Abstract
Background
Given its pathophysiology and evidence on its long-term effects, coronavirus disease 2019 (COVID-19) may have altered the incidence, types, and severity of other respiratory infections (RIs). Therefore, this study aimed to evaluate the potential impact of COVID-19 on non-SARS-CoV-2 RIs in COVID-19 survivors.
Methods
This cross-sectional study was conducted using a self-administered, online survey from February 2022 to April 2022 in the United Arab Emirates. Participants included adults diagnosed with COVID-19 before February 2021. Those who did not undergo nasopharyngeal swab tests for contraction of other RIs after recovering from COVID-19 were excluded.
Results
Of the 384 participants, 24.5% were aged 18–24 years, and 55.5% were women. The mean body mass index (BMI) was 29.6 ± 3.7 kg/m2. Among the participants, 34.4% experienced at least one non-SARS-CoV-2 RI after recovering from COVID-19, of which 25.8% reported that the RI lasted longer than those previously, 27.3% sought physician advice, 31.8% reported increased coughing as a symptom of the non-SARS-CoV-2 RI, and 38.6% reported increased sputum. Based on patient responses, the degree of COVID-19 severity was significantly associated with non-SARS-CoV-2 RIs (p = 0.003), and depression was found to be a significant predictor of changes in shortness of breath symptoms (adjusted odds ratio [AOR] = 1.29; 95% confidence interval [CI], 1.17–1.46).
Conclusions
The participants of this study reported changes in the duration and severity of non-SARS-CoV-2 RIs after recovering from COVID-19. Further research is needed to confirm these findings, which suggest a need for swift action to protect the public against RIs, particularly seasonal pathogens.
Keywords: SARS-CoV-2, Respiratory infections, Post-COVID-19, United Arab Emirates
1. Introduction
Respiratory infections (RIs) are a leading cause of acute illness and death worldwide [1]. Viruses such as rhinovirus, adenovirus, and coronavirus are common causes of upper respiratory tract infections (URIs) such as epiglottitis, laryngitis, pharyngitis, and sinusitis. Occasionally, bacterial pathogens can also lead to URIs, which are typically treated with antimicrobial agents, including amoxicillin [1].
The global rate of URIs surpassed 17.2 billion in 2019. Although symptoms are typically mild and self-limiting, some cases can lead to life-threatening conditions that impair quality of life and productivity of patients [2]. A study in the United Arab Emirates (UAE) found that 28.5% of patients with acute RIs (ARIs) were admitted to the hospital, with the admission rate rising to 44.4% among the elderly population [3].
The recent outbreak of SARS-CoV-2 (also known as SARS-CoV-2) has further compounded concerns over acute respiratory infections (ARIs), which already accounted for more than 70% of all contagious diseases before COVID-19 [4] emerged. SARS-CoV-2 can infect both the upper and lower respiratory tracts, with replication taking place mainly in the upper airways. This explains its high infectiousness rate as well as cold-like symptoms that often accompany it. However, infection of the lower airways can lead to serious illness in vulnerable groups like newborns and the elderly [5]. As a result of SARS-CoV-2, patients are now frequently suffering from acute respiratory distress syndrome, which necessitates hospital admission [6]. Although much has been written about the COVID-19 pandemic and its consequences, an accurate assessment of its effects on non-SARS-CoV-2 respiratory infection RIs for those who have recovered from COVID-19 has yet to be done. This study seeks to provide a comprehensive look at changes in respiratory infection RI patterns, nature and severity one year after recovery from COVID-19.
2. Methods
2.1. Study design and population
This cross-sectional study, conducted from February 2022 to April 2022, investigated patients who had recovered from COVID-19 at least one year prior to contracting RIs in the UAE using an online, self-administered survey. The study included COVID-19 survivors who had been diagnosed with COVID-19 (based on positive SARS-CoV-2 polymerase chain reaction tests on nasopharyngeal swabs) before February 2021. To achieve the study objectives, participants who did not undergo nasopharyngeal swabs when contracting other RIs after recovering from COVID-19, as well as those with memory problems or who were aged <18 years, were excluded. The study aimed to recruit 384 participants, based on a sample size calculation that considered a population size of 306,339, a 5% margin of error, and a 95% confidence interval (CI). The Research Ethics Committee at Al Ain University deemed the study exempt from ethics approval.
2.2. Development of survey
For this study, a new written questionnaire was designed through a multistep process. First, the literature was reviewed in the context of the study objectives. Because no studies in the published literature addressed our hypothesis, the review focused on studies that investigated post-COVID-19 complications [[7], [8], [9]]. Second, the first version of the questionnaire, which was drafted in the English language by the research group, included only close-ended questions. The operational definitions of medical terminologies mentioned in the first draft were adopted from national and global guidelines. Third, a content validation of the study instrument was performed by distributing a validation score sheet to three academic (pathophysiologist and medical laboratory scientist) and professional (general practitioner) experts. The score sheet enabled the experts to grade each question in the survey from 0 to 10 in terms of relevance, appropriateness, and clarity. Then, the overall mean score for each category was calculated. Specifically, the mean scores of relevance, appropriateness, and clarity were 9.6 ± 1.6, 9.3 ± 2.1, and 8.9 ± 1.4, respectively. The experts suggested adding two additional questions and modifying three others. Fourth, the drafted questionnaire was professionally translated into the Arabic language using the forward and backward translation method [10]. Fifth, to assess reliability and understandability of the questionnaire, a small pilot test was performed by collecting data from 20 random participants. Cronbach's alpha, which is an indicator for the internal consistency of the questionnaire items, was calculated using SPSS Version 26 (Armonk, NY: IBM Corp). The value of the alpha coefficient was 0.83, which indicated a high degree of consistency between the questionnaire items. The final version of the questionnaire consisted of three main sections comprising 25 questions (Supplementary file 1). The first section included nine questions about socio-demographic information of participants such as age, sex, weight, height, education, marital status, ethnicity, and comorbidities. Additionally, we inquired participants about their smoking habits. A smoker was defined as “an adult who has smoked at least 100 conventional cigarettes in his or her lifetime, and who now smokes every day, or has smoked e-cigarettes daily for the last month.” A former smoker was defined as “an adult who has smoked at least 100 cigarettes in his or her lifetime, but who had quit smoking at the time of the survey, or has smoked e-cigarettes daily for at least one month in his or her lifetime, but who had quit smoking at the time of the survey.” A dual smoker was defined as “an adult who is currently an e-cigarette and cigarette smoker.” A never smoker was defined as “an adult who has smoked fewer than 100 cigarettes in his/her lifetime; conventional or electronic” [11,12]. In the second section of the questionnaire, we asked participants one question about the severity of COVID-19 infection that each experienced, one question about medications taken during the infection, and one question about vaccination status. Additionally, we asked each participant whether they experienced one or more COVID-19 infections and whether they contracted the infection before or after taking the vaccine. The severity of COVID-19 infection was categorized as follows [1]: asymptomatic, “no signs or symptoms” [2]; mild, “experiencing one or more of the following symptoms: fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell” [3]; moderate, “experiencing shortness of breath and abnormal chest imaging” [4]; severe, “hospitalization”; and [5] critical, “the patient was admitted to the intensive care unit” [13]. The third section of the questionnaire consisted of 13 questions designed to find any potential differences in the frequency, symptoms, or severity of RI before and after contracting COVID-19. Therefore, only participants who reported that they experienced at least one RI after COVID-19 were able to complete this section.
2.3. Data collection
Because we did not have access to the national vaccine database in the UAE, identification of eligible participants and face-to-face interviews were not feasible. Therefore, we constructed the final version of the questionnaire as an online survey using Google tools [14] and distributed it using the word-of-mouth technique. Research associates approached their friends, relatives, and colleagues and asked them to approach their contacts. Additionally, the link to the survey was distributed on WhatsApp, Facebook, Twitter, and Telegram, the most popular social network platforms in the UAE. The online survey began with a cover letter explaining the purpose of the study, inclusion and exclusion criteria, and the privacy policy of the research. Participants' information was kept confidential and used only for this research.
2.4. Statistical analysis
The data analysis was conducted using SPSS (Armonk, NY: IBM Corp, Version 26) and included two parts [1]: descriptive statistics, in which the frequencies of categorical data were presented as absolute numbers (n) and proportions (%), whereas the frequencies of non-categorical variables were presented as means with standard deviations (SDs) or as medians with interquartile ranges (IQRs) for data with abnormal trends, and [2] inferential statistics, in which Chi-square test was used to compare categorical variables under parametric conditions, and Student's t-test was performed to compare non-categorical variables with normal distribution. For abnormally distributed, non-categorical data, the Mann–Whitney U test was performed. Additionally, to assess predictors for changes in frequency, type, symptoms, and severity of RIs (other than COVID-19), we performed multivariate regression analysis and presented the outcomes as adjusted odds ratios (AORs) with 95% CIs. The variance inflation factor (VIF) test was performed to test multicollinearity issues, and independent variables with VIFs of ≤3 were considered to have acceptable collinearity [15]. All analysis outcomes were considered significant at a p-value of <0.05.
3. Results
A total of 429 individuals participated in the questionnaire, but 45 participants were excluded from the analysis, as they did not provide answers to significant numbers of questions. Of the 384 remaining COVID-19 survivors who completed the questionnaire, 24.5% were aged 18–24 years, and 55.5% were women. The mean body mass index (BMI) was 29.6 ± 3.7 kg/m2. Among the participants, 23.7% and 10.2% were identified as current and dual smokers, respectively (Table 1 ). Around one-quarter of participants (22.1%) reported having at least one medical condition.
Table 1.
Demographic and clinical characteristics of participants (N = 384).
| Parameter | Total, n (%) |
|---|---|
| Age (years) | |
| 18–24 | 94 (24.5%) |
| 25–34 | 76 (19.8%) |
| 35–44 | 81 (21.1%) |
| 45–54 | 65 (16.9%) |
| 55–64 | 41 (10.7%) |
| ≥65 | 27 (7.0%) |
| Gender | |
| Female | 213 (55.5%) |
| Male | 171 (44.5%) |
| BMI, mean (SD) | 29.6 ± 3.7 |
| Educational background | |
| College | 276 (71.9%) |
| School | 96 (25.0%) |
| None | 12 (3.1%) |
| Marital status | |
| Married | 143 (37.2%) |
| Single | 207 (53.9%) |
| Divorced | 23 (6.0%) |
| Widowed | 11 (2.9%) |
| Origin | |
| Arab | 189 (49.2%) |
| Asian | 101 (26.3%) |
| African | 37 (9.6%) |
| American | 22 (5.7%) |
| European | 21 (5.4%) |
| Australian | 14 (3.6%) |
| Smoking status | |
| Current smoker | 91 (23.7%) |
| Former smoker | 67 (17.4%) |
| Dual smoker | 39 (10.2%) |
| Never smoker | 187 (48.7%) |
| Health conditionsa | |
| Any, yes | 85 (22.1%) |
| Heart diseases, yes | 12 (3.1%) |
| Diabetes, yes | 27 (7.0%) |
| Chronic kidney diseases, yes | 5 (1.3%) |
| Chronic liver disease, yes | 6 (1.6%) |
| Chronic respiratory diseases, yes | 39 (10.2%) |
| Stroke, yes | 3 (0.8%) |
| Osteoarthritis, yes | 20 (5.2%) |
| Depression, yes | 36 (9.4 |
| Cancer, yes | 1 (0.3%) |
| Immunodeficiency, yes | 4 (1.0%) |
SD: standard deviation.
Participants could pick more than one response.
The findings of the study indicated that 26.6% and 45.8% of the participants reported that they had symptomatic and mild COVID-19 infections, respectively (Table 2 ). Additionally, the vast majority of participants received either two or more doses of the COVID-19 vaccine. Among the COVID-19 survivors, 34.4% experienced at least one non-SARS-CoV-2 RI after recovery from COVID-19. The findings of this study demonstrated a potential association between RI contraction after recovery from COVID-19 and clinical and social parameters such as smoking (p = 0.012), taking antiseptic gargles during COVID-19 infection (p = 0.041), and the degree of COVID-19 severity (p = 0.003). Specifically, the prevalence of contracting at least one RI among current and never smokers was 56.0% and 28.3%, respectively. Among COVID-19 survivors who contracted at least one RI after recovery from COVID-19, 25.8% reported that RIs persisted longer than those contracted prior to COVID-19, 27.3% reported that they needed to seek physician advice due to RIs, and 8.3% were hospitalized due to RIs. Differences in vaccination status were significantly associated with the duration of RIs (p = 0.035). Smoking status (p = 0.021) and the degree of COVID-19 severity (p = 0.033) were significantly different among participants who sought physician advice due to RIs and those who did not. Smoking status was also significantly different among participants who were hospitalized and those who did not need hospitalization (p = 0.046).
Table 2.
Assessment of factors related to changes in the patterns, duration, and severity of respiratory infections after recovery from COVID-19.
| Parameter | Total, n (%) | Contracted any respiratory infection (other than SARS-COV-2) after recovery from COVID-19 | aAfter COVID-19, respiratory infections last longer than before | aAfter COVID-19, needed to seek physician advice due to respiratory infections | aAfter COVID-19, needed admission to hospital due to respiratory infections | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Yes | No | P value | Yes | No | P value | Yes | No | P value | Yes | No | P value | ||
| COVID-19 history | 0.003 | 0.162 | 0.033 | 0.131 | |||||||||
| Asymptomatic | 102 (26.6%) | 28 (27.5%) | 74 (72.5%) | 4 (14.3%) | 24 (85.7%) | 5 (17.8%) | 23 (82.1%) | 0 (0.0%) | 28 (100.0%) | ||||
| Mild | 176 (45.8%) | 67 (38.1%) | 109 (61.9%) | 14 (20.9%) | 53 (79.1%) | 14 (20.9%) | 53 (79.1%) | 5 (7.4%) | 62 (92.6%) | ||||
| Moderate | 85 (22.1%) | 27 (31.7%) | 58 (68.2%) | 12 (44.4%) | 15 (55.6%) | 11 (40.7%) | 16 (59.3%) | 2 (7.4%) | 25 (92.6%) | ||||
| Severe | 16 (4.2%) | 8 (50.0%) | 8 (50.0%) | 4 (50.0%) | 4 (50.0%) | 5 (62.5%) | 3 (37.5%) | 3 (37.5%) | 5 (62.5%) | ||||
| Critical | 5 (1.3%) | 2 (40.0%) | 3 (60.0%) | 0 (0.0%) | 2 (100.0%) | 1 (50.0%) | 1 (50.0%) | 1 (50.0%) | 1 (50.0%) | ||||
| Vaccination status | 0.162 | 0.136 | 0.231 | ||||||||||
| Received the first dose | 21 (5.5%) | 11 (52.4%) | 10 (47.6%) | 4 (36.4%) | 7 (63.6%) | 0.035 | 4 (36.4%) | 7 (63.6%) | 2 (18.2%) | 9 (81.8%) | |||
| Received both the first and second doses | 296 (77.1) | 103 (34.8%) | 193 (65.2%) | 25 (24.2%) | 78 (75.7%) | 23 (22.3%) | 80 (77.7%) | 7 (6.8%) | 96 (93.2%) | ||||
| Received three or more doses | 63 (16.4%) | 16 (25.4%) | 47 (74.6%) | 3 (18.8%) | 13 (81.3%) | 7 (43.8%) | 9 (56.2%) | 1 (6.2%) | 15 (93.8%) | ||||
| Non-vaccinated | 4 (1.0%) | 2 (50.0%) | 2 (50.0%) | 2 (100.0%) | 0 (0.0%) | 2 (100.0%) | 0 (0.0%) | 1 (50.0%) | 1 (50.0%) | ||||
| Timing of COVID-19 infection | 0.091 | 0.113 | 0.236 | 0.132 | |||||||||
| Before vaccination | 176 (45.8%) | 89 (50.6%) | 87 (49.4%) | 27 (30.3%) | 62 (69.7%) | 22 (24.7%) | 67 (75.3%) | 9 (10.1%) | 80 (89.9%) | ||||
| After vaccination | 208 (54.2%) | 43 (20.7%) | 165 (79.3%) | 7 (12.3%) | 36 (83.7%) | 14 (32.5%) | 29 (67.5%) | 2 (4.6%) | 41 (94.4%) | ||||
| Medications used during COVID-19 | |||||||||||||
| Any, yes | 303 (78.9%) | 106 (34.9%) | 197 (65.1%) | 0.139 | 14 (3.8%) | 92 (96.2%) | 0.119 | 9 (8.5%) | 94 (91.5%) | 0.162 | 5 (4.7%) | 101 (95.3%) | 0.139 |
| Paracetamol | 289 (75.3%) | 119 (41.2%) | 170 (58.8%) | 0.158 | 19 (7.6%) | 100 (92.4%) | 0.121 | 14 (11.8%) | 105 (88.2%) | 0.136 | 2 (1.7%) | 117 (98.3%) | 0.122 |
| Amoxicillin | 76 (19.8%) | 13 (17.1%) | 63 (82.9%) | 0.115 | 4 (30.8%) | 9 (69.2%) | 0.069 | 3 (23.1%) | 10 (76.9%) | 0.151 | 2 (15.4%) | 11 (84.6%) | 0.161 |
| Azithromycin | 48 (12.5%) | 9 (18.8%) | 39 (81.2%) | 0.320 | 2 (22.2%) | 7 (77.8%) | 0.081 | 2 (22.2%) | 7 (77.8%) | 0.136 | 1 (11.1%) | 8 (88.9%) | 0.133 |
| Ibuprofen | 111 (28.9%) | 56 (50.5%) | 55 (49.5%) | 0.114 | 8 (14.3%) | 48 (85.7%) | 0.136 | 6 (10.7%) | 50 (89.3%) | 0.169 | 4 (7.1%) | 52 (92.9%) | 0.102 |
| Dexamethasone | 39 (10.2%) | 19 (48.7%) | 20 (51.3%) | 0.142 | 6 (31.6%) | 13 (15.8%) | 0.126 | 7 (36.8%) | 12 (63.2%) | 0.098 | 4 (21.1%) | 15 (78.9%) | 0.215 |
| Vitamin C | 174 (45.3%) | 114 (65.5%) | 60 (34.5%) | 0.632 | 16 (14.0%) | 98 (86.0%) | 0.230 | 5 (4.4%) | 109 (95.6%) | 0.133 | 1 (0.9%) | 113 (99.1%) | 0.136 |
| Antiseptic gargles | 56 (14.6%) | 6 (10.7%) | 50 (89.3%) | 0.041 | 3 (50.0%) | 3 (50.5%) | 0.163 | 1 (16.7%) | 5 (83.3%) | 0.211 | 0 (0.0%) | 6 (100.0%) | 0.133 |
| Cough syrup | 214 (55.7%) | 113 (52.8%) | 81 (47.2%) | 0.120 | 8 (7.1%) | 105 (92.9%) | 0.133 | 3 (2.6%) | 110 (97.4%) | 0.368 | 1 (0.9%) | 112 (99.1%) | 0.361 |
| Other medications | 32 (8.3%) | 8 (25.0%) | 24 (75.0%) | 0.109 | 2 (25.0%) | 6 (75.0%) | 0.187 | 1 (12.5%) | 7 (87.5%) | 0.118 | 3 (37.5%) | 5 (62.5%) | 0.122 |
| Smoking status | 0.012 | 0.091 | 0.021 | 0.046 | |||||||||
| Current smoker | 91 (23.7%) | 51 (56.0%) | 40 (44.0%) | 13 (25.5%) | 38 (74.5%) | 12 (23.5%) | 39 (76.5%) | 3 (5.9%) | 48 (94.1%) | ||||
| Former smoker | 67 (17.4%) | 9 (13.4%) | 58 (86.6%) | 4 (44.4%) | 5 (65.6%) | 1 (11.1%) | 8 (88.9%) | 1 (11.1%) | 8 (88.9%) | ||||
| Dual smoker | 39 (10.2%) | 19 (48.7%) | 20 (51.3%) | 4 (21.1%) | 15 (78.9%) | 13 (68.4%) | 6 (31.6%) | 5 (26.3%) | 14 (73.7%) | ||||
| Never smoker | 187 (48.7%) | 53 (28.3%) | 134 (71.7%) | 13 (24.5%) | 40 (75.5%) | 10 (18.7%) | 43 (81.3%) | 1 (1.9%) | 53 (98.1%) | ||||
The denominator for each item is the number of participants who contracted any respiratory infection (other than SARS-COV-2) after recovery from COVID-19. Bold values indicate a significant difference (p < 0.05).
The findings of this study showed no significant changes in the types and frequency of RIs after recovery from COVID-19 (Fig. 1 ). However, a significant change was identified in the proportions of participants who sought physician advice after recovery from COVID-19. Among COVID-19 survivors who contracted at least one non-SARS-CoV-2 RI, 31.8% reported that cough as a symptom of non-SARS-CoV-2 RIs increased after recovery from COVID-19, 38.6% indicated that sputum was increased, and 20.5% reported that shortness of breath was increased (Fig. 2 ).
Fig. 1.
Changes in the prognosis of respiratory infections after recovery from COVID-19. (a)Changes in types of respiratory infections. (b)Changes in frequency of respiratory infections. (c)Changes in the severity of respiratory infection.
Fig. 2.
Changes in symptoms of respiratory infections after recovery from COVID-19.
The logistic regression results showed that age (18–24 years vs. ≥65 years) (AOR = 0.76; 95% CI, 0.32–0.89), obesity (AOR = 1.69; 95% CI, 1.21–1.89], and smoking status (current vs. never smoker) (AOR = 2.46; 95% CI, 2.15–2.74) were significant predictors for changes in cough after recovery from COVID-19 (Table 3 ). Additionally, smoking status (current vs. never smoker) was a significant predictor for changes in sputum (AOR = 1.83; 95% CI, 1.62–2.17). Presence of a health condition (depression vs. chronic respiratory disease) was a significant predictor for changes in shortness of breath symptoms (AOR = 1.29; 95% CI, 1.17–1.46).
Table 3.
Predictors for changes in symptoms of respiratory infections (other than SARS-CoV-2) after recovery from COVID-19.
| Predicting changes in cough | Predicting changes in sputum | Predicting changes in shortness of breath | |
|---|---|---|---|
| Parameter (parameter versus reference) | AOR (95%CI), p value | AOR (95%CI), p value | AOR (95%CI), p value |
| Age (years) | |||
| 18-24 vs. ≥65 | 0.76 (0.32–0.89), 0.01 | 1.18 (0.87–1.38), 0.21 | 1.23 (0.91–3.42), 0.13 |
| 25-34 vs. ≥65 | 0.98 (0.56–1.23), 0.13 | 0.73 (0.46–1.47), 0.10 | 0.88 (0.63–1.54), 0.10 |
| 35-44 vs. ≥65 | 1.23 (0.92–1.46), 0.11 | 0.80 (0.64–1.27), 0.17 | 0.69 (0.57–1.22), 0.14 |
| 45-54 vs. ≥65 | 0.79 (0.69–1.09), 0.09 | 1.38 (0.84–2.47), 0.21 | 1.08 (0.91–1.28), 0.09 |
| 55-64 vs. ≥65 | 0.87 (0.76–1.13), 0.23 | 0.92 (0.79–1.14), 0.12 | 0.91 (0.67–1.58), 0.31 |
| Sex | |||
| Female vs. Male | 0.82 (0.73–1.19), 0.21 | 0.98 (0.56–1.23), 0.13 | 1.39 (0.65–2.76), 0.19 |
| BMI | |||
| Obese vs. Non-obese | 1.69 (1.21–1.89), 0.03 | 1.28 (0.86–1.76), 0.09 | 0.81 (0.77–1.48), 0.20 |
| Origin | |||
| Asian vs. Arab | 0.86 (0.59–1.23), 0.11 | 1.03 (0.96–1.57), 0.11 | 0.78 (0.64–1.28), 0.32 |
| African vs. Arab | 1.10 (0.92–1.29), 0.34 | 1.13 (0.69–1.59), 0.34 | 0.91 (0.75–2.48), 0.13 |
| American vs. Arab | 0.69 (0.53–2.19), 0.25 | 0.89 (0.69–1.23), 0.12 | 1.31 (0.92–2.48), 0.51 |
| European vs. Arab | 0.73 (0.69–1.49), 0.17 | 1.16 (0.86–1.57), 0.31 | 1.21 (0.67–3.48), 0.35 |
| Australian vs. Arab | 0.81 (0.74–1.21), 0.33 | 0.79 (0.66–1.27), 0.11 | 0.78 (0.51–2.55), 0.39 |
| Smoking status | |||
| Current smoker vs. Never smoker | 2.46 (2.15–2.74), 0.02 | 1.83 (1.62–2.17), 0.03 | 1.93 (0.67–4.58), 0.61 |
| Former smoker vs. Never smoker | 1.36 (0.92–1.95), 0.21 | 1.17 (0.84–1.36), 0.13 | 0.87 (0.62–1.91), 0.19 |
| Dual smoker vs. Never smoker | 1.26 (0.94–1.44), 0.18 | 0.93 (0.86–1.41), 0.13 | 1.26 (0.76–1.46), 0.38 |
| Health conditions | |||
| HD vs. CRD | 0.48 (0.12–2.01), 0.41 | 0.83 (0.62–2.37), 0.43 | 0.68 (0.54–1.68), 0.10 |
| Diabetes vs. CRD | 0.77 (0.52–1.90), 0.27 | 0.76 (0.56–2.56), 0.21 | 1.08 (0.90–2.55), 0.24 |
| CKD vs. CRD | 1.76 (0.82–3.49), 0.22 | 1.17 (0.76–3.43), 0.26 | 2.38 (0.67–4.32), 0.53 |
| CLD vs. CRD | 0.81 (0.52–2.13), 0.18 | 1.25 (0.86–3.17), 0.21 | 0.42 (0.35–4.18), 0.52 |
| Stroke vs. CRD | 0.61 (0.42–1.12), 0.27 | 0.71 (0.59–1.92), 0.19 | 0.78 (0.67–1.24), 0.14 |
| Osteoarthritis vs. CRD | 0.96 (0.74–2.43), 0.13 | 0.65 (0.46–4.43), 0.51 | 1.28 (0.72–4.58), 0.21 |
| Depression vs. CRD | 1.36 (0.82–1.59), 0.09 | 1.69 (0.76–3.58), 0.23 | 1.29 (1.17–1.46), 0.04 |
| Cancer vs. CRD | 1.06 (0.80–1.79), 0.12 | 1.15 (0.76–2.16), 0.16 | 0.67 (0.54–2.18), 0.19 |
| Immunodeficiency vs. CRD | 1.38 (0.88–2.44), 0.21 | 0.90 (0.86–1.83), 0.19 | 1.48 (0.91–3.58), 0.41 |
BMI: body mass index, HD: heart disease, CRD: chronic respiratory disease, CKD: chronic kidney disease, CLD: chronic liver disease, VS: versus, bold values indicate significant predictors.
4. Discussion
While SARS-CoV-2 primarily targets lung cells, causing acute hypoxia and leading to death in severe cases, COVID-19 survivors may continue to experience persistent symptoms and long-term effects [[16], [17], [18]]. In this study, we address an unexplored area in the long-term sequelae of COVID-19 by examining whether SARS-CoV-2 infection causes changes in the rate, nature, severity, and outcomes of non-SARS-CoV-2 RIs. To our knowledge, this is the first study that reports potential changes in the prognosis of non-SARS-CoV-2 RIs one year after recovery from COVID-19.
The rate of non-SARS-CoV-2 RIs in COVID-19 survivors was similar to that reported in the UAE months before the outbreak of COVID-19. However, given the self-reported nature of our study, we expect that the incidence of non-SARS-CoV-2 RIs may have been underestimated. Some participants may not have been able to recall minor infections that they encountered after recovery from COVID-19, which aligns with the findings of Amar et al. [19], who screened the clinical and laboratory data of 386,711 patients in primary healthcare facilities and found that the rate of non-SARS-CoV-2 RIs in the community was substantially increased in 2021, compared with that in 2018 and 2019.
Kim and colleagues surveyed COVID-19 survivors and found that some respiratory symptoms may persist one year after recovery [20]. Our findings also suggest a potential association between smoking and the occurrence, duration, and severity of non-SARS-CoV-2 RIs, which is consistent with previous literature [21,22]. Additionally, our findings indicate that the severity of COVID-19 may increase the risk and severity of non-SARS-CoV-2 RIs, possibly due to the extent of damage to lung cells, particularly the alveolar epithelium, caused by severe SARS-CoV-2 infections [23]. Furthermore, our study found that taking antiseptic gargles during SARS-CoV-2 infection may reduce the risk for future non-SARS-CoV-2 RIs, which is consistent with published literature [24].
While the findings of our study indicated no significant changes in the frequency and types of non-SARS-CoV-2 RIs after recovery from COVID-19, compared with those during the pre-pandemic period, a considerable difference was identified in the proportions of participants who sought physicians' advice due to non-SARS-CoV-2 RIs before and after the pandemic. This may indicate a change in the severity of non-SARS-CoV-2 RIs after recovery from COVID-19, which could be attributed to damage in the airways caused by SARS-CoV-2 infections. Previous studies have reported conflicting findings regarding the impact of SARS-CoV-2 infection on the epidemiological characteristics of respiratory pathogens. While some studies have reported a decrease in the frequency of non-SARS-CoV-2 RIs during the pandemic and justified their findings with infection prevention and control practices implemented worldwide [25], other studies have suggested an increase in the circulation of certain respiratory viruses [26]. However, these studies did not examine COVID-19 survivors, but rather included participants from the general public. Therefore, their findings may not reflect the direct impact of SARS-CoV-2 infection on the prognosis of non-SARS-CoV-2 RIs.
Many participants in our study reported increases in symptoms of non-SARS-CoV-2 RIs, such as cough, sputum, and shortness of breath, after recovering from COVID-19. Age, obesity, and smoking were found to be significant determinants of these increases; this relationship is supported by the findings of a large meta-analysis that reported a significant association between high BMI and an increased incidence of respiratory symptoms [27]. Surprisingly, depression was found to be a significant predictor of changes in shortness of breath symptoms. Leander et al. reported a significant association between depression and respiratory symptoms in patients with asthma [28]. Mazza et al. indicated that contraction of SARS-CoV-2 could be associated with psychiatric problems [29]. Despite the different methodological approaches of the above studies, the psychopathology of COVID-19 survivors should be assessed on a regular basis. Additionally, community-based surveillance and serological studies are becoming increasingly important to better understand the effects of COVID-19 on the epidemiology and prognosis of non-SARS-CoV-2 RIs.
This study has several limitations. First, the self-reported nature of the data collection may have resulted in response bias or the omission of important information. Additionally, as the assessment of RIs was conducted one year after recovery from COVID-19, some participants may have had difficulty recalling all respiratory events, particularly minor ones. Furthermore, the sampling technique used in this study may not have resulted in a representative sample. Finally, this study lacked a control group for COVID-19 survivors, which could have affected the accuracy of the findings. Despite these limitations, this study aims to provide a new perspective on the long-term impact of SARS-CoV-2 infection on non-SARS-CoV-2 RIs and to serve as a foundation for future research. To this end, it is suggested that future studies include an appropriate number of COVID-19 survivors and their controls, along with professionally reported clinical data.
5. Conclusion
The results of this study suggest that COVID-19 may have an impact on the severity and duration of non-SARS-CoV-2 RIs. The analysis also revealed that depression was associated with changes in shortness of breath among COVID-19 survivors. However, to gain a more comprehensive understanding of related trends, a large-scale case-control study is necessary.
Funding
The authors of this research received no funding.
Ethics approval
The study was approved by the Institutional Ethics Committee of Al Ain University (B3, 23/03/2022) and all participants gave written informed consent.
Authors contribution statement
AZM: Proposal writing, conceptualization and study design development, data collection, data analysis, results’ interpretation, manuscript drafting. NS: study design development, survey securing and translation, manuscript drafting and review.
Conflict of Interest
The authors have no relevant financial or non-financial interests to disclose.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.resinv.2023.03.006.
Appendix A. Supplementary data
The following is the Supplementary data to this article.
References
- 1.MacIntyre C.R., Chughtai A.A., Zhang Y., Seale H., Yang P., Chen J., et al. Viral and bacterial upper respiratory tract infection in hospital health care workers over time and association with symptoms. BMC Infect Dis. 2017 Aug;17 doi: 10.1186/s12879-017-2649-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Jin X., Ren J., Li R., Gao Y., Zhang H., Li J., et al. Global burden of upper respiratory infections in 204 countries and territories, from 1990 to 2019. EClinicalMedicine. 2021 Jul;37 doi: 10.1016/j.eclinm.2021.100986. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Jeon J.H., Han M., Chang H.E., Park S.S., Lee J.W., Ahn Y.J., et al. Incidence and seasonality of respiratory viruses causing acute respiratory infections in the Northern United Arab Emirates. J Med Virol. 2019 Aug;91:1378–1384. doi: 10.1002/jmv.25464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Salzberger B., Buder F., Lampl B., Ehrenstein B., Hitzenbichler F., Holzmann T., et al. Epidemiology of SARS-CoV-2. Infection. 2021 Apr;49:233–239. doi: 10.1007/s15010-020-01531-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Vabret A., Mourez T., Gouarin S., Petitjean J., Freymuth F. An outbreak of coronavirus OC43 respiratory infection in Normandy, France. Clin Infect Dis. 2003 Apr;36:985–989. doi: 10.1086/374222. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Zangrillo A., Belletti A., Palumbo D., Calvi M.R., Guzzo F., Fominskiy E.V., et al. One-year multidisciplinary follow-up of patients with COVID-19 requiring invasive mechanical ventilation. J Cardiothorac Vasc Anesth. 2022 May;36:1354–1363. doi: 10.1053/j.jvca.2021.11.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Salamanna F., Veronesi F., Martini L., Landini M.P., Fini M. Post-COVID-19 syndrome: the persistent symptoms at the post-viral stage of the disease. A Systematic Review of the Current Data. 2021;8 doi: 10.3389/fmed.2021.653516. Frontiers in medicine. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Wang X., Xu H., Jiang H., Wang L., Lu C., Wei X., et al. Clinical features and outcomes of discharged coronavirus disease 2019 patients: a prospective cohort study. QJM An Int J Med [Internet] 2020;113:657–665. doi: 10.1093/qjmed/hcaa178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Bliddal S., Banasik K., Pedersen O.B., Nissen J., Cantwell L., Schwinn M., et al. Acute and persistent symptoms in non-hospitalized PCR-confirmed COVID-19 patients. Sci Rep. 2021;11:1–11. doi: 10.1038/s41598-021-92045-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Wild D., Grove A., Martin M., Eremenco S., McElroy S., Verjee-Lorenz A., et al. Principles of good practice for the translation and cultural adaptation process for patient-reported outcomes (PRO) measures: report of the ISPOR task force for translation and cultural adaptation. Value Heal J Int Soc Pharmacoeconomics Outcomes Res. 2005;8:94–104. doi: 10.1111/j.1524-4733.2005.04054.x. [DOI] [PubMed] [Google Scholar]
- 11.Garcia-Aymerich J., Lange P., Benet M., Schnohr P., Antó J.M. Regular physical activity modifies smoking-related lung function decline and reduces risk of chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2007 Mar;175:458–463. doi: 10.1164/rccm.200607-896OC. [DOI] [PubMed] [Google Scholar]
- 12.Pericot-valverde I., Gaalema D.E., Priest J.S. Higgins S.T. E-Cigarette awareness, perceived harmfulness, and ever use among U.S. Adults (Baltim) [Internet] Prev Med. 2017;104:92–99. doi: 10.1016/j.ypmed.2017.07.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Son K-B, Lee T, Hwang S. Disease severity classification and COVID-19 outcomes, Republic of Korea. Bull World Health Organ [Internet]. vol. 99:62–66. [DOI] [PMC free article] [PubMed]
- 14.Google. Google Forms; Survey Adminstration Application [Internet]. 2020 [cited 2020 Nov 22]. Available from: https://www.google.com/forms/about/.
- 15.García C.B., García J., Martín M.M.L., Salmerón R. Collinearity: revisiting the variance inflation factor in ridge regression. J Appl Stat [Internet] 2015;42:648–661. [Google Scholar]
- 16.Premraj L., Kannapadi N.V., Briggs J., Seal S.M., Battaglini D., Fanning J., et al. Mid and long-term neurological and neuropsychiatric manifestations of post-COVID-19 syndrome: a meta-analysis. J Neurol Sci [Internet] 2022;434:120–162. doi: 10.1016/j.jns.2022.120162. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ahmed J.O., Ahmad S.A., Hassan M.N., Kakamad F.H., Salih R.Q., Abdulla B.A., et al. Post COVID-19 neurological complications; a meta-analysis. Ann Med Surg [Internet] 2022;76:103–440. doi: 10.1016/j.amsu.2022.103440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Mumoli N., Bonaventura A., Colombo A., Vecchié A., Cei M., Vitale J., et al. vol. 5. Mayo Clin Proc Innov Qual Outcomes [Internet; 2021. pp. 907–915. (Lung function and symptoms in post–COVID-19 patients: a single-center experience). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Amar S., Avni Y.S., O’Rourke N., Michael T. Prevalence of common infectious diseases after COVID-19 vaccination and easing of pandemic restrictions in Israel. JAMA Netw Open [Internet] 2022 Feb 1;5 doi: 10.1001/jamanetworkopen.2021.46175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kim Y., Bitna-Ha, Kim S.-W., Chang H.-H., Kwon K.T., Bae S., et al. Post-acute COVID-19 syndrome in patients after 12 months from COVID-19 infection in Korea. BMC Infect Dis [Internet] 2022;22:93. doi: 10.1186/s12879-022-07062-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Marcy T.W., Merrill W.W. Cigarette smoking and respiratory tract infection. Clin Chest Med. 1987 Sep;8:381–391. [PubMed] [Google Scholar]
- 22.Jiang C., Chen Q., Xie M. Smoking increases the risk of infectious diseases: a narrative review. Tob Induc Dis [Internet] 2020 Jul 14;18:60. doi: 10.18332/tid/123845. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Bridges J.P., Vladar E.K., Huang H., Mason R.J. vol. 77. 2022. pp. 203–209. (Respiratory epithelial cell responses to SARS-CoV-2 in COVID-19). Thorax [Internet] [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Ahmad L. Impact of gargling on respiratory infections. All Life. 2021 Jan 1;14:147–158. [Google Scholar]
- 25.Liu P., Xu M., Cao L., Su L., Lu L., Dong N., et al. Impact of COVID-19 pandemic on the prevalence of respiratory viruses in children with lower respiratory tract infections in China. Virol J [Internet] 2021;18:159. doi: 10.1186/s12985-021-01627-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Uhteg K., Amadi A., Forman M., Mostafa H.H. Circulation of non-SARS-CoV-2 respiratory pathogens and coinfection with SARS-CoV-2 amid the COVID-19 pandemic. Open Forum Infect Dis [Internet] 2022 Mar 1;9 doi: 10.1093/ofid/ofab618. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Beuther D.A., Sutherland E.R. Overweight, obesity, and incident asthma: a meta-analysis of prospective epidemiologic studies. Am J Respir Crit Care Med. 2007 Apr;175:661–666. doi: 10.1164/rccm.200611-1717OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Leander M., Lampa E., Rask-Andersen A., Franklin K., Gislason T., Oudin A., et al. Impact of anxiety and depression on respiratory symptoms. Respir Med [Internet] 2014;108:1594–1600. doi: 10.1016/j.rmed.2014.09.007. [DOI] [PubMed] [Google Scholar]
- 29.Mazza M.G., De Lorenzo R., Conte C., Poletti S., Vai B., Bollettini I., et al. Anxiety and depression in COVID-19 survivors: role of inflammatory and clinical predictors. Brain Behav Immun. 2020 Oct;89:594–600. doi: 10.1016/j.bbi.2020.07.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.