Study setting and design
To assess the incidence rate and the accuracy of HAIs diagnosis, we designed a 3-month longitudinal study from 23th September to 21th December 2021, with daily follow-ups. In parallel, we compared the incidence of HAIs using data extracted from the routine surveillance during the same period in a tertiary-care teaching hospital, Afzalipour hospital, in Kerman city, southeastern Iran. Furthermore, we conducted a qualitative study in 18 hospitals in different cities to identify the barriers of HAIs diagnosis.
Part 1: quantitative study
In the longitudinal study, we included patients from six wards among 24 active wards in the hospital, based on disease variety, ward turnover, and the variety of HAI reports, including internal medicine, surgery intensive care unit (ICU), gastrointestinal diseases, pulmonary diseases, general surgery, and women’s surgery. We included all the admitted patients with a central venous catheter (CVC), mechanical ventilation, or Foley catheter from four wards (internal medicine, surgery ICU, gastrointestinal diseases, and pulmonary diseases). Patients in two wards (general surgery and women’s surgery) were not followed up for device infection because Foley catheters were fixed in the patients at the time of operation. Severe patients in two wards were transformed into surgery ICUs. We also added two wards, general surgery, and women’s surgery, to detect SSIs because these two wards were responsible for most of the SSIs reported in the hospital.
Follow-ups of patients were started after admission to the ward and ended if they were discharged or died. A trained and experienced nurse (NN) referred to six wards daily and asked the nurses who were in charge of infection control and other staff about HAIs. He followed up patients and asked them or their relatives and the responsible nurse to look for signs or symptoms of infection. He also recorded the mode of ventilators set according to the bedside chart and observed the mode on the ventilator screen. We excluded all infections acquired in the community before the admission and secondary infection events in BSI.
In order to reach the objectives of the study, these data were recorded during the follow-up period: the admission code, hospitalization history, cause of the hospitalization, signs of infection, urine catheter date, CVC date, endotracheal tube date, registered vital signs, clinical symptoms, laboratory findings, surgical intervention record, positive end-expiratory pressure (PEEP) of the ventilator, and FiO2 daily. Also, we registered demographic data (age, sex), comorbidities, disease history, date of hospitalization, and discharge or death date.
Definition of HAIs in the longitudinal study and routine surveillance was based on the standard definitions of INIS . HAIs were diagnosed according to the case definition of INIS and consulted with an infectious diseases specialist (IG) or according to the therapeutic physician’s opinion. The definition of included HAIs is presented in Table 1. Central Line-associated Bloodstream Infection (CLABSI) is one of the four main causes of HAIs, but in our study, it is not investigated because, up to now, we do not have a detection method in use. In particular, we or some of our hospitals do not apply. The Infectious Diseases Society of America published recommendations for the diagnosis and treatment of CLABSI. They describe preferential culturing of catheter tips and avoidance of broth culturing techniques and define the interpretation of roll plate (> 15 CFU from a 5 cm segment) and sonication techniques (> 102 CFU) when assessing for colonization. CLABSI diagnosis can be made when culture results identify the same organism in at least the culture obtained as a peripheral stick and from a culture of the catheter tip. If the catheter is left in place, the diagnosis can be made if there are two blood samples being drawn (one from the catheter and one from a peripheral stick) that meet specific criteria for quantitative blood cultures or differential time to positivity [17, 18]. To assess the discrepancy between the report of routine surveillance and the result of the longitudinal study, we referred to the hospital archive and reviewed the patients’ records.
Data management and analysis
After completing the follow-up period, another author (AK) was referred to the hospital’s infection control center and received data on the routine surveillance systems for each HAI. The admission code was used to link data from the routine surveillance system and the longitudinal study. We used hospital information systems (HIS) data to reduce missing data and confirm data validity using laboratory and pharmacy information.
Data were analyzed using descriptive statistics (proportion and percentage for categorical data, mean and standard deviation for continuous data). Also, the incidence rate of HAIs in general and for each specific HAIs (per 1000 patient-days), and their 95% Confidence Intervals (CI) were calculated. The Chi-square test was used to compare proportions in two approaches (longitudinal study and routine surveillance). Kappa, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. All analyses were calculated by Stata software version 14.2 (StataCorp, College Station, TX, USA).
Part 2: qualitative study
To assess the barriers of the detection of HAIs, we carried out a qualitative study. In this study, using convenient sampling, 18 infection control experts in different hospitals around the country were recruited for interview. The interview was conducted in a private, public, or military hospital with more than 100 active beds. They were asked about their experiences in controlling infection and their opinion on barriers of detection and registration of HAIs. Data were obtained using semi-structured interviews in 20–40 min. We obtained explicit consent from participants to record their interviews with an audio recording device. Data saturation was reached after interview 14.
The interviews were analyzed using content analysis. The procedures applied were as follows: after implementing the interviews by the first author, they were transcribed verbatim by two separate authors. The credibility, transferability, dependability, and confirmability of the interviews were considered and confirmed [19, 20]. Each interview was read to get a general understanding and meaning units were identified and condensed to be labeled with codes. Then, codes were classified into subcategories and categories (the primary codes), and finally the underlying meaning was extracted. MAXQDA software version 10 (VERBI Software; Udo Kuckartz, Berlin, Germany) was used to analyze the data.