Objective This longitudinal study aimed to measure precarious employment in the US using a multidimensional indicator. Methods We used data from the National Longitudinal Survey of Youth (1988–2016) and the Occupational Information Network database to create a longitudinal precarious employment score (PES) among 7568 employed individuals over 18 waves (N=101 290 observations). We identified 13 survey indicators to operationalize 7 dimensions of precarious employment, which we included in our PES (range: 0–7, with 7 indicating the most precarious): material rewards, working-time arrangements, stability, workers’ rights, collective organization, interpersonal relations, and training. Using generalized estimating equations, we estimated the mean PES and change over time in the PES overall and by race/ethnicity, gender, education, income, and region. Results On average, the PES was 3.17 [standard deviation (SD) 1.19], and was higher among women (3.34, SD 1.20), people of color (Hispanics: 3.24, SD 1.23; non-Hispanic Blacks: 3.31, SD 1.23), those with less education (primary: 3.99, SD 1.07; high school: 3.43, SD 1.19), and with lower-incomes (3.84, SD 1.08), and those residing in the South (3.23, SD 1.17). From 1988 to 2016, the PES increased by 9% on average [0.29 points; 95% confidence interval (CI) 0.26–0.31]. While precarious employment increased over time across all subgroups, the increase was largest among males (0.35 points; 95% CI 0.33–0.39), higher-income (0.39 points; 95% CI 0.36–0.42) and college-educated (0.37 points; 95% CI 0.33–0.41) individuals. Conclusions Long-term decreases in employment quality are widespread in the US. Women and those from racialized and less-educated populations remain disproportionately precariously employed; however, we observed the largest increases among men, college graduates and higher-income individuals.

Objective: There is growing recognition that precarious employment is an important determinant of health, which may increase BMI through multiple mechanisms, including stress. It was investigated whether increases in precarious employment were associated with changes in BMI in the United States.Methods: Data were from the National Longitudinal Survey of Youth adult cohort (1996–2016) (N = 7280). Thirteen indicators were identified to operationalize seven dimensions of precarious employment (range: 0–7, 7 indicating most precarious): material rewards, working‐time arrangements, stability, workers' rights, collective organization, interpersonal relationships, and training. The precarious employment–BMI association was estimated using linear regression models and an instrumental variables approach; state‐ and individual‐level firm sizes were the instruments for precarious employment. Models also included individual and year fixed effects and controlled for age, marital status, education, region, and industry.Results: The average precarious employment score (PES) was 3.49 (95% CI: 3.46–3.52). The PES was the highest among Hispanic (4.04; 95% CI: 3.92–4.15) and non‐Hispanic Black (4.02; 95% CI: 3.92–4.12) women with lower education. A 1‐point increase in the PES was associated with a 2.18‐point increase in BMI (95% CI: 0.30–4.01).Conclusions: Given that even small changes in weight affect chronic disease risk, policies to improve employment quality warrant consideration.

In the context of urban development, bridging the gap between urban regeneration and people’s demand for high-quality built environments is a current focus of research. Exploring the vitality of certain kinds of urban districts is imperative for comprehending human needs for specific built environments and fostering urban renaissance and advancement. This urgency arises from the prevailing lack of in-depth studies on district vitality, as current research primarily provides a general assessment of street vitality. Thus, this study aims to explore the correlations between indicators of urban vitality in cultural districts and built environments, using Haikou and Suzhou as case studies and employing multiple data sources (e.g., Baidu heat maps and nighttime light) and measurements. By applying a logit regression model, we find the following: (1) Traffic network integration has a positive impact on daytime vitality in Haikou and nighttime vitality in Suzhou, but it negatively affects nighttime vitality in Haikou. (2) In terms of nighttime vitality, both commercial density and greenery positively influence the overall blocks and various subgroups. (3) The proportion of cultural facilities in Suzhou has a detrimental effect on daytime vitality, especially for blocks with fewer permanent residents and lower land values. The present study, while limited to selected cultural blocks in Suzhou and Haikou, establishes the groundwork for a better comprehension of how spatial vitality can be enhanced at the street segment level, thereby contributing to the investigation of the varying impacts that built environment factors have on urban vitality in tourism cities at different stages of development. It uncovers the inherent latent characteristics found within cultural blocks across diverse regions and offers innovative perspectives and recommendations for optimizing the sustainable development of urban blocks.