Correlations among the Number of Inpatients in Obesity, Diabetes, Hypertension, and Heart Diseases
Article Sidebar
Main Article Content
Abstract
Obesity occurs when there is an excess amount of body fat and may cause diseases and other health problems such as diabetes, high blood pressure, and heart diseases. In a correlation study using time series data on the number of inpatients in four diseases from Thailand's inpatient statistics, the summary of illness reports from the Ministry of Public Health since 2021 dated back to 2012 for 10 years. When analyzed using Spearman’s rank correlation, the number of inpatients in obesity was related to the number of inpatients in diabetes. and the number of inpatients with high blood pressure with statistical significance equal to 0.964 (p-value < .001) and 0.952 (p-value < .001), respectively. The number of inpatients in diabetes and the number of inpatients in hypertension had a statistically significant Spearman’s rank correlation coefficient of 0.998 (p-value < .001).As for the number of inpatients in heart diseases and the number of inpatients in obesity, the number of inpatients in diabetes, and the number of inpatients in hypertension, there was no statistically significant correlation; the Spearman’s rank correlation coefficients were -0.442 (p-value = 0.204), -0.370 (p-value = 0.296), and -0.394 (p-value = 0.263), respectively. The number of inpatients in obesity was highly related with the number of inpatients in diabetes and the number of inpatients in hypertension.
Article Details
References
Patel SA, et al. (2016). Obesity and its relation with diabetes and hypertension: A cross-sectional study across 4 geographical regions. Global Heart Journal. 11(1), 71-79. doi: 10.1016/j.gheart.2016.01.003
วิชัย เอกพลากร (บรรณาธิการ). (2564). รายงานการสำรวจสุขภาพประชาชนไทยโดยการตรวจร่างกาย ครั้งที่ 6 พ.ศ. 2562 – 2563. กรุงเทพฯ: สำนักพิมพ์อักษรกราฟฟิคแอนด์ดีไซน์.
Altman N, and Krzywinski M. (2015). Association, correlation and causation. Nat Methods. 12, 899–900. https://doi.org/10.1038/nmeth.3587
Statistics Solutions. (2023). Correlation (Pearson, Kendall, Spearman). Retrieved July 30, 2023, from https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/correlation-pearson-kendall-spearman/
Chen D, and Utter B. (2021). When correlation does not imply causation: Why your gut microbes may not (yet) be a silver bullet to all your problems. Retrieved July 30, 2023, from https://sitn.hms.harvard.edu/flash/2021/when-correlation-does-not-imply-causation-why-your-gut-microbes-may-not-yet-be-a-silver-bullet-to-all-your-problems/
JMP Statistical Discovery LLC. (2023). Correlation vs. Causation.Retrieved July 30, 2023, from https://www.jmp.com/en_au/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html
กองยุทธศาสตร์และแผนงาน สำนักงานปลัดกระทรวงสาธารณสุข. (2564-2556). (2565-2557). สรุปรายงานการป่วย ปี 2564 ถึง 2556. ค้นเมื่อ 30 กรกฎาคม 2566, จาก https://spd.moph.go.th/illness-report/
Healthkpi. (2564). อัตราตายของผู้ป่วยโรคหลอดเลือดหัวใจ. สืบค้นเมื่อ 30 กรกฎาคม 2566, จาก http://healthkpi.moph.go.th/kpi2/kpi-list/view/?id=45
The jamovi project. (2022). Jamovi. (Version 2.3) [Computer Software]. Retrieved July 10, 2023, from https://www.jamovi.org.
R Core Team. (2021). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. Retrieved July 10, 2023, from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2022-01-01).
Patel SA, et al. (2016). Obesity and its Relation With Diabetes and Hypertension: A Cross-Sectional Study Across 4 Geographical Regions. Glob Heart.11(1):71-79. e4.
Wei OY. (2020). The Relationship Between Obesity, Diabetes and the Heart. Mount Elizabeth Hospitals. Retrieved July 10, 2023, from https://www.mountelizabeth.com.sg/health-plus/article/the-relationship-between-obesity-diabetes-and-the-heart
Deng J. (1982). Control problems of grey system. System and Control Letter. 1: 288-294.
Li GD, et al. 2007. New proposal and accuracy evaluation of grey prediction GM. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences. E90, 1188-1197.
Gujarati, DN, and Porter DC. (2009). "Causality in Economics: The Granger Causality Test". Basic Econometrics (5th international ed.). New York: McGraw-Hill. pp. 652–658.
Granger, CWJ. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society. 37(3):424–438.
Grande, AF, et al. (2022). Granger causality and time series regression for modelling the migratory dynamics of infuenza into Brazil. SORT-Statistics and Operations Research Transactions. 46 (2), 161-188 DOI: 10.2436/20.8080.02.122
