Future Blood Debt: Projecting Blood Supply and Demand of Korea Based on Subnational Population Projections (2021-2050).

BACKGROUND: South Korea faces a critical challenge with its rapidly declining fertility rates and an increasingly aging population, which significantly impacts the country's blood supply and demand. Despite these nationwide trends, regional disparities in blood supply and demand have not been thoroughly studied. METHODS: This research utilized blood donation data from the Korean Red Cross and blood transfusion data from the Health Insurance Review and Assessment Service. We analyzed these datasets in conjunction with regional population projections to simulate blood supply and demand from 2021 to 2050 across South Korea. Sensitivity analyses were conducted to assess the impact of various factors, including the number of donors, age eligibility criteria for donations, frequency of donations, and blood discard rates. RESULTS: Our projections indicate a decreasing trend in blood supply, from 2.6 million units in 2021 to 1.4 million units by 2050, while demand is expected to peak at 5.1 million units by 2045 before declining. Metropolitan areas, particularly Gyeonggi Province, are projected to experience the most severe shortages. Sensitivity analyses suggest that increasing the donation frequency of existing donors and relaxing age eligibility criteria are more effective strategies in addressing these imbalances than merely increasing the number of new donors. Blood discard rates showed minimal impact on the overall blood shortage. CONCLUSION: The findings emphasize the urgent need for targeted strategies to mitigate national and regional blood supply shortages in South Korea. Encouraging frequent donations from experienced donors and broadening eligibility criteria are critical steps toward stabilizing the blood supply amidst demographic shifts. These strategies must be prioritized to address the impending regional disparities in blood availability.

Place-Based Developmental Research: Conceptual and Methodological Advances in Studying Youth Development in Context.

Scientists have, for some time, recognized that development unfolds in numerous settings, including families, schools, neighborhoods, and organized and unorganized activity settings. Since the turn of the 20th century, the body of mainstream neighborhood effects scholarship draws heavily from the early 20th century Chicago School of Sociology frameworks and have been situating development in neighborhood contexts and working to identify the structures and processes via which neighborhoods matter for a range of developmental outcomes, especially achievement, behavioral and emotional problems, and sexual activity. From this body of work, two new areas of developmental scholarship are emerging. Both areas are promising for advancing an understanding of child development in context. First, cultural-developmental neighborhood researchers are advancing neighborhood effects research that explicitly recognizes the ways that racial, ethnic, cultural, and immigrant social positions matter for neighborhood environments and for youths' developmental demands, affordances, experiences, and competencies. This body of work substantially expands the range of developmental outcomes examined in neighborhood effects scholarship to recognize normative physical, emotional, cognitive, behavioral, social, and cultural competencies that have largely been overlooked in neighborhood effects scholarship that espoused a more color-blind developmental approach. Second, activity space neighborhood researchers are recognizing that residential neighborhoods have important implications for broader activity spaces-or the set of locations and settings to which youth are regularly exposed, including, for example, schools, work, organized activities, and hang-outs. They are using newer technologies and geographic frameworks to assess exposure to residential neighborhood and extra-neighborhood environments. These perspectives recognize that time (i.e., from microtime to mesotime) and place are critically bound and that exposures can be operationalized at numerous levels of the ecological system (i.e., from microsystems to macrosystems). These frameworks address important limitations of prior development in context scholarship by addressing selection and exposure. Addressing selection involves recognizing that families have some degree of choice when selecting into settings and variables that predict families' choices (e.g., income) also predict development. Considering exposure involves recognizing that different participants or residents experience different amounts of shared and nonshared exposures, resulting in both under-and over-estimation of contextual effects. Activity space scholars incorporate exposure to the residential neighborhood environments, but also to other locations and settings to which youth are regularly exposed, like schools, after-school settings, work, and hang-outs. Unfortunately, the cultural-development and activity space streams, which have both emerged from early 20th century work on neighborhood effects on development, have been advancing largely independently. Thus, the overarching aim of this monograph is to integrate scholarship on residential neighborhoods, cultural development, and activity spaces to advance a framework that can support a better understanding of development in context for diverse groups. In Chapters I and II we present the historical context of the three streams of theoretical, conceptual, and methodological research. We also advance a comprehensive cultural-developmental activity space framework for studying development in context among children, youth, and families that are ethnically, racially, and culturally heterogeneous. This framework actively recognized diversity in ethnic, racial, immigrant, and socioeconomic social positions. In Chapters III-V we advance specific features of the framework, focusing on: (1) the different levels of nested and nonnested ecological systems that can be captured and operationalized with activity space methods, (2) the different dimensions of time and exposures or experiences that can be captured and operationalized by activity space methods, and (3) the importance of settings structures and social processes for identifying underlying mechanisms of contextual effects on development. Structures are setting features related to the composition and spatial arrangement of people and institutions (e.g., socioeconomic disadvantage, ethnic/racial compositions). Social processes represent the collective social dynamics that take place in settings, like social interactions, group activities, experiences with local institutions, mechanisms of social control, or shared beliefs. In Chapter VI, we highlight a range of methodological and empirical exemplars from the United States that are informed by our comprehensive cultural-developmental activity space framework. These exemplars feature both quantitative and qualitative methods, including method mixing. These exemplars feature both quantitative and qualitative methods, including method mixing. The exemplars also highlight the application of the framework across four different samples from populations that vary in terms of race, ethnicity, gender, age, socioeconomic status (SES), geographic region, and urbanicity. They capture activity space characteristics and features in a variety of ways, in addition to incorporating family shared and nonshared activity space exposures. Finally, in Chapter VII we summarize the contributions of the framework for advancing a more comprehensive science of development in context, one that better realizes major developmental theories emphasizing persons, processes, contexts, and time. Additionally, we offer a place-based, culturally informed developmental research agenda to meet the needs of an increasingly diverse population.

Social Vulnerability and the Prevalence of Opioid Use Disorder Among Older Medicare Beneficiaries in U.S. Counties.

OBJECTIVES: Recent research has investigated the factors associated with the prevalence of opioid use disorder (OUD) among older adults (65+), which has rapidly increased in the past decade. However, little is known about the relationship between social vulnerability and the prevalence of OUD, and even less is about whether the correlates of the prevalence of OUD vary across the social vulnerability spectrum. This study aims to fill these gaps. METHODS: We assemble a county-level data set in the contiguous United States (U.S.) by merging 2021 Medicare claims with the CDC's social vulnerability index and other covariates. Using the total number of older beneficiaries with OUD as the dependent variable and the total number of older beneficiaries as the offset, we implement a series of nested negative binomial regression models and then analyze by social vulnerability quartiles. RESULTS: Higher social vulnerability is associated with higher prevalence of OUD in U.S. counties. This association cannot be fully explained by the differences in the characteristics of older Medicare beneficiaries (e.g., average age) and/or other social conditions (e.g., social capital) across counties. Moreover, the group comparison tests indicate correlates of the prevalence of OUD vary across social vulnerability quartiles in that the average number of mental disorders is positively related to OUD prevalence in the least and the most vulnerable counties and social capital benefits the less vulnerable counties. DISCUSSION: A perspective drawing upon contextual factors, especially social vulnerability, may be more effective in reducing OUD among older adults in U.S. counties than a one-size-fits-all approach.

The Role of Infant Health Problems in Constraining Interneighborhood Mobility: Implications for Citywide Employment Networks.

Infant health problems are a persistent concern across the United States, disproportionally affecting socioeconomically vulnerable communities. We investigate how inequalities in infant health contribute to differences in interneighborhood commuting mobility and shape neighborhoods' embeddedness in the citywide structure of employment networks in Chicago over a 14-year period. We use the Census Bureau's Longitudinal Employer-Household Dynamics' Origin-Destination Employment Statistics to analyze commuting networks between 2002 and 2015. Results from longitudinal network analyses indicate two main patterns. First, after the Great Recession, a community's infant health problems began to significantly predict isolation from the citywide employment network. Second, pairwise dissimilarity in infant health problems predicts a lower likelihood of mobility ties between communities throughout the entire study period. The findings suggest that infant health problems present a fundamental barrier for communities in equally accessing the full range of jobs and opportunities across the city-compounding existing inequalities.

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties.

INTRODUCTION: This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate. METHODS: This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022. RESULTS: The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local. CONCLUSIONS: The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

One-year spatiotemporal database of Emergency Medical Service (EMS) calls in Mashhad, Iran: data on 224,355 EMS calls.

OBJECTIVES: Emergency Medical Services (EMS) is the first point of service for the people who are in critical condition and in need of urgent health care. In Iran, as in other countries, people in need of emergency services often die or are left with a permanent injury due to the poor EMS-related infrastructure. It has been shown that a detailed examination of the response times and the spatiotemporal pattern of EMS calls for service can lead to improvements in time-sensitive patient outcomes. We performed a spatiotemporal study in city of Mashhad, the second-most populous city of Iran, to investigate the pattern of the EMS calls and now wish to release a comprehensive dataset resulting from this study. DATA DESCRIPTION: The data include three data files plus a data dictionary file. Data file 1 contains the characteristics of EMS requests including sex, age group, date of call, different time periods of each EMS missions, the census tracts' ID of callers, the chief complaint, and the EMS mission result. Two spatial data files include the boundaries of the census tracts in Mashhad and the point location of all EMS stations, respectively. A data dictionary file defines all fields and values across the data files.

Depopulation, super aging, and extreme heat events in South Korea.

South Korea's population is declining and its composition changing, associated with lowest-low fertility rates and rapid aging (super aging). When estimating changes in future exposure to extreme heat events (EHE), events that are predicted to be intensified due to climate change, it is important to incorporate demographic dynamics. We analyze business-as-usual (BAU) population and climate scenarios-where BAU refers to no significant change in current processes and trends in either domain-from 2010 to 2060 for South Korea. Data for both BAU scenarios are spatially linked and used to measure and identify national and sub-national and age-group specific EHE exposure. The results reveal an increasing exposure to EHE over time at the national level, but this varies widely within the country, measured at the municipal level. The most intensive exposure levels will be in the decade ending in 2040 driven by high estimated severe EHE. Sub-nationally, Seoul will be the most vulnerable municipality associated with super aging, while severe EHE not demographic factors will be relevant in Daegu, the second-most vulnerable metropolitan area. By 2060, national estimates suggest the older population will be up to four times more exposed to EHE than today. While the population of South Korea will decline, the rapid aging of the population ensures that specific regions of the country will become exceedingly vulnerable to EHE.

Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach.

Pre-hospital care is provided by emergency medical services (EMS) staff, the initial health care providers at the scene of disaster. This study aimed to describe the characteristics of EMS callers and space-time distribution of emergency requests in a large urban area. Descriptive thematic maps of EMS requests were created using an empirical Bayesian smoothing approach. Spatial, temporal and spatio-temporal clustering techniques were applied to EMS data based on Kulldorff scan statistics technique. Almost 225,000 calls were registered in the EMS dispatch centre during the study period. Approximately two-thirds of these calls were associated with an altered level of patient consciousness, and the median response time for rural and urban EMS dispatches was 12.2 and 10.1 minutes, respectively. Spatio-temporal clusters of EMS requests were mostly located in central parts of the city, particularly near the downtown area. However, high-response time clustered areas had a low overlap with these general, spatial clusters. This low convergence shows that some unknown factors, other than EMS requests, influence the high-response times. The findings of this study can help policymakers to better allocate EMS resources and implement tailored interventions to enhance EMS system in urban areas.

A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

PURPOSE: This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States. METHODS: We assemble a county-level data set that contains COVID-19-confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence. RESULTS: The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties. CONCLUSIONS: Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

Racial/ethnic segregation and health disparities: Future directions and opportunities.

Health researchers have investigated the association between racial segregation and racial health disparities with multilevel approaches. This study systematically reviews these multilevel studies and identifies broad trends and potential directions for future research on racial segregation and health disparities in the US. After searching databases including CINAHL and MEDLINE, we identified and systematically reviewed 66 articles published between 2003 and 2019 and found four major gaps in racial/ethnic segregation and health disparities: (a) the concept of segregation was rarely operationalized at the neighborhood level, (b) except for the evenness and exposure dimension, other dimensions of segregation are overlooked, (c) little attention was paid to the segregation between whites and non-black minorities, particularly Hispanics and Asians, and (d) mental health outcomes were largely absent. Future directions and opportunities include: First, other segregation dimensions should be explored. Second, the spatial scales for segregation measures should be clarified. Third, the theoretical frameworks for black and non-black minorities should be tested. Fourth, mental health, substance use, and the use of mental health care should be examined. Fifth, the long-term health effect of segregation has to be investigated, and finally, other competing explanations for why segregation matters at the neighborhood level should be answered.

Communal Innovations: Inspiring Neighborhoods of Hope and Advocacy.

Innovations promise a better future, which may generate feelings of hope and inspire advocacy. Some innovations are more communal in nature: attempting to address a social problem, through community engagement and wide-spread adoption. For such innovations, the social processes that involve collective aspects of community life may play important roles in fostering hope and interpersonal advocacy. This study uses communication infrastructure theory and discrete emotions theory to investigate hope and advocacy within a field trial for a salient, visible, community-bound innovation to reduce transmission of malaria. Heads of households in one community (N = 119) in West Africa were interviewed. Results showed that innovation hope was predicted by appraisals of innovation attributes. Better appraisals of the innovation's attributes, greater perceived collective efficacy, and recent malaria illness predicted more innovation advocacy. The spatial analysis showed that innovation advocacy was geographically clustered within the community, but hope was not. The implications for theory and practice are discussed.

The geography of sentiment towards the Women's March of 2017.

The Women's March of 2017 generated unprecedented levels of participation in the largest, single day, protest in history to date. The marchers protested the election of President Donald Trump and rallied in support of several civil issues such as women's rights. "Sister marches" evolved in at least 680 locations across the United States. Both positive and negative reactions to the March found their way into social media, with criticism stemming from certain, conservative, political sources and other groups. In this study, we investigate the extent to which this notable, historic event influenced sentiment on Twitter, and the degree to which responses differed by geographic area within the continental U.S. Tweets about the event rose to an impressive peak of over 12% of all geo-located tweets by mid-day of the March, Jan. 21. Messages included in tweets associated with the March tended to be positive in sentiment, on average, with a mean of 0.34 and a median of 0.07 on a scale of -4 to +4. In fact, tweets associated with the March were more positive than all other geo-located tweets during the day of the March. Exceptions to this pattern of positive sentiment occurred only in seven metropolitan areas, most of which involved very small numbers of tweets. Little evidence surfaced of extensive patterns of negative, aggressive messages towards the event in this set of tweets. Given the widespread nature of online harassment and sexist tweets, more generally, the results are notable. In sum, online reactions to the March on this social media platform suggest that this modern arm of the Women's Movement received considerable, virtual support across the country.

Exploring heterogeneities with geographically weighted quantile regression: An enhancement based on the bootstrap approach.

Geographically weighted quantile regression (GWQR) has been proposed as a spatial analytical technique to simultaneously explore two heterogeneities, one of spatial heterogeneity with respect to data relationships over space and one of response heterogeneity across different locations of the outcome distribution. However, one limitation of GWQR framework is that the existing inference procedures are established based on asymptotic approximation, which may suffer computation difficulties or yield incorrect estimates with finite samples. In this paper, we suggest a bootstrap approach to address this limitation. Our bootstrap enhancement is first validated by a simulation experiment and then illustrated with an empirical US mortality data. The results show that the bootstrap provides a practical alternative for inference in GWQR and enhances the utilization of GWQR.

Neighborhood Walkability and BMI Change: A National Study of Veterans in Large Urban Areas.

OBJECTIVE: Improving neighborhood walkability has been proposed as a policy intervention to reduce obesity. The objective of this study was to evaluate longitudinal relationships between neighborhood walkability and body weight among adults living in large urban areas. METHODS: In this retrospective longitudinal study of United States military veterans using Department of Veterans Affairs health care, Veterans Affairs clinical and administrative data (2007-2014) were linked to environmental measures constructed from public (2006-2014) and proprietary (2008-2014) sources, and linear regression models with person fixed effects were used to estimate associations between walkability and BMI among 758,434 men and 70,319 women aged 20 to 80 years in 2009 to 2014. RESULTS: Neighborhood walkability was associated with small reductions in BMI. Effects were most pronounced among men aged 30 to 49 and 50 to 64. For women, differences were largest in the two youngest age groups, 20 to 29 and 30 to 49, though only estimates for all women combined were statistically significant. For women aged 30 to 49, effect sizes grew when the sample was limited to those who remained in the same neighborhood during the entire follow-up period. CONCLUSIONS: Investments in the built environment to improve walkability may be a useful strategy for weight control in some segments of the adult population.

Stability of activity space footprint, size, and environmental features over six months.

As activity space measures are increasingly used to estimate exposure to environmental determinants of health, little is known about the stability of these measures over time. To test the stability of GPS-derived measures of activity-space footprint, size, and environmental features over time, we compared 14-day measures at baseline and six months later for 35 adults in a large city. Activity-space measures were based on convex hulls and 500 m route buffers, and included the geographic footprint (i.e. location of the activity space), size (i.e., area in square miles; (Cummins, 2007)), and environmental features including supermarket, fast-food restaurant, and parkland density. The proportion of the participants' smaller geographic footprint covered by the larger was, on average, 0.64 (SD 0.17) for the 500 m route buffer and 0.84 (SD 0.18) for the convex hull. Mean percent change in activity space size ranged from 36.3% (mean daily 500 m route buffer) to 221.3% (cumulative convex hull). Mean percent change in the density of environmental features ranged from 28.8 to 66.5%. Forty-one percent to 92.4% of the variance at one timepoint was predicted by environmental features measured within approximately six months. Activity-space size and environmental features were moderately to highly stable over six months, although there was considerable variation in stability between measures. Strategies for addressing measurement error in studies of activity space-health associations are discussed.

From Census Tracts to Local Environments: An Egocentric Approach to Neighborhood Racial Change.

Most quantitative studies of neighborhood racial change rely on census tracts as the unit of analysis. However, tracts are insensitive to variation in the geographic scale of the phenomenon under investigation and to proximity among a focal tract's residents and those in nearby territory. Tracts may also align poorly with residents' perceptions of their own neighborhood and with the spatial reach of their daily activities. To address these limitations, we propose that changes in racial structure (i.e., in overall diversity and group-specific proportions) be examined within multiple egocentric neighborhoods, a series of nested local environments surrounding each individual that approximate meaningful domains of experience. Our egocentric approach applies GIS procedures to census block data, using race-specific population densities to redistribute block counts of whites, blacks, Hispanics, and Asians across 50-meter by 50-meter cells. For each cell, we then compute the proximity-adjusted racial composition of four different-sized local environments based on the weighted average racial group counts in adjacent cells. The value of this approach is illustrated with 1990-2000 data from a previous study of 40 large metropolitan areas. We document exposure to increasing neighborhood racial diversity during the decade, although the magnitude of this increase in diversity-and of shifts in the particular races to which one is exposed-differs by local environment size and racial group membership. Changes in diversity exposure at the neighborhood level also depend on how diverse the metro area as a whole has become.

Modeling the Importance of Within- and Between-County Effects in an Ecological Study of the Association Between Social Capital and Mental Distress.

INTRODUCTION: Levels of mental distress in the United States are a health policy concern. The association between social capital and mental distress is well documented, but evidence comes primarily from individual-level studies. Our objective was to examine this association at the county level with advanced spatial econometric methods and to explore the importance of between-county effects. METHODS: We used County Health Rankings and Roadmaps data for 3,106 counties of the contiguous United States. We used spatial Durbin modeling to assess the direct (within a county) and indirect (between neighboring counties) effects of social capital on mental distress. We also examined the spatial spillover effects from neighboring counties based on higher-order spatial weights matrices. RESULTS: Counties with the highest prevalence of mental distress were found in regional clusters where levels of social capital were low, including the Black Belt, central/southern Appalachia, on the Mississippi River, and around some Indian Reservations. Most of the association between social capital and mental distress was indirect, from the neighboring counties, although significant direct effects showed the within-county association. Models also confirmed the importance of county-level socioeconomic status. CONCLUSION: We found that county social capital is negatively related to mental distress. Counties are not isolated places and are often part of wider labor and housing markets, so understanding spatial dependencies is important in addressing population-level mental distress.