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The rise in health care costs, caused by older and more complex patient populations, requires Population Health Management approaches including risk stratification. With risk stratification, patients are assigned individual risk scores based on medical records. These patient stratifications focus on future high costs and expensive care utilization such as hospitalization, for which different models exist. With this study, the research team validated the accuracy of risk prediction scores for future hospitalization and high health care costs, calculated by the Adjusted Clinical Group (ACG)'s risk stratification models, using Dutch primary health care data registries. In addition, they aimed to adjust the US-based predictive models for Dutch primary care. The statistical validity of the existing models was assessed. In addition, the underlying prediction models were trained on 95,262 patients' data from de Zoetermeer region and externally validated on data of 48,780 patients from Zeist, Nijkerk, and Urk. Information on age, sex, number of general practitioner visits, International Classification of Primary Care coded information on the diagnosis and Anatomical Therapeutic Chemical Classification coded information on the prescribed medications, were incorporated in the model. C-statistics were used to validate the discriminatory ability of the models. Calibrating ability was assessed by visual inspection of calibration plots. Adjustment of the hospitalization model based on Dutch data improved C-statistics from 0.69 to 0.75, whereas adjustment of the high-cost model improved C-statistics from 0.78 to 0.85, indicating good discrimination of the models. The models also showed good calibration. In conclusion, the local adjustments of the ACG prediction models show great potential for use in Dutch primary care. [ABSTRACT FROM AUTHOR]

Aims: To determine the association between registered mental illness and type 2 diabetes mellitus treatment targets, while taking into account the effects of health expenditure and social determinants of health. Methods: This observational cross‐sectional study was based on routine primary care data, linked to socio‐economic and medical claims data. The main outcomes, analysed by multivariate logistic regression, were achieving primary care guideline treatment targets for HbA1c, systolic blood pressure (SBP) and LDL‐cholesterol in 2017. We examined the association with diagnosed mental illness registered by the general practitioner (GP) or treated via specialist' mental healthcare between 2016 and 2018, adjusting for, medication use, body mass index, co‐morbidity, smoking, and additionally examining effect‐modification of healthcare expenditures, migration status, income and demographics. Results: Overall (N = 2862), 64.0% of participants achieved their treatment targets for HbA1c, 65.1% for SBP and 53.0% for LDL‐cholesterol. Adjusted for migrant background, income and care expenditures, individuals <65 years of age with mental illness achieved their HbA1c treatment target more often than those without (OR (95% CI)): treatment by GP: 1.46 (1.01, 2.11), specialist care: 1.61 (1.11, 2.34), as did men with mental illness for SBP: GP OR 1.61 (1.09, 2.40), specialist care OR 1.59 (1.09, 2.45). LDL‐cholesterol target was not associated with mental illness. A migrant background or low income lowered the likelihood of reaching HbA1c targets. Conclusions: People with registered mental illness appear comparable or better able to achieve diabetes treatment targets than those without. Achieving HbA1c targets is influenced by social disadvantage. [ABSTRACT FROM AUTHOR]