Assessment of Value of Neighborhood Socioeconomic Status in Models That Use Electronic Health Record Data to Predict Health Care Use Rates and Mortality

Association between socioeconomic status and risk of chronic obstructive pulmonary disease in China: a prospective cohort study

OBJECTIVE

Socioeconomic status (SES) has been proven to be associated with chronic obstructive pulmonary disease (COPD) in Western populations, but the evidence is very limited in China. This study aimed to investigate the association between SES and the risk of COPD incident.

METHODS

This study was based on the China Kadoorie Biobank (CKB) project in Wuzhong District, Suzhou. A total of 45,484 adults aged 30-79 were included in the analysis during 2004-2008. We used Cox proportional hazard models to investigate the association between SES and the risk of COPD. Household income, education, private property and consumption potential was used to measure SES. Incident COPD cases were ascertained using hospitalization records, death certificates, and active follow-up.

RESULTS

A total of 524 COPD cases were identified during a median follow-up of 11.2 years. Household income was inversely associated with the risk of COPD (Ptrend<0.005). The adjusted hazard ratios (95% confidence intervals) for incident COPD were 0.88 (0.69-1.14), 0.77 (0.60-0.99), and 0.42 (0.31-0.57) for participants with annual household income of 10,000 ~ 19,999 yuan, 20,000 ~ 34,999 yuan and ≥ 35,000 yuan respectively, in comparison to participants with an annual household income < 10,000 yuan. Furthermore, we found that education level, refrigerator use, private toilet, private phone, and motor vehicle were adversely associated with COPD risk, while ownership of newly renovated flats was positively correlated with COPD incident.

CONCLUSIONS

This prospective study suggests that SES is associated with the risk of COPD in Chinese adults. Population-based COPD prevention strategies tailored for people with different SES could help reduce the burden of COPD in Chinese.

Clinical characteristics and risk of all-cause mortality in low education patients with chronic obstructive pulmonary disease in the Chinese population

Background

Education levels play a critical role in the development of chronic obstructive pulmonary disease (COPD), which mainly affects the elderly, who generally have a low level of education in China. We aimed to investigate the association between education level and COPD clinical characteristics and outcomes, especially the effects of education level on the all-cause mortality of COPD in the Chinese population.

Methods

We retrieved data collected between December 2016 and June 2020 in the RealDTC, an ongoing multicenter, real-world study on the status of diagnosis and treatment of COPD. The patients were classified into low- and high-education groups. We extracted data on demographics, pulmonary function, Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades, modified Medical Research Council (mMRC) scores, COPD Assessment Test (CAT) scores, exacerbation history, therapy, and comorbidities, and on mortality during three years of follow-up.

Results

We included 4098 patients with COPD, of whom 3258 (79.5%) were of low education. This group had higher ages, CAT scores, mMRC scores, and numbers of exacerbations, as well as a greater proportion of females, never smokers, biofuel exposure, and GOLD grade 3. Logistic regression showed that being aged ≥65 years, being female, having biofuel exposure, having CAT scores of 20-29, and having ≥2 exacerbations were independently associated with having low education (P < 0.05). Furthermore, low-education COPD patients had a higher cumulative mortality risk during three years of follow-up than their high-education counterparts (hazard ratio (HR) = 1.75; 95% confidence interval (CI) = 1.17-2.61, P = 0.006).

Conclusions

Low-education COPD patients, who accounted for most of our sample, had a higher symptom burden, risk of exacerbation, and risk of all-cause mortality. Clinicians attending COPD patients should be more attentive of individuals with low education levels.

Electronic health records perception among three healthcare providers specialties in Saudi Arabia: A cross-sectional study

Worldwide, more health care facilities are adapting the use of electronic health record (EHR). Healthcare providers (HCP) have different perceptions toward the use of EHR. To investigate the perception of three classes of HCP in Saudi Arabia toward using EHR, a questionnaire (targeting satisfaction, easiness, and benefits of use as major perception indicators) was prepared. The questionnaire was assessed by an expert panel for content validity. The questionnaire internal consistency was examined using Cronbach's alpha. 108 physicians, physical therapists (PT) and respiratory care therapists (RT) from different hospitals in Saudi Arabia answered the questionnaire. Most of respondents perceived EHR systems as beneficial and made work easier. Most HCP were satisfied with the use of EHR, however, with the use of EHR more time was needed to finish the work. Age, experience, job, and job rank of HCP are of different importance in determining responses, perception, and obstacles of using EHR. Moreover, the perception of using EHR seems to be field specific. There is a positive perception among Saudi Arabia HCP about EHR use.

Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European countries

BACKGROUND

Pandemics such as the COVID-19 pandemic and other severe health care disruptions endanger individuals to miss essential care. Machine learning models that predict which patients are at greatest risk of missing care visits can help health administrators prioritize retentions efforts towards patients with the most need. Such approaches may be especially useful for efficiently targeting interventions for health systems overburdened during states of emergency.

METHODS

We use data on missed health care visits from over 55,500 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE) COVID-19 surveys (June - August 2020 and June - August 2021) with longitudinal data from waves 1-8 (April 2004 - March 2020). We compare the performance of four machine learning algorithms (stepwise selection, lasso, random forest, and neural networks) to predict missed health care visits during the first COVID-19 survey based on common patient characteristics available to most health care providers. We test the prediction accuracy, sensitivity, and specificity of the selected models for the first COVID-19 survey by employing 5-fold cross-validation, and test the out-of-sample performance of the models by applying them to the data from the second COVID-19 survey.

RESULTS

Within our sample, 15.5% of the respondents reported any missed essential health care visit due to the COVID-19 pandemic. All four machine learning methods perform similarly in their predictive power. All models have an area under the curve (AUC) of around 0.61, outperforming random prediction. This performance is sustained for data from the second COVID-19 wave one year later, with an AUC of 0.59 for men and 0.61 for women. When classifying all men (women) with a predicted risk of 0.135 (0.170) or higher as being at risk of missing care, the neural network model correctly identifies 59% (58%) of the individuals with missed care visits, and 57% (58%) of the individuals without missed care visits. As the sensitivity and specificity of the models are strongly related to the risk threshold used to classify individuals, the models can be calibrated depending on users' resource constraints and targeting approach.

CONCLUSIONS

Pandemics such as COVID-19 require rapid and efficient responses to reduce disruptions in health care. Based on characteristics available to health administrators or insurance providers, simple machine learning algorithms can be used to efficiently target efforts to reduce missed essential care.

Social Determinants of Health Influence Future Health Care Costs in the Medicaid Cohort of the District of Columbia Study

Policy Points Social determinants of health are an important predictor of future health care costs. Medicaid must partner with other sectors to address the underlying causes of its beneficiaries' poor health and high health care spending.

CONTEXT

Social determinants of health are an important predictor of future health care costs but little is known about their impact on Medicaid spending. This study analyzes the role of social determinants of health (SDH) in predicting future health care costs for adult Medicaid beneficiaries with similar past morbidity burdens and past costs.

METHODS

We enrolled into a prospective cohort study 8,892 adult Medicaid beneficiaries who presented for treatment at an emergency department or clinic affiliated with two hospitals in Washington, DC, between September 2017 and December 31, 2018. We used SDH information measured at enrollment to categorize our participants into four social risk classes of increasing severity. We used Medicaid claims for a 2-year period; 12 months pre- and post-study enrollment to measure past and future morbidity burden according to the Adjusted Clinical Groups system. We also used the Medicaid claims data to characterize total annual Medicaid costs one year prior to and one year after study enrollment.

RESULTS

The 8,892 participants were primarily female (66%) and Black (91%). For persons with similar past morbidity burdens and past costs (p < 0.01), the future morbidity burden was significantly higher in the upper two social risk classes (1.15 and 2.04, respectively) compared with the lowest one. Mean future health care spending was significantly higher in the upper social risk classes compared with the lowest one ($2,713, $11,010, and $17,710, respectively) and remained significantly higher for the two highest social risk classes ($1,426 and $3,581, respectively), given past morbidity burden and past costs (p < 0.01). When we controlled for future morbidity burden (measured concurrently with future costs), social risk class was no longer a significant predictor of future health care costs.

CONCLUSIONS

SDH are statistically significant predictors of future morbidity burden and future costs controlling for past morbidity burden and past costs. Further research is needed to determine whether current payment systems adequately account for differences in the care needs of highly medically and socially complex patients.

Prospective evaluation of social risks, physical function, and cognitive function in prediction of non-elective rehospitalization and post-discharge mortality

Background

Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients’ rehospitalization risk. Understanding a patient’s readmission risk may help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone.

Methods

We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR–derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman’s rho statistic. For all models, the results reported were obtained after fivefold cross validation.

Results

The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors).

Conclusions

In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07910-w.

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models

BACKGROUND

Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are unclear. There is a critical need to develop methods to identify children at high-risk for future exacerbation to allow targeted prevention measures. We sought to evaluate the utility of models using spatiotemporally resolved climatic data and individual electronic health records (EHR) in predicting pediatric asthma exacerbations.

METHODS

We extracted retrospective EHR data for 5982 children with asthma who had an encounter within the Duke University Health System between January 1, 2014 and December 31, 2019. EHR data were linked to spatially resolved environmental data, and temporally resolved climate, pollution, allergen, and influenza case data. We used xgBoost to build predictive models of asthma exacerbation over 30-180 day time horizons, and evaluated the contributions of different data types to model performance.

RESULTS

Models using readily available EHR data performed moderately well, as measured by the area under the receiver operating characteristic curve (AUC 0.730-0.742) over all three time horizons. Inclusion of spatial and temporal data did not significantly improve model performance. Generating a decision rule with a sensitivity of 70% produced a positive predictive value of 13.8% for 180 day outcomes but only 2.9% for 30 day outcomes.

CONCLUSIONS

EHR data-based models perform moderately wellover a 30-180 day time horizon to identify children who would benefit from asthma exacerbation prevention measures. Due to the low rate of exacerbations, longer-term models are likely to be most clinically useful.

TRIAL REGISTRATION

Not applicable.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Association between socioeconomic status and chronic obstructive pulmonary disease in Jiangsu province, China: a population-based study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a common public health problem worldwide. Recent studies have reported that socioeconomic status (SES) is related to the incidence of COPD. This study aimed to investigate the association between SES and COPD among adults in Jiangsu province, China, and to determine the possible direct and indirect effects of SES on the morbidity of COPD.

METHODS

A cross-sectional study was conducted among adults aged 40 years and above between May and December of 2015 in Jiangsu province, China. Participants were selected using a multistage sampling approach. COPD, the outcome variable, was diagnosed by physicians based on spirometry, respiratory symptoms, and risk factors. Education, occupation, and monthly family average income (FAI) were used to separately indicate SES as the explanatory variable. Mixed-effects logistic regression models were introduced to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for examining the SES-COPD relationship. A pathway analysis was conducted to further explore the pulmonary function impairment of patients with different SES.

RESULTS

The mean age of the 2421 participants was 56.63 ± 9.62 years. The prevalence of COPD was 11.8% (95% CI: 10.5%-13.1%) among the overall sample population. After adjustment for age, gender, residence, outdoor and indoor air pollution, body weight status, cigarette smoking, and potential study area-level clustering effects, educational attainment was negatively associated with COPD prevalence in men; white collars were at lower risk (OR: 0.60, 95% CI: 0.43-0.83) of experiencing COPD than blue collars; compared with those within the lower FAI subgroup, participants in the upper (OR: 0.68, 95% CI: 0.49-0.97) tertiles were less likely to experience COPD. Such negative associations between all these three SES indicators and COPD were significant among men only. Education, FAI, and occupation had direct or indirect effects on pulmonary function including post-bronchodilator forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC), FEV1, FVC, and FEV1 percentage of predicted. Education, FAI, and occupation had indirect effects on pulmonary function indices of all participants mainly through smoking status, indoor air pollution, and outdoor air pollution. We also found that occupation could affect post-bronchodilator FEV1/FVC through body mass index.

CONCLUSIONS

Education, occupation, and FAI had an adverse relationship with COPD prevalence in Jiangsu province, China. SES has both direct and indirect associations with pulmonary function impairment. SES is of great significance for COPD morbidity. It is important that population-based COPD prevention strategies should be tailored for people with different SES.

Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions

BACKGROUND

Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions.

OBJECTIVE

Identify existing approaches to inclusion of a broad set of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions.

METHODS

A systematic review of scientific literature published and indexed in PubMed, Web of Science, Association of Computing Machinery (ACM) and SCOPUS from 2010 through October 2020 was performed. To be included, articles had to include search terms related to Electronic Health Record (EHR) data Neighborhood-Level Risk Factors (NLRFs), and Machine Learning (ML) Methods. Citations of relevant articles were also reviewed for additional articles for inclusion. Articles were reviewed and coded by two independent reviewers to capture key information including data sources, linkage of EHR to NRLFs, methods, and results. Articles were assessed for quality using a modified Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO).

RESULTS

A total of 334 articles were identified for abstract review. 36 articles were identified for full review with 19 articles included in the final analysis. All but two of the articles included socio-demographic data derived from the U.S. Census and we found great variability in sources of NLRFs beyond the Census. The majority or the articles (14 of 19) included broader clinical (e.g. medications, labs and co-morbidities) and demographic information about the individual from the EHR in addition to the clinical outcome variable. Half of the articles (10) had a stated goal to predict the outcome(s) of interest. While results of the studies reinforced the correlative association of NLRFs to clinical outcomes, only one article found that adding NLRFs into a model with other data added predictive power with the remainder concluding either that NLRFs were of mixed value depending on the model and outcome or that NLRFs added no predictive power over other data in the model. Only one article scored high on the quality assessment with 13 scoring moderate and 4 scoring low.

CONCLUSIONS

In spite of growing interest in combining NLRFs with EHR data for clinical prediction, we found limited evidence that NLRFs improve predictive power in clinical risk models. We found these data and methods are being used in four ways. First, early approaches to include broad NLRFs to predict clinical risk primarily focused on dimension reduction for feature selection or as a data preparation step to input into regression analysis. Second, more recent work incorporates NLRFs into more advanced predictive models, such as Neural Networks, Random Forest, and Penalized Lasso to predict clinical outcomes or predict value of interventions. Third, studies that test how inclusion of NLRFs predict clinical risk have shown mixed results regarding the value of these data over EHR or claims data alone and this review surfaced evidence of potential quality challenges and biases inherent to this approach. Finally, NLRFs were used with unsupervised learning to identify underlying patterns in patient populations to recommend targeted interventions. Further access to computable, high quality data is needed along with careful study design, including sub-group analysis, to better determine how these data and methods can be used to support decision making in a clinical setting.