Abstract
In general, the use of indirect methods is limited to developing countries. Developed countries are usually assumed to have no need to apply such methods because detailed demographic data exist. However, the potentialities of demographic analysis with direct methods are limited to the characteristics of available macro data on births, deaths, and migration. For instance, in many Western countries, official population statistics do not permit the estimation of mortality by socioeconomic status (SES) or migration background, or for estimating the relationship between parity and mortality. In order to overcome these shortcomings, I modify and extend the socalled orphanhood method for indirect estimation of adult mortality from survey information on maternal and paternal survival to allow its application to populations of developed countries. The method is demonstrated and tested with data from two independent Italian crosssectional surveys by estimating overall and SESspecific life expectancy. The empirical applications reveal that the proposed method can be used successfully for estimating levels and trends of mortality differences in developed countries and thus offers new prospects for the analysis of mortality.
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Notes
 1.
The gestation period has two opposing effects on the survivorship ratio of respondents’ parents: (1) an increase of the survival time by 0.75 years, and (2) a decrease of the initial age of the survivorship ratio by 0.75 years. While the first effect leads to an increase of mortality, the latter causes the risk of dying to decrease. Consequently, the effects cancel each other out to some extent and thus are expected to be negligible.
 2.
The number of deceased parents during year y after respondents’ birth, D(n)_{ y }, can be estimated from the real cohort survival of the population analyzed, which is known for most developed countries (see Appendix 1).
 3.
The reported proportions of mothers and fathers alive reflect survival experiences of approximately n + 2.5 years. I use a time span of n + 3 years in order to provide a survivorship estimate that is directly comparable to the official period life tables.
 4.
Life expectancy at age 35 was chosen because this is the youngest age for which all methods can be employed.
 5.
Alternatively, two or more fiveyear age groups could be combined to form age groups with higher case numbers. This flexibility regarding the size of the age intervals is another major advantage of the MOM over the traditional variants of the orphanhood method.
 6.
The period was extended by one year to 1991 only for the estimates of tertiary education level and of the occupation group professional.
 7.
The estimates were derived using the MOM, and the resulting survivorship probabilities were transferred into complete life tables from age 30 with Brass’s logit life table model. Values for the Brass parameter β were estimated from education and occupationspecific death rates for age groups 18–29, 30–44, 45–54, 55–64, and 65–74 published by Istat (2001) for the years 1991–1992. Brass’s β was set to 1.0 only for economically inactive and selfemployed women and men because the death rates available from Istat are not fully comparable owing to different compositions of the occupation groups as compared with the multipurpose survey.
 8.
Murphy et al. (2006) arrived at a similar conclusion in their indirect analysis of mortality by education level in Russia based on information about survival of spouses and siblings.
 9.
Because the Italian multipurpose surveys entail the age at childbearing only for those parents who are still alive at the time of the survey, these proportions had to be estimated from the available information on parents still alive (see Appendix 2).
 10.
For males, I assumed the female fertility rates for ages 15–49 to be shifted by four years to ages 19–53. For the case of France, Caselli and Vallin (2006) showed that this assumption fits empirical reality in populations from developed countries very well for the distant past and approximately well for more recent years.
 11.
A fertility schedule leading to the average age at childbearing of \( {\hat{\bar{x}}^{*}} \) years could also be derived directly from the Schmertmann model. I chose the described combination of the Schmertmann and Gompertz models because I wanted to keep a systematic relation between the Schmertmann parameters H and P, which is not possible when using the Schmertmann model alone. This is, however, a personal preference, with minor impacts on the final orphanhoodbased estimates for life expectancy. In principle, any fertility model can be used to implement Step 2.
References
Bobak, M., Murphy, M., Pikhart, H., Martikainen, P., Rose, R., & Marmot, M. (2002). Mortality patterns in the Russian federation: Indirect technique using widowhood data. Bulletin of the World Health Organization, 80, 876–881.
Bobak, M., Murphy, M., Rose, R., & Marmot, M. (2003). Determinants of adult mortality in Russia. Estimates from sibling data. Epidemiology, 14, 603–611.
Bradshaw, D., & Timæus, I. M. (2006). Levels and trends of adult mortality. In D. T. Jamison, R. G. Feachem, M. W. Makgoba, E. R. Bos, F. K. Baingana, K. J. Hofman, & K. O. Rogo (Eds.), Disease and mortality in SubSaharan Africa (pp. 31–42). Washington, DC: World Bank.
Brass, W. (1971). On the scale of mortality. In W. Brass (Ed.), Biological aspects of demography (pp. 69–110). London, UK: Taylor and Francis.
Brass, W. (1975). Methods for estimating fertility and mortality from limited and defective data. Chapel Hill: University of North Carolina.
Brass, W. (1981). The use of the Gompertz relational model to estimate fertility. In International Union for the Scientific Study of Population (IUSSP) (Ed.), International Population Conference, Manila 1981. Solicited papers (pp. 345–362). Liège, Belgium: IUSSP.
Brass, W., & Bamgboye, E. A. (1981). The time location of reports of survivorship: Estimates for maternal and paternal orphanhood and the everwidowed (Working Paper No. 81–1). London, UK: London School of Hygiene & Tropical Medicine, Centre for Population Studies.
Brass, W., & Hill, K. (1973). Estimating adult mortality from orphanhood. In International Union for the Scientific Study of Population (IUSSP) (Ed.), Proceedings of the International Population Conference, Liège 1973 (pp. 111–123). Liège, Belgium: IUSSP.
Butt, S., Borgquist, S., Garne, J. P., Landberg, G., Tengrup, I., Olsson, Å., & Manjer, J. (2009). Parity in relation to survival following breast cancer. European Journal of Surgical Oncology, 35, 702–708.
Caselli, G., & Vallin, J. (2006). Population replacement. In G. Caselli, J. Vallin, & G. Wunsch (Eds.), Demography: Analysis and synthesis (Vol. I, pp. 237–247). London, UK: Academic Press.
Chackiel, J., & Orellana, H. (1985). Adult female mortality trends from retrospective questions about maternal orphanhood included in censuses and surveys. In International Union for the Scientific Study of Population (IUSSP) (Ed.), IUSSP International Population Conference, Florence 1985 (Vol. 4, pp. 39–51). Liège, Belgium: IUSSP.
Crimmins, E. M., & Saito, Y. (2001). Trends in healthy life expectancy in the United States, 1970–1990: Gender, racial, and educational differences. Social Science & Medicine, 52, 1629–1641.
Deboosere, P., Gadeyne, S., & Van Oyen, H. (2009). The 1991–2004 evolution in life expectancy by educational level in Belgium based on linked census and population register data. European Journal of Population, 25, 175–196.
Doblhammer, G. (2000). Reproductive history and mortality in later life: A comparative study of England and Wales and Austria. Population Studies, 54, 169–176.
Festy, P. (1995). Adult mortality and proportions orphaned in Austria in 1991. Population: An English Selection, 7, 232–238.
Friedlander, N. J. (1996). The relation of lifetime reproduction to survivorship in women and men: A prospective study. American Journal of Human Biology, 8, 771–783.
Goldman, N., & Hu, Y. (1993). Excess mortality among the unmarried: A case study of Japan. Social Science & Medicine, 36, 533–546.
Green, A., Beral, V., & Moser, K. (1988). Mortality in women in relation to their childbearing history. British Medical Journal, 297, 391–395.
Grundy, E., & Kravdal, Ø. (2010). Fertility history and causespecific mortality: A registerbased analysis of complete cohorts of Norwegian women and men. Social Science & Medicine, 70, 1847–1857.
Grundy, E., & Tomassini, C. (2005). Fertility history and health in later life: A record linkage study in England and Wales. Social Science & Medicine, 61, 217–228.
Henry, L. (1960). Mesure indirecte de la mortalité des adultes [Indirect measurement of adult mortality]. Population, 15, 457–466.
Hill, K. (1984). An evaluation of indirect methods for estimating mortality. In J. Vallin, J. H. Pollard, & L. Heligman (Eds.), Methodologies for the collection and analysis of mortality data (pp. 145–177). Liège, Belgium: Ordina Editions.
Hill, K. (2001, August). Methods for measuring adult mortality in developing countries: A comparative review. Paper presented at the XXIV IUSSP General Conference, Salvador, Brazil. Retrieved from http://www.iussp.org/Brazil2001/s10/S14_01_Hill.pdf
Hill, K. (2006). Indirect estimation methods. In G. Caselli, J. Vallin, & G. Wunsch (Eds.), Demography: Analysis and synthesis (Vol. IV, pp. 619–631). London, UK: Academic Press.
Hill, K., Choi, Y., & Timæus, I. M. (2005). Unconventional approaches to mortality estimation. Demographic Research, 13, article 12, 281–300. doi:10.4054/DemRes.2005.13.12
Hill, K., & Trussell, J. (1977). Further developments in indirect mortality estimation. Population Studies, 31, 313–334.
Hill, K., Zlotnik, H., & Trussell, J. (1983). Manual X. Indirect techniques for demographic estimation. New York: United Nations.
Hu, Y., & Goldman, N. (1990). Mortality differentials by marital status: An international comparison. Demography, 27, 233–250.
Human Mortality Database (2009). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Retrieved from http://www.mortality.org or http://www.humanmortality.de (data downloaded on 07312009)
Hurt, L. S., Ronsmans, C., & Thomas, S. L. (2006). The effect of number of births on women’s mortality: Systematic review of the evidence for women who have completed their childbearing. Population Studies, 60, 55–71.
Istat (2001). La mortalità differenziale secondo alcuni fattori sociodemografici, anni 1991–1992 [Differential mortality by some sociodemographic characteristics, 1991–1992]. Rome, Italy: Istat.
Kitagawa, E. M., & Hauser, P. M. (1973). Differential mortality in the United States: A study in socioeconomic epidemiology (Vital and Health Statistics Monographs). Cambridge, MA: Harvard University Press.
Kvåle, G., Heuch, I., & Nilssen, S. (1994). Parity in relation to mortality and cancer incidence: A prospective study of Norwegian women. International Journal of Epidemiology, 23, 691–699.
Le Bourg, É. (2007). Does reproduction decrease longevity in human beings? Ageing Research Reviews, 6, 141–149.
Lotka, A. J. (1939). Théorie analytique des associations biologiques. Deuxième partie [Analytical theory of biological associations. Part two]. Paris, France: Hermann et C^{ie}.
Lund, E., Arnesen, E., & Borgan, J.K. (1990). Pattern of childbearing and mortality in married women—A national prospective study from Norway. Journal of Epidemiology and Community Health, 44, 237–240.
Luy, M., Di Giulio, P., & Caselli, G. (2011). Differences in life expectancy by education and occupation in Italy, 1980–94: Indirect estimates from maternal and paternal orphanhood. Population Studies, 65, 137–155.
Molla, M. T., Madans, J. H., & Wagener, D. K. (2004). Differentials in adult mortality and activity limitation by years of education in the United States at the end of the 1990s. Population and Development Review, 30, 625–646.
Murphy, M., Bobak, M., Nicholson, A., Rose, R., & Marmot, M. (2006). The widening gap in mortality by educational level in the Russian Federation, 1980–2001. American Journal of Public Health, 96, 1293–1299.
Ngom, P., & Bawah, A. A. (2004). INDEPTH model life tables for subSaharan Africa. Aldershot, Burlington: Ashgate.
Nicholson, A., Bobak, M., Murphy, M., Rose, R., & Marmot, M. (2005). Alcohol consumption and increased mortality in Russian men and women: A cohort study based on the mortality of relatives. Bulletin of the World Health Organization, 83, 812–819.
Palloni, A., Massagli, M., & Marcotte, J. (1984). Estimating adult mortality with maternal orphanhood data: Analysis of sensitivity of the techniques. Population Studies, 38, 255–279.
Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and modeling population processes. Oxford, UK: Blackwell.
Rogers, R. G. (1995). Marriage, sex, and mortality. Journal of Marriage and the Family, 57, 515–526.
Schmertmann, C. P. (2003). A system of model fertility schedules with graphically intuitive parameters. Demographic Research, 9, article 5, 81–110. doi:10.4054/DemRes.2003.9.5
Shkolnikov, V. M., Andreev, E. M., Jasilionis, D., Leinsalu, M., Antonova, O. I., & McKee, M. (2006). The changing relation between education and life expectancy in central and eastern Europe in the 1990s. Journal of Epidemiology and Community Health, 60, 875–881.
Spence, N. J., & Eberstein, I. W. (2009). Age at first union, parity, and postreproductive mortality among white and black women in the US, 1982–2002. Social Science & Medicine, 68, 1625–1632.
Stewart, Q. T. (2004). Brass’ relational model: A statistical analysis. Mathematical Population Studies, 11, 51–72.
Timæus, I. M. (1986). An assessment of methods for estimating adult mortality from two sets of data on maternal orphanhood. Demography, 23, 435–450.
Timæus, I. M. (1991a). Estimation of adult mortality from orphanhood before and since marriage. Population Studies, 45, 455–472.
Timæus, I. M. (1991b). Estimation of adult mortality from orphanhood in adulthood. Demography, 28, 213–227.
Timæus, I. M. (1991c). Measurement of adult mortality in less developed countries: A comparative review. Population Index, 57, 552–568.
Timæus, I. M. (1992). Estimation of adult mortality from paternal orphanhood: A reassessment and a new approach. Population Bulletin of the United Nations, 33, 47–63.
Timæus, I. M., & Graham, W. (1989). Measuring adult mortality in developing countries. A review and assessment of methods (Policy, Planning, and Research Working Papers WPS 155). Washington, DC: World Bank.
Timæus, I. M., & Nunn, A. J. (1997). Measurement of adult mortality in populations affected by AIDS: An assessment of the orphanhood method. Health Transitions Review, 7(Suppl. 2), 23–43.
United Nations (2002). Methods for estimating adult mortality. New York: United Nations. Retrieved from http://www.un.org/esa/population/techcoop/DemEst/methods_adultmort/methods_adultmort.html
United Nations (2006). World population prospects. The 2004 revision. Volume III: Analytical report. New York: United Nations.
Valkonen, T. (2006). Social inequalities in mortality. In G. Caselli, J. Vallin, & G. Wunsch (Eds.), Demography: Analysis and synthesis (Vol. II, pp. 195–206). London, UK: Academic Press.
Zlotnik, H., & Hill, K. (1981). The use of hypothetical cohorts in estimating demographic parameters under conditions of changing fertility and mortality. Demography, 18, 103–122.
Acknowledgments
I thank Paola Di Giulio for preparing the data of the Italian multipurpose surveys and for running specific computer programs; Graziella Caselli and Griffith Feeney for fruitful discussions; and two anonymous reviewers for their careful reading and very helpful comments and suggestions on earlier versions of this article.
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Appendices
Appendix 1
The complete conversion of empirical values for \( \hat{S}(n) \) into period survivorship probabilities from age 30 to age 33 + n is demonstrated with the example of mortality of mothers with primary education of respondents aged 40–44 from the Italian multipurpose survey of 1998. Of all 3,219 respondents of that age group who declared that their mother has primary education, 2,635 reported that their mothers are still alive, and 584 reported that their mothers are already deceased. From these numbers, I calculated the proportion of respondents aged 40–44 with mother alive: \( \hat{S}(40) = 2,635/\left( {2,635 + 584} \right) = 0.8186 \). From the birth years of respondents and their mothers I could derive further that at the time of respondents’ birth, 0.10% of the mothers were 15 years old, 0.37% were 16 years, 0.79% were 17 years, 1.34% were 18 years, and so forth.^{Footnote 9} Applying these proportions as weights \( {\hat{w}_x} \) (with \( {\hat{w}_{{15}}} = 0.0010 \), \( {\hat{w}_{{16}}} = 0.0037 \), and so forth) to the cohort life tables for the total Italian female population (reconstructed from official population statistics) enabled me to calculate the proportion of women alive with the same age structure as respondents’ mothers but with the cohort mortality of Italian women (representing the denominator of Eq. 5):
With Brass’s logit life table model, I transformed this reconstructed cohort survival function to a new survival function with a survival rate of 0.8186—the empirical value \( \hat{S}(40) \) from the survey data (setting Brass’s parameter β = 1.0). From this survival function, I derived that the average time of death of respondents’ mothers was 11.92 years prior to the end of the observed survival time (the term in parentheses in Eq. 3). Thus, the reference time for the period estimate was 1998.5 – 11.92 = 1986.58. From the probabilities of dying q _{ x } for single ages of the Italian period life tables for women in 1986 and 1987, I estimated the single q _{ x } for 1986.58 by linear interpolation, producing a life table with a life expectancy at age 30 of 50.55 years. The survivorship probability from age 30 to 33 + 40 of this life table is 0.7932, representing the enumerator on the righthand side of Eq. 5. Thus, the survivorship probability from age 30 to 73 for the mothers with primary education of respondents aged 40–44 from the 1998 survey could be estimated from Eq. 5 by
By again using Brass’s logit life table model, I shifted the reference life table with the survivorship probability from age 30 to 73 of 0.7932 to a life table with the estimated survivorship probability of 0.8363, producing a Brass parameter α of 0.1431 and an estimate for life expectancy at age 30 of 52.28 years (with the Brass parameter β = 1.0). Changing the Brass parameter β to 0.90674 (see footnote 7) provides the final survivorship function for women with primary education, with an estimated life expectancy at age 30 of 51.92 years.
Appendix 2
The Italian multipurpose survey allows the determination of the age at childbearing only for those parents who are still alive at the time of the survey. Because the weights \( {\hat{w}_x} \) in Eq. 5 refer to the ages at childbearing of all parents at the time of respondents’ birth, the corresponding schedule of agespecific fertility rates \( {\hat{f}_x} \) (in the following short \( {\hat{f}_x} \) schedule) has to be estimated. Therefore, I used a threestep procedure (which is necessary only when the weights \( {\hat{w}_x} \) cannot be derived directly from the survey data):

1.
From Eq. 2, I reconstructed the w _{ x } for all age groups of respondents from ages 20–24 to 60–64 with N _{ x } and f _{ x } taken from official Italian population statistics for single calendar years from 1933 to 1982 (i.e., the birth years of survey respondents), averaged for the five calendar years in which the respondents of a specific fiveyear age group were born.^{Footnote 10} The series of f _{ x } were smoothed with the fertility model proposed by Schmertmann (2003), setting α = 14 (the youngest age at which fertility rises above age zero). The other parameters of the model were estimated from the empirical f _{ x } of official Italian statistics. For instance, for mothers of respondents aged 40–44 from the 1998 survey, the fertility rates resulted in P = 26.0 (the age at which fertility reaches its peak level), f(P) = 0.1473 (fertility rate at age P), and H = 36.3 (the age above P at which fertility falls to half of its peak level). The resulting fertility schedule and the given N _{ x } of the years 1953–1957 (birth years of respondents) led to an average age at childbearing of \( \bar{x} = 29.24\,{\text{years}} \) (i.e., the average age at childbearing of the entire Italian population at that time).

2.
In the next step, I modified the agespecific fertility rates f _{ x } from Step 1 to a new \( \hat{f}_x^{*} \) schedule with an average age at childbearing of \( {\hat{\bar{x}}^{*}} \) years, which is the age at childbearing of parents still alive at the time of respondents’ birth; hats indicate that the parameters refer to the analyzed subpopulation of the survey, and the asterisks indicate that they refer to parents still alive at the time of the interview (for the example of mothers with primary education of respondents aged 40–44 from the 1998 survey, \( {\hat{\bar{x}}^{*}} = 28.38 \)). For that purpose, the relational Gompertz fertility model of Brass (1981) was used to shift the agespecific fertility rates f _{ x } from Step 1 along the age axes by varying Brass’s α and setting Brass’s β = 1.0 (in the current example, using Brass’s α = 0.1619 shifts the f _{ x } schedule from Step 1 to a new set of \( \hat{f}_x^{*} \), with \( {\hat{\bar{x}}^{*}} = 28.38 \)). In cases where \( \bar{x} \) and \( {\hat{\bar{x}}^{*}} \) deviated by more than one year, I used a combination of the relational Gompertz fertility model and the Schmertmann fertility model. The Schmertmann model was used to shift the basic agespecific fertility rates f _{ x } along the age axes by systematic changes of parameters P and H. The system of changes was oriented on the number of years which \( {\hat{\bar{x}}^{*}} \) differed from \( \bar{x} \). For each year that \( {\hat{\bar{x}}^{*}} \) was higher or lower than \( \bar{x} \), P and H were increased by 2.0 and 1.0, or decreased by 1.0 and 2.0 years, respectively. The other two parameters of the Schmertmann model were always kept constant at α = 14 and f(P) as given by the basic fertility schedule of the total Italian population. By using the relational Gompertz fertility model, keeping Brass’s β = 1.0 constant and varying Brass’s α, the fertility schedule was then further modified to provide a set of agespecific fertility rates \( \hat{f}_x^{*} \), leading to an age at childbearing of \( {\hat{\bar{x}}^{*}} \) years.^{Footnote 11}

3.
Applying the fertility rates \( \hat{f}_x^{*} \) from Step 2 and the N _{ x } from Step 1 to the series of cohort life tables for the Italian population yields a specific survival function for the cohorts of parents of respondents aged (n, n + 4) with an average age at childbearing of \( {\hat{\bar{x}}^{*}} \) years. From this survival function, I derived an approximate estimate of the age at childbearing of all parents, \( \hat{\bar{x}} \), by assuming that \( {\hat{\bar{x}}^{*}} \) refers only to the survivors at the end of the observation time (time of interview) and by adding the deceased individuals to the calculation with their ages at childbearing. Then I shifted the fertility rates f _{ x } from Step 1 as described in Step 2 to provide the final \( {\hat{f}_x} \) schedule, leading to an age at childbearing of \( \hat{\bar{x}} \) years (for the example of mothers with primary education of respondents aged 40–44 from the 1998 survey, the estimated \( \hat{\bar{x}} = 29.29 \)). Finally, this fertility schedule and the N _{ x } for the entire Italian population from Step 1 were used to determine the weights \( {\hat{w}_x} \) by applying Eq. 2.
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Luy, M. Estimating Mortality Differences in Developed Countries From Survey Information on Maternal and Paternal Orphanhood. Demography 49, 607–627 (2012). https://doi.org/10.1007/s1352401201014
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Keywords
 Mortality
 Mortality differences
 Life expectancy
 Indirect estimation techniques
 Orphanhood method