Identification of child mental health problems by combining electronic health record information from different primary healthcare professionals: a population-based cohort study.

OBJECTIVES: To investigate the potential value of combining information from electronic health records from Dutch general practitioners (GPs) and preventive youth healthcare professionals (PYHPs) in predicting child mental health problems (MHPs). DESIGN: Population-based retrospective cohort study. SETTING: General practice, children who were registered with 76 general practice centres from the Leiden University Medical Centre (LUMC) primary care academic network Extramural LUMC Academic Network in the Leiden area, the Netherlands. For the included children we obtained data regarding a child's healthy development from preventive youth healthcare. PARTICIPANTS: 48 256 children aged 0-19 years old who were registered with participating GPs between 2007 and 2017 and who also had data available from PYHPs from the period 2010-2015. Children with MHPs before 2007 were excluded (n=3415). PRIMARY OUTCOME: First MHPs based on GP data. RESULTS: In 51% of the children who had MHPs according to GPs, PYPHs also had concerns for MHPs. In 31% of the children who had no MHPs according to GPs, PYHPs had recorded concerns for MHPs. Combining their information did not result in better performing prediction models than the models based on GP data alone (c-statistics ranging from 0.62 to 0.64). Important determinants of identification of MHPs by PYHPs 1 year later were concerns from PHYPs about MHPs, borderline or increased problem scores on mental health screening tools, life events, family history of MHPs and an extra visit to preventive youth healthcare. CONCLUSIONS: Although the use of combined information from PYHPs and GPs did not improve prediction of MHPs compared with the use of GP data alone, this study showed the feasibility of analysing a combined dataset from different healthcare providers what has the potential to inform future studies aimed at improving child MHP identification.

The Usefulness of Electronic Health Records From Preventive Youth Healthcare in the Recognition of Child Mental Health Problems.

Background and Objectives: Early identification of child mental health problems (MHPs) is important to provide adequate, timely treatment. Dutch preventive youth healthcare monitors all aspects of a child's healthy development. We explored the usefulness of their electronic health records (EHRs) in scientific research and aimed to develop prediction models for child MHPs. Methods: Population-based cohort study with anonymously extracted electronic healthcare data from preventive youth healthcare centers in the Leiden area, the Netherlands, from the period 2005-2015. Data was analyzed with respect to its continuity, percentage of cases and completeness. Logistic regression analyses were conducted to develop prediction models for the risk of a first recorded concern for MHPs in the next scheduled visit at age 3/4, 5/6, 10/11, and 13/14 years. Results: We included 26,492 children. The continuity of the data was low and the number of concerns for MHPs varied greatly. A large number of determinants had missing data for over 80% of the children. The discriminatory performance of the prediction models were poor. Conclusions: This is the first study exploring the usefulness of EHRs from Dutch preventive youth healthcare in research, especially in predicting child MHPs. We found the usefulness of the data to be limited and the performance of the developed prediction models was poor. When data quality can be improved, e.g., by facilitating accurate recording, or by data enrichment from other available sources, the analysis of EHRs might be helpful for better identification of child MHPs.

Quantitative and temporal approach to utilising electronic medical records from general practices in mental health prediction.

This study proposes a framework for mining temporal patterns from Electronic Medical Records. A new scoring scheme based on the Wilson interval is provided to obtain frequent and predictive patterns, as well as to accelerate the mining process by reducing the number of patterns mined. This is combined with a case study using data from general practices in the Netherlands to identify children at risk of suffering from mental disorders. To develop an accurate model, feature engineering methods such as one hot encoding and frequency transformation are proposed, and the pattern selection is tailored to this type of clinical data. Six machine learning models are trained on five age groups, with XGBoost achieving the highest AUC values (0.75-0.79) with sensitivity and specificity above 0.7 and 0.6 respectively. An improvement is demonstrated by the models learning from patterns in addition to non-temporal features.

Identification of children at risk for mental health problems in primary care-Development of a prediction model with routine health care data.

BACKGROUND: Despite being common and having long lasting effects, mental health problems in children are often under-recognised and under-treated. Improving early identification is important in order to provide adequate, timely treatment. We aimed to develop prediction models for the one-year risk of a first recorded mental health problem in children attending primary care. METHODS: We carried out a population-based cohort study based on readily available routine healthcare data anonymously extracted from electronic medical records of 76 general practice centers in the Leiden area, the Netherlands. We included all patients aged 1-19 years on 31 December 2016 without prior mental health problems. Multilevel logistic regression analyses were used to predict the one-year risk of a first recorded mental health problem. Potential predictors were characteristics related to the child, family and healthcare use. Model performance was assessed by examining measures of discrimination and calibration. FINDINGS: Data from 70,000 children were available. A mental health problem was recorded in 27•7% of patients during the period 2007-2017. Age independent predictors were somatic complaints, more than two GP visits in the previous year, one or more laboratory test and one or more referral/contact with other healthcare professional in the previous year. Other predictors and their effects differed between age groups. Model performance was moderate (c-statistic 0.62-0.63), while model calibration was good. INTERPRETATION: This study is a first promising step towards developing prediction models for identifying children at risk of a first mental health problem to support primary care practice by using routine healthcare data. Data enrichment from other available sources regarding e.g. school performance and family history could improve model performance. Further research is needed to externally validate our models and to establish whether we are able to improve under-recognition of mental health problems.

Factors associated with the identification of child mental health problems in primary care-a systematic review.

Background: Although common and often with long-lasting effects, child mental health problems (MHPs) are still under-recognized and under-treated. A better understanding of the factors associated with the identification of MHPs in primary care may improve the recognition of MHPs. Objectives: To review studies on factors associated with the identification of child MHPs in primary care. Methods: Six leading databases were systematically searched until 1 October 2018. Two independent researchers selected articles and extracted data on study characteristics and factors associated with MHP identification. Inclusion criteria were the investigation of factors associated with MHP identification by primary care professionals (PCPs) in children aged 0-18 years. Results: Of the 6215 articles identified, 26 were included. Prevalence rates of PCP-identified MHPs varied between 7 and 30%. PCPs identified 26-60% of children with an increased risk of MHPs as indicated by MHP assessment tools, but associated factors were investigated in relatively few studies. MHPs were more often identified in children with a family composition other than married parents, with worse mental health symptoms, prior MHPs, among boys in elementary school, when contact with PCPs was related to parental psychosocial concerns or routine health check-ups, when PCPs were recently trained in MHPs or when PCPs felt less burdened treating MHPs. Conclusion: MHP identification varied substantially between studies and PCPs and was related to several child, family and practice factors. Future studies should systematically investigate factors associated with MHP identification by PCPs and specifically in children with an increased risk of MHPs according to mental health assessment tools.

[Collaboration between general practitioners and preventive youth health physicians: room for improvement]. / Samenwerking tussen huisarts en jeugdarts.

OBJECTIVE: To investigate how general practitioners and preventive youth health physicians experience their collaboration and to analyse factors involved. DESIGN: Qualitative research. METHOD: 14 general practitioners and 11 preventive youth health physicians from the Leiden and The Hague areas were interviewed in a semistructured manner. Data were analysed by thematic analysis using the 'Framework method', to identify important themes for collaboration. RESULTS: Contact frequency between general practitioners and preventive youth health physicians varied from biannually to weekly. Important conditions for good collaboration were not met by most participants. General practitioners were not always aware of competencies and tasks of preventive youth health physicians and had little trust in them. They also reported less often than preventive youth health physicians that there were mutual agreements or guidelines. Both parties experienced little support from municipalities or their own organisations. For both, exchange of information mainly took place in case of medical necessity or when the other party requested it. Accessibility of the other party was experienced as inconsistent. Better information exchange was mentioned as the most important point for improvement of collaboration. CONCLUSION: Current collaboration between general practitioners and preventive youth health physicians is suboptimal. There is room for improvement with respect to knowledge of each other's competencies and tasks, trust, information exchange and support from within their own organisations and municipalities. These insights could help to shape and improve interprofessional collaboration in primary care for children, also regarding the youth teams created recently.

Identification of patients at risk for colorectal cancer in primary care: an explorative study with routine healthcare data.

BACKGROUND: Early diagnosis of colorectal cancer (CRC) is likely to reduce burden of disease and improve treatment success. Estimation of the individual patient risk for CRC diagnostic determinants in a primary care setting has not been very successful as yet. The aim of our study is to improve prediction of CRC in patients selected for colonoscopy in the primary healthcare setting using readily available routine healthcare data. PATIENTS AND METHODS: A cross-sectional study was carried out in the Julius General Practitioners' Network database. Patients referred for colonoscopy by their general practitioner (GP) between 2007 and 2012 were selected. We evaluated the association between long-term registered patient characteristics, symptoms and conditions, and colonoscopy test results with multivariable logistic regression. RESULTS: Two per cent (2787/140 000) of the patients between 30 and 85 years were found to be newly referred for colonoscopy by their GP, of whom 57 (2%) were diagnosed with CRC. Age 50 years or over, hypertension and the absence of preceding consultations for abdominal pain were independent predictors for CRC and/or high-risk adenomas, with an area under the curve of 0.65. CONCLUSION: Three factors in routine care data combined might prove valuable in future strategies to improve the prediction of CRC risk in primary care. Improvement in quality and availability of routine care data for research and risk stratification is needed to optimize its usability for prediction purposes in daily practice. IMPACT: Only referring patients at the highest risk for colonoscopy by the GP could decrease superfluous colonoscopies.