Higher COVID-19 Vaccination And Narrower Disparities In US Cities With Paid Sick Leave Compared To Those Without

Occupation and Industry Data Quality Among Select Notifiable Conditions in Washington State

CONTEXT

Occupation and industry are basic data elements that, when collected during public health investigations, can be key to understanding patterns of disease transmission and developing effective prevention measures.

OBJECTIVE

To assess the completeness and quality of occupation and industry data among select notifiable conditions in Washington and discuss potential improvements to current data collection efforts.

DESIGN

We evaluated occupation and industry data, collected by local health departments during routine case investigations, for 11 notifiable conditions, selected for inclusion based on an established or plausible link to occupational exposure.

SETTING AND PARTICIPANTS

Confirmed cases of select notifiable conditions among Washington residents aged 16 to 64 years, for years 2019-2021.

MAIN OUTCOME MEASURES

We calculated the percentage of cases among working-age adults reported as employed, the percentage with occupation and industry data collected, and the percentage assigned standard occupation and industry codes. We identified the most common responses for occupation and industry and challenges of assigning codes to those responses.

RESULTS

Among the 11 conditions evaluated, one-third of cases aged 16 to 64 years were reported as employed. Among the cases reported as employed, 91.5% reported occupation data and 30.5% reported industry data. "Self-employed" was among the top responses for occupation, a response that does not describe a specific job and could not be assigned an occupation code. In the absence of additional information, 4 of the most common responses for industry could not be coded: "health care," "technology," "tech," and "food."

CONCLUSION

Routine collection of informative occupation and industry data among working-age adults is largely absent from case investigations in Washington. Methods of data collection that improve quality while minimizing the burden of collection should be pursued. Suggestions for improving data quality are discussed.

Disparities in unmet needed paid leave across race, ethnicity and citizenship status among employed Californians: a cross-sectional study

OBJECTIVES

Paid leave is essential for public health in order to prevent presenteeism (i.e., working while sick), provide an economic safety net for workers when ill, and promote family well-being through parental leave. While racial and ethnic disparities in unmet paid leave (or needing but not being able to take paid leave) are well documented, little evidence of the intersecting role of citizenship status exists. This study examined disparities in unmet paid leave across race, ethnicity and citizenship status.

STUDY DESIGN

This was a cross-sectional study of employed adults in California, USA.

METHODS

Weighted, multivariable logistic regressions were used to assess disparities in unmet needed paid leave across race, ethnicity and citizenship status categories, including non-citizen, naturalised, and citizen Latinx and Asian respondents, and naturalised and non-citizen White respondents, relative to US-born White respondents, controlling for demographic, familial, health-related and work-related covariates. This study examined a representative sample of Californian adults using the 2021 California Health Interview Survey (CHIS). A total of 24,453 people completed the CHIS from March to October 2021. This analysis was restricted to individuals who had complete data, were employed at the time of the survey and were part of the study race and ethnic groups of interest, leading to an analytical sample of 12,485 respondents.

RESULTS

While 16.9% of employed Californians reported forgoing needed paid leave, disparities across race, ethnicity and citizenship status were evident. Specifically, 31.8% of non-citizen Latinx respondents, compared to 11% of US-born White respondents, did not use paid leave when they needed it due to fear of job loss, fear of negative impacts on job advancement, employers denying it, lack of information or knowledge regarding the process or ineligibility. In the fully adjusted analyses, respondents identifying as non-citizen Latinx (adjusted odds ratio [aOR] = 2.57, 95% confidence interval [CI] = 1.94-3.40), naturalised Latinx (aOR = 1.90, 95% CI = 1.46-2.48), US-born Latinx (OR = 1.30, 95% CI = 1.06-1.60), non-citizen Asian (aOR = 2.34, 95% CI = 1.69-3.23) and naturalised Asian (aOR = 1.78, 95% CI = 1.35-2.34) had a statistically significantly higher likelihood of experiencing unmet needed paid leave compared to US-born White respondents.

CONCLUSIONS

Despite its importance for health, disparities across race, ethnicity and citizenship status exist in those who experience unmet paid leave. It is recommended that the administrative and enforcement agencies in California further communicate eligibility, facilitate the application process and enforce equitable access to paid leave for all workers.

Which Curve Are We Flattening? The Disproportionate Impact of COVID-19 Among Economically Marginalized Communities in Ontario, Canada, Was Unchanged From Wild-Type to Omicron

Person-level surveillance (N = 14 million) and neighborhood-level income data were used to explore magnitude of inequalities in COVID-19 hospitalizations and deaths over 5 waves in Ontario, Canada. Despite attempts at equity-informed policies alongside fluctuating levels of public health measures, the magnitude of inequalities in hospitalizations and deaths remained unchanged across waves.