All-cause mortality attributable to type 2 diabetes mellitus in Peru: a comparative risk assessment analysis

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Quispe-Villegas, Giovani Fabrizio Luna-Venturo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8297456/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives. To quantify all-cause mortality attributable to type 2 diabetes mellitus (T2DM) across the 25 regions of Peru using a comparative risk assessment approach. Materials and methods. We estimated population attributable fractions (PAFs) by combining T2DM prevalence from the Peruvian Demographic and Health Survey (ENDES) with relative risks (RRs) for the association between T2DM and all-cause mortality. Sex-, age-, and region-specific PAFs were multiplied by the number of registered deaths in 2018 to obtain the absolute number and proportion of deaths attributable to T2DM in each region. Results. In 2018, an estimated 19,102 deaths were attributable to T2DM, corresponding to 16.6% of all-cause deaths among adults aged ≥ 20 years. The burden was slightly higher among men (9,985 deaths; 8.7% of all-cause deaths) than among women (9,117 deaths; 7.9%). The highest proportions and absolute numbers of deaths attributable to T2DM were observed in Lima, Lambayeque, and Callao (all coastal regions), whereas the lowest estimates were found in Pasco, Madre de Dios, and Amazonas. Lima concentrated the largest number of T2DM-attributable deaths and the highest age-standardized mortality rates. Conclusions. T2DM is a major contributor to all-cause mortality in Peru, particularly in Lima and other coastal regions. National and regional policies should prioritize T2DM prevention, early detection, and access to effective treatment to reduce this mortality burden. Epidemiology Statistical Epidemiology Endocrinology & Metabolism Risk assessment Health metrics Type 2 diabetes mellitus Figures Figure 1 Figure 2 INTRODUCTION Type 2 diabetes mellitus (T2DM) is a major contributor to morbidity, disability, and premature mortality worldwide, with a disproportionate impact on low- and middle-income countries (LMICs) ( 1 , 2 ). In 2019, T2DM was responsible for an estimated 1.4 million deaths globally, of which 60.7% occurred in LMICs ( 3 ). In response, global (e.g., United Nations) and regional (e.g., Latin American Diabetes Association) organizations have endorsed specific targets to reduce T2DM-related mortality, such as those embedded within Sustainable Development Goal 3.4 ( 2 , 4 , 5 ). To inform progress toward these goals, up-to-date and granular evidence is needed, particularly in LMIC settings. Global and local studies have quantified T2DM-related mortality using death records in which T2DM is registered as the underlying cause of death ( 3 , 7 ). In Peru, 25,704 deaths were attributed to T2DM between 2005 and 2014 (with 3,782 deaths in 2014) based on Ministry of Health records ( 7 ). However, many LMICs face challenges in maintaining high-quality mortality registries ( 7 ). Consequently, mortality estimates based solely on underlying causes of death may underestimate the true mortality burden of T2DM. This is consistent with previous work showing that mortality derived from death records is usually lower than mortality estimated using comparative risk assessment approaches ( 2 ). Comparative risk assessment methods have been used to quantify mortality attributable to T2DM at global ( 6 ), regional ( 8 ), and national levels ( 6 , 8 ). To our knowledge, however, no subnational analysis using this approach has been conducted in Peru. Such analyses are particularly relevant in this country because of marked heterogeneity in healthcare access, quality of care and follow-up, methods of data collection, and the prevalence and incidence of T2DM across departments ( 9 ). Assuming a single attributable fraction for the entire country could obscure important regional differences and lead to ecological fallacies. A systematic review reported an increasing prevalence of T2DM in Peru; nevertheless, evidence from Amazonian and rural areas remains limited, and internal migration dynamics may further influence regional estimates ( 10 ). Understanding these subnational patterns is essential to design targeted policies and interventions. Therefore, this study aimed to quantify the mortality attributable to T2DM across the 25 regions of Peru using a comparative risk assessment approach. Our findings may guide the design and prioritization of public policies tailored to the specific needs and epidemiological profiles of each region ( 11 ). MATERIALS AND METHODS Study design Using a comparative risk assessment framework, we estimated the proportion and absolute number of all-cause deaths in 2018 attributable to T2DM in adults aged ≥ 20 years across the 25 regions of Peru. First, we calculated population attributable fractions (PAFs) by combining T2DM prevalence estimates with relative risks (RRs) for the association between T2DM and all-cause mortality. Second, we multiplied these PAFs by the absolute number of deaths, stratified by sex, region, and age group, to obtain the number of all-cause deaths attributable to T2DM ( 12 – 14 ). Data Sources Prevalence of T2DM Age- and sex-specific prevalence estimates of T2DM were obtained from the Peruvian Demographic and Health Survey (ENDES). ENDES is an annual, nationally and subnationally representative survey of individuals aged ≥ 15 years that collects self-reported information on physician-diagnosed T2DM ( 12 ). To maximize sample size and obtain stable regional estimates, we pooled ENDES data from 2014 to 2018. This allowed us to estimate T2DM prevalence by region, sex, and age group. The self-reported prevalence of T2DM in each region was then adjusted to approximate total T2DM (diagnosed plus undiagnosed cases). This adjustment was based on the proportion of individuals living in rural and urban areas and a correction factor derived from a population-based study conducted in three Peruvian cities, including both urban and rural settings ( 15 ). Because survey data from five consecutive years (2014–2018) were pooled, we assumed that the resulting prevalence estimates corresponded to the mid-point year of 2016. Although five survey waves were combined, the effective sample size was still insufficient to estimate prevalence in five-year age groups. Therefore, we calculated prevalence for 10-year age groups and assumed the same prevalence for the corresponding adjacent five-year categories. Relative Risks RRs for the association between T2DM and all-cause mortality were obtained from a meta-analysis of population-based cohort studies conducted in Latin America and the Caribbean (LAC) ( 13 ). This meta-analysis reported age-specific RRs for two age ranges: 35–59 and 60–74 years. Using interpolation methods ( 16 ), we derived RRs for fourteen five-year age groups: 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70–74, 75–79, 80–84, and ≥ 85 years (Supplementary Table 1). Age-specific RRs were derived from cohorts of individuals with self-reported T2DM. The same age-specific RRs were applied to men and women because the meta-analysis did not report sex-specific RRs. Using RRs based on self-reported T2DM rather than total T2DM is feasible in this context because the available LAC cohorts rely on self-reported diagnoses ( 8 ). Some evidence suggests that RRs for all-cause mortality are slightly higher among individuals with self-reported T2DM than among those with total T2DM, which may lead to modest overestimation of mortality attributable to T2DM. However, sensitivity analyses restricted to diagnosed cases did not materially change the main findings in the underlying meta-analysis ( 10 ). Therefore, relying on self-reported data ensures consistency with local evidence and transparency in the analytical approach ( 8 ). All-cause mortality The number of all-cause deaths in 2018, disaggregated by sex, region, and five-year age group, was obtained from the national death registry maintained by the Peruvian Ministry of Health (Ministerio de Salud, MINSA). This registry compiles mortality data from multiple sources, including healthcare facility records, the National Registry of Identification and Civil Status (RENIEC), and the Public Ministry. These data are publicly accessible and can be requested online ( 14 ). Population Population counts by sex, region, and five-year age groups were extracted from the 2017 National Census ( 17 ). Using these population data, we expressed T2DM-attributable deaths as age-standardized mortality rates per 100,000 people, using the WHO world standard population ( 18 ). Statistical analysis The PAF for T2DM quantifies the proportion of deaths from all causes that can be attributed to T2DM. We calculated the PAF using the standard formula ( 19 ): $$\:PAF=\:\frac{{P}_{\:}({RR}_{\:\:}-1)}{P\left({RR}_{\:\:}-1\right)+1}$$ where 𝑃 is the prevalence of T2DM and RR is the relative risk of all-cause mortality associated with T2DM. For each sex, region, and five-year age group, we multiplied the corresponding PAF by the number of recorded all-cause deaths to estimate the absolute number of deaths attributable to T2DM: $$\:{D}_{T2DM}=\:PAF\times\:{D}_{all-cause}$$ Because prevalence estimates for T2DM represented the mid-point year 2016, whereas mortality data corresponded to 2018, we assumed a two-year lag between T2DM exposure and subsequent all-cause mortality. Thus, we interpreted the T2DM prevalence in 2016 as the exposure giving rise to deaths observed in 2018. To incorporate uncertainty from both prevalence and RR estimates into our final results, we used a simulation-based approach. For each region-, sex-, and age-specific prevalence estimate and each age-specific RR, we generated 1,000 random draws assuming appropriate distributions. This yielded 1,000 PAFs for each sex-, region-, and age-specific stratum. For each stratum, we report the median as the point estimate and the 2.5th and 97.5th percentiles as the 95% uncertainty interval. All analyses and figures were generated using R (version 4.0.3). Prevalence estimates accounted for the complex sampling design of the ENDES survey ( 20 ). The code used in this study and the harmonized dataset are openly available ( 21 , 22 ). 2.4 Ethics This study used anonymized, nationally representative survey data in the public domain ( 12 ) and de-identified individual-level mortality data that can be requested from the Ministry of Health ( 14 ). No direct contact with human participants occurred. In accordance with national regulations, formal ethics committee approval was not required for this secondary analysis of anonymized data. RESULTS National results In 2018, there were 114,836 all-cause deaths among adults aged ≥ 20 years in Peru. Of these, 19,102 deaths (16.6%) were attributable to T2DM. The burden of T2DM-attributable mortality was slightly higher among men (9,985 deaths; 8.7% of all-cause deaths) than among women (9,117 deaths; 7.9% of all-cause deaths). Regional results Substantial regional variation was observed in both the proportion and absolute number of all-cause deaths attributable to T2DM, and this pattern was consistent in men and women (Figs. 1 and 2 ). Among men, the highest proportions of all-cause deaths attributable to T2DM were observed in Lima (21.3%), Callao (2.8%), and Lambayeque (2.7%); notably, all three regions are located on the coast. The lowest proportions were observed in Pasco (0.1%), Madre de Dios (0.3%), and Amazonas (0.3%), the latter two being Amazonian regions. Similar patterns were observed among women. The highest proportions of all-cause deaths attributable to T2DM in women occurred in Lima (19.7%), Lambayeque (2.7%), and Callao (2.5%), whereas the lowest proportions were observed in Pasco (0.3%), Madre de Dios (0.2%), and Amazonas (0.3%). In terms of absolute numbers, Lima was the region with the largest number of T2DM-attributable deaths in 2018 for both sexes (Fig. 2 ), with 4,068 deaths among men and 3,754 among women. In contrast, most other regions contributed far fewer T2DM-attributable deaths, with several regions accounting for fewer than 500 deaths in each sex (Supplementary Table 2, Fig. 2 ). T2DM-attributable age-standardized all-cause mortality rates Patterns for T2DM-attributable age-standardized mortality rates were consistent with those observed for the absolute numbers of deaths. Among men, the highest age-standardized mortality rates attributable to T2DM were found in Lima, followed by Callao and Lambayeque, all coastal regions. The lowest rates were observed in Pasco, Madre de Dios, and Amazonas. A similar geographic pattern was observed among women, with Lima, Lambayeque, and Callao having the highest age-standardized mortality rates and Pasco, Madre de Dios, and Amazonas the lowest (Supplementary Table 3). DISCUSSION Main findings In this comparative risk assessment analysis, we quantified the subnational burden of all-cause mortality attributable to T2DM in Peru. Our findings complement global and regional evidence on T2DM-related mortality ( 6 , 8 ) by providing region-specific estimates in a middle-income country with marked geographic and socio-demographic heterogeneity. We found that T2DM is responsible for a substantial proportion of all-cause mortality in Peru, with 16.6% of deaths among adults aged ≥ 20 years attributable to T2DM in 2018. The burden was slightly higher in men than in women. At the subnational level, there was considerable variation in both the proportion and number of T2DM-attributable deaths, with coastal regions—particularly Lima—bearing the greatest burden in terms of both absolute numbers and age-standardized mortality rates (Supplementary Table 3). Public health implications The concentration of T2DM-attributable mortality in coastal regions, especially Lima, has important implications for the design and prioritization of national health policies. T2DM prevention and control strategies in Peru should explicitly consider regional disparities in disease burden and healthcare resources in order to optimize the allocation of limited health system capacity. Reducing mortality associated with T2DM will require improvements across the continuum of care, including screening, early diagnosis, linkage to care, and long-term management to prevent complications. The validation and implementation of T2DM risk scores (e.g., FINDRISC) adapted to the Peruvian population could facilitate the identification of high-risk individuals and support targeted screening programs ( 10 , 24 ). In the Peruvian context, interventions must also consider low-cost and scalable strategies suitable for regions with constrained resources ( 25 ). For instance, digital tools such as the “Zucar” application, which focuses on education for T2DM prevention and control, could be expanded and integrated into broader health promotion programs ( 26 ). Similarly, mobile health technologies and telemedicine services, which were strengthened in Peru during the COVID-19 pandemic ( 27 , 28 ), could be leveraged to improve access to diabetes care, particularly in remote and underserved regions. However, the implementation and uptake of these tools remain limited in many parts of the country, partly due to insufficient dissemination and promotion by health institutions ( 24 , 29 ). Public health strategies should also directly address lifestyle-related risk factors, including unhealthy diets, physical inactivity, and obesity ( 30 , 31 ). Several countries have implemented policies to tackle these risk factors, such as front-of-package warning labels for processed and ultra-processed foods ( 29 , 32 ). In Peru, the government has introduced legislation requiring front-of-package warning labels to inform consumers and encourage healthier choices ( 33 – 35 ). While it is still too early to fully evaluate the impact of this policy on T2DM outcomes, our findings underscore the need for sustained and comprehensive efforts to reduce the burden of non-communicable diseases. Importantly, national policies alone are unlikely to be sufficient. Cultural, socioeconomic, and structural barriers that hinder the adoption of healthy behaviors must also be addressed. Efforts to improve T2DM outcomes should therefore combine population-wide policy measures with community- and individual-level interventions tailored to local contexts ( 35 ). Research in context The Global Burden of Disease (GBD) 2019 study provides global, regional, national, and, in some countries, subnational estimates of mortality attributable to cardiometabolic risk factors, including high fasting plasma glucose ( 36 ). However, Peru was not included among the countries with subnational estimates ( 3 , 36 ). Our study adds to this literature by providing region-specific estimates of T2DM-attributable mortality using nationally representative data sources. Our national estimates of T2DM-attributable mortality are higher than those reported by GBD 2019. For 2018, we estimated 9,985 T2DM-attributable deaths among men and 9,117 among women, whereas GBD reported 5,904 deaths in men and 6,457 in women for Peru ( 3 ). Differences in data sources and methods may explain this discrepancy. The GBD study relies on modeled estimates of all-cause mortality (e.g., Gaussian process regression), whereas we used mortality counts from the Peruvian national death registry ( 36 ). Additionally, we used ENDES-based self-reported T2DM prevalence adjusted to approximate total T2DM, which may differ from the prevalence inputs used in GBD ( 7 ). A previous national study estimated deaths caused by T2DM in Peru between 2005 and 2014 using death certificates ( 6 ). In 2014, that study reported 3,782 deaths due to T2DM out of 96,460 all-cause deaths, corresponding to a mortality rate of 12 deaths per 100,000 people. In contrast, our 2018 estimates indicate 5,727 deaths due to T2DM out of 125,325 deaths and a higher mortality rate. These differences may reflect both a real increase in T2DM-related mortality over time and methodological differences. Our approach, based on PAFs derived from prevalence and RRs, captures deaths in which T2DM may contribute as a risk factor even when it is not listed as the underlying cause of death on death certificates. Strengths and limitations This study has several strengths. We used nationally and subnationally representative T2DM prevalence estimates ( 19 ), RRs derived from a LAC-specific meta-analysis ( 13 ), and the most recent mortality data available from the national death registry ( 14 ). Our analysis provides, for the first time, region-specific estimates of T2DM-attributable all-cause mortality in Peru using a comparative risk assessment framework. The availability of open code and data further enhances the transparency and reproducibility of our findings ( 21 , 22 ). Nevertheless, several limitations must be considered. First, T2DM prevalence from ENDES is based on self-reported physician diagnosis and thus captures diagnosed T2DM only. The survey question—“Has a doctor ever diagnosed you with diabetes or high blood sugar?”—may also introduce misclassification by combining diabetes with hyperglycemia. In addition, ENDES interviews only one individual aged ≥ 15 years per selected household, which may not fully capture the distribution of T2DM and other health conditions in larger or multigenerational households. This sampling approach can also complicate longitudinal interpretations of trends. Second, local evidence suggests that RRs for all-cause mortality may be slightly higher among individuals with self-reported T2DM than among those with total T2DM ( 13 ), which could lead to some overestimation of T2DM-attributable mortality. We attempted to mitigate this by applying a correction factor to prevalence estimates to approximate total T2DM ( 15 ). Third, the RRs used were obtained from a meta-analysis including LAC countries but not Peru specifically ( 13 ). Given the heterogeneity in healthcare access and policies within LAC and within Peru, our findings should be interpreted with these contextual differences in mind. Fourth, we applied the same age-specific RRs to men and women because sex-specific estimates were not available. Although the meta-analysis reported similar RRs for both sexes, international evidence indicates that mortality risks associated with T2DM may differ between men and women ( 13 ). Finally, our analysis focused on 2018 because that was the latest year for which mortality data were fully available at the time of analysis. Future updates incorporating more recent data will be important to monitor trends over time. CONCLUSIONS T2DM is a major contributor to all-cause mortality in Peru, accounting for approximately one in six deaths among adults in 2018. The burden of T2DM-attributable mortality varies substantially across regions, with coastal regions—particularly Lima—experiencing the highest proportions, absolute numbers, and age-standardized mortality rates. These findings highlight the need to strengthen national and regional health policies aimed at preventing, detecting, and managing T2DM, with a particular focus on high-burden regions. Tailored strategies that combine policy measures, health system strengthening, and community-based interventions are essential to reduce the mortality burden associated with T2DM and to advance toward national and global non-communicable disease targets. Declarations Funding: The authors have no funding to report. Disclosures: The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. 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Int J Obes (Lond) 38(12):1483–1490. 10.1038/ijo.2014.75 GBD 2019 Risk Factors Collaborators (2020) Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396(10258):1223–1249. 10.1016/S0140-6736(20)30752-2 Regional Committee for the Western Pacific, 067. Sustainable development goals (‎Resolution) ‎. WHO Regional Office for the Western Pacific [Internet] (2016) Available: https://apps.who.int/iris/handle/10665/361406 Additional Declarations The authors declare no competing interests. Supplementary Files T2DMMortalitySupplementarymaterials.docx All-cause mortality attributable to type 2 diabetes mellitus in Peru: a comparative risk assessment analysis: Supplementary Materials Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8297456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556431115,"identity":"9e11d194-43e0-459e-b18c-f7465f391efd","order_by":0,"name":"Fritz Fidel Váscones-Román","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBAC9gbGBmS+DRDz4NfCc4CxEVlPGjFaGFCsOUyEFunD7Q9/MByWl28/fOzBhz/nE7eznz3AXFCBRwtfYmODBMNhww1n0tINZ7bdTtzZk5fAPOMMbi32PEC/GDCkMW6Q4DGT5m24nbjhBo8BM28bHltAWhIY0uznzwBq+fPnHFTLPwJaDjDYJDbcAGphYDsA1dKAX8vMBgObZKBf0iR725KNd/bkGByecQyfFvYHH39USNjOB4aYxI8/drLb2c8YPi6owa0FAgzQ2IcJacDUzkyallEwCkbBKBjmAABPS07J3iZdEgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9564-0710","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":true,"prefix":"","firstName":"Fritz","middleName":"Fidel","lastName":"Váscones-Román","suffix":""},{"id":556431116,"identity":"54d890f7-adcf-4e86-8ed9-d5c55c1925d1","order_by":1,"name":"Gustavo A. Quispe-Villegas","email":"","orcid":"https://orcid.org/0000-0001-8833-5072","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":false,"prefix":"","firstName":"Gustavo","middleName":"A.","lastName":"Quispe-Villegas","suffix":""},{"id":556431117,"identity":"7af85fd9-dc57-4191-b287-290580c6c318","order_by":2,"name":"Giovani Fabrizio Luna-Venturo","email":"","orcid":"https://orcid.org/0000-0002-8473-2318","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":false,"prefix":"","firstName":"Giovani","middleName":"Fabrizio","lastName":"Luna-Venturo","suffix":""},{"id":556431118,"identity":"bc09e975-f964-4ad3-b416-41515855f803","order_by":3,"name":"Samanta Janet Fuentes-García","email":"","orcid":"https://orcid.org/0009-0009-8522-1737","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":false,"prefix":"","firstName":"Samanta","middleName":"Janet","lastName":"Fuentes-García","suffix":""},{"id":556431119,"identity":"e87b73b5-4db9-42db-b526-5439b8ba9211","order_by":4,"name":"Brigith Avila-Lucas","email":"","orcid":"https://orcid.org/0000-0002-1151-8759","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":false,"prefix":"","firstName":"Brigith","middleName":"","lastName":"Avila-Lucas","suffix":""},{"id":556431120,"identity":"7edc40fe-2b4c-4617-a4dc-adc160815d6a","order_by":5,"name":"Wilmer Cristobal Guzman-Vilca","email":"","orcid":"https://orcid.org/0000-0002-2194-8496","institution":"Instituto Nacional de Salud","correspondingAuthor":false,"prefix":"","firstName":"Wilmer","middleName":"Cristobal","lastName":"Guzman-Vilca","suffix":""}],"badges":[],"createdAt":"2025-12-07 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05:19:34","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89146,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8297456/v1/0a6b338065189dd1abc70548.html"},{"id":97759238,"identity":"3b797ee5-37d6-4806-9106-8a36b47cd037","added_by":"auto","created_at":"2025-12-09 05:19:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66552,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of all-cause deaths in 2018 attributable to type 2 diabetes mellitus (T2DM) in 2016, by region and sex in Peru. Exact estimates and 95% confidence intervals are presented in Supplementary Table 2.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8297456/v1/90b88a34724004c2a9244eac.jpg"},{"id":97759241,"identity":"177e1a92-93a3-431c-9f9b-cdc5dce1f300","added_by":"auto","created_at":"2025-12-09 05:19:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58850,"visible":true,"origin":"","legend":"\u003cp\u003eAbsolute number of all-cause deaths in 2018 attributable to type 2 diabetes mellitus (T2DM) in 2016, by region and sex in Peru. Exact estimates and 95% confidence intervals are presented in Supplementary Table 2.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8297456/v1/d51dfa890dee7d553c8ea33f.jpg"},{"id":97902751,"identity":"58c358d3-b6a5-48fb-aea3-2803283e8db0","added_by":"auto","created_at":"2025-12-10 15:53:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":715189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8297456/v1/c1cb74af-c471-4e5c-a9c8-0d70c326838b.pdf"},{"id":97759243,"identity":"7f947ea7-268d-4dbe-a93a-593606002829","added_by":"auto","created_at":"2025-12-09 05:19:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3055555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAll-cause mortality attributable to type 2 diabetes mellitus in Peru: a comparative risk assessment analysis: Supplementary Materials\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"T2DMMortalitySupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8297456/v1/80a92f19ec260d14df6385cf.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAll-cause mortality attributable to type 2 diabetes mellitus in Peru: a comparative risk assessment analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a major contributor to morbidity, disability, and premature mortality worldwide, with a disproportionate impact on low- and middle-income countries (LMICs) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In 2019, T2DM was responsible for an estimated 1.4\u0026nbsp;million deaths globally, of which 60.7% occurred in LMICs (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In response, global (e.g., United Nations) and regional (e.g., Latin American Diabetes Association) organizations have endorsed specific targets to reduce T2DM-related mortality, such as those embedded within Sustainable Development Goal 3.4 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). To inform progress toward these goals, up-to-date and granular evidence is needed, particularly in LMIC settings.\u003c/p\u003e\u003cp\u003eGlobal and local studies have quantified T2DM-related mortality using death records in which T2DM is registered as the underlying cause of death (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In Peru, 25,704 deaths were attributed to T2DM between 2005 and 2014 (with 3,782 deaths in 2014) based on Ministry of Health records (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, many LMICs face challenges in maintaining high-quality mortality registries (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Consequently, mortality estimates based solely on underlying causes of death may underestimate the true mortality burden of T2DM. This is consistent with previous work showing that mortality derived from death records is usually lower than mortality estimated using comparative risk assessment approaches (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eComparative risk assessment methods have been used to quantify mortality attributable to T2DM at global (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), regional (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and national levels (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). To our knowledge, however, no subnational analysis using this approach has been conducted in Peru. Such analyses are particularly relevant in this country because of marked heterogeneity in healthcare access, quality of care and follow-up, methods of data collection, and the prevalence and incidence of T2DM across departments (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Assuming a single attributable fraction for the entire country could obscure important regional differences and lead to ecological fallacies.\u003c/p\u003e\u003cp\u003eA systematic review reported an increasing prevalence of T2DM in Peru; nevertheless, evidence from Amazonian and rural areas remains limited, and internal migration dynamics may further influence regional estimates (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Understanding these subnational patterns is essential to design targeted policies and interventions.\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to quantify the mortality attributable to T2DM across the 25 regions of Peru using a comparative risk assessment approach. Our findings may guide the design and prioritization of public policies tailored to the specific needs and epidemiological profiles of each region (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eUsing a comparative risk assessment framework, we estimated the proportion and absolute number of all-cause deaths in 2018 attributable to T2DM in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years across the 25 regions of Peru. First, we calculated population attributable fractions (PAFs) by combining T2DM prevalence estimates with relative risks (RRs) for the association between T2DM and all-cause mortality. Second, we multiplied these PAFs by the absolute number of deaths, stratified by sex, region, and age group, to obtain the number of all-cause deaths attributable to T2DM (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003ePrevalence of T2DM\u003c/h2\u003e\u003cp\u003eAge- and sex-specific prevalence estimates of T2DM were obtained from the Peruvian Demographic and Health Survey (ENDES). ENDES is an annual, nationally and subnationally representative survey of individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years that collects self-reported information on physician-diagnosed T2DM (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo maximize sample size and obtain stable regional estimates, we pooled ENDES data from 2014 to 2018. This allowed us to estimate T2DM prevalence by region, sex, and age group. The self-reported prevalence of T2DM in each region was then adjusted to approximate total T2DM (diagnosed plus undiagnosed cases). This adjustment was based on the proportion of individuals living in rural and urban areas and a correction factor derived from a population-based study conducted in three Peruvian cities, including both urban and rural settings (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause survey data from five consecutive years (2014\u0026ndash;2018) were pooled, we assumed that the resulting prevalence estimates corresponded to the mid-point year of 2016. Although five survey waves were combined, the effective sample size was still insufficient to estimate prevalence in five-year age groups. Therefore, we calculated prevalence for 10-year age groups and assumed the same prevalence for the corresponding adjacent five-year categories.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRelative Risks\u003c/h3\u003e\n\u003cp\u003eRRs for the association between T2DM and all-cause mortality were obtained from a meta-analysis of population-based cohort studies conducted in Latin America and the Caribbean (LAC) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This meta-analysis reported age-specific RRs for two age ranges: 35\u0026ndash;59 and 60\u0026ndash;74 years. Using interpolation methods (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), we derived RRs for fourteen five-year age groups: 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, 45\u0026ndash;49, 50\u0026ndash;54, 55\u0026ndash;59, 60\u0026ndash;64, 65\u0026ndash;69, 70\u0026ndash;74, 75\u0026ndash;79, 80\u0026ndash;84, and \u0026ge;\u0026thinsp;85 years (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eAge-specific RRs were derived from cohorts of individuals with self-reported T2DM. The same age-specific RRs were applied to men and women because the meta-analysis did not report sex-specific RRs.\u003c/p\u003e\u003cp\u003eUsing RRs based on self-reported T2DM rather than total T2DM is feasible in this context because the available LAC cohorts rely on self-reported diagnoses (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Some evidence suggests that RRs for all-cause mortality are slightly higher among individuals with self-reported T2DM than among those with total T2DM, which may lead to modest overestimation of mortality attributable to T2DM. However, sensitivity analyses restricted to diagnosed cases did not materially change the main findings in the underlying meta-analysis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, relying on self-reported data ensures consistency with local evidence and transparency in the analytical approach (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAll-cause mortality\u003c/h3\u003e\n\u003cp\u003eThe number of all-cause deaths in 2018, disaggregated by sex, region, and five-year age group, was obtained from the national death registry maintained by the Peruvian Ministry of Health (Ministerio de Salud, MINSA). This registry compiles mortality data from multiple sources, including healthcare facility records, the National Registry of Identification and Civil Status (RENIEC), and the Public Ministry. These data are publicly accessible and can be requested online (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePopulation\u003c/h2\u003e\u003cp\u003ePopulation counts by sex, region, and five-year age groups were extracted from the 2017 National Census (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Using these population data, we expressed T2DM-attributable deaths as age-standardized mortality rates per 100,000 people, using the WHO world standard population (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe PAF for T2DM quantifies the proportion of deaths from all causes that can be attributed to T2DM. We calculated the PAF using the standard formula (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:PAF=\\:\\frac{{P}_{\\:}({RR}_{\\:\\:}-1)}{P\\left({RR}_{\\:\\:}-1\\right)+1}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003e\u0026#119875;\u003c/em\u003e is the prevalence of T2DM and \u003cem\u003eRR\u003c/em\u003e is the relative risk of all-cause mortality associated with T2DM.\u003c/p\u003e\u003cp\u003eFor each sex, region, and five-year age group, we multiplied the corresponding PAF by the number of recorded all-cause deaths to estimate the absolute number of deaths attributable to T2DM:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{D}_{T2DM}=\\:PAF\\times\\:{D}_{all-cause}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBecause prevalence estimates for T2DM represented the mid-point year 2016, whereas mortality data corresponded to 2018, we assumed a two-year lag between T2DM exposure and subsequent all-cause mortality. Thus, we interpreted the T2DM prevalence in 2016 as the exposure giving rise to deaths observed in 2018.\u003c/p\u003e\u003cp\u003eTo incorporate uncertainty from both prevalence and RR estimates into our final results, we used a simulation-based approach. For each region-, sex-, and age-specific prevalence estimate and each age-specific RR, we generated 1,000 random draws assuming appropriate distributions. This yielded 1,000 PAFs for each sex-, region-, and age-specific stratum. For each stratum, we report the median as the point estimate and the 2.5th and 97.5th percentiles as the 95% uncertainty interval.\u003c/p\u003e\u003cp\u003eAll analyses and figures were generated using R (version 4.0.3). Prevalence estimates accounted for the complex sampling design of the ENDES survey (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The code used in this study and the harmonized dataset are openly available (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 Ethics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study used anonymized, nationally representative survey data in the public domain (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and de-identified individual-level mortality data that can be requested from the Ministry of Health (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). No direct contact with human participants occurred. In accordance with national regulations, formal ethics committee approval was not required for this secondary analysis of anonymized data.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eNational results\u003c/h2\u003e\u003cp\u003eIn 2018, there were 114,836 all-cause deaths among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years in Peru. Of these, 19,102 deaths (16.6%) were attributable to T2DM. The burden of T2DM-attributable mortality was slightly higher among men (9,985 deaths; 8.7% of all-cause deaths) than among women (9,117 deaths; 7.9% of all-cause deaths).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRegional results\u003c/h2\u003e\u003cp\u003eSubstantial regional variation was observed in both the proportion and absolute number of all-cause deaths attributable to T2DM, and this pattern was consistent in men and women (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong men, the highest proportions of all-cause deaths attributable to T2DM were observed in Lima (21.3%), Callao (2.8%), and Lambayeque (2.7%); notably, all three regions are located on the coast. The lowest proportions were observed in Pasco (0.1%), Madre de Dios (0.3%), and Amazonas (0.3%), the latter two being Amazonian regions. Similar patterns were observed among women. The highest proportions of all-cause deaths attributable to T2DM in women occurred in Lima (19.7%), Lambayeque (2.7%), and Callao (2.5%), whereas the lowest proportions were observed in Pasco (0.3%), Madre de Dios (0.2%), and Amazonas (0.3%).\u003c/p\u003e\u003cp\u003eIn terms of absolute numbers, Lima was the region with the largest number of T2DM-attributable deaths in 2018 for both sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with 4,068 deaths among men and 3,754 among women. In contrast, most other regions contributed far fewer T2DM-attributable deaths, with several regions accounting for fewer than 500 deaths in each sex (Supplementary Table\u0026nbsp;2, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eT2DM-attributable age-standardized all-cause mortality rates\u003c/h2\u003e\u003cp\u003ePatterns for T2DM-attributable age-standardized mortality rates were consistent with those observed for the absolute numbers of deaths. Among men, the highest age-standardized mortality rates attributable to T2DM were found in Lima, followed by Callao and Lambayeque, all coastal regions. The lowest rates were observed in Pasco, Madre de Dios, and Amazonas. A similar geographic pattern was observed among women, with Lima, Lambayeque, and Callao having the highest age-standardized mortality rates and Pasco, Madre de Dios, and Amazonas the lowest (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMain findings\u003c/h2\u003e\u003cp\u003eIn this comparative risk assessment analysis, we quantified the subnational burden of all-cause mortality attributable to T2DM in Peru. Our findings complement global and regional evidence on T2DM-related mortality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) by providing region-specific estimates in a middle-income country with marked geographic and socio-demographic heterogeneity.\u003c/p\u003e\u003cp\u003eWe found that T2DM is responsible for a substantial proportion of all-cause mortality in Peru, with 16.6% of deaths among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years attributable to T2DM in 2018. The burden was slightly higher in men than in women. At the subnational level, there was considerable variation in both the proportion and number of T2DM-attributable deaths, with coastal regions\u0026mdash;particularly Lima\u0026mdash;bearing the greatest burden in terms of both absolute numbers and age-standardized mortality rates (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePublic health implications\u003c/h2\u003e\u003cp\u003eThe concentration of T2DM-attributable mortality in coastal regions, especially Lima, has important implications for the design and prioritization of national health policies. T2DM prevention and control strategies in Peru should explicitly consider regional disparities in disease burden and healthcare resources in order to optimize the allocation of limited health system capacity.\u003c/p\u003e\u003cp\u003eReducing mortality associated with T2DM will require improvements across the continuum of care, including screening, early diagnosis, linkage to care, and long-term management to prevent complications. The validation and implementation of T2DM risk scores (e.g., FINDRISC) adapted to the Peruvian population could facilitate the identification of high-risk individuals and support targeted screening programs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the Peruvian context, interventions must also consider low-cost and scalable strategies suitable for regions with constrained resources (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). For instance, digital tools such as the \u0026ldquo;Zucar\u0026rdquo; application, which focuses on education for T2DM prevention and control, could be expanded and integrated into broader health promotion programs (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Similarly, mobile health technologies and telemedicine services, which were strengthened in Peru during the COVID-19 pandemic (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), could be leveraged to improve access to diabetes care, particularly in remote and underserved regions. However, the implementation and uptake of these tools remain limited in many parts of the country, partly due to insufficient dissemination and promotion by health institutions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePublic health strategies should also directly address lifestyle-related risk factors, including unhealthy diets, physical inactivity, and obesity (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Several countries have implemented policies to tackle these risk factors, such as front-of-package warning labels for processed and ultra-processed foods (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In Peru, the government has introduced legislation requiring front-of-package warning labels to inform consumers and encourage healthier choices (\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). While it is still too early to fully evaluate the impact of this policy on T2DM outcomes, our findings underscore the need for sustained and comprehensive efforts to reduce the burden of non-communicable diseases.\u003c/p\u003e\u003cp\u003eImportantly, national policies alone are unlikely to be sufficient. Cultural, socioeconomic, and structural barriers that hinder the adoption of healthy behaviors must also be addressed. Efforts to improve T2DM outcomes should therefore combine population-wide policy measures with community- and individual-level interventions tailored to local contexts (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eResearch in context\u003c/h2\u003e\u003cp\u003eThe Global Burden of Disease (GBD) 2019 study provides global, regional, national, and, in some countries, subnational estimates of mortality attributable to cardiometabolic risk factors, including high fasting plasma glucose (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, Peru was not included among the countries with subnational estimates (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Our study adds to this literature by providing region-specific estimates of T2DM-attributable mortality using nationally representative data sources.\u003c/p\u003e\u003cp\u003eOur national estimates of T2DM-attributable mortality are higher than those reported by GBD 2019. For 2018, we estimated 9,985 T2DM-attributable deaths among men and 9,117 among women, whereas GBD reported 5,904 deaths in men and 6,457 in women for Peru (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Differences in data sources and methods may explain this discrepancy. The GBD study relies on modeled estimates of all-cause mortality (e.g., Gaussian process regression), whereas we used mortality counts from the Peruvian national death registry (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additionally, we used ENDES-based self-reported T2DM prevalence adjusted to approximate total T2DM, which may differ from the prevalence inputs used in GBD (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA previous national study estimated deaths caused by T2DM in Peru between 2005 and 2014 using death certificates (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In 2014, that study reported 3,782 deaths due to T2DM out of 96,460 all-cause deaths, corresponding to a mortality rate of 12 deaths per 100,000 people. In contrast, our 2018 estimates indicate 5,727 deaths due to T2DM out of 125,325 deaths and a higher mortality rate. These differences may reflect both a real increase in T2DM-related mortality over time and methodological differences. Our approach, based on PAFs derived from prevalence and RRs, captures deaths in which T2DM may contribute as a risk factor even when it is not listed as the underlying cause of death on death certificates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThis study has several strengths. We used nationally and subnationally representative T2DM prevalence estimates (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), RRs derived from a LAC-specific meta-analysis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and the most recent mortality data available from the national death registry (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Our analysis provides, for the first time, region-specific estimates of T2DM-attributable all-cause mortality in Peru using a comparative risk assessment framework. The availability of open code and data further enhances the transparency and reproducibility of our findings (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNevertheless, several limitations must be considered. First, T2DM prevalence from ENDES is based on self-reported physician diagnosis and thus captures diagnosed T2DM only. The survey question\u0026mdash;\u0026ldquo;Has a doctor ever diagnosed you with diabetes or high blood sugar?\u0026rdquo;\u0026mdash;may also introduce misclassification by combining diabetes with hyperglycemia. In addition, ENDES interviews only one individual aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years per selected household, which may not fully capture the distribution of T2DM and other health conditions in larger or multigenerational households. This sampling approach can also complicate longitudinal interpretations of trends.\u003c/p\u003e\u003cp\u003eSecond, local evidence suggests that RRs for all-cause mortality may be slightly higher among individuals with self-reported T2DM than among those with total T2DM (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), which could lead to some overestimation of T2DM-attributable mortality. We attempted to mitigate this by applying a correction factor to prevalence estimates to approximate total T2DM (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Third, the RRs used were obtained from a meta-analysis including LAC countries but not Peru specifically (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Given the heterogeneity in healthcare access and policies within LAC and within Peru, our findings should be interpreted with these contextual differences in mind.\u003c/p\u003e\u003cp\u003eFourth, we applied the same age-specific RRs to men and women because sex-specific estimates were not available. Although the meta-analysis reported similar RRs for both sexes, international evidence indicates that mortality risks associated with T2DM may differ between men and women (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Finally, our analysis focused on 2018 because that was the latest year for which mortality data were fully available at the time of analysis. Future updates incorporating more recent data will be important to monitor trends over time.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eT2DM is a major contributor to all-cause mortality in Peru, accounting for approximately one in six deaths among adults in 2018. The burden of T2DM-attributable mortality varies substantially across regions, with coastal regions\u0026mdash;particularly Lima\u0026mdash;experiencing the highest proportions, absolute numbers, and age-standardized mortality rates.\u003c/p\u003e\u003cp\u003eThese findings highlight the need to strengthen national and regional health policies aimed at preventing, detecting, and managing T2DM, with a particular focus on high-burden regions. Tailored strategies that combine policy measures, health system strengthening, and community-based interventions are essential to reduce the mortality burden associated with T2DM and to advance toward national and global non-communicable disease targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors have no funding to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures:\u003c/strong\u003e The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfshin A, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J et al (2015) The impact of dietary habits and metabolic risk factors on cardiovascular and diabetes mortality in countries of the Middle East and North Africa in 2010: A comparative risk assessment analysis. 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WHO Regional Office for the Western Pacific [Internet] (2016) Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/361406\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/361406\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidad Peruana Cayetano Heredia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Risk assessment, Health metrics, Type 2 diabetes mellitus","lastPublishedDoi":"10.21203/rs.3.rs-8297456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8297456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives.\u003c/h2\u003e\u003cp\u003eTo quantify all-cause mortality attributable to type 2 diabetes mellitus (T2DM) across the 25 regions of Peru using a comparative risk assessment approach.\u003c/p\u003e\u003ch2\u003eMaterials and methods.\u003c/h2\u003e\u003cp\u003eWe estimated population attributable fractions (PAFs) by combining T2DM prevalence from the Peruvian Demographic and Health Survey (ENDES) with relative risks (RRs) for the association between T2DM and all-cause mortality. Sex-, age-, and region-specific PAFs were multiplied by the number of registered deaths in 2018 to obtain the absolute number and proportion of deaths attributable to T2DM in each region.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e\u003cp\u003eIn 2018, an estimated 19,102 deaths were attributable to T2DM, corresponding to 16.6% of all-cause deaths among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years. The burden was slightly higher among men (9,985 deaths; 8.7% of all-cause deaths) than among women (9,117 deaths; 7.9%). The highest proportions and absolute numbers of deaths attributable to T2DM were observed in Lima, Lambayeque, and Callao (all coastal regions), whereas the lowest estimates were found in Pasco, Madre de Dios, and Amazonas. Lima concentrated the largest number of T2DM-attributable deaths and the highest age-standardized mortality rates.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e\u003cp\u003eT2DM is a major contributor to all-cause mortality in Peru, particularly in Lima and other coastal regions. National and regional policies should prioritize T2DM prevention, early detection, and access to effective treatment to reduce this mortality burden.\u003c/p\u003e","manuscriptTitle":"All-cause mortality attributable to type 2 diabetes mellitus in Peru: a comparative risk assessment analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 05:19:29","doi":"10.21203/rs.3.rs-8297456/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f583b9e-9da4-431b-9c98-61e6d2eea802","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59222514,"name":"Epidemiology"},{"id":59222515,"name":"Statistical Epidemiology"},{"id":59222516,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2025-12-09T05:19:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 05:19:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8297456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8297456","identity":"rs-8297456","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}