Global, regional, and national burden of hypertensive heart disease during 1990–2019: an analysis of the global burden of disease study 2019

Abstract

Background Hypertensive heart disease (HHD) is a major public health issue worldwide. We analyzed the global, regional, and national burden of HHD between the years 1990 and 2019 in relation to age, gender, and socioeconomic factors. Methods The prevalence and death rates, the disability adjusted life-years (DALY), and the corresponding age-standardized rates of HHD were extracted from the Global Burden of Disease study 2019. The epidemiological trends were evaluated by calculating the estimated annual percentage changes (EAPC) of the above variates. Results A total of 19.60 million HHD cases were documented in 2019 compared to 7.82 million in 1990, corresponding to an EAPC of 0.17. Contrarily, the global age-standardized death rate (ASDR) and age-standardized DALYs decreased with respective EAPCs of − 0.74 and − 1.02. HHD mostly occurred in people aged over 65. The disease burden of HHD varied considerably between countries, and univariate linear regression indicated that many socioeconomic variables had significantly negative correlations with age-standardized DALY rate. Conclusion HHD cases have increased over the last three decades; however the mortality rate has declined. Multi-faceted improvements in health, education and income could help to alleviate the disease burden of HHD, specially in some regions with lower socio-demographic index and higher ASDR.

Non‐homogeneous continuous‐time Markov chain with covariates: Applications to ambulatory hypertension monitoring

“…In the United States, high blood pressure affects nearly 78 million people and greatly increases the risk of developing cardiovascular disease 1‐3 . Among adults aged 18 and over, 48.1% are expected to experience hypertension, 4 with the prevalence of hypertensive heart failure continuing to increase globally 5,6 . Studies have found correlation between hypertension and other factors such as diet, gender, age, and BMI 7‐10 .…”

“…[1][2][3] Among adults aged 18 and over, 48.1% are expected to experience hypertension, 4 with the prevalence of hypertensive heart failure continuing to increase globally. 5,6 Studies have found correlation between hypertension and other factors such as diet, gender, age, and BMI. [7][8][9][10] Some previous studies have modeled hypertension through linear models or machine learning techniques.…”

Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA

“…Current hypertension studies (Lu and Lan, 2022) have improved our understanding in tracing the determinants of a high rise in hypertension prevalence globally and the US. Most hypertension research relies on linear spatial models (Bravo et al, 2019; Roy et al, 2019; Sarkar et al, 2018).…”

“…Most hypertension research relies on linear spatial models (Bravo et al, 2019; Roy et al, 2019; Sarkar et al, 2018). However, health outcomes are non-linear and context-dependent outcomes of a variety of dynamics, including biological, city size, population, personal behavior, interpersonal behavior, social politics, spatial governance, carbon emission, urban heat, crime, mental distress, or inactive lifestyle (Grekousis et al, 2022; Lu and Lan, 2022; Kandala et al, 2021). According to Rocha et al (2015), health outcomes in large cities are non-linear due to the non-linear effects of larger populations’ social networks.…”

“…Also, collinearity among socioeconomic data samples of linear models causes results to be misrepresented, necessitating the development of models that account for collinearity (Grekousis et al, 2022b). Similarly, the association between socio-spatial variables and the prevalence of hypertension is multifaceted and not usually linear (Lu and Lan, 2022). Only a few studies (Kandala et al, 2021) have used non-linear spatial modeling to investigate the relationships between tract-level hypertension prevalence and socioeconomic factors.…”