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.