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Article

Disability-Adjusted Life Years for Cancer in 2010–2014: A Regional Approach in Mexico

by
Efrén Murillo-Zamora
1,
Oliver Mendoza-Cano
2,*,
Mónica Ríos-Silva
3,
Ramón Alberto Sánchez-Piña
4,
Martha Alicia Higareda-Almaraz
5,
Enrique Higareda-Almaraz
5 and
Agustin Lugo-Radillo
6,*
1
Unidad de Medicina Familiar No. 19, Instituto Mexicano del Seguro Social, Av. Javier Mina 301, Col. Centro, Colima 28000, Colima, Mexico
2
Facultad de Ingeniería Civil, Universidad de Colima, Km. 9.0 Carretera Colima-Coquimatlán, Coquimatlán 28400, Colima, Mexico
3
Profesora Investigadora Cátedras CONACyT-Universidad de Colima, Centro Universitario de Investigaciones Biomédicas, Colima 28040, Colima, Mexico
4
Center for Health and the Global Environment, Department of Environmental Health, Harvard TH Chan School of Public Health, 401 Park Drive, P.O. Box 15677, 4th Floor West, Suite 415, Boston, MA 02215, USA
5
Jefatura de Servicios de Prestaciones Médicas, Instituto Mexicano del Seguro Social, Álvaro Obregón 184, Col. Centro, Colima 28000, Colima, Mexico
6
CONACYT-Facultad de Medicina y Cirugía, Universidad Autónoma Benito Juárez de Oaxaca, Ex Hacienda de Aguilera S/N, Carretera a San Felipe del Agua, Oaxaca 68020, Oaxaca, Mexico
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(5), 864; https://doi.org/10.3390/ijerph15050864
Submission received: 15 March 2018 / Revised: 18 April 2018 / Accepted: 21 April 2018 / Published: 26 April 2018
(This article belongs to the Special Issue Social, Cultural and Economic Context of Health and Social Care)
Versions Notes

Abstract

:
The disability-adjusted life years (DALYs) were used to estimate the regional (state of Colima, Mexico) cancer burden in 2010–2014. The years of life lost (YLL) were estimated with mortality data and years lived with disability (YLD) using incidence data. The DALYs were calculated as the arithmetic addition of YLL and YLD. Sex and cancer site-specific estimations were made and DALY rates were used to identify the leading causes of disease burden. Data from 2532 deaths were analyzed and, for all malignant tumors combined, 18,712.9 DALYs and 20,243.3 DALYs were estimated in males and females respectively. The overall contribution of YLL in DALY estimates was higher among females (93.7% vs. 87.4%). Age-standardized DALY rates (and 95% confidence intervals, CI) per 100,000 inhabitants were used to rank the leading causes of disease burden and, among males, malignant tumors from the prostate, lower respiratory tract, and colon and rectum accounted the highest rates (45.7, 95% CI 32.7–59.3; 37.6, 95% CI 25.7–49.9; and 25.9, 95% CI 16.0–36.1 DALYs). Breast, cervix uteri, and lower respiratory tract cancer showed the highest burden in females (66.0, 95% CI 50.3–82.4; 44.4, 95% CI 31.5–57.7; and 20.9, 95% CI 12.0–30.0 DALYs). The present study provides an indication of the burden of cancer at the regional level, underscoring the need to expand cancer prevention, screening, and awareness programs, as well as to improve early diagnosis and medical treatment.

1. Introduction

Cancer is a leading cause of death worldwide. Due to growing aging population and high prevalence of exposure to known risks factors [1], an increase in cancer burden has been observed and it is expected to almost double by 2030 despite the advances in timely diagnosis and medical treatment [2].
The methodology of the Global Burden of Disease study (GBD) provides a comprehensive assessment of major diseases and injuries. The disability-adjusted life years (DALYs) measure the premature mortality (years of life lost, YLL) and spent time in states of reduced health (years lived with disability, YLD) [3]. An age-weighting function is used in DALY computation and values life years differently depending on the age of illness onset [4]. They are a useful analytical tool in cost-effectiveness analysis [5] and are commonly used for comparison purposes [6].
Regional patterns of cancer incidence and mortality rates have been described [7] and, from an economic and health-policy perspective, the regional estimation of cancer burden represents a helpful analytical tool to identify and prioritize decisions related to the implementation and evaluation of preventive strategies [8]. Located in western Mexico, the state of Colima (650,000 inhabitants) [9] has epidemiological information systems that make the computing of DALYs attributable to malignant tumors viable.
This study aimed to estimate the cancer burden in the state of Colima from 2010–2014 using the DALYs as a health measure.

2. Materials and Methods

DALYs for cancer were estimated following the methods described by the GBD study [10] as the arithmetic addition of YLL and YLD. First, data from cancer-related mortality was collected from the Statistical and Epidemiological Death Registration (SEDR) from the National System for Epidemiological Surveillance. The SEDR integrates data from issued death certificates in the state of Colima and its function is regulated by specific governmental normative lineaments [11]. The underlying causes of death (International Classification of Diseases 10th revision, ICD-10) included in this analysis were malignant tumors from: mouth and oropharynx (C00–C14); esophagus (C15); stomach (C16); colon and rectum (C18–C21); liver (C22); pancreas (C25); larynx (C32); trachea, bronchus and lung (C33–C34); melanoma and other skin cancers (C43–C44); breast (C50); cervix uteri (C53); corpus uteri (C54–C55); ovary (C56); prostate (C61); bladder (C67); central nervous system (C70–C72); lymphomas and multiple myeloma (C81–C90, C96); leukemia (C91–C95) and other malignant tumors (C00–C97, except for those previously cited). Age-standardized mortality rates (ASMR) per 100,000 inhabitants were estimated using the World Standard Population (2000–2025) [12].
Second, the YLL were computed by multiplying the number of cancer deaths in the study period by the number of expected remaining life at the respective age interval according to the Tables of Life 2013 (Global Health Observatory) from Mexico [13]. The next parameters were fixed: discount rate (r) = 0.03, age-weighting (β) = 0.04, adjustment constant for age weights (C) = 0.1658, and age-weighting modulation (K) = 0 [14]. The total population, by sex and age group, was obtained from the National Census of Population and Housing 2010 [9].
Third, the sex-stratified cancer incidence data of Mexico were obtained from the GLOBOCAN project 2012 [15]. The incidence of each malignant tumor was multiplied by the average duration (years) of the disease and the corresponding disability weight to compute the YLD. The disease durations for countries categorized as having ‘established market economics’ were used [10] since this data is not available for Mexico. Two disability weights were employed in DALYs computation: 0.484 (limitation in ≥2 areas; malignant tumors from mouth and oropharynx; esophagus; liver; pancreas; larynx; lower respiratory tract (trachea, bronchus and lung); ovary; bladder and central nervous system) and 0.294 (limitation in 1 area; malignant tumors from stomach; colon and rectum; melanoma and other skin cancers; breast; cervix/corpus uteri; prostate; lymphomas and multiple myeloma; leukemia and other). The assessed functional areas include recreation, education, procreation, and occupation [16].
Finally, the cancer-site DALYs were aggregated to obtain the total estimation by sex and age-standardized DALYs rates per 100,000 inhabitants were used to rank the leading causes of burden of disease; 95% confidence intervals (CI) were computed. Spreadsheets (Microsoft® Excel®) from the GBD study [17] were used to compute the parameters of interest and the summary statistics were estimated using Stata® MP 13.0 (StataCorp LP, College Station, TX, USA).

Ethical Considerations

This study was approved by the Ethics of Health Research Committee. Data regarding the identification of individuals included in the study sample were omitted in order to preserve their anonymity.

3. Results

In the study period, a total 20,418 deaths from all causes were registered and data from 2532 cancer-attributable deaths were analyzed (males, 51.8%). The sex-stratified cancer mortality is shown in Table 1. The overall ASMRs were 69.0 and 61.8 deaths per 100,000 inhabitants in males and females respectively. The malignant tumors from the prostate (11.1 deaths per 100,000 inhabitants) and from the breast (11.4 deaths per 100,000 inhabitants) were the most frequent underlying causes of death in males and females as corresponding. Cancer of the lower respiratory tract (trachea, bronchus, and lung) was also a frequent cause of mortality, mainly among male individuals (10.1 vs. 4.1 deaths per 100,000 inhabitants).
Table 2 shows the YLL and YLD by sex and cancer site. For all malignant neoplasms combined, the YLL were higher among females than in males (18,971.6 vs. 16,345.9). The computed YLD were lower among female individuals (1271.7 vs. 2367.0).
Among males, five malignant tumors (from prostate, lower respiratory tract (trachea, bronchus and lungs), colorectal, leukemia, and liver) were responsible of nearly a half (48.4%) of total YLL computed. Breast, cervix uteri, and ovarian cancer contributed to 39.0% of premature life lost among females.
The cancer-attributable DALYs were 18,712.9 and 20,243.3 in males and females as corresponding (Table 3). The overall mean DALYs from all cancer sites was 7791.2 per year. The highest age-standardized DALY rates per 100,000 inhabitants among males were observed in prostate (45.7, 95% CI 32.7–59.3), lower respiratory tract (37.6, 95% CI 25.7–49.9), and colon and rectum (25.9, 95% CI 16.0–36.1) cancer. Malignant tumors from the breast (66.0, 95% CI 50.3–82.4), cervix uteri (44.4, 95% CI 31.5–57.7), and lower respiratory tract (20.9, 95% CI 12.0–30.0) were the leading causes of cancer burden among female participants.

4. Discussion

The regional cancer burden from 2010 to 2014 was estimated in this study using DALYs as a health measure, which combines incidence and mortality data. To our best knowledge, this is the first study estimating the disease burden at state-level. Our findings provide quantitative evidence that may be useful for implementation, prioritization, and evaluation of specific health policies focused on the prevention and early diagnosis of malignant tumors.
Cancer is a leading cause of disease burden in Mexico mainly due to a high premature death; the observed contribution of YLL to the overall DALYs estimates in our study was high (87.4% and 93.7% in males and females, respectively). A high proportion of YLL in DALYs for cancer has been previously described in other populations [18,19,20]. Higher cancer-related burden has been in urban areas when compared with rural locations [21]; information regarding place of residence of enrolled individuals was not collected in our study.
We observed that breast and prostate cancer had the highest disease burden (3937.2 and 2730.4 DALYs) among females and males, respectively. A published research ranking the cancer burden among users from the Mexican Institute of Social Security (from Spanish Instituto Mexicano del Seguro Social) had similar findings [22]. The YLL proportion in that study was lower than ours (breast, 56.5% vs. 94.1%; prostate, 45.5% vs. 80.9%) maybe due to the existence of institutional protocols (e.g., OncoIMSS) [22] implemented to provide a timely diagnosis and treatment and also to improve the prognosis of cancer patients.
Malignant tumors from the prostate are the leading cause of cancer death among adult males [23] and the mortality rate from the state de Colima, where this study took place, is one of the highest in the country [24]. High occurrence of late stages (Gleason score ≥ 7) at the time of diagnosis have been documented, in a population from northern Mexico [25], and may be determining in the observed mortality. In addition, Mexico lacks a population-based screening program regarding this malignant tumor [26]. Interestingly, economic analyses have shown that early detection of prostate tumors among individuals aged 70 years and older is not cost-effective [27].
Breast cancer is the leading cause of cancer mortality among women of reproductive age and an increase in its incidence has been documented in Mexico [28]. The screening program is based on breast self-examination and mammography among women aged 40–69 years old, however, the screening coverage is low (40–49 years old, 11.7%; 50–59, 22.8%) [28] and more than 80% of breast neoplasms are diagnosed in late clinical stages [29]. A significant role of perception from organizational and structural factors has been observed in both breast and prostate cancer screening use [25,30].
Plausible strategies to decrease the breast cancer burden include the reduction in exposure to environmental risk factors (i.e., alcohol use) [31], promoting healthy life habits [32], the implementation of a well-organized screening program, control of quality in mammography screening, and standardization of medical management protocols [33].
Considering both genders, the lung cancer mortality rate among Mexican adults is the highest [34]. In our study, the computed DALYs were higher among males (2244.4 vs. 1246.7) and this scenario is consistent with the higher prevalence of smoking habit among them [35]. Smoking is a major risk factor for malignant tumors of respiratory tract [36] and smoke-free legislation has been promoted, and a decrease in the exposure to secondhand tobacco smoke has been documented [37]. The former law in Mexico took effect in April 2008 and prohibits smoking in enclosed public spaces (i.e., restaurants, bars, commercial establishments, and public vehicles) [38].
Overweight and obesity are associated with increased risk of malignant tumors [39] and, among Mexican individuals aged 20 years and older, are associated with high prevalence of exposure to excess body weight (overweight, 32.4%; obesity, 38.8%) [40]. In the state of Colima, where this study took place, the cancer-related premature death attributable to overweight and obesity has been assessed [41]. Targeting overweight and obesity may improve cancer prevention and outcomes after diagnosis, also the burden reduction may be potentially observed [42].
The discount rate (r = 0.03) and standard age weighting (β = 0.04, C = 0.1658) were used for DALYs computation in our study. The discount rate reflects the social value of year lived in a state of health and, from a health-economics perspective; age weighting enables to give more value to a year of live in young adulthood than a year in the extremes of live [10]. There is not a general consensus regarding the social weighting and lower DALYs are computed when age weighting and discount rate are employed [14].
In addition, the usefulness of cancer site-specific causes of death reported on death certificates has been evidenced [43]. However, and among patients with multiple cancer sites, attributing the underlying cause of death may be challenging and impacts on the quality of registered data [43].
There are some alternatives to DALYs approach in economic evaluation and they include the healthy year equivalent (HYE) [44], willingness-to-pay (WTP) [45], and the quality-adjusted life year (QALY) [46]. The HYE reflects the utility function of an individual over their lifetime and health states and is a measure of life quality [44]. However, the HYE have been criticized for the difficulty of implementation in practice and its estimation is considered unworkable [47]. The WTP approach uses a cost-benefit framework to obtain, in monetary terms, how much the individuals would be willing to pay to obtain or avoid specific health effects. The ability to pay is closely associated with the WTP evaluation and this approach will disadvantage people with lower incomes [48]. The QALY incorporates the impact on both the quantity and quality of life from a specific health-related event and are widely used in health economics evaluation [49]. However, they do not incorporate equity weights, which may potentially limit the use of QALY for public health interventions [50].
The limitations of our study must be cited. First, only deaths that occurred in the state of Colima were analyzed and these results may not be reproduced in other territories from Mexico, since regional patterns in cancer-related morbidity and mortality have been described [51]. However, the demographic characteristics of the population of Colima are similar to those observed at national level. Second, life expectancy estimates for Mexico were employed in YLL computing instead of standard life expectancy. Mexico is currently going through a deep epidemiological transition [39,52] and the use of specific estimates may result more accurate according to the aim of this study, however this fact may limit the comparability of our findings. The standard life expectancy is approximately 7% lower than the life expectancy of Mexico, and lower YLL and DALYs rates would have been obtained by using global estimates. Third, reliability of cancer incidence and cancer-related mortality are major limitations since Mexico lacks a population-based cancer registry [52]. Incidence data employed on the computing of cancer burden was obtained from the GLOBOCAN project and it was estimated from national mortality data and modelled survival [15]. On the other hand, the local system of death registration is considered one of the best worldwide in terms of quality and integrity of data [42]. The proportion of cancer-attributable deaths in our study (12.4%) was similar to the observed proportion in a national analysis (12.8%) [52]. Fourth, since no year-stratified analyses were performed, the 2010 total population of the state of Colima was employed (according to government data) and no projections were considered. However, and in accordance with official projections, the local average annual population growth (2010–2014) was <2% [53] and it minimizes the overall effect on the estimates. Lower morbidity and mortality rates would have been observed by using denominators from 2014.

5. Conclusions

Cancer is a leading cause of disease burden, and regional estimates were provided in this study from a cost-effectiveness perspective using DALYs as a health measure. There is an urgent need to implement effective cancer prevention programs, including screening and awareness. Ensuring access to early diagnosis and treatment is also a need.

Author Contributions

E.M.-Z. and O.M.-C. conceived the study, collected and analyzed data, and wrote the manuscript. R.A.S.-P. and A.L.-R. analyzed data and wrote the manuscript. M.A.H.-A., M.R.-S., and E.H.-A. analyzed data and wrote the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

DALYsdisability-adjusted life years
YLLyears of life lost
YLDyears lived with disability
GBDGlobal Burden of Disease study
SEDRStatistical and Epidemiological Death Registration system from Mexico
ASMRage-standardized mortality rates
HYEhealthy year equivalent
WTPwillingness-to-pay
QALYquality-adjusted life year

References

  1. Grimard, F.; Laszlo, S.; Lim, W. Health, aging and childhood socio-economic conditions in Mexico. J. Health Econ. 2010, 29, 630–640. [Google Scholar] [CrossRef] [PubMed]
  2. Media Centre–IARC News. World Cancer Day 2016. ‘We Can. I Can’. Available online: https://www.iarc.fr/en/media-centre/iarcnews/2016/worldcancerday2016.php (accessed on 2 February 2018).
  3. Feigin, V.L.; Roth, G.A.; Naghavi, M.; Parmar, P.; Krishnamurthi, R.; Chugh, S.; Mensah, G.A.; Norrving, B.; Shiue, I.; Ng, M.Y.; et al. Global burden of stroke and risk factors in 188 countries, during 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurol. 2016, 15, 913–924. [Google Scholar] [CrossRef]
  4. Sassi, F. Calculating QALYs, comparing QALY and DALY calculations. Health Policy Plan. 2006, 21, 402–408. [Google Scholar] [CrossRef] [PubMed]
  5. Fox-Rushby, J.A.; Hanson, K. Calculating and presenting disability adjusted life years (DALYs) in cost-effectiveness analysis. Health Policy Plan. 2001, 16, 326–331. [Google Scholar] [CrossRef] [PubMed]
  6. Tan-Torres Edejer, T.; Evans, D.B. Generalized cost-effectiveness analysis: An aid to decision making in health. In Making Choices in Health: WHO Guide to Cost-Effectiveness Analysis; World Health Organization: Geneva, Switzerland, 2003; pp. 277–288. [Google Scholar]
  7. Jemal, A.; Center, M.M.; DeSantis, C.; Ward, E.M. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol. Biomark. Prev. 2010, 19, 1893–1907. [Google Scholar] [CrossRef] [PubMed]
  8. Global Burden of Disease Cancer Collaboration; Fitzmaurice, C.; Dicker, D.; Pain, A.; Hamavid, H.; Moradi-Lakeh, M.; MacIntyre, M.F.; Allen, C.; Hansen, G.; Woodbrook, R.; et al. The global burden of cancer 2013. JAMA Oncol. 2015, 1, 505–527. [Google Scholar] [CrossRef] [PubMed]
  9. Instituto Nacional de Estatística y Geografía. Censo de Población y Vivienda 2010. Available online: http://www.inegi.org.mx/est/lista_cubos/consulta.aspx?p=pob&c=1 (accessed on 17 November 2017).
  10. Murray, C.J.; Lopez, A.D.; World Health Organization; World Bank; Harvard School of Public Health. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020; Harvard University Press: Boston, MA, USA, 1996. [Google Scholar]
  11. Secretaría de Salud, Dirección General de Información en Salud, Subsistema Epidemiológico y Estadístico de Defunciones. Available online: http://187.191.75.115/gobmx/salud/documentos/manuales/30_Manual_SEED.pdf (accessed on 12 December 2017).
  12. National Cancer Institute, Standard Population Data, World (WHO 2000–2025) Standard. Available online: https://seer.cancer.gov/stdpopulations/world.who.html (accessed on 14 November 2017).
  13. Global Health Observatory, Life Tables by Country, Mexico. Available online: http://apps.who.int/gho/data/?theme=main&vid=61060 (accessed on 14 November 2017).
  14. Devleesschauwer, B.; Havelaar, A.H.; Maertens de Noordhout, C.; Haagsma, J.A.; Praet, N.; Dorny, P.; Duchateau, L.; Torgerson, P.R.; Van Oyen, H.; Speybroeck, N. Calculating disability-adjusted life years to quantify burden of disease. Int. J. Public Health 2014, 59, 565–569. [Google Scholar] [CrossRef] [PubMed]
  15. Ferlay, J.; Soerjomataram, I.; Dikshit, R.; Eser, S.; Mathers, C.; Rebelo, M.; Parkin, D.M.; Forman, D.; Bray, F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 2015, 136, E359–E386. [Google Scholar] [CrossRef] [PubMed]
  16. Murray, C.J. Quantifying the burden of disease: The technical basis for disability-adjusted life years. Bull. World Health Organ. 1994, 72, 429–445. [Google Scholar] [PubMed]
  17. World Health Organization. Health Statistics and Information Systems, Global Health Estimates: National Burden of Disease Supplementary Files. Available online: http://www.who.int/healthinfo/global_burden_disease/tools_national/en/ (accessed on 13 November 2017).
  18. Lapostolle, A.; Lefranc, A.; Gremy, I.; Spira, A. Sensitivity analysis in summary measure of population health in France. Eur. J. Public Health 2008, 18, 195–200. [Google Scholar] [CrossRef] [PubMed]
  19. Fernandez de Larrea-Baz, N.; Alvarez-Martin, E.; Morant-Ginestar, C.; Genova-Maleras, R.; Gil, A.; Perez-Gomez, B.; Lopez-Abente, G. Burden of disease due to cancer in Spain. BMC Public Health 2009, 9, 42. [Google Scholar] [CrossRef] [PubMed]
  20. Pham, T.M.; Kubo, T.; Fujino, Y.; Ozasa, K.; Matsuda, S.; Yoshimura, T. Disability-adjusted life years (DALY) for cancer in Japan in 2000. J. Epidemiol. 2011, 21, 309–312. [Google Scholar] [CrossRef] [PubMed]
  21. Li, S.; Zhang, X.; Yan, Y.; Wang, K.; Rui, D.; Pang, L.; Li, F. High cancer burden in elderly Chinese, 2005–2011. Int. J. Environ. Res. Public Health 2015, 12, 12196–12211. [Google Scholar] [CrossRef] [PubMed]
  22. González-León, M.; Fernández-Gárate, J.E.; Rascón-Pacheco, R.A.; Valladares-Aranda, M.A.; Dávila-Torres, J.; Borja-Aburto, V.H. The burden of disease of cancer in the Mexican Social Security Institute. Salud Publica Mex. 2016, 58, 132–141. [Google Scholar] [CrossRef] [PubMed]
  23. Castillejos-Molina, R.A.; Gabilondo-Navarro, F.B. Prostate cancer. Salud Publica Mex. 2016, 58, 279–284. [Google Scholar] [CrossRef] [PubMed]
  24. Sanchez-Barriga, J.J. Mortality trends and years of potential life lost from prostate cancer in the 32 states and 7 socioeconomic regions of Mexico, 2000–2010. Gac. Med. Mex. 2013, 149, 576–585. [Google Scholar] [PubMed]
  25. Gomez-Guerra, L.S.; Martinez-Fierro, M.L.; Alcantara-Aragon, V.; Ortiz-Lopez, R.; Martinez-Villarreal, R.T.; Morales-Rodriguez, I.B.; Garza-Guajardo, R.; Ponce-Camacho, M.A.; Rojas-Martinez, A. Population based prostate cancer screening in north Mexico reveals a high prevalence of aggressive tumors in detected cases. BMC Cancer 2009, 9, 91. [Google Scholar] [CrossRef] [PubMed]
  26. Liang, Y.; Messer, J.C.; Louden, C.; Jimenez-Rios, M.A.; Thompson, I.M.; Camarena-Reynoso, H.R. Prostate cancer risk prediction in a urology clinic in Mexico. Urol. Oncol. 2013, 31, 1085–1092. [Google Scholar] [CrossRef] [PubMed]
  27. Howard, D.H. Life expectancy and the value of early detection. J. Health Econ. 2005, 24, 891–906. [Google Scholar] [CrossRef] [PubMed]
  28. Valencia-Mendoza, A.; Sanchez-González, G.; Bautista-Arredondo, S.; Torres-Mejia, G.; Bertozzi, S.M. Cost-effectiveness of breast cancer screening policies in Mexico. Salud Publica Mex. 2009, 51 (Suppl. 2), s296–s304. [Google Scholar] [CrossRef] [PubMed]
  29. Mohar, A.; Bargallo, E.; Ramirez, M.T.; Lara, F.; Beltran-Ortega, A. Available resources for the treatment of breast cancer in Mexico. Salud Publica Mex. 2009, 51 (Suppl. 2), s263–s269. [Google Scholar] [PubMed]
  30. Wall, K.M.; Nunez-Rocha, G.M.; Salinas-Martinez, A.M.; Sanchez-Pena, S.R. Determinants of the use of breast cancer screening among women workers in urban Mexico. Prev. Chronic Dis. 2008, 5, 1–8. [Google Scholar]
  31. Murillo-Zamora, E.; Moreno-Macias, H.; Ziv, E.; Romieu, I.; Lazcano-Ponce, E.; Angeles-Llerenas, A.; Perez-Rodriguez, E.; Vidal-Millan, S.; Fejerman, L.; Torres-Mejia, G. Association between rs2981582 polymorphism in the FGFR2 gene and the risk of breast cancer in Mexican women. Arch. Med. Res. 2013, 44, 459–466. [Google Scholar] [CrossRef] [PubMed]
  32. Torres-Mejia, G.; Angeles-Llerenas, A.; Ortega-Olvera, C.; Lazcano-Ponce, E.; Ziv, E.; Pulido-Rodriguez, J.; Garcia-Solis Mde, J.; Murillo-Zamora, E.; Vazquez-Lara, J.; Romieu, I. Moderate-intensity physical activity ameliorates the breast cancer risk in diabetic women. Diabetes Care 2012, 35, 2500–2502. [Google Scholar] [CrossRef] [PubMed]
  33. Torres-Mejia, G.; Ortega-Olvera, C.; Angeles-Llerenas, A.; Villalobos-Hernandez, A.L.; Salmeron-Castro, J.; Lazcano-Ponce, E.; Hernandez-Avila, M. Utilization patterns of prevention and early diagnosis for cancer in women. Salud Publica Mex. 2013, 55 (Suppl. 2), S241–S248. [Google Scholar] [PubMed]
  34. Ruiz-Godoy, L.; Rizo Rios, P.; Sanchez Cervantes, F.; Osornio-Vargas, A.; Garcia-Cuellar, C.; Meneses Garcia, A. Mortality due to lung cancer in Mexico. Lung Cancer 2007, 58, 184–190. [Google Scholar] [CrossRef] [PubMed]
  35. Franco-Marina, F.; Lazcano-Ponce, E. Adult smoking trends in Mexico between 1988 and 2008. Salud Publica Mex. 2010, 52 (Suppl. 2), S108–S119. [Google Scholar] [PubMed]
  36. Freedman, N.D.; Abnet, C.C.; Caporaso, N.E.; Fraumeni, J.F., Jr.; Murphy, G.; Hartge, P.; Hollenbeck, A.R.; Park, Y.; Shiels, M.S.; Silverman, D.T. Impact of changing US cigarette smoking patterns on incident cancer: Risks of 20 smoking-related cancers among the women and men of the NIH-AARP cohort. Int. J. Epidemiol. 2016, 45, 846–856. [Google Scholar] [CrossRef] [PubMed]
  37. Thrasher, J.F.; Swayampakala, K.; Arillo-Santillan, E.; Sebrie, E.; Walsemann, K.M.; Bottai, M. Differential impact of local and federal smoke-free legislation in Mexico: A longitudinal study among adult smokers. Salud Publica Mex. 2010, 52 (Suppl. 2), S244–S253. [Google Scholar] [CrossRef] [PubMed]
  38. Lopez, C.M.; Ruiz, J.A.; Shigematsu, L.M.; Waters, H.R. The economic impact of Mexico City’s smoke-free law. Tob. Control 2011, 20, 273–278. [Google Scholar] [CrossRef] [PubMed]
  39. Stevens, G.; Dias, R.H.; Thomas, K.J.; Rivera, J.A.; Carvalho, N.; Barquera, S.; Hill, K.; Ezzati, M. Characterizing the epidemiological transition in Mexico: National and subnational burden of diseases, injuries, and risk factors. PLoS Med. 2008, 5, e125. [Google Scholar] [CrossRef] [Green Version]
  40. Barquera, S.; Campos-Nonato, I.; Hernandez-Barrera, L.; Pedroza, A.; Rivera-Dommarco, J.A. Prevalence of obesity in Mexican adults 2000–2012. Salud Publica Mex. 2013, 55 (Suppl. 2), S151–S160. [Google Scholar] [CrossRef] [PubMed]
  41. Murillo-Zamora, E.; Garcia-Ceballos, R.; Delgado-Enciso, I.; Garza-Guajardo, R.; Barboza-Quintana, O.; Rodriguez-Sanchez, I.P.; Mendoza-Cano, O. Regional-level estimation of expected years of life lost attributable to overweight and obesity among Mexican adults. Glob. Health Action 2016, 9, 31642. [Google Scholar] [CrossRef] [PubMed]
  42. Mathers, C.D.; Fat, D.M.; Inoue, M.; Rao, C.; Lopez, A.D. Counting the dead and what they died from: An assessment of the global status of cause of death data. Bull. World Health Organ. 2005, 83, 171–177. [Google Scholar] [PubMed]
  43. Lund, J.L.; Harlan, L.C.; Yabroff, K.R.; Warren, J.L. Should cause of death from the death certificate be used to examine cancer-specific survival? A study of patients with distant stage disease. Cancer Investig. 2010, 28, 758–764. [Google Scholar] [CrossRef] [PubMed]
  44. Mehrez, A.; Gafni, A. Quality-adjusted life years, utility theory, and healthy-years equivalents. Med. Decis. Mak. 1989, 9, 142–149. [Google Scholar] [CrossRef] [PubMed]
  45. Thompson, M.S. Health versus money. Value judgments in the perspective of decision analysis. Med. Decis. Mak. 1983, 3, 285–297. [Google Scholar] [CrossRef] [PubMed]
  46. Prieto, L.; Sacristan, J.A. Problems and solutions in calculating quality-adjusted life years (QALYs). Health Qual. Life Outcomes 2003, 1, 80. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Drummond, M.; Brixner, D.; Gold, M.; Kind, P.; McGuire, A.; Nord, E.; Consensus Development Group. Toward a consensus on the QALY. Value Health 2009, 12 (Suppl. 1), S31–S35. [Google Scholar] [CrossRef] [PubMed]
  48. Hurley, J. An overview of the normative economics of the health sector. In Handbook of Health Economics, 1st ed.; Culyer, A.J., Newhouse, J.P., Eds.; Elsevier: Amsterdam, The Netherlands, 2000; pp. 55–118. [Google Scholar]
  49. Whitehead, S.J.; Ali, S. Health outcomes in economic evaluation: The QALY and utilities. Br. Med. Bull. 2010, 96, 5–21. [Google Scholar] [CrossRef] [PubMed]
  50. Dolan, P.; Tsuchiya, A. The elicitation of distributional judgements in the context of economic evaluation. In The Elgar Companion to Health Economics, 1st ed.; Elgar: Cheltenham, UK, 2006. [Google Scholar]
  51. Institute for Health Metrics and Evaluation, Data Visualizations (Global Burden of Disease). Available online: https://vizhub.healthdata.org/gbd-compare/ (accessed on 13 April 2018).
  52. Mohar-Betancourt, A.; Reynoso-Noverón, N.; Armas-Texta, D.; Gutiérrez-Delgado, C.; Torres-Domínguez, J.A. Cancer trends in Mexico: Essential data for the creation and follow-up of public policies. J. Glob. Oncol. 2017, 3, 740–748. [Google Scholar] [CrossRef] [PubMed]
  53. Consejo Nacional de Población, Población Proyectada 2010–2050 (Estado de Colima, México). Available online: http://www.conapo.gob.mx/es/CONAPO/Proyecciones_Datos (accessed on 6 April 2018).
Table
Table 1. Sex-stratified cancer mortality in the study population, Colima, Mexico 2010–2014.
Table 1. Sex-stratified cancer mortality in the study population, Colima, Mexico 2010–2014.
Cancer SiteICD-10MalesFemales
Deaths (n)ASMR aDeaths (n)ASMR a
1Mouth and oropharynxC00–C14361.9150.7
2EsophagusC15201.160.2
3StomachC16633.5612.7
4Colon and rectumC18–C21915.1663.4
5LiverC22934.9844.2
6PancreasC25663.6492.3
7LarynxC32482.630.2
8Lower respiratory tract bC33–C3418910.1874.1
9Melanoma and other skin cancersC43–C44291.4200.8
10BreastC5030.221511.4
11Cervix uteriC53--1447.6
12Corpus uteriC54–C55--231.2
13OvaryC56--693.7
14ProstateC6125011.1--
15BladderC67190.960.2
16Central nervous systemC70–C72563.2462.5
17Lymphomas and multiple myelomaC81–C90, C96643.6371.8
18LeukemiaC91–C95643.8462.7
19Other malignant tumors-22112.324312.2
All sitesC00–C97131269.0122061.8
Abbreviations: ICD-10, International Classification of Diseases 10th Revision; ASMR, age-standardized mortality rate. a Per 100,000 inhabitants; estimated using the World Standard Population (2000–2025). b Trachea, bronchus, and lung.
Table
Table 2. Years of life lost, years lived with disability, and unadjusted rates per 1000 inhabitants, Colima, Mexico 2010–2014.
Table 2. Years of life lost, years lived with disability, and unadjusted rates per 1000 inhabitants, Colima, Mexico 2010–2014.
Cancer SiteICD-10MalesFemales
YLL (n/Rate)YLD (n/Rate)YLL (n/Rate)YLD (n/Rate)
1Mouth and oropharynxC00–C14462.81.487.00.3219.30.749.60.2
2EsophagusC15281.60.921.20.173.80.210.30.03
3StomachC16801.02.5200.40.6775.82.4129.50.4
4Colon and rectumC18–C211254.83.9292.40.9957.32.9220.30.7
5LiverC221134.33.587.50.31144.73.540.90.1
6PancreasC25871.22.762.30.2633.21.943.00.1
7LarynxC32599.01.974.00.251.00.29.00.03
8Lower respiratory tract bC33–C342141.06.6103.40.31179.13.667.60.2
9Melanoma and other skin cancersC43–C44311.11.014.30.04218.90.710.80.03
10BreastC5043.30.12.10.013706.011.3231.20.7
11Cervix uteriC53- - 2508.27.7139.00.4
12Corpus uteriC54–C55- - 365.71.127.60.1
13OvaryC56- - 1190.53.631.20.1
14ProstateC612208.96.8521.51.6- -
15BladderC67198.90.634.50.173.70.214.80.05
16Central nervous systemC70–C72841.22.673.10.2849.22.661.60.2
17Lymphomas and multiple myelomaC81–C90, C96932.42.997.60.3532.71.637.40.1
18LeukemiaC91–C951179.83.7149.70.5879.22.787.80.3
19Other malignant tumors-3084.69.6546.01.73613.311.060.10.2
All sitesC00–C9716,345.950.62367.07.318,971.657.91271.73.9
Abbreviations: ICD-10, International Classification of Diseases 10th Revision; YLL, years of life lost; YLD, years lived with disability. b Trachea, bronchus, and lung.
Table
Table 3. Sex-stratified leading cancer causes of disability-adjusted life years, Colima, Mexico 2010–2014.
Table 3. Sex-stratified leading cancer causes of disability-adjusted life years, Colima, Mexico 2010–2014.
Cancer SiteICD-10%YLL in DALYsDALYs
n (%)Rate (95% CI) a
Males
1ProstateC6180.92730.4(14.6)45.7(32.7–59.3)
2Lower respiratory tract bC33–C3495.42244.4(12.0)37.6(25.7–49.9)
3Colon and rectumC18–C2181.11547.2(8.3)25.9(16.0–36.1)
4LeukemiaC91–C9588.71329.5(7.1)22.3(13.1–31.7)
5LiverC2292.81221.8(6.5)20.5(11.7–29.5)
6Lymphomas and multiple myelomaC81–C90, C9690.51030.0(5.5)17.3(9.2–25.5)
7StomachC1680.01001.4(5.4)16.8(8.8–24.9)
8PancreasC2593.3933.5(5.0)15.6(7.9–23.5)
9Central nervous systemC70–C7292.0914.3(4.9)15.3(7.7–23.1)
10LarynxC3289.0673(3.6)11.3(4.7–18.0)
Other sites-86.15087.4(27.2)85.2(67.5–103.9)
All sitesC00–C9787.418,712.9 313.5(280.4–350.2)
Females
1BreastC5094.13937.2(19.4)66.0(50.3–82.4)
2Cervix uteriC5394.72647.2(13.1)44.4(31.5–57.7)
3Lower respiratory tract bC33–C3494.61246.7(6.2)20.9(12.0–30.0)
4OvaryC5697.41221.7(6.0)20.5(11.7–29.5)
5LiverC2296.61185.6(5.9)19.9(11.2–28.8)
6Colon and rectumC18–C2181.31177.6(5.8)19.7(11.1–28.6)
7LeukaemiaC91–C9590.9967(4.8)16.2(8.4–24.2)
8Central nervous systemC70–C7293.2910.8(4.5)15.3(7.6–23.0)
9StomachC1685.7905.3(4.5)15.2(7.6–22.9)
10PancreasC2593.6676.2(3.3)11.3(4.8–18.0)
Other sites-95.95368.0(26.5)89.9(71.8–109.2)
All sitesC00–C9793.720,243.3 339.2(304.8–377.4)
Abbreviations: ICD-10, International Classification of Diseases 10th Revision; YLL, years of life lost; DALYs, disability-adjusted life years; CI, confidence interval. a Age-standardized DALYs rate per 100,000 inhabitants; estimated using the World Standard Population (2000–2025). b Trachea, bronchus, and lung.

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Murillo-Zamora, E.; Mendoza-Cano, O.; Ríos-Silva, M.; Sánchez-Piña, R.A.; Higareda-Almaraz, M.A.; Higareda-Almaraz, E.; Lugo-Radillo, A. Disability-Adjusted Life Years for Cancer in 2010–2014: A Regional Approach in Mexico. Int. J. Environ. Res. Public Health 2018, 15, 864. https://doi.org/10.3390/ijerph15050864

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Murillo-Zamora E, Mendoza-Cano O, Ríos-Silva M, Sánchez-Piña RA, Higareda-Almaraz MA, Higareda-Almaraz E, Lugo-Radillo A. Disability-Adjusted Life Years for Cancer in 2010–2014: A Regional Approach in Mexico. International Journal of Environmental Research and Public Health. 2018; 15(5):864. https://doi.org/10.3390/ijerph15050864

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Murillo-Zamora, Efrén, Oliver Mendoza-Cano, Mónica Ríos-Silva, Ramón Alberto Sánchez-Piña, Martha Alicia Higareda-Almaraz, Enrique Higareda-Almaraz, and Agustin Lugo-Radillo. 2018. "Disability-Adjusted Life Years for Cancer in 2010–2014: A Regional Approach in Mexico" International Journal of Environmental Research and Public Health 15, no. 5: 864. https://doi.org/10.3390/ijerph15050864

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