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Despite growing recognition of the importance of social, economic, and political contexts for population health and health inequalities, research on pain disparities relies heavily on individual-level data, while neglecting overarching macrolevel factors such as state-level policies and characteristics. Focusing on moderate or severe arthritis-attributable joint pain-a common form of pain that considerably harms individuals quality of life-we (1) compared joint pain prevalence across US states; (2) estimated educational disparities in joint pain across states; and (3) assessed whether state sociopolitical contexts help explain these 2 forms of cross-state variation. We linked individual-level data on 407,938 adults (ages 25-80 years) from the 2017 Behavioral Risk Factor Surveillance System with state-level data on 6 measures (eg, the Supplemental Nutrition Assistance Program [SNAP], Earned Income Tax Credit, Gini index, and social cohesion index). We conducted multilevel logistic regressions to identify predictors of joint pain and inequalities therein. Prevalence of joint pain varies strikingly across US states: the age-adjusted prevalence ranges from 6.9% in Minnesota to 23.1% in West Virginia. Educational gradients in joint pain exist in all states but vary substantially in magnitude, primarily due to variation in pain prevalence among the least educated. At all education levels, residents of states with greater educational disparities in pain are at a substantially higher risk of pain than peers in states with lower educational disparities. More generous SNAP programs (odds ratio [OR] = 0.925; 95% confidence interval [CI]: 0.963-0.957) and higher social cohesion (OR = 0.819; 95% CI: 0.748-0.896) predict lower overall pain prevalence, and state-level Gini predicts higher pain disparities by education.

Objectives This article investigates the role of pain in disability trends in the United States, within the context of recent unfavorable disability trends and the concurrent rise in pain. Methods We conducted a 2-part analysis using National Health Interview Survey data from 2002 to 2018 for U.S. adults aged 45–84. First, we assessed how changes in the prevalence of 5 site-specific types of pain (headaches/migraines, joint, low back, neck, and facial/jaw pain) associated with disability trends. Second, we used self-reported causes of disability and examined whether there has been a change in the proportion of individuals who attribute their disability to 1 of 5 chronic or acute painful conditions. Results The 5 site-specific types of pain, individually and collectively, were significantly associated with increases in disability. If site-specific chronic pain had not increased during the study period, the trend for functional limitations would have been 40% lower, and that for activity limitations would have shown a slight decline instead of an increase. Attributions of functional limitations to painful conditions increased by 23% during the 2002–2018 period, representing an additional 9.82 million Americans experiencing pain-attributable disability. Arthritis/rheumatism, back/neck problems, and other musculoskeletal/connective conditions were the primary sources of pain-related disability. Discussion Our research provides the first systematic, national examination of how pain is contributing to disability trends in the United States. The findings have implications for disability reduction policies and shed light on the far-reaching consequences of pain for overall population health. [ABSTRACT FROM AUTHOR]

Race-based and other demographic information on COVID-19 patients is not being collected consistently across provinces in Canada. Therefore, whether the burden of COVID-19 is falling disproportionately on the shoulders of particular demographic groups is relatively unknown. In this article, we first provide an overview of the available geographic and demographic data related to COVID-19. We then make creative use of these existing data to fill the vacuum and identify key demographic risk factors for COVID-19 across Canada's health regions. Drawing on COVID-19 counts and tabular census data, we examine the association between communities' demographic composition and the number of COVID-19 infections. COVID-19 infections are higher in communities with larger shares of Black and low-income residents. Our approach offers a way for researchers and policymakers to use existing data to identify communities nationwide that are vulnerable to the pandemic in the absence of more detailed demographic and more granular geographic data. Les renseignements fondes sur la race et d'autres donnees demographiques sur les patients atteints du COVID-19 ne sont pas recueillis de maniere uniforme dans toutes les provinces du Canada. Par consequent, si le fardeau du COVID-19 tombe de maniere disproportionnee sur les epaules de groupes demographiques particuliers est relativement inconnu. Dans cet article, nous fournissons d'abord un apercu des donnees geographiques et demographiques disponibles liees au COVID-19. Nous utilisons ensuite de maniere creative ces donnees existantes pour combler le vide et identifier les principaux facteurs de risque demographiques du COVID-19 dans les regions sociosanitaires du Canada. En nous basant sur les denombrements de COVID-19 et les donnees tabulaires du recensement, nous examinons l'association entre la composition demographique des communautes et le nombre d'infections au COVID-19. Les infections au COVID-19 sont plus elevees dans les communautes avec une plus grande proportion de residents Noirs et a faible revenu. Notre approche offre aux chercheurs et aux decideurs un moyen d'utiliser les donnees existantes pour identifier les communautes a l'echelle nationale qui sont vulnerables a la pandemie en l'absence de donnees demographiques plus detaillees et geographiques plus granulaires., INTRODUCTION The global novel Coronavirus disease 2019 (COVID-19) pandemic has been raging for more than one year. In Canada, an initial wave emerged in mid-March, and by mid-April, there were [...]

Supplemental Digital Content is Available in the Text. County-level prevalences of arthritis and arthritis-attributable pain outcomes have significant spatial clustering patterns, and factors shaping these patterns are different for different outcomes. Research on the geographic distribution of pain and arthritis outcomes, especially at the county level, is limited. This is a high-priority topic, however, given the heterogeneity of subnational and substate regions and the importance of county-level governments in shaping population health. Our study provides the most fine-grained picture to date of the geography of pain in the United States. Combining 2011 Behavioral Risk Factor Surveillance System data with county-level data from the Census and other sources, we examined arthritis and arthritis-attributable joint pain, severe joint pain, and activity limitations in US counties. We used small area estimation to estimate county-level prevalences and spatial analyses to visualize and model these outcomes. Models considering spatial structures show superiority over nonspatial models. Counties with higher prevalences of arthritis and arthritis-related outcomes are mostly clustered in the Deep South and Appalachia, while severe consequences of arthritis are particularly common in counties in the Southwest, Pacific Northwest, Georgia, Florida, and Maine. Net of arthritis, county-level percentages of racial/ethnic minority groups are negatively associated with joint pain prevalence, but positively associated with severe joint pain prevalence. Severe joint pain is also more common in counties with more female individuals, separated or divorced residents, more high school noncompleters, fewer chiropractors, and higher opioid prescribing rates. Activity limitations are more common in counties with higher percentages of uninsured people. Our findings show that different spatial processes shape the distribution of different arthritis-related pain outcomes, which may inform local policies and programs to reduce the risk of arthritis and its consequences. [ABSTRACT FROM AUTHOR]

Over the past half century the gap in mortality across education levels has grown in the United States, and since the mid-1980s, the growth has been especially pronounced among white women. The reasons for the growth among white women are unclear. We investigated three explanations--social-psychological factors, economic circumstances, and health behaviors--for the widening education gap in mortality from 1997 to 2006 among white women aged 45 to 84 years using data from the National Health Interview Survey Linked Mortality File (N = 46,744; 4,053 deaths). Little support was found for social-psychological factors, but economic circumstances and health behaviors jointly explained the growing education gap in mortality to statistical nonsignificance. Employment and smoking were the most important individual components. Increasing high school graduation rates, reducing smoking prevalence, and designing work-family policies that help women find and maintain desirable employment may reduce mortality inequalities among women. (Contains 3 tables and 5 notes.)

An interval estimation procedure for proportion of explained observed variance in latent curve analysis is discussed, which can be used as an aid in the process of choosing between linear and nonlinear models. The method allows obtaining confidence intervals for the R[squared] indexes associated with repeatedly followed measures in longitudinal studies. In addition to facilitating evaluation of local model fit, the approach is helpful for purposes of differentiating between plausible models stipulating different patterns of change over time, and in particular in empirical situations characterized by large samples and high statistical power. The procedure is also applicable in cross-sectional studies, as well as with general structural equation models. The method is illustrated using data from a nationally representative study of older adults. (Contains 2 tables and 2 footnotes.)