Quality of care for anxiety and depression in different ethnic groups by family practitioners in urban areas in the Netherlands

Fassaert, Thijs; Nielen, M.; Verheij, Robert; Verhoeff, Arnoud P.; Dekker, Jack; Beekman, Aartjan T.F.; Wit, Matty de

doi:10.1016/j.genhosppsych.2010.04.010

Cited by 19 publications

(9 citation statements)

References 69 publications

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“…Since such studies are research question-driven rather than based on patients consulting the GP, the lower prevalences in our study seem expected. However, we also found mostly lower prevalences in comparison with other primary care registry based studies with anxiety symptom prevalences of 1.7%, anxiety disorder 0.7%, anxiety (ICPC P01 + P74) 2.2%-2.3% and all anxiety codes 7.2% [9,21,47]. These could either be true differences or could be caused by under-registration or under-recognition.…”

Section: Interpretation Of Findingscontrasting

confidence: 56%

Shifts in the Care for Patients Presenting in Primary Care With Anxiety; Stepped Collaborative Care Parameters From More than a Decade

et al. 2015

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Section: Interpretation Of Findingscontrasting

confidence: 56%

Shifts in the Care for Patients Presenting in Primary Care With Anxiety; Stepped Collaborative Care Parameters From More than a Decade

et al. 2015

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“…In addition, linking these EHR data to other distinct data sources increases the research possibilities enormously. For example, data from NIVEL’s Primary Care Database [ 5 ] have been linked to many other data sources providing environmental characteristics [ 52 , 53 ], migration background [ 54 ], income, school dropout rates [ 55 ], insurance claims [ 56 ], and pharmacy data [ 57 ]. EHR data are also increasingly used for public health forecasts and surveillance [ 58 , 35 , 59 , 60 ].…”

Section: Resultsmentioning

confidence: 99%

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

et al. 2018

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BackgroundEnormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use these data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems.ObjectiveIn this paper, we discuss whether data that are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are fit for another purpose, and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the use and reuse of these type of data.MethodsThis paper is based on the authors’ experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.ResultsWe first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step that can generate biased outcomes. We identified 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.ConclusionsThere are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor- and purpose-oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven health care will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda.

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“…It is currently unknown whether detection of emotional distress and referral rates in immigrants with diabetes differ from non‐migrant patients with diabetes. There is evidence to suggest that some ethnic groups have more difficulty accessing mental health services . Future studies should investigate which screening and subsequent intervention strategies are the most effective to tackle the problem of increased level of emotional distress in patients with diabetes with different ethnic backgrounds.…”

Section: Discussionmentioning

confidence: 99%

Ethnic aspects of emotional distress in patients with diabetes – the Amsterdam Health Monitor Study

et al. 2012

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Quality of care for anxiety and depression in different ethnic groups by family practitioners in urban areas in the Netherlands

Abstract: Prevalence of and quality of care for anxiety and depression were comparable between ethnic minority clients, but some differences suggest that efforts to educate primary care providers in management of anxiety/depression should be continued and tailored to specific ethnic groups.

Cited by 19 publications

References 69 publications

Shifts in the Care for Patients Presenting in Primary Care With Anxiety; Stepped Collaborative Care Parameters From More than a Decade

Shifts in the Care for Patients Presenting in Primary Care With Anxiety; Stepped Collaborative Care Parameters From More than a Decade

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

Ethnic aspects of emotional distress in patients with diabetes – the Amsterdam Health Monitor Study

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