Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 Jan 5.
Published in final edited form as: Demogr Res. 2018 Jan 5;38(3):95–108. doi: 10.4054/DemRes.2018.38.3

High life expectancy and reversed socioeconomic gradients of elderly people in Mexico and Costa Rica

Luis Rosero-Bixby 1
PMCID: PMC5863751  NIHMSID: NIHMS948481  PMID: 29576742

Abstract

Background

Some existing estimates suggest, controversially, that life expectancy at age 60 (LE60) of Latin American males is exceptionally high. Knowledge of adult mortality in Latin America is often based on unreliable statistics or indirect demographic methods.

Objectives

This study aimed to gather direct estimates of mortality at older ages in two Latin American countries (Mexico and Costa Rica) using recent longitudinal surveys and to determine the socioeconomic status (SES) gradients for LE60.

Methods

Data were collected from independent panels of approximately 7,000 older adults followed over more than a decade—the MHAS and CRELES surveys. The age-specific death rates were modeled with Gompertz regression, and thousands of life tables were simulated to estimate LE60 and its confidence interval.

Results

LE60 estimates obtained from MHAS and CRELES are similar to those obtained from traditional statistics, confirming the exceptionally high LE60 of men in the two countries. The expected gradients of higher LE60 with higher SES are not present, especially among males, who even show reverse gradients (some exaggerated by data issues).

Conclusions

Vital statistics correctly estimate elderly mortality in Mexico and Costa Rica. The higher-than-expected LE60 among Latin American males in general, and particularly among low-SES individuals, seems to be real; their determinants should be thoroughly investigated.

Contribution

This study shows with hard, reliable data, independent of traditional statistics, that elderly males in tropical Latin America enjoy an exceptionally high life expectancy and that SES gradients are absent or even reverse.

Introduction

In each cohort of Latin Americans, 84% of life table deaths occur after age 60 (UNPD 2015). Despite this preponderance, estimates of old-age mortality in Latin America are controversial, often based on unreliable statistics or indirect demographic methods. Only five countries2 have vital statistics that are considered adequate to allow valid estimates of mortality (CELADE 2010); only Chile is included in the Human Mortality Database of populations with complete data (HMD 2017). In most Latin American countries, adult mortality estimates are based on indirect demographic methods using questionable assumptions (Hill, Choi, and Timæus 2005).

A peculiarity of Latin America is that elderly males show better-than-expected life expectancy given the level of development of the countries. Figure 1 shows the life expectancy of males at age 60 (LE60-M) (UNPD 2015) in relation to the per capita gross domestic product parity purchasing power (GDP-PPP) (World Bank 2013) in 170 countries of the world. As expected, countries with higher GDP show higher LE60-M. Strikingly, all tropical Latin American countries are above the regression line that best fit the data, which means that these countries have a higher-than-expected LE60-M3. Costa Rica, Cuba, Ecuador or Mexico with a GDP-PPP per capita of about $10,000 show LE60-M of approximately 22 years, which would normally agree with economies of about $70,000.

Figure 1. Life expectancy of males aged 60 years by GDP-PPP. World’s countries circa 2010.

Figure 1

Open in a new tab

BRA=Brazil, COL=Colombia, CRI=Costa Rica, CUB=Cuba, DOM=Dominican Republic, ECU=Ecuador, SLV=El Salvador, GTM=Guatemala, HND=Honduras, MEX=Mexico, NIC=Nicaragua, PAN=Panama, PRY=Paraguay, PER=Peru, VEN=Venezuela.

Although the exceptionally low Latin American mortality at older ages has been documented (UNPD 1982), it has been dismissed as a result of faulty data, particularly age-misreport in censuses (Coale and Kisker 1986) (Dechter and Preston 1991, Preston, Elo, and Stewart 1999). Recent evidence, however, suggests that health at old ages in Latin America may be as good as in the United States (Payne 2015).

Latin America has the highest income distribution inequality in the world (Ravallion 2014). If mortality were determined exclusively by income, the region should also show extreme social inequalities in life expectancy. Indeed, Behm and collaborators documented extreme socioeconomic inequalities in child mortality in the 1970s (Behm 1980). In Guatemala, for example, the children of uneducated mothers died at a rate four times higher than that for the children of mothers with 10 or more years of education (Chackiel and Plaut 1996). It has often been assumed that similar inequalities occur in adult mortality, although the statistical evidence supporting this assumption is exiguous. For example, in a book on adult mortality in Latin America that compiles 18 papers, not a single item of hard data shows socioeconomic status (SES) inequalities (Timaeus, Chackiel, and Ruzicka 1996). Only in Chile and Argentina has it been documented that low-educated or low-income adults tend to die at higher rates (Sandoval and Turra 2015, Peláez and Acosta 2011, Rofman 1994). By contrast, studies of Hispanics in the United States and of adult Costa Ricans have questioned the existence of SES gradients in adult mortality, especially at older ages (Rosero-Bixby and Dow 2016, Turra and Goldman 2007, Lariscy, Hummer, and Hayward 2015).

This report, based on longitudinal surveys in Mexico and Costa Rica, has the double purpose of (1) directly and independently determining adult mortality in those countries and to assess the validity of existing estimates and (2) documenting the SES gradients for mortality as a first step toward understanding its determinants.

Both Mexico and Costa Rica are middle-income economies with large income distribution inequality (Gini index of 0.5 (Underwood 2014)). While 70% of Mexicans had health insurance in 2008 (Gómez-Dantés et al. 2011), 85% of Costa Rica had it according to the 2011 census.

Methods

This report uses existing databases from two independently conducted longitudinal surveys: (1) the Mexican Health and Aging Study (MHAS 2004, Wong et al. 2015) and (2) the mother sample of the Costa Rican Study of Longevity and Healthy Aging (CRELES) (Rosero-Bixby and Dow 2009).

MHAS is a representative panel of the older Mexican population initiated in 2001 with follow-up waves in 2003 and 2012. Its micro-databases are publicly available on the project’s web site (MHAS 2012). For comparability purposes, ages younger than 55 and older than 99 as well as interviewed spouses were excluded. The analytical sample size used here is 6,700 individuals (Table 1). Relatives and neighbors provided information on the date of death for 87% of the deceased participants; 4% of the missing dates were randomly imputed for 2001–03 and 9% for 2003–12. The sample includes 4% of the deaths recovered from the MHAS data files of no-interviews. The analytical panel has an attrition rate of 7% that clearly increases with SES. Dropouts were censored at the date of the last contact. A sensitivity analysis to attrition-mortality scenarios is provided as supplementary material. The observation time started on 1/1/2002, several months after the baseline interviews, and stopped on the date of the 2012 interview.

Table 1.

Sample sizes and attrition rate by gender, age, and SES groups.

Groups Persons Person-years Deaths % attrition
Mexico Costa Rica Mexico Costa Rica Mexico Costa R. Mexico Costa R.
Total 6,748 7,629 57,995 59,147 2,049 3,290 6.9 1.5
Gender
 Male 2,908 3,620 24,541 27,742 969 1,651 6.7 1.1
 Female 3,840 4,009 33,454 31,405 1,080 1,639 7.0 1.9
Baseline age
 55–74 5,508 4,076 49,829 39,402 1,275 975 6.9 0.7
 75–99 1,240 3,553 8,166 19,745 774 2,315 6.9 2.4
Observed age
 55–74 39,853 28,521 768 509
 75–99 18,142 30,626 1,281 2,781
Baseline residence
 <100,000 inhab. 2,825 3,488 25,103 27,226 864 1,486 4.0 1.4
 100,000+ inhab. 3,923 4,141 32,892 31,921 1,185 1,804 8.9 1.6
Education
 None 2,017 1,500 16,858 10,767 738 724 5.1 1.9
 Primary 3,566 4,979 31,186 38,705 1,059 2,152 6.0 1.5
 Secondary 833 712 7,196 5,954 184 266 11.3 0.8
 Post-secondary 332 438 2,755 3,722 68 148 15.4 1.4
Wealth tercile
 Low 1,948 2,957 16,710 22,479 672 1,332 5.1 1.4
 Medium 2,409 2,162 20,510 17,129 771 911 6.4 1.7
 High 2,391 2,510 20,775 19,539 606 1,047 8.8 1.5
Open in a new tab

The CRELES panel is a national sample of residents aged 55 or more drawn from 2000 census files linked to the death registry (details in (Rosero-Bixby, Brenes-Camacho, and Collado-Chaves 2004). After excluding foreigners (3%) and centenarians the analytical sample size is 7,200. In addition to the follow-up in the death registry, the survival of individuals was verified using the voting lists for the presidential elections of 2002, 2006, 2010 and 2014, resulting in an attrition rate of 1.5% of non-death individuals who disappeared from the voting lists. These lost individuals were excluded from observation on the closing date of the voting registry in which they first disappeared. Observation started on 1/1/2001 (six months after the census) and stopped on 12/31/2011. A nested subsample of 3,000 individuals was contacted in person in three waves of visits mostly in 2005, 2007 and 2009. In waves 2 and 3, relatives or neighbors reported 566 deaths in this subsample. Only 5 of these deaths (< 1%) were missing in the death registry follow-up, suggesting that the Costa Rican registry is essentially complete.

SES groups were defined with three indicators at baseline: (1) educational attainment (no formal education, some elementary, some secondary, and postsecondary education); (2) tercile of household wealth (measured by the count of 8 household assets: tap water inside the house, toilet, television, refrigerator, washer, telephone, hot water, and car); and (3) whether the place of residence is a city of more than 100,000 inhabitants. People in cities usually enjoy better economic opportunities and services (clean water, electricity, transportation, banking, health care, and so on) than in rural areas and thus they are considered of a higher SES. Table 1 shows the analytical sample sizes of the 40 groups defined with these variables.

Age in each observation segment was established from the date of birth (DoB) in months. DoB in CRELES was taken from the linked birth registry, which makes it error-free. Participants in MHAS reported their DoB in the 2001 wave. An assessment of the accuracy of DoB in MHAS, based on confirmatory reports during the 2012 wave, is included as supplementary material. Only 13% of MHAS participants changed their reported birth year; 8% changed their five-year bracket.

A two-parameter Gompertz function (Pollard 1991) was estimated for each SES group using hazard regression (Hosmer and Lemeshow 1999). The LE60 and its 95% confidence interval (95% CI) were estimated for each group using the Gompertz parameters (and their standard errors) to generate 1,000 sets of death rates, and the corresponding life tables, with Monte Carlo simulation. The median value of LE60 in 1,000 simulations is taken as the point indicator of mortality in a group along with the 2.5 and 97.5 percentiles as estimates of the 95% CI 4.

The national estimates of LE60 in this report are compared to estimates5 from the following other sources:

  • Vital statistics: deaths in the last intercensus period and, as rates denominator, the population average of the census of 2000 and 2010 in Mexico and of 2000 and 2011 in Costa Rica (Palloni, Pinto, and Beltrán-Sánchez 2014).

  • Official estimates: the age-specific death rates from 2000–2010 used by the Consejo Nacional de Población (CONAPO 2012) in the population projections of Mexico and the average of the Costa Rican death rates of 2000–05 and 2005–10 in the life tables by the Pension Superintendent (SUPEN) (CCP 2014).

  • LAMBdA project: death rates in the life tables estimated by the project “Latin American Mortality Database” (LAMBdA), Mexico 2000–10 and Costa Rica 2000–11 (Palloni, Pinto, and Beltrán-Sánchez 2014).

  • GBD: average of the death rates reported for 2000 and 2010 by the project “Global Burden of Disease” (GBD) (IHME 2015).

  • UNPD: Average of death rates of 2000–05 and 2005–10 in (UNPD 2015).

Results

The resulting LE60 for males (21.2 years in Mexico and 21.9 years in Costa Rica) is essentially the same as those from vital statistics considering the 95% CI (Table 2). Among women, the MHAS estimate (23.4 years) and its 95% CI is slightly higher than that from the Mexican vital statistics, whereas the CRELES estimate (24.4 years) is slightly lower. The UNPD estimates for these two countries, as well as the official life tables of Costa Rica, also yield estimates similar to those of MHAS and CRELES. The official mortality estimate for Mexico found in the CONAPO seems slightly up-biased (down-biased LE60).

Table 2.

Comparing estimates of life expectancy at age 60

Country, period and source Males Females
Mexico 2000–10
 MHAS 2002–11 21.2 (20.5–21.8) 23.4 (22.8–24.0)
 Vital statistics 20.4 22.4
 Official CONAPO 19.9 22.1
 LAMBdA 18.0 19.6
 GBD 21.8 24.7
 UNPD 20.8 22.6
Costa Rica 2000–10
 CRELES 2002–12 21.9 (21.5–22.2) 24.3 (23.9–24.8)
 Vital statistics 21.8 24.8
 Official SUPEN 21.8 24.4
 LAMBdA 19.3 21.4
 GBD 20.7 24.0
 UNPD 21.5 24.2
Open in a new tab

In parenthesis the 95% confidence interval

Estimates from the multicountry projects LAMBdA and GBD tend to be out of range. LAMBdA yields substantially higher mortality (lower LE60) in the two countries. For example, LE60 of Mexican males is just 18.0 years, according to LAMBdA. The LE60 GBD estimate is too high for Mexico and too low for Costa Rica.6

Figure 2 shows the resulting SES gradients in LE60. Strikingly, the expected gradients of higher LE60 with better SES do not show up in Figure 2, except among Costa Rican women. There are no significant differences in LE60 by education among Mexican women. Males in the two countries clearly show declining LE60 with increased education—a reverse gradient. Mexicans with postsecondary education, for example, have just 16.0 years (13.6–18.5 CI) of LE60 compared to 22.9 years (21.7–23.9 CI) for those with no education.

Figure 2.

Figure 2

Open in a new tab

Life expectancy at age 60 (LE60) by gender and SES groups. Mexico and Costa Rica

Residents in large cities tend to have lower LE60, with the exception of Costa Rican women. For example, LE60 among Mexican males residing in cities larger than 100,000 inhabitants is 20.0 years compared to 21.7 years for people living in small towns or rural areas.

The stratification by three groups (terciles) of household wealth does not yield gradients, negative or positive, in LE60.

Discussion

Panels of approximately 7,000 older adults followed over more than a decade in Mexico and Costa Rica yielded direct and independent estimates of adult mortality that allow an assessment of existing estimates. Two results are apparent: (1) LE60 from these two panels is similar to the raw estimates from vital statistics in the two countries, and (2) SES gradients are lacking; moreover, some “reverse inequality” shows up among males, with LE60 declining with increasing education or in large cities.

The lack of SES gradients challenges the assumption that SES-based inequality of mortality at older ages is similar to the inequalities documented for child mortality in Latin America (Behm 1980) or those observed among adults in developed countries (Mackenbach et al. 2008). Earlier Costa Rican studies had noted this lack of SES gradients among elderly Costa Ricans (Rosero-Bixby and Dow 2009, Rosero-Bixby and Dow 2016) as well as among the Hispanic population (which has a large Mexican component) in the United States (Turra and Goldman 2007, Lariscy, Hummer, and Hayward 2015). Consistent with these results, an analysis of cardiovascular risk factors (obesity, smoking, hypertension, and diabetes) with data from CRELES and MHAS found a weak or null association between the prevalence of these factors and education, particularly in rural areas, where reverse gradients even occur (Hummer et al. 2014). Another study using partial data from CRELES and MHAS found functional health levels comparable to the United States (Payne 2015).

The paradoxically high life expectancy of older Latin American males could very well be an expression of another paradox unveiled in this study: low-SES adults do not endure lower life expectancy.

Additional research is needed to determine the origin of these paradoxes. Part of the explanation could be the relatively low prevalence of obesity among older males in the region, particularly in rural areas. For example, the prevalence of obesity (BMI ≤ 30 kg/m2) among elderly males in Costa Rica is 20%, compared to 38% in the United States (Rosero-Bixby and Dow 2016).

A different approach from the explanation that focuses on a risk factor would be explanations that focus on past survival to very high mortality rates in childhood and the corresponding selection of the fittest, which would have resulted in cohorts of older adults with gene mutations that are protective against some diseases. Examples of promising studies that take this approach are those of polymorphisms in the enzymes ACP1 (Gloria-Bottini et al. 2010) and G6PD (Manganelli et al. 2013) that protect against both cardiovascular diseases and some types of cancer. The key to these polymorphisms is that they are gender specific and more prevalent among malaria survivors, which would explain the concentration of exceptional longevity only in males from tropical countries. The survival-selection argument has been used to explain the black/white crossover in old age mortality in the U.S. (Manton, Poss, and Wing 1979).

A third explanation is faulty data: age-misreport and attrition-caused biases from excluding healthy out-migrants or individuals who were lost because of death. As mentioned before, age-misreport does not exist in the CRELES data. Age errors in MHAS, assessed from comparing DoB reports in waves of 2001 and 2012, are lower than errors documented in census data (Preston, Elo, and Stewart 1999). Corrections of age errors in MHAS reduce little the LE60 estimates and essentially do not change SES gradients. Attrition of healthier out-migrants could be exaggerating some of the reverse SES gradients found especially in Mexico. In an extreme scenario of zero-mortality of dropouts, the contrast between education groups would cut by half in Mexican males. Attrition of high-mortality participants may be up-biasing LE60 estimates only in a few decimal points. Sensitivity analysis of these potential errors is included as supplementary material.

Strengths of estimates in this report are that rates were computed using information from the same source for the numerator (deaths) and denominator (population), which may be crucial in studying SES gradients, and the similarity of the results from two independent surveys. The estimates in this report also have the advantage that they did not require assumptions about population dynamics as those required by indirect methods. Notably, the estimates excluded the approximately six-month period immediately following the baseline contact, thus avoiding the possibility that ill individuals close to death were omitted from the sample.

A weakness of the estimates in this report is the noise from sampling errors that necessitate smoothing out the age-specific rates, which was performed with a Gompertz function. It must be noted, however, that this function is reputed to be a very good fit in the age range studied (Bongaarts and Feeney 2002).

Supplementary Material

Supplementary materials

Figure S1. Life expectancy of males aged 60 years by child mortality 0q5. World’s countries 2005–2010 (Data from United Nations Population Division 2016)

Figure S2. Age-specific death rates from MHAS/CRELES, compared to other estimates.

Table S1. Gompertz parameters α and γ describing mortality of people aged 55 to 99 in Mexico and Costa Rica SES groups and life expectancy at age 60 (LE60).

Sensitivity analysis: Assessing the effect of attrition and age-misreport on LE60 estimates (it includes Tables S2 and S3 and Figure S3)

STATA data file “MHAS_CRELES_morta_short2.dta”.

MHAS_CRELES_morta.do: STATA program code to compute age-specific death rates and national Gompertz regressions

Footnotes

2

Argentina, Chile, Costa Rica, Cuba, and Uruguay.

3

An analogous figure (provided as supplemental material to this article) with child mortality instead of GDP on the x-axis shows that LE60-M is also higher than expected in tropical Latin American countries given their child mortality levels.

4

Supplementary material includes a STATA data file with the microdata used to estimate death rates and the hazard regression models. Table S1 shows the two Gompertz parameters for each of the 40 groups and their standard errors as well as the corresponding LE60 estimate and its 95% CI, along with estimates of life expectancy for ages 55 and 65.

5

To avoid discrepancies due to differences in the method used by each source to compute LE60, this indicator was re-estimated with the set of age specific death rates in ages 55–99 years for each source modeled with a Gompertz function to obtain smoothed rates for ages 55 to 114 years and the corresponding life table.

6

A more detailed comparison of the age-specific death rates (instead of the summary LE60) is shown in Figure S2, included as supplementary material.

References

  1. Behm H. Socio-economic determinants of mortality in Latin America. Population Bulletin of the United Nations. 1980;13:1–15. [PubMed] [Google Scholar]
  2. Bongaarts J, Feeney G. How long do we live? Population and Development Review. 2002;28(1):13–29. [Google Scholar]
  3. CCP. Tablas de vida completas quinquenales. Centro Centroamericano de Población (CCP), Universidad de Costa Rica; 2014. [Accessed 3/12/2016]. http://ccp.ucr.ac.cr/observa/CRindicadores/TVcompletas.html. [Google Scholar]
  4. CELADE; Centro Latinoamericano y Caribeño de Demografía (CELADE), editor Observatorio Demográfico N. 9. Santiago, Chile: 2010. América Latina y el Caribe. Mortalidad. [Google Scholar]
  5. Chackiel J, Plaut R. Adult Mortality in Latin America. New York: Oxford University Press; 1996. Demographic trends with emphasis on mortality; pp. 14–41. [Google Scholar]
  6. Coale AJ, Kisker EE. Mortality crossovers: reality or bad data. Population Studies. 1986;40(3):389–401. [Google Scholar]
  7. CONAPO; Consejo Nacional de Población (CONAPO), editor Documento metodológico: Proyecciones de la población de México 2010–2050. México: Subdirección de Desarrollo Editorial CONAPO; 2012. http://www.gob.mx/conapo/documentos/documentos-metodologicos-sobre-la-estimacion-de-los-fenomenos-demograficos-y-proyeccion-de-poblaciones?idiom=es-MX. [Google Scholar]
  8. Dechter AR, Preston SH. Age misreporting and its effects on adult mortality estimates in Latin America. Population Bulletin of the United Nations. 1991;31/32:1–16. [PubMed] [Google Scholar]
  9. Gloria-Bottini F, Saccucci P, Magrini A, Bottini E. Is there a role of ACP1-ADA1 genetic complex in immune reaction? Association with T1D and with past malarial morbidity. American Journal of the Medical Sciences. 2010;340(4):268–270. doi: 10.1097/MAJ.0b013e3181e62ae6. [DOI] [PubMed] [Google Scholar]
  10. Gómez-Dantés O, Sesma S, Becerril VM, Knaul FM, Arreola H, Frenk J. Sistema de salud de México. Salud Pública de México. 2011;53(Sup 2):S220–S232. [PubMed] [Google Scholar]
  11. Hill K, Choi Y, Timæus IM. Unconventional approaches to mortality estimation. Demographic Research. 2005;13(12):281–299. [Google Scholar]
  12. HMD. Human Mortality Database. University of California, Berkeley and Max Planck Institute for Demographic Research; 2017. [Accessed 23/11/2017]. http://www.mortality.org. [Google Scholar]
  13. Hosmer DW, Lemeshow S. Applied Survival Analysis. New York: John Wiley & Sons; 1999. [Google Scholar]
  14. Hummer RA, Dondero M, Rosero-Bixby L, WHDow WH, Wong R. Educational Attainment and Older Adult Cardiovascular Health: A Comparison Between Mexico and Costa Rica. Annual Meeting of the Population Association of America; Boston. 2014. [Google Scholar]
  15. IHME. Global Burden of Disease Study 2015 (GBD 2015) Results. Institute for Health Metrics and Evaluation (IHME); 2015. [Accessed Nov 7]. http://ghdx.healthdata.org/gbd-results-tool. [Google Scholar]
  16. Lariscy JT, Hummer RA, Hayward MD. Hispanic Older Adult Mortality in the United States: New Estimates and an Assessment of Factors Shaping the Hispanic Paradox. Demography. 2015;52(1):1–14. doi: 10.1007/s13524-014-0357-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Mackenbach JP, Stirbu I, Roskam AJR, Schaap MM, Menvielle G, Leinsalu M, Kunst AE. Socioeconomic inequalities in health in 22 European countries. New England Journal of Medicine. 2008;358(23):2468–2481. doi: 10.1056/NEJMsa0707519. [DOI] [PubMed] [Google Scholar]
  18. Manganelli G, Masullo U, Passarelli S, Filosa S. Glucose-6-phosphate dehydrogenase deficiency: disadvantages and possible benefits. Cardiovascular & Haematological Disorders-Drug Targets. 2013;13(1):73–82. doi: 10.2174/1871529x11313010008. [DOI] [PubMed] [Google Scholar]
  19. Manton KG, Poss SS, Wing S. The black/white mortality crossover: Investigation from the perspective of the components of aging. The Gerontologist. 1979;19(3):291–300. doi: 10.1093/geront/19.3.291. [DOI] [PubMed] [Google Scholar]
  20. MHAS. Reporte de Proyecto. Versión 2.” [Electronic document] Mexican Health and Aging Study (MHAS); 2004. [Accessed 7/11/2016]. Estudio Nacional de Salud y Envejecimiento en México (ENASEM). Documento Metodológico. http://mhasweb.org/Resources/DOCUMENTS/2001/Methodological_Document_2001.pdf. [Google Scholar]
  21. MHAS. Data Files and Documentation (public use): Mexican Health and Aging Study, (Raw data files) Mexican Health and Aging Study (MHAS); 2012. [Accessed 12/11/2015]. http://www.mhasweb.org/Data.aspx. [Google Scholar]
  22. Palloni A, Pinto G, Beltrán-Sánchez H. Machine-readable database. University of Wisconsin; 2014. [Accessed 19/10/2015]. Latin American Mortality Database (LAMBdA) https://http://www.ssc.wisc.edu/cdha/latinmortality/?page_id=11. [Google Scholar]
  23. Payne CF. Aging in the Americas: Disability-free life expectancy among adults aged 65 and older in the United States, Costa Rica, Mexico, and Puerto Rico. Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2015:1–12. doi: 10.1093/geronb/gbv076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Peláez E, Acosta L. Educación y mortalidad diferencial de adultos. Provincia de Córdoba, República Argentina. Papeles de Población. 2011;70:9–31. [Google Scholar]
  25. Pollard JP. Fun with Gompertz. Genus. 1991;47(1/2):1–20. [PubMed] [Google Scholar]
  26. Preston SH, Elo IT, Stewart Q. Effects of age misreporting on mortality estimates at older ages. Population Studies. 1999;53(2):165–177. [Google Scholar]
  27. Ravallion M. Income inequality in the developing world. Science. 2014;344(6186):851–855. doi: 10.1126/science.1251875. [DOI] [PubMed] [Google Scholar]
  28. Rofman R. Diferenciales de mortalidad adulta en Argentina. Notas de Población. 1994;22(59):73–91. [PubMed] [Google Scholar]
  29. Rosero-Bixby L, Brenes-Camacho G, Collado-Chaves A. Tablas de vida para cálculo actuarial de rentas vitalicias y retiro programado. Costa Rica circa 2000. Población y Salud En Mesoamérica. Revista Electrónica. 2004;1(2) Art. 4. [Google Scholar]
  30. Rosero-Bixby L, Dow W. Surprising SES gradients in mortality, health and biomarkers in a Latin American population of adults. Journal of Gerontology Social Sciences. 2009;64(1):105–117. doi: 10.1093/geronb/gbn004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Rosero-Bixby L, Dow William H. Exploring why Costa Rica outperforms the United States in life expectancy: A tale of two inequality gradients. Proceedings of the National Academy of Sciences. 2016;113(5):1130–1137. doi: 10.1073/pnas.1521917112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Sandoval MH, Turra CM. El gradiente educativo en la mortalidad adulta en Chile. Revista Latinoamericana de Población. 2015;17:7–35. [Google Scholar]
  33. Timaeus I, Chackiel J, LRuzicka L. Adult Mortality in Latin America. New York: Oxford University Press; 1996. [Google Scholar]
  34. Turra CM, Goldman N. Socioeconomic differences in mortality among US adults: insights into the Hispanic paradox. The Journals of Gerontology Series B: Psychological and Social Sciences. 2007;62(3):S184–S192. doi: 10.1093/geronb/62.3.s184. [DOI] [PubMed] [Google Scholar]
  35. Underwood E. A world of difference. Countries vary widely in inequality. Science. 2014;344(6186):820–821. doi: 10.1126/science.344.6186.820. [DOI] [PubMed] [Google Scholar]
  36. UNPD. Model Life Tables for Developing Countries, Population Studies. New York: United Nations Publications; 1982. [Google Scholar]
  37. UNPD. World Mortality Report 2015, CD-ROM Edition - Datasets in Excel formats (POP/DB/MORT/2015) New York: United Nations Population Division; 2015. [accessed 13/08/2016]. United Nations Population Division. http://www.un.org/en/development/desa/population/publications/mortality/world-mortality-cdrom-2015.shtml. [Google Scholar]
  38. Wong R, Michaels-Obregon A, Palloni A, Gutierrez-Robledo LM, Gonzalez-Gonzalez C, Lopez-Ortega M, Tellez-Rojo MM, Mendoza-Alvarado LR. Progression of aging in Mexico: the Mexican Health and Aging Study (MHAS) 2012. Salud Publica Mex. 2015;57(Suppl 1):S79–89. doi: 10.21149/spm.v57s1.7593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. World Bank. World Development Indicators. World Bank: 2013. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary materials

Figure S1. Life expectancy of males aged 60 years by child mortality 0q5. World’s countries 2005–2010 (Data from United Nations Population Division 2016)

Figure S2. Age-specific death rates from MHAS/CRELES, compared to other estimates.

Table S1. Gompertz parameters α and γ describing mortality of people aged 55 to 99 in Mexico and Costa Rica SES groups and life expectancy at age 60 (LE60).

Sensitivity analysis: Assessing the effect of attrition and age-misreport on LE60 estimates (it includes Tables S2 and S3 and Figure S3)

STATA data file “MHAS_CRELES_morta_short2.dta”.

MHAS_CRELES_morta.do: STATA program code to compute age-specific death rates and national Gompertz regressions

NIHMS948481-supplement-Supplementary_materials.pdf (401.8KB, pdf)

RESOURCES