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Addressing the diagnostic gap in hypertension through possible interventions and scale-up: A microsimulation study [1]

['Lisa Koeppel', 'Division Of Infectious Diseases', 'Tropical Medicine', 'Heidelberg University Hospital', 'Heidelberg', 'Sabine Dittrich', 'Find', 'Geneva', 'Nuffield Department Of Medicine', 'Faculty Of Tropical Medicine']

Date: 2022-12 

Abstract Background Cardiovascular diseases (CVDs) are the leading cause of mortality globally with almost a third of all annual deaths worldwide. Low- and middle-income countries (LMICs) are disproportionately highly affected covering 80% of these deaths. For CVD, hypertension (HTN) is the leading modifiable risk factor. The comparative impact of diagnostic interventions that improve either the accuracy, the reach, or the completion of HTN screening in comparison to the current standard of care has not been estimated. Methods and findings This microsimulation study estimated the impact on HTN-induced morbidity and mortality in LMICs for four different scenarios: (S1) lower HTN diagnostic accuracy; (S2) improved HTN diagnostic accuracy; (S3) better implementation strategies to reach more persons with existing tools; and, lastly, (S4) the wider use of easy-to-use tools, such as validated, automated digital blood pressure measurement devices to enhance screening completion, in comparison to the current standard of care (S0). Our hypothetical population was parametrized using nationally representative, individual-level HPACC data and the global burden of disease data. The prevalence of HTN in the population was 31% out of which 60% remained undiagnosed. We investigated how the alteration of a yearly blood pressure screening event impacts morbidity and mortality in the population over a period of 10 years. The study showed that while improving test accuracy avoids 0.6% of HTN-induced deaths over 10 years (13,856,507 [9,382,742; 17,395,833]), almost 40 million (39,650,363 [31,34,233, 49,298,921], i.e., 12.7% [9.9, 15.8]) of the HTN-induced deaths could be prevented by increasing coverage and completion of a screening event in the same time frame. Doubling the coverage only would still prevent 3,304,212 million ([2,274,664; 4,164,180], 12.1% [8.3, 15.2]) CVD events 10 years after the rollout of the program. Our study is limited by the scarce data available on HTN and CVD from LMICs. We had to pool some parameters across stratification groups, and additional information, such as dietary habits, lifestyle choice, or the blood pressure evolution, could not be considered. Nevertheless, the microsimulation enabled us to include substantial heterogeneity and stochasticity toward the different income groups and personal CVD risk scores in the model. Conclusions While it is important to consider investing in newer diagnostics for blood pressure testing to continuously improve ease of use and accuracy, more emphasis should be placed on screening completion.

Author summary Why was this study done? Cardiovascular diseases (CVDs) are the leading cause of mortality globally, affecting low- and middle-income countries (LMICs) disproportionally highly.

Hypertension (HTN) is the leading modifiable risk factor for CVDs.

The diagnosis of HTN and thus the access to treatment is hampered by the necessity of at least one repeated measurement for a final diagnosis and the operator-dependent variability of blood pressure measurement.

It is unclear which strategies would be the most impactful to close the diagnostic gap: more accurate, easy-to-use and/or more scalable tools or better implementation strategies to reach more persons with existing tools. What did the researchers do and find? We developed a stochastic microsimulation model that examines the impact of possible diagnostic interventions and implementation strategies on HTN-induced morbidity and mortality in LMICs.

The different scenarios were applied over a period of 10 years and affected the individual risk of experiencing a CVD event.

While improving test accuracy avoids only 0.6% of HTN-induced deaths over 10 years, scaling up test coverage and completion can avoid almost 40 million HTN-induced CVD events and 14 million (13.7%) related deaths. What do these findings mean? This simulation demonstrates the importance of increasing the coverage of testing for HTN and the improvement of screening completion over diagnostic accuracy of HTN testing.

Strategies to narrow the diagnostic gap in HTN should put more emphasis on screening completion.

Citation: Koeppel L, Dittrich S, Brenner Miguel S, Carmona S, Ongarello S, Vetter B, et al. (2022) Addressing the diagnostic gap in hypertension through possible interventions and scale-up: A microsimulation study. PLoS Med 19(12): e1004111. https://doi.org/10.1371/journal.pmed.1004111 Academic Editor: Aaron S. Kesselheim, Harvard University, Brigham and Women’s Hospital, UNITED STATES Received: February 17, 2022; Accepted: September 15, 2022; Published: December 6, 2022 Copyright: © 2022 Koeppel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Requests for additional information on HPACC can be directed to hpacc@uni-heidelberg.de. Funding: This work was funded by FIND (CMD, LK) and through support from the UK government (SD, SC, SO, BV). PG is a Chan Zuckerberg Biohub investigator. The funders provided feedback on the design of the analysis, edited and revised the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CMD is an Academic Editor on PLOS Medicine’s editorial board. TB has been Editor-in-Chief of PLOS Medicine since September 2022. DS, SC, SO, BV are FIND employees. Abbreviations: BMI, body mass index; CVD, cardiovascular disease; GBD, Global Burden of Disease; HTN, hypertension; LIC, low-income country; LMIC, low- and middle-income country; LoMIC, lower-middle-income country; STEPS, STEPwise approach to noncommunicable diseases Surveillance; UMIC, upper-middle-income country

Introduction Cardiovascular diseases (CVDs) are the leading cause of mortality globally; an estimated 17.9 million people die each year, which makes up about 32% of all annual deaths worldwide [1]. Low- and middle-income countries (LMICs) account for approximately 80% of these deaths. With a prevalence of about 31% globally, hypertension (HTN) is the leading modifiable risk factor for CVD and premature death worldwide [2–5]. The age-standardized prevalence of HTN increased by 7.7% in LMICs between 2000 and 2010 [6]. HTN is diagnosed if the measurement of the systolic blood pressure is above 140 mm Hg or the diastolic blood pressure is above 90 mm Hg on two different days [7]. However, the necessity of at least a repeated measurement for a final diagnosis constitutes a barrier in access to treatment and management of the condition. Moreover, the accuracy of devices for taking a blood pressure measurement can be highly operator and device dependent. Manually operated mercury or aneroid blood pressure devices are still widely used in many LMICs, relying on skilled operators and a suitable environment to correctly operate the devices and obtain an accurate reading. Automated, digital blood pressure devices can be used more easily by less trained operators in a range of environments and have the potential to obtain more accurate readings [8]. These limitations contribute to the diagnostic gap (approximately 61%), denoting the percentage of people with HTN not having completed their second measurement and being diagnosed [9]. To improve HTN diagnosis, better implementation strategies to reach more persons with existing tools or the use of more accurate, easy-to-use tools are necessary. This simulation study aims to estimate impact on morbidity and mortality from HTN through altering the diagnostic accuracy of HTN tools or increasing the coverage of screening for hypertension to achieve more active outreach and follow-up compared to the current standard of care.

Discussion This analysis provided insight on the potential effects of scaling up the screening for HTN for different scenarios. Across analyses, extending the coverage of a blood pressure measurement has a much higher potential impact than improving the accuracy of the test. Whereas the improvement of the test accuracy had only a small potential impact on CVD events and related mortality over 10 years (reduction of 0.5% and 0.6%, respectively), by doubling of the coverage of a first screen, 6.6% of HTN-induced deaths could be avoided over a period of 10 years, and by increasing screen completion in addition, even 13.7% of deaths would not occur. The base case of our model was calibrated in such a way that the HTN prevalence and the increase in HTN prevalence over time is in line with the recent Global Burden of Disease (GBD) study report [3]. Moreover, according to a review on available data on HTN in the literature [30], in 2015, an estimated 7.5 million deaths were attributable to systolic blood pressure in LMICs. This number coincides with the number of deaths in our model for the baseline (S0). Additionally, in the baseline case (S0), the total number of people with HTN diagnosed per year over time and thus under control is decreasing (Fig 2B). This is in line with short-term trends in prevalence and control of HTN found in the literature [5,31,32]. These findings have implications on policy development and resource allocation. First of all, it shows that diagnosis and early identification plays a critical role in mitigating long-term mortality of CVD. Further, based on our data, programmatic expansion of screening interventions to scale up testing capacity might have a greater benefit than improving diagnostic accuracy. Models for scaling up testing could draw on successfully implemented community based mass screenings as implemented in Northern California [33] or annual HTN awareness campaigns, such as the May Measure Month [34]. This should be considered at the national and global level by funders of health interventions as well as in budget allocations for local healthcare improvements and international donor investments when weighing product versus access funding. Estimates of costs and cost effectiveness of the different scenarios are beyond the scope of this paper. Although all efforts were made to mimic reality, our model also has several limitations. Due to the simplification of the population initialization, our model does not consider differences that arise from local heterogeneities. Moreover, genetic predisposition, ethnic background, family history of HTN, and dietary and exercise habits were not included in the model. Further, smoking and diabetes, which may contribute to HTN and CVD risk and their evolution over time were not included in the model due to lack of data on the direct relationship to parameterize the model. Furthermore, we acknowledge that blood pressure levels depend on more variables than what we can consider in the model, e.g., diet or lifestyle. The parametrization of the development of HTN was inferred from a logistic regression of the HPACC dataset and thus estimated prevalent and not incident hypertension. Also, we did not take into account adverse events accrued from putting false positively diagnosed individuals on treatment. The model parameters (Table 2) are assumed to be valid across the entire population and main stratification factors. Furthermore, we adapted model input sources that stem from high-income countries (i.e., Framingham Heart Study) and scaled them to our purpose where possible. We assumed complete treatment adherence, which we acknowledge to be far from reality (i.e., full adherence to treatment is estimated to be only 10.3%) [9]. While this assumption is a simplification of the problems in the care cascade that follow the diagnosis, it enabled us to focus on the impact of diagnostics. The absolute reduction in the number of CVD-related deaths modeled would likely be lower in reality, though the relative reduction across our scenarios would still give realistic estimates given a likely similar treatment adherence. We further did not model chronic kidney disease as an outcome due to the limited data availability for the parametrization. For the probability of being screened, we utilize numbers from Geldsetzer and colleagues [9] (at least one blood pressure measurement, diagnostic gap) to the population at large, even though they pertain only to persons with HTN. This might result in an overestimate in the absolute number of people screened, in particular when wanting to scale it down to a region or single country. In this case, the STEPS data can aid as additional external validation source [12]. Further, we do not consider the many years someone had HTN prior to a blood pressure measurement in detail. To reduce this bias, we assumed an estimate for the average years of opportunity for a blood pressure measurement and scaled it per annum. However, all model scenarios have the same underlying assumption and these parameters have little effect on the differences of the model scenarios, which is what we are primarily interested in. Although the microsimulation added computational complexity compared to, for example, a model on a population level, it allowed us to integrate risk factor heterogeneity across individuals and include the possibility for each individual to develop CVD given their personal risk. In conclusion, our simulation shows the promising impact in reducing HTN-induced morbidity and mortality in LMICs when increasing the coverage of testing for HTN and the improvement of screening completion; strategies to narrow the diagnostic gap in hypertension should focus on these two aspects.

Acknowledgments Furthermore, we are thankful for the input received from Dr. Liam Keegan at the Scientific Software Center (SSC), Interdisciplinary Center of Scientific Computing, and Prof. Dr. Jan Johannes from the Institute of Applied Mathematics at Heidelberg University.

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[1] Url: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004111

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