Blending data to understand the economic impact of COVID-19.

The data demands during the pandemic heightened the need to blend information from numerous sources to get a more timely and granular picture of economic developments. Ongoing efforts include the Chicago Fed's weekly retail sales estimate, the Census Bureau's work on higher-frequency state-level retail sales data, the Federal Reserve Board's computations of business closures and weekly payrolls, and the academic Opportunity Insights team's estimates of spending, business revenues and employment by income and ZIP code.

Association of Severe Trauma With Work and Earnings in a National Cohort in Canada.

Importance: Traumatic injury disproportionately affects adults of working age. The ability to work and earn income is a key patient-centered outcome. The association of severe injury with work and earnings appears to be unknown. Objective: To evaluate the association of severe traumatic injury with subsequent employment and earnings in long-term survivors. Design, Setting, and Participants: This is a retrospective, matched, national, population-based cohort study of adults who had employment and were hospitalized with severe traumatic injury in Canada between January 2008 and December 2010. All acute care hospitalizations for severe injury were included if they involved adults aged 30 to 61 years who were hospitalized with severe traumatic injury, working in the 2 years prior to injury, and alive through the third calendar year after their injury. Patients were matched with unexposed control participants based on age, sex, marital status, province of residence, rurality, baseline health characteristics, baseline earnings, self-employment status, union membership, and year of the index event. Data analysis occurred from March 2019 to December 2019. Main Outcomes and Measures: Changes in employment status and annual earnings, compared with unexposed control participants, were evaluated in the third calendar year after injury. Weighted multivariable probit regression was used to compare proportions of individuals working between those who survived trauma and control participants. The association of injury with mean yearly earnings was quantified using matched difference-in-difference, ordinary least-squares regression. Results: A total of 5167 adults (25.6% female; mean [SD] age, 47.3 [8.8] years) with severe injuries were matched with control participants who were unexposed (25.6% female; mean [SD] age, 47.3 [8.8] years). Three years after trauma, 79.3% of those who survived trauma were working, compared with 91.7% of control participants, a difference of -12.4 (95% CI, -13.5 to -11.4) percentage points. Three years after injury, patients with injuries experienced a mean loss of $9745 (95% CI, -$10 739 to -$8752) in earnings compared with control participants, representing a 19.0% difference in annual earnings. Those who remained employed 3 years after injury experienced a 10.8% loss of earnings compared with control participants (-$6043 [95% CI, -$7101 to -$4986]). Loss of work was proportionately higher in those with lower preinjury income (lowest tercile, -18.5% [95% CI, -20.8% to -16.2%]; middle tercile, -11.5% [95% CI, -13.2% to -9.9%]; highest tercile, -6.0% (95% CI, -7.8% to -4.3%]). Conclusions and Relevance: In this study, severe traumatic injury had a significant association with employment and earnings of adults of working age. Those with lower preinjury earnings experienced the greatest relative loss of employment and earnings.

Effects of Cardiovascular Health Shocks on Spouses' Work and Earnings: A National Study.

BACKGROUND: Acute health shocks can reduce the ability to work and earn among working-age survivors. The full economic impact includes labor market effects on spouses/partners, but there is a knowledge gap in this area. OBJECTIVES: The objective of this study was to assess how 3 common health shocks, acute myocardial infarction, stroke, and cardiac arrest, influence work and earnings of spouses aged 35-61 years. RESEARCH DESIGN: This retrospective cohort study of case and control couples used population-based, linked Canadian income tax and hospitalization data from 2005 to 2013. SUBJECTS: Case couples comprised 1 partner aged 41-61 years who experienced a health shock in the index year and survived 3 years hence, and a working-age partner. Control couples were matched up to 5:1 on 12 characteristics, with neither experiencing the health shock of interest in the index year. MEASURES: Primary outcome was the change in spousal annual earnings between the year prior and 3 years after the event. Pre-to-post spousal income changes were categorized into 9 levels and compared between case spouses and control spouses by the Pearson χ test. RESULTS: There were 11,208 matched case couples for acute myocardial infarction, 622 for cardiac arrest, and 2288 for stroke. Overall, case and control spouses experienced similar distributional changes in preevent to postevent earning (all P≥0.27). Heterogeneity analysis indicated that spouses of more severe stroke sufferers ceased working at a higher rate than for control spouses. CONCLUSION: Beyond assessing average values, detailed analysis of changes in spousal earnings after common cardiovascular health shocks did not demonstrate effects attributable to those health shocks.

Effects of cardiovascular and cerebrovascular health events on work and earnings: a population-based retrospective cohort study.

BACKGROUND: Survivors of acute health events can experience lasting reductions in functional status and quality of life, as well as reduced ability to work and earn income. We aimed to assess the effect of acute myocardial infarction (MI), cardiac arrest and stroke on work and earning among working-age people. METHODS: For this retrospective cohort study, we used the Canadian Hospitalization and Taxation Database, which contains linked hospital and income tax data, from 2005 to 2013 to perform difference-in-difference analyses. We matched patients admitted to hospital for acute MI, cardiac arrest or stroke with controls who were not admitted to hospital for these indications. Participants were aged 40-61 years, worked in the 2 years before the event and were alive 3 years after the event. Patients were matched to controls for 11 variables. The primary outcome was working status 3 years postevent. We also assessed earnings change attributable to the event. We matched 19 129 particpants who were admitted to hospital with acute MI, 1043 with cardiac arrest and 4395 with stroke to 1 820 644, 307 375 and 888 481 controls, respectively. RESULTS: Fewer of the patients who were admitted to hospital were working 3 years postevent than controls for acute MI (by 5.0 percentage points [pp], 95% confidence interval [CI] 4.5-5.5), cardiac arrest (by 12.9 pp, 95% CI 10.4-15.3) and stroke (by 19.8 pp, 95% CI 18.5-23.5). Mean (95% CI) earnings declines attributable to the events were $3834 (95% CI 3346-4323) for acute MI, $11 143 (95% CI 8962-13 324) for cardiac arrest, and $13 278 (95% CI 12 301-14 255) for stroke. The effects on income were greater for patients who had lower baseline earnings, comorbid disease, longer hospital length of stay or needed mechanical ventilation. Sex, marital status or self-employment status did not affect income declines. INTERPRETATION: Acute MI, cardiac arrest and stroke all resulted in substantial loss in employment and earnings that persisted for at least 3 years after the events. These outcomes have consequences for patients, families, employers and governments. Identification of subgroups at high risk for these losses may assist in targeting interventions, policies and legislation to promote return to work.

The Association Between Income and Life Expectancy in the United States, 2001-2014.

IMPORTANCE: The relationship between income and life expectancy is well established but remains poorly understood. OBJECTIVES: To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation. DESIGN AND SETTING: Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy. EXPOSURE: Pretax household earnings as a measure of income. MAIN OUTCOMES AND MEASURES: Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas. RESULTS: The sample consisted of 1,408,287,218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61,175 per year). There were 4,114,380 deaths among men (mortality rate, 596.3 per 100,000) and 2,694,808 deaths among women (mortality rate, 375.1 per 100,000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r = -0.69, P < .001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r = 0.72, P < .001), fraction of college graduates (r = 0.42, P < .001), and government expenditures (r = 0.57, P < .001). CONCLUSIONS AND RELEVANCE: In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.