Effects of Marital Status and Income on Hypertension: The Korean Genome and Epidemiology Study (KoGES)

Abstract

Objectives: This study aimed to analyze the associations of income, marital status, and health behaviors with hypertension in male and female over 40 years of age in the Korea.Methods: The data were derived from the Korean Genome and Epidemiology Study (KoGES; 4851-302) which included 211 576 participants. To analyze the relationships of income, marital status, and health behaviors with hypertension in male and female over 40 years of age, multiple logistic regression was conducted with adjustments for these variables.Results: The prevalence of hypertension increased linearly as income decreased. The odds ratio for developing hypertension in people with an income of <0.5 million Korean won (KRW) compared to ≥6.0 million KRW was 1.55 (95% confidence interval [CI], 1.25 to 1.93) in the total population, 1.58 (95% CI, 1.27 to 1.98) in male, and 1.07 (95% CI, 0.35 to 3.28) in female. The combined effect of income level and marital status on hypertension was significant. According to income level and marital status, in male, low income and divorce were most associated with hypertension (1.76 times; 95% CI, 1.01 to 3.08). However, in female, the low-income, married group was most associated with hypertension (1.83 times; 95% CI, 1.71 to 1.97).Conclusions: The results of this study show that it is necessary to approach male and female marital status separately according to income in health policies to address inequalities in the prevalence of hypertension.

If You Build It, Will They Come? A Mixed-Method Formative Evaluation of the Start-up of a Post-Discharge Clinic to Improve Care Transitions

“…Marital status was association with appointment adherence; divorced, separated, or widowed individuals had higher cancellation and no-show rates, while single patients were more likely to not show up, possibly re ecting varying levels of social support affecting healthcare access and adherence. 23,24 Patients with commercial insurance or Medicare attended appointments more consistently than those with Medicaid or no insurance, highlighting the accessibility and affordability issues, perhaps as a proxy for socioeconomic status. 25,26 Addressing these barriers, e.g., reducing insurance complexities and out-of-pocket costs, and providing community health workers or patient navigators to those lacking endogenous social support, could improve appointment adherence.…”

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

“…These methods often have better accuracy, potentially due to their ability to leverage non‐linear and interactive relationships when performing prediction 28,30,42,43 . However, due to the complexity of the non‐parametric algorithms that are common in machine‐learning methods, it is impossible for a human to analyze each tree and execute an explanation of how the machine‐learning method works 7,28,30,35,42–45 . Without methods that explain how machine learning algorithms reach their predictions, clinicians will not be able to identify if models are reliable and generalizable or just replicating the biases within the training datasets 11,35–37,46 .…”

“… 28 , 30 , 42 , 43 However, due to the complexity of the non‐parametric algorithms that are common in machine‐learning methods, it is impossible for a human to analyze each tree and execute an explanation of how the machine‐learning method works. 7 , 28 , 30 , 35 , 42 , 43 , 44 , 45 Without methods that explain how machine learning algorithms reach their predictions, clinicians will not be able to identify if models are reliable and generalizable or just replicating the biases within the training datasets. 11 , 35 , 36 , 37 , 46 This study is one of the first in the literature that predicts risk for hypertension from nutritional covariates using machine‐learning methods and executes model explanation algorithms to add transparency to the methods.…”

“…Hypertension is the leading cause of worldwide mortality, responsible for over 40% of deaths from cardiovascular disease, type 2 diabetes, and chronic kidney disease, per the World Health Organization 1–5 . Over 50% of the complications of hypertension may be prevented through diet modifications 6–9 . Guidelines for treatment and control of hypertension by the American Heart Association have increasingly leveraged lifestyle recommendations in addition to pharmaceutical‐based therapy 1,3,10,11 .…”

“… 1 , 2 , 3 , 4 , 5 Over 50% of the complications of hypertension may be prevented through diet modifications. 6 , 7 , 8 , 9 Guidelines for treatment and control of hypertension by the American Heart Association have increasingly leveraged lifestyle recommendations in addition to pharmaceutical‐based therapy. 1 , 3 , 10 , 11 Therefore, using patient diets as a potential risk factor for hypertension may enhance personalized medical care.…”