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Postal codes are often the only geographic identifier available to match subjects in a health dataset to census geography. This paper describes the characteristics of postal codes reported by the Canadian population on the census and, as an indicator of geocoding accuracy, the proportion that are linked to a single dissemination area (DA). Postal codes reported on the 2016 Census questionnaire were matched to a combination of the Postal Code Conversion File (PCCF) and the Postal Code Conversion File Plus (PCCF+ version 7B) (reference date November 2018) to calculate population-weighted counts and the number of matches to DAs by province or territory, delivery mode type (DMT), population centre or rural area size, and census metropolitan area. The number of single matches to census tracts (CTs), census subdivisions (CSDs) and census divisions (CDs) was also calculated. In Canada, 72.6% of the population reported postal codes that matched to a single DA. This proportion was higher in urban cores (87.1%) and among postal codes for an urban street address (DMT=A) (85.3%) or apartment building (DMT=B) (95.3%), and was lower in rural areas (26.2% to 38.1%) and among rural postal codes (13.9%). In comparison, 89.3% and 95.4% of the population reported postal codes matching to a single CSD or CD, respectively, while 92.1% of the population that live within CT boundaries were matched to a single CT. Matching postal codes to census geography is relatively accurate and frequently one to one in urban centres. In rural areas and for some types of postal code DMTs, alternative approaches to using the PCCF and PCCF+ for attaching census geography might be explored.

This book is a detailed, empirical investigation into the question of whether academic social research can compete with the commercial sector, with its new technologies and big data, in order to classify, profile, and understand us.

Monitoring and improving rural health is challenging because of varied and conflicting concepts of just what rural means. Federal, state, and local agencies and data resources use different definitions, which may lead to confusion and inequity in the distribution of resources depending on the definition used. This article highlights how inconsistent definitions of rural may lead to measurement bias in research, the interpretation of research outcomes, and differential eligibility for rural-focused grants and other funding. We conclude by making specific recommendations on how policy makers and researchers could use these definitions more appropriately, along with definitions we propose, to better serve rural residents. We also describe concepts that may improve the definition of and frame the concept of rurality.

Introduction The nature of sleep can leave sleepers vulnerable to environmental threats, requiring a balance between the demands for vigilance and the physiological requirement for sleep. Understanding the sleeping context is particularly relevant to understanding the dynamics of sleep disturbance observed in individuals with posttraumatic stress symptoms (PTSS) because of subsequent increased vigilance to threats. This study examined the associations between neighborhood disadvantage, sleep respiratory sinus arrhythmia (RSA), fear of sleep, and nightmare frequency in a sample of trauma-exposed Veterans. Methods Baseline data from an ongoing study examining sleep with trauma nightmares using ambulatory sleep measurement in a sample of U.S. military Veterans were analyzed. Participants completed assessments of nightmare frequency and fear of sleep, and slept on a mattress actigraphy system for at least seven nights, which captured sleep-period RSA as an index of autonomic activation. Neighborhood disadvantage was assessed with the Area Deprivation Index (ADI), a census-based socioeconomic index. For this study, participants’ zip codes were linked to 2020 ADI scores using the Neighborhood Atlas, and the census-block groups were ranked into state-level 1 to 10 deciles (split into halves- low and high). Higher ADI scores indicate greater disadvantage. The differences between the least and most disadvantaged groups were analyzed with binary logistic regression models. Results Data were available from 33 Veterans. The mean state-level ADI was 6.2 (SD = 2.7). Low and high state-level ADI scores were 4.3 (SD = 2, n = 19) and 8.7 (SD = 0.8, n = 14), respectively. Residing in zip codes with greater neighborhood disadvantage was associated with elevated fear of sleep (OR = 1.2, p = 0.04), and a trend for reduced sleep-period RSA (OR = 0.4, p = 0.05). No differences were observed for past week nightmare frequency. Conclusion In this sample of Veterans, living in the most disadvantaged neighborhoods was significantly associated with greater fear of sleep, and may be associated with reduced RSA. Sleep context may increase hypervigilance in turn serving as a mechanism by which trauma-induced sleep disruptions are maintained. Support (if any) Funded by the U.S. Department of Veterans Affairs, Veterans Health Administration Clinical Science Research and Development Service - IK2 CX001874.

Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health 1 – 8 . However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers 9 , we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date 10 , 11 . Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality 12 – 14 . To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org . Analyses of data on 21 billion friendships from Facebook in the United States reveal associations between social capital and economic mobility.

Hospital admissions in the US fell dramatically with the onset of the coronavirus disease 2019 (COVID-19) pandemic. However, little is known about differences in admissions patterns among patient groups or the extent of the rebound. In this study of approximately one million medical admissions from a large, nationally representative hospitalist group, we found that declines in non-COVID-19 admissions from February to April 2020 were generally similar across patient demographic subgroups and exceeded 20 percent for all primary admission diagnoses. By late June/early July 2020, overall non-COVID-19 admissions had rebounded to 16 percent below prepandemic baseline volume (8 percent including COVID-19 admissions). Non-COVID-19 admissions were substantially lower for patients residing in majority-Hispanic neighborhoods (32 percent below baseline) and remained well below baseline for patients with pneumonia (-44 percent), chronic obstructive pulmonary disease/asthma (-40 percent), sepsis (-25 percent), urinary tract infection (-24 percent), and acute ST-elevation myocardial infarction (-22 percent). Health system leaders and public health authorities should focus on efforts to ensure that patients with acute medical illnesses can obtain hospital care as needed during the pandemic to avoid adverse outcomes.