Model design
To predict the estimated future number of deaths due to ischemic heart disease (IHD) and stroke that could be averted, we developed a regression model to quantify the impact of population hypertension control on cardiovascular disease deaths, specifically on IHD and stroke. Six independent sex- and cause-specific models were fitted by regression analysis using estimates of population hypertension control as independent variable and estimates of IHD and stroke mortality as response variables for 36 countries of the Americas from 1990 to 2019. The regional best-fitted regression models are used for predicting the expected levels of IHD and stroke mortality for a given level of population hypertension control.
For the regression modeling, two separate data sources were used: 1) Estimates of age-standardized death rates per 100,000 population caused by IHD, and stroke and stroke subtypes ―e.g., ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage― by sex for the Region of the Americas and the 36 countries and territories of the Region from 1990 to 2019 from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019), and 2) estimates of the age-standardized prevalence of population hypertension control by sex for the Region of the Americas and 36 countries and territories, extracted from the WHO Global Health Observatory. These estimates were produced by the NCD Risk Factors Collaboration (NCD RisC).
Intervention definitions to scaling up population hypertension control
Three interventions are considered for intervention scenarios:
- Scaling up the detection or diagnosis of people with hypertension.
- Improving the rate of treatment among people with hypertension with a diagnosis of hypertension or who know their condition.
- Increasing hypertension control among people with hypertension that are receiving treatment for lowering high blood pressure.
The prevalence of these three hypertension treatment cascade measures equates to the level of population hypertension control by the equation:
pHTNctrl = Aw x Tt_Aw x HTNctrl_Tt
Where pHTBctrl is the prevalence of population hypertension control, Aw is the prevalence of people with hypertension with a diagnosis of hypertension, Tt_Aw is the prevalence of people under treatment for lowering high blood pressure among those with a diagnosis of hypertension, and HTNctrl_Tt is the prevalence of hypertension control among those under treatment for lowering high blood pressure.
For the intervention analysis, we assume that population hypertension control is scaled up from a given baseline to a target level in a given number of years in equal units per year. This means that in a set scenario where population HTN control will be scaled up by 5% in five years, 1% increase will be reached each year. Then, the model is used to estimate the deaths averted in each year, which it accumulates over the given years.
Users can change the default value of parameters -e.g., location name and population size, baseline and target level of awareness, treated among those aware of the condition, hypertension control among treated, population hypertension control, and the number of years to reach the target- to set specific scenarios and visualize the impact on IHD and stroke mortality for a given population.
Estimation of future deaths averted
To estimate the deaths that could be averted in a future number of years by scaling up population hypertension control from a given current (baseline) level to target, first, the predicted death rates per 100,000 population for the baseline and target levels for the given baseline and target levels of population hypertension control is calculated separately for IHD, and stroke. Then, the difference between those two death rates is calculated to obtain the death averted per 100 000 population. These obtained death rates are interpreted as the predicted number of deaths averted per 100,000 population by scaling up population hypertension control from the baseline to the target level. Then, the absolute number of deaths averted is derived from the predicted averted death rate by applying the given population size of the location or population group separately for IHD, and stroke. The total deaths averted from IHD and Stroke is calculated by the addition of deaths averted from IHD and deaths averted from Stroke.
Limitations
These models have limitations and results should be interpreted with caution. First, prediction models were built in an ecological observational study. Second, each of the two data sources used in the analysis could inherit limitations from their primary data sources, particularly in terms of data completeness and quality. Modeling approaches to produce comparable estimates for both mortality and hypertension treatment cascade measures could be also sources of biased results, mainly in low- and middle-income countries with suboptimal vital registration systems and limited population-based risk factors surveys. Despite these limitations and knowing that there is much room for improvement, most of the countries in the Region of the Americas, including the most populous ones, have good quality and nationally represented population surveys. Finally, the prediction of deaths averted from IHD and stroke does not take into account population growth and aging which are factors for changes in the prevalence of hypertension and the hypertension treatment cascade.
Additional information about methods
Detailed information about methods is available in the article:
Martinez R, Soliz P, Campbell NRC, Lackland DT, Whelton PK, Ordunez P. Association between population hypertension control and ischemic heart disease and stroke mortality in 36 countries of the Americas, 1990-2019: an ecological study. Rev Panam Salud Publica. 2022;46:e143. https://doi.org/10.26633/RPSP.2022.143