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Respiratory tract infections are the most common causes of infection, and viruses account for the majority of these infections, leading to significant levels of morbidity and mortality. Emergency rooms (ERs) serve as the frontline for patients at highest risk for respiratory infection diseases, especially because of the acute nature of these illnesses.
The prevalence of respiratory viral infection (RVI) in adults admitted to the ER is largely unexplored, as most relevant data concern infants and children.[3, 4] RVI can be severe in elderly patients, especially in those with underlying respiratory or cardiac disease. During winter months, RVI can account for many of the admissions to hospitals.[5, 6] The etiology of respiratory infections in adults remains undetermined in more than 50% of cases.[7, 8] In a study with 510 adults hospitalized with pulmonary diseases, an overall prevalence of respiratory viruses (RVs) in the lower respiratory tract was of 42·2%, with rhinoviruses and influenza A virus being the most common. In adults with acute asthma admitted to the ER, a prevalence of 12·2% of RVI was found. In another study, with adults admitted to hospital with respiratory symptoms, viruses accounted for 15% of hospital admissions for respiratory infections.
Seasonality of certain acute respiratory tract infection pathogens can be explained by meteorological variations. In a study, temperature was highly inversely correlated with respiratory syncytial virus (RSV), influenza A, and adenovirus frequency; rhinovirus was also associated with relative humidity (RH). Climatic factors may influence the interaction among the host, pathogen, and environment, increasing the probability of exposure, susceptibility, and infection. In addition, experimental data have shown that air pollutants affect lung immune responses and inflammatory reactions and that these effects may underlie the increased risk for respiratory infections.[13, 14]
Because of the large impact respiratory virus infections have on morbidity and even mortality, it is important to understand whether and how meteorological factors and exposure to air pollutants could influence respiratory virus infections. The aims of this study were to determine the number of emergency room visits for influenza-like illness (ILI) and severe acute respiratory infection (SARI) and to evaluate the association between ILI/SARI frequency, respiratory virus prevalence, and meteorological factors/air pollution, especially in adult population, in a humid subtropical climate.
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During the 12-month study period, there were 37 059 admissions to the ER (24 189 adults and 12 870 children), of which 11 953 (32·3%) presented with respiratory symptoms. The most common symptoms were cough (73·4%), fever (56·1%), dyspnea (40·9%), chest pain (24·5%), and coryza (20·9%). The median duration of symptoms before admission was 3 days (IQR: 1–6 days). A total of 2205 (18·5%) patients admitted to ER needed to be hospitalized; of these patients, 242 (2·0%) required ICU admission. ILI and SARI were diagnosed in 3698 (30·9%) and 2063 (17·7%) patients, respectively. The overall mortality rate among all study participants was 280 of 11 953 (2·3%). Demographic and clinical characteristics of the study population are shown in Table 1.
Table 1. Characteristics of patients with respiratory symptoms (November 2008–2009; n = 11 953)a
|Characteristics||Adults (n = 6546)||Children (n = 5407)|
|Age, year||50·6 ± 19·2||2 (0·01–17)|
|Male sex||2942 (45·0)||2977 (55·1)|
|White race||5593 (85·6)||4336 (80·3)|
|<8 year of schooling||2764 (47·8)||–|
|Current smokers||609 (9·3)||–|
|Cough||4265 (65·2)||4501 (83·3)|
|Fever||2708 (41·4)||3994 (73·9)|
|Dyspnea/tachypnea||3401 (52·0)||1482 (27·4)|
|Chest pain||2583 (39·5)||343 (6·3)|
|Coryza||615 (9·4)||1882 (34·8)|
|Sore throat||767 (11·7)||602 (11·1)|
|Duration of symptoms before admission||3 (1–6)||2 (1–4)|
|ILI||1321 (20·2)||2376 (44·0)|
|SARI||845 (12·9)||1218 (22·5)|
|Need for hospitalization||1526 (23·3)||679 (12·6)|
|Mortality rate||260 (4·0)||20 (0·4)|
According to the selection criteria of the study, 425 patients met the inclusion criteria and were invited to participate, and 15 patients declined to participate. Then, 410 adults were enrolled for virological investigation, 255 in the first year and 155 in the second one. There were no differences in age, sex, race, years of schooling, and symptoms between selected sample and all adults admitted to the ER in the same period. However, as expected, the median duration of symptoms was lower in the selected sample as compared to all adults admitted to the ER [3·0 days (2·0–4·0) versus 3·0 days (1·0–7·0); P < 0·0001]. Thirty-seven (9·0%) samples were positive by IFI: 11 influenza A, 11 RSV, 8 PIV type 3, 3 adenovirus, 2 PIV type 2, 1 PIV type 1, and 1 influenza B. The characteristics of IFI-positive and IFI-negative patients were described in Table 2.
Table 2. Characteristics of patients with IFI positive and IFI negative (November 2008–2009; n = 410)a
|Characteristics||IFI positive (n = 37)||IFI negative (n = 373)||P value|
|Age, year||50·4 ± 19·5||51·4 ± 18·4||0·741|
|Male sex||16 (43·2)||148 (39·7)||0·673|
|White race||29 (78·4)||269 (72·1)||0·415|
|Years of schooling||8 (5–11)||7 (4–10)||0·139|
|Current smokers||7 (31·8)||78 (37·1)||0·622|
|Presence of smokers at home||12 (32·4)||141 (37·8)||0·520|
|Air conditioning at home or at work||13 (35·1)||68 (18·2)||0·014|
|Use of wood stoves||6 (16·2)||51 (13·7)||0·670|
|Mold in home||15 (40·5)||118 (31·6)||0·270|
|Cough||36 (97·3)||315 (84·5)||0·034|
|Fever||24 (64·9)||190 (50·9)||0·106|
|Dyspnea||32 (86·5)||314 (84·2)||0·713|
|Chest pain||28 (75·7)||233 (62·5)||0·111|
|Coryza||20 (5·1)||171 (46·0)||0·347|
|Sore throat||8 (21·6)||94 (25·2)||0·631|
|Wheezing||27 (73·0)||199 (53·4)||0·022|
|Duration of symptoms before admission||2 (2–4)||3 (2–4)||0·613|
|ILI||10 (27·0)||43 (11·5)||0·007|
|SARI||23 (62·2)||143 (38·3)||0·005|
|Need for hospitalization||9 (24·3)||96 (25·8)||0·931|
|Length of hospital stay, days||4 (2–5)||6 (3–15·8)||0·033|
|Mortality rate||1 (2·7)||5 (1·3)||0·342|
Samples from 180 patients were sent for RT-PCR analysis. Sixty-five (36·1%) samples were positive, with 70 viruses being identified (five patients had two viruses): 26 influenza A (with 15 H1N1), 19 rhinovirus, eight hCoV 229E/NL63, five PIV type 3, three hMPV, two adenovirus, two PIV type 1, two RSV B, one RSV A, one PIV type 2, and one PIV type 4. Seven samples considered to be unsatisfactory for IFI were positive by PCR. Another 50 samples that were negative in IFI were positive by PCR. Of the 37 samples positive in IFI, 18 were sent for PCR too, and in nine, the result was positive. In seven of these cases, the results of IFI and PCR were concordant. In one patient, adenovirus was identified on IFI and influenza A was found in PCR. In another case, IFI detected PIV type 2, and PCR identified PIV types 1 and 3. The characteristics of patients with PCR positive and negative were shown in Table 3. Figure 1 shows times-series graph for virus percent positive by IFI and by PCR by month.
Table 3. Characteristics of patients with PCR positive and negative (November 2008 to September 2009; n = 180)a
|Characteristics||PCR positive (n = 65)||PCR negative (n = 115)||P value|
|Age, year||48·3 ± 20·1||51·7 ± 18·3||0·248|
|Male sex||24 (36·9)||44 (38·3)||0·986|
|White race||48 (73·8)||87 (75·7)||0·929|
|Years of schooling||8 (5·0–11·0)||8 (4·5–11·0)||0·929|
|Current smokers||22 (36·7)||20 (20·2)||0·036|
|Presence of smokers at home||29 (44·6)||39 (33·9)||0·207|
|Air conditioning at home or at work||17 (26·2)||15 (13·0)||0·045|
|Use of wood stoves||5 (7·7)||15 (13·0)||0·395|
|Mold in home||17 (26·2)||37 (32·2)||0·498|
|Cough||59 (90·8)||99 (86·1)||0·494|
|Fever||43 (66·2)||66 (57·4)||0·319|
|Dyspnea||53 (81·5)||101 (87·8)||0·351|
|Chest pain||43 (66·2)||71 (61·7)||0·668|
|Coryza||34 (52·3)||49 (43·0)||0·295|
|Sore throat||12 (18·5)||25 (21·7)||0·741|
|Wheezing||30 (46·2)||68 (59·1)||0·128|
|Duration of symptoms before admission||3 (1·25-4·0)||3 (2·0-4·0)||0·837|
|ILI||17 (26·2)||16 (13·9)||0·066|
|SARI||32 (49·2)||52 (45·2)||0·717|
|Need for hospitalization||7 (10·8)||32 (27·8)||0·013|
|Length of hospital stay, days||0·5 (0·5–2·5)||2 (0·5–6·0)||0·008|
|Mortality rate||0 (0)||2 (1·7)||0·536|
Figure 1. Times-series graph for virus percent positive by IFI and by PCR by month (IFI: November 2008–2009; n = 255 and PCR: November 2008 to September 2009; n = 180).
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Table 4 shows the descriptive statistics corresponding to the environmental variables considered in this study. Figure 2 shows the modeled and observed values for ILI and SARI cases. Figures 3 and 4 show the daily number of patients with ILI and SARI and meteorological parameters. We checked the autocorrelation between the covariates of climate data and observations from previous days, and we considered the following lags (in days) for each variable: average temperature (5), rainfall (2), sunshine duration (6), relative humidity (5), mean concentration of pollutants (3), and absolute humidity (2).
Table 4. Statistics for environmental variables (November 2008–2009)
|Absolute humidity (g/kg)||3·8||19·3||11·8||3·5|
|Relative humidity (%)||47·0||98·3||75·8||9·6|
|Sunshine duration (number of sunshine hours per day)||0||13·0||6·0||3·9|
|Mean concentration of pollutants (μm/m3)||1||72·0||27·4||10·9|
Figure 2. Modeled and observed values for ILI and SARI cases (ILI: November 2008–2009; n = 1·321 and SARI: November 2008–2009; n = 845).
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Figure 3. Daily number of patients with ILI and SARI against (A) daily average temperature, (B) daily absolute humidity, and (C) relative humidity. D shows daily total respiratory admissions (ILI: November 2008–2009; n = 1·321 and SARI: November 2008–2009; n = 845).
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Figure 4. Daily number of patients with ILI and SARI against (A) sunshine duration, (B) rainfall, and (C) mean concentration of pollutants. (D) shows daily total respiratory admissions (ILI: November 2008–2009; n = 1·321 and SARI: November 2008–2009; n = 845).
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The number of ILI and SARI cases tends to be higher between July 5, 2009, and August 22, 2009. In this period, the mean temperatures (Tmin: 9·52 ± 3·89°C and Tmax: 18·9 ± 4·92°C) and the sunshine duration (4·13 ± 3·31 hours of sun per day) were lower, as expected in winter. The rainfall tends to be higher than the median calculated for the entire year (0 mm, IQR: 0–5·4 mm). In addition, the absolute humidity (AH; 7·8 ± 2·1 g/kg) and mean concentration of pollutants (20·0 ± 7·0 μm/m3) were lower in this period compared with the annual values.
Table 5 shows multivariate logistic regression model for IFI and PCR. We included 255 patients in logistic regression for IFI and 180 patients in logistic regression for PCR, because we have climate data only for the first year of study. IFI positivity was statistically associated with AH (OR: 0·72; 95% CI 0·59–0·86), use of air conditioning (OR: 4·16; 95% CI 1·45–11·83), and presence of mold in home (OR: 2·95; 95% CI 1·10–8·29). On the other hand, PCR positivity was statistically associated with use of air conditioning (OR: 2·27; 95% CI 1·04–4·97), average temperature (OR: 0·92; 95% CI 0·86–0·98), and mean concentration of pollutants (OR: 1·04; 95% CI 1·00–1·08).
Table 5. Multivariate logistic regression model for IFI and PCR (IFI: November 2008–2009; n = 255 and PCR: November 2008 to September 2009; n = 180)
|Mold in home||2·95||1·10–8·29|
|Mean concentration of pollutants||1·04||1·00–1·08|
The multivariate time-series models for ILI and SARI cases are summarized in Table 6. Sunshine duration was the only independent covariate that was significantly associated with the frequency of ILI cases. The β-coefficient for this parameter was negative, indicating increasing ILI frequency with decreasing sunshine duration. In the model for SARI cases, the following variables proved to be significant: mean temperature (β = 0·399; P = 0·025), sunshine duration (β = −0·392; P = 0·007), RH (β = −0·098; P = 0·05), and mean concentration of pollutants (β = −0·079; P = 0·018).
Table 6. Multivariate time-series models for ILI and SARI (ILI: November 2008–2009; n = 1·321 and SARI: November 2008–2009; n = 845)
| ||β||SE||P value|
|ARIMA (1,1,3) – ARCH (2) model|
|AIC|| ||2264·2|| |
|BIC|| ||2287·6|| |
|Jarque–Bera test|| || ||<0·001|
|Ljung–Box test|| || ||0·973|
|ARIMA (0,1,3) – ARCH (4) model|
|Mean concentration of pollutants||−0·079||0·037||0·018|
|AIC|| ||1857·2|| |
|BIC|| ||1888·3|| |
|Jarque–Bera test|| || ||0·014|
|Ljung–Box test|| || ||0·949|
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Acute RVIs are responsible for causing significant levels of morbidity and mortality. The most common respiratory syndrome caused by these pathogens is ILI. A more severe presentation, named SARI, was also related to some RVs.[16, 17] In this study, we have examined the relationship between ILI and SARI cases, meteorological variables, and air pollution using multivariate time-series analyses. We found that ILI cases were inversely correlated with sunshine duration. In addition, SARI cases were significantly associated with mean temperature, sunshine duration, RH, and concentration of pollutants.
Seasonal cycles of infectious diseases have been attributed to changes in atmospheric variables, the prevalence or virulence of the pathogen, or the behavior of the host. Earlier investigations have demonstrated that lower temperatures and sunshine duration, conditions usually encountered in winter, were associated with admissions for RVI.[12, 19] Temperature was found to be highly inversely correlated with RSV, influenza A, and adenovirus frequency. Interestingly, we found a positive correlation between temperature and SARI cases. One possible explanation is that it was demonstrated that for every one degree Celsius rise in temperature, the risk of premature death and acute morbidity especially among respiratory patients is up to six times higher than in the rest of the population. Second, evidence is emerging that increasing temperature is associated with increases in air pollution, especially ground-level ozone, and can amplify the adverse effects of poor air quality. Taking this evidence into account, we could expect that higher temperatures may have increased concentration of pollutants, leading to more SARI cases. However, our data showed a decrease in air pollution during the months with a higher prevalence of SARI. The third hypothesis to explain the relationship between higher temperatures and SARI cases was related to El Niño Southern Oscillation (ENSO) phenomenon. ENSO undergoes cycles between warm phases (El Niño episodes) and reverse cold phases (La Niña episodes). In the southern region of Brazil, this phenomenon is associated with elevated temperatures and rainfall, especially in spring and in the period between May and July. Previous reports have determined that El Niño events were associated with increased hospitalizations and more severe influenza epidemics.[21, 22]
Severe acute respiratory infection cases were found to be negatively related to RH in our study. Previous studies have demonstrated that higher RH decreases the survival of lipid-enveloped virus, like influenza A, influenza B, RSV, and PIV.[23-25] The use of indoor heating in winter lowers the RH; breathing dry air could cause desiccation of the nasal mucosa, epithelial damage, and reduced mucociliary clearance, increasing the host susceptibility to RVIs. However, even in tropical regions with humid climate (RH >70%), a higher activity of influenza can be found. This observation could be explained by the variation of viral stability in different RH levels. The stability of aerosolized influenza virions is maximal at lower RH (20–40%), moderate at higher RH (60–80%), and minimum at a mid-range RH (50%).
In a multivariate logistic regression model for IFI-positive patients, we found that AH was a protect factor for RVI. A recent study suggested that AH may better correlate with influenza virus survival and transmission. Unlike RH, AH measures the actual water vapor content of air irrespective of temperature and has a prominent wintertime low, both indoor and outdoor. Such findings suggest that humidification measures could be helpful decreasing survival and transmissibility of influenza.
Air pollution has been associated with adverse health outcomes. Studies have suggested acute effects causing respiratory symptoms, cardiovascular events, hospital admissions, and mortality. Although the available evidences indicate associations between exposure to pollutants and increased risk of RVI, potential mechanisms mediating these effects are largely unexplored.[27, 28] Surprisingly, our results showed that SARI cases were associated with a decrease in mean concentration of pollutants. In fact, this could be a reflection of higher rainfall in the same period, as rain acts washing out or scattering pollutants from atmosphere. On the other hand, we cannot exclude an effect of indoor pollution. In the last years, indoor pollution has been recognized as an emerging health problem, as about 90% of our time is spent indoors where we are exposed to chemical and biological contaminants. We estimated indoor pollution indirectly in our study, questioning patients about the use of wood stoves and air conditioning, and the presence of mold in home. Our findings suggested that IFI-positive patients were more prone to live in a residence with mold growth. Dampness and mold are two important sources of indoor pollution, consistently associated with respiratory symptoms. Home dampness may be a marker for mold growth, dust mites, endotoxins, and reduced ventilation, which could increase concentrations of indoor pollutants. Cough, wheezing, and upper respiratory symptoms were associated with dampness and mold in a meta-analysis. According to these results, the prevalence of cough and wheezing was higher in patients with mold in home and IFI positive.
Air conditioning was also positively related to IFI test in this study. Air conditioning use was associated with fewer hospital admissions for cardiovascular diseases, chronic obstructive pulmonary disease, and pneumonia on days with high concentrations of PM10, as individuals are less exposed to outdoor pollutants. Nevertheless, the majority of virus transmission occurs within indoor, air-conditioned (i.e., cooler, lower humidity) environments that favor airborne virus survival and transmission.[24, 25] In hot and humid conditions, indoor transmission in air conditioning environments may account for most of the transmission.
We found a prevalence of 22% of RV, which is higher than that previous studies have demonstrated (between 12% and 15% in adults).[12, 13] Moreover, the length of stay was lower in our IFI- and/or PCR-positive patients. This finding is consistent with existing knowledge that virus identification allows the prompt initiation of therapy when indicated and avoids the unnecessary use of antibiotics, decreasing the length of hospital stay.
The present study has some limitations. First, it was based on data collected from a single center, which may have potential biases because of the characteristics of the catchment population, like vaccination coverage. Second, it is also important to note that this investigation was performed in a group of hospitalized patients, which is a bias toward the most severe disease cases. Additionally, we do not have the concentrations of individual air pollutants, but it is implausible to reliably separate the effects of air pollutants because they frequently react with each other, sometimes potentiating individual effects.[10, 35] The short study period should also be considered a limitation. Finally, the use of molecular techniques (PCR) in all study patients could be useful, increasing the number of viruses detected, as limited sensitivity of IFI method is well known.[10, 36] Despite these limitations, this is the first study, to our knowledge, to analyze the relationship between RV, meteorological parameters, and air pollution in an adult population.
In conclusion, we found that in adult patients admitted to ER with respiratory complaints, at least 22% of infections were caused by RV. The correlations found among meteorological variables, air pollution, ILI/SARI cases, and RV demonstrated the relevance of climate factors as significant underlying contributors to the prevalence of RVI in a temperate region. There is still a need of additional investigations to clarify and confirm these data, perhaps using longer time-series observations.