Skip to main content

Impact of hypertension diagnosis on morbidity and mortality: a retrospective cohort study in primary care



Hypertension is responsible for a huge burden of disease. The aim of this study was to evaluate the impact of newly diagnosed hypertension on the occurrence of kidney or cardiovascular events (K/CVEs) and on mortality among community dwellers.


Retrospective cohort study, conducted from January, 2007, to December, 2018. All patients (age > 18) newly diagnosed with hypertension and no previous K/CVEs in 2007 and 2008, in the primary care centers of Madrid (Spain) (n = 71,770), were enrolled. The control group (n = 72,946) included patients without hypertension, matched by center, sex and age. The occurrence of kidney or CV events, including mortality from these causes and total mortality were evaluated using Cox regression and multistate models. Data were collected from three sources: personal data from administrative records, clinical data from medical records, and mortality data from regional and national databases.


The median follow-up was 138.61 months (IQR: 124.68–143.97 months). There were 32,896 K/CVEs (including 3,669 deaths from these causes) and 12,999 deaths from other causes. Adjusted for sex, smoking, diabetes and socioeconomic status, K/CVEs HR was 4.36 (95% CI: 3.80–5.00) for diagnoses before 45 years of age, 2.45(95% CI: 2.28- 2.63) for diagnosis between 45 to 54 years, and HR decreased to 1.86 (95% CI: 1.64–210) for diagnoses over age 85. Total mortality risk was only higher for hypertension diagnosed before 55 years of age (HR: 2.47, 95% CI: 1.90–3.19 for ages 18 to 44; and HR: 1.14, 95% CI: 1.02–1.28 for ages 45 to 54).


The diagnosis of hypertension in the community environment, in patients without evidence of previous kidney or CV disease, is associated with a large increase in the risk of K/CVEs, but especially in individuals diagnosed before the age of 55. This diagnosis is only associated with an increase in kidney or cardiovascular mortality or overall mortality when it occurs before age 55.

Peer Review reports


Arterial hypertension (HTN) is one of the most prevalent pathological conditions. One in three people over age 30 has been diagnosed with HTN, and although the age-adjusted prevalence has remained stable, the total number of diagnoses has doubled in the past 30 years [1].

HTN is an enormous burden responsible for the loss of 143 million disability-adjusted life years (DALYs) worldwide by 2015, considering the threshold of 140/90 mm Hg for its definition. These figures represented an increase of more than 30% of the DALYs lost for the same reason in 1990 [2].

The prevalence of HTN increases throughout life; among a cohort of previously healthy patients ages 25, 45, and 65, 0.3%, 6.5%, and 37% were diagnosed, respectively [3]. The number of people over 65 is growing steadily. Specifically, in Europe, it is expected that their number will double during the next 50 years, reaching 150 million people, and those who reach the average life expectancy without HTN have more than a 90% probability of developing the disease during their remaining life [4].

The excess mortality produced by HTN is mainly mediated by CV disease [2, 5]. Although the control of HTN through pharmacological and lifestyle measures has been shown to decrease mortality from these causes [6,7,8,9,10], it seems that hypertensive patients have an excess risk of CVEs [11] and overall mortality [12,13,14]. There has been a reduction in mortality in hypertensive patients over time, but there is a differential mortality compared to nonhypertensive patients with the same characteristics [15].

However, some authors have questioned this interpretation [16]. The diagnosis of hypertension occurs more frequently in people with other cardiovascular risk factors (CVRFs) [17]. Further, the presence of certain inflammatory markers, which have been associated with cardiovascular disease, is associated with the risk of being diagnosed with hypertension [18]. In fact, the existence of other CVRFs in patients newly diagnosed with hypertension is more frequent than in the general population of the same age [19]. Although the association between hypertension and cardiovascular disease, and mortality seems solid, the causality is debatable.

The impact of hypertension on overall mortality is attenuated as a function of the age of onset, going from mortality risks 2.5 times higher when hypertension is diagnosed before the age of 45 to excess mortality risk lower than 30% when this diagnosis is made over 65 years [19]. It is possible that the increase in blood pressure levels with age may constitute a protective mechanism against the dysfunction of certain organs [20, 21]. Additionally, it has been described that the differences in mortality (by cardiovascular or global cause) between patients with and without hypertension disappear when only the group of patients with treated and good control is considered [22].

In this context, the objective of evaluating the impact of newly diagnosed hypertension on the occurrence of kidney or cardiovascular events, mortality from these causes and total mortality in different age groups in the community under clinical practice conditions was proposed.


A retrospective cohort study was designed.

Inclusion criteria for the hypertensive cohort included to be over 18 years of age at recruitment, have been diagnosed of hypertension (code CIAP2 K86) from January 1, 2007, to December 31, 2008, and the absence of kidney or cardiovascular disease prior to such diagnosis. The diagnosis of hypertension implied that the mean of two or more correctly measured systolic blood pressure readings at each of two or more clinic visits was ≥ 140 mmHg or that diastolic blood pressure readings at each of two or more clinic visits was ≥ 90 mmHg. When the record referred to a diagnosis prior to that time, the subject was excluded.

The comparison cohort was constructed by pairing each individual with another person from the same Primary Care Center (PCC), without hypertension of the same sex and age range who did not have kidney or cardiovascular (CV) disease.

Exclusion criteria were being younger than 18 years, having suffered a kidney disease or CV event or having been diagnosed with hypertension before the start of the study.

Subjects in each cohort were selected from all PCC in the Community of Madrid.

Additional file 1 details the construction of both cohorts.

The follow-up lasted until December 31, 2018, or until the patient was removed from the community health records or died.

Sociodemographic and clinical variables were collected.

Age in years at diagnosis, sex and deprivation index of the area at the time of inclusion in the study were recorded. This deprivation index was developed for the MEDEA Project using Principal Component Analysis with the Census data. MEDEA index detects small areas of large cities with unfavorable socioeconomic characteristics and is related to general mortality [23]. The index was assigned to each census area using the following five socioeconomic indicators: manual workers, unemployment, temporary wage earners, total insufficient education and in youth. Each patient was assigned the MEDEA Index (in quintiles, the fifth quintile represents the least advantaged group) of their PCC, as an approximation to the place of residence.

The presence of the following clinical conditions recorded in the Clinical History of Primary Care, which uses the International Classification of Primary Care (ICPC-2), was collected [24]:

Diabetes Mellitus (DM)-ICPC2 T89 and T90-, tobacco use -ICPC2 P17-, or any reference to active tobacco consumption in the Electronic Health Record (HER) at the time of inclusion or in the year prior to inclusion.

In the follow-up, three types of dependent variables were collected:

  • Occurrence of kidney or cardiovascular event (K/CVE): ischemic heart disease (acute myocardial infarction (ICPC2 K75), angina–(ICPC2 K74), chronic ischemia (ICPC2 K76), heart failure (ICPC2 K77), cerebrovascular disease (ICPC2 K90, K91), peripheral arterial disease (ICPC2 K92), chronic kidney disease (ICPC2 U99.1), or appearance of maintained urinary microalbuminuria, or proteinuria.

  • Mortality from all causes. The International Classification of Diseases 10th edition (ICD-10) was used to study the causes of mortality [25].

  • Kidney or CV mortality: deaths due to chronic kidney disease (ICD10: N18), cerebrovascular accident (ICD10: G46; I60-I69), ischaemic heart disease (ICD10: I20-I25), heart failure (ICD10: I50) and peripheral arterial disease (ICD10: I70, I71, I72, I74), were classified as kidney or CV mortality.

The appearance of hypertension in the cohort that initially did not express this condition was also collected.

Data sources

Potential participants were identified by applying the eligibility criteria to the Center for Basic Strategic Information for Health care Environments (CIBELES). Clinical data were collected using a coding algorithm from the Centralized EHR for Primary Care PC of the Community of Madrid (AP-Madrid®). The EHR electronic source was linked to the mortality database of the Statistics National Institute and copied to a normalized database.


Prior to analysis, investigators implemented and verified several data quality processes for error identification and had access to the database population.

For the study of the occurrence of events, it should be taken into account that the subjects of the unexposed cohort could be diagnosed with hypertension in the follow-up. To use time-dependent covariates, the observation periods must be broken down into parts, depending on whether there is exposure. Once this procedure is performed, the data can be analyzed using a Cox proportional hazards model [26]. Given that contextual data were used, standard errors were calculated using robust methods and adjusted for 401 clusters (centers) [27]. The first analytical approach was Cox regression with time-dependent covariates.

On the other hand, mortality from kidney or CV causes and mortality from other causes can be considered competitive risks. One of the events could increase by decreasing the other. To address this problem, multistate models were constructed [28]. These models assume that the probability of transitioning to another state only depends on the present situation and allows treating some competitive risks as mutually exclusive absorbing states. Each transition between the states can be evaluated by Cox regressions, and the probabilities of transition to the same state from two different intermediate states can be compared [26].

The transitions defined for the multistate models can be seen in Fig. 1a and b. In our case, the comparisons of interest were transitions 2 versus 4 and 3 versus 5 to assess the risk of death from other causes and death from kidney or CV causes, respectively (Fig. 1a). To assess the impact of the association between HTN and total mortality, transitions 2 and 3 of Fig. 1b were studied. The comparison between models was made by assessing the Akaike Information Criteria (AIC) and Bayes Information Criteria (BIC) [29]. The analyses were performed with Stata® 14 using the “multistate” module designed by Crowther and Lambert [30].

Fig. 1
figure 1

Transitions evaluated using multistate models. a Mortality from different causes. b Total mortality

Ethical and legal aspects

A positive opinion was obtained from the Ethical Committee of the Alcorcon Foundation University Hospital.

This study was funded by the Carlos III Health Institute (ISCIII) through project PI18/00370 and co-financed by the European Union.


A total of 71,770 patients with an incidental diagnosis of hypertension and 72,946 matched controls by age group, sex and health center were included in the study (see Additional file 1).

Table 1 shows the baseline characteristics of both cohorts.

Table 1 Characteristics of the selected cohorts

The median follow-up was 138.61 months (interquartile range, IQR 124.68–143.97 months).

During the follow-up time, 15,042 patients in the unexposed cohort were diagnosed with hypertension. Of these, 1,327 had suffered a K/CVE prior to the diagnosis of hypertension, so their observation period ended at that time (with the occurrence of K/CVE). The remaining 13,715 were considered subjects with hypertension.

Study of the occurrence of kidney or CV events 

A total of 32,896 K/CVEs were recorded, including death due to these causes, 13,008 in the initially unexposed cohort (17.83%; 95% CI: 17.55–18.11%) and 19,888 (27.71%; 95% CI: 27.38–28.03%) in the cohort diagnosed with hypertension.

For patients who had a K/CVE, the median follow-up until the event was 70.14 months (IQR: 39.79–100.86 months). The follow-up was performed on 144,716 subjects, with a total of 16,462,184.38 person-months of observation and an event incidence rate of 0.0020 per person-month.

The risk of event occurrence was studied for each age range (constructing an age-hypertension interaction) and adjusting it for the following covariates: sex, smoking, diabetes diagnosis and socioeconomic status of the area.

Table 2 shows the results of the best Cox model. The risk of event occurrence is up to four times higher when hypertension diagnosis is made between ages 18 and 44 and gradually decreases with increasing age of diagnosis, but the association is relevant in all age ranges.

Table 2 Cox model for cardiovascular events, including kidney or cardiovascular mortality, hypertensive vs. non-hypertensive subjects

Study of global mortality

The follow-up was performed on 144,716 subjects, with a total of 18,137,117.42 person-months of observation and an incidence rate of death of 0.0009 per person-month. At the end of the follow-up, 16,668 subjects had died, 10.74% (95% CI: 10.51–10.97%) of the cohort initially diagnosed with hypertension and 12.28% (95% CI: 12.05- 12.52%) of the cohort initially undiagnosed. Of the deaths observed, 3,669 were caused by kidney or CV events and 12,999 by other causes.

For patients who died, the median follow-up was 84.40 months (IQR: 52.53–112.21 months).

The overall mortality risk was studied for each age range (constructing an age-hypertension interaction) and adjusting it for the same variables as in the previous case.

Table 3 shows the results of the best model. Adjusted for sex, smoking, diabetes and the socioeconomic level of the area, hypertension increases the risk of mortality up to 54 years but stops doing so when diagnosed after this age.

Table 3 Cox model for the total mortality event, hypertensive vs. non-hypertensive subjects

Study of mortality from different causes as competitive risks

A multistate model was developed in which the probability of mortality due to kidney or cardiovascular causes or other causes in the two cohorts was studied.

Table 4 shows the results of the association of hypertension with mortality in the different age groups. The adjustment variables were the same: sex, smoking, diabetes and socioeconomic status of the area.

Table 4 Proportional risks of death, hypertensive vs. non-hypertensive patients by age groups and causes (use of multistate models, Figs. 1a and b). Models adjusted for age, sex, diabetes mellitus, smoking and socioeconomic status of the area

Hypertension is strongly associated with kidney or cardiovascular mortality when diagnosed in individuals less than 45 years of age, and this association remains but with lower intensity until 54 years of age. After this age, there is no association between the two. When other causes of mortality are evaluated, hypertension is associated with an increase in mortality only if it is diagnosed before age 45 and presents an inverse association after the age of 55.


The diagnosis of HTN in patients without previous kidney or CV disease is associated with an increase in the occurrence of K/CVEs (including death due to these causes) throughout the entire life course, but especially when HTN is diagnosed before the age of 55.

The diagnosis of hypertension is only associated with kidney or cardiovascular mortality or total mortality, when it occurs before 55 years. An inverse association has been observed between HTN diagnosis over 55 years and overall mortality. The described associations were found in patients without previous kidney or CV disease who were followed and treated in a health system with full access to the general population and adjusted for the effect of DM, smoking, and socioeconomic situation.

HTN is associated with an increase in CVEs [2, 22, 31] and an association has also been described between HTN and all-cause mortality [14]. In some studies, CVEs’ incidence was twice if HTN was diagnosed under the age of 45 and an the excess of risk was about 60% for patients diagnosed between 45 and 55 years [19]. The results presented indicate higher risks (HR 4.36 and 2.45 for each of these age ranges) but include kidney or CV death as an event. But association with mortality from all causes has only been found when HTN is diagnosed at the earliest ages of life. The differences are more subtle when we compare the results with studies that analyze newly diagnosed HTN by age strata. While the diagnosis of HTN has been associated with an excess risk of mortality from all causes of 2.5 times (HR 2.59) when it occurs before age 45, this excess mortality does not reach 30% (HR 1.29) when it occurs over age 65 [19]. In the age group under 45 years, our results are very similar for this association (HR 2.47). The decrease in risk with the latest diagnosis of HTN is consistent with what has been previously described. An increase in the probability of developing target organ injury has been reported in patients with HTN diagnosed before 35 years, which was not observed when the diagnosis was made over age 45 [32]. Some studies have reported an association between well-controlled HTN and all-cause mortality in patients younger than 70 years [33], but other ones reported no association observed with all-cause mortality in patients with HTN under treatment, older than 75 years [7].

The differences found when assessing the risk of total mortality may be due to several reasons. Some of the primary studies that mentioned cohorts were recruited more than two decades ago and those that have more recent recruitments find more uncertain results for the association of HTN with all-cause mortality [14]. Improved survival in HTN patients has been demonstrated over time, and when blood pressure levels are better controlled with antihypertensive medication [7, 9]. In one of the studies with the longest reported follow-up (median 19.1 years), although a strong association between HTN and the occurrence of CVEs and all-cause mortality for untreated or poorly controlled patients was established, no such association was found in treated and controlled patients [22].

The inverse association found between HTN and total mortality over 55 years should not be explained from a causal perspective, as it is not plausible. It has been reported that a more intense use of PC services was associated with lower mortality in hypertensive patients [34], and certain promotional interventions have been shown to decrease CV risk in elderly hypertensive patients in PC [35]. Additionally a strong association has been reported between higher continuity of care and reduced mortality rate among hypertensive patients [36]. Our health system has practically universal coverage. Chronic disease is the main explanatory factor for the use of family doctor visits [37], and HTN occupies a relevant consultation time in PC [38]. The health care to which hypertensive patients older than 55 years are subjected, as well as the action on other coexisting CVRFs, may contribute to explaining, at least in part, the association.

Regarding the confounding role of the variables studied, both the occurrence of kidney or CV events and mortality from all causes increased in diabetic patients and smokers. The role of these CVRFs in mortality is well known, and both factors are used as adjustment variables in most of the studies discussed [14, 31, 33, 39]. In women with HTN, the risk is lower. It has already been described that avoidable mortality is lower in women than in hypertensive men [40], and it has been estimated that the burden of disease for hypertension is lower in women than in men for all ages except over 75 years [2]. The association of a worse socioeconomic situation and the events associated with HTN has also been widely described [41, 42].

This study has limitations inherent to retrospective cohort studies. The strength of the data is determined by the quality of the information collected. Some of the classical CVRFs, such as hypercholesterolemia or obesity, or other clinical circumstances such as the time at which diabetes was diagnosed, were not included as adjustment variables because they had not been validated in the EHR or their collection could have been differential for the groups compared. The diagnoses recorded in the EHR of PC for HTN and DM have been previously validated [43].

Among the strengths, all cases diagnosed in PC in the Autonomous Community during a period of two years were included and the secondary data sources allowed us to identify the final state in a reliable way, with very limited losses to follow-up. Given the characteristics of the health system (in 2020, 86% of assigned people were visited in their PCC) the generalizability of the results is important.

The value of the findings presented is based on the fact that they are data in real clinical practice conditions, in a specific environment, in which paradoxically, although healthy lifestyle habits are not very prevalent, cardiovascular mortality remains comparatively low [44].


The diagnosis of hypertension in the community environment, in patients without evidence of previous kidney or CV disease, is associated with a large increase in the risk of K/CVEs. This increased risk depends on the individual’s age at diagnosis; risk is highest if diagnosis is made before the age of 55 years and decreases with age. This diagnosis is only associated with an increase in kidney or cardiovascular or overall mortality when it occurs before age 55. Thus, the health system should increase secondary prevention measures, especially in hypertensive patients diagnosed before the age of 55, when excess risk is most evident.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.



Akaike Information Criteria


Bayes Information Criteria


Center for Basic Strategic Information for Healthcare Environments




Cardiovascular event


Cardiovascular risk factor


Diabetes Mellitus


Disability Adjusted Life Years


(Arterial) hypertension


International Classification of Diseases, 10th edition


International Classification of Primary Care 2nd ed.


Kidney or cardiovascular events


Models and their Effects on Development paths an Ethnographic and comparative Approach (a method to examine mortality and socioeconomic and environmental inequalities in Spanish communities)


Primary Care


Primary Care Center


  1. Zhou B, Carrillo-Larco RM, Danaei G, Riley LM, Paciorek CJ, Stevens GA, et al. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957–80.

    Article  Google Scholar 

  2. Forouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, et al. Global burden of hypertension and systolic blood pressure of at least 110 to 115mmHg, 1990–2015. JAMA. 2017;317(2):165–82.

    Article  PubMed  Google Scholar 

  3. Shihab HM, Meoni LA, Chu AY, Wang N-Y, Ford DE, Liang K-Y, et al. Body Mass Index and Risk of Incident Hypertension Over the Life Course: The Johns Hopkins Precursors Study. Circulation. 2012;126(25):2983–9.

    Article  PubMed  PubMed Central  Google Scholar 

  4. O’Brien E. The Lancet Commission on hypertension: Addressing the global burden of raised blood pressure on current and future generations. J Clin Hypertens. 2017;19(6):564–8.

    Article  Google Scholar 

  5. Shah NS, Molsberry R, Rana JS, Sidney S, Capewell S, O’Flaherty M, et al. Heterogeneous trends in burden of heart disease mortality by subtypes in the United States, 1999–2018: observational analysis of vital statistics. BMJ. 2020;13(370): m2688.

    Article  Google Scholar 

  6. Law MR, Morris JK, Wald NJ. Use of blood pressure lowering drugs in the prevention of cardiovascular disease: Meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies. BMJ. 2009;338(7705):1245.

    Google Scholar 

  7. Banach M, Bromfield S, Howard G, Howard VJ, Zanchetti A, Aronow WS, et al. Association of systolic blood pressure levels with cardiovascular events and all-cause mortality among older adults taking antihypertensive medication. Int J Cardiol. 2014;176(1):219–26.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bundy J, Li C, Stuclik P, Bu X, Kellly T, Mills KT, et al. Systolic Blood Pressure Reduction and Risk of Cardiovascular Disease and Mortality A Systematic Review and Network Meta-analysis. JAMA Cardiol. 2017;2(7):775–81.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Yun D, Lee H, Choi W, Chang H, Son D, Lee J. Association of optimal blood pressure with mortality in patients taking antihypertensive medications. J Clin Hypertens. 2020;22(11):2035–43.

    Article  CAS  Google Scholar 

  10. Wei J, Galaviz KI, Kowalski AJ, Magee MJ, Haw JS, Narayan KMV, et al. Comparison of cardiovascular events among users of different classes of antihypertension medications. JAMA Netw Open. 2020;3(2): e1921618.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Almgren T, Persson B, Wilhelmsen L, Rosengren A, Andersson OK. Stroke and coronary heart disease in treated hypertension - A prospective cohort study over three decades. J Intern Med. 2005;257(6):496–502.

    Article  CAS  PubMed  Google Scholar 

  12. Sepanlou SG, Sharafkhah M, Poustchi H, Malekzadeh MM, Etemadi A, Khademi H, et al. Hypertension and mortality in the Golestan Cohort Study: A prospective study of 50000 adults in Iran. J Hum Hypertens. 2016;30:260–7 Macmillan Publishers Limited.

    Article  CAS  PubMed  Google Scholar 

  13. da Silva TLN, Klein CH, da Rocha Nogueira A, Salis LHA, de Souza E Silva NA, Bloch KV. Cardiovascular mortality among a cohort of hypertensive and normotensives in Rio de Janeiro - Brazil - 1991-2009. BMC Public Health. 2015;15:623.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lawlor DA, Kim L, Morris R, Amuzu A, Whincup P, Ebrahim S. Survival with treated and well-controlled blood pressure: Findings from a prospective cohort study. PLoS One. 2011;6(4):e17792.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ford ES. Trends in mortality from all causes and cardiovascular disease among hypertensive and nonhypertensive adults in the united states. Circulation. 2011;123(16):1737–44.

    Article  PubMed  Google Scholar 

  16. Makridakis S, DiNicolantonio JJ. Hypertension: empirical evidence and implications in 2014. Open Hear. 2014;1(1): e000048.

    Article  Google Scholar 

  17. Kringeland E, Tell GS, Midtbø H, Haugsgjerd TR, Igland J, Gerdts E. Factors associated with increase in blood pressure and incident hypertension in early midlife: the Hordaland Health Study. Blood Press. 2020;29(5):267–75.

    Article  CAS  PubMed  Google Scholar 

  18. Kansui Y, Matsumura K, Morinaga Y, Inoue M, Kiyohara K, Ohta Y, et al. C-reactive protein and incident hypertension in a worksite population of Japanese men. J Clin Hypertens. 2019;21(4):524–32.

    Article  CAS  Google Scholar 

  19. Wang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, et al. Association of Age of Onset of Hypertension With Cardiovascular Diseases and Mortality. J Am Coll Cardiol. 2020;75(23):2921–30.

    Article  PubMed  Google Scholar 

  20. Lip S, Tan LE, Jeemon P, McCallum L, Dominiczak AF, Padmanabhan S. Diastolic Blood Pressure J-Curve Phenomenon in a Tertiary-Care Hypertension Clinic. Hypertension. 2019;74(4):767–75.

    Article  CAS  PubMed  Google Scholar 

  21. Bae E, Rocco MV, Lee J, Park JY, Kim YC, Yoo KD, et al. Impact of DBP on all-cause and cardiovascular mortality: results from the National Health and Nutrition Examination survey, 1999–2014. J Hypertens. 2022;40(1):108–16.

    Article  CAS  PubMed  Google Scholar 

  22. Zhou D, Xi B, Zhao M, Wang L, Veeranki SP. Uncontrolled hypertension increases risk of all-cause and cardiovascular disease mortality in US adults: The NHANES III Linked Mortality Study. Sci Rep. 2018;8(1):1–7.

    Google Scholar 

  23. Domínguez-Berjón MF, Borrell C, Cano-Serral G, Esnaola S, Nolasco A, Pasarín MI, et al. Construcción de un índice de privación a partir de datos censales en grandes ciudades españolas (Proyecto MEDEA). Gac Sanit. 2008;22(3):179–87.

    Article  PubMed  Google Scholar 

  24. World Health Organization (WHO). International Classification of Primary Care, -ICPC-2. World Health Organization; 2009.

    Google Scholar 

  25. World Health Organization (WHO). ICD-10: international statistical classification of diseases and related health problems: tenth revision. World Health Organization; 2004.

    Google Scholar 

  26. Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007;26(11):2389–430.

    Article  CAS  PubMed  Google Scholar 

  27. Williams RL. A Note on Robust Variance Estimation for Cluster-Correlated Data. Biometrics. 2000;56(2):645–6.

    Article  CAS  PubMed  Google Scholar 

  28. Barnett A, Graves N. Competing risks models and time-dependent covariates. Crit Care. 2008;12(2):134.

  29. Fabozzi FJ, Focardi SM, Rachev ST, Arshanapalli BG. Appendix E: Model Selection Criterion: AIC and BIC. In: The Basics of Financial Econometrics. Hoboken: Wiley; 2014. p. 399–403.

  30. Crowther MJ, Lambert P. MULTISTATE: Stata module to perform multi-state survival analysis. Statistical Software Components S458207, Boston College Department of Economics. 2016. Revised 18 Jan 2023.

  31. Rapsomaniki E, Timmis A, George J, Pujades-Rodriguez M, Shah AD, Denaxas S, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. Lancet. 2014;383(9932):1899–911.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Suvila K, McCabe EL, Lehtonen A, Ebinger JE, Lima JAC, Cheng S, et al. Early Onset Hypertension Is Associated With Hypertensive End-Organ Damage Already by MidLife. Hypertension. 2019;74(2):305–12.

    Article  CAS  PubMed  Google Scholar 

  33. Park S, Han K, Lee S, Kim Y, Lee Y, Kang MW, et al. Cardiovascular or mortality risk of controlled hypertension and importance of physical activity. Heart. 2021;107(18):1472–9.

    Article  CAS  PubMed  Google Scholar 

  34. Clement FM, Chen G, Khan N, Tu K, Campbell NRC, Smith M, et al. Primary care physician visits by patients with incident hypertension. Can J Cardiol. 2014;30(6):653–60.

    Article  PubMed  Google Scholar 

  35. Arija V, Villalobos F, Pedret R, Vinuesa A, Jovani D, Pascual G, et al. Physical activity, cardiovascular health, quality of life and blood pressure control in hypertensive subjects: randomized clinical trial. Health Qual Life Outcomes. 2018;16(1):184.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Chan K-S, Wan EY-F, Chin W-Y, Cheng WH-G, Ho MK, Yu EY-T, et al. Effects of continuity of care on health outcomes among patients with diabetes mellitus and or hypertension: a systematic review. BMC Fam Pract. 2021;22(1):145.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Martín-Fernández J, Gómez-Gascón T, Del Cura - González MI, Tomás-García N, Vargas-Machuca C, Rodríguez-Martínez G. [Quality of life related to health as a factor explaining the use of family medical consultation: a study on the behavioral model]. Rev Esp Salud Publica. 2010;84(3):293–319.

    PubMed  Google Scholar 

  38. Levine DM, Dixon RF, Linder JA. Association of Structured Virtual Visits for Hypertension Follow-Up in Primary Care with Blood Pressure Control and Use of Clinical Services. J Gen Intern Med. 2018;33(11):1862–7.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Franco OH, Peeters A, Bonneux L, de Laet C. Blood Pressure in Adulthood and Life Expectancy With Cardiovascular Disease in Men and Women: Life Course Analysis. Hypertension. 2005;46(2):280–6.

    Article  CAS  PubMed  Google Scholar 

  40. Redon J, Tellez-Plaza M, Orozco-Beltran D, Gil-Guillen V, Pita Fernandez S, Navarro-Pérez J, et al. Impact of hypertension on mortality and cardiovascular disease burden in patients with cardiovascular risk factors from a general practice setting. J Hypertens. 2016;34(6):1075–83.

    Article  CAS  PubMed  Google Scholar 

  41. King JB, Pinheiro LC, Ringel JB, Bress AP, Shimbo D, Muntner P, et al. Multiple social vulnerabilities to health disparities and hypertension and death in the regards study. Hypertension. 2022;79(1):196–206.

    Article  CAS  PubMed  Google Scholar 

  42. Lawlor DA, Ebrahim S, Smith GD. Adverse socioeconomic position across the lifecourse increases coronary heart disease risk cumulatively: Findings from the British women’s heart and health study. J Epidemiol Community Health. 2005;59(9):785–93.

    Article  PubMed  PubMed Central  Google Scholar 

  43. de Burgos-Lunar C, Salinero-Fort MA, Cárdenas-Valladolid J, Soto-Díaz S, Fuentes-Rodríguez CY, Abánades-Herranz JC, et al. Validation of diabetes mellitus and hypertension diagnosis in computerized medical records in primary health care. BMC Med Res Methodol. 2011;11(1):146.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Graciani A, León-Muñoz LM, Guallar-Castillón P, Rodríguez-Artalejo F, Banegas JR. Cardiovascular Health in a Southern Mediterranean European Country. Circ Cardiovasc Qual Outcomes. 2013;6(1):90–8.

    Article  PubMed  Google Scholar 

Download references


Not applicable.


The study was funded by the Carlos III Health Institute (ISCIII, PI1800370) and co-financed by the European Union.

Author information

Authors and Affiliations



Conceived and designed the experiments: JMF, TAS, EPC, MDEV, GAC, MIGA, LSP, GRM, ABG. Performed the experiments: JMF, TAS, EPC, MDEV, GAC, LSP. Analyzed the data: JMF, TAS. Discussed the results: JMF, TAS, EPC, MDEV, GAC, MIGA, GRM, RRC, ABG. Draft the manuscript: JMF, TAS. Revised and approved the manuscript: JMF, TAS, EPC, MDEV, GAC, MIGA, LSP, GRM, RRC, ABG. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jesus Martín-Fernández.

Ethics declarations

Ethics approval and consent to participate

Authorization was obtained from the Ethics Committee at the Alcorcon Foundation University Teaching Hospital (18/115).

The study was carried out in accordance with the guidelines of good research practices, the principles of the Declaration of Helsinki (Fortaleza 2013), the provisions of Organic Law 3/2018 of December 5 for the Protection of Personal Data and guarantee of digital rights, and Law 14/2007 on Biomedical Research. Once all the databases were related, the data were dissociated, eliminating any potential identifiers.

Waiver of informed consent was granted by the Ethic Review Board of the Alcorcon Foundation University Teaching Hospital (ref 18/115).

Consent for publication

Not applicable (see above).

Competing interests

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

 Generation of Cohorts.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Martín-Fernández, J., Alonso-Safont, T., Polentinos-Castro, E. et al. Impact of hypertension diagnosis on morbidity and mortality: a retrospective cohort study in primary care. BMC Prim. Care 24, 79 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Hypertension
  • Survival
  • Cardiovascular disease
  • Primary health care