ABSTRACT
Caries and periodontitis remain prevalent in the Netherlands. Given the assumption that increasing the accessibility and affordability of dental care can improve oral health outcomes, policy interventions aimed at improving these aspects may contribute to better oral health. To identify possible solutions, this scoping review firstly identifies policy interventions from around the world that have effectively improved the accessibility or affordability of dental care. Secondly, this review discusses the potential of the policy interventions identified that are applicable to the Dutch healthcare sector specifically. A literature search was performed in four databases. Two reviewers independently screened all potentially relevant titles and abstracts before doing the same for the full texts. Only studies that had quantitatively evaluated the effectiveness of policy interventions aimed at improving the accessibility or affordability of dental care were included. 61 of the 1288 retrieved studies were included. Interventions were grouped into four categories. Capacity interventions (n = 5) mainly focused on task delegation. Coverage interventions (n = 25) involved the expansion of covered dental treatments or the group eligible for coverage. Managed care interventions (n = 20) were frequently implemented in school or community settings. Payment model interventions (n = 11) focused on dental reimbursement rates or capitation. 199 indicators were identified throughout the 61 included studies. Indicators were grouped into three categories: accessibility (n = 137), affordability (n = 21), and oral health status (n = 41). Based on the included studies, increasing managed care interventions for children and adding dental coverage to the basic health insurance plan for adults could improve access to dental care in the Netherlands. Due to possible spillover effects, it is advisable to investigate a combination of these policy interventions. Further research will be necessary for the development of effective policy interventions in practice.
ABSTRACT
BACKGROUND: Esophagectomy for esophageal cancer has a complication rate of up to 60%. Prediction models could be helpful to preoperatively estimate which patients are at increased risk of morbidity and mortality. The objective of this study was to determine the best prediction models for morbidity and mortality after esophagectomy and to identify commonalities among the models. PATIENTS AND METHODS: A systematic review was performed in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and was prospectively registered in PROSPERO ( https://www.crd.york.ac.uk/prospero/ , study ID CRD42022350846). Pubmed, Embase, and Clarivate Analytics/Web of Science Core Collection were searched for studies published between 2010 and August 2022. The Prediction model Risk of Bias Assessment Tool was used to assess the risk of bias. Extracted data were tabulated and a narrative synthesis was performed. RESULTS: Of the 15,011 articles identified, 22 studies were included using data from tens of thousands of patients. This systematic review included 33 different models, of which 18 models were newly developed. Many studies showed a high risk of bias. The prognostic accuracy of models differed between 0.51 and 0.85. For most models, variables are readily available. Two models for mortality and one model for pulmonary complications have the potential to be developed further. CONCLUSIONS: The availability of rigorous prediction models is limited. Several models are promising but need to be further developed. Some models provide information about risk factors for the development of complications. Performance status is a potential modifiable risk factor. None are ready for clinical implementation.
Subject(s)
Esophagectomy , Humans , Esophagectomy/adverse effects , Prognosis , Morbidity , Bias , Risk FactorsABSTRACT
Aims: Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF. Methods and results: PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC's ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction. Conclusion: AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.
