of RCD and GS data sources and definitions, and test statistics were reviewed. Summary sensitivities and specificities for studies ascertaining acute and prevalent HF were estimated using a bivariate random effects meta-analysis. Heterogeneity was evaluated using I2 statistics and hierarchical summary receiver operating characteristic (HSROC) curves. RESULTS: A total of 58 studies of 48,643 GS-adjudicated HF events were included in this review. Strategies used to improve case identification included the use of broader coding definitions, combining multiple data sources, and using machine learning algorithms to search free text data, but these methods were not always successful and at times reduced specificity in individual studies. Meta-analysis of 17 acute HF studies showed that RCD algorithms have high specificity (96.2%, 95% confidence interval [CI] 91.5-98.3), but lacked sensitivity (63.5%, 95% CI 51.3-74.1) with similar results for 21 prevalent HF studies. There was considerable heterogeneity between studies. CONCLUSIONS: RCD can correctly identify HF outcomes but may miss approximately one-third of events. Methods used to improve case identification should also focus on minimizing false positives.

The epidemiology and societal costs of myasthenia gravis in Norway: A non-interventional study using national registry data.

BACKGROUND AND PURPOSE: With the emergence of new treatment options for myasthenia gravis (MG), there is a need for information regarding epidemiology, healthcare utilization, and societal costs to support economic evaluation and identify eligible patients. We aimed to enhance the understanding of these factors using nationwide systematic registry data in Norway. METHODS: We received comprehensive national registry data from five Norwegian health- and work-related registries. The annual incidence and prevalence were estimated for the period 2013-2021 using nationwide hospital and prescription data. The direct, indirect (productivity losses) and intangible costs (value of lost life-years [LLY] and health-related quality of life [HRQoL]) related to MG were estimated over a period of 1 year. RESULTS: In 2021, the incidence of MG ranged from 15 to 16 cases per year per million population depending on the registry used, while the prevalence varied between 208.9 and 210.3 per million population. The total annual societal costs of MG amounted to EUR 24,743 per patient, of which EUR 3592 (14.5%) were direct costs, EUR 8666 (35.0%) were productivity loss, and EUR 12,485 (50.5%) were lost value from LLY and reduced HRQoL. CONCLUSION: The incidence and prevalence of MG are higher than previously estimated, and the total societal costs of MG are substantial. Our findings demonstrate that productivity losses, and the value of LLY and HRQoL constitute a considerable proportion of the total societal costs.

A multi-state analysis of disease trajectories and mental health transitions in patients with type 2 diabetes: A population-based retrospective cohort study utilizing health administrative data.

AIMS: To investigate the risk of major depression and dementia in patients with type 2 diabetes, including dementia resulting from depression, and their impact on diabetes-related complications and mortality. METHODS: We conducted a population-based retrospective cohort study including 11,441 incident cases of diabetes in 2015-2017, with follow-up until 2022. A multi-state survival analysis was performed on a seven-state model with 15 transitions to capture disease progression and onset of mental disorders. RESULTS: Eight-year probabilities of depression, dementia, diabetes-related complications, and death were 9.7% (95% CI 8.7-10.7), 0.9% (95% CI 0.5-1.3), 10.4% (95% CI 9.5-11.4), and 14.8% (95% CI 13.9-15.7), respectively. Depression increased the risk of dementia up to 3.7% (95% CI 2.0-5.4), and up to 10.3% (95% CI 0.3-20.4) if coupled with diabetes complications. Eight-year mortality was 37.5% (95% CI 33.1-42.0) after depression, 74.1% (95% CI 63.7-84.5) after depression plus complications, 76.4% (95% CI 68.8-83.9) after dementia, and 98.6% (95% CI 96.1-100.0) after dementia plus complications. CONCLUSIONS: The interconnections observed across depression, dementia, complications, and mortality underscore the necessity for comprehensive and integrated approaches in managing diabetes. Early screening for depression, followed by timely and targeted interventions, may mitigate the risk of dementia and improve diabetes prognosis.

Population-Based Prognostic Models for Head and Neck Cancers Using National Cancer Registry Data from Taiwan.

PURPOSE: This study aims to raise awareness of the disparities in survival predictions among races in head and neck cancer (HNC) patients by developing and validating population-based prognostic models specifically tailored for Taiwanese and Asian populations. METHODS: A total of 49,137 patients diagnosed with HNCs were included from the Taiwan Cancer Registry (TCR). Six prognostic models, divided into three categories based on surgical status, were developed to predict both overall survival (OS) and cancer-specific survival using the registered demographic and clinicopathological characteristics in the Cox proportional hazards model. The prognostic models underwent internal evaluation through a tenfold cross-validation among the TCR Taiwanese datasets and external validation across three primary racial populations using the Surveillance, Epidemiology, and End Results database. Predictive performance was assessed using discrimination analysis employing Harrell's c-index and calibration analysis with proportion tests. RESULTS: The TCR training and testing datasets demonstrated stable and favorable predictive performance, with all Harrell's c-index values ≥ 0.7 and almost all differences in proportion between the predicted and observed mortality being < 5%. In external validation, Asians exhibited the best performance compared with white and black populations, particularly in predicting OS, with all Harrell's c-index values > 0.7. CONCLUSIONS: Survival predictive disparities exist among different racial groups in HNCs. We have developed population-based prognostic models for Asians that can enhance clinical practice and treatment plans.