Erratum: “Associations of Combined Exposures to Surrounding Green, Air Pollution, and Road Traffic Noise with Cardiometabolic Diseases”

For the interaction analyses, we reported the differences in conditional effects (compared to the reference category) instead of the conditional effects, without clearly stating this and discussing results as conditional effects. We calculated the conditional effects; the association of an exposure (e.g., NDVI $300\mathrm{m}$) with an outcome (e.g., odds of diabetes) conditioned on the level of another exposure (e.g., ${\text{OP}}^{\text{DTT}}$). $\mathrm{P}$-values for interaction did not change. However, two paragraphs of text and three tables were affected and need to be corrected.

The text below should replace the “Potential for interaction” section in the Results.

We hypothesized that the association with exposure to air pollution is strongest (increased odds) in the highest road traffic noise quintile and vice versa. Further, we hypothesized that the association with air pollution and road traffic noise is strongest (increased odds) in the lowest surrounding green quintile and that the association with surrounding green is strongest (decreased odds) in the lowest air pollution or road traffic noise quintile. The conditional associations showed some indications for multiplicative interactions in the hypothesized directions between combinations of exposure variables and the odds of diabetes (Table 5). In the lowest ${\text{OP}}^{\text{DTT}}$ quintiles, an IQR increase in NDVI $300\mathrm{m}$ was associated with lower odds of diabetes, whereas in the highest ${\text{OP}}^{\text{DTT}}$ quintiles, no significant association of NDVI $300\mathrm{m}$ was observed. The associations of ${\text{OP}}^{\text{DTT}}$ with the odds of diabetes was slightly stronger in the highest road traffic noise quintile compared to the other quintiles. The conditional associations showed no clear pattern of interactions for the odds of hypertension (Table S7). When we assessed interactions using continuous by continuous exposures terms, we found similar patterns as when we used continuous by categorical interaction terms (Table S8).

Table 5.

Multiplicative interactions of surrounding green (NDVI $300\mathrm{m}$), air pollution (${\text{OP}}^{\text{DTT}}$), and road traffic noise (Lden) on the odds of diabetes.

Stratified exposure variable	Quintile	Linear exposure variable
		OR (95% CI)
		NDVI $300\mathrm{m}$	${\text{OP}}^{\text{DTT}}$	Road traffic noise (Lden)
NDVI $300\mathrm{m}$	Q1 ($\le \mathrm{0}\mathrm{.44}$)	—	1.00 (0.94, 1.05)	0.98 (0.95, 1.01)
	Q2 ($\le 0.50$)	—	1.00 (0.94, 1.06)	1.02 (0.99, 1.05)
	Q3 ($\le 0.55$)	—	1.08 (1.02, 1.14)	1.01 (0.98, 1.05)
	Q4 ($\le 0.61$)	—	1.08 (1.02, 1.14)	1.02 (0.99, 1.06)
	Q5 ($>0.61$)	—	1.09 (1.02, 1.15)	1.00 (0.97, 1.03)
	p.inta	—	0.05	0.34
${\text{OP}}^{\text{DTT}}$	Q1 ($<1.01$)	0.89 (0.85, 0.93)	—	1.00 (0.97, 1.02)
	Q2 ($\le 1.14$)	0.88 (0.83, 0.93)	—	1.00 (0.97, 1.03)
	Q3 ($\le 1.24$)	0.91 (0.86, 0.96)	—	1.01 (0.97, 1.04)
	Q4 ($\le 1.35$)	0.99 (0.94, 1.05)	—	1.02 (0.99, 1.05)
	Q5 ($>1.35$)	1.04 (0.99, 1.10)	—	1.00 (0.97, 1.03)
	p.inta	$<0.005$	—	0.78
Road traffic noise (Lden)	Q1 ($<49.3$)	0.90 (0.86, 0.95)	1.08 (1.03, 1.13)	—
	Q2 ($\le 52.0$)	0.88 (0.84, 0.93)	1.09 (1.03, 1.14)	—
	Q3 ($\le 54.7$)	0.92 (0.88, 0.97)	1.08 (1.03, 1.14)	—
	Q4 ($\le 58.7$)	0.92 (0.88, 0.96)	1.07 (1.02, 1.13)	—
	Q5 ($>58.7$)	0.93 (0.90, 0.97)	1.12 (1.07, 1.17)	—
	p.inta	0.48	0.78	—

Note: Results of multiplicative interactions are given as OR (95% CI) per 1-unit increase (IQR for NDVI $300\mathrm{m}$: 0.13; IQR for ${\text{OP}}^{\text{DTT}}$: $0.27\text{\hspace{0.17em}nmol}$ ${\text{DTT}/\text{min}/\mathrm{m}}^3$, 5 dB for Lden) in quintiles of the second variable (conditional effects). Models were adjusted for sex, age, marital status, region of origin, education, work, standardized household income, smoking habits, number of cigarettes/day, alcohol consumption, number of alcohol glasses/week, physical activity, body mass index and neighborhood socioeconomic status (SES). —, no data; CI, confidence interval; Lden, daily average noise level; NDVI, Normalized Difference Vegetation Index; ${\text{OP}}^{\text{DTT}}$, oxidative potential (OP) metric with dithiothreitol (DTT) assay; OR, odds ratio.

^a p.int shows the $p$-value for the overall interaction.

Table S7.

Multiplicative interaction effects of surrounding green (NDVI $300\mathrm{m}$), air pollution (${\text{OP}}^{\text{DTT}}$) and road-traffic noise on the odds of hypertension^a.

Stratified exposure variable	Quintile	Linear exposure variable
		NDVI $300\mathrm{m}$	${\text{OP}}^{\text{DTT}}$	Road-traffic noise (Lden)
		OR (95% CI)	OR (95% CI)	OR (95% CI)
NDVI $300\mathrm{m}$	Q1 ($\le 0.44$)	—	1.02 (0.98, 1.06)	0.97 (0.95, 0.99)
	Q2 ($\le 0.50$)	—	1.06 (1.02, 1.11)	1.01 (0.99, 1.03)
	Q3 ($\le 0.55$)	—	1.07 (1.03, 1.11)	0.99 (0.97, 1.01)
	Q4 ($\le 0.61$)	—	1.03 (0.99, 1.07)	1.00 (0.98, 1.02)
	Q5 ($>0.61$)	—	1.07 (1.03, 1.11)	1.00 (0.98, 1.02)
	p.intb	—	0.22	0.10
${\text{OP}}^{\text{DTT}}$	Q1 ($<1.01$)	0.96 (0.94, 0.99)	—	1.00 (0.99, 1.02)
	Q2 ($\le 1.14$)	0.97 (0.93, 1.01)	—	1.00 (0.98, 1.02)
	Q3 ($\le 1.24$)	1.01 (0.97, 1.04)	—	0.99 (0.97, 1.01)
	Q4 ($\le 1.35$)	1.04 (1.00, 1.08)	—	0.98 (0.96, 1.00)
	Q5 ($>1.35$)	1.01 (0.97, 1.05)	—	0.98 (0.96, 1.00)
	p.intb	0.02	—	0.21
Road-traffic noise (Lden)	Q1 ($<49.3$)	0.95 (0.92, 0.97)	1.07 (1.04, 1.11)	—
	Q2 ($\le 52.0$)	0.98 (0.95, 1.01)	1.07 (1.03, 1.10)	—
	Q3 ($\le 54.7$)	0.98 (0.94, 1.01)	1.07 (1.03, 1.11)	—
	Q4 ($\le 58.7$)	0.97 (0.94, 1.00)	1.06 (1.02, 1.10)	—
	Q5 ($>58.7$)	0.99 (0.96, 1.02)	1.03 (1.00, 1.07)	—
	p.intb	0.34	0.49	—

^a Results of multiplicative interaction effects are given as OR (95% CI) per IQR increase (IQR for NDVI $300\mathrm{m}$: 0.13, IQR for ${\text{OP}}^{\text{DTT}}$: $0.27\text{\hspace{0.17em}nmol}$ ${\text{DTT}/\text{min}/\mathrm{m}}^3$) or per 5 dB. Models were adjusted for sex, age, marital status, region of origin, education, work, standardized household income, smoking habits, number of cigarettes/day, alcohol consumption, number of alcohol glasses/week, physical activity, body mass index and neighborhood SES.

^b P.int shows the $\mathrm{p}$-value for the overall interaction effect.

Table S8.

Multiplicative interaction effects (continuous $*\text{\hspace{0.17em}continuous}$) of surrounding green (NDVI $300\mathrm{m}$), air pollution (${\text{OP}}^{\text{DTT}}$) and road-traffic noise on the odds of diabetes and hypertension^a.

Exposure	Effect modifier	Level effect modifier	Diabetes		Hypertension
			OR (95% CI)a	P.intb	OR (95% CI)a	P.intb
NDVI $300\mathrm{m}$	${\text{OP}}^{\text{DTT}}$ (nmol ${\text{DTT}/\text{min}/\mathrm{m}}^3$)	1.05	0.92 (0.90, 0,94)	$<0.005$	0.99 (0.97, 1.00)	$<0.005$
		1.32	0.96 (0,94, 0.99)		1.01 (0.99, 1.03)
	Road-traffic noise (dB)	50.0	0.90 (0.88, 0.92)	0.04	0.96 (0.94, 0.98)	0.008
		57.5	0.92 (0.90, 0.94)		0.98 (0.96, 1.00)
${\text{OP}}^{\text{DTT}}$	NDVI $300\mathrm{m}$	0.46	1.03 (1.00, 1.06)	$<0.005$	1.04 (1.02, 1.06)	$<0.005$
		0.59	1.08 (1.05, 1.11)		1.06 (1.05, 1.08)
	Road-traffic noise (dB)	50.0	1.09 (1.06, 1.12)	0.86	1.07 (1.05, 1.09)	0.005
		57.5	1.09 (1.06, 1.12)		1.05 (1.03, 1.07)
Road-traffic noise	${\text{OP}}^{\text{DTT}}$ (${\text{nmol}\hspace{0.17em}\text{DTT}/\text{min}/\mathrm{m}}^3$)	1.05	1.00 (0.99, 1.02)	0.86	1.00 (0.99, 1.01)	0.005
		1.32	1.00 (0.99, 1.02)		0.98 (0.97, 0.99)
	NDVI $300\mathrm{m}$	0.46	1.00 (0.98, 1.01)	0.04	0.99 (0.97, 1.00)	0.008
		0.59	1.01 (1.00, 1.03)		1.00 (0.99, 1.01)

^a Results of multiplicative interaction effects are given as OR (95% CI) per IQR increase (IQR for NDVI $300\mathrm{m}$: 0.13, IQR for ${\text{OP}}^{\text{DTT}}$: $0.27\text{\hspace{0.17em}nmol}$ ${\text{DTT}/\text{min}/\mathrm{m}}^3$) or per 5 dB. As it can be hard to interpret patterns of effect modification based on effect estimates from a model with continuous $*\text{\hspace{0.17em}continuous}$ interaction terms, we calculated effect estimates of the continuous terms (exposure) when the other continuous term (effect modifier) is held constant at its 25^th and 75^th percentile (level effect modifier). For example, the associations of NDVI $300\mathrm{m}$ with the odds of diabetes is stronger ($\text{OR}=0.92$, 95% CI: 0.90, 0.94) when ${\text{OP}}^{\text{DTT}}$ is at its 25^th percentile ($1.05\text{\hspace{0.17em}nmol}$ ${\text{DTT}/\text{min}/\mathrm{m}}^3$) than when ${\text{OP}}^{\text{DTT}}$ is at its 75^th percentile ($1.32\text{\hspace{0.17em}nmol}$ ${\text{DTT}/\text{min}/\mathrm{m}}^3$; $\text{OR}=0.96$, 95% CI: 0.94, 0.99). Models were adjusted for sex, age, marital status, region of origin, education, work, standardized household income, smoking habits, number of cigarettes/day, alcohol consumption, number of alcohol glasses/week, physical activity, body mass index and neighborhood SES.

^b P.int shows the $\mathrm{p}$-value for the interaction effect.

The following changes should be made to the “Interaction” section in the Discussion.

• The first two sentences, reading “We did not find indications for interactions in the hypothesized direction for any cardiometabolic outcome. Some interaction terms were significant; however, no clear pattern was observed.” should be deleted.

• The last two sentences in the paragraph should be replaced with the following: “Surrounding green was associated with lower odds of diabetes in areas with lower air pollution levels, but not in areas with high air pollution levels. This could be because surrounding green in low air pollution areas might be more aesthetically attractive and therefore have a stronger impact on psychological stress, which could in turn lead to diabetes. Further, for diabetes, we found a very weak indication for potential effect measure modification between ${\text{OP}}^{\text{DTT}}$ and road traffic noise. Most other studies reported no indications for interactions between air pollution and road traffic noise (Bodin et al. 2016; Sørensen et al. 2013; Selander et al. 2009), except for Sørensen et al. (2014).”

The authors regret the error and would like to apologize for any inconvenience caused.

Table 5 and Tables S7 and S8 should be replaced with the following:

Environ Health Perspect. 127(8):087003, (2019), https://doi.org/10.1289/EHP3857