What factors are associated with Covid-19 diagnosis in symptomatic patients in primary care practices?

Although more and more people are being vaccinated against Covid-19, the weekly number of new cases remains relatively high worldwide. In addition, several SARS-CoV-2 variants have been described (Gómez et al., 2021), and there is a significant risk of repeated waves of Covid-19 in the coming years. In this context, further research is needed to better understand the epidemiology of Covid-19. In recent months, several studies have examined the prevalence of Covid-19 and the characteristics of people diagnosed with the disease. The proportion of people diagnosed with Covid-19 has been found to vary greatly by setting and country (Elimian et al., 2020; Fan et al., 2020; Guerriero et al., 2021; Haimovich et al., 2020; Lusignan et al., 2020; Mani et al., 2020; Pouwels et al., 2021; Santos-Hövener et al., 2020; Shen et al., 2020; Shepard et al., 2021; Sundaram et al., 2021 ). Although these studies have advanced the field, most of them included participants regardless of their potential symptomatology, so little is known about the specific prevalence of Covid-19 in people with common Covid-19 symptoms (e.g. cough, fatigue, or fever) is known. Regarding the sociodemographic and clinical characteristics of people diagnosed with Covid-19, the literature has also provided some contradictory results. For example, one study found that people with diabetes have an increased risk of Covid-19 compared to people without diabetes (Fan et al., 2020), while another study found that this was not the case ( Lusignan et al., 2020). Given these discrepancies, more data are needed on the frequency of Covid-19 diagnosis among individuals with common Covid-19 symptoms, as well as on the sociodemographic and clinical characteristics of this population.

>> The aim of this study was therefore to investigate the prevalence and factors associated with Covid-19 diagnosis in symptomatic patients who were tested and observed for Covid-19 in general practitioners' practices in Germany between March 2020 and March 2021.
methods

Database
This retrospective study used data from the Disease Analyzer Database (IQVIA), a database containing demographic, clinical and pharmaceutical data collected in primary care practices (Rathmann et al., 2018). This data is collected anonymously by general and specialist practices. The quality of the data available in the database is regularly assessed based on various criteria such as completeness of documentation and linkage between clinical and pharmaceutical data (Rathmann et al., 2018). The Disease Analyzer database contains data from around 3-4% of all general and specialist practices in Germany. The selection of practices for inclusion in the database is based on several characteristics (e.g. age of the doctor, specialty, community size class and federal state). Finally, previous studies have shown that this database is representative of all outpatient practices in Germany (Rathmann et al., 2018).

study population
The study included all patients aged ≥14 years with Covid-19-like symptoms who were tested for Covid-19 and followed up between March 2020 and March 2021 in one of 962 general practices in Germany. The index date corresponded to the date on which the Covid-19 test was documented. If a person had multiple Covid-19 tests carried out, the index date applied to the first test. The Covid-19 diagnosis was based on two ICD-10 codes: U07.1 (Covid-19, virus identified) and U07.2 (Covid-19, virus not identified).

Variables in the study
Variables examined for their influence on positive Covid-19 tests included gender, age and a range of medical conditions documented in at least 3% of the sample within 12 months before the index date. These diseases were hypertension, thyroid disease, gastritis and duodenitis, dermatitis and eczema, lipid metabolism disorders, chronic sinusitis, osteoarthritis, sleep disorders, reflux diseases, chronic headaches, asthma, diabetes mellitus, obesity, phlebitis and thrombosis, allergic rhinitis, cardiac arrhythmias, non-infectious enteritis and Colitis, chronic obstructive pulmonary disease (COPD), spondylosis, vitamin D deficiency, neuropathies, cancer, iron deficiency anemia. Depression, anxiety disorders, somatoform disorders, reaction to severe stress and adjustment disorders.

Statistical evaluations
The prevalence of Covid-19 was calculated by dividing the number of people diagnosed with Covid-19 by the number of symptomatic patients tested for Covid-19. Finally, the association between demographic and clinical variables and Covid-19 diagnosis was examined using a logistic regression model. This logistic regression model included Covid-19 diagnosis as the dependent variable and gender, age and all of the above conditions as independent variables. The results of the regression analysis are presented in the form of odds ratios (ORs) and 95% confidence intervals (CIs). P values less than 0.05 were considered statistically significant. Analyzes were performed using SAS version 9.4.
Results
This study involved 301,290 symptomatic patients who were tested and observed for Covid-19 in 962 general practices. Of these, 54.7% were women, and the mean (standard deviation) age was 44.6 (18.5) years (Table 1). The three most common diseases were hypertension (16.4%), thyroid disease (12.3%) and depression (12.0%). The prevalence of Covid-19 in the total sample was 13.8%. The results of the adjusted logistic regression analysis are shown in Figure 1. Covid-19 diagnosis was positive and significantly associated with male gender (OR = 1.04, 95% CI: 1.02-1.06) and age (reference: 14-25 years; ORs between 1.07 [95% CI: 1.03-1.11] in the age group 26-35 years and 1.80 [95% CI: 1.73-1.88] in the age group >65 years). Regarding comorbidities, there was a positive and significant association between Covid-19 diagnosis and cardiac arrhythmias (OR = 1.33, 95% CI: 1.26-1.39), depression (OR = 1.18, 95% CI : 1.14-1.23), obesity (OR = 1.17, 95% CI: 1.12-1.23), iron deficiency anemia (OR = 1. 11, 95% CI: 1.05-1.18), Diabetes mellitus (OR = 1.10, 95% CI: 1.05-1.15), phlebitis and thrombosis (OR = 1.06, 95% CI: 1.01-1.11), gastritis and duodenitis (OR = 1 .05, 95% CI: 1.01-1.09), osteoarthritis (OR = 1.05, 95% CI: 1.01-1.09) and hypertension (OR = 1.04, 95% CI: 1.00- 1.07). On the other hand, the factors that were negatively and significantly associated with the likelihood of a Covid-19 diagnosis included chronic sinusitis (OR = 0.75, 95% CI: 0.72-0.79), asthma (OR = 0.81 , 95% CI: 0. 77-0.85), anxiety disorders (OR = 0.82, 95% CI: 0.77-0.87), somatoform disorders (OR = 0.83, 95% CI: 0.81-0.87), vitamin D deficiency (OR = 0.84, 95% CI: 0.79-0.89), COPD (OR = 0.85, 95% CI: 0. 80-0.90), reaction to severe stress and adjustment disorders (OR = 0.85, 95% CI: 0.82-0.89), Cancer (OR = 0.88, 95% CI: 0.83-0.93), chronic headaches (OR = 0.88, 95% CI: 0.84-0.92), sleep disorders (OR = 0.89, 95% CI: 0.85-0.93), allergic rhinitis (OR = 0.90, 95% CI: 0.86-0.96), spondylosis (OR = 0.94, 95% CI: 0.89-0.99 ) and lipid metabolism disorders (OR = 0.96, 95% CI: 0.93-1.00).
discussion
As far as the author is aware, this is one of the largest studies to examine the prevalence of Covid-19 and factors associated with diagnosis in primary care.
This German study showed that slightly fewer than one in seven symptomatic patients tested for Covid-19 were actually diagnosed with Covid-19. It is difficult to compare this result with the results of previous studies because most of these studies included symptomatic and asymptomatic participants and were conducted in other settings and countries. Based on these results, the prevalence of Covid-19 reported in the present study appears to be relatively high. This finding highlights the important role that general practices play in the diagnosis of Covid-19 in Germany and also highlights the importance of preventive measures to control the transmission of the virus between patients of the same practice in this country (Eisele et al., 2021).
The second aim of the present study was to identify demographic and clinical factors significantly associated with the likelihood of a Covid-19 diagnosis. Interestingly, in line with previous research (Fan et al., 2020; Lusignan et al., 2020), there was a positive association between male gender, older age and Covid-19 diagnosis. Although the strength of the association between gender and Covid-19 diagnosis was relatively weak (i.e. OR = 1.04), at the population level this association could result in a difference of hundreds of thousands of Covid-19 positive cases between men and women. There are significant sex differences in human physiological response to SARS-CoV-2 at different stages of infection (e.g. virus entry, virus recognition, and innate immune response), and these differences may result in a higher risk of Covid-19 or in men lead to more persistent detection of viral RNA than in women (Scully et al., 2020; Xu et al., 2020). Finally, various age-related changes such as immunosenescence, inflammation, and a dysregulated renin-angiotensin system may increase susceptibility to Covid-19 in older adults compared to their younger counterparts (Mueller et al., 2020).
Regarding comorbidities, Covid-19 diagnosis was strongly and positively associated with cardiac arrhythmias, depression and obesity. There is considerable evidence in the literature that cardiac arrhythmias are relatively common in people with Covid-19 (Cho et al., 2020; Gopinathannair et al., 2020; Rav-Acha et al., 2021).
Regarding mental health, previous literature has also identified depression as a risk factor for Covid-19, and the association between depression and Covid-19 may be mediated by factors such as difficulty adhering to preventive measures and limited access to healthcare (Wang et al., 2021).
Finally, we found that people with obesity were more likely to get Covid-19 than people without obesity, and this result is consistent with previous data. A meta-analysis of 50 studies found a positive association between obesity and SARS-CoV-2 infection and severe Covid-19 symptoms (Yang et al., 2020). Since adipose tissue expresses angiotensin-converting enzyme 2 (ACE2) (Al-Benna, 2020), a receptor that plays a key role in cellular entry of SARS-CoV-2 (Ziegler et al., 2020), excessive obesity may increase the risk of a Covid-19 diagnosis. Furthermore, there is strong evidence of the negative impact of obesity on the immune response to infection (Milner and Beck, 2012).
In contrast, this study found a relatively strong negative association between chronic sinusitis, asthma and Covid-19. Because previous research has yielded mixed results in this regard, these relationships should be interpreted with caution. There is overlap in the symptoms of Covid-19, asthma and chronic sinusitis, and for example, the prevalence of Covid-19 has been found to be significantly lower in asthma patients with suspected Covid-19 than in their counterparts without asthma (Cao et al ., 2021). Since fear of a Covid-19 diagnosis is relatively common in individuals with asthma or other respiratory diseases (de Boer et al., 2021; Mousing and Sørensen, 2021), adherence to prevention measures may be higher in these individuals than in the general population.
Finally, the present German study found a negative association between anxiety disorders and the Covid-19 diagnosis. Although psychiatric disorders have been associated with an increased risk of Covid-19 (Taquet et al., 2021), less is known about the specific impact of pre-existing anxiety disorders on the frequency of SARS-CoV-2 infections, and some data suggest this indicate that adherence to prevention measures is higher in the presence of anxiety (Apisarnthanarak et al., 2020; Mevorach et al., 2021). These data could explain why patients with anxiety disorders in the present German study were less likely to be diagnosed with Covid-19 than patients without anxiety disorders.
The major strengths of this study are the large sample size and the use of data collected from more than 960 general practices. However, several limitations must be taken into account when interpreting the study results. First, no Covid-19-like symptoms were documented in the database, so it was not possible to determine the prevalence of specific symptoms (e.g. cough, fatigue or fever) in the sample and their individual association with Covid-19 diagnosis to rate. Second, data on health behaviors (e.g. physical activity or adherence to preventive measures) were missing, although these behaviors can predict the risk of a Covid-19 diagnosis. Third, a significant proportion of patients with Covid-19 may have been diagnosed in emergency departments, so the prevalence of Covid-19 may have been underestimated in the present study. In addition, people at particular risk of developing severe Covid-19 disease (e.g. people with obesity or COPD) may have been treated in specialized practices, potentially biasing the estimates obtained based on the logistic regression analysis.
Conclusions
In this study, which included around 301,300 symptomatic patients who were tested and observed for Covid-19 in general practices in Germany between March 2020 and March 2021, around 14% of patients were diagnosed with Covid-19. Male gender, older age and multiple comorbidities (e.g. cardiac arrhythmias, depression and obesity) were significantly and positively associated with Covid-19, while there was also a negative association between a variety of medical conditions (e.g. chronic sinusitis, asthma and anxiety disorders) and the diagnosis of Covid-19. Further longitudinal studies are needed to confirm or refute the results of this study. <<