Nitrogen dioxide increases cardiorespiratory admissions in Torrelavega (Spain).

Background

Since the pioneering papers of the 1960s (Sterling, Pollack, & Phair, 1967; Sterling, Pollack, & Weinkam, 1969), the effects of air pollution on human health have frequently been studied (Dockery et al., 1993; Katsouyanni et al., 1996). Most papers have analyzed the effects of pollution with respect to daily mortality (Anderson, Ponce de Leon, Bland, & Coger, 1996; Kan & Chen, 2003; Michelozzi et al., 1998) or daily hospital admissions for respiratory or cardiovascular diseases (Atkinson et al., 1999; Hajat, Haines, Goubet, Atkinson, & Andersson, 1999; Schouten, Vonk, de Graaf, 1996; Wong et al., 1999; Yang, Chen, Yang, & Ho, 2004). A more recent trend is to study the effects of pollution on specific diseases such as asthma (Tolbert et al., 2000; Tolbert et al., 2004) and chronic pulmonary obstructive disease (Tolbert et al., 2000).

The principal multicity effort has probably been Air Pollution and Health--A European Approach (APHEA), a study in which relations between several pollutants and morbidity and mortality have been analyzed with data from several large and largely industrialized European cities (Katsouyanni et al., 1996). The APHEA project found a relationship between daily deaths and nitrogen dioxide concentrations (Toulomi et al., 1997), asthma admissions and nitrogen dioxide (Sunyer et al., 1997), and daily admissions for respiratory diseases and ozone levels (Spix et al., 1998).

As outdoor air pollution is of major concern in large cities, most studies have been centered in cities where many people would be exposed to high levels of pollution. Therefore, there is little knowledge about the effect of air pollution in cities with low levels of pollution (Vedal, Brauer, White, & Petkau, 2003) or in small cities. In the study reported here, the goal was to analyze the short-term effects of air pollutants on cardiac and respiratory admissions in Torrelavega. What is new in this study is that Torrelavega (located in northern Spain) has far fewer inhabitants than the cities in the APHEA project; therefore, one would expect that levels of pollution from human sources (e.g., traffic vehicles) would be more unstable and that pollutants would be less persistent in the air.

Methods

Population and Period

Data relative to Torrelavega from January 1, 1992, to December 31, 1995, were analyzed. Torrelavega is the second largest city in the administrative region of Cantabria (Spain); it has about 60,000 inhabitants and is located 27 kilometers to the southwest of the capital (Santander). An industrialized area is located to the north and northwest of Torrelavega; this direction coincides with the dominant winds. Until October 31, 1994, Torrelavega inhabitants received hospital treatment in the Marques de Valdecilla University Hospital (in Santander); this is a tertiary hospital with about 1,200 beds. The Sierrallana Hospital (a new general hospital) opened near Torrelavega on November 1, 1994. For a considerable time, however, most people in Torrelavega continued using the Marques de Valdecilla University Hospital because of its emergency services.

Variables and Data Source

The number of cardiac (ICD-9 codes 390-459) or respiratory (ICD-9 codes 460-519) emergency admissions among people living in Torrelavega was analyzed as output. The authors did not control for multiple visits by the same individual. Admissions of tourists or other nonresidents were excluded. Pollutants included were hydrogen sulfide ([H.sub.2]S), sulfur dioxide (S[O.sub.2]), nitrogen oxide (NO), nitrogen dioxide (N[O.sub.2]), and total suspended particles (TSP). As both pollutants and admissions would be influenced by several meteorological factors, the authors have obtained data on a daily basis for rain (in millimeters); temperature (maximum, minimum, and mean); dominant wind direction (classified, by quadrant, as northeast, northwest, southwest, and southeast); and maximum wind speed.

The authors controlled for short-wave autocorrelations by introducing the number of admissions in the six preceding days. Long-wave autocorrelations were controlled via sine and cosine functions: sin(2[pi]T/365k) and cos(2[pi]T/365k), where T is the number of days from the beginning of the study, and k takes integer values between 1 and 6. If k = 1, autocorrelations of annual wave are controlled, if k = 2, autocorrelations of biannual waves, and so on (Vedal et al., 2003).

The daily numbers of admissions was obtained from the Marques de Valdecilla University Hospital Admission Service for the whole period, and from the Sierrallana Hospital Admission Service for the period between November 1, 1994, and December, 31, 1995. The Sierrallana data were, however, not of sufficient quality (probably because of a lack of uniformity in the register), so these data were not used. The average number of admissions excluded was 4.3 (standard deviation: 3.5). This number would produce a misinformation bias; to test it, the authors performed all the analyses in duplicate--for the whole period and for the period before the opening of the Sierrallana Hospital. In this paper, only the whole-period results are shown because excess risk (i.e., rate ratio--1) was always under 0.2, so no appreciable differences between the two periods were found.

Meteorological data were obtained from the National Meteorology Institute in Santander. Pollutant data were provided by the secretary of environment for the Cantabrian Regional Government. There were three stations for air pollution monitoring in Torrelavega; they were located in the industrialized area, the city center, and a residential area. For each pollutant, the arithmetic mean of all the measures available each day was its daily-value input (in [micro]g/[m.sup.3]).

Influenza incidence was provided by the secretary of health for the Cantabrian Regional Government and comes from the compulsory-declaration-of-diseases system.

Statistical Analysis

Meteorological factors influencing air pollutants were studied by linear correlation, as an exploratory method, and by stepwise multiple linear regression. The relationship between the daily level of air pollutants (for each 100 [micro]g/[m.sup.3]) and daily hospital admissions (as output) was analyzed by Poisson regression. Poisson regression allows rate ratios and their confidence intervals to be obtained. Rate ratios were adjusted for meteorological factors, day, and influenza incidence. As pollutants are correlated with each other, the authors made their calculations on the basis of two models: first, for each pollutant; second, including all five pollutants in the same model.

All the statistical analyses were performed with the package Stata Intercooled, Release 6 (Stata Corporation, Texas).

Results

Throughout the four years of the study, 18,137 admissions for respiratory and cardiac diseases were produced. The mean number (standard deviation) of emergency admissions was 12.54 (4.68) per day; 7.61 (3.52) from cardiac cause and 4.93 (2.58) from respiratory disease. Both cardiac and respiratory admissions were higher on Mondays (8.38 and 5.39, respectively) and Tuesdays (8.16 and 5.28, respectively), and lower on Sundays (6.46 and 4.08, respectively). The mean air pollutant levels were 48.8 (23.7) [micro]g/[m.sup.3] for TSP, 13.3 (16.7) [micro]g/[m.sup.3] for S[O.sub.2], 7.2 (9.2) [micro]g/[m.sup.3] for S[H.sub.2], 12.2 (15.2) [micro]g/[m.sup.3] for NO, and 21.3 (16.5) [micro]g/[m.sup.3] for N[O.sub.2]. In all cases, a great daily variability could be observed.

The mean daily rainfall was 3 L/[m.sup.2]. No rainfall occurred on 793 days (out of 1,461 days studied). The mean temperatures were 18.2[degrees]C, 11.8[degrees]C, and 15.0[degrees]C for the maximum, minimum, and mean, respectively. The mean of the maximum gust of wind was 38 kilometres per hour. Dominant winds were from the northeast on 432 days, from the northwest on 600 days, and from the southwest on 350 days. The weekly average for influenza cases over the whole period was 121.

Weather and Pollution

Table 1 gives the Pearson's correlation coefficients for pollutants and weather variables. As expected, rain was negatively correlated with all pollutants, although this relationship was more intense with particulate matter. Nitrogen oxides were negatively associated with all weather variables.

The level of TSP dropped with the rain (a decrease of 3.1 [micro]g/[m.sup.3] for every 10 liters; p = .001), and wind speed (17.6 [micro]g/[m.sup.3] less for every 10 km/hour; p < .001). An independent decrease of 34.5 [micro]g/[m.sup.3] occurred if the wind came from the northwest. Temperatures and other wind directions did not show an independent relation with TSP level.

S[O.sub.2] concentration increased 1.02 [micro]g/[m.sup.3] if the minimum temperature went down a degree (p < .001) and if the maximum temperature rose a degree (p < .0001). Other meteorological factors did not show any relation with S[O.sub.2].

An increase in N[O.sub.2] levels was produced by wind speed (9.7 [micro]g/[m.sup.3] for every 10 km/hour), p = .008), northwest wind (19.5 [micro]g/[m.sup.3], p = .03), and maximum temperature (0.85 [micro]g/[m.sup.3] for each centigrade degree, p < .001), and a decrease was produced by minimum temperature (1.56 [micro]g/[m.sup.3] for each degree, p < .001).

Finally, NO declined with wind speed (6.5 [micro]g/[m.sup.3] for every 10 km/hour, p = .05), and northwest and northeast wind (81.5 [micro]g/[m.sup.3], p < .001, for northwest wind and 72.4 [micro]g/[m.sup.3], p < .001, for northeast wind). NO levels increased with minimum and maximum temperatures: 1.4 [micro]g/[m.sup.3] for each degree in minimum (p < .001) and 0.6 [micro]g/[m.sup.3] each degree in maximum (p < .001).

Pollutants and Admissions

The relationships between pollutants and admissions are delineated in Table 2. Most associations between sulfur compounds (S[O.sub.2] and [H.sub.2]S), TSP, and admissions disappear in the five-pollutant model.

Nitrogen oxides were associated with a rise in admissions for both respiratory and cardiac cause. In the case of N[O.sub.2], this association remained in the five-pollutant model: All admissions rose 20 percent for each 100 [micro]g/[m.sup.3] increase in nitrogen dioxide (equivalent to 1.8 percent increase for each 10 [micro]g/[m.sup.3]); most of this effect was due to respiratory admissions (69 percent increase for each 100 [micro]g/[m.sup.3], equivalent to 5.4 percent increase for each 10 [micro]g/[m.sup.3]). The effect was less intense in the warm seasons, during which N[O.sub.2] was lower (results not shown).

Discussion

Nitrogen oxide pollution was associated with an increase in hospital admissions in Torrelavega for both respiratory and cardiac causes. Particle and sulfur-compound levels were associated with admissions.

N[O.sub.2] levels fell with wind speed and the northwest wind, and rose with extreme temperatures. The impact of N[O.sub.2] on the number of cardiac-respiratory admissions was a bit higher than previously reported: In Atlanta, an increase in N[O.sub.2] concentration of 20 ppb was associated with 0.9 percent more emergency department visits (Tolbert et al., 2000), while other studies have failed to find such an association (Petroeschevsky, Simpson, Thalib, & Rutherford, 2001). Other studies have explored relationships between nitrogen dioxide and specific diseases: A positive association was found with visits for wet cough (Jalaludin, O'Toole, & Leeder, 2004), chronic lower-respiratory diseases (Martins, Latorre Mdo, Saldiva, & Braga, 2002), asthma (Atkinson et al., 1999), and asthma in children (Tolbert et al., 2004). When mortality is studied, results are far more consistent; the APHEA-2 project shows little increase in risk until nitrogen dioxide levels go up 80 [micro]g/[m.sup.3] (Samoli et al., 2003). That study, however, was performed in cities with high N[O.sub.2] levels (medians above 110 [micro]g/[m.sup.3]), which reduces its comparability. The EMECAMEMECAS project studied short-term mortality by air pollution in 13 Spanish cities, finding increases of 0.6 percent, 0.8 percent, and 1.2 percent in all-cause, cardiac, and respiratory mortalities, respectively, per 10 [micro]g/[m.sup.3] rise in N[O.sub.2] (Ballester et al., 2003). Increases in mortality were also found in Shanghai (1.5 percent per 10 [micro]g/[m.sup.3]) (Kan & Chen, 2003) and Rome (0.3 percent per 10 [micro]g/[m.sup.3]) (Michelozzi et al., 1998), but not in Taipei (Yang et al., 2004).

Nitrogen dioxide mainly originates from human activities; because there are no nitrogen-related industries (e.g., fertilizer or explosive industries) in Torrelavega, the main sources for N[O.sub.2] should be traffic and heating. Traffic increases in summer with tourism; however, higher levels of N[O.sub.2] and the higher health effect occurred in cold seasons, which suggests that heating is a more important source in Torrelavega.

Particle concentration dropped with rainfall and wind speed. The authors did not find any association between particles and admissions. Other studies have some methodological heterogeneity, because 1) while some of them analyzed TSP (as did the authors of the study reported here), others limited their analyses to particles smaller than 2.5 [micro]m or to black smoke, and 2) most analyzed mortality--not admissions--as outcomes. The APHEA-2 project found that admissions rose by 1 percent per 10 [micro]g/[m.sup.3] increase in particulate matter with an aerodynamic diameter of less than 10 (P[M.sub.10]) (Atkinson et al., 2001); in the Study of Particulates and Health in Atlanta (SOPHIA) project, only P[M.sub.2.5-10] and P[M.sub.2.5] elemental carbon, but not P[M.sub.10], were found associated with dysrhythmia and all cardiovascular diseases admissions for adult asthma. Studies on mortality have shown positive relationships with P[M.sub.10]: 0.69 percent for cardiovascular diseases and 0.74 percent for respiratory diseases (Zanobetti et al., 2003), 0.8 percent for all of admissions, 1.2 percent for cardiovascular diseases and 1.3 percent for respiratory diseases (Ballester et al., 2003), and 0.6 percent for all admissions (black smoke) (Samoli et al., 2001).

A few factors might explain the discrepancy between the authors' results with respect to particulate matter and those previously reported. First, Torrelavega is located in a rainy area (the Cantabric coast) with about 1,000 liters/[m.sup.2] per year, nearly equally distributed by season. This circumstance may entail lower particle concentrations, resulting in lesser effects on health. Second, particle size seems to be important with respect to health effects; the authors have data only for TSP, and any effect of smaller particles (say, P[M.sub.2.5] or P[M.sub.10]) would be diluted. Third, TSP and N[O.sub.2] were positively associated in fall and winter (Spearman correlation coefficient = .340) and negatively in spring and summer (correlation coefficient = -.577); this result suggests heating as the shared source for both pollutants in cold seasons; therefore, the effect of TSP would be masked by N[O.sub.2]. Fourth, the authors cannot rule out an interaction between particulate matter and N[O.sub.2], but their model does not have enough power to detect such interaction. In this regard, a case-crossover study designed to estimate the effect of particulate air pollution matched with other pollutants found a positive effect of P[M.sub.10] on mortality; it could not, however, analyze interactions (Schwartz, 2004).

The authors did not find any relation between sulfur compounds and hospital admissions in Torrelavega. [H.sub.2]S has not been studied in previous papers. Other studies show contradictory results for S[O.sub.2]; Mackenbach and co-authors found a crude association that disappeared after temperature was adjusted for (Mackenbach, Looman, & Kunst, 1993). The study reported here confirms that S[O.sub.2] rises with extreme temperatures; these temperatures may produce an increase in cardiac-respiratory admissions. On the other hand, some papers have shown a positive association with mortality (Derrienic, Richardson, Mollie, & Lellouch, 1989; Hatzakis, Katsouyanni, Kalandidi, Day, & Trichopoulos, 1986; Pope et al., 1995). It must be remarked that most of these studies were performed in cities that were larger and more industrialized than Torrelavega.

In conclusion, N[O.sub.2] was associated with a rise in emergency admissions in Torrelavega, mainly in cold seasons; most of this effect occurred in admissions for respiratory diseases. Other pollutants have not shown any association with admissions. The small number of daily admissions and the variability of pollutant concentrations make these results difficult to generalize. This problem may be frequent in small cities, so more research is needed on the health effects of outdoor air pollution in cities with low levels of pollution or in less urban areas; such research would provide new insight into threshold levels of pollutants and differences between persistent pollution (in urban areas) and less persistent pollution (in less urban or rural areas).

Corresponding Author: Javier Llorca, Profesor, Division of Preventive Medicine and Public Health, University of Cantabria School of Medicine, Avda. Cardenal Herrera Oria s/n, Santander, Spain 39011. E-mail: [e-mail].

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Javier Llorca, M.D., Ph.D.

Ana Salas, M.D., Ph.D.

Dolores Prieto-Salceda, M.D., Ph.D.

Veronica Chinchon-Bengoechea, M.D., Ph.D.

Miguel Delgado-Rodriguez, M.D., Ph.D.

TABLE 1 Spearman Correlation Coefficients Between Pollutants and Weather
Variables

                      Maximum       Minimum      Average      Wind
Pollutants  Rain      Temperature   Temperature  Temperature  Speed

TSP*        -0.339     0.071        -0.003        0.035       -0.357
S[O.sub.2]   0.021    -0.076        -0.141       -0.113        0.089
S[H.sub.2]   0.040    -0.038        -0.091        0.012        0.087
NO          -0.092    -0.178        -0.260       -0.225       -0.153
N[O.sub.2]  -0.060    -0.116        -0.184       -0.156       -0.142

Pollutants  TSP*       S[O.sub.2]  S[H.sub.2]    NO

TSP*        --         --          --            --
S[O.sub.2]  -0.400     --          --            --
S[H.sub.2]  -0.392     0.957       --            --
NO          -0.109     0.544       0.474         --
N[O.sub.2]  -0.120     0.588       0.545         0.855

*TSP = total suspended particles.

TABLE 2 Rate Ratios (RR) Obtained by Poisson Regression for Each
Pollutant

Pollutant            RR (One-Pollutant Model)*   95% CI     p

Cardiorespiratory admissions
S[O.sub.2]           0.98                        0.89-1.07   0.58
S[H.sub.2]           0.86                        0.73-1.02   0.08
Particles            0.92                        0.86-0.98   0.006
NO                   1.33                        1.22-1.46  <0.001
N[O.sub.2]           1.37                        1.26-1.49  <0.001

Cardiac admissions
S[O.sub.2]           0.94                        0.84-1.05   0.26
S[H.sub.2]           0.79                        0.64-0.98   0.03
Particles            0.88                        0.81-0.95   0.002
NO                   1.32                        1.17-1.48  <0.001
N[O.sub.2]           1.27                        1.14-1.42  <0.001

Respiratory admissions
S[O.sub.2]           1.04                        0.90-1.19   0.61
S[H.sub.2]           0.99                        0.77-1.29   0.96
Particles            0.98                        0.89-1.08   0.66
NO                   1.35                        1.17-1.56  <0.001
N[O.sub.2]           1.54                        1.34-1.76  <0.001

Pollutant            RR (Five-Pollutant Model)  95% CI     p

Cardiorespiratory admissions
S[O.sub.2]           0.98                       0.80-1.21   0.88
S[H.sub.2]           0.93                       0.63-1.37   0.71
Particles            1.05                       0.97-1.14   0.21
NO                   0.93                       0.79-1.09   0.36
N[O.sub.2]           1.20                       1.05-1.39   0.01

Cardiac admissions
S[O.sub.2]           1.09                       0.83-1.42   0.54
S[H.sub.2]           0.74                       0.45-1.22   0.24
Particles            1.02                       0.92-1.12   0.77
NO                   1.07                       0.88-1.31   0.49
N[O.sub.2]           1.10                       0.92-1.32   0.28

Respiratory admissions
S[O.sub.2]           0.89                       0.64-1.24   0.50
S[H.sub.2]           0.78                       0.43-1.43   0.42
Particles            0.91                       0.80-1.02   0.11
NO                   0.87                       0.67-1.13   0.28
N[O.sub.2]           1.69                       1.34-2.13  <0.001

*Adjusted for influenza, day of week, wind speed, northeast and
southwest winds, and minimum and maximum temperatures.