Selected article for: "positive strong correlation and strong correlation"

Author: Gregor Singer; Joshua Graff Zivin; Matthew Neidell; Nicholas Sanders
Title: Air Pollution Increases Influenza Hospitalizations
  • Document date: 2020_4_10
  • ID: kbv9kh6z_5
    Snippet: As our measure of pollution, we use the U.S. Environmental Protection Agency's Air Quality Index (AQI), which we aggregate to county-bymonth-by-year to match outcomes. The AQI is a measure of overall air quality based on the primary criteria pollutants specified in the Clean Air Act. Aggregation of pollutants means there are no real "units" for the measure. It is designed such that higher AQI values indicate worse air quality. To ensure we captur.....
    Document: As our measure of pollution, we use the U.S. Environmental Protection Agency's Air Quality Index (AQI), which we aggregate to county-bymonth-by-year to match outcomes. The AQI is a measure of overall air quality based on the primary criteria pollutants specified in the Clean Air Act. Aggregation of pollutants means there are no real "units" for the measure. It is designed such that higher AQI values indicate worse air quality. To ensure we capture exposure to air pollution before diagnosis, we lag the AQI by one month. In all of our analyses, we focus on the influenza season (October to March). Figure 1 shows the seasonality of inpatient hospitalizations in our data (Figure 1a) , which matches closely with general influenza-like illnesses reported by the Centers for Disease Control and Prevention (CDC) (Figure 1b ). Figure 1c shows the age distribution of hospital admissions, which has important implications for vaccine effectiveness, described in more detail below. Figure 2a shows a clear positive correlation between air quality and count of influenza admissions in the raw data; higher AQI correlates with more influenza admissions (43). Figure 2b shows the correlation after adjusting both variables for fixed effects and weather controls. After this adjustment, a strong, positive correlation remains. Table 1 shows estimates from Poisson Pseudo-Maximum Likelihood regressions given the count nature of the dependent variable. The coefficients represent the change in the expected log of inpatient admission counts, which approximates a percentage change in number of county-year-month admissions within our data (44). Column (1) implies a 1-unit increase in the lagged monthly AQI results in a 0.56% increase in inpatient influenza admissions. To put this estimate in national context, a one standard deviation increase in AQI (12.79-unit increase in our data) amounts to approximately 4,064 additional inpatient hospitalizations for the 6-month influenza season in the U.S. (45).

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