Loss of Paid Employment up to 4 Years after Colorectal Cancer Diagnosis-A Nationwide Register-Based Study with a Population-Based Reference Group.

Cancer survivors consider work as a key aspect of cancer survivorship while previous research indicated that cancer survivors have a higher risk of unemployment. The objectives were to assess: (1) whether colorectal cancer survivors less often have paid employment at diagnosis compared to a population-based reference group, (2) whether colorectal cancer survivors with paid work have a higher risk of loss of employment up to 4 years after diagnosis compared to a population-based reference group and (3) which colorectal cancer survivors are at highest risk of loss of paid employment. In a nationwide register-based study, persons diagnosed with colorectal cancer (N = 12,007) as registered in the Netherlands Cancer Registry, were compared on loss of paid employment with a sex and age-matched population-based reference group (N = 48,028) from Statistics Netherlands. Cox regression analyses were conducted. Colorectal cancer survivors had a higher risk of loss of paid employment (HR 1.56 [1.42, 1.71]). Within the group of colorectal cancer survivors, risk of loss of paid employment was lower for older survivors (>60 vs. 45-55) (HR 0.64 [0.51, 0.81]) and higher for those with a more advanced cancer stage (IV vs. I) (HR 1.89 [1.33, 2.70]) and those receiving radiotherapy (HR 1.37 [1.15, 1.63]). Colorectal cancer survivors at high risk of loss of paid employment may benefit from work support interventions as part of cancer survivorship.

Predicting thromboembolic complications in COVID-19 ICU patients using machine learning.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is a challenge for intensive care units (ICU) in part due to the failure to identify risks for patients early and the inability to render an accurate prognosis. Previous reports suggest a strong association between hypercoagulability and poor outcome. Factors related to hemostasis may, therefore, serve as tools to improve the management of COVID-19 patients. AIM: The purpose of this report is to develop a model to determine whether it is possible to early identify COVID-19 patients at risk for thromboembolic complications (TCs). METHODS: We analyzed electronic health record data of 108 consecutive COVID-19 patients admitted to the adult ICU of the Erasmus University Medical Center between February 27 and May 20, 2020. By training a decision tree classifier on 66% of the available data, a model for the prediction of TCs was developed. RESULTS: The median (interquartile range) age was 62 (53-70) years and 73% were male. Forty-three patients (40%) developed a TC during their ICU stay. Mortality was higher for patients in the TCs group compared to the control group (26% vs. 8%, P=0.03). Lactate dehydrogenase, standardized bicarbonate, albumin, and leukocytes were identified by the Decision Tree classifier as the most powerful predictors for TCs 2 days before the onset of the TC, with a sensitivity of 73% and a positive likelihood ratio of 2.7 on the test dataset. CONCLUSIONS: Clinically relevant TCs frequently occur in critically ill COVID-19 patients. These can successfully be predicted using a decision tree model. Although this model could be of special importance to aid clinical decision making, its generalizability and clinical impact should be determined in a larger population. RELEVANCE FOR PATIENTS: Recently, severe TCs were observed in COVID-19 patients with progressive respiratory failure warranting ICU treatment. Timely identification of patients at risk of developing TCs is critical inasmuch as it would enable clinicians to initiate potentially salvaging therapeutic anticoagulation.

Defining care products to finance health care in the Netherlands.

A case-mix project started in the Netherlands with the primary goal to define a complete set of health care products for hospitals. The definition of the product structure was completed 4 years later. The results are currently being used for billing purposes. This paper focuses on the methodology and techniques that were developed and applied in order to define the casemix product structure. The central research question was how to develop a manageable product structure, i.e., a limited set of hospital products, with acceptable cost homogeneity. For this purpose, a data warehouse with approximately 1.5 million patient records from 27 hospitals was build up over a period of 3 years. The data associated with each patient consist of a large number of a priori independent parameters describing the resource utilization in different stages of the treatment process, e.g., activities in the operating theatre, the lab and the radiology department. Because of the complexity of the database, it was necessary to apply advanced data analysis techniques. The full analyses process that starts from the database and ends up with a product definition consists of four basic analyses steps. Each of these steps has revealed interesting insights. This paper describes each step in some detail and presents the major results of each step. The result consists of 687 product groups for 24 medical specialties used for billing purposes.

Principal agent relationships and the efficiency of hospitals.

The efficiency of hospitals is an important political issue and has been the subject of a number of studies. Most studies find evidence for inefficiency but provide no theoretical explanations for differences in efficiency. This study used principal agent theory to explain differences in efficiency between hospitals. Two agency issues are examined: (1) quality of care in the relationship between hospital and patient, and (2) internal organisation, i.e. the relationship between the hospital and its main departments. It was found that efficiency and quality go together. This implies that the potential harmful information asymmetry between hospitals and patients does not appear to be a major problem, because increasing efficiency does not seem to reduce quality. Further, we find no relationship between the efficiency of departments and the efficiency of the entire hospital. The interest of hospital departments is currently not in line with the interests of the entire hospital.

Hospital efficiency and transaction costs: a stochastic frontier approach.

The make-or-buy decision of organizations is an important issue in the transaction cost theory, but is usually not analyzed from an efficiency perspective. Hospitals frequently have to decide whether to outsource or not. The main question we address is: Is the make-or-buy decision affected by the efficiency of hospitals? A one-stage stochastic cost frontier equation is estimated for Dutch hospitals. The make-or-buy decisions of ten different hospital services are used as explanatory variables to explain efficiency of hospitals. It is found that for most services the make-or-buy decision is not related to efficiency. Kitchen services are an important exception to this. Large hospitals tend to outsource less, which is supported by efficiency reasons. For most hospital services, outsourcing does not significantly affect the efficiency of hospitals. The focus on the make-or-buy decision may therefore be less important than often assumed.