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Environmental pollution is associated with increased risk of psychiatric disorders in the US and Denmark

Fig 3

Relationship between environmental factors and neurological and psychiatric disorders in US.

The results from the US data analysis in which all predictor variables are divided into septiles (7 groups) with each septile representing approximately 400 counties. Septile 1 (counties with the least exposure or the least percentage) is used as a referent to compare the disorder rates in the higher septiles (counties with systematically higher exposures or percentages). For air, water, land, and built qualities, a higher septile corresponds to the group of counties with poor quality. Similarly, for all other variables, a higher septile represents a higher fraction or the corresponding percentages. The estimated disorder rate from the mixed-effects regression model is shown for (A) bipolar disorder, (B) schizophrenia, (C) personality disorder, (D) major depression, (E) epilepsy, and (F) Parkinson disease. (G) Map showing the aggregated state-level random effects. The random effects for the 6 disorders are aggregated to produce 1 representative map. States shaded red show higher disorder diagnoses, and those shaded blue show lower disorder diagnoses that is not captured by our model. An apparent high neurological and psychiatric disorder rate in the states of Michigan, Missouri, Georgia, and New Mexico, and the apparent low rate in the states of South Dakota, Iowa, Wyoming, and North Carolina could be associated with reporting biases. (H) Map showing aggregated, county-level random effects. Random effects for the 6 disorders are aggregated to produce 1 representative map. Counties in red show higher disorder rates, and those in blue show lower disorder rates not captured by our model. County-level random effects can be thought of as residual variations not explained by fixed-effect predictors and state-level random effects. There are relatively few counties in which the county-level random effect is consistently low. For example, several counties are consistently low (San Diego, Imperial, Orange, and San Bernardino Counties in Southern California), and several counties are consistently high (San Luis Obispo County in California and Snohomish and King Counties in Washington). The underlying data for this figure can be found in S2 Table.

Fig 3