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The warnings of potential climate migration first appeared in the scientific literature in the late 1970s when increased recognition that disintegrating ice sheets could drive people to migrate from coastal cities. Since that time, scientists have modeled potential climate migration without integrating other population processes, potentially obscuring the demographic amplification of this migration. Climate migration could amplify demographic change--enhancing migration to destinations and suppressing migration to origins. Additionally, older populations are the least likely to migrate, and climate migration could accelerate population aging in origin areas. Here, we investigate climate migration under sea-level rise (SLR), a single climatic hazard, and examine both the potential demographic amplification effect and population aging by combining matrix population models, flood hazard models, and a migration model built on 40 y of environmental migration in the United States to project the US population distribution of US counties. We find that the demographic amplification of SLR for all feasible Representative Concentration Pathway-Shared Socioeconomic Pathway (RCP-SSP) scenarios in 2100 ranges between 8.6-28 M [5.7-53 M]--5.3 and 18 times the number of migrants (0.4-10 M). We also project significant aging of coastal areas as youthful populations migrate but older populations remain, accelerating population aging in origin areas. As the percentage of the population lost due to climate migration increases, the median age also increases--up to 10+ y older in some highly impacted coastal counties. Additionally, our population projection approach can be easily adapted to investigate additional or multiple climate hazards. [ABSTRACT FROM AUTHOR]

We investigate a modern statistical approach to a classic deterministic demographic estimation technique. When vital event registration is missing or inadequate, it is possible to approximate a population's total fertility rate (TFR) from information about its distribution by age and sex. For example, if under-five child mortality is low then TFR is often close to seven times the child/woman ratio (CWR), the number of 0-4 year olds per 15-49-year-old woman. We analyse the formal relationship between CWR and TFR to identify sources of uncertainty in indirect estimates. We construct a Bayesian model for the statistical distribution of TFR conditional on the population's age-sex structure, in which unknown demographic quantities in the standard approximation are parameters with prior distributions. We apply the model in two case studies: to a small indigenous population in the Amazon region of Brazil that has extremely high fertility rates, and to the set of 159 counties in the US state of Georgia. A statistical approach yields important insights into the sources of error in indirect estimation, and their relative magnitudes. [ABSTRACT FROM AUTHOR]

Although general patterns of food insecurity in the United States are known, few studies have attempted to estimate small area food security or account for ongoing socioeconomic changes. Here we address these issues by producing small area estimates of food insecurity in the Atlanta metropolitan area using two methodologies: fixed effects modeling and demographic metabolism. In both cases, we use county-level data from the Current Population Survey to determine the association between food insecurity and demographic predictors. These associations are then applied to tract-level data from the 2009 to 2013 American Community Survey and projected data for 2020 to create small area estimates of food insecurity. We find broad consensus between our two methods. For both time periods, food insecurity is highest in southern sections of the city of Atlanta and its neighboring suburbs. Projections to 2020, however, show that food insecurity rates are projected to increase in outer-ring suburbs east and west of the city while decreasing in the urban core. These results highlight the need to further adapt antihunger efforts for often sprawling suburban communities, where poverty rates are increasing but spatial mismatch combined with poor transit access might hinder access to food assistance. [ABSTRACT FROM AUTHOR]