About

Sadie Costello is an occupational and environmental epidemiologist at the UC Berkeley School of Public Health. She has studied a range of chronic disease outcomes, including heart disease and cancer, in relation to particulate exposures in light metal manufacturing, metalworking fluids in automobile manufacturing, and to diesel exhaust and silica in miners. Sadie has focused on methods to address healthy worker survivor bias and on using large administrative datasets to answer causal research questions. Her research interests include exposure-response models for health outcomes from occupational exposures, healthy workers survivor bias, and chronic disease.

Research topics

• Internal medicine
• Medicine
• Environmental health
• Ecology
• Biology
• Mechanical engineering
• Environmental science
• Atmospheric sciences
• Materials science
• Physiology
• Genetics
• Meteorology
• Geology
• Chemistry
• Engineering
• Composite material
• Geography

Selected publications

• Response to Drs. Igor Burstyn and George Luta’s letter: Advice on better utilization of validation data to adjust odds ratios for differential exposure misclassification (recall bias)

  Scandinavian Journal of Work Environment & Health · 2025-04-24

  letterOpen access

  We want to thank Drs. Burstyn & Luta (1) for their recognition of our recent study (2) suggesting that estimates of breast cancer risk following retrospective self-reported night shift work are inflated by recall bias. The main strength of the study was a gold standard based on individual, prospective, objective and detailed information on night shift work that allowed validation of self-reported night shift work obtained after breast cancer was diagnosed – the usual situation for case–control studies (3). The study confirmed what textbooks have long taught but rarely documented empirically (4–6). We also want to thank Drs. Burstyn & Luta for their advice on how we could have utilized this precious dataset not only for simple but also probabilistic and Bayesian quantitative bias analyses. Even if highly instructive, this may still require strong statistical involvement. Using data provided in our paper, Drs. Burstyn & Luta’s bias-corrected odds ratio (OR) estimate of breast cancer following night shift work was centered around 1.0 (95% credible interval 0.3–1.7) and suggests that recall bias could completely, and not only partly as in our analysis (OR 1.05; correctly computed 95% confidence interval 0.88–1.27), explain the observed associations between night shift work and breast cancer found in case–control studies with retrospective self-reported exposure information. This finding strengthens our concern that breast cancer studies based on retrospective self-reports of night shifts may not provide convincing evidence. The gold standard of this validation study was based on a cohort of healthcare workers with day-by-day night shift information from a pay roll register and has earlier been used for breast cancer risk assessment showing no increased risk (7). A recent Swedish study using comparable data neither showed an overall increased risk (8). However, these studies included only information on recent night shift work. The next step should be a follow up of the cohorts when information on more distant night shift work becomes available with an emphasis on analyses that explore the timing of shift work on breast cancer risk. References 1. Burstyn I, Luta G. Advice on better utilization of validation data to adjust odds ratios for differential exposure misclassification (recall bias). Scand J Work Environ Health – online first. https://doi.org/10.5271/sjweh.4226 2. Vestergaard JM, Haug JN, Dalbøge A, Bonde JP, Garde AH, Hansen J et al. Validity of self-reported night shift work among women with and without breast cancer. Scand J Work Environ Health. 2024;50(3):152–7. https://doi.org/10.5271/sjweh.4142. 3. Cordina-Duverger E, Menegaux F, Popa A, Rabstein S, Harth V, Pesch B et al. Night shift work and breast cancer: a pooled analysis of population-based case-control studies with complete work history. Eur J Epidemiol. 2018; 33(4):369–79. https://doi.org/10.1007/s10654-018-0368-x. 4. Berrington de González A, Richardson DB, Schubauer-Berigan MK. Statistical methods in cancer research, Volume V. Bias assessment in case–control and cohort studies for hazard identification. Lyon, France: International Agency for Research on Cancer; 2024. 5. Checkoway H, Pearce N, Kriebel D. Research Methods in Occupational Epidemiology. New York: Oxford University Press 2004. p372. 6. Rothman KJ. Modern Epidemiology. Boston/Toronto: Little, Brown and Company; 1986. p358. 7. Vistisen HT, Garde AH, Frydenberg M, Christiansen P, Hansen AM, Hansen J et al. Short-term effects of night shift work on breast cancer risk: a cohort study of payroll data. Scand J Work Environ Health. 2017;43(1):59–67. https://doi.org/10.5271/sjweh.3603. 8. Gustavsson P, Bigert C, Andersson T, Kader M, Härmä M, Selander J et al. Night work and breast cancer risk in a cohort of female healthcare employees in Stockholm, Sweden. Occup Environ Med. 2023;80(7):372–6. https://doi.org/10.1136/oemed-2022-108673. Henrik Albert Kolstad, MD,1, 2 Jesper Medom Vestergaard, MIT,1 Jens Peter Bonde, MD,3 Sadie Costello, PhD,4 Annett Dalbøge, PhD,1 Åse Marie Hansen, PhD,5, 6 Ann Dyreborg Larsen, PhD,6 Anne Helene Garde, PhD 5, 6 1 Occupational and Environmental Medicine, Danish Ramazzini Centre, Aarhus University Hospital, Aarhus, Denmark. 2 Department of Clinical Medicine, Aarhus University, Denmark. 3 Department of Occupational and Environmental Medicine, Bispebjerg and Frederiksberg Hospital, Denmark. 4 Environmental Health Science, School of Public Health, University of California, Berkeley, USA. 5 The National Research Centre for the Working Environment, Denmark. 6 Department of Public Health, University of Copenhagen, Denmark. Correspondence to: Henrik Kolstad, Occupational and Environmental Medicine, Danish Ramazzini Centre, Aarhus University Hospital, Aarhus, Denmark. [E-mail: kolstad@clin.au.dk]

  PublisherOA PDFDOI

• Occupational risk assessment: lessons from the MSHA Silica Rule

  Occupational and Environmental Medicine · 2025-10-01

  editorialOpen access
  PublisherOA PDFDOI

• 8290478 Working evening and night shifts in the health care sector and incidence of myocardial infarction: a national cohort study in Denmark

  2025-10-01

  articleOpen access

  <h3>Objective</h3> To examine the exposure-response relations between cumulative evening and night shifts and incident myocardial infarction among hospital employed health care workers, including nurses, physicians, and others. <h3>Material and Methods</h3> This national register-based cohort study includes all newly hired health care workers ever working evening or night shifts with day by day payroll information on evening and night shifts, from 2008/2009 to 2020. In total, we included 137 184 health care workers (mean age 33.3 years at inclusion, 77% women) who were followed from first registered evening or night shift for an average of 6.4 years. First time hospital contacts for myocardial infarction were identified in national health registers. Incidence rates of myocardial infarction by cumulative evening and night shifts were estimated by Poisson regression adjusted for sex, age, calendar year, diabetes, obesity, hypertension, hypercholesterolemia, education, and family history of cardiovascular disease; and for a subset of 13 080 workers, additionally adjusted for self-reported smoking, body mass index, and alcohol consumption. <h3>Results</h3> During follow-up, 477 workers (49% women) were diagnosed with myocardial infarction. Men and women showed overall incidence rates of 12.5 and 3.4 per 10 000 person years. No increasing incidence rates of myocardial infarction with cumulative number of evening or night shifts were observed. <h3>Conclusions</h3> This study of health care workers found no exposure-response relation between cumulative evening or night shifts and incident myocardial infarction during up to 13 years after the first recorded evening or night shift.

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• Diesel exhaust and respiratory dust exposure and mortality from ischemic heart disease in the Diesel Exhaust in Miners Study II

  ISEE Conference Abstracts · 2024-07-31

  articleOpen access
  PublisherDOI

• Re: Adjustment for duration of employment in occupational epidemiology

  Annals of Epidemiology · 2024-08-08

  letterOpen access
  PublisherOA PDFDOI

• Validity of self-reported night shift work among women with and without breast cancer

  Scandinavian Journal of Work Environment & Health · 2024-02-08 · 8 citations

  articleOpen access

  OBJECTIVES: This study aimed to estimate the validity of self-reported information on ever-night shift work among women with and without breast cancer and illustrate the consequences for breast cancer risk estimates. METHODS: During 2015-2016, 225 women diagnosed with breast cancer and 1800 matched controls without breast cancer employed within the Danish hospital regions during 2007-2016 participated in a questionnaire-based survey. Their reported night shift work status was linked with objective payroll register day-by-day working hour data from the Danish Working Hour Database and the Danish Cancer Registry. For the breast cancer patients and their matched controls, we estimated sensitivity and specificity for ever-working night shifts using the payroll data as the gold standard. We also used quantitative bias analysis to estimate the impact on relative risk estimates for a hypothetical population. RESULTS: For breast cancer patients, we observed a sensitivity of ever-night shifts of 86.2% and a specificity of never-night shifts of 82.6%. For controls, the sensitivity was 80.6% and the specificity 83.7%. Odds ratio for breast cancer in a hypothetical population decreased from 1.12 [95% confidence interval (CI) 1.03-1.21] to 1.05 (95% CI 0.95-1.16) when corrected by the sensitivity and specificity estimates. CONCLUSION: This study shows that female breast cancer patients had slightly better recall of previous night shift work than controls. Additionally, both breast cancer patients and controls recalled previous never-night shift work with low specificity. The net effect of this misclassification is a small over-estimation of the relative breast cancer risk due to night shift work.

  PublisherOA PDFDOI

• Diesel exhaust and respiratory dust exposure in miners and chronic obstructive pulmonary disease (COPD) mortality in DEMS II

  Environment International · 2024-02-24 · 5 citations

  articleOpen access

  BACKGROUND: Diesel exhaust and respirable dust exposures in the mining industry have not been studied in depth with respect to non-malignant respiratory disease including chronic obstructive pulmonary disease (COPD), with most available evidence coming from other settings. OBJECTIVES: To assess the relationship between occupational diesel exhaust and respirable dust exposures and COPD mortality, while addressing issues of survivor bias in exposed miners. METHODS: The study population consisted of 11,817 male workers from the Diesel Exhaust in Miners Study II, followed from 1947 to 2015, with 279 observed COPD deaths. We fit Cox proportional hazards models for the relationship between respirable elemental carbon (REC) and respirable dust (RD) exposure and COPD mortality. To address healthy worker survivor bias, we leveraged the parametric g-formula to assess effects of hypothetical interventions on both exposures. RESULTS: Cox models yielded elevated estimates for the associations between average intensity of REC and RD and COPD mortality, with hazard ratios (HR) corresponding to an interquartile range width increase in exposure of 1.46 (95 % confidence interval (CI): 1.12, 1.91) and 1.20 (95 % CI: 0.96, 1.49), respectively for each exposure. HRs for cumulative exposures were negative for both REC and RD. Based on results from the parametric g-formula, the risk ratio (RR) for COPD mortality comparing risk under an intervention eliminating REC to the observed risk was 0.85 (95 % CI: 0.55, 1.06), equivalent to an attributable risk of 15 %. The corresponding RR comparing risk under an intervention eliminating RD to the observed risk was 0.93 (95 % CI: 0.56, 1.31). CONCLUSIONS: Our findings, based on data from a cohort of nonmetal miners, are suggestive of an increased risk of COPD mortality associated with REC and RD, as well as evidence of survivor bias in this population leading to negative associations between cumulative exposures and COPD mortality in traditional regression analysis.

  PublisherDOI

• The health benefits of reducing micro-heat islands: A 22-year analysis of the impact of urban temperature reduction on heat-related illnesses in California's major cities

  The Science of The Total Environment · 2024-08-03 · 5 citations

  articleOpen access
  PublisherOA PDFDOI

• Evaluating natural course performance in parametric g-formula: review of current practice and illustration based on the United Autoworkers–General Motors cohort

  American Journal of Epidemiology · 2024-10-23 · 2 citations

  article

  The parametric g-formula is a causal inference method that appropriately adjusts for time-varying confounding affected by prior exposure. Like all parametric methods, it assumes correct model specification, usually assessed by comparing the observed outcome with the simulated outcome under no intervention (natural course). However, it is unclear how to evaluate natural course performance and whether other variables should also be considered. We reviewed current practices for evaluating model misspecification in applications of the parametric g-formula. To illustrate the pitfalls of current practices, we then applied the parametric g-formula to examine cardiovascular disease mortality in relation to occupational exposure in the United Autoworkers-General Motors cohort (UAW-GM), comparing 20 parametric model sets and qualitatively assessing natural course performance for all time-varying variables over follow-up. We found that current practices of evaluating model misspecification are often insufficient, increasing risk of bias and statistical cherry-picking. Based on our motivational analyses of the UAW-GM cohort, good natural course performance of the outcome does not guarantee good simulations of other covariates; poor predictions of exposures and covariates may still exist. We recommend reporting natural course performance for all time-varying variables at all time points. Objective criteria for evaluating model misspecification in the parametric g-formula need to be developed.

  PublisherDOI

• Associations between prenatal and early-life air pollution exposure and lung function in young children: Exploring influential windows of exposure on lung development

  Environmental Research · 2023-02-03 · 25 citations

  articleOpen access
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Frequent coauthors

• Ellen A. Eisen

  University of California, Berkeley

  89 shared

• Andreas M. Neophytou

  Colorado State University

  62 shared

• Sally Picciotto

  Berkeley Public Health Division

  47 shared

• Daniel Brown

  42 shared

• Mark R. Cullen

  36 shared

• Elizabeth M. Noth

  University of California, Berkeley

  34 shared

• John R. Balmes

  University of California, Berkeley

  31 shared

• Ellen Eisen

  University of California, Berkeley

  31 shared