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Deepwater Horizon oil spill exposure and child health: a longitudinal analysis

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Abstract

The BP Deepwater Horizon oil spill (DHOS) created widespread concern about threats to health among residents of the Louisiana Gulf Coast. This study uses data from the Resilient Children, Youth, and Communities study—a longitudinal cohort survey of households with children in DHOS-affected areas of South Louisiana—to consider the effect of DHOS exposure on health trajectories of children, an especially vulnerable population subgroup. Results from latent linear growth curve models show that family DHOS exposure via physical contact and job/income loss both negatively influenced initial child health. However, the effects of physical exposure dissipated over time while the effects of job/income loss persisted. This pattern holds for both general child health and the number of recent physical health problems children had experienced. These findings help to bridge the literature on disaster impacts and resilience/vulnerability, with the literature on socioeconomic status as a fundamental cause of health outcomes over the life course.

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Notes

  1. The DHOS is surpassed in volume only by the Persian Gulf oil spill of 1991, when Iraqi forces intentionally released over 300 million gallons of oil (CNN 2018). This was a purposeful action in the context of war; a disaster, but not an accident.

  2. Henceforth we use the term parent for ease of expression.

  3. For further methodological information, see Abramson et al. (2013).

  4. In rare cases a new caregiver was selected due to chronic unavailability of the original adult respondent.

  5. In a supplemental analysis (not shown), we examined correlations of all Time 1 outcome and exposure variables with study attrition. One component of physical DHOS exposure (whether the parent could smell the oil in the first 6 months following the oil spill) carried a negative and association at the p = .039 level, whereas the other components of physical DHOS exposure (parent or child direct contact with the oil) did not carry significant associations, nor did spill-related job/income loss, general child health, or recent physical child health problems. That the outcome variables were not significantly associated with study attrition, provides added confidence in the results.

  6. Correlations between the two outcomes at each time point are shown in Appendix 1.

  7. Two months is a standard for recent health recollections used elsewhere in the literature. See, for example, the National Health Interview Survey (National Center for Health Statistics 2019).

  8. There were approximately 50 people who in 2016 or 2018 who reported losing job/income due to the DHOS since the prior wave. Allowing these individuals to affect the slope once they had been exposed does not change our results.

  9. In our analysis child age is modeled age as a linear relationship. Sensitivity analysis modeling child age as curvilinear or categorical (i.e., quartiles) yielded similar results.

  10. Ancillary analyses controlling for whether the respondent had moved during the survey yielded similar results.

  11. Likelihood ratio (LR) test for OLS vs. unconditional means model: χ2 = 310.12, p < .001; LR test for unconditional means vs. unconditional growth curve without covariance between random intercept and random slope: χ2 = 16.31 p < .001; LR test for adding covariance between random intercept and random slope: 6.74; χ2 = p < .01.

  12. All figures were estimated using the margins command. While the sem command allows for FIML estimation of missing values it does not provide predicted values for any of the cases missing on model predictors. Further, the sem command does not allow factor notation, which is required for margins to provide valid estimates. Therefore, we estimated margins from multilevel models using the mixed command, which does not use FIML, so for these models we handled missing data on income by using mean imputation and including a dichotomous indicator of whether cases were missing data on income.

  13. Likelihood Ratio (LR) test for OLS vs. unconditional means model: χ2 = 321.19, p < .001; LR test for unconditional means vs. unconditional growth curve without covariance between random intercept and random slope: χ2 = 14.39; p < .001; LR test for adding covariance between random intercept and random slope: χ2 = 0.80; p = .372.

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Acknowledgments

We are grateful to all who participated in the Resilient Children, Youth, and Communities (RCYC) study, which was made possible by grants from the Baton Rouge Area Foundation and the Gulf of Mexico Research Initiative (GoMRI).

Funding

Data funded by GoMRI are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org (DOI: https://doi.org/10.7266/n7-hjz4-w930. DOI: https://doi.org/10.7266/n7-9ftv-yd07).

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  1. Department of Sociology, Louisiana State University, 126 Stubbs Hall, Baton Rouge, LA, 70803, USA

    Tim Slack

  2. Louisiana State University, Baton Rouge, USA

    Rhiannon A. Kroeger, Samuel Stroope & Kathryn Sweet Keating

  3. Columbia University, New York, USA

    Jonathan Sury, Jeremy Brooks, Thomas Chandler & Jaishree Beedasy

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Appendix. Correlations for outcome variables

Appendix. Correlations for outcome variables

  

Current general child health

Number of recent physical child health problems

  

Time 1

Time 2

Time 3

Time 1

Time 2

Time 3

Current general child health

Time 1

1.00

–

–

–

–

–

Time 2

.55

1.00

–

–

–

–

Time 3

.43

.62

1.00

–

–

–

Number of recent physical child health problems

Time 1

− .53

− .37

− .36

1.00

–

–

Time 2

− .32

− .48

− .35

.52

1.00

–

Time 3

− .30

− .38

− .42

.51

.60

1.00

  1. RCYC survey (N = 393 )
  2. All correlations are significant (p < .001)

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Slack, T., Kroeger, R.A., Stroope, S. et al. Deepwater Horizon oil spill exposure and child health: a longitudinal analysis. Popul Environ 42, 477–500 (2021). https://doi.org/10.1007/s11111-020-00354-6

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