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Public Health Pract (Oxf)
Public Health Pract (Oxf)
Public Health in Practice
2666-5352
Elsevier

S2666-5352(24)00074-0
10.1016/j.puhip.2024.100537
100537
Original Research
Mortality attributable to diabetes in Cuba: Estimates for 2019
Seuc A.H. ahseuc@gmail.com
⁎
Mirabal-Sosa M.
Garcia-Serrano Y.
Alfonso-Sague K.
Fernandez-Gonzalez L.
Instituto Nacional de Higiene, Epidemiología y Microbiología (INHEM), Infanta 1151, e/ Clavel y Llinas, Centro Habana, La Habana, 10300, Cuba
⁎ Corresponding author. INHEM, Infanta 1151 e/ Clavel y Llinas, Centro Habana, La Habana, 10300, Cuba. ahseuc@gmail.com
22 8 2024
12 2024
22 8 2024
8 1005375 3 2024
21 7 2024
6 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective

To estimate the national and provincial number of excess deaths due to diabetes across Cuba in 2019.

Study design

Cross-sectional design with secondary data.

Methods

We used DISMODII, a computerized generic disease model, to assess disease burden by modelling the relationships between incidence, prevalence, and disease-specific mortality. Baseline input data included population structure, total mortality, and age- and sex-specific estimates for diabetes prevalence from the Cuban National Health Survey 2019, and available published estimates of the relative risk of death for people with diabetes compared to people without diabetes. The results were internally validated with DISMODII output for duration of diabetes (years).

Results

In 2019, we estimated an excess of mortality attributable to diabetes of 7.5 times the diabetes mortality reported by the National Death Registry, which is equivalent to 16.4 % of all deaths in Cuba. The percentages of all-cause mortality among provinces varied between 10.7 % in Villa Clara and 24.5 % in Ciego de Avila.

Conclusions

These are the first estimates of mortality attributable to diabetes in Cuba and its provinces. Diabetes is likely to be a much more prominent leading cause of death than the 9th ranking reported by the Cuban National Death Registry 2019. Disease models similar to DISMODII are important tools to validate the epidemiologic indicators used in the burden of disease calculations.

Keywords

Burden of disease
Diabetes
Mortality
Epidemiology
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pmc1 Introduction

Globally, diabetes ranks among the top 10 causes of mortality. An estimated 537 million adults aged 20–79 years worldwide (10.5 % of all adults in this age group) had diabetes in 2021 and, by 2045, 783 million adults are projected to be living with this condition. Thus, while the world's population is estimated to grow 20 % over this period, the number of people with diabetes is estimated to increase by 46 % [1]. Excluding the mortality risks associated with the COVID-19 pandemic, approximately 6.7 million adults (20–79 years) are estimated to have died as a result of diabetes or its complications (e.g., cardiovascular and/or kidney diseases) in 2021, which corresponds to 12.2 % of global deaths from all causes in this age group. Moreover, approximately one-third (32.6 %) of all deaths from diabetes occurs in people of working age (under 60 years), corresponding to 11.8 % of total global deaths in people under 60 [1].

Routinely reported statistics based on death certification seriously underestimate mortality from diabetes [[2], [3], [4], [5]], as individuals with diabetes most often die of cardiovascular and kidney disease and not from a cause exclusively related to diabetes, such as ketoacidosis or hypoglycaemia [6,7]. Complex methods have been developed for estimating cause-specific mortality for some conditions (AIDS, tuberculosis) but not for diabetes [6].

The Cuban National Death Registry 2019 [8], estimated a total number of 109,085 deaths in Cuba. From this total, diabetes (ICD10: E10-E14) was the 9th highest cause of death in Cuba (2313 deaths), behind heart diseases (I05-I52; 26,736 deaths), cancer (C00-C97; 25,035 deaths), cerebrovascular diseases (I60-I69; 10,008 deaths), influenza and pneumonia (J09-J18; 8923 deaths), accidents (V01-X59, Y85-Y86; 5429 deaths), chronic obstructive pulmonary disease (J40-J47; 4310 deaths), and arteries/arterioles/capillary vessels diseases (I70-I79; 2794 deaths).

Diabetes mortality is a complex indicator [9], and on top of this the proportion of undiagnosed diabetes is high (in the South and Central America International Diabetes Federation (IDF) region, where Cuba is included, the proportion of undiagnosed diabetes is 32.8 %, and globally it is close to 50 % [1]); therefore we expect the mortality attributable to diabetes to be much higher, particularly in Cuba.

The epidemiologic indicators of type 1 and type 2 diabetes are quite different. However, most countries do not have data about type 1 diabetes, particularly in south Asia, sub-Saharan Africa, and parts of central and South America [10], particularly in Cuba. This is why we have decided in this study, as in other relevant papers on this topic [6], to consider them together.

DISMODII is a software package made available to the public domain by the World Health Organization; using a set of differential equations, it exploits the causal relations between the various variables that describe a disease process [11], in this particular case, diabetes. This program is routinely used by World Health Organization (WHO) and the IDF Diabetes Atlas [1,7], and has been externally validated by several authors [6,[12], [13], [14]]. According to Ref. [6], in 2000 mortality attributable to diabetes was about 3 times the “official” mortality from death certificates.

This study aimed to provide a more realistic estimate of the number of deaths attributable to diabetes in Cuba and to assess the usefulness of DISMODII to obtain them.

2 Research design and methods

Model background. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries [15]. In this evidence, the burden of mortality (or morbidity) of a disease is calculated using mortality (or morbidity) attributable to the disease, which is defined using a counterfactual causality approach. To estimate the number of deaths attributable to diabetes in the year 2019, we used a software program, DisMod II, developed for the Global Burden of Disease 2000 study [11,16]. As already mentioned, this program is also routinely used by World Health Organization (WHO) and the IDF Diabetes Atlas ([1,7], and externally validated by several authors [6,[12], [13], [14]]. The DisMod II disease model, been described elsewhere [6,11], is a multistate life table that describes a single disease; it is freely available and can be downloaded from http://www.epigear.com/index.htm.

The model uses six disease-specific input variables: incidence, remission, case fatality/relative risk [RR] for total mortality, prevalence, duration, and mortality. Three variables are strictly needed, along with the population size and total mortality, to calculate the others in the model.

2.1 Model input

Input data for this study included population structure, all-cause mortality rates, age- and sex-specific diabetes prevalence, remission, and available published estimates of RR of death for people with diabetes.

A study in Scotland reported a type 2 diabetes remission prevalence rate close to 5 % [17]; the remission incidence rate should be quite smaller, and a key study assumed it to be zero [6]. Finally, we expect these rates to be lower in Cuba as they are associated to some health technologies (e.g. bariatric surgery) [17] that are not easily available in this country. So we considered adequate to assume the remission (incidence) rate to be zero for this modelling study.

Estimates for prevalence were those obtained from the National Health Survey conducted in 2019 by the Cuban National Institute of Hygiene, Epidemiology, and Microbiology (INHEM). This survey uses a national representative stratified multi-stage sampling design, involving 14,339 adults (15+) and 1556 children (6–14 years); unpublished results. Based on this survey, diabetes prevalence estimates were obtained by age and sex groups, for Cuba and its 16 provinces: i) Pinar del Rio, ii) Artemisa, iii) La Habana, iv) Mayabeque, v) Matanzas, vi) Villa Clara, vii) Cienfuegos, viii) Sancti Spiritus, ix) Ciego de Avila, x) Camaguey, xi) Las Tunas, xii) Holguin, xiii) Granma, xiv) Santiago de Cuba, xv) Guantánamo, and xvi) Isla de la Juventud, [18]. These 16 provinces have been in place since 2011 [19].

2.2 Statistical analysis

Published studies reporting estimates of RR of mortality (RRMORT) for diabetics are scarce; the paper by Roglic [6] reported/assumed approximate RRs values of 2.5, 6.72, 3.54, 2.25, and 1, for age groups 0–19, 20–39, 40–59, 60–79, and 80+ respectively, also described elsewhere [20]. These RRMORT patterns were used as the basis of our DISMODII analyses. The DISMODII time trend option was used (different values were used, particularly a 5-year length period, with a 1 % annual increase) for calculations of point estimates, and binomial and Poisson distribution were assumed for prevalence and RRMORT inputs, respectively, for uncertainty calculations. The uncertainty module in DISMODII was used to get 95 % uncertainty interval (UI95 %) for output indicators, particularly for mortality attributable to diabetes.

The only study that have published in detail the application of DISMODII to assess internal consistency of diabetes indicators [6], does not mention the use of the “time-trend” option; we decided to explore this option to see if it accounted for the very high levels of mortality attributable to diabetes that we observed during the estimation process. A 1 % annual increase during a 5-year length period before 2019 does represent a significant increase, and this is why we tried it.

Previous studies [11] have used the binomial and Poisson distributions for prevalence and mortality related input indicators to DISMODII, and this is why we decided to use them in this study. We tried different distributions, but it impacted negatively in the uncertainty intervals, e.g. they were too narrow, or the upper value was smaller than the lower value.

Several studies have approached validation of DISMODII results using external data on the indicator of interest, e.g., mortality attributable to diabetes [6] and incidence of acute myocardial infarction [13]. We tried an internal validation, aiming at DISMODII results on duration to be “plausible”. For example, at least 1–2 years below life expectancy in the 0–5 age group; life expectancy in Cuba is approximately 76 years for men and 80 years for women [21]. The aim was for the duration of diabetes to gradually decreases with incident age in both genders [22].

3 Results

The use of the time trend option in DISMODII did have a negligible impact on final results, though it was in the expected direction, i.e. with a time trend the mortality rate output was slightly smaller than without a time trend. Results are presented without a time trend.

Numerous sets of RRMORT values across the age groups 0–19, 20–39, 40–59, 60–79, and 80+ were tried, having as a guide the reference pattern used in Ref. [6]. For example for Cuba, men and women we used the RRMORT inputs 4, 14, 8, 8, and 8, and 8, 30, 14.1, 14.1, and 14.1, respectively, Option 1; the corresponding DISMODII diabetes durations (years) were 48, 43, 28, 25 and 24, and 49, 43, 29, 28 and 28, for age groups 0–4, 5–14, 15–44, 45–59 and 60+, men and women respectively, which are close to the durations estimated by Ref. [22].

It should be noted that introducing RRMORT values closer to one for the last two age groups, as suggested by the published reference patterns [6], generated important oscillations in the duration indicator, and for this reason, the mortality results corresponding to these modifications were not followed up.

Using Option 1 of RRMORT values, however, generated very high results for diabetes-attributable mortality rates (x100000); for example, for Cuba the rates were 254.2 and 1102.1 for men and women respectively, 13.7 and 45.9 times the mortality rates reported by the Cuban National Death Registry for 2019.

Different sets of RRMORT values, increasing from (close to) 1 in the first three age groups, and larger values in both the fourth and fifth age groups, both for men and women, were used and succeeded in generating diabetes durations close but below life expectancies in Cuba [23]. For example, for Cuba the RRMORT input values 1.1, 3, 4, 4.5, and 4.5, and 1.1, 3.6, 7.5, 8.4, and 8.4, for men and women respectively (Option 2), generated diabetes durations of 74, 69, 54, 44 and 43 years, and 79, 73, 59, 47, and 46 years, for men and women respectively, both corresponding to age groups 0–4, 5–14, 15–44, 45–59, and 60+; RRMORT values with similar patterns were used for each of the 16 provinces. Results in Table 1, Table 2 are obtained using the Option 2 for RRMORT values. Life expectancy at birth in Cuba in 2019 was approximately 76 and 80 for men and women respectively [23].Table 1 Estimated rates and number of deaths, attributable to diabetes [1] and from the National Death Registry [2], by sex and Cuba's provinces: 2019.

Table 1Province	Rate (x100000)	Number of deaths	
male	female	all	male	female	all	
[1]	[2]	[1]	[2]	[1]	[2]	[1]	[2]	[1]	[2]	[1]	[2]	
Pinar del Rio	51.5	10.5	163.0	10.8	106.5	10.6	152	31	470	31	623	62	
Artemisa	92.1	11.2	261.3	15.3	176.2	13.3	238	29	666	39	904	68	
La Habana	153.9	29.4	234.4	34.8	196.0	32.2	1566	299	2612	388	4178	687	
Mayabeque	105.8	27.3	158.8	33.7	132.0	30.5	205	53	301	64	507	117	
Matanzas	121.9	16.2	175.4	22.0	148.7	19.1	436	58	629	79	1066	137	
Villa Clara	92.6	15.5	137.8	17.7	115.2	16.6	359	60	537	69	896	129	
Cienfuegos	113.7	12.7	216.5	12.9	164.6	12.8	233	26	436	26	669	52	
Sancti Spiritus	169.1	18.0	163.3	25.1	166.2	21.5	395	42	378	58	773	100	
Ciego de Avila	104.8	19.6	286.3	23.1	194.9	21.4	230	43	619	50	849	93	
Camaguey	188.2	17.8	284.0	28.4	236.2	23.1	718	68	1088	109	1806	177	
Las Tunas	127.9	21.4	239.3	25.1	182.8	23.2	347	58	630	66	976	124	
Holguin	122.4	11.1	143.0	13.5	132.6	12.3	629	57	729	69	1358	126	
Granma	94.9	14.3	166.3	20.2	130.2	17.2	393	59	675	82	1068	141	
Santiago de Cuba	140.9	15.6	157.0	25.0	149.0	20.4	732	81	828	132	1560	213	
Guantanamo	55.8	19.0	273.8	24.9	165.0	21.9	141	48	694	63	835	111	
MEIJa	169.8	42.7	27.0	60.4	99.0	51.5	72	18	11	25	83	43	
CUBA	117.4	18.5	201.9	24.0	159.9	21.2	6539	1030	11,371	1350	17,910	2380	
a Municipio Especial Isla de la Juventud.

Table 2 Diabetes attributable mortality percentages of all-cause deaths by sex and Cuba's provinces: 2019.

Table 2Province	% of all-cause deaths	
male	female	total	
Pinar del Rio	4.8	18.9	11.0	
Artemisa	9.6	31.4	19.6	
La Habana	13.0	22.3	17.6	
Mayabeque	9.0	16.9	12.5	
Matanzas	11.5	19.8	15.2	
Villa Clara	7.8	14.1	10.7	
Cienfuegos	10.9	24.7	17.2	
Sancti Spiritus	15.6	18.7	17.0	
Ciego de Avila	10.1	35.8	21.2	
Camaguey	17.4	33.4	24.5	
Las Tunas	12.9	30.1	20.5	
Holguin	12.0	18.2	14.7	
Granma	9.2	20.9	14.2	
Santiago de Cuba	13.8	19.9	16.5	
Guantanamo	6.1	38.3	20.3	
MEIJa	17.8	3.9	12.0	
CUBA	11.0	23.0	16.4	
a Municipio Especial Isla de la Juventud.

An alternative approach to estimate excess mortality attributable to diabetes [9] was explored considering all deaths where diabetes was mentioned in the death certificate in the National Death Registry; mortality rates (x100000) obtained using this approach were 0, 0, 0, 12 and 55 for men, and 6, 0, 4, 8, and 62 for women, for age groups 0–5, 6–14, 15–44, 45–59, and 60+, respectively. However, the duration of diabetes from DISMODII was too high: 115, 110, 90, 78, and 76 years for men, and 194, 194, 179, 180, and 178 years for women, for the same age groups, so we finally did not use this alternative approach (Option 3).

Mortality rates (x100000) and numbers of deaths, attributable to diabetes and from the National Death Registry based on the underlying cause of death, using Option 2 of RRMORT values, are presented in Table 1.

The diabetes attributable mortality percentages of all-cause (total) mortality, by sex and provinces, using Option 2 for RRMORT values, are presented in Table 2.

Diabetes excess deaths in Cuba are 7.5 (17,910/2380) times the deaths from diabetes reported by the National Death Registry, 6.3 (6539/1030) times in men and 8.4 (11,371/1350) in women; the lowest ratios were observed in MEIJ (1.9 times) and Mayabeque (6.6 times) and the highest in Artemisa (13.3 times) and Cienfuegos (12.9 times).

Excess mortality attributable to diabetes is estimated at 17,910 deaths (UI95 %: 15,083–20176), 6539 for men (UI95 %: 5527–7370) and 11,371 for women (UI95 %: 9556–12806), which is equivalent to 16.4 % of Cuba all-cause mortality in the year 2019, 11 % and 23 % for men and women respectively. The percentage of excess deaths was lowest in Villa Clara (10.7 %) and Pinar del Rio (11.0 %) and highest in Camaguey (24.5 %) and Ciego de Avila (21.2 %).

The number of deaths in Cuba in 2019 due to Heart Diseases (I05-I52) and Cancer (C00-C97), the two leading causes of death in Cuba, were 14,355 and 14,264 respectively [8], both lower than (each about 80 %) the number of deaths attributable to diabetes estimated in this study, 17,910.

4 Discussion

This study aimed to provide a more realistic estimate of the number of deaths attributable to diabetes in Cuba and to assess the usefulness of DISMODII to obtain them.

DISMODII did prove to be an extremely useful tool to assess the consistency of epidemiologic indicators, and to identify the knowledge gaps and potential sources to bridge them. It allowed us to confront prejudices and pre-conceptions about our knowledge of diabetes epidemiology in Cuba, particularly when we faced the necessary balance between (relative risk of) mortality and disease duration. For example, in order to have rates of mortality attributable to diabetes close to those reported by the Cuban National Death Registry, we had to allow for duration of diabetes to be larger than corresponding life expectancies. Finally, its use rightly introduces important uncertainties in the estimation of burden of diseases components, mortality and morbidity.

Several options for RRMORT values were tried, and we decided to present in Tables those with, in our opinion, more credible estimations of diabetes attributable mortality in Cuba and its provinces, in 2019. In general, we identified that the excess diabetes-attributable mortality is about 7.5 times higher than the diabetes mortality reported by the National Death Registry, 6.3 and 8.4 times in men and women respectively. Excess mortality attributable to diabetes is estimated at 17,910 deaths, 6539 for men and 11,371 for women, which is equivalent to 16.4 % of Cuba all-cause mortality in the year 2019, 11 % and 23 % for men and women respectively.

As mentioned previously, the results of this study have been generated using the most updated epidemiologic indicators of diabetes available for Cuba 2019, particularly diabetes prevalence from the 2019 Cuban National Health Survey (unfortunately, the corresponding database is not open access). These results indicate that in Cuba, as probably in many other countries, the National Death Registries seriously underestimate the real number of deaths attributable to diabetes. Our results are higher than those reported by Ref. [6], which reported excess mortality attributable to diabetes to be 5.2 % of the world all-cause mortality in the year 2000, much lower than our 16.4 % estimation. It is possible that in Cuba, the frequency of diabetes complications related to heart and renal diseases is much higher than in other comparable countries, which might explain why diabetes mortality is so highly underestimated by the Cuban National Death Registry.

According to the IHME-GHDx data platform [24], mortality attributable to diabetes in Cuba 2019 was (95 % UI): 2029 (1900, 2150), for men 869 (793, 940), and for women 1160 (1067, 1243). In fact, quite close to mortality from the Cuban National Death Registry. Apart from the fact that GBD Studies use not DISMODII but a different tool (DISMOD-MR) for assessing consistency of disease indicators, we think they do not identify under-registration because the Cuban National Health Survey data were not available to them.

The estimated percentage of excess deaths for Cuba, 16.4 %, seems too high, but it is compatible with results from other studies, e.g. i) 15.7 % in North America 2010 in age group 20–79 [12], ii) 21 % in Germany 2010 [5], and iii) 24.5 % in the Middle East and North Africa IDF Region in 2021 [1].

It might also be possible different RRMORT values could generate estimates of diabetes-attributable mortality lower than those we have reported here for Cuba 2019, but definitely, it would imply even larger diabetes durations than those we have presented here, which is close to impossible. Therefore, it seems the real excess mortality attributable to diabetes in Cuba in 2019 could be even larger.

Although not standardized, the behaviour of mortality attributable to diabetes across provinces seems to be quite different. In some provinces (Guantánamo, Las Tunas, Camaguey and Ciego de Avila) this indicator represents more than 20 % of total mortality from all causes, while in other provinces (Pinar del Rio, Villa Clara, and MEIJ) it represents 12 % or less. This heterogeneity might be the result of differences in prevalences and/or case fatality, which are eventually related to differences in the efficacy of prevention and/or treatment programs. This is something that would need to be explored further in the future.

4.1 Limitations of the study

Given the lack of evidence on RRMORT and diabetes duration, we have manipulated the RRMORT input to DISMODII to get diabetes duration 1–2 years below life expectancy for all provinces in Cuba; there is no published evidence on the real duration of diabetes in Cuba, nor on how it varies across provinces At the same time, these durations might be above the real duration values [22], meaning our DISMODII results on mortality attributable to diabetes presented here might still be below the real figures.

5 Conclusions

This is the first study reporting excess deaths attributable to diabetes in Cuba.

The results of this study show that diabetes should have a higher priority for prevention programs and treatment services within the Cuban health sector, than it has had so far. Diabetes is probably very close to Cardiovascular Diseases and Cancer, the two main health concerns in Cuba in terms of DALYs.

The causes of the probable heterogeneity across provinces in Cuba in terms of mortality attributable to diabetes should be identified, through additional and relevant subnational studies.

This study demonstrates the importance of assessing the internal consistency of disease epidemiologic indicators before burden calculations [11,25], both for infectious and for chronic diseases. Tools like DISMODII are essential for this purpose.

The results of this study should be a reference point for future debates on the epidemiologic characterization of diabetes and other diseases in Cuba.

Declaration of competing interest

The authors have no competing interests to declare.

Acknowledgements

Funding: This work was partially funded from the framework agreement between the Institute of Tropical Medicine and the Belgian Directorate-General for Development Cooperation and Humanitarian Aid (DGD). The funders had no role in study design, data manipulation and analysis, or decision to publish.
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References

1 International Diabetes Federation IDF Diabetes Atlas tenth ed. 2021 IDF Diabetes Atlas
2 Fuller J. Elford J. Goldblatt en P.A. A. Diabetes mortality: new light on an underestimated public health problem Diabetologia 1983 336 341
3 Brinks R. Tonnies T. Hoyer en A. Assessing two methods for estimating excess mortality of chronic diseases from aggregated data BMC Res. Notes 216 2020
4 Boyko E. Progress in the estimation of mortality due to diabetes Diabetes Care 2005 2320 2321 16123511
5 Jacobs E. Hoy A. Brinks R. Rathmann O. Kuss en W. Burden of mortality attributable to diagnosed diabetes: a nationwide analysis based on claims data from 65 million people in Germany Diabetes Care 2017 1703 1709 28993421
6 Roglic G. Unwin N. Bennett P. Mathers C. Tuomilehto J. Nag S. King V. Connolly en H. The burden of mortality attributable to diabetes Diabetes Care 2005 2130 2135 16123478
7 IDF Diabetes Atlas Group Update of mortality attributable to diabetes for the IDF Diabetes Atlas: estimates for the year 2011 Diabetes Res. Clin. Pract. 2013 277 279
8 MINSAP, Direccion de Registros Medicos y Estadisticas de Salud, „2019 Anuario Estadistico de Salud CEDISAP, La Habana 2020
9 Bracco P. Gregg E. Rolka D. Schmidt M. Barreto S. Lotufo P. Bensenor I. Chor D. Duncan en B. A nationwide analysis of the excess death attributable to diabetes in Brazil J. Global Health 10 1 2020
10 Gregory G.A. Robinson T.I.G. Linklater S.E. Wang F. Colagiuri S. de Beaufort en C. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study Lancet Diabetes Endocrinol. 10 2022 741 760 36113507
11 Barendregt J. Oortmarssen G.V.T. Murray en C. A generic model for the assessment of disease epidemiology: the computational basis of DisMod II Popul. Health Metrics 1 2003 4
12 Unwin G. Roglic en N. Mortality attributable to diabetes: estimates for the year 2010 Diabetes Res. Clin. Pract. 2010 15 19
13 Scarborough P. Smolina K. Mizdrak A. Briggs L. Coblac en A. Assessing the external validity of model-based estimates of the incidence of heart attack in England: a modelling study BMC Publ. Health 1135 2016
14 Kruijshaar M. Hoeymans J. Barendregt en N. The use of models in the estimation of disease epidemiology Bull. World Health Organ. 80 8 2002 622 628 2002 12219152
15 GBD 2019 Diseases and Injuries Collaborators Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019 Lancet 2020 1204 1222 33069326
16 Murray C. Lopez en A. Regional patterns of disability-free life expectancy and disability-adjusted life expectancy: global Burden of Disease Study Lancet 349 1997 1347 1352 9149696
17 Captieux M. Fleetwood K. Kennon B. Sattar N. Lindsay R. Guthrie en B. Epidemiology of type 2 diabetes remission in Scotland in 2019: a cross-sectional population-based study PLoS Med. 18 11 2021 e1003828
18 Revueltas-Aguero M. Molina-Esquivel E. Suarez-Medina R. Bonet-Gorbea M. Varona-Perez P. Benitez-Martinez en M. La hipertensión arterial en Cuba según la Encuesta Nacional de Salud 2018-2019 Arch méd Camagüey. 2022 26 2022 e9239 e9239
19 Lloret C. Mendez en M. La division politico administrativa en Cuba: antecedentes y actualidad 2021 Revista Territorios y Regionalismos 1 11
20 Tancredi M. Rosengren A. Svensson A. Kosiborod M. Pivodic A. Gudbjornsdottir S W.H. Clements M. Dahlqvist S. Lind en M. Excess mortality among persons with type 2 diabetes N. Engl. J. Med. 2015 1720 1732 26510021
21 ONEI, Oficina Nacional de Estadistica e Informacion La Esperanza de Vida 2011-2013, Calculos para Cuba y provincias, por sexo y edades Centro de Estudios de Poblacion y Desarrollo, La Habana 2014
22 Cho S. Kim S. Kim Y. Jo S. Yoon en M. Estimating lifetime duration of diabetes by age and gender in the Korean population using a markov model 2019 Mar 26 J Korean Med Sci. 34 1 2019 34 e74
23 ONEI. Oficina Nacional de Estadistica e Informacion. Cuba. LA esperanza de vida 2011-2013 Centro de Estudios de Poblacion y Desarrollo, La Habana 2014
24 Global Burden of Disease Collaborative Network Global Burden of Disease Studay 2021 (GBD 2021) Results 2022 Institute for Health Metrics and Evaluation (IHME) Seattle, United States [Online]. Available: https://vizhub.healthdata.org/gbd-results/ [Geopend 2024]
25 Mathers C. Lopez C. Murray en A. Epidemiological evidence: improving validity through consistency analysis. Editorial Bull. World Health Organ. 611 2002
