Sociodemographic determinants of health care in heart failure mortality in Mexico, 019 and 2023

ABSTRACT

Objective:  To analyze sociodemographic determinants associated with medical care before death from heart failure (HF) in Mexico, comparing 2019 and 2023.

Methods:  A retrospective cross-sectional analytical study was conducted using national mortality records from the General Directorate of Epidemiology (DGE). HF deaths (ICD-10: I50.0-I50.9) from 2019 and 2023 were selected, representing pre- and post-pandemic (COVID-19) contexts. The variables included gender, age, education, marital status, health insurance coverage, area of residence, region, place of death, and year of occurrence. Multiple logistic regression was applied to estimate odds ratios (OR) with 95% confidence intervals (95% CI).

Results:  In the 2019-2023 period, 13,510 HF deaths were recorded (6,077 in 2019 and 7,433 in 2023). The probability of receiving medical care before death was 22% higher in 2023 compared to 2019 (OR 1.22; 95%CI 1.10-1.36). Medical care was more likely among individuals aged ≥80 years old, with higher educational levels, living in urban areas, and with health insurance coverage. Being male, lacking health coverage, and dying at home were associated with a lower probability of receiving medical care. The interaction between health insurance coverage and place of death revealed reduced access to medical care among individuals without social security who died at home.

Conclusions:  Substantial gaps in access to medical care persist, associated with structural and social inequalities. The post-pandemic recovery of coverage indicates a partial strengthening of the health system. Strengthening primary health care, expanding effective universal coverage, and developing strategies targeting vulnerable groups are recommended.

Keywords:
Heart failure; Mortality; Socioeconomic factors; Social security; Mexico

RESUMEN

Objetivo:  Analizar los determinantes sociodemográficos asociados con la asistencia médica antes del fallecimiento por ICa en México, comparando 2019 y 2023.

Métodos:  Estudio transversal retrospectivo, con enfoque analítico, basado en registros nacionales de mortalidad de la Dirección General de Epidemiología (DGE). Se seleccionaron defunciones por ICa (CIE-10: I50.0-I50.9) correspondientes a 2019 y 2023, periodos representativos del contexto pre y pospandemia (COVID-19). Las variables incluidas fueron sexo, edad, escolaridad, estado civil, derechohabiencia, área de residencia, región, sitio de defunción y año de ocurrencia. Se aplicó regresión logística múltiple para estimar odds ratio (OR) con intervalos de confianza al 95% (IC95%).

Resultados:  En el periodo 2019-2023 se registraron 13.510 defunciones por ICa (6.077 en 2019 y 7.433 en 2023). La probabilidad de recibir asistencia médica antes del fallecimiento fue 22% mayor en 2023 en comparación con 2019 (OR 1,22; IC95% 1,10-1,36). La asistencia fue más probable en personas de 80 años o más, con mayor escolaridad, residencia urbana y derechohabiencia. Ser Hombre, no contar con cobertura médica y fallecer en el hogar se asociaron con una menor probabilidad de recibir atención. La interacción entre derechohabiencia y sitio de defunción reveló una reducción de la atención médica en quienes no contaban con seguridad social y fallecieron en el hogar.

Conclusiones:  Persisten brechas sustanciales en el acceso a la atención médica asociadas con desigualdades estructurales y sociales. La recuperación de la cobertura posterior a la pandemia evidencia un fortalecimiento parcial del sistema de salud. Se recomienda fortalecer la atención primaria, ampliar la cobertura universal efectiva y desarrollar estrategias dirigidas a grupos vulnerables.

Palabras clave:
Insuficiencia cardíaca; Mortalidad; Factores Socioeconómicos; Seguridad social; México

INTRODUCTION

Heart failure (HF) is a clinical syndrome characterized by signs and symptoms resulting from structural and/or functional cardiac abnormalities that impair the heart’s ability to pump sufficient blood to meet the body’s metabolic demands¹. The principal risk factors for the development of HF include coronary heart disease, hypertension, diabetes mellitus, obesity, a family history of cardiovascular disease, chronic lung disease, chronic inflammation or infection, metabolic disorders, exposure to cardiotoxic agents, cocaine use, anthracycline therapy in oncology, and alcohol abuse².

Since the late 20^th century, HF has been recognized as an epidemic due to the exponential rise in hospitalizations attributable to this condition³. In 2002, it was estimated that approximately 6.5 million individuals in Europe, 5 million in the United States, and 2.4 million in Japan were living with the disease⁴. This increase has been associated with population aging⁵, as well as with a higher prevalence of comorbidities, greater physical disability, and an increased incidence of adverse events, all of which contribute to higher healthcare utilization and mortality. Consequently, HF has emerged as a major public health concern⁶.

Cardiovascular diseases are currently the second leading cause of death worldwide, following cancer⁷, and represent the leading cause of death in Mexico. According to estimates from the Global Burden of Disease 2023 study, more than 64 million individuals are living with HF globally, with an age-adjusted prevalence of 831 cases per 100,000 inhabitants⁸. The greatest burden is concentrated in low- and middle-income countries, where incidence and mortality rates remain high due to limited access to healthcare services and a higher prevalence of metabolic risk factors. In contrast, high-income countries have demonstrated a sustained reduction in HF-related mortality, attributable to the expansion of evidence-based pharmacological therapies and the strengthening of primary cardiovascular care⁹.

However, although current advances in the treatment of HF have improved patients’ quality of life and survival, an increase in comorbidities, disability, and functional dependence has also been observed, leading to higher healthcare system costs. This phenomenon is related not only to increased longevity but also to population aging, which is recognized as one of the main risk factors for the development of HF¹⁰.

Although current technologies have made it possible to stabilize or even reduce the incidence of HF in older adults in some populations, opposite trends have been observed in younger individuals, possibly related to the increased prevalence of other pathologies, such as obesity¹¹. Additionally, it has been demonstrated that characteristics such as race influence the incidence of HF before the age of 50 years, with higher frequency observed in Black individuals compared with White individuals¹².

Despite the importance of this disease, estimates of the incidence of HF and their temporal trends remain scarce and inconsistent, even in high-income countries¹³. In Mexico, although precise data are not available, it is estimated that 750,000 individuals suffer from HF, with approximately 75,000 new cases occurring each year¹⁴. This situation is further aggravated by structural inequalities in access to healthcare services.

The health system in Mexico comprises public and private sectors that operate in a complementary manner, although with marked inequalities. The public sector includes social security institutions - the Mexican Social Security Institute (Instituto Mexicano del Seguro Social - IMSS), the Institute for Social Security and Services for State Workers (Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado - ISSSTE), Petróleos Mexicanos (PEMEX), the Ministry of National Defense (Secretaría de la Defensa Nacional - SEDENA), and the Ministry of the Navy (Secretaría de Marina - SEMAR) - which provide coverage to approximately 52% of the population¹⁵. Meanwhile, the system for the care of the uninsured population, formerly known as Seguro Popular and, since 2023, the Mexican Social Security Institute for Well-being (Instituto Mexicano de Seguridad Social Bienestar - IMSS Bienestar), covers approximately 35% of the population, while the remaining 13% rely on private services or have limited effective access to healthcare¹⁶. These structural differences in affiliation and resource availability affect equity in healthcare delivery, particularly in the management of chronic diseases such as HF¹⁷.

The magnitude of this disease, its impact on mortality, and the high costs associated with its treatment highlight its relevance to public health. In this context, the study of mortality related to HF represents an essential tool for health planning, allowing the identification of inequalities, the prioritization of resources, and the formulation of evidence-based public health policies¹⁸.

Therefore, the objective of this study was to perform a comparative analysis of the sociodemographic determinants related to medical care among individuals who died from HF in Mexico during the years 2019 and 2023.

METHODS

Design and information sources

A retrospective, cross-sectional study with an analytical approach was conducted using individual death records obtained from the General Directorate of Epidemiology (Dirección General de Epidemiología - DGE) of Mexico. Deaths in which the underlying cause was HF were selected using codes I50.0 to I50.9 of the International Classification of Diseases, Tenth Revision (ICD-10).

The years 2019 and 2023 were selected to represent the periods before and after the COVID-19 pandemic, thereby avoiding potential bias from the intervening years (2020-2022), during which alterations in medical care delivery and in death certification processes were observed.

Place and area of study

The study was conducted nationwide in Mexico, covering all 32 federal entities. The country has an area of 1.96 million km² and an estimated population of 129 million inhabitants in 2023. The national Human Development Index (HDI) was 0.78, and the per capita income was US$12,900. The Central and Northern regions have the most extensive healthcare infrastructure, whereas the South and Southeast regions have historically shown lower coverage and quality of healthcare services¹⁹.

Data source

Mortality data were obtained from the Epidemiological and Statistical Subsystem of Deaths (Subsistema Epidemiológico y Estadístico de Defunciones - SEED), administered by the DGE. This database is composed of death certificates processed by the Civil Registry and the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía - INEGI), with an estimated national coverage of 98%²⁰. Previous studies have documented underreporting of less than 2%, with slight regional variations, mainly in southern states.

Variables and information processing

The dependent variable was “medical assistance prior to death” (yes/no). Independent variables included state; geographic region (North, Central-North, Central, South, and Southeast); gender; age (under 60 years; 60 to 80 years; and over 80 years); month of death (December-January and other months); educational level (no schooling; primary/secondary education; and high school education or higher); marital status (married/other); health insurance coverage (with or without coverage); area of residence (urban or rural); year of death (2023/2019); speaks indigenous language (yes/no); place of death (home or other place); and birth cohort.

The dependent variable “medical assistance prior to death” demonstrated a completeness rate of 97.6%, which supports the validity of the analyses performed.

Information analysis

Mortality rates per 100,000 inhabitants were calculated, stratified by age range and gender, with their respective 95% confidence intervals. The rates were estimated based on deaths registered by the DGE and population projections provided by the National Population Council (Consejo Nacional de Población - CONAPO) and INEGI.

Bivariate analysis

The associations between medical care and categorical variables were explored using the chi-square test. For continuous variables, such as age, the Student’s t-test for independent samples was applied. Variables with p<0.05 in the bivariate analysis were included in the multiple logistic regression model.

Multiple analysis

Based on the results of the cross-sectional design, a binary logistic regression model was applied to estimate odds ratios (ORs), 95% confidence intervals (95% CIs), and p-values. Categorical variables were transformed into dummy variables by excluding a reference category to avoid multicollinearity. The interaction between health insurance status and place of death was evaluated using an additive term (dh#soc) to identify combined effects on the probability of having received medical care prior to death.

The model was estimated using maximum likelihood estimation with the Logit link function from the statsmodels library in Python, supplemented with pandas and numpy. A robust variance-covariance matrix estimator (cov_type=’HC0’) was used to control for heteroscedasticity. ORs and their respective 95% CIs were reported.

Ethical considerations

This study was classified as low risk, as it exclusively used anonymized, publicly available secondary databases. According to Article 17 of the Regulations of the General Health Law on Health Research, studies that use publicly available and anonymized data are considered low risk and are exempt from ethics committee review²¹.

Data availability statement:

The full dataset supporting the results of this study is available from the corresponding author upon reasonable request.

RESULTS

In Mexico, 13,510 deaths due to HF were registered between 2019 and 2023, of which 6,077 (45%) occurred in 2019 and 7,433 (55%) in 2023. The mortality rate per 100,000 inhabitants was higher in 2023 (5.61) than in 2019 (4.89), and was slightly higher among women than among men in both years (Figure 1).

Figure 1.
Comparison of mortality rates due to heart failure in men and women. Mexico, 2019 and 2023.

In men, the rate increased from 4.67 to 5.34 per 100,000 inhabitants, while in women it increased from 5.11 to 5.87 per 100,000 inhabitants, reflecting a sustained rise in both genders, although more pronounced among men.

Table 1 summarizes the sociodemographic characteristics of individuals who died from HF according to medical care received prior to death. Overall, the majority of individuals received medical care before death. Women showed a higher proportion of receiving care (86.7%) than men (84.4%). Regarding age, the group aged 80 years and older accounted for the largest number of deaths (97%), and 15.1% of individuals in this age group did not receive medical care. By region, the central region of the country registered the highest proportion of individuals receiving care (92.2%), whereas the North and Southeast regions showed the lowest proportions of care received (19.2% and 19.3% without care, respectively).

Medical care was more frequent among individuals residing in urban areas (87.9%) than among those in rural areas (77.6%), as well as among married individuals (88.8%) compared with unmarried individuals (84.2%) (Table 1).

Table 1.
Characteristics of individuals who died from heart failure according to sociodemographic aspects and medical care received. Mexico, 2019 and 2023.

Characteristic	Category	Received medical care (%)	Did not receive medical care (%)	95%CI
Gender	Male	84.4	15.6	83.2-85.6
	Female	86.7	13.3	85.5-87.9
Age (years)	<60	78.3	21.7	76.8-79.8
	60-79	85.2	14.8	84.1-86.3
	≥80	97.0	3.0	96.5-97.5
Education	No schooling	82.6	17.4	81.1-84.1
	Primary/secondary	88.9	11.1	87.8-90.0
	High school or higher	91.5	8.5	90.1-92.9
Marital status	Married	88.8	11.2	87.6-90.0
	Other	84.2	15.8	82.9-85.5
Health insurance coverage	With coverage	92.3	7.7	91.1-93.5
	Without coverage	78.4	21.6	77.0-79.8
Month of death	December-January	83.1	16.9	81.8-.84.5
	Other months	86.3	13.7	85.7-.86.9
Place of death	Home	80.6	19.4	79.6-81.5
	Other locations	90.9	9.1	90.2-91.6
Area of residence	Urban	87.9	12.1	86.5-89.3
	Rural	77.6	22.4	75.8-79.4
Region	North	80.8	19.2	78.9-82.7
	Central-North	89.1	10.9	87.8-90.4
	Central	92.2	7.8	91.0-93.4
	South	86.7	13.3	85.2-88.2
	Southeast	80.7	19.3	78.9-82.5
Year of death	2019	84.5	15.5	83.2-85.8
	2023	86.8	13.2	85.6-88.0

• 95%CI: 95% confidence interval.

The multiple logistic regression model (Table 2) showed that individuals who died in 2023 were 22% more likely to have received medical care than those who died in 2019 (OR 1.22; 95%CI 1.10-1.36). Men were 20% less likely to have received medical care than women (OR 0.80; 95%CI 0.73-0.89). Urban residence was associated with a higher likelihood of receiving care (OR 1.64; 95%CI 1.46-1.84), as was older age: 60-79 years old (OR 1.58; 95%CI 1.36-1.84) and those aged 80 years old or older (OR 1.95; 95%CI 1.67-2.27).

Table 2.
Factors associated with receiving medical care before death from heart failure. Mexico, 2019 and 2023.

Characteristics	Categories	Adjusted OR	95%CI	P-value
Year of death	2019	Ref.	-	-
	2023	1.22	1.10-1.36	<0.001
Gender	Female	Ref.	-	-
	Male	0.80	0.73-0.89	<0.001
Age (years)	<60	Ref.	-	-
	60-79	1.58	1.36-1.84	<0.001
	≥80	1.95	1.67-2.27	<0.001
Education	No schooling	Ref.	-	-
	Primary/secondary	1.51	1.35-1.69	<0.001
	High school or higher	2.05	1.70-2.47	<0.001
Marital status	Other	Ref.	-	-
	Married	1.31	1.16-1.48	<0.001
Health insurance coverage	Without coverage	Ref.	-	-
	With coverage	2.42	2.18-2.69	<0.001
Area of residence	Rural	Ref.	-	-
	Urban	1.64	1.46-1.84	<0.001
Region of the country	North	Ref.	-	-
	Central-North	1.93	1.65-2.27	<0.001
	Central	Ref.	2.64-3.64	<0.001
	South	1.91	1.61-2.26	<0.001
	Southeast	1.45	1.22-1.73	<0.001
Month of death	December - January	Ref.
	Other months	1.27	1.13-1.43	<0.001
Place of death	Hospital/clinic	Ref.	-	-
	Home	0.37	0.33-0.43	<0.001
Year of death	2019	Ref.	-	-
	2023	1.22	1.10-1.36	<0.001
Interaction terms	Without health insurance and death at home	0.35	0.30-0.41	<0.001
	With health insurance and death at home	0.37	0.33-0.43	<0.001
	Without health insurance and death outside home	0.85	0.69-1.05	0.128

• OR: odds ratio; 95%: 95% confidence interval. Multiple logistic regression model adjusted for gender, age, education, marital status, health insurance coverage, area of residence, region, place of death, and year.

Furthermore, a higher level of education was associated with a progressively increased likelihood of receiving medical care. Individuals with primary or secondary education had an OR of 1.51 (95%CI 1.35-1.69), while those with high school education or higher had an OR of 2.05 (95%CI 1.70-2.47). Marital status was also significantly associated with medical care, with a higher likelihood of receiving care among married individuals (OR 1.31; 95%CI 1.16-1.48).

Regarding geographic region, compared with the northern region of the country, a higher probability of receiving medical attention was observed in the Central (OR 3.10), Central-North (OR 1.93), South (OR 1.91), and Southeast (OR 1.45) regions, with statistically significant differences (p<0.001) (Figure 2).

Figure 2.
Forest plot of the adjusted odds ratio for receiving medical care before death from heart failure. Mexico, 2019 and 2023.

Finally, death occurring at home was associated with a significant reduction in the probability of having received medical care (OR 0.37; 95%CI 0.33-0.43) (Table 2).

Interaction between health insurance and place of death

A significant reduction in the probability of receiving medical care was observed when individuals without health insurance died at home (OR 0.35; 95%CI 0.30-0.41). Similarly, individuals with health insurance who died at home had a lower probability of receiving care (OR 0.37; 95%CI 0.33-0.43). In contrast, lack of health insurance combined with death occurring outside the home was not significantly associated with reduced medical care (OR 0.85; 95%CI 0.69-1.05), suggesting that the effect of health insurance coverage is conditioned by the place of death (Table 2). The model demonstrated adequate discriminatory performance (AUC=0.713), with a sensitivity of 85.6%.

DISCUSSION

This study identified the sociodemographic determinants associated with medical care prior to death from HF in Mexico during 2019 and 2023. The results showed that male and rural populations, as well as individuals with lower levels of education and those without health insurance, had a lower probability of receiving medical care prior to death, confirming the persistence of structural inequalities in access to healthcare services.

The finding that the probability of receiving medical care in 2023 was 22% higher than in 2019 may reflect an expansion of medical care following the COVID-19 pandemic. This result may be explained by the reorganization of the healthcare system and the reactivation of primary and hospital care services after the saturation observed in 2020 and 2021, when human and material resources were concentrated on emergency healthcare responses. Previous studies in Latin America and Mexico documented a considerable decrease in care for chronic diseases during the pandemic^22,23, followed by an increase in consultations and hospitalizations during 2022-2023, which is consistent with the trend observed in this analysis.

In relation to gender, men showed a 20% lower probability of having received medical assistance prior to death, in accordance with international evidence indicating lower healthcare-seeking behavior among males^24,25.

Men constitute a vulnerable group in access to healthcare services due to sociocultural factors that discourage seeking care, expressing symptoms, and adhering to treatment.

Several studies suggest that traditional patterns of masculinity are associated with lower risk perception, reduced use of preventive services, and higher mortality from preventable causes²⁶.

In contrast, women tend to access medical services more frequently, partly due to greater contact with the healthcare system throughout their lives and a higher perception of risk, factors that positively influence timely medical care^27,28.

The increased likelihood of receiving medical care among individuals aged 60 years old and older is consistent with international literature, which reports greater interaction with the healthcare system as age increases²⁹.

This finding can be explained by a greater burden of comorbidities, a more acute perception of functional decline, and more frequent use of specialized healthcare services.

Depending on their place of residence, individuals living in urban areas were significantly more likely to have received medical attention prior to death. This difference reflects the unequal distribution of healthcare resources, which have historically been concentrated in the urban areas of the central and northern regions of the country.

In contrast, rural areas face infrastructure deficiencies, shortages of healthcare personnel, and limitations in transportation and access to specialized services^30,31.

Furthermore, linguistic barriers can exacerbate inequalities, especially in indigenous communities, where more than 68 native languages are spoken in the Mexican Republic³². The absence of interpreters and the limited intercultural training of healthcare personnel restrict communication, reduce diagnostic quality, and discourage the search for timely medical attention^33,34.

Educational level acted as a positive determinant of the probability of receiving medical care.

Individuals with secondary education or higher were more likely to receive medical assistance, which may be attributed to better knowledge of the disease, higher health literacy, and improved economic conditions and access to healthcare services.

This finding is consistent with research indicating that education acts as a protective factor against mortality from HF and other chronic diseases³⁵.

Similarly, being married was associated with a higher likelihood of having received medical care.

The presence of a partner or a close family network can facilitate the decision to seek healthcare services, as well as support healthcare-seeking behavior, therapeutic adherence, and continuity of care³⁶.

One of the most significant findings was the interaction between health insurance coverage and place of death. Individuals without health insurance coverage who died at home were the least likely to have received medical attention, followed by those with health insurance coverage who also died at home. This result suggests the existence of structural barriers that transcend formal health insurance coverage, related to economic, geographic, or cultural factors.

Previous studies on avoidable mortality in Mexico have documented that lack of health coverage, higher levels of marginalization, and residence in rural areas increase the probability of dying without medical attention³⁷.

The regression model demonstrated adequate discriminatory power, which reinforces the internal validity of the findings.

However, this study has limitations that should be considered.

The retrospective, cross-sectional study design prevents the establishment of causal relationships between variables.

Furthermore, the database used does not include detailed clinical information, such as time since diagnosis, ejection fraction, type of treatment, or the presence of additional comorbidities, which could influence the likelihood of receiving medical care.

Despite these limitations, the use of official sources with broad coverage and methodological validation ensures the reliability of the results. Taken together, the findings demonstrate that, although the Mexican healthcare system has shown progress in coverage and post-pandemic reorganization, significant inequities persist in access to medical services, particularly among rural populations, individuals with low levels of education, and those without health insurance. These results reinforce the need to strengthen effective universal coverage, expand medical care in rural and indigenous areas, and develop specific strategies targeting male and socially vulnerable populations to guarantee timely and equitable medical care.

ACKNOWLEDGMENTS:

We thank the General Directorate of Epidemiology of Mexico for providing access to the online mortality records for the study period.

REFERENCES

• ^1. Bozkurt B, Coats AJS, Tsutsui H, Abdelhamid CM, Adamopoulos S, Albert N, et al. Universal definition and classification of heart failure: a report of the Heart Failure Society of America, Heart Failure Association of the European Society of Cardiology, Japanese Heart Failure Society and Writing Committee of the Universal Definition of Heart Failure. Eur J Heart Fail 2021; 23(3): 352-80. https://doi.org/10.1002/ejhf.2115
  » https://doi.org/https://doi.org/10.1002/ejhf.2115
• ^2. Schwinger RHG. Pathophysiology of heart failure. Cardiovasc Diagn Ther 2021; 11(1): 263-76. https://doi.org/10.21037/cdt-20-302
  » https://doi.org/https://doi.org/10.21037/cdt-20-302
• ^3. Roger VL. Epidemiology of heart failure. Circ Res 2021; 128(10): 1421-34. https://doi.org/10.1161/CIRCRESAHA.121.318172
  » https://doi.org/https://doi.org/10.1161/CIRCRESAHA.121.318172
• ^4. McMurray JJV, Stewart S. The burden of heart failure. Eur Heart J Suppl 2002; 4(suppl_D): D50-8. https://doi.org/10.1093/ehjsupp/4.suppl_D.D50
  » https://doi.org/https://doi.org/10.1093/ehjsupp/4.suppl_D.D50
• ^5. Dmitrieva NI, Liu D, Wu CO, Boehm M. Middle age serum sodium levels in the upper part of normal range and risk of heart failure. Eur Heart J 2022; 43(35): 3335-48. https://doi.org/10.1093/eurheartj/ehac138
  » https://doi.org/https://doi.org/10.1093/eurheartj/ehac138
• ^6. Hoffman C, Rice D, Sung HY. Persons with chronic conditions: their prevalence and costs. JAMA 1996; 276(18): 1473-9. https://doi.org/10.1001/jama.1996.03540180029029
  » https://doi.org/https://doi.org/10.1001/jama.1996.03540180029029
• ^7. López CP, Jaramillo-Gómez N, Mejía CAV, Cardona-Vélez J, Contreras-Martínez H, Jaramillo-Restrepo V. Conceptos actuales sobre el envejecimiento y la enfermedad cardiovascular. Rev Colomb Cardiol 2016; 23(3): 210-7. https://www.elsevier.es/es-revista-revista-colombiana-cardiologia-203-articulo-conceptos-actuales-sobre-el-envejecimiento-S0120563316000048
  » https://doi.org/https://www.elsevier.es/es-revista-revista-colombiana-cardiologia-203-articulo-conceptos-actuales-sobre-el-envejecimiento-S0120563316000048
• ^8. GBD 2023 Disease and Injury and Risk Factor Collaborators. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. The Lancet 2023; 406(10513): 1873-922. https://doi.org/10.1016/S0140-6736(25)01637-X
  » https://doi.org/https://doi.org/10.1016/S0140-6736(25)01637-X
• ^9. Savarese G, Stolfo D, Sinagra G, Lund LH. Heart failure with mid-range or mildly reduced ejection fraction. Nat Rev Cardiol 2022; 19(2): 100-16. https://doi.org/10.1038/s41569-021-00605-5
  » https://doi.org/https://doi.org/10.1038/s41569-021-00605-5
• ^10. Dahlström U. Frequent non-cardiac comorbidities in patients with chronic heart failure. Eur J Heart Fail 2005; 7(3): 309-16. https://doi.org/10.1016/j.ejheart.2005.01.008
  » https://doi.org/https://doi.org/10.1016/j.ejheart.2005.01.008
• ^11. Groenewegen A, Rutten FH, Mosterd A, Hoes AW. Epidemiology of heart failure. Eur J Heart Fail 2020; 22(8): 1342-56. https://doi.org/10.1002/ejhf.1858
  » https://doi.org/https://doi.org/10.1002/ejhf.1858
• ^12. Bibbins-Domingo K, Pletcher MJ, Lin F, Vittinghoff E, Gardin JM, Arynchyn A, et al. Racial differences in incident heart failure among young adults. N Engl J Med 2009; 360(12): 1179-90. https://doi.org/10.1056/nejmoa0807265
  » https://doi.org/https://doi.org/10.1056/nejmoa0807265
• ^13. Conrad N, Judge A, Tran J, Mohseni H, Hedgecott D, Crespillo AP, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. The Lancet 2018; 391(10120): 572-80. https://doi.org/10.1016/S0140-6736(17)32520-5
  » https://doi.org/https://doi.org/10.1016/S0140-6736(17)32520-5
• ^14. Torres PEC, Salas AP, Mendoza LMG, Yanes-Lane M. Panorama de la mortalidad en México a través de cuatro indicadores de 1990 a 2012. Gac Médica México 2017; 153(1): 16-25.
• ^15. Barraza-Lloréns M, Bertozzi S, González-Pier E, Gutiérrez JP. Addressing inequity in health and health care in Mexico. Health Affairs (Millwood) 2002; 21(3): 47-56. https://doi.org/10.1377/hlthaff.21.3.47
  » https://doi.org/https://doi.org/10.1377/hlthaff.21.3.47
• ^16. Gómez-Dantés H, Fullman N, Lamadrid-Figueroa H, Cahuana-Hurtado L, Darney B, Avila-Burgos L, et al. Dissonant health transition in the states of Mexico, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet 2016; 388(10058): 2386-402. https://doi.org/10.1016/S0140-6736(16)31773-1
  » https://doi.org/https://doi.org/10.1016/S0140-6736(16)31773-1
• ^17. Gutiérrez JP, Castañeda A, Agudelo-Botero M, Martínez-Valle A, Knight M, Lozano R. Performance evaluation of Mexico’s health system at the national and subnational level, 1990-2019: an analysis of the Health Access and Quality Index. Public Health 2024; 236: 7-14. https://doi.org/10.1016/j.puhe.2024.07.009
  » https://doi.org/https://doi.org/10.1016/j.puhe.2024.07.009
• ^18. Connelly PJ, Azizi Z, Alipour P, Delles C, Pilote L, Raparelli V. The importance of gender to understand sex differences in cardiovascular disease. Can J Cardiol 2021; 37(5): 699-710. https://doi.org/10.1016/j.cjca.2021.02.005
  » https://doi.org/https://doi.org/10.1016/j.cjca.2021.02.005
• ^19. Consejo Nacional de Población. Proyecciones de la población de México y de las entidades federativas, 2020-2050. México: Consejo Nacional de Población; 2023.
• ^20. Dirección General de Epidemiología. Subsistema Epidemiológico y Estadístico de Defunciones. México: Secretaría de Salud; 2020 [citado 2026 Feb 2]. Disponible en: https://www.gob.mx/salud/acciones-y-programas/sistema-nacional-de-vigilancia-epidemiologica
  » https://www.gob.mx/salud/acciones-y-programas/sistema-nacional-de-vigilancia-epidemiologica
• ^21. Secretaría de Salud. Reglamento de la Ley General de Salud en Materia de Investigación para la Salud. Diario Oficial de la Federación. 2023.
• ^22. Parkinson A, Matenge S, Desborough J, Dykgraaf SH, Ball L, Wright M, et al. The impact of COVID-19 on chronic disease management in primary care: lessons for Australia from the international experience. Med J Aust 2022; 216(9): 445-8. https://doi.org/10.5694/mja2.51497
  » https://doi.org/https://doi.org/10.5694/mja2.51497
• ^23. Gutierrez JP, Bertozzi SM. Non-communicable diseases and inequalities increase risk of death among COVID-19 patients in Mexico. PLoS One 2020; 15(10): e0240394. https://doi.org/10.1371/journal.pone.0240394
  » https://doi.org/https://doi.org/10.1371/journal.pone.0240394
• ^24. Courtenay WH. Constructions of masculinity and their influence on men’s well-being: a theory of gender and health. Soc Sci Med 2000; 50(10): 1385-401. https://doi.org/10.1016/s0277-9536(99)00390-1
  » https://doi.org/https://doi.org/10.1016/s0277-9536(99)00390-1
• ^25. Baker P, Dworkin SL, Tong S, Banks I, Shand T, Yamey G. The men’s health gap: men must be included in the global health equity agenda. Bull World Health Organ 2014; 92(8): 618-20. https://doi.org/10.2471/BLT.13.132795
  » https://doi.org/https://doi.org/10.2471/BLT.13.132795
• ^26. Galdas PM, Cheater F, Marshall P. Men and health help-seeking behaviour: literature review. J Adv Nurs 2005; 49(6): 616-23. https://doi.org/10.1111/j.1365-2648.2004.03331.x
  » https://doi.org/https://doi.org/10.1111/j.1365-2648.2004.03331.x
• ^27. Alcaraz-Moreno N, Serván-Mori E, Heredia-Pi I, Nigenda G, Lozano R. Gender differences in healthcare use in Mexico: analysis of the ENSANUT 2021. Salud Publica Mex 2023; 65(5): 458-67. https://doi.org/10.21149/15263--- 27
  » https://doi.org/https://doi.org/10.21149/15263--- 27
• ^28. Sen G, Östlin P. Gender inequity in health: why it exists and how we can change it. Global Public Health 2008; 3(Suppl. 1): 1-12. https://doi.org/10.1080/17441690801900795
  » https://doi.org/https://doi.org/10.1080/17441690801900795
• ^29. Niccoli T, Partridge L. Ageing as a risk factor for disease. Curr Biol 2012; 22(17): R741-R752. https://doi.org/10.1016/j.cub.2012.07.024
  » https://doi.org/https://doi.org/10.1016/j.cub.2012.07.024
• ^30. Gutiérrez JP, García-Saisó S, Peña RE, Balandrán-Durán M, Hernández-Ávila M. Inequality in medical care in Mexico: challenges for universal coverage. Salud Publica Mex 2022; 64(1): 35-44.
• ^31. Polanco N, Cediel N, Benavides E, Villamil LC. Covid-19 como sindemia en la ruralidad colombiana: brechas y desigualdades. Equidad Desarro 2021; (37): 53-74. https://doi.org/10.19052/eq.vol1.iss37.3
• ^32. Pérez NM. Conocimiento, Ciencia y tecnología en un mundo intercultural. Diversidad Lingüística de México [Internet] 2020 [cite don Feb 2, 2026]; (1): 37. Available at: https://www.researchgate.net/profile/Maria-Alvarez-Velazquez2/publication/356540562_Las_mas_monas_primatologas_de_ayer_y_hoy/links/619fc572f8565a76fdf47df1/Las-mas-monas-primatologas-de-ayer-y hoy.pdf#page=37
  » https://www.researchgate.net/profile/Maria-Alvarez-Velazquez2/publication/356540562_Las_mas_monas_primatologas_de_ayer_y_hoy/links/619fc572f8565a76fdf47df1/Las-mas-monas-primatologas-de-ayer-y hoy.pdf#page=37
• ^33. Sritharan S, Wilsmore B, Wiggers J, Butel-Simoes L, Fakes K, McGee M, et al. Rural-urban differences in outcomes of acute cardiac admissions in a large health service. JACC Adv 2024; 3(11): 101328. https://doi.org/10.1016/j.jacadv.2024.101328
  » https://doi.org/https://doi.org/10.1016/j.jacadv.2024.101328
• ^34. Vértiz-Ramírez JJ, Ramírez-Palacios P, Ríos-Cázares G, Sánchez-Domínguez MS, Ochoa-Torres MP, Negri-Filho AA, et al. Vulnerabilidad y barreras de acceso a los servicios de salud. Salud Pública México 2024; 66(4): 556-70. https://doi.org/10.21149/16199
  » https://doi.org/https://doi.org/10.21149/16199
• ^35. Petermann-Rocha F, Apolinar E, Nazar G, Diaz-Toro F, Celis A, Deo S, et al. Associations of diabesity with all-cause and cardiovascular disease mortality: findings from the Mexico City Prospective Study. Diabetes Obes Metab 2024; 26(6): 2199-208. https://doi.org/10.1111/dom.15528
  » https://doi.org/https://doi.org/10.1111/dom.15528
• ^36. Manzoli L, Villari P, Pirone GM, Boccia A. Marital status and mortality in the elderly: a systematic review and meta-analysis. Soc Sci Med 2007; 64(1): 77-94. https://doi.org/10.1016/j.socscimed.2006.08.031
  » https://doi.org/https://doi.org/10.1016/j.socscimed.2006.08.031
• ^37. Barraza-Lloréns M, Bertozzi S, González-Pier E, Gutiérrez JP. Addressing inequity in health and health care in Mexico. Health Aff (Millwood) 2002; 21(3): 47-56. https://doi.org/10.1377/hlthaff.21.3.47
  » https://doi.org/https://doi.org/10.1377/hlthaff.21.3.47

• HOW TO CITE THIS ARTICLE:
  Campuzano JC, Rodríguez JM, Chaparro PE, Urrego DC, Hernández A. Sociodemographic determinants of health care in heart failure mortality in Mexico, 2019 and 2023. Rev Bras Epidemiol. 2026; 29: e260019. https://doi.org/10.1590/1980-549720260019.2
• ETHICS COMMITTEE:
  This study was classified as minimal risk, as it used exclusively secondary, anonymized, and publicly available databases. Therefore, it did not require approval from an Institutional Review Board or Ethics Committee.
• FUNDING:
  Instituto Nacional de Salud Pública de México, Pontificia Universidad Javeriana and Observatorio Nacional de Salud.

Edited by

• ASSOCIATED EDITOR:
  Márcia Furquim de Almeida https://orcid.org/0000-0003-0052-1888

• SCIENTIFIC EDITOR:
  Cassia Maria Buchalla https://orcid.org/0000-0001-5169-5533

Publication Dates

• Publication in this collection
  20 Apr 2026
• Date of issue
  2026

History

• Received
  06 Aug 2025
• Reviewed
  30 Oct 2025
• Accepted
  25 Dec 2025