Health impacts of local air pollution reduction Sample‑size– based feasibility analysis. Table 1 exhibits the percent reduction in con- centrations for a given pollutant that would be needed if we assumed a specific RR (1.01, 1.05, 1.10, 1.15, and 1.20) and an estimated reduction in adverse health outcome (2.5%, 5%, 10%, 15%, and 20%) using the assump- tions stated above. Table 2 lists the health out- comes that we explored with corresponding minimum statistically detectable decreases in each outcome for the New Haven study popu- lation given the baseline rate of the outcome. The New Haven study population area is suf- ficient in size to examine reductions in adverse health outcomes ranging from a low of 2.5% (adult asthma prevalence) to a high of 10% (all-cause mortality and hospital discharge for cardiovascular diseases and respiratory causes). Based on the percentage decrease air pol- lution projected for 2010, 2020, and 2030 within New Haven for NOx (61%, 2010; 78%, 2020; 81%, 2030) and PM2.5 (8%, 2010; 9%, 2020 and 2030), the percent reduction in air pollution needed to produce a given change in the outcome (Table 1), and the minimum statistically significant percent decrease in each health outcome that can be detected in the New Haven study popula- tion (Table 2), we can assess the feasibility for detecting beneficial health effects of air pollu- tion reductions. Of the 26 different air pollu- tion–health outcome linkages assessed, only five, all NOx related, are potentially feasible (Table 2, last column): all-cause mortality, cardiovascular disease hospitalization, respira- tory disease hospitalization discharge, current Table 1. Percent reduction in air pollution needed to reduce the risk of an outcome by 2.5–20% for a range of assumed RRs.a Assumed RR Percent reduction in outcome 1.01 1.05 1.10 1.15 1.20 2.5 > 100 52 27 18 14 5 > 100 > 100 54 37 28 10 > 100 > 100 > 100 75 58 15 > 100 > 100 > 100 > 100 89 20 > 100 > 100 > 100 > 100 > 100 aEstimates based on Equation 2; the percent reduction in outcome corresponds to Δy/y, and the estimated values for percent reduction in air pollution required correspond to Δcreq. The Δcreq value ≥ 100% indicates that the corresponding value for Δy/y is not feasible, because exposure would have to be reduced by more than 100% to achieve it. prevalence of asthma in children, and current prevalence of asthma in adults. Simulation‑ based epidemiological feasibil‑ ity analysis. The average number of hospitaliza- tions for CHD among those ≥ 65 years of age in each census block group decreased between 2001 and 2010, whereas average numbers of asthma hospitalizations increased (Table 3). We were unable to detect associations between small reductions in PM2.5 pollution concentra- tions and health outcomes, so we restricted our analysis to census block groups with PM2.5 reductions of > 4 μg/m 3 (n = 30). For these census block groups, numbers of CHD hos- pitalizations were inversely associated with the estimated reduction in PM2.5 concentrations, indicating that numbers of hospitalizations decreased as the reduction in PM2.5 increased (p < 0.1; Table 4, Figure 3A). Asthma hospi- talizations were also inversely associated with reductions in PM2.5 concentrations based on our simulations, suggesting that greater reduc- tions in PM2.5 may slow the increase in asthma hospitalizations over time (Table 4, Figure 3B). However, the inverse association was weaker than for CHD hospitalizations. Finally, an exploratory analysis we conducted by including additional surrogate variables in the regres- sion models aimed at capturing neighborhood effects caused substantial increases in model R2 values, whereas the PM2.5 effect estimates Table 2. Minimum statistically detectable difference in health reduction for a given reference rate and affected population, based on sample size calculations. Outcome All-cause mortality (except injury) Cardiovascular disease Mortality (ICD-10 codes I00–I87) Hospitalization discharge (ICD-9 codes 390–459) Respiratory disease Mortality (ICD-10 codes J00–J98) Hospitalization discharge (ICD-9 codes 460–519) Reference rate (per 100,000) 812.9 316.9 1059.67 89.5 948.05 Chronic obstructive pulmonary disease and related disorders Mortality (ICD-10 codes J40–J44) 40.7 Hospitalization discharge (ICD-9 codes 490–496) 266.81 Asthma Current prevalence, adults (≥ 18 years of age) Current prevalence, children (< 18 years of age) Hospitalizations (ICD-9 code 493), adults (≥ 18 years of age) Hospitalizations (ICD-9 code 493), children (< 18 years of age) 7,900 8,800 314 144 716 290 Reference rate source information State of Connecticut, 1999–2006 (Connecticut age-adjusted rate)b State of Connecticut, 1999–2006 (Connecticut age-adjusted rate)b County of New Haven, 2006 (Connecticut age-adjusted rate)c State of Connecticut, 1999–2006 (Connecticut age-adjusted rate)b County of New Haven, 2006 (Connecticut age-adjusted rate)c State of Connecticut, 1999–2006 (Connecticut age-adjusted rate)b County of New Haven, 2006 (Connecticut age-adjusted rate)c County of New Haven, 2006d County of New Haven, 2006d City of New Haven, 2001–2005e County of New Haven, 2001–2005e City of New Haven, 2001–2005e County of New Haven, 2001–2005e New Haven Study Area estimated 2007 population 367,173 Minimum statistically detectable percent decrease 10 367,173 367,173 367,173 367,173 367,173 367,173 283,232 83,941 283,232 283,232 83,941 83,941 15 10 20 10 30 15 5 15 20 15 25 2.5 ICD-9 (World Health Organization 2004); ICD-10 (World Health Organization 2007). aBased on Table 1, the minimum statistically detectable percent decrease in this table, and the forecasted reductions in NOx 2010 (61%), 2020 (78%), and 2030 (81%). An air pollutant– health outcome combination is considered feasible for assessment of air pollution reductions if the reduction in an air pollutant for a given health minimum statistically detectable percent decrease is within the range of given RRs. bData from the Centers for Disease Control and Prevention (2009). cData from the CDPH (2006). dConnecticut Behavioral Risk Factor Surveillance System, 2006 (see Peng et al. 2008). eOffice of Health Care Access Discharge Database (see Peng et al. 2008). Environmental Health Perspectives • volume 119 | number 4 | April 2011 NOx feasibility based on sample sizea 2020, 2030 (RR ≥ 1.15); 2010 (RR = 1.20) Not feasible 2020, 2030 (RR ≥ 1.15); 2010 (RR = 1.20) Not feasible 2020, 2030 (RR ≥ 1.15); 2010 (RR = 1.20) Not feasible Not feasible 2010, 2020, 2030 (RR ≥ 1.05) 2010, 2020, 2030 (RR ≥ 1.10) Not feasible Not feasible Not feasible Not feasible 491