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Exposure to particulate air pollution and long-term incidence of frailty after myocardial infarction
Annals of Epidemiology 23 (2013) 395e400
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Exposure to particulate air pollution and long-term incidence of frailty after myocardial infarction Vicki Myers MSc a, David M. Broday DSc b, David M. Steinberg PhD c, Yuval PhD b, Yaacov Drory MD d, Yariv Gerber PhD a, * a
Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Division of Environmental, Water and Agricultural Engineering, Faculty of Civil & Environmental Engineering, Technion, Israel Institute of Technology, Haifa, Israel Department of Statistics and Operations Research, School of Mathematical Sciences, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel d Department of Rehabilitation, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 January 2013 Accepted 13 May 2013
Purpose: Frailty, a multidimensional syndrome of increased vulnerability, is prevalent post-myocardial infarction (MI) and predicts mortality and recurrent events. We investigated whether chronic exposure to particulate matter 2.5 mm in diameter (PM2.5) is associated with the development of post-MI frailty. Methods: Participants (n ¼ 1120) were aged 65 or less and admitted to hospital in central Israel with first MI in 1992 and 1993. Daily measures of PM2.5 recorded at air quality monitoring stations were summarized and chronic exposure was estimated individually using the geo-coded residential location. Frailty assessment was conducted via an index based on deficit accumulation, and those defined as frail (applying a threshold of 0.25) at baseline were excluded. Remaining participants who survived to follow-up 10 to 13 years post-MI (n ¼ 848) were reassessed for frailty. Logistic regression models were constructed to evaluate the role of PM2.5 exposure in frailty risk prediction. Results: Mean exposure to PM2.5 was 24.2 mg/m3 (range, 16.9e28.6). A total of 301 participants (35.5%) developed frailty during follow-up. Adjusting for sociodemographic and clinical variables, PM2.5 exposure was associated with increased odds of developing frailty (odds ratio, 1.53; 95% confidence interval, 1.22e1.91, comparing the 75th vs. 25th percentiles). Addition of PM2.5 exposure to the multivariable model resulted in an integrated discrimination improvement of 1.60% (P ¼ .005) and a net reclassification index of 6.51% (P ¼ .02). Conclusions: An association was observed between exposure to PM2.5 and incidence of frailty, providing a potential intermediary between air pollution and post-MI outcomes. Ó 2013 Elsevier Inc. All rights reserved.
Keywords: Frailty Myocardial infarction Follow-up studies Air pollution Particulate matter Epidemiology Risk factors Environmental exposures Elderly Ageing
Introduction Chronic exposure to air pollution, even at low levels, has been associated with the development of a multitude of terminal illnesses, including coronary heart disease, cerebrovascular disease, respiratory diseases, diabetes and cancer [1e6]. Patients with myocardial infarction (MI)da vulnerable subgroup at risk of mortality and reinfarction [7,8]dmay be particularly susceptible to the effects of air pollution [9,10]. Furthermore, long-term exposure to particulate matter (PM) has been associated with increased post-MI See Acknowledgments for a list of participating medical centers and investigators. * Corresponding author. Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel. Tel.: þ972-3-6409867; fax: þ 972-3-6409868. E-mail address: [email protected] (Y. Gerber). 1047-2797/$ e see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.annepidem.2013.05.001
mortality, readmissions, and adverse events [11e13]. However, despite intense efforts [3], the pathways underlying these associations remain unclear. Frailty describes the heterogeneity of vulnerability in older people and has been shown to predict mortality, disability, and institutionalization [14]. Frailer individualsdthose with multiple deficitsdhave diminished reserve and resistance to stressors [15]. Although there has been little research on the significance of frailty in MI survivors, several studies have reported excess morbidity and mortality in frail survivors of MI [16,17] and a recent study found frailty to be clinically relevant, predicting long-term mortality and hospitalizations in this population [18]. The prevalence of frailty in cardiovascular patients is estimated to be between 25% and 50% [19]. Frailty has been shown to be related to several factors including advanced age, female gender and socioeconomic status (SES), with lower SES associated with greater frailty in both general and clinical
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populations [20]. Additional factors that may influence the development of frailty remain to be identified; it is plausible that environmental factors may play a role. In a previous follow-up study [12], we reported comparable associations between chronic PM exposure and multiple adverse outcomes post-MI, including all-cause mortality, cardiac mortality, recurrent MI, heart failure, and stroke, suggesting a nonspecific association, whereby pollutants may influence multiple systems through a common pathway. Here, we investigated whether exposure to air pollution is a risk factor for development of frailty post-MI, thus illustrating a potential intermediary between exposure to air pollution and various clinical outcomes. Methods Participants were drawn from the Israel Study of First Acute Myocardial Infarction [21,22], comprising individuals aged 65 who were hospitalized for incident MI in 1992 and 1993 in one of the eight hospitals in central Israel, and were followed longitudinally. Of an initial 1626 consecutive patients admitted, 81 died during initial hospitalization and 24 were lost to follow-up. Of the remaining 1521, particulate matter 2.5 mm in diameter (PM2.5) data were available for 1120 patients, of whom 59 met the definition of frailty at baseline and an additional 213 died before the second frailty assessment, 10 to 13 years post-MI. Analyses were conducted on the remaining 848 patients. Demographic, socioeconomic, and clinical data were collected at baseline and followup interviews. All aspects of the study were approved by the appropriate Institutional Ethics Committees. Exposure assessment Exposure to PM2.5 was assessed using data from air quality monitoring stations in central Israel, as previously described in detail [23]. Chronic exposure was defined as the mean pollutant concentration during a representative period. The mean PM2.5 concentrations during this period in the monitoring stations were interpolated to the residential addresses of the study participants using ordinary kriging, a statistical interpolation scheme suitable to the case given the flat topography and the similar meteorologic conditions along the coastal area where the study took place. Halfhourly records of 12 PM2.5 monitoring stations were available. Additional half-hourly PM2.5 records were modeled using nine PM10 data series and the simultaneous meteorologic variables. The conversion from PM10 to PM2.5 data was carried out by a linear regression algorithm that was developed using the records of three stations observing both pollutants, two of which were in the study area. Nonlinear neural network regression models were also developed, but had only a marginal advantage; thus, the simpler linear models were preferred. The performance of the regression models was measured using Pearson correlation between the halfhourly series, and relative chronic long-term exposure error. Crossvalidated modeling performance measures are given in Supplementary Tables 1 and 2. A final model providing the PM2.5 series in the locations of the nine PM10 stations was developed using the mean PM2.5 value of the two dual-pollutant stations, elevating the total number of PM2.5 series available for the interpolation to 21. Because an increasing number of monitoring stations were fully operable over time (Fig. 1), we selected a representative exposure period, namely, the years 2003 through 2005. Using this relatively short period to characterize the exposure during the study’s duration is justified given that most of the spatial variability in PM2.5 in the study area is due to the spatial variability in the vehicular particulate emissions, which have remained relatively stable since the beginning of the study. For example, the spatial
Fig. 1. The temporal coverage of the PM2.5 series used in this study. Operational periods are marked with light lines while missing data are marked by dark lines. The series of stations 1 through 12 are observed whereas those of stations 13 through 21 are modeled (see text).
pattern of exposure to nitric oxide, a good traffic pollution proxy which has been monitored in the study area over a longer time period, has not changed much during the study’s duration. Frailty assessment A multidimensional frailty index, consisting of 40 items, was constructed from data collected from medical records and during follow-up interviews 10 to 13 years post-MI. The frailty index was adapted from a model developed by Rockwood et al. [24,25] and included data on self-rated health, functional limitations, comorbid conditions, weight loss, physical activity and psychosocial status (see Supplementary Table 3 for a list of frailty index variables). Full details of the frailty index and its derivation in this study have been previously published [18]. For dichotomous items, a score of 0 was given if the deficit was absent and 1 if it was present; ordinal or continuous variables were graded into a score between 0 and 1 (0 [no impairment], 0.5 [minor impairment], 1 [major impairment]). Scores were subsequently summed and divided by the total number of variables to give a frailty index between 0 and 1, indicating proportion of deficits accumulated, with 1 representing the greatest frailty. Patients were categorized into frail and non-frail groups using a threshold of 0.25 or higher, based on values used in previous research [25,26]. The latter cutoff was achieved by comparing a phenotypic definition of frailty with a frailty index [25]. Additional variables Individual SES data were self-reported at study entry and included family income relative to the national average (categorized as below average, average, or above average), education (years of schooling), and pre-MI employment status (full or part time vs. none) [27,28]. Neighborhood-level SES was estimated through a 20-point scale index developed by the Israel Central Bureau of Statistics, with a score of 20 representing the highest SES [29]. Cardiovascular risk factors, MI characteristics, and disease severity indices were recorded at the index hospitalization. Cigarette use was classified into current smoking versus never or past smoking. Hypertension and hypercholesterolemia were defined according to standard criteria based on clinical and laboratory data. Comorbidity was defined based on the Charlson index [30]. MI characteristics and severity indicators included infarct type (Q wave or non-Q wave), anterior MI, and Killip class.
V. Myers et al. / Annals of Epidemiology 23 (2013) 395e400
Statistical analysis Differences in baseline characteristics across PM2.5 tertiles were examined using the c2 test for trend (categorical variables) or generalized linear models (continuous variables). Generalized additive models were used to determine whether the relationship between frailty and PM2.5 might be nonlinear; however, results showed no indication of a nonlinear relationship. The main exposure variable had a clearly significant linear component, but the nonlinear part was not significant (P ¼ .66). Subsequently, logistic regression models were constructed to assess the odds ratios (OR) and 95% confidence intervals (CI) for frailty associated with PM2.5 exposure. PM2.5 was assessed both as a categorical variable, divided into tertiles of the exposure distribution, and as a continuous variable, for which OR are reported comparing the 75th versus 25th percentiles of PM2.5 (corresponding to 25.5 vs. 22.9 mg/m3). Adjustment was initially made for sociodemographic variables (age, gender, family income, education, pre-MI employment, and neighborhood SES; model 1) and additionally for clinical variables (hypertension, smoking, hypercholesterolemia, obesity, Q-wave MI, anterior MI, Killip class, comorbidity, self-rated health, and baseline frailty score; model 2). These potential predictors were chosen based on previous literature and data availability. Of the 1061 non-frail participants at baseline with available PM2.5 assessment, 213 had died before the 10- to 13-year follow-up interview, so that their frailty score could not be evaluated at the end of follow-up. This type of selection bias was addressed by applying the Diehr method, which considered as frail patients who had died before frailty assessment [31,32]. Predicted probabilities of frailty at end of follow-up were calculated for the full model (model 2) with and without PM2.5
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(modeled as a continuous variable) and categorized as less than 20%, 20% to 50%, and greater than 50%, corresponding with low, intermediate, and high risk, respectively. The incremental discriminatory value of adding PM2.5 exposure data over all other covariates (i.e., the amount by which the addition of this factor improves the ability to differentiate events from non-events) was examined by established methods proposed by Pencina et al. [33]. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indices were calculated. In general, IDI measures the improvement in average sensitivity associated with the more advanced model, and subtracts any decrease in average specificity. NRI, in contrast, measures the extent to which individuals with and without the event of interest are appropriately reclassified into clinically accepted higher or lower risk categories with the extended versus basic model. This was done by using a SAS macro (rocplus) written by Erik Bergstralh (available from: http:// mayoresearch.mayo.edu/mayo/research/biostat/sasmacros.cfm). Missing values for family income (12%) and neighborhood SES (4%), as well as for variables comprising the frailty index, which ranged from 0% to 14% (mean, 4%), were imputed using multiple imputation methodology [34]. The Markov Chain Monte Carlo method was used for this purpose. Five datasets were created, with missing values replaced by imputed values based on models incorporating demographic, socioeconomic, psychosocial, anthropometric, and clinical variables. The results from analyzing these datasets were then combined using Rubin’s rules [34]. A complete-case analysis was subsequently performed, to ensure the robustness of the multiple imputation results. Because both approaches yielded similar results, only the multiple imputation results are presented. Analyses were performed using SAS 9.2 (SAS Institute Inc, Cary, NC) and IBM SPSS Statistics version 19 (IBM SPSS Inc, Chicago, IL).
Table 1 Baseline patient characteristics Characteristic
Missing, n
Overall (n ¼ 848)
PM2.5 tertiles Lower (n ¼ 283)
d 24.2 (16.9e28.6) 22.2 (16.9e23.3) PM2.5, mean (range), mg/m3 Age, mean (SD), y d 53.2 (8.2) 53.4 (8.1) Men, n (%) d 710 (83.7) 240 (84.8) Socioeconomic measures Family income, n (%) 98 d d Below average d 315 (42.0) 120 (45.1) Average d 203 (27.1) 69 (25.9) Above average d 232 (30.9) 77 (28.9) Education, mean (SD), y d 11.5 (4.0) 11.1 (4.1) Pre-MI employment, n (%) d 703 (83.0) 235 (83.0) Neighborhood SES, mean (SD) 34 12.9 (3.8) 12.1 (3.6) Risk factors, comorbid conditions, and incident MI characteristics, n (%) unless otherwise specified Comorbidity index d d d Severe d 14 (1.7) 6 (2.1) Mild d 275 (32.4) 105 (37.1) None d 559 (65.9) 172 (60.8) Self-rated health d d d Poor d 117 (13.8) 45 (15.9) Average d 198 (23.3) 66 (23.3) Good d 533 (62.9) 172 (60.8) Hypertension d 304 (35.8) 96 (33.9) Current smoking d 437 (52) 149 (52.7) Hypercholesterolemia d 331 (39.0) 117 (41.3) Obesity 6 143 (17.0) 55 (19.6) Q-wave MI d 634 (74.8) 223 (78.8) Anterior MI d 342 (40.3) 113 (39.9) Killip >1 d 154 (18.2) 54 (19.1) Baseline frailty score, mean (SD) d 0.081 (0.054) 0.088 (0.055) Baseline frailty categories d d d 187 (66.1) <0.1 d 600 (70.8) 0.1e0.2 d 212 (25.0) 80 (28.3) 0.2e0.25 d 36 (4.2) 16 (5.7)
P for trend Medium (n ¼ 283) 23.9 (23.3e24.9) 53.1 (8.1) 238 (84.1) d 118 65 71 11.1 230 11.7
26.6 (24.7e28.6) 53.3 (8.4) 232 (82.3) d
(46.5) (25.6) (28.0) (4.0) (81.3) (3.4)
d 3 84 196 d 36 68 179 93 155 106 48 207 113 54 0.078 d 206 64 13
Upper (n ¼ 282)
77 69 84 12.2 238 14.8
(33.5) (30.0) (36.5) (3.8) (84.7) (3.6)
d (1.1) (29.7) (69.3) (12.7) (24.0) (63.3) (32.9) (54.8) (37.5) (17.1) (73.1) (39.9) (19.1) (0.054) (72.8) (22.6) (4.6)
MI ¼ myocardial infarction; PM2.5 ¼ particulate matter 2.5 mm in diameter; SD ¼ standard deviation; SES ¼ socioeconomic status.
5 86 191 d 36 64 182 115 133 108 40 204 116 46 0.076 d 207 68 7
(1.8) (30.5) (67.7) (12.8) (22.7) (64.5) (40.8) (47.2) (38.3) (14.2) (72.3) (41.1) (16.3) (0.053) (73.4) (24.1) (2.5)
d .83 .42 .015 d d d .002 .60 <.001 .09 d d d .26 d d d .09 .19 .46 .09 .08 .77 .39 .005 .03 d d d
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Results Of the 848 patients included in the study, 301 (35.5%) developed frailty during follow-up. The median frailty score at the end of follow-up was 0.18 (interquartile range, 0.10e0.30). Participants had a mean standard deviation age of 53.2 8.2 years at recruitment, and mean exposure to PM2.5 was 24.2 mg/m3 (range, 16.9e28.6; Table 1). Participants were divided into three equal groups based on exposure to PM2.5. There were no differences between exposure groups according to age, gender, self-rated health, clinical risk factors, or MI characteristics. However, greater exposure was associated with greater SES, at both individual and neighborhood levels, as well as with slightly lower baseline frailty score (P ¼ .005; Table 1). The inverse association between exposure and baseline frailty was substantially attenuated on adjustment for SES and clinical variables (P ¼ .23; data not shown). Logistic regression models showed higher PM2.5 exposure to be associated with increased odds of developing frailty during followup (Table 2). Patients in the upper tertile of exposure had an OR of 1.78 (95% CI, 1.17e2.72) of developing frailty compared with those in the lower tertile, after adjustment for sociodemographic and clinical variables. In a sensitivity analysis, which considered as frail patients who had died before frailty assessment, the OR of developing frailty was similar at 1.71 (95% CI, 1.18e2.49) for upper compared with lower tertiles of exposure. With exposure modeled as a continuous variable, an increase from the 25th to 75th percentile of exposure was associated with a 50% increase in the odds of frailty development (OR, 1.53; 95% CI, 1.22e1.91 after multivariable adjustment). A linear regression was additionally conducted, avoiding dichotomization of the outcome measure, which supported the results of the logistic regression (P < .001 for PM2.5 in the adjusted model). Reclassification tables show the added value of PM2.5 exposure (extended model), beyond sociodemographic and clinical variables (basic model), in predicting frailty at the end of follow-up (Table 3). In total, 9% (n ¼ 76) of events (developing frailty) and non-events (not developing frailty) were appropriately reclassified and 6% (n ¼ 51) were inappropriately reclassified with the extended model. Inclusion of PM2.5 exposure in the multivariable model resulted in an IDI of 1.60% (P ¼ .0005) and an NRI of 6.51% (P ¼ .02), indicating significant gains in predictive value. The IDI and NRI for baseline frailty (with PM2.5 included in the basic model) were 2.60% (P < .001) and 9.55% (P ¼ .003), indicating that baseline frailty is a strong predictor of future frailty status.
Table 2 Odds ratios (95% confidence intervals) for frailty development 10e13 years after MI associated with cumulative PM2.5 exposure PM2.5 tertiles Lower
Medium
Upper
Unadjusted 1 (ref.) 1.03 (0.73e1.45) 1.10 (0.78e1.56) Model 1 1 (ref.) 0.98 (0.67e1.42) 1.47 (0.99e2.19) Model 2 1 (ref.) 1.05 (0.71e1.57) 1.78 (1.17e2.72) Sensitivity analysis applying the Diehr methody Unadjusted 1 (ref.) 1.10 (0.82e1.47) 1.18 (0.88e1.58) Model 1 1 (ref.) 1.00 (0.73e1.38) 1.55 (1.11e2.18) Model 2 1 (ref.) 1.10 (0.77e1.55) 1.71 (1.18e2.49)
75th vs. 25th percentiles* 1.12 (0.93e1.34) 1.33 (1.08e1.63) 1.53 (1.22e1.91) 1.11 (0.95e1.30) 1.31 (1.09e1.56) 1.44 (1.19e1.76)
MI ¼ myocardial infarction; PM2.5 ¼ particulate matter 2.5 mm in diameter. Model 1: Age, gender, and SES measures (family income, education, pre-MI employment, and neighborhood socioeconomic status). Model 2: Model 1 plus clinical variables (hypertension, smoking, hypercholesterolemia, obesity, q-wave MI, anterior MI, Killip >1, comorbidity, and selfrated health) and baseline frailty index score. 3 * PM2.5 modeled as a continuous variable (corresponds to 25.5 vs. 22.9 mg/m ). y With patients who died before frailty assessment coded as frail [32,33].
Table 3 Predicted risk categories by basic (sociodemographic þ clinical variables) and extended (PM2.5 added) model Basic model
Non-events <20% 20e50% >50% Total Events <20% 20e50% >50% Total
Extended model <20%
20e50%
204 38 0 242 15 8 0 23
Reclassified as higher risk
Reclassified as lower risk
223 248 76 547
19 15 NA 34
NA 38 8 46
22 148 131 301
7 23 NA 30
NA 8 9 17
>50%
Total
19 195 8 222
0 15 68 83
7 117 9 133
0 23 122 145
PM2.5 ¼ particulate matter 2.5 mm in diameter; NA ¼ not applicable. Basic model: Age, gender, family income, education, pre-MI employment, neighborhood socioeconomic status, hypertension, smoking, hypercholesterolemia, obesity, q-wave myocardial infarction (MI), anterior MI, Killip >1, comorbidity, self-rated health and baseline frailty index score. Extended model: Basic model plus PM2.5 (as a continuous variable).
Discussion Post-MI patients exposed to higher levels of PM2.5 were more likely to develop frailty than were their less exposed counterparts, after adjusting for key prognostic factors. Frailty was previously demonstrated to be significantly associated with post-MI mortality and hospitalizations [16,18]. To date, research on the health effects of air pollution has most often focused on death as the outcome of interest, via either timeseries or cohort studies [35]. The few studies that investigated frailty in the context of air pollution health effects did find some evidence for a relationship, demonstrating for example that frailty history modified the effects of PM and ozone on lung function [36]. To the best of our knowledge, this is the first study to specifically examine the relationship between exposure to air pollution and post-MI frailty. These findings, demonstrating a role for PM2.5 exposure in long-term frailty development after MI, could identify an intermediary step between air pollution and adverse cardiovascular outcomes. The frailty index provides a comprehensive and multidimensional evaluation of the patient. Detection of an association with air pollution provides a possible explanation for the multitude of adverse health outcomes associated with PM2.5, all driven by a common mechanism. Indeed, particulate air pollution exposure has been implicated in the incidence of conditions as diverse as acute cardiovascular events [13], coronary heart disease, dysrhythmias, heart failure [37], stroke [38], decreased lung function [2], lung cancer, and chronic obstructive pulmonary disease [39]. Thus, beyond damage to specific systems, chronic PM exposure may add to the burden of illness, an accumulation of changes to multiple physiological systems, which eventually lead to disease, disability, and death [40]. Of note, patients exposed to higher levels of PM2.5 exhibited higher SES, which is generally associated with a better prognosis [22,27,41] and lower odds of frailty [20]. For example, the Women’s Health and Ageing Study found increased odds of frailty associated with low SES [20]. In the current study, exposure to air pollution was associated with greater odds of frailty, a relationship that was substantially enhanced upon adjustment for multiple SES measures. This highlights the importance of controlling for multiple measures of SES, both at the individual and area levels, in the study of air pollution effects. Owing to the challenges inherent in exposure assessment, previous studies of frailty have failed to address the impact of environmental determinants. A large-scale study of Hong Kong residents found significant differences in frailty by district;
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attempts to examine associations with air pollution were limited by availability of exposure data and found little association [42]. Numerous studies have, however, reported both elevated cardiovascular events in the general population [2,3], and excess postMI mortality [11], associated with exposure to air pollutants. Several mechanisms have been suggested to explain the increase in adverse cardiovascular events associated with chronic exposure to air pollutants, for example an adverse effect on cardiac autonomic tone [37]. MI patients are generally at increased risk of death and recurrent ischemic events, and this risk seems to be associated with higher levels of markers of systemic inflammation. Indeed, the inflammatory pathway may provide some support for the relationship between exposure and frailty development, since low grade chronic inflammation, and raised levels of the inflammatory markers interleukin-6 and C-reactive protein, have been associated with both frailty [19,43] and exposure to fine particle pollution [44,45]. However, the relationship with frailty demonstrated here suggests that air pollutants may exert influences on multiple systems, involving more than a single process. Kunzli et al. [35] proposed that the pathways leading to air pollution attributable deaths often involve frailty, either by increasing the risk of underlying disease leading to frailty or by hastening death in those already frail. Given that exposure to pollutants exacerbates many conditions including cardiovascular and respiratory processes [2], it is plausible that exposure above a certain level may add to the burden of frailty found in post-MI patients. Accordingly, many survivors of an MI may already have decreased homeostatic reserve, thus being particularly susceptible to air pollution effects in terms of their subsequent frailty risk. Methodologic considerations The current study had several advantages that strengthen the results. First, the study sample is a well-defined post-MI cohort, a population established as vulnerable to the effects of air pollution, as well as being geographically defined, thereby improving relevance and generalizability. Moreover, it has been suggested that cohort studies are more effective than time-series studies in assessing the health impact of air pollution [35]. The frailty index used has been previously validated in this cohort and was shown to predict mortality and hospitalizations [18]. Sensitivity analyses were performed to reduce the effects of selection bias, caused by exclusion of decedents. Extensive high quality clinical and sociodemographic data were available from multiple sources, including medical records and structured interviews, allowing us to more accurately control for confounding factors, rather than relying on administrative data. Assessment of PM exposure was individual, according to home address, and blinded to outcome, thereby precluding differential misclassification bias. Several limitations should be also considered when interpreting the study findings. PM2.5 data were incomplete, particularly for the earlier years of the study period, leading to some unavoidable exposure misclassification; however, the exposure period chosen had high reliability and minimal uncertainty. It has been proposed that assessment of exposure over the most recent 1 or 2 years can accurately estimate health risks of long-term PM exposure [3]. Indeed, the spatial differences in traffic emissionsdthe main contributor to PM2.5 in the study regiondhave not changed over the study period, whereas other sources such as desert dust and transboundary sulphites and nitrates impact on a very large spatial scale, making any spatial differences within the study area negligible. Owing to the possibility of exposure misclassification, ranks of exposure were considered more reliable than absolute values. Results are therefore presented by tertiles or percentiles of exposure, and the findings should be interpreted accordingly. Frailty was
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assessed via an index, rather than direct phenotypic assessment [46]; however, the index of accumulated deficits has been demonstrated to correlate well with frailty phenotype [17]. Some data, including frailty index variables, were missing for a small proportion of the sample, necessitating the use of multiple imputations. However, similar results were obtained in the complete-case analysis. The relatively young cohort limits the generalizability of these findings because post-MI populations are generally older. In addition, more recent cohorts use definitions of MI based on cardiac biomarkers, which were not yet in use at the time of study recruitment. Implications The findings of this study propose a potential intermediary mechanism, bridging the relationship between particulate air pollution and post-MI outcomes. We detected a significant association between exposure to PM2.5 and development of frailty in MI survivors, indicating that chronic exposure to PM might affect multiple physiological systems and contributes to the decline of patients in this vulnerable subpopulation. Because the frailty index encompasses functional, clinical, and psychosocial deficits, the results presented here suggest that exposure to PM2.5 may contribute to excess mortality, not just through single processes such as atherosclerosis, but by a broader assault on bodily systems. The onset of frailty is a predecessor to overt disease, heralding a decline that eventually leads to adverse events, disease expression, and death, “an intermediate stage between robust health and end of life” [42]. Understanding the determinants of frailty in cardiovascular populations is important, both to implement preventive interventions and in an attempt to reduce adverse outcomes. Acknowledgment The following investigators and institutions took part in the Israel Study Group on First Acute Myocardial Infarction: Yaacov Drory, MD, Principal Investigator, Department of Rehabilitation, Sackler Medical School, Tel Aviv University, Tel Aviv; Yeheskiel Kishon, MD, Michael Kriwisky, MD, and Yoseph Rosenman, MD, Wolfson Medical Center, Holon; Uri Goldbourt, PhD, Hanoch Hod, MD, Eliezer Kaplinsky, MD, and Michael Eldar, MD, Sheba Medical Center, Tel Hashomer; Itzhak Shapira, MD, Amos Pines, MD, Margalit Drory, MSW, Arie Roth, MD, Shlomo Laniado, MD, and Gad Keren, MD, Tel-Aviv Sourasky Medical Center, Tel-Aviv; Daniel David, MD, Morton Leibowitz, MD, and Hana Pausner, MD, Meir Medical Center, Kfar Sava; Zvi Schlesinger, MD, and Zvi Vered, MD, Assaf Harofeh Medical Center, Zerifin; Alexander Battler, MD, Alejandro Solodky, MD, and Samuel Sclarovsky, MD, Beilinson Medical Center, Petach Tikvah; Izhar Zehavi, MD, and Rachel MaromKlibansky, MD, Hasharon Medical Center, Petah Tikvah; and Ron Leor, MD, Laniado Medical Center, Netanya. The authors are also indebted to Zalman Kaufman, MSc, for assistance with the Geographic Information System analysis. Funding: This work was supported by the Environment and Health Fund, Israel [Grant Award Nos. SGA 1204 and RGA 0904]. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.annepidem.2013.05.001. References [1] Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, et al. Risk of nonaccidental and cardiovascular mortality in relation to long-term
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[24] Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal 2001;1:323e36. [25] Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci 2007;62(7):738e43. [26] Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J Am Geriatr Soc 2010;58(4):681e7. [27] Gerber Y, Goldbourt U, Drory Y. Interaction between income and education in predicting long-term survival after acute myocardial infarction. Eur J Cardiovasc Prev Rehabil 2008;15(5):526e32. [28] Molshatzki N, Drory Y, Myers V, Goldbourt U, Benyamini Y, Steinberg DM, et al. Role of socioeconomic status measures in long-term mortality risk prediction after myocardial infarction. Med Care 2011;49(7):673e8. [29] Burck L, Feinstein Y. Characterization and classification of geographical units by the socioeconomic level of the population. Series of 1995 census of population and housing publications. Jerusalem: Israel Central Bureau of Statistics; 2000. Report No.: 13. [30] Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373e83. [31] Diehr P, Patrick DL. Trajectories of health for older adults over time: accounting fully for death. Ann Intern Med 2003;139:416e20. [32] Diehr P, Patrick DL, Spertus JA, Kiefe CI, McDonell M, Fihn SD. Transforming self-rated health and the SF-36 scales to include death and improve interpretability. Med Care 2001;39:670e80. [33] Pencina MJ, D’Agostino Sr RB, D’Agostino Jr RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27(2):157e72. [34] Rubin D. Multiple imputations for nonresponse in surveys. New York: John Wiley & Sons, Inc; 1987. [35] Kunzli N, Medina S, Kaiser R, Quenel P, Horak Jr F, Studnicka M. Assessment of deaths attributable to air pollution: should we use risk estimates based on time series or on cohort studies? Am J Epidemiol 2001;153(11):1050e5. [36] Eckel SP, Louis TA, Chaves PH, Fried LP, Margolis AH. Modification of the association between ambient air pollution and lung function by frailty status among older adults in the Cardiovascular Health Study. Am J Epidemiol 2012;176(3):214e23. [37] Pope 3rd CA, Hansen ML, Long RW, Nielsen KR, Eatough NL, Wilson WE, et al. Ambient particulate air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly subjects. Environ Health Perspect 2004; 112(3):339e45. [38] Wellenius GA, Burger MR, Coull BA, Schwartz J, Suh HH, Koutrakis P, et al. Ambient air pollution and the risk of acute ischemic stroke. Arch Intern Med 2012;172(3):229e34. [39] Naess O, Nafstad P, Aamodt G, Claussen B, Rosland P. Relation between concentration of air pollution and cause-specific mortality: four-year exposures to nitrogen dioxide and particulate matter pollutants in 470 neighborhoods in Oslo, Norway. Am J Epidemiol 2007;165(4):435e43. [40] Walston J. Frailty - the search for underlying causes. Sci Aging Knowledge Environ 2004;2004(4):pe4. [41] Salomaa V, Miettinen H, Niemela M, Ketonen M, Mahonen M, ImmonenRaiha P, et al. Relation of socioeconomic position to the case fatality, prognosis and treatment of myocardial infarction events: the FINMONICA MI register study. J Epidemiol Community Health 2001;55:475e82. [42] Woo J, Chan R, Leung J, Wong M. Relative contributions of geographic, socioeconomic, and lifestyle factors to quality of life, frailty, and mortality in elderly. PLoS One 2010;5(1):e8775. [43] Li H, Manwani B, Leng SX. Frailty, inflammation, and immunity. Aging Dis 2011;2(6):466e73. [44] Brook RD, Franklin B, Cascio W, Hong Y, Howard G, Lipsett M, et al. Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation 2004;109(21):2655e71. [45] Hoffmann B, Moebus S, Dragano N, Stang A, Mohlenkamp S, Schmermund A, et al. Chronic residential exposure to particulate matter air pollution and systemic inflammatory markers. Environ Health Perspect 2009;117(8):1302e8. [46] Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):M146e56.
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Exposure to particulate air pollution and long-term incidence of frailty after myocardial infarction Vicki Myers MSc a, David M. Broday DSc b, David M. Steinberg PhD c, Yuval PhD b, Yaacov Drory MD d, Yariv Gerber PhD a, * a
Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Division of Environmental, Water and Agricultural Engineering, Faculty of Civil & Environmental Engineering, Technion, Israel Institute of Technology, Haifa, Israel Department of Statistics and Operations Research, School of Mathematical Sciences, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel d Department of Rehabilitation, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 January 2013 Accepted 13 May 2013
Purpose: Frailty, a multidimensional syndrome of increased vulnerability, is prevalent post-myocardial infarction (MI) and predicts mortality and recurrent events. We investigated whether chronic exposure to particulate matter 2.5 mm in diameter (PM2.5) is associated with the development of post-MI frailty. Methods: Participants (n ¼ 1120) were aged 65 or less and admitted to hospital in central Israel with first MI in 1992 and 1993. Daily measures of PM2.5 recorded at air quality monitoring stations were summarized and chronic exposure was estimated individually using the geo-coded residential location. Frailty assessment was conducted via an index based on deficit accumulation, and those defined as frail (applying a threshold of 0.25) at baseline were excluded. Remaining participants who survived to follow-up 10 to 13 years post-MI (n ¼ 848) were reassessed for frailty. Logistic regression models were constructed to evaluate the role of PM2.5 exposure in frailty risk prediction. Results: Mean exposure to PM2.5 was 24.2 mg/m3 (range, 16.9e28.6). A total of 301 participants (35.5%) developed frailty during follow-up. Adjusting for sociodemographic and clinical variables, PM2.5 exposure was associated with increased odds of developing frailty (odds ratio, 1.53; 95% confidence interval, 1.22e1.91, comparing the 75th vs. 25th percentiles). Addition of PM2.5 exposure to the multivariable model resulted in an integrated discrimination improvement of 1.60% (P ¼ .005) and a net reclassification index of 6.51% (P ¼ .02). Conclusions: An association was observed between exposure to PM2.5 and incidence of frailty, providing a potential intermediary between air pollution and post-MI outcomes. Ó 2013 Elsevier Inc. All rights reserved.
Keywords: Frailty Myocardial infarction Follow-up studies Air pollution Particulate matter Epidemiology Risk factors Environmental exposures Elderly Ageing
Introduction Chronic exposure to air pollution, even at low levels, has been associated with the development of a multitude of terminal illnesses, including coronary heart disease, cerebrovascular disease, respiratory diseases, diabetes and cancer [1e6]. Patients with myocardial infarction (MI)da vulnerable subgroup at risk of mortality and reinfarction [7,8]dmay be particularly susceptible to the effects of air pollution [9,10]. Furthermore, long-term exposure to particulate matter (PM) has been associated with increased post-MI See Acknowledgments for a list of participating medical centers and investigators. * Corresponding author. Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel. Tel.: þ972-3-6409867; fax: þ 972-3-6409868. E-mail address: [email protected] (Y. Gerber). 1047-2797/$ e see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.annepidem.2013.05.001
mortality, readmissions, and adverse events [11e13]. However, despite intense efforts [3], the pathways underlying these associations remain unclear. Frailty describes the heterogeneity of vulnerability in older people and has been shown to predict mortality, disability, and institutionalization [14]. Frailer individualsdthose with multiple deficitsdhave diminished reserve and resistance to stressors [15]. Although there has been little research on the significance of frailty in MI survivors, several studies have reported excess morbidity and mortality in frail survivors of MI [16,17] and a recent study found frailty to be clinically relevant, predicting long-term mortality and hospitalizations in this population [18]. The prevalence of frailty in cardiovascular patients is estimated to be between 25% and 50% [19]. Frailty has been shown to be related to several factors including advanced age, female gender and socioeconomic status (SES), with lower SES associated with greater frailty in both general and clinical
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populations [20]. Additional factors that may influence the development of frailty remain to be identified; it is plausible that environmental factors may play a role. In a previous follow-up study [12], we reported comparable associations between chronic PM exposure and multiple adverse outcomes post-MI, including all-cause mortality, cardiac mortality, recurrent MI, heart failure, and stroke, suggesting a nonspecific association, whereby pollutants may influence multiple systems through a common pathway. Here, we investigated whether exposure to air pollution is a risk factor for development of frailty post-MI, thus illustrating a potential intermediary between exposure to air pollution and various clinical outcomes. Methods Participants were drawn from the Israel Study of First Acute Myocardial Infarction [21,22], comprising individuals aged 65 who were hospitalized for incident MI in 1992 and 1993 in one of the eight hospitals in central Israel, and were followed longitudinally. Of an initial 1626 consecutive patients admitted, 81 died during initial hospitalization and 24 were lost to follow-up. Of the remaining 1521, particulate matter 2.5 mm in diameter (PM2.5) data were available for 1120 patients, of whom 59 met the definition of frailty at baseline and an additional 213 died before the second frailty assessment, 10 to 13 years post-MI. Analyses were conducted on the remaining 848 patients. Demographic, socioeconomic, and clinical data were collected at baseline and followup interviews. All aspects of the study were approved by the appropriate Institutional Ethics Committees. Exposure assessment Exposure to PM2.5 was assessed using data from air quality monitoring stations in central Israel, as previously described in detail [23]. Chronic exposure was defined as the mean pollutant concentration during a representative period. The mean PM2.5 concentrations during this period in the monitoring stations were interpolated to the residential addresses of the study participants using ordinary kriging, a statistical interpolation scheme suitable to the case given the flat topography and the similar meteorologic conditions along the coastal area where the study took place. Halfhourly records of 12 PM2.5 monitoring stations were available. Additional half-hourly PM2.5 records were modeled using nine PM10 data series and the simultaneous meteorologic variables. The conversion from PM10 to PM2.5 data was carried out by a linear regression algorithm that was developed using the records of three stations observing both pollutants, two of which were in the study area. Nonlinear neural network regression models were also developed, but had only a marginal advantage; thus, the simpler linear models were preferred. The performance of the regression models was measured using Pearson correlation between the halfhourly series, and relative chronic long-term exposure error. Crossvalidated modeling performance measures are given in Supplementary Tables 1 and 2. A final model providing the PM2.5 series in the locations of the nine PM10 stations was developed using the mean PM2.5 value of the two dual-pollutant stations, elevating the total number of PM2.5 series available for the interpolation to 21. Because an increasing number of monitoring stations were fully operable over time (Fig. 1), we selected a representative exposure period, namely, the years 2003 through 2005. Using this relatively short period to characterize the exposure during the study’s duration is justified given that most of the spatial variability in PM2.5 in the study area is due to the spatial variability in the vehicular particulate emissions, which have remained relatively stable since the beginning of the study. For example, the spatial
Fig. 1. The temporal coverage of the PM2.5 series used in this study. Operational periods are marked with light lines while missing data are marked by dark lines. The series of stations 1 through 12 are observed whereas those of stations 13 through 21 are modeled (see text).
pattern of exposure to nitric oxide, a good traffic pollution proxy which has been monitored in the study area over a longer time period, has not changed much during the study’s duration. Frailty assessment A multidimensional frailty index, consisting of 40 items, was constructed from data collected from medical records and during follow-up interviews 10 to 13 years post-MI. The frailty index was adapted from a model developed by Rockwood et al. [24,25] and included data on self-rated health, functional limitations, comorbid conditions, weight loss, physical activity and psychosocial status (see Supplementary Table 3 for a list of frailty index variables). Full details of the frailty index and its derivation in this study have been previously published [18]. For dichotomous items, a score of 0 was given if the deficit was absent and 1 if it was present; ordinal or continuous variables were graded into a score between 0 and 1 (0 [no impairment], 0.5 [minor impairment], 1 [major impairment]). Scores were subsequently summed and divided by the total number of variables to give a frailty index between 0 and 1, indicating proportion of deficits accumulated, with 1 representing the greatest frailty. Patients were categorized into frail and non-frail groups using a threshold of 0.25 or higher, based on values used in previous research [25,26]. The latter cutoff was achieved by comparing a phenotypic definition of frailty with a frailty index [25]. Additional variables Individual SES data were self-reported at study entry and included family income relative to the national average (categorized as below average, average, or above average), education (years of schooling), and pre-MI employment status (full or part time vs. none) [27,28]. Neighborhood-level SES was estimated through a 20-point scale index developed by the Israel Central Bureau of Statistics, with a score of 20 representing the highest SES [29]. Cardiovascular risk factors, MI characteristics, and disease severity indices were recorded at the index hospitalization. Cigarette use was classified into current smoking versus never or past smoking. Hypertension and hypercholesterolemia were defined according to standard criteria based on clinical and laboratory data. Comorbidity was defined based on the Charlson index [30]. MI characteristics and severity indicators included infarct type (Q wave or non-Q wave), anterior MI, and Killip class.
V. Myers et al. / Annals of Epidemiology 23 (2013) 395e400
Statistical analysis Differences in baseline characteristics across PM2.5 tertiles were examined using the c2 test for trend (categorical variables) or generalized linear models (continuous variables). Generalized additive models were used to determine whether the relationship between frailty and PM2.5 might be nonlinear; however, results showed no indication of a nonlinear relationship. The main exposure variable had a clearly significant linear component, but the nonlinear part was not significant (P ¼ .66). Subsequently, logistic regression models were constructed to assess the odds ratios (OR) and 95% confidence intervals (CI) for frailty associated with PM2.5 exposure. PM2.5 was assessed both as a categorical variable, divided into tertiles of the exposure distribution, and as a continuous variable, for which OR are reported comparing the 75th versus 25th percentiles of PM2.5 (corresponding to 25.5 vs. 22.9 mg/m3). Adjustment was initially made for sociodemographic variables (age, gender, family income, education, pre-MI employment, and neighborhood SES; model 1) and additionally for clinical variables (hypertension, smoking, hypercholesterolemia, obesity, Q-wave MI, anterior MI, Killip class, comorbidity, self-rated health, and baseline frailty score; model 2). These potential predictors were chosen based on previous literature and data availability. Of the 1061 non-frail participants at baseline with available PM2.5 assessment, 213 had died before the 10- to 13-year follow-up interview, so that their frailty score could not be evaluated at the end of follow-up. This type of selection bias was addressed by applying the Diehr method, which considered as frail patients who had died before frailty assessment [31,32]. Predicted probabilities of frailty at end of follow-up were calculated for the full model (model 2) with and without PM2.5
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(modeled as a continuous variable) and categorized as less than 20%, 20% to 50%, and greater than 50%, corresponding with low, intermediate, and high risk, respectively. The incremental discriminatory value of adding PM2.5 exposure data over all other covariates (i.e., the amount by which the addition of this factor improves the ability to differentiate events from non-events) was examined by established methods proposed by Pencina et al. [33]. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indices were calculated. In general, IDI measures the improvement in average sensitivity associated with the more advanced model, and subtracts any decrease in average specificity. NRI, in contrast, measures the extent to which individuals with and without the event of interest are appropriately reclassified into clinically accepted higher or lower risk categories with the extended versus basic model. This was done by using a SAS macro (rocplus) written by Erik Bergstralh (available from: http:// mayoresearch.mayo.edu/mayo/research/biostat/sasmacros.cfm). Missing values for family income (12%) and neighborhood SES (4%), as well as for variables comprising the frailty index, which ranged from 0% to 14% (mean, 4%), were imputed using multiple imputation methodology [34]. The Markov Chain Monte Carlo method was used for this purpose. Five datasets were created, with missing values replaced by imputed values based on models incorporating demographic, socioeconomic, psychosocial, anthropometric, and clinical variables. The results from analyzing these datasets were then combined using Rubin’s rules [34]. A complete-case analysis was subsequently performed, to ensure the robustness of the multiple imputation results. Because both approaches yielded similar results, only the multiple imputation results are presented. Analyses were performed using SAS 9.2 (SAS Institute Inc, Cary, NC) and IBM SPSS Statistics version 19 (IBM SPSS Inc, Chicago, IL).
Table 1 Baseline patient characteristics Characteristic
Missing, n
Overall (n ¼ 848)
PM2.5 tertiles Lower (n ¼ 283)
d 24.2 (16.9e28.6) 22.2 (16.9e23.3) PM2.5, mean (range), mg/m3 Age, mean (SD), y d 53.2 (8.2) 53.4 (8.1) Men, n (%) d 710 (83.7) 240 (84.8) Socioeconomic measures Family income, n (%) 98 d d Below average d 315 (42.0) 120 (45.1) Average d 203 (27.1) 69 (25.9) Above average d 232 (30.9) 77 (28.9) Education, mean (SD), y d 11.5 (4.0) 11.1 (4.1) Pre-MI employment, n (%) d 703 (83.0) 235 (83.0) Neighborhood SES, mean (SD) 34 12.9 (3.8) 12.1 (3.6) Risk factors, comorbid conditions, and incident MI characteristics, n (%) unless otherwise specified Comorbidity index d d d Severe d 14 (1.7) 6 (2.1) Mild d 275 (32.4) 105 (37.1) None d 559 (65.9) 172 (60.8) Self-rated health d d d Poor d 117 (13.8) 45 (15.9) Average d 198 (23.3) 66 (23.3) Good d 533 (62.9) 172 (60.8) Hypertension d 304 (35.8) 96 (33.9) Current smoking d 437 (52) 149 (52.7) Hypercholesterolemia d 331 (39.0) 117 (41.3) Obesity 6 143 (17.0) 55 (19.6) Q-wave MI d 634 (74.8) 223 (78.8) Anterior MI d 342 (40.3) 113 (39.9) Killip >1 d 154 (18.2) 54 (19.1) Baseline frailty score, mean (SD) d 0.081 (0.054) 0.088 (0.055) Baseline frailty categories d d d 187 (66.1) <0.1 d 600 (70.8) 0.1e0.2 d 212 (25.0) 80 (28.3) 0.2e0.25 d 36 (4.2) 16 (5.7)
P for trend Medium (n ¼ 283) 23.9 (23.3e24.9) 53.1 (8.1) 238 (84.1) d 118 65 71 11.1 230 11.7
26.6 (24.7e28.6) 53.3 (8.4) 232 (82.3) d
(46.5) (25.6) (28.0) (4.0) (81.3) (3.4)
d 3 84 196 d 36 68 179 93 155 106 48 207 113 54 0.078 d 206 64 13
Upper (n ¼ 282)
77 69 84 12.2 238 14.8
(33.5) (30.0) (36.5) (3.8) (84.7) (3.6)
d (1.1) (29.7) (69.3) (12.7) (24.0) (63.3) (32.9) (54.8) (37.5) (17.1) (73.1) (39.9) (19.1) (0.054) (72.8) (22.6) (4.6)
MI ¼ myocardial infarction; PM2.5 ¼ particulate matter 2.5 mm in diameter; SD ¼ standard deviation; SES ¼ socioeconomic status.
5 86 191 d 36 64 182 115 133 108 40 204 116 46 0.076 d 207 68 7
(1.8) (30.5) (67.7) (12.8) (22.7) (64.5) (40.8) (47.2) (38.3) (14.2) (72.3) (41.1) (16.3) (0.053) (73.4) (24.1) (2.5)
d .83 .42 .015 d d d .002 .60 <.001 .09 d d d .26 d d d .09 .19 .46 .09 .08 .77 .39 .005 .03 d d d
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Results Of the 848 patients included in the study, 301 (35.5%) developed frailty during follow-up. The median frailty score at the end of follow-up was 0.18 (interquartile range, 0.10e0.30). Participants had a mean standard deviation age of 53.2 8.2 years at recruitment, and mean exposure to PM2.5 was 24.2 mg/m3 (range, 16.9e28.6; Table 1). Participants were divided into three equal groups based on exposure to PM2.5. There were no differences between exposure groups according to age, gender, self-rated health, clinical risk factors, or MI characteristics. However, greater exposure was associated with greater SES, at both individual and neighborhood levels, as well as with slightly lower baseline frailty score (P ¼ .005; Table 1). The inverse association between exposure and baseline frailty was substantially attenuated on adjustment for SES and clinical variables (P ¼ .23; data not shown). Logistic regression models showed higher PM2.5 exposure to be associated with increased odds of developing frailty during followup (Table 2). Patients in the upper tertile of exposure had an OR of 1.78 (95% CI, 1.17e2.72) of developing frailty compared with those in the lower tertile, after adjustment for sociodemographic and clinical variables. In a sensitivity analysis, which considered as frail patients who had died before frailty assessment, the OR of developing frailty was similar at 1.71 (95% CI, 1.18e2.49) for upper compared with lower tertiles of exposure. With exposure modeled as a continuous variable, an increase from the 25th to 75th percentile of exposure was associated with a 50% increase in the odds of frailty development (OR, 1.53; 95% CI, 1.22e1.91 after multivariable adjustment). A linear regression was additionally conducted, avoiding dichotomization of the outcome measure, which supported the results of the logistic regression (P < .001 for PM2.5 in the adjusted model). Reclassification tables show the added value of PM2.5 exposure (extended model), beyond sociodemographic and clinical variables (basic model), in predicting frailty at the end of follow-up (Table 3). In total, 9% (n ¼ 76) of events (developing frailty) and non-events (not developing frailty) were appropriately reclassified and 6% (n ¼ 51) were inappropriately reclassified with the extended model. Inclusion of PM2.5 exposure in the multivariable model resulted in an IDI of 1.60% (P ¼ .0005) and an NRI of 6.51% (P ¼ .02), indicating significant gains in predictive value. The IDI and NRI for baseline frailty (with PM2.5 included in the basic model) were 2.60% (P < .001) and 9.55% (P ¼ .003), indicating that baseline frailty is a strong predictor of future frailty status.
Table 2 Odds ratios (95% confidence intervals) for frailty development 10e13 years after MI associated with cumulative PM2.5 exposure PM2.5 tertiles Lower
Medium
Upper
Unadjusted 1 (ref.) 1.03 (0.73e1.45) 1.10 (0.78e1.56) Model 1 1 (ref.) 0.98 (0.67e1.42) 1.47 (0.99e2.19) Model 2 1 (ref.) 1.05 (0.71e1.57) 1.78 (1.17e2.72) Sensitivity analysis applying the Diehr methody Unadjusted 1 (ref.) 1.10 (0.82e1.47) 1.18 (0.88e1.58) Model 1 1 (ref.) 1.00 (0.73e1.38) 1.55 (1.11e2.18) Model 2 1 (ref.) 1.10 (0.77e1.55) 1.71 (1.18e2.49)
75th vs. 25th percentiles* 1.12 (0.93e1.34) 1.33 (1.08e1.63) 1.53 (1.22e1.91) 1.11 (0.95e1.30) 1.31 (1.09e1.56) 1.44 (1.19e1.76)
MI ¼ myocardial infarction; PM2.5 ¼ particulate matter 2.5 mm in diameter. Model 1: Age, gender, and SES measures (family income, education, pre-MI employment, and neighborhood socioeconomic status). Model 2: Model 1 plus clinical variables (hypertension, smoking, hypercholesterolemia, obesity, q-wave MI, anterior MI, Killip >1, comorbidity, and selfrated health) and baseline frailty index score. 3 * PM2.5 modeled as a continuous variable (corresponds to 25.5 vs. 22.9 mg/m ). y With patients who died before frailty assessment coded as frail [32,33].
Table 3 Predicted risk categories by basic (sociodemographic þ clinical variables) and extended (PM2.5 added) model Basic model
Non-events <20% 20e50% >50% Total Events <20% 20e50% >50% Total
Extended model <20%
20e50%
204 38 0 242 15 8 0 23
Reclassified as higher risk
Reclassified as lower risk
223 248 76 547
19 15 NA 34
NA 38 8 46
22 148 131 301
7 23 NA 30
NA 8 9 17
>50%
Total
19 195 8 222
0 15 68 83
7 117 9 133
0 23 122 145
PM2.5 ¼ particulate matter 2.5 mm in diameter; NA ¼ not applicable. Basic model: Age, gender, family income, education, pre-MI employment, neighborhood socioeconomic status, hypertension, smoking, hypercholesterolemia, obesity, q-wave myocardial infarction (MI), anterior MI, Killip >1, comorbidity, self-rated health and baseline frailty index score. Extended model: Basic model plus PM2.5 (as a continuous variable).
Discussion Post-MI patients exposed to higher levels of PM2.5 were more likely to develop frailty than were their less exposed counterparts, after adjusting for key prognostic factors. Frailty was previously demonstrated to be significantly associated with post-MI mortality and hospitalizations [16,18]. To date, research on the health effects of air pollution has most often focused on death as the outcome of interest, via either timeseries or cohort studies [35]. The few studies that investigated frailty in the context of air pollution health effects did find some evidence for a relationship, demonstrating for example that frailty history modified the effects of PM and ozone on lung function [36]. To the best of our knowledge, this is the first study to specifically examine the relationship between exposure to air pollution and post-MI frailty. These findings, demonstrating a role for PM2.5 exposure in long-term frailty development after MI, could identify an intermediary step between air pollution and adverse cardiovascular outcomes. The frailty index provides a comprehensive and multidimensional evaluation of the patient. Detection of an association with air pollution provides a possible explanation for the multitude of adverse health outcomes associated with PM2.5, all driven by a common mechanism. Indeed, particulate air pollution exposure has been implicated in the incidence of conditions as diverse as acute cardiovascular events [13], coronary heart disease, dysrhythmias, heart failure [37], stroke [38], decreased lung function [2], lung cancer, and chronic obstructive pulmonary disease [39]. Thus, beyond damage to specific systems, chronic PM exposure may add to the burden of illness, an accumulation of changes to multiple physiological systems, which eventually lead to disease, disability, and death [40]. Of note, patients exposed to higher levels of PM2.5 exhibited higher SES, which is generally associated with a better prognosis [22,27,41] and lower odds of frailty [20]. For example, the Women’s Health and Ageing Study found increased odds of frailty associated with low SES [20]. In the current study, exposure to air pollution was associated with greater odds of frailty, a relationship that was substantially enhanced upon adjustment for multiple SES measures. This highlights the importance of controlling for multiple measures of SES, both at the individual and area levels, in the study of air pollution effects. Owing to the challenges inherent in exposure assessment, previous studies of frailty have failed to address the impact of environmental determinants. A large-scale study of Hong Kong residents found significant differences in frailty by district;
V. Myers et al. / Annals of Epidemiology 23 (2013) 395e400
attempts to examine associations with air pollution were limited by availability of exposure data and found little association [42]. Numerous studies have, however, reported both elevated cardiovascular events in the general population [2,3], and excess postMI mortality [11], associated with exposure to air pollutants. Several mechanisms have been suggested to explain the increase in adverse cardiovascular events associated with chronic exposure to air pollutants, for example an adverse effect on cardiac autonomic tone [37]. MI patients are generally at increased risk of death and recurrent ischemic events, and this risk seems to be associated with higher levels of markers of systemic inflammation. Indeed, the inflammatory pathway may provide some support for the relationship between exposure and frailty development, since low grade chronic inflammation, and raised levels of the inflammatory markers interleukin-6 and C-reactive protein, have been associated with both frailty [19,43] and exposure to fine particle pollution [44,45]. However, the relationship with frailty demonstrated here suggests that air pollutants may exert influences on multiple systems, involving more than a single process. Kunzli et al. [35] proposed that the pathways leading to air pollution attributable deaths often involve frailty, either by increasing the risk of underlying disease leading to frailty or by hastening death in those already frail. Given that exposure to pollutants exacerbates many conditions including cardiovascular and respiratory processes [2], it is plausible that exposure above a certain level may add to the burden of frailty found in post-MI patients. Accordingly, many survivors of an MI may already have decreased homeostatic reserve, thus being particularly susceptible to air pollution effects in terms of their subsequent frailty risk. Methodologic considerations The current study had several advantages that strengthen the results. First, the study sample is a well-defined post-MI cohort, a population established as vulnerable to the effects of air pollution, as well as being geographically defined, thereby improving relevance and generalizability. Moreover, it has been suggested that cohort studies are more effective than time-series studies in assessing the health impact of air pollution [35]. The frailty index used has been previously validated in this cohort and was shown to predict mortality and hospitalizations [18]. Sensitivity analyses were performed to reduce the effects of selection bias, caused by exclusion of decedents. Extensive high quality clinical and sociodemographic data were available from multiple sources, including medical records and structured interviews, allowing us to more accurately control for confounding factors, rather than relying on administrative data. Assessment of PM exposure was individual, according to home address, and blinded to outcome, thereby precluding differential misclassification bias. Several limitations should be also considered when interpreting the study findings. PM2.5 data were incomplete, particularly for the earlier years of the study period, leading to some unavoidable exposure misclassification; however, the exposure period chosen had high reliability and minimal uncertainty. It has been proposed that assessment of exposure over the most recent 1 or 2 years can accurately estimate health risks of long-term PM exposure [3]. Indeed, the spatial differences in traffic emissionsdthe main contributor to PM2.5 in the study regiondhave not changed over the study period, whereas other sources such as desert dust and transboundary sulphites and nitrates impact on a very large spatial scale, making any spatial differences within the study area negligible. Owing to the possibility of exposure misclassification, ranks of exposure were considered more reliable than absolute values. Results are therefore presented by tertiles or percentiles of exposure, and the findings should be interpreted accordingly. Frailty was
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assessed via an index, rather than direct phenotypic assessment [46]; however, the index of accumulated deficits has been demonstrated to correlate well with frailty phenotype [17]. Some data, including frailty index variables, were missing for a small proportion of the sample, necessitating the use of multiple imputations. However, similar results were obtained in the complete-case analysis. The relatively young cohort limits the generalizability of these findings because post-MI populations are generally older. In addition, more recent cohorts use definitions of MI based on cardiac biomarkers, which were not yet in use at the time of study recruitment. Implications The findings of this study propose a potential intermediary mechanism, bridging the relationship between particulate air pollution and post-MI outcomes. We detected a significant association between exposure to PM2.5 and development of frailty in MI survivors, indicating that chronic exposure to PM might affect multiple physiological systems and contributes to the decline of patients in this vulnerable subpopulation. Because the frailty index encompasses functional, clinical, and psychosocial deficits, the results presented here suggest that exposure to PM2.5 may contribute to excess mortality, not just through single processes such as atherosclerosis, but by a broader assault on bodily systems. The onset of frailty is a predecessor to overt disease, heralding a decline that eventually leads to adverse events, disease expression, and death, “an intermediate stage between robust health and end of life” [42]. Understanding the determinants of frailty in cardiovascular populations is important, both to implement preventive interventions and in an attempt to reduce adverse outcomes. Acknowledgment The following investigators and institutions took part in the Israel Study Group on First Acute Myocardial Infarction: Yaacov Drory, MD, Principal Investigator, Department of Rehabilitation, Sackler Medical School, Tel Aviv University, Tel Aviv; Yeheskiel Kishon, MD, Michael Kriwisky, MD, and Yoseph Rosenman, MD, Wolfson Medical Center, Holon; Uri Goldbourt, PhD, Hanoch Hod, MD, Eliezer Kaplinsky, MD, and Michael Eldar, MD, Sheba Medical Center, Tel Hashomer; Itzhak Shapira, MD, Amos Pines, MD, Margalit Drory, MSW, Arie Roth, MD, Shlomo Laniado, MD, and Gad Keren, MD, Tel-Aviv Sourasky Medical Center, Tel-Aviv; Daniel David, MD, Morton Leibowitz, MD, and Hana Pausner, MD, Meir Medical Center, Kfar Sava; Zvi Schlesinger, MD, and Zvi Vered, MD, Assaf Harofeh Medical Center, Zerifin; Alexander Battler, MD, Alejandro Solodky, MD, and Samuel Sclarovsky, MD, Beilinson Medical Center, Petach Tikvah; Izhar Zehavi, MD, and Rachel MaromKlibansky, MD, Hasharon Medical Center, Petah Tikvah; and Ron Leor, MD, Laniado Medical Center, Netanya. The authors are also indebted to Zalman Kaufman, MSc, for assistance with the Geographic Information System analysis. Funding: This work was supported by the Environment and Health Fund, Israel [Grant Award Nos. SGA 1204 and RGA 0904]. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.annepidem.2013.05.001. References [1] Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, et al. Risk of nonaccidental and cardiovascular mortality in relation to long-term
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