Parental survival data: Some results of the application of Ledermann's model life tables

Summary Ledermann's one- and two-parameter model life tables are used in order to summarize and compare adult mortality estimates derived from parental survival data, and also to link parental survival with child survival data. The Ledermann models provide an alternative to the logit model used by Brass and Hill. Examination of life tables derived from actual child and adult mortality estimates reveals that although the two types of models yield similar overall levels of mortality, they show marked differences in the estimated patterns by sex and age. It has not been possible to disentangle completely how much of this divergence is due to the models themselves and how much to inadequacies in the data available. Finally, we question whether it is always wise to establish a full life table from child and adult mortality estimates when these are based on data which refer to different periods of exposure to the risk of dying, without allowance for possible distortions resulting from mortality change.     

Subnational migration in the United Kingdom: producing a consistent time series using a combination of available data and estimates

There is no single framework in place in the United Kingdom for collating and harmonizing the data on migration taking place between the subnational administrative units that constitute the home nations, together with the flows of international migration between these areas and the ‘rest of the world’. This paper proposes the construction of complete matrices of subnational migration statistics on an annual basis which can then be used to monitor migration trends throughout the UK in a comprehensive manner. The paper reviews how various ‘known’ flows are estimated by the national statistics agencies and develops estimates to fill in the gaps in the matrices. It then looks at changing patterns of migration in the UK between 2001/2002 and 2010/2011, showing that migration exhibits most activity in the middle of the decade. Flows within the home nations are the most consistent over time, while the patterns of international migration and migration across the borders of the home nations exhibit more substantial change. The latter flows, although relatively small in magnitude, have not previously been studied at the subnational level in any detail. Patterns of subnational internal and international net migration are found to be negatively correlated.     

An Application of Bayesian Methods to Small Area Poverty Rate Estimates

Efforts to estimate various sociodemographic variables in small geographic areas are proving difficult with the replacement of the Census long-form with the American Community Survey (ACS). Researchers interested in subnational demographic processes have previously relied on Census 2000 long-form data products in order to answer research questions. ACS data products promise to begin providing up-to-date profiles of the nation’s population and economy; however, unit- and item-level nonresponse in the ACS have left researchers with gaps in subnational coverage resulting in unstable and unreliable estimates for basic demographic measures. Borrowing information from neighboring areas and across time with a spatiotemporal smoothing process based on Bayesian statistical methods, it is possible to generate more stable and accurate estimates of rates for geographic areas not represented in the ACS. This research evaluates this spatiotemporal smoothing process in its ability to derive estimates of poverty rates at the county level for the contiguous United States. These estimates are then compared to more traditional estimates produced by the US Census Bureau, and comparisons between the two methods of estimation are carried out to evaluate the practical application of this smoothing method. Our findings suggest that by using available data from the ACS only, we are able to recreate temporal and spatial patterns of poverty in US counties even in years where data are sparse. Results show that the Bayesian methodology strongly agrees with the estimates produced by the SAIPE program, even in years with little data. This methodology can be expanded to other demographic and socioeconomic data with ease.     

Place and Child Health: The Interaction of Population Density and Sanitation in Developing Countries

A long literature in demography has debated the importance of place for health, especially children’s health. In this study, we assess whether the importance of dense settlement for infant mortality and child height is moderated by exposure to local sanitation behavior. Is open defecation (i.e., without a toilet or latrine) worse for infant mortality and child height where population density is greater? Is poor sanitation is an important mechanism by which population density influences child health outcomes? We present two complementary analyses using newly assembled data sets, which represent two points in a trade-off between external and internal validity. First, we concentrate on external validity by studying infant mortality and child height in a large, international child-level data set of 172 Demographic and Health Surveys, matched to census population density data for 1,800 subnational regions. Second, we concentrate on internal validity by studying child height in Bangladeshi districts, using a new data set constructed with GIS techniques that allows us to control for fixed effects at a high level of geographic resolution. We find a statistically robust and quantitatively comparable interaction between sanitation and population density with both approaches: open defecation externalities are more important for child health outcomes where people live more closely together.     

Infant mortality,birth order,and sibship size: The role of heterogeneous risk and the previous‐death effect

This paper derives an analytic model to study biases in infant mortality estimates by birth order and sibship size, which occur when the death of an infant tends to shorten the next birth interval and mortality risk varies among families. We find that order‐specific and sibship‐size‐specific estimates are biased by a selection for high‐risk women across birth orders, since women with higher risk will tend to have shorter intervals, and more births, within a given period of time. Sibship‐size‐specific estimates are, in addition, biased by a selection of women who have experienced deaths, even if there is no heterogeneity in risk. Numerical examples based on data from Matlab, Bangladesh, are used to illustrate the possible magnitude of these biases. The results resemble patterns of infant mortality by birth order and sibship size which are often observed empirically.     

Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas With Defective Vital Records

High sampling variability complicates estimation of demographic rates in small areas. In addition, many countries have imperfect vital registration systems, with coverage quality that varies significantly between regions. We develop a Bayesian regression model for small-area mortality schedules that simultaneously addresses the problems of small local samples and underreporting of deaths. We combine a relational model for mortality schedules with probabilistic prior information on death registration coverage derived from demographic estimation techniques, such as Death Distribution Methods, and from field audits by public health experts. We test the model on small-area data from Brazil. Incorporating external estimates of vital registration coverage though priors improves small-area mortality estimates by accounting for underregistration and automatically producing measures of uncertainty. Bayesian estimates show that when mortality levels in small areas are compared, noise often dominates signal. Differences in local point estimates of life expectancy are often small relative to uncertainty, even for relatively large areas in a populous country like Brazil.     

The estimation of adult mortality in Africa from data on orphanhood

Summary Until recently, very little information has been available about the levels and patterns of adult mortality in tropical Africa, but during the past decade several countries have included questions in censuses and surveys as to whether a person's father and mother are still alive. From the data so obtained, estimates of adult mortality have been prepared. This paper compares the results of three such exercises with alternative estimates of adult mortality derived from other sources. In the case of Chad, the orphanhood data obtained in the demographic sample survey of 1964 yielded estimates of mortality which agreed reasonably closely with those obtained from questions on deaths of household members occurring during the twelve months preceding the survey. The latter data however were themselves subject to substantial errors and had to be corrected using techniques based on stable population theory. For Kenya, the orphanhood questions were included in the 1969 census and the results were compared with the mortality estimates derived from inter-censal survival from 1962 to 1969. Once again, the data obtained from the latter were subject to error but in general appeared to be consistent with the orphanhood estimates. The third comparison was made from Malawi, where alternative mortality figures were available from the Malawi Population Change survey which was a 'dual record' type of operation, conducted in 1971/2. The agreement in this case was remarkably close, once the number of deaths had been corrected for omissions by both systems with allowance for positive correlation. It is concluded that as a simple and inexpensive technique of estimating adult mortality, the orphanhood approach has much to recommend it.     

Combining methods of insect pest control: Partitioning mortality and predicting complementarity

Summary An age-structured population model is used as a vehicle for presenting a method for the analysis of interactions between pairs of insect pest control methods. This analysis is based on partitioning the total mortality acting on a population into its constituent components from all known sources. Pairwise critical mortality curves are then constructed which represent the combined mortality required for eradicating the pest population. Effort curves are then constructed from computing the mortality resulting from a given amount of control effort. The convolution of the critical mortality curves and the effort curves then yields the isoclines formed by the effort required of two control methods in combination to achieve eradication. This analysis allows the prediction of either synergism or interference between the control methods and also helps explain patterns observed in previous modelling of such combinations of pest control methods.     

Assessing post-census state poverty estimates

While the decennial census provides poverty figures for states and other subnational geographic units every ten years, their utility declines over the course of a decade. Consequently, there is growing interest in producing post-census estimates for a variety of indicators. This study extends recent efforts to estimate post-census poverty figures for states by producing such estimates using a multiple regression approach. The accuracy of the multiple regression estimates along with recently produced estimates from the Current Population Survey (CPS) are evaluated relative to the decennial census. The mean absolute percentage point error (MAPPE) using the ratio-correlation technique (1.56 percentage points) was somewhat higher than the MAPPE of 1989 CPS (1.37 percentage points) and an average of 1988–1990 CPS data (1.15 percentage points). However, a simple regression technique using data from 1979 to estimate poverty in 1989 produced a set of estimates where the MAPPE (1.37 percentage points) is nearly as accurate as the single-year CPS estimates. Estimates which average regression estimates and CPS-based estimates are more accurate than either regression or CPS estimates used alone. Several suggestions are offered for improving regression estimates.This article is a revised version of a paper presented at the annual meeting of the Population Association of America, 1–4 April 1993, Cincinnati, OH, USA.     

A model for analyzing insect stage-frequency data when mortality varies with time

Summary A model is developed for the analysis of insect stage-frequency data which may be applied to populations with age-dependent mortality. The analysis of stage-frequency data is divided into two steps. In the first step, the number of different mortality rates and their values are estimated. The second step provides estimates of developmental rates and variances for each developmental stage and in addition provides estimates of the number of recruits to each stage. The model may be used both in analysis and prediction of insect stage frequencies. Hence, in addition to estimating developmental and mortality rates from stage-frequency data, it may also be used as a simulation model for an insect population. The model is applied to two populations ofHemileuca oliviae Cockerell, a lepidopterous pest of New Mexico grasslands. The model identifies, in the two populations, different mortality rates that are related to plant productivity.     

The synthesis economic fertility model: a latent variable investigation of some critical attributes

"This study uses Nicaraguan data to estimate a latent variable system of reduced-form demands for births, infant mortality, contraception, nutrition and breastfeeding. The estimates support some of the synthesis extensions to the standard fertility model, such as the concurrent increase of contraception, health, nutrition and fertility and decline in breastfeeding with income increases from initial low levels.... The initial stages of development may experience an increase in family size despite an increase in contraceptive use...as well as a profertility impact of reduced breastfeeding." Data are from a survey of women aged 15-45 that was conducted in Nicaragua from 1977 to 1978.     

A flexible two-dimensional mortality model for use in indirect estimation

Mortality estimates for many populations are derived using model life tables, which describe typical age patterns of human mortality. We propose a new system of model life tables as a means of improving the quality and transparency of such estimates. A flexible two-dimensional model was fitted to a collection of life tables from the Human Mortality Database. The model can be used to estimate full life tables given one or two pieces of information: child mortality only, or child and adult mortality. Using life tables from a variety of sources, we have compared the performance of new and old methods. The new model outperforms the Coale-Demeny and UN model life tables. Estimation errors are similar to those produced by the modified Brass logit procedure. The proposed model is better suited to the practical needs of mortality estimation, since both input parameters are continuous yet the second one is optional.     

Revised regional model life tables at very low levels of mortality

This paper presents and discusses new model life tables at very low mortality, which make use of age-specific death rates from the 1960s, 1970s, and 1980s. These life tables fit recorded death rates in very low mortality populations better than do the existing ones at expectations of life of 77.5 and 80 years. The old tables incorporate too-high mortality at the higher ages and in infancy and they incorporate regional differences that no longer exist. The new tables "close out" the mortality schedules above age 80 more realistically. The convergence of age patterns of mortality at very high life expectancies in populations that used to conform to different families is in itself of demographic interest. Some convergence may perhaps be expected. Sullivan (1973) found that, in Taiwan, the comparison of mortality at ages 1-5 to mortality at 5-35 in the late 1950s showed higher mortality at the younger ages relative to the ensuing 30-year age interval than was found in any of the models, including the South model, which has the highest relative mortality from ages 1-5 among the 4 regional patterns. Then, in the late 1960s, the relation of mortality at 1-5 to mortality at 5-35 in Taiwan fell to a position intermediate between the West and South tables. Sullivan found in data on mortality by cause of death a large reduction in mortality from diarrhea and enteritis, no doubt as a result of environmental sanitation. Mortality from these causes is concentrated among young children, and reduction in deaths from these causes would naturally diminish the excess mortality in this age interval. The East pattern, characterized by very high mortality in infancy (but not from 1-5), may be the result of the prevalence of early weaning or avoidance of breast feeding altogether in the populations characterized by this pattern. As health conditions have improved, evidenced by the overall design of mortality, these special factors are diminished or erased. Model life tables at these very low mortality levels have different uses from most applications of model life tables at higher mortality. The use of model tables to estimate accurate schedules of mortality when the basic data are incomplete or inaccurate is less relevant in this range of mortality levels.     

An epidemiological model for health policy analysis

Health programs needing health and other related data have failed in their solutions because they have lacked a rational framework for analysis. A cursory analysis of present disease patterns reveals chronic conditions for which the present system of organized health care has no immediate cures. On the other hand, infectious diseases of decades past have been all but eliminated by vaccines and antibiotics. Before we are able either to prevent or to arrest current disease processes, we must dissect the health field into more manageable elements that reflect a creative area for epidemiological models. An epidemiological model that supports health policy analysis and decisiveness must be broad, comprehensive, and must include all matters affecting health. Consequently, four primary divisions have been identified: (1) System of Health Care Organization; (2) Life Style (self-created risks); (3) Environment; and (4) Human Biology. An application of the epidemiological model involves four steps: (1) the selection of diseases that are of high risk and that contribute substantially to the overall morbidity and mortality; (2) to proportionately allocate the contributing factors of the disease to the four elements of the epidemiological model; (3) to proportionately allocate total health expenditures to the four elements of the epidemiological model; and (4) to determine the difference in proportions between (2) and (3) above. Five tables illustrate how the epidemiological model is applied, showing the diseases selected for analysis; the contributing factors of each disease to the four components of the epidemiological model; the distribution of Federal outlays for medical and health-related activities by category; the distribution of Federal outlays of health expenditures by category; and a comparison of Federal health expenditures to the allocation of mortality in accordance with the epidemiological model. The conclusion to be drawn from this study is that, based on current procedures for reducing mortality and morbidity, little or no change in our present disease patterns will be accomplished unless we dramatically shift our health policy.     

Migration bias in indirect estimates of regional childhood mortality levels

Demographers often use Brass‐style indirect methods to obtain childhood mortality estimates for regions within developing countries. Regional populations are not closed to migration, however, and mortality reports of women resident in a certain region on the survey date may contain information on events and exposure that occurred elsewhere as the mother migrated. Including this “imported”; mortality information may cause significant bias in regional estimates. In this paper the authors: (1) investigate the possible magnitude of migration bias using a multiregional simulation model, (2) propose a modification to standard methods which should reduce bias in many circumstances, and (3) apply the modified technique to data from Brazil's 1980 Census. We find that migration bias can indeed be significant, and that in the specific case of São Paulo state, imported mortality information may result in overestimates of local mortality levels of 10–15% when using Brass‐style methods.     

The sources of error in Brass's method for estimating child survival: the case of Bangladesh

Abstract Brass's method for estimating child mortality is based on an ingeniously simplified model. However, it frequently leads to values of q(x) that are not consistent with each other. This is most obvious for estimates of q(1). This paper examines the extent to which such inconsistencies are caused by simplifications in the model. Three assumptions are relaxed by adjusting for differences in infant mortality by birth order, taking account of annual fluctuations in mortality, and using a different age pattern of fertility for each cohort. These adjustments are applied to data from the 1974 Bangladesh Retrospective Survey of Fertility and Mortality and the 1975 Bangladesh Fertility Survey in which additional data from the Cholera Research Laboratory are used. The resulting estimates are more consistent both internally and with estimates from other surveys and by other procedures.     

Socio-economic Differentials in Infant and Child Mortality during Mortality Decline: England and Wales, 1890–1911

In this paper data from the 1911 Census of the Fertility of Marriage of England and Wales are used to study patterns of mortality decline by socio-economic characteristics, principally the occupation of husband. That census reported data on number of wives, children ever born, and children dead by marriage-duration cohorts for 190 non-overlapping occupations of husband. These results, along with those on number of rooms in the dwelling of the family are used to make indirect estimates of childhood mortality using the techniques described in United Nations, Manual X. These procedures produce values of q(a), the probability of dying before reaching some exact age ‘a’. Estimates for q(2), q(3), q(5), q(10), q(15), and q(20) are derived from data on women married 0–4, 5–9, 10–14, 15–19, 20–24, and 25–29 years, respectively. These estimates can also be dated to a point in the past. These values can also be converted to a corresponding level of a Model West life table, which describes the ‘average’ mortality regime which the children of those women experienced. This furnishes a basis to look at mortality decline for various social classes and occupational groups. Ordinary least squares regressions of the levels of Model West life tables implied by the 1(a) values on time give one measure of mortality decline. Another is the absolute amount of the increase in the level of the Model West life tables from marriage-duration cohort 20–24 years to 0–4 years. The aggregate results indicate that social class in England and Wales during the 1890s and 1900s tended to be related to the speed of mortality decline: childhood mortality declined more rapidly in the higher and more privileged social class groups. But the results were neither nearly as strong nor as regular as those which predicted the level of mortality within any marriage-duration cohort. These outcomes are not particularly sensitive to the three different social-class stratification schemes used: the 1911 English Registrar General's classification; the 1951 English Registrar General's classification; and the 1950 U.S. Census classification. There was also a fairly regular and predictable gradient for the number of rooms in the home: child mortality was higher in families who lived in larger dwellings. Analysis of 190 detailed male occupational groups revealed that considerably more of the variation in mortality levels than of trends could be explained by social-class categories. Between 20 and 40 per cent of variation in mortality trend could be accounted for by social class alone, as opposed to 50 to 80 per cent of mortality levels for different marriage-duration cohorts. Results for a more restricted sample of 116 occupations for which income estimates could be made revealed a similar pattern. In addition, income was virtually unrelated to the pattern of mortality decline, and improvement was more rapid in groups who were more urban. This reflects the role of rapidly improving urban sanitation in the late nineteenth and early twentieth centuries in England. In contrast, income was significantly related to childhood morality levels for various marriage-duration cohorts (with higher income associated with lower mortality), while urbanization was inversely correlated with mortality levels (more urban groups experienced higher mortality). Overall, social class (or occupation group), income, and urbanization were more successful in explaining mortality levels than time trends across occupations, although social class and the extent of urbanization did reasonably well in accounting for trends. Over a longer period, the transition in child mortality was under way by the 1890s, but its pace and timing varied in different occupations and social class groupings. Although absolute differences in infant mortality were reduced after about 1911, relative inequality persisted even as infant and child survival improved for all groups.     

Modeling and Forecasting Health Expectancy: Theoretical Framework and Application

Life expectancy continues to grow in most Western countries; however, a major remaining question is whether longer life expectancy will be associated with more or fewer life years spent with poor health. Therefore, complementing forecasts of life expectancy with forecasts of health expectancies is useful. To forecast health expectancy, an extension of the stochastic extrapolative models developed for forecasting total life expectancy could be applied, but instead of projecting total mortality and using regular life tables, one could project transition probabilities between health states simultaneously and use multistate life table methods. In this article, we present a theoretical framework for a multistate life table model in which the transition probabilities depend on age and calendar time. The goal of our study is to describe a model that projects transition probabilities by the Lee-Carter method, and to illustrate how it can be used to forecast future health expectancy with prediction intervals around the estimates. We applied the method to data on the Dutch population aged 55 and older, and projected transition probabilities until 2030 to obtain forecasts of life expectancy, disability-free life expectancy, and probability of compression of disability.     

The Epidemiologic Transition Revisited: Compositional Models for Causes of Death by Age and Sex

For decades, researchers have noted systematic shifts in cause‐of‐death patterns as mortality levels change. The notion of the “epidemiologic transition” has influenced thinking about the evolution of health in different societies and the response of the health system to these changes. This article re‐examines the epidemiologic transition in terms of empirical regularities in the cause composition of mortality by age and sex since 1950, and considers whether the theory of epidemiologic transition presents a durable framework for understanding more recent patterns. Age‐sex‐specific mortality rates from three broad cause groups are analyzed: Group 1 (communicable diseases, maternal and perinatal causes, and nutritional deficiencies); Group 2 (noncommunicable diseases); and Group 3 (injuries), using the most extensive international database on mortality by cause, including 1,576 country‐years of observation, and new statistical models for compositional data. The analyses relate changes in cause‐of‐death patterns to changing levels of all‐cause mortality and income per capita. The results confirm that declines in overall mortality are accompanied by systematic changes in the composition of causes in many age groups. These changes are most pronounced among children, for whom Group 1 causes decline as overall mortality falls, and in younger adults, where strikingly different patterns are found for men (shift from Group 3 to Group 2) compared to women (shift toward Group 2 then Group 3). The underlying patterns that emerge from this analysis offer insights into the epidemiologic transition from high‐mortality to low‐mortality settings.     

Estimating maternal mortality differentials using census data: experience in Honduras

High levels of maternal mortality in developing countries are considered a major public health problem. Over the past decade several international conferences on health have stated the necessity to reduce maternal mortality in developing countries. This is a challenge not only in terms of achieving it but also from the point of view of monitoring it. I use national population censuses to measure maternal mortality and study mortality regional differentials in Honduras, which identified maternal mortality in its most recent census. I also use standard demographic methods to evaluate the census data quality, for both population and death counts, and to evaluate the completeness and coverage of household death data.