Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

Wu, Chia‐Yi; Chang, Chin‐Kuo; Robson, Deborah; Jackson, Roy; Chen, Shaw-Ji; Hayes, Richard D.; Stewart, Robert

doi:10.1371/journal.pone.0074262

Cited by 68 publications

(65 citation statements)

References 30 publications

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“…Fourth, while we adjusted our obesity and smoking estimates for nonresponse to reduce the impact of data missing differentially across strata, doing so did not change the obesity estimate and only changed the smoking estimate by 0.01 percentage points. Fifth, contrary to our expectation and findings from Wu et al,56 our chart review of 48 EHRs found that scanning unstructured fields only minimally improved indicator sensitivity. This finding is important and reassuring because natural language processing to extract unstructured data is not possible within NYC Macroscope and is complicated and burdensome in any setting.…”

Section: Discussioncontrasting

confidence: 99%

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

McVeigh¹,

Newton-Dame²,

Chan³

et al. 2016

eGEMs

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Introduction:Electronic health records (EHRs) offer potential for population health surveillance but EHR-based surveillance measures require validation prior to use. We assessed the validity of obesity, smoking, depression, and influenza vaccination indicators from a new EHR surveillance system, the New York City (NYC) Macroscope. This report is the second in a 3-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the infrastructure underlying the NYC Macroscope; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. This second report, which addresses concerns related to sampling bias and data quality, describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods described in this report to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.Methods:NYC Macroscope prevalence estimates, overall and stratified by sex and age group, were compared to reference survey estimates for adult New Yorkers who reported visiting a doctor in the past year. Agreement was evaluated against 5 a priori criteria. Sensitivity and specificity were assessed by examining individual EHR records in a subsample of 48 survey participants.Results:Among adult New Yorkers in care, the NYC Macroscope prevalence estimate for smoking (15.2%) fell between estimates from NYC HANES (17.7 %) and CHS (14.9%) and met all 5 a priori criteria. The NYC Macroscope obesity prevalence estimate (27.8%) also fell between the NYC HANES (31.3%) and CHS (24.7%) estimates, but met only 3 a priori criteria. Sensitivity and specificity exceeded 0.90 for both the smoking and obesity indicators. The NYC Macroscope estimates of depression and influenza vaccination prevalence were more than 10 percentage points lower than the estimates from either reference survey. While specificity was > 0.90 for both of these indicators, sensitivity was < 0.70.Discussion:Through this work we have demonstrated that EHR data from a convenience sample of providers can produce acceptable estimates of smoking and obesity prevalence among adult New Yorkers in care; gained a better understanding of the challenges involved in estimating depression prevalence from EHRs; and identified areas for additional research regarding estimation of influenza vaccination prevalence. We have also shared lessons learned about how EHR indicators should be constructed and offer methodologic suggestions for validating them.Conclusions:This work adds to a rapidly emerging body of literature about how to define, collect and interpret EHR-based surveillance measures and...

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Section: Discussioncontrasting

confidence: 99%

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

McVeigh¹,

Newton-Dame²,

Chan³

et al. 2016

eGEMs

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“…Other natural language-processing applications developed in the Clinical Record Interactive Search system in current research use include those ascertaining text on tobacco 22 and cannabis use, 23 medications for diabetes and other physical disorders, 7 and more than 70 different mental health symptoms 24 . These efforts have been supplemented by a range of new algorithms for ascertaining recorded body-mass index and mentions of comorbid physical disorders and use of several common illicit drugs.…”

Section: Discussionmentioning

confidence: 99%

Ethnicity and excess mortality in severe mental illness: a cohort study

Das‐Munshi

Chang

Dutta

et al. 2017

The Lancet Psychiatry

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SummaryBackgroundExcess mortality in severe mental illness (defined here as schizophrenia, schizoaffective disorders, and bipolar affective disorders) is well described, but little is known about this inequality in ethnic minorities. We aimed to estimate excess mortality for people with severe mental illness for five ethnic groups (white British, black Caribbean, black African, south Asian, and Irish) and to assess the association of ethnicity with mortality risk.MethodsWe conducted a longitudinal cohort study of individuals with a valid diagnosis of severe mental illness between Jan 1, 2007, and Dec 31, 2014, from the case registry of the South London and Maudsley Trust (London, UK). We linked mortality data from the UK Office for National Statistics for the general population in England and Wales to our cohort, and determined all-cause and cause-specific mortality by ethnicity, standardised by age and sex to this population in 2011. We used Cox proportional hazards regression to estimate hazard ratios and a modified Cox regression, taking into account competing risks to derive sub-hazard ratios, for the association of ethnicity with all-cause and cause-specific mortality.FindingsWe identified 18 201 individuals with a valid diagnosis of severe mental illness (median follow-up 6·36 years, IQR 3·26–9·92), of whom 1767 died. Compared with the general population, age-and-sex-standardised mortality ratios (SMRs) in people with severe mental illness were increased for a range of causes, including suicides (7·65, 95% CI 6·43–9·04), non-suicide unnatural causes (4·01, 3·34–4·78), respiratory disease (3·38, 3·04–3·74), cardiovascular disease (2·65, 2·45–2·86), and cancers (1·45, 1·32–1·60). SMRs were broadly similar in different ethnic groups with severe mental illness, although the south Asian group had a reduced SMR for cancer mortality (0·49, 0·21–0·96). Within the cohort with severe mental illness, hazard ratios for all-cause mortality and sub-hazard ratios for natural-cause and unnatural-cause mortality were lower in most ethnic minority groups relative to the white British group.InterpretationPeople with severe mental illness have excess mortality relative to the general population irrespective of ethnicity. Among those with severe mental illness, some ethnic minorities have lower mortality than the white British group, for which the reasons deserve further investigation.FundingUK Health Foundation and UK Academy of Medical Sciences.

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“…For example, smoking has been found to be under-reported in EHRs, so using only structured smoking fields to characterize patients may inaccurately identify current and former smokers. 56 While illuminating to the analysis, geocoding patient data presents its own set of challenges. Some patients had missing or inaccurate address data, forcing us to drop them from this portion of the analysis.…”

Section: Discussionmentioning

confidence: 99%

The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study

Roth

Payne

Weier

et al. 2016

Journal of Biomedical Informatics

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Background Community-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health. Purpose This study sought to link community and EHR data from an older female patient cohort participating in an ongoing intervention at the Ohio State University Wexner Medical Center to associate community-level data with patient-level cardiovascular health (CVH) as well as to assess the utility of this EHR integration methodology. Materials and Methods CVH was characterized among patients using available EHR data collected May through July of 2013. EHR data for 153 patients were linked to United States census-tract level data to explore feasibility and insights gained from combining these disparate data sources. Analyses were conducted in 2014. Results Using the linked data, weekly per capita expenditure on fruits and vegetables was found to be significantly associated with CVH at the p<0.05 level and three other community-level attributes (median income, average household size, and unemployment rate) were associated with CVH at the p<0.10 level. Conclusions This work paves the way for future integration of community and EHR-based data into patient care as a novel methodology to gain insight into multi-level factors that affect CVH and other health outcomes. Further, our findings demonstrate the specific architectural and functional challenges associated with integrating decision support technologies and geographic information to support tailored and patient-centered decision making therein.

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Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

Cited by 68 publications

References 30 publications

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

Ethnicity and excess mortality in severe mental illness: a cohort study

The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study

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