Aging and the Left Behind: Puerto Rico and Its Unconventional Rapid Aging.

Puerto Rico is aging more rapidly than almost any country, with 2020 estimates placing its population share of adults older than 65 as being the 10th highest in the world. Unlike most locales, Puerto Rico's aging is driven by both (a) the culmination of long-running fertility and mortality trends and (b) high levels of outmigration of working-age adults, which contributes both directly (removal of young people) and indirectly (reduced births) to its pace of population aging. This article offers an overview of the main issues surrounding population aging in Puerto Rico. Policymakers and government leaders must plan for Puerto Rico's unconventional population aging, which will exacerbate traditional concerns about the sustainability of government services and long-term economic prospects. Additional concerns emerge related to reduced social support networks and their impact on caregiving dynamics and implications for health. Puerto Rico's unique history and political relationship with the United States present challenges and benefits for its aging population. Research on aging in Puerto Rico and public health policies must adapt to the needs of the country's aging society.

Estimating Hidden Population Sizes with Venue-based Sampling: Extensions of the Generalized Network Scale-up Estimator.

BACKGROUND: Researchers use a variety of population size estimation methods to determine the sizes of key populations at elevated risk of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), an important step in quantifying epidemic impact, advocating for high-risk groups, and planning, implementing, and monitoring prevention, care, and treatment programs. Conventional procedures often use information about sample respondents' social network contacts to estimate the sizes of key populations of interest. A recent study proposes a generalized network scale-up method that combines two samples-a traditional sample of the general population and a link-tracing sample of the hidden population-and produces more accurate results with fewer assumptions than conventional approaches. METHODS: We extended the generalized network scale-up method from link-tracing samples to samples collected with venue-based sampling designs popular in sampling key populations at risk of HIV. Our method obviates the need for a traditional sample of the general population, as long as the size of the venue-attending population is approximately known. We tested the venue-based generalized network scale-up method in a comprehensive simulation evaluation framework. RESULTS: The venue-based generalized network scale-up method provided accurate and efficient estimates of key population sizes, even when few members of the key population were sampled, yielding average biases below ±6% except when false-positive reporting error is high. It relies on limited assumptions and, in our tests, was robust to numerous threats to inference. CONCLUSIONS: Key population size estimation is vital to the successful implementation of efforts to combat HIV/AIDS. Venue-based network scale-up approaches offer another tool that researchers and policymakers can apply to these problems.