| Literature DB >> 22538298 |
Amar J Mehta1, Martin Adam, Emmanuel Schaffner, Jean-Claude Barthélémy, David Carballo, Jean-Michel Gaspoz, Thierry Rochat, Christian Schindler, Joel Schwartz, Jan-Paul Zock, Nino Künzli, Nicole Probst-Hensch.
Abstract
BACKGROUND: Household cleaning products are associated with adverse respiratory health outcomes, but the cardiovascular health effects are largely unknown.Entities:
Mesh:
Substances:
Year: 2012 PMID: 22538298 PMCID: PMC3404664 DOI: 10.1289/ehp.1104567
Source DB: PubMed Journal: Environ Health Perspect ISSN: 0091-6765 Impact factor: 9.031
Characteristics of participants who reported cleaning in their homes (n = 581).
| Characteristic | Used spray or scented products (n = 515) | Did not use spray or scented products (n = 66) | p-Valuea | |||
|---|---|---|---|---|---|---|
| Age (years) [median (IQR)] | 59.8 (54.6, 65.6) | 60.4 (56.1, 68.0) | 0.38 | |||
| Male (%) | 50 (9.7) | 9 (13.6) | 0.32 | |||
| BMI (kg/m2) [median (IQR] | 26.0 (22.9, 28.9) | 24.6 (22.8, 27.4) | 0.12 | |||
| Smoking status [n (%)] | ||||||
| Never | 272 (52.8) | 44 (66.7) | 0.04 | |||
| Former | 159 (30.9) | 15 (22.7) | 0.20 | |||
| Current | 84 (16.3) | 7 (10.6) | 0.28 | |||
| ETS exposure (hr/day) [n (%)] | ||||||
| 0 | 415 (80.6) | 56 (84.9) | 0.50 | |||
| < 3 | 68 (13.2) | 7 (10.6) | 0.70 | |||
| ≥ 3 | 32 (6.2) | 3 (4.5) | 0.79 | |||
| Alcohol consumption (drinks/day) [n (%)] | ||||||
| < 1 | 339 (65.8) | 45 (68.2) | 0.78 | |||
| ≥ 1 | 176 (34.2) | 21 (31.8) | ||||
| Physical activity (hr/week) [n (%)] | ||||||
| < 0.5 | 235 (45.7) | 34 (51.5) | 0.43 | |||
| 0.5 – 2.0 | 183 (35.5) | 17 (25.8) | 0.13 | |||
| > 2.0 | 97 (18.8) | 15 (22.7) | 0.51 | |||
| Uric acid (μmol/L) [median (IQR)] | 289 (243, 337) | 293 (243, 367) | 0.22 | |||
| Employment status [n (%)] | ||||||
| Fully/partially employed, in military, or student | 76 (14.8) | 9 (13.6) | 1.00 | |||
| Unemployed housewife/househusband | 218 (42.3) | 27 (40.9) | 0.89 | |||
| Retired, sick/disabled, or other | 221 (42.9) | 30 (44.5) | 0.69 | |||
| Tertiary education levelb [n (%)] | ||||||
| Low | 60 (11.7) | 8 (12.1) | 0.84 | |||
| Medium | 360 (69.9) | 47 (71.2) | 0.89 | |||
| High | 95 (18.5) | 11 (16.7) | 0.87 | |||
| Taking cardiovascular medication [n (%)] | 125 (24.3) | 11 (16.7) | 0.22 | |||
| Symptoms and markers of OBSc [n (%)] | 212 (54.5) | 34 (59.7) | 0.32 | |||
| IQR, interquartile range. ap-Values are based on chi-square and two-sample comparison tests for categorical variables and continuous variables, respectively. bLow, primary school; medium, secondary school/middle school/apprenticeship school; and high, technical college/university. cPercentages represent the 404 exposed and 57 unexposed participants who completed prebronchodilator spirometry and who did not report ever having asthma or taking respiratory medication. | ||||||
Figure 1Adjusted average percent change (95% CIs) in 24-hr SDNN, TP, LF, and HF associated with the use of cleaning sprays (A), air freshening sprays (B), scented products (C), and the number of sprays used weekly (D). Twenty-four-hour SDNN, TP, LF, and HF were modeled on the logarithmic scale in multiple linear regression as a function of each exposure in separate models and then transformed into average percent change relative to unexposed participants (n = 66), after adjusting for sex, age, age2, BMI, BMI2, alcohol consumption, physical activity, smoking status, environmental tobacco smoke exposure, education, employment status, cardiovascular medication intake, uric acid levels, street and railway noise, traffic-related PM10, seasonal effects, and study area. *Ordinal exposure variable p < 0.05. **Ordinal exposure variable p < 0.10.
Figure 2Adjusted average percent changes (95% CIs) in 24-hr SDNN, TP, LF, and HF associated with the use of cleaning sprays (A), air freshening sprays (B), scented products (C), and the number of sprays used weekly (D) after stratification by OBS. Twenty-four-hour SDNN, TP, LF, and HF were modeled on the logarithmic scale in multiple linear regression as a function of each exposure in separate models and then transformed into average percent change relative to unexposed participants (n = 34, OBS; n = 23, no OBS), after adjusting for OBS, sex, age, age2, BMI, BMI2, alcohol consumption, physical activity, smoking status, environmental tobacco smoke exposure, education, employment status, cardiovascular medication intake, uric acid levels, street and railway noise, traffic-related PM10, seasonal effects and study area. Participants who reported doctor-diagnosed asthma or asthma medication use were excluded from this analysis.