Big Data, Decision Models, and Public Health

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Abstract

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

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The Outcome and Implications of Public Precautionary Measures in Taiwan–Declining Respiratory Disease Cases in the COVID-19 Pandemic

Chih-Chia Hsieh, Chih-Hao Lin, William Wang … (2020)

With the rapid development of the COVID-19 pandemic, countries are trying to cope with increasing medical demands, and, at the same time, to reduce the increase of infected numbers by implementing a number of public health measures, namely non-pharmaceutical interventions (NPIs). These public health measures can include social distancing, frequent handwashing, and personal protective equipment (PPE) at the personal level; at the community and the government level, these measures can range from canceling activities, avoiding mass gatherings, closing facilities, and, at the extreme, enacting national or provincial lockdowns. Rather than completely stopping the infectious disease, the major purpose of these NPIs in facing an emerging infectious disease is to reduce the contact rate within the population, and reduce the spread of the virus until the time a vaccine or reliable medications become available. The idea is to avoid a surge of patients with severe symptoms beyond the capacity of the hospitals’ medical resources, which would lead to more mortality and morbidity. While many countries have experienced steep curves in new cases, some, including Hong Kong, Vietnam, South Korea, New Zealand, and Taiwan, seem to have controlled or even eliminated the infection locally. From its first case of COVID-19 on the 21 January until the 12 May, Taiwan had 440 cases, including just 55 local infections, and seven deaths in total, representing 1.85 cases per 100,000 population and a 1.5% death rate (based on the Worldometer 2020 statistics of Taiwan’s population of 23.8 million). This paper presents evidence that spread prevention involving mass masking and universal hygiene at the early stage of the COVID-19 pandemic resulted in a 50% decline of infectious respiratory diseases, based on historical data during the influenza season in Taiwan. These outcomes provide potential support for the effectiveness of widely implementing public health precaution measures in controlling COVID-19 without a lockdown policy.

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Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals

Chin-Chuan Shih, Chi-Jie Lu, Gin-Den Chen … (2020)

Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes datasets from 19,270 patients, provided by an adult health examination program from 32 chain clinics and three special physical examination centers, between 2015 and 2019. There were 11 independent variables, and the glomerular filtration rate (GFR) was used as the predictive variable. The C4.5 decision tree algorithm outperformed the three comparison models for predicting early CKD based on accuracy, sensitivity, specificity, and area under the curve metrics. It is, therefore, a promising method for early CKD prediction. The experimental results showed that Urine protein and creatinine ratio (UPCR), Proteinuria (PRO), Red blood cells (RBC), Glucose Fasting (GLU), Triglycerides (TG), Total Cholesterol (T-CHO), age, and gender are important risk factors. CKD care is closely related to primary care level and is recognized as a healthcare priority in national strategy. The proposed risk prediction models can support the important influence of personality and health examination representations in predicting early CKD.

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40-Year Projections of Disability and Social Isolation of Older Adults for Long-Range Policy Planning in Singapore

Reuben Ng, Si Qi Lim, Su Saw … (2020)

Against a rapidly aging population, projections are done to size up the demand for long-term care (LTC) services for long-range policy planning. These projections are typically focused on functional factors such as disability. Recent studies indicate the importance of social factors, for example, socially isolated seniors living alone are more likely to be institutionalized, resulting in higher demand for LTC services. This is one the first known studies to complete a 40-year projection of LTC demand based on disability and social isolation. The primary micro dataset was the Retirement and Health Survey, Singapore’s first nationally representative longitudinal study of noninstitutionalized older adults aged 45 to 85 with over 15,000 respondents. Disability prevalence across the mild to severe spectrum is projected to increase five-fold over the next 40 years, and the number of socially isolated elders living alone is projected to grow four-fold. Regression models of living arrangements revealed interesting ethnic differences: Malay elders are 2.6 times less likely to live alone than their Chinese counterparts, controlling for marital status, age, and housing type. These projections provide a glimpse of the growing demand for LTC services for a rapidly aging Singapore and underscore the need to shore up community-based resources to enable seniors to age-in-place.

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Journal

Journal ID (nlm-ta): Int J Environ Res Public Health

Journal ID (iso-abbrev): Int J Environ Res Public Health

Journal ID (publisher-id): ijerph

Title: International Journal of Environmental Research and Public Health

Publisher: MDPI

ISSN (Print): 1661-7827

ISSN (Electronic): 1660-4601

Publication date (Electronic): 15 September 2020

Publication date (Print): September 2020

Volume: 17

Issue: 18

Electronic Location Identifier: 6723

Affiliations

[1 ]Department of Information Management, Yuan Ze University, Taoyuan 320, Taiwan; clchan@ 123456saturn.yzu.edu.tw

[2 ]Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan

[3 ]School of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan

Author notes

[* ]Correspondence: threec@ 123456csmu.edu.tw ; Tel.: +886-4-24730022

Author information

Chien-Lung Chan https://orcid.org/0000-0002-7486-7075

Chi-Chang Chang https://orcid.org/0000-0001-6513-9212

Article

Publisher ID: ijerph-17-06723

DOI: 10.3390/ijerph17186723

PMC ID: 7558933

PubMed ID: 32942728

SO-VID: b03a1db9-5f19-47c7-b3e3-cb182722614a

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

Big Data, Decision Models, and Public Health

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Most cited references 23

The Outcome and Implications of Public Precautionary Measures in Taiwan–Declining Respiratory Disease Cases in the COVID-19 Pandemic

Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals

40-Year Projections of Disability and Social Isolation of Older Adults for Long-Range Policy Planning in Singapore

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