Sunny-Day Flooding and Mortality Risk in Coastal Florida.

Sea-level rise is likely to worsen the impacts of hurricanes, storm surges, and tidal flooding on coastal access to basic services. We investigate the historical impact of tidal flooding on mortality rates of the elderly population in coastal Florida using administrative records of individual deaths, demographics, and residential location combined with tidal gauge and high-resolution elevation data. We incorporate data capturing storm and precipitation events into our empirical model to distinguish between disruptions from routine sunny-day flooding and less predictable tropical storm-induced flooding. We find that a 1-standard-deviation (20-millimeter) increase in tidal flooding depth increases mortality rates by 0.46% to 0.60% among those aged 65 or older. Our estimates suggest that future sea-level rises may contribute to an additional 130 elderly deaths per year in Florida relative to 2019, all else being equal. The enhanced risk is concentrated among residents living more than nine minutes away from the nearest hospital. Results suggest that tidal flooding may augment elderly mortality risk by delaying urgent medical care.

Climate migration amplifies demographic change and population aging.

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.

Integrating climate change induced flood risk into future population projections.

Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years.

Research Note: Demographic Change on the United States Coast, 2020-2100.

Prospective demographic information of the United States is limited to national-level analyses and subnational analyses of the total population. With nearly 40% of the U.S. population being residents of coastal areas, understanding the anticipated demographic changes in coastal counties is important for long-range planning purposes. In this research note, we use long-range, county-level population projections based on a simplified cohort-component method to discuss demographic changes by age, sex, and race and ethnicity for coastal counties between 2020 and the end of the century, and we compare these changes to inland counties. Presently, coastal counties are statistically significantly different from inland counties by race and ethnicity (more diverse) and sex (more women) but not by age, yet by 2025, we expect coastal counties to become significantly older than inland counties. We note several important trajectories of predicted demographic outcomes in coastal counties across the remainder of the century: (1) the non-Hispanic White population is expected to decrease, both numerically and as a percentage of the population; (2) the population older than 65 is projected to increase, both numerically and as a percentage of the population; and (3) the ratio of women to men remains constant over the century at 1.03. These trends combine to suggest that the future U.S. coastline will likely be both increasingly diverse racially and ethnically and significantly older than it is today.

User Beware: Concerning Findings from the Post 2011-2012 U.S. Internal Revenue Service Migration Data.

The U.S. Internal Revenue Service (IRS) makes publicly and freely available period migration data at the state and county levels. Among their uses, these data inform estimates of net-migration as part of the U.S. Census Bureau's Population Estimates Program, which, in turn, are used for producing other annual statistics, survey design, business and community planning, and federal funding allocations. Building on and extending prior research, we devote this Research Brief to documenting from multiple new angles a highly concerning and apparently systemic problem with the IRS migration data since the IRS took over responsibilities for preparing these data from the U.S. Census Bureau in 2011. As we then discuss, despite the fact that the IRS provides documentation detailing changes that it made to how it prepares these data relative to how the U.S. Census Bureau prepared them, it is not clear why or how these changes would result in the problem detailed in our analysis. Given that this problem appears to be an internal one within the IRS, we conclude by suggesting that the post 2011-12 IRS migration data not be used until this problem is resolved, and we encourage the IRS to do so quickly, transparently, and collaboratively.

Housing unit and urbanization estimates for the continental U.S. in consistent tract boundaries, 1940-2019.

Subcounty housing unit counts are important for studying geo-historical patterns of (sub)urbanization, land-use change, and residential loss and gain. The most commonly used subcounty geographical unit for social research in the United States is the census tract. However, the changing geometries and historically incomplete coverage of tracts present significant obstacles for longitudinal analysis that existing datasets do not sufficiently address. Overcoming these barriers, we provide housing unit estimates in consistent 2010 tract boundaries for every census year from 1940 to 2010 plus 2019 for the entire continental US. Moreover, we develop an "urbanization year" indicator that denotes if and when tracts became "urbanized" during this timeframe. We produce these data by blending existing interpolation techniques with a novel procedure we call "maximum reabsorption." Conducting out-of-sample validation, we find that our hybrid approach generally produces more reliable estimates than existing alternatives. The final dataset, Historical Housing Unit and Urbanization Database 2010 (HHUUD10), has myriad potential uses for research involving housing, population, and land-use change, as well as (sub)urbanization.

Racial disparities for COVID19 mortality in Georgia: Spatial analysis by age based on excess deaths.

INTRODUCTION: This study uses multiple measures of excess deaths to analyze racial disparities in COVID-19 mortality across Georgia. METHODS: The Georgia Department of Public Health provided monthly mortality data for 2010-2020 stratified by race/ethnicity, age, county, and recorded cause of death. We first calculate crude mortality rates by health district during the time period for all groups for March through June for our historical period to identify significant time-series outliers in 2020 distinguishable from general trend variations. We then calculate the mean and standard deviation of mortality rates by age and racial subgroup to create historic confidence intervals that contextualize rates in 2020. Lastly, we use risk ratios to identify disparities in mortality between Black and White mortality rates both in the 2010-2019 period and in 2020. RESULTS: Time-series analysis identified three health districts with significant increases in mortality in 2020, located in metro Atlanta and Southwest Georgia. Mortality rates decreased sharply in 2020 for children in both racial categories in all sections of the state, but rose in a majority of districts for both categories in adult and older populations. Risk ratios also increased significantly in 2020 for children and older populations, showing rising disparities in mortality during the pandemic even as crude mortality rates declined for children classified as Black. CONCLUSIONS: Increased mortality during the COVID-19 outbreak disproportionately affected African-Americans, possibly due, in part, to pre-existing disparities prior to the pandemic linked to social determinants of health. The pandemic deepened these disparities, perhaps due to unequal resources to effectively shelter-in-place or access medical care. Future research may identify local factors underlying geographically heterogenous differences in mortality rates to inform future policy interventions.

Assessing population exposure to coastal flooding due to sea level rise.

The exposure of populations to sea-level rise (SLR) is a leading indicator assessing the impact of future climate change on coastal regions. SLR exposes coastal populations to a spectrum of impacts with broad spatial and temporal heterogeneity, but exposure assessments often narrowly define the spatial zone of flooding. Here we show how choice of zone results in differential exposure estimates across space and time. Further, we apply a spatio-temporal flood-modeling approach that integrates across these spatial zones to assess the annual probability of population exposure. We apply our model to the coastal United States to demonstrate a more robust assessment of population exposure to flooding from SLR in any given year. Our results suggest that more explicit decisions regarding spatial zone (and associated temporal implication) will improve adaptation planning and policies by indicating the relative chance and magnitude of coastal populations to be affected by future SLR.

Evacuees and Migrants Exhibit Different Migration Systems After the Great East Japan Earthquake and Tsunami.

Research on the destinations of environmentally induced migrants has found simultaneous migration to both nearby and long-distance destinations, most likely caused by the comingling of evacuee and permanent migrant data. Using a unique data set of separate evacuee and migration destinations, we compare and contrast the pre-, peri-, and post-disaster migration systems of permanent migrants and temporary evacuees of the Great East Japan Earthquake and Tsunami. We construct and compare prefecture-to-prefecture migration matrices for Japanese prefectures to investigate the similarity of migration systems. We find evidence supporting the presence of two separate migration systems-one for evacuees, who seem to emphasize short distance migration, and one for more permanent migrants, who emphasize migration to destinations with preexisting ties. Additionally, our results show that permanent migration in the peri- and post-periods is largely identical to the preexisting migration system. Our results demonstrate stability in migration systems concerning migration after a major environmental event.

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. We test accuracy across a diverse set of data sources that comprise more than 2,400 fertility schedules with known TFR values, including the Human Fertility Database, Demographic and Health Surveys, U.S. counties, and nonhuman species. We show that even the simplest and least accurate variant has a median error of only 0.09 births per woman over 2,400 fertility schedules, suggesting accurate TFR estimation over a wide range of demographic conditions. We anticipate that this framework will extend fertility analysis to new subpopulations, periods, geographies, and even species. To demonstrate the framework's utility in new applications, we produce subnational estimates of African fertility levels, reconstruct historical European TFRs for periods up to 150 years before the collection of detailed birth records, and estimate TFR for the United States conditional on race and household income.

Population projections for U.S. counties by age, sex, and race controlled to shared socioeconomic pathway.

Small area and subnational population projections are important for understanding long-term demographic changes. I provide county-level population projections by age, sex, and race in five-year intervals for the period 2020-2100 for all U.S. counties. Using historic U.S. census data in temporally rectified county boundaries and race groups for the period 1990-2015, I calculate cohort-change ratios (CCRs) and cohort-change differences (CCDs) for eighteen five-year age groups (0-85+ ), two sex groups (Male and Female), and four race groups (White NH, Black NH, Other NH, Hispanic) for all U.S counties. I then project these CCRs/CCDs using ARIMA models as inputs into Leslie matrix population projection models and control the projections to the Shared Socioeconomic Pathways. I validate the methods using ex-post facto evaluations using data from 1969-2000 to project 2000-2015. My results are reasonably accurate for this period. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States.