Commentary on van Raalte et al.'s "The Dangers of Drawing Cohort Profiles From Period Data: A Research Note".

van Raalte et al. (2023) alerted demographers to the potential dangers of calculating cohort measures from the "diagonals" of gridded age-period (AP) data. In the case of cohort fertility, however, a minor change to the estimation procedure can mitigate the trend and cohort size biases that the authors identify.

District-Level Life Expectancy in Germany.

Background: Identifying regions with low life expectancy is important to policy makers, in particular for allocating resources in the health system. Life expectancy estimates for small regions are, however, often unreliable and lead to statistical uncertainties when the underlying populations are relatively small.

Methods: We combine the most recent German data available (2015-2017) with a Bayesian model that includes several methodological advances.

Population Pyramids Yield Accurate Estimates of Total Fertility Rates.

The primary fertility index for a population, the total fertility rate (TFR), cannot be calculated for many areas and periods because it requires disaggregation of births by mother's age. Here we discuss a flexible framework for estimating TFR using inputs as minimal as a population pyramid. We develop five variants, each with increasing complexity and data requirements.

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.

Adjusting for population shifts and covariates in space-time interaction tests.

Statistical tests for epidemic patterns use measures of space-time event clustering, and look for high levels of clustering that are unlikely to appear randomly if events are independent. Standard approaches, such as Knox's (1964, Applied Statistics 13, 25-29) test, are biased when the spatial distribution of population changes over time, or when there is space-time interaction in important background variables. In particular, the Knox test is too sensitive to coincidental event clusters in such circumstances, and too likely to raise false alarms.

Bayes plus Brass: estimating total fertility for many small areas from sparse census data.

Estimates of fertility in small areas are valuable for analysing demographic change, and important for local planning and population projection. In countries lacking complete vital registration, however, small-area estimates are possible only from sparse survey or census data that are potentially unreliable. In these circumstances estimation requires new methods for old problems: procedures must be automated if thousands of estimates are required; they must deal with extreme sampling variability in many areas; and they should also incorporate corrections for possible data errors.

Changes in service delivery patterns after introduction of telemedicine provision of medical abortion in Iowa.

Objectives: We assessed the effect of a telemedicine model providing medical abortion on service delivery in a clinic system in Iowa.

Methods: We reviewed Iowa vital statistic data and billing data from the clinic system for all abortion encounters during the 2 years prior to and after the introduction of telemedicine in June 2008 (n = 17,956 encounters). We calculated the distance from the patient's residential zip code to the clinic and to the closest clinic providing surgical abortion.

Knox meets Cox: adapting epidemiological space-time statistics to demographic studies.

Many important questions and theories in demography focus on changes over time, and on how those changes differ over geographic and social space. Space-time analysis has always been important in studying fertility transitions, for example. However demographers have seldom used formal statistical methods to describe and analyze time series of maps.

Mapping the Timing, Pace, and Scale of the Fertility Transition in Brazil.

Between 1960 and 2000, fertility fell sharply in Brazil, but this transition was unevenly distributed in space and time. Using Bayesian spatial statistical methods and microdata from five censuses, we develop and apply a procedure for fitting logistic curves to the fertility transitions in more than 500 small regions of Brazil over this 40-year period. Doing so enables us to map the main features of the Brazilian fertility transition in considerable detail.

Empirical Bayes estimation of demographic schedules for small areas.

In this article, we analyze empirical Bayes (EB) methods for estimating small-area rate schedules. We develop EB methods that treat schedules as vectors and use adaptive neighborhoods to keep estimates appropriately local. This method estimates demographic rates for local subpopulations by borrowing strength not only from similar individuals elsewhere but also from other groups in the same area and from regularities in schedules across locations.

Fertility and development: evidence from Brazil.

Using microdata from the Brazilian demographic censuses of 1960, 1970, 1980, and 1991, aggregated into 518 consistently defined spatial units called microregions, we estimated fertility and mortality and constructed indicators of development and living conditions in the rural and urban areas of the microregions in each census. We then estimated cross-sectional and fixed-effects models to answer questions about the degree to which changes in these indicators are associated with changes in fertility and whether the relationship between fertility and development shifts through time. We found strong and consistent relationships between the decline in fertility and measurable changes in social and economic circumstances.

A simple method for estimating age-specific rates from sequential cross sections.

I develop and demonstrate a simple formula for estimating age-specific event rates for a period from "before" and "after" cross sections. The general approach applies to a wide range of estimation problems in demography, the social sciences, and epidemiology. The method arises from the formal mathematics of unstable populations and is similar in spirit to "variable-r" methods.

Fertility estimation from open birth-interval data.

Censuses and surveys frequently collect information on period fertility through questions on the timing of last births. The standard approach to estimating fertility with open-interval data uses the proportion of women giving birth in the year before the interview. I propose a more efficient, maximum likelihood method for estimating fertility from open-interval data.

Three strikes and you're out: demographic analysis of mandatory prison sentencing.

Much of the debate about the costs and benefits of "three-strikes" laws for repeat felony offenders is implicitly demographic, relying on unexamined assumptions about prison population dynamics. However, even state-of-the-art analysis has omitted important demographic details. We construct a multistate life-table model of population flows to and from prisons, incorporating age-specific transition rates estimated from administrative data from Florida.

An introduction to nonparametric regression in demographic research.

"My intent in this...

Demographic influences on the number of children at school entry ages, with examples from three states.

"This paper analyzes the effects of changes in fertility, mortality, and net migration patterns on the growth of school entry-age populations in three states (Florida, South Carolina, and West Virginia) over the period 1950-1990. Fertility changes have had the largest influence on growth of these young populations, as common sense suggests. Changing migration patterns have been quite important, however, in explaining intertemporal and interspatial variations in growth rates.

Immigrant's ages and the structure of stationary populations with below-replacement fertility.

A sustained regime of low fertility plus immigration yields an unusual kind of stationary population. The author demonstrates that all stationary populations have a common structure, and that the familiar replacement-level fertility population is the youngest among the many stationary populations corresponding to a particular life table. This finding has important consequences for policy because although fertility increase and immigration are equally effective at halting population decline, immigration is inferior as a means of rejuvenating low-fertility populations.