Abstract

From March to May 2020, 1306 oilfield workers in Kazakhstan tested positive for SARS-CoV-2. We conducted a case-control study to assess factors associated with SARS-CoV-2 transmission. The cases were PCR-positive for SARS-CoV-2 during June–September 2020. Controls lived at the same camp and were randomly selected from the workers who were PCR-negative for SARS-CoV-2. Data was collected telephonically by interviewing the oil workers. The study had 296 cases and 536 controls with 627 (75%) men, and 527 (63%) were below 40 years of age. Individual factors were the main drivers of transmission, with little contribution by environmental factors. Of the twenty individual factors, rare hand sanitizer use, travel before shift work, and social interactions outside of work increased SARS-CoV-2 transmission. Of the twenty-two environmental factors, only working in air-conditioned spaces was associated with SARS-CoV-2 transmission. Communication messages may enhance workers’ individual responsibility and responsibility for the safety of others to reduce SARS-CoV-2 transmission.

Highlights

  • MethodsTCO is one of the largest producers and marketers of oil and gas in Kazakhstan

  • We excluded from the study 815 (58%) COVID-19 patients who were off rotation, and not available for the interview, and 298 (21%) workers who were identified as positive for COVID-19 prior to arriving to their work rotation (Figure 1)

  • A total of 296 eligible COVID-19 patients were identified during their rotation in the selected camps and all of them agreed to participate in the study as case patients (Table 1)

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Summary

Methods

TCO is one of the largest producers and marketers of oil and gas in Kazakhstan. It is a limited liability partnership (LLP) between four international companies and one national company to develop the Tengiz and Korolevskoye oilfields in the Atyrau region of Kazakhstan [15]. Data were analyzed using Stata version 16 (StataCorp LP, College Station, TX, USA). Epi Info version 7.2.4.0 (CDC, Atlanta, GA, USA). The Chi-square test was used to determine if the difference between proportions was statistically significant. Multivariable logistic regression analysis was completed to generate adjusted ORs (aOR) and 95% CIs. Multicollinearity was assessed by the Generalized Variance Inflation Factor (GVIF) [18,19]

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