Development and Validation of the Chronic Disease Population Risk Tool (CDPoRT) to Predict Incidence of Adult Chronic Disease

Revolutionizing Chronic Heart Disease Management: The Role of IoT-Based Ambulatory Blood Pressure Monitoring System

Chronic heart disease (CHD) is a widespread and persistent health challenge that demands immediate attention. Early detection and accurate diagnosis are essential for effective treatment and management of this condition. To overcome this difficulty, we created a state-of-the-art IoT-Based Ambulatory Blood Pressure Monitoring System that provides real-time blood pressure readings, systolic, diastolic, and pulse rates at predefined intervals. This unique technology comes with a module that forecasts CHD's early warning score. Various machine learning algorithms employed comprise Naïve Bayes, K-Nearest Neighbors (K-NN), random forest, decision tree, and Support Vector Machine (SVM). Using Naïve Bayes, the proposed model has achieved an impressive 99.44% accuracy in predicting blood pressure, a vital aspect of real-time intensive care for CHD. This IoT-based ambulatory blood pressure monitoring (IABPM) system will provide some advancement in the field of healthcare. The system overcomes the limitations of earlier BP monitoring devices, significantly reduces healthcare costs, and efficiently detects irregularities in chronic heart diseases. By implementing this system, we can take a significant step forward in improving patient outcomes and reducing the global burden of CHD. The system's advanced features provide an accurate and reliable diagnosis that is essential for treating and managing CHD. Overall, this IoT-based ambulatory blood pressure monitoring system is an important tool for the early identification and treatment of CHD in the field of healthcare.

Modeling chronic disease risk across equity factors using a population-based prediction model: the Chronic Disease Population Risk Tool (CDPoRT)

BACKGROUND

Predicting chronic disease incidence at a population level can help inform overall future chronic disease burden and opportunities for prevention. This study aimed to estimate the future burden of chronic disease in Ontario, Canada, using a population-level risk prediction algorithm and model interventions for equity-deserving groups who experience barriers to services and resources due to disadvantages and discrimination.

METHODS

The validated Chronic Disease Population Risk Tool (CDPoRT) estimates the 10-year risk and incidence of major chronic diseases. CDPoRT was applied to data from the 2017/2018 Canadian Community Health Survey to predict baseline 10-year chronic disease estimates to 2027/2028 in the adult population of Ontario, Canada, and among equity-deserving groups. CDPoRT was used to model prevention scenarios of 2% and 5% risk reductions over 10 years targeting high-risk equity-deserving groups.

RESULTS

Baseline chronic disease risk was highest among those with less than secondary school education (37.5%), severe food insecurity (19.5%), low income (21.2%) and extreme workplace stress (15.0%). CDPoRT predicted 1.42 million new chronic disease cases in Ontario from 2017/2018 to 2027/2028. Reducing chronic disease risk by 5% prevented 1500 cases among those with less than secondary school education, prevented 14 900 cases among those with low household income and prevented 2800 cases among food-insecure populations. Large reductions of 57 100 cases were found by applying a 5% risk reduction in individuals with quite a bit workplace stress.

CONCLUSION

Considerable reduction in chronic disease cases was predicted across equity-defined scenarios, suggesting the need for prevention strategies that consider upstream determinants affecting chronic disease risk.

Increasing Health Behaviors and Psychological Measures with an Adapted Version of the ACCELERATION Program

BACKGROUND

Recent evidence highlights the importance of interventions tackling physical inactivity and unhealthy eating in lower-income countries. The purpose of this study was to examine the effectiveness of the Canadian ACCELERATION lifestyle program adapted to Brazilians. The main outcomes of the study were changes in the engagement in weekly moderate-to-vigorous physical activity (MVPA) and in the daily consumption of fruits/vegetables.

METHODS

The adapted intervention consisted of a 12-week quasi-randomized controlled trial delivered through email. The data from the original Canadian experimental group (CE, n = 194) and the two groups of Portuguese-speaking Brazilians living in Canada in the adapted program - Brazilian experimental (BE, n = 41) and Brazilian control (BC, n = 35) - were assessed at baseline and post-intervention. The data of the 270 participants were analyzed using two-way repeated measures factorial ANCOVA (group x time) for ratio variables and Chi-square and McNemar tests for the categorical variables.

RESULTS

The BE group had a significant increase in MVPA (mean difference, 95% CI: 86.3, 38.1-134.4 min/week) and fruits/vegetables intake (3.2, 1.4-5.1 servings/day) after the intervention (both p < 0.001). The proportion of participants engaging in ≥ 150 min of MVPA increased from 4.9% to 73.2%, while adoption of a healthy diet increased from 4.9% to 53.7% in the BE group (both p < 0.001). The CE group also improved on these variables (p < 0.05) with no difference vs the BE group (p > 0.05), whereas BC did not show changes (p > 0.05).

CONCLUSION

The Brazilian version of the ACCELERATION program effectively promoted positive health behavior changes in its participants and has the potential to contribute to the fight against risk factors for chronic diseases in Brazilians.

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT)

INTRODUCTION

Avoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitalizations at the population level represents a significant advantage for health system decision-makers that could facilitate proactive intervention for ambulatory care-sensitive conditions (ACSCs). The aim of this study is to develop and validate the Avoidable Hospitalization Population Risk Tool (AvHPoRT) that will predict the 5-year risk of first avoidable hospitalization for seven ACSCs using self-reported, routinely collected population health survey data.

METHODS AND ANALYSIS

The derivation cohort will consist of respondents to the first 3 cycles (2000/01, 2003/04, 2005/06) of the Canadian Community Health Survey (CCHS) who are 18-74 years of age at survey administration and a hold-out data set will be used for external validation. Outcome information on avoidable hospitalizations for 5 years following the CCHS interview will be assessed through data linkage to the Discharge Abstract Database (1999/2000-2017/2018) for an estimated sample size of 394,600. Candidate predictor variables will include demographic characteristics, socioeconomic status, self-perceived health measures, health behaviors, chronic conditions, and area-based measures. Sex-specific algorithms will be developed using Weibull accelerated failure time survival models. The model will be validated both using split set cross-validation and external temporal validation split using cycles 2000-2006 compared to 2007-2012. We will assess measures of overall predictive performance (Nagelkerke R2), calibration (calibration plots), and discrimination (Harrell's concordance statistic). Development of the model will be informed by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement.

ETHICS AND DISSEMINATION

This study was approved by the University of Toronto Research Ethics Board. The predictive algorithm and findings from this work will be disseminated at scientific meetings and in peer-reviewed publications.

Modelling population-level and targeted interventions of weight loss on chronic disease prevention in the Canadian population

Obesity is a known risk factor for major chronic diseases. Prevention of chronic disease is a top global priority. The study aimed to model scenarios of population-level and targeted weight loss interventions on 10-year projected risk of chronic disease in Canada using a population-level risk prediction algorithm. The validated Chronic Disease Population Risk Tool (CDPoRT) forecasts 10-year risk of chronic disease in the adult population. We applied CDPoRT to the 2013/14 Canadian Community Health Survey to generate prospective chronic disease estimates for adults 20 years and older in Canada (n = 83,220). CDPoRT was used to model the following scenarios: British Columbia's (BC) and Quebec's (QC) provincial population-level weight reduction targets, a population-level intervention that could achieve weight loss, targeted weight loss interventions for overweight and obese groups, and the combination of a population-level and targeted weight loss intervention. We estimated chronic disease risk reductions and number of cases prevented in each scenario compared with the baseline. At baseline, we predicted an 18.4% risk and 4,151,929 new cases of chronic disease in Canada over the 10-year period. Provincial weight loss targets applied to the Canadian population estimated chronic disease reductions of 0.6% (BC) and 0.1% (QC). The population-level intervention estimated a greater reduction in risk (0.2%), compared to the targeted interventions (0.1%). The combined approach estimated a 0.3% reduction in chronic disease risk. Our modelling predicted that population-level approaches that achieve weight loss in combination with targeted weight loss interventions can substantially decrease the chronic disease burden in Canada.

Study protocol for development and validation of a single tool to assess risks of stroke, diabetes mellitus, myocardial infarction and dementia: DemNCD-Risk

INTRODUCTION

Current efforts to reduce dementia focus on prevention and risk reduction by targeting modifiable risk factors. As dementia and cardiometabolic non-communicable diseases (NCDs) share risk factors, a single risk-estimating tool for dementia and multiple NCDs could be cost-effective and facilitate concurrent assessments as compared with a conventional single approach. The aim of this study is to develop and validate a new risk tool that estimates an individual's risk of developing dementia and other NCDs including diabetes mellitus, stroke and myocardial infarction. Once validated, it could be used by the public and general practitioners.

METHODS AND ANALYSIS

Ten high-quality cohort studies from multiple countries were identified, which met eligibility criteria, including large representative samples, long-term follow-up, data on clinical diagnoses of dementia and NCDs, recognised modifiable risk factors for the four NCDs and mortality data. Pooled harmonised data from the cohorts will be used, with 65% randomly allocated for development of the predictive model and 35% for testing. Predictors include sociodemographic characteristics, general health risk factors and lifestyle/behavioural risk factors. A subdistribution hazard model will assess the risk factors' contribution to the outcome, adjusting for competing mortality risks. Point-based scoring algorithms will be built using predictor weights, internally validated and the discriminative ability and calibration of the model will be assessed for the outcomes. Sensitivity analyses will include recalculating risk scores using logistic regression.

ETHICS AND DISSEMINATION

Ethics approval is provided by the University of New South Wales Human Research Ethics Committee (UNSW HREC; protocol numbers HC200515, HC3413). All data are deidentified and securely stored on servers at Neuroscience Research Australia. Study findings will be presented at conferences and published in peer-reviewed journals. The tool will be accessible as a public health resource. Knowledge translation and implementation work will explore strategies to apply the tool in clinical practice.

Predicting work disability among people with chronic conditions: a prospective cohort study

Few risk prediction scores are available to identify people at increased risk of work disability, particularly for those with an existing morbidity. We examined the predictive performance of disability risk scores for employees with chronic disease. We used prospective data from 88,521 employed participants (mean age 43.1) in the Finnish Public Sector Study including people with chronic disorders: musculoskeletal disorder, depression, migraine, respiratory disease, hypertension, cancer, coronary heart disease, diabetes, comorbid depression and cardiometabolic disease. A total of 105 predictors were assessed at baseline. During a mean follow-up of 8.6 years, 6836 (7.7%) participants were granted a disability pension. C-statistics for the 8-item Finnish Institute of Occupational Health (FIOH) risk score, comprising age, self-rated health, number of sickness absences, socioeconomic position, number of chronic illnesses, sleep problems, BMI, and smoking at baseline, exceeded 0.72 for all disease groups and was 0.80 (95% CI 0.80-0.81) for participants with musculoskeletal disorders, 0.83 (0.82-0.84) for those with migraine, and 0.82 (0.81-0.83) for individuals with respiratory disease. Predictive performance was not significantly improved in models with re-estimated coefficients or a new set of predictors. These findings suggest that the 8-item FIOH work disability risk score may serve as a scalable screening tool in identifying individuals with increased risk for work disability.

The effect of video-based multimedia training on knowledge, attitude, and performance in breast self-examination

Background/Objectives

Breast neoplasm is one of the most common cancers in Iranian women due to the late diagnosis. Awareness of breast neoplasm and using Breast Self-Examination (BSE) assist in the early detection and treatment of cancer. This study examined the effectiveness of video-based multimedia training versus face-to-face training in awareness of breast neoplasm and BSE and possible factors affecting their effectiveness.

Methods

This research was a pre-test, a post-test experimental study comparing the knowledge, attitude, and performance of women about BSE across two training intervention groups (face-to-face versus video-based multimedia). The study was conducted at Shiraz University of Medical Sciences (SUMS), and 100 women between 20 to 60 years old were allocated to each intervention group via multi-stage cluster sampling (n:110). Three valid and reliable researcher-made questioners were used. Data were analyzed using SPSS 24 with independent t-test, paired t-test, and ANOVA.

Results

Both video-based multimedia and face-to-face training methods significantly increased the participant's knowledge, attitude, and skills about breast self-examination (P < 0.001). In the sub-categories, the results showed that the face-to-face training improved negligence and forgetfulness in applying BSE (P = 0.03) and correcting or modifying the previous knowledge around the issue (P = 0.02). The effect of the video-based method on participants with university education was more than on non-university (P = 0.04).

Conclusion

Incorporating video-based multimedia training in awareness of breast neoplasm and breast self-examination provides an easy, flexible, and affordable way for detection, particularly considering crisis restrictions. This can be of particular attention in more populated, developing/low-income countries and rural and remote areas to enhance equitable access to training and facilitation diagnosis and treatment if applicable.

Social isolation among older veterans: findings from the National Health and Aging Trends Study

OBJECTIVES

Social isolation is a critical public health issue that affects multiple domains of well-being among older adults, but little is known about social isolation among older military veterans. As such, the purpose of this study was to estimate the prevalence of social isolation among older veterans and to examine risk factors for social isolation among older veterans.

METHOD

Data were derived from Round 1 of the National Health and Aging Trends Study, which is an annual, longitudinal panel survey of Medicare beneficiaries aged 65 and older. The sample included 1,683 veterans, who were primarily White and male. Weighted logistic regression models were used to predict severe social isolation (having no social participation) and social isolation (having only one source of social participation) among older veterans, while controlling for age, sex, race, marital status, education, income, and metropolitan residency.

RESULTS

After accounting for other predictors, results show that veterans who are 85 and older, male, White, unmarried or unpartnered, with lower educational attainment and lower income are greatly at risk of both severe social isolation and social isolation.

CONCLUSION

The results of this study support past research showing that veterans with limited social and economic capital are at great risk of experiencing adverse outcomes in older adulthood, including social isolation. Interventions should therefore aim to improve social connectedness among this population and should address the risk-factors that contribute to social isolation among older veterans.

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.

Hepatocellular Carcinoma Risk Prediction in the NIH-AARP Diet and Health Study Cohort: A Machine Learning Approach

BACKGROUND

Prediction of hepatocellular carcinoma (HCC) development in persons with known risk factors remain a challenge and is an urgent unmet need, considering projected increases in HCC incidence and mortality in the US. We aimed to use machine learning techniques to identify a set of demographic, lifestyle, and health history information that can be used simultaneously for population-level HCC risk prediction.

METHODS

Data from 377,065 participants of the NIH-AARP Diet and Health Study, among whom 647 developed HCC over 16 years of follow-up, were analyzed. The sample was randomly divided into independent training (60%) and validation (40%) sets. We evaluated 123 participant characteristics and tested 15 different machine learning algorithms for robustness in predicting HCC risk. Separately, we evaluated variables selected from multivariable logistic regression for risk prediction.

RESULTS

The random under-sampling boosting (RUSBoost) algorithm performed best during model testing. Fourteen participant characteristics were selected for risk prediction based on differences between cases and controls (Bonferroni-corrected p-values <0.0004) and from the most frequently used variables in the initial two decision trees of the RUSBoost learner trees. A predictive model based on the 14 variables had an AUC of 0.72 (sensitivity=0.68, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.68, specificity=0.63). A subset of 9 variables identified through logistic regression also had an AUC of 0.72 (sensitivity=0.67, specificity=0.63) and independent validation AUC of 0.65 (sensitivity=0.70, specificity=0.61).

CONCLUSION

Population-level HCC risk prediction can be performed with a machine learning-based algorithm and could inform strategies for improving HCC risk reduction in at-risk groups.

Risk stratification for COVID-19 hospitalization: a multivariable model based on gradient-boosting decision trees

BACKGROUND

The COVID-19 pandemic has led to an increased demand for health care resources and, in some cases, shortage of medical equipment and staff. Our objective was to develop and validate a multivariable model to predict risk of hospitalization for patients infected with SARS-CoV-2.

METHODS

We used routinely collected health records in a patient cohort to develop and validate our prediction model. This cohort included adult patients (age ≥ 18 yr) from Ontario, Canada, who tested positive for SARS-CoV-2 ribonucleic acid by polymerase chain reaction between Feb. 2 and Oct. 5, 2020, and were followed up through Nov. 5, 2020. Patients living in long-term care facilities were excluded, as they were all assumed to be at high risk of hospitalization for COVID-19. Risk of hospitalization within 30 days of diagnosis of SARS-CoV-2 infection was estimated via gradient-boosting decision trees, and variable importance examined via Shapley values. We built a gradient-boosting model using the Extreme Gradient Boosting (XGBoost) algorithm and compared its performance against 4 empirical rules commonly used for risk stratifications based on age and number of comorbidities.

RESULTS

The cohort included 36 323 patients with 2583 hospitalizations (7.1%). Hospitalized patients had a higher median age (64 yr v. 43 yr), were more likely to be male (56.3% v. 47.3%) and had a higher median number of comorbidities (3, interquartile range [IQR] 2-6 v. 1, IQR 0-3) than nonhospitalized patients. Patients were split into development (n = 29 058, 80.0%) and held-out validation (n = 7265, 20.0%) cohorts. The gradient-boosting model achieved high discrimination (development cohort: area under the receiver operating characteristic curve across the 5 folds of 0.852; validation cohort: 0.8475) and strong calibration (slope = 1.01, intercept = -0.01). The patients who scored at the top 10% captured 47.4% of hospitalizations, and those who scored at the top 30% captured 80.6%.

INTERPRETATION

We developed and validated an accurate risk stratification model using routinely collected health administrative data. We envision that modelling such risk stratification based on routinely collected health data could support management of COVID-19 on a population health level.

The role of familial confounding in the associations of physical activity, smoking and alcohol consumption with early exit from the labour market

We investigated the associations between health behaviors and sustainable working life outcomes including all-cause disability pension, disability pensions due to musculoskeletal and mental diagnoses and unemployment. The role of familial factors behind these associations was studied by analysing discordant twin pairs. Our data included Swedish twins born in 1925-1986 (51891 twin individuals). Baseline data based on two independent surveys in 1998-2003 and 2005-2006 for health behaviors were linked to national registers on disability pension and unemployment until 2016. Cox proportional hazards models for hazard ratios (HR) with 95% confidence intervals (CI) were estimated for the whole sample adjusting for covariates. Analyses of health behavior discordant twin pairs (n = 5903 pairs) were conducted using conditional Cox models. In the whole cohort, the combination of healthy behaviors was associated with lower risk for all-cause disability pension, disability pension due to musculoskeletal diagnoses or mental diagnoses, and for unemployment (HRs 0.56-0.86, 95% CIs 0.51-0.92) as did being physically active (HRs 0.69-0.87, 95% CI 0.65-0.92). The discordant pair analyses confirmed the lower risk among those having healthy behaviors (HR 0.70-0.86) or being physically active (HR 0.86-0.87) for all-cause disability pension, disability pension due to musculoskeletal diagnoses, and for unemployment. To conclude, controlling the effects of covariates or familial confounding (i.e. discordant twin pair analyses) shows that being physically active or having several healthy behaviors predict better working life outcomes. This points towards independent association between healthy behavior and longer working life.

ESSE-RF study: epidemiology and public health promotion

The largest population-based study in Russian modern history the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) for 8 years has become a platform for public health research and projects, relevant for the whole country. Results of the ESSE-RF study were used to identify Demography National Project parameters, to model mortality and morbidity risk at the population level, to estimate the economic burden of risk factors, to predict the economic effect of population prevention measures, to assess the feasibility of using novel biomarkers for risk stratification, as well as for external evaluation of health care system. Further, results can be used to develop a novel cardiovascular risk score, to analyze COVID-19-related risk factors, and to study health protection environment. Epidemiological studies ESSE-RF1 and ESSE-RF2 have already become a significant component of public health system in Russia, and taking into account the scope of the ESSE-RF3 study (30 regions), the role of epidemiology will increase.

Development of a prognostic prediction model to estimate the risk of multiple chronic diseases: constructing a copula-based model using Canadian primary care electronic medical record data

Introduction

The ability to estimate risk of multimorbidity will provide valuable information to patients and primary care practitioners in their preventative efforts. Current methods for prognostic prediction modelling are insufficient for the estimation of risk for multiple outcomes, as they do not properly capture the dependence that exists between outcomes.

Objectives

We developed a multivariate prognostic prediction model for the 5-year risk of diabetes, hypertension, and osteoarthritis that quantifies and accounts for the dependence between each disease using a copula-based model.

Methods

We used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2009 onwards, a collection of electronic medical records submitted by participating primary care practitioners across Canada. We identified patients 18 years and older without all three outcome diseases and observed any incident diabetes, osteoarthritis, or hypertension within 5-years, resulting in a large retrospective cohort for model development and internal validation (n=425,228). First, we quantified the dependence between outcomes using unadjusted and adjusted Ø coefficients. We then estimated a copula-based model to quantify the non-linear dependence between outcomes that can be used to derive risk estimates for each outcome, accounting for the observed dependence. Copula-based models are defined by univariate models for each outcome and a dependence function, specified by the parameter θ. Logistic regression was used for the univariate models and the Frank copula was selected as the dependence function.

Results

All outcome pairs demonstrated statistically significant dependence that was reduced after adjusting for covariates. The copula-based model yielded statistically significant θ parameters in agreement with the adjusted and unadjusted Ø coefficients. Our copula-based model can effectively be used to estimate trivariate probabilities.

Discussion

Quantitative estimates of multimorbidity risk inform discussions between patients and their primary care practitioners around prevention in an effort to reduce the incidence of multimorbidity.

A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)

BACKGROUND

Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches.

METHODS

PreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007-2008, 2009-2010, and 2011-2012) from the development cohort (CCHS cycles 2000-2001, 2003-2004, and 2005-2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke's R2), calibration (e.g., calibration plots), and discrimination (e.g., Harrell's concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers.

DISCUSSION

Using routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.