Current Data Gaps in Modeling Essential Worker Absenteeism Due to COVID-19

Area of Science:

• Occupational Health
• Epidemiology
• Health Economics

Background:

• The COVID-19 pandemic presented significant physical and mental health challenges for employees.
• Companies adopted various measures, primarily reducing in-person interactions, to minimize virus transmission risks.
• Existing data for modeling employee absenteeism during the pandemic is limited, impacting model accuracy.

Purpose of the Study:

• To identify data gaps hindering the development of precise absenteeism models.
• To highlight areas for improved data collection to enhance decision support for companies.
• To inform strategies for managing workforce health and productivity post-pandemic.

Main Methods:

• Preliminary analysis of available data on COVID-19's impact on workforce absenteeism.
• Identification of key variables and data deficiencies in current absenteeism modeling.
• Literature review on factors influencing employee return-to-work post-illness.

Main Results:

• Current absenteeism models rely on assumptions due to insufficient data, limiting their precision.
• Key data gaps include time-to-recovery post-hospitalization and absenteeism related to family illness.
• The impact of mental health on returning to work is an under-researched area.

Conclusions:

• More comprehensive data is required to build robust absenteeism models.
• Addressing data gaps in recovery times, household illness, and mental health is crucial for effective workforce management.
• Improved analytical approaches are needed to support companies in navigating pandemic-related workforce challenges.