Personal Care Products, Socioeconomic Status, and Endocrine-Disrupting Chemical Mixtures in Black Women.
Samantha SchildrothTraci N BetheaAmelia K WesselinkAlexa FriedmanVictoria FruhAntonia M CalafatGanesa WegienkaSymielle A GastonDonna D BairdLauren A WiseBirgit Claus HennPublished in: Environmental science & technology (2024)
Personal care products (PCPs) are sources of exposure to endocrine-disrupting chemicals (EDCs) among women, and socioeconomic status (SES) may influence these exposures. Black women have inequitable exposure to EDCs from PCP use, but no study has investigated how exposure to EDCs through PCPs may vary by SES, independent of race. Using data from the Study of Environment, Lifestyle, and Fibroids, a cohort of reproductive-aged Black women ( n = 751), we quantified associations between PCPs and urinary biomarker concentrations of EDC mixtures (i.e., phthalates, phenols, parabens) within SES groups, defined using k- modes clustering based on education, income, marital status, and employment. Information about PCP use and SES was collected through questionnaires and interviews. We used principal component analysis to characterize the EDC mixture profiles. Stratified linear regression models were fit to assess associations between PCP use and EDC mixture profiles, quantified as mean differences in PC scores, by SES group. Associations between PCP use and EDC mixture profiles varied by SES group; e.g., vaginal powder use was associated with a mixture of phenols among lower SES women, whereas this association was null for higher SES women. Findings suggest that SES influences PCP EDC exposure in Black women, which has implications for public health interventions.
Keyphrases
- polycystic ovary syndrome
- pregnancy outcomes
- public health
- healthcare
- cervical cancer screening
- breast cancer risk
- physical activity
- cardiovascular disease
- type diabetes
- quality improvement
- machine learning
- ionic liquid
- chronic pain
- air pollution
- social media
- mass spectrometry
- single molecule
- deep learning
- skeletal muscle
- single cell
- electronic health record