Implementing a healthcare-associated bloodstream infection surveillance network in India: a mixed-methods study on the best practices, challenges and opportunities, 2022

Abstract

Background

Healthcare-associated bloodstream infections (BSI) threaten patient safety and are the third most common healthcare-associated infection (HAI) in low- and middle-income countries. An intensive-care-unit (ICU) based HAI surveillance network recording BSIs was started in India in 2017. We evaluated this surveillance network’s ability to detect BSI to identify best practices, challenges, and opportunities in its implementation.

Methods

We conducted a mixed-methods descriptive study from January to May 2022 using the CDC guidelines for evaluation. We focused on hospitals reporting BSI surveillance data to the HAI network from May 2017 to December 2021, and collected data through interviews, surveys, record reviews, and site visits. We integrated quantitative and qualitative results and present mixed methods interpretation.

Results

The HAI surveillance network included 39 hospitals across 22 states of India. We conducted 13 interviews, four site visits, and one focus-group discussion and collected 50 survey responses. Respondents included network coordinators, surveillance staff, data entry operators, and ICU physicians. Among surveyed staff, 83% rated the case definitions simple to use. Case definitions were correctly applied in 280/284 (98%) case reports. Among 21 site records reviewed, 24% reported using paper-based forms for laboratory reporting. Interviewees reported challenges, including funding, limited human resources, lack of digitalization, variable blood culture practices, and inconsistent information sharing.

Conclusion

Implementing a standardized HAI surveillance network reporting BSIs in India has been successful, and the case definitions developed were simple. Allocating personnel, digitalizing medical records, improving culturing practices, establishing feedback mechanisms, and funding commitment are crucial for its sustainability.

Introduction

Globally, healthcare-associated infections (HAI) pose a significant threat to patient safety. Bloodstream infections (BSIs) are among the most common HAIs in low- and middle-income countries (LMIC), prolong hospital stays, and increase mortality rates [1,2,3,4]. Prospective and active surveillance is associated with reductions in HAI rates by up to 30% in high-income countries when used to measure the disease burden and direct targeted infection prevention and control (IPC) measures [1, 5,6,7]. Furthermore, HAI surveillance systems can be instrumental in the timely detection of (multidrug-resistant) MDR pathogens in hospitals, especially in tertiary care, which are potential sites for the emergence of MDR pathogens owing to high antimicrobial pressure [8]. However, only 16% (23/147) of LMICs reported having a functional national HAI surveillance system in a survey conducted by the World Health Organization (WHO) in 2010 [2]. There is limited information available from LMICs on the impact of these HAI surveillance systems and the implementation challenges faced.

In 2017, an HAI surveillance network was started in India by the All India Institute of Medical Sciences (AIIMS), New Delhi, with technical coordination by the Indian Council of Medical Research (ICMR) and the National Centre for Disease Control (NCDC), India, and with support from the United States Centers for Disease Control and Prevention (US CDC) [9] to document BSI trends [10]. The BSI surveillance was implemented in intensive-care units (ICUs) in selected tertiary-care hospitals across the country.

After five years, we evaluated BSI surveillance in India’s first HAI surveillance network. We identified best practices, challenges, and opportunities in its implementation to help develop context-specific, cost-effective, and sustainable HAI surveillance systems in limited-resource settings.

Methods

Study design, population, and period

We conducted a descriptive mixed-methods study using a convergent parallel (concurrent) study design from January to May 2022. The evaluation focused on hospitals that reported BSI surveillance data to the HAI surveillance network. The evaluation focused on staff trained to conduct and report active BSI surveillance using the HAI surveillance protocol within each hospital.

Operational definitions

Healthcare-associated BSI was defined in the HAI BSI protocol (available at haisindia.com) for patients admitted for more than two calendar days in a selected hospital ICU participating in the HAI surveillance network. This standard operational definition used for BSI in the HAI network was modified for the Indian setting from the US CDC’s National Healthcare Safety Network (NHSN) case definition [11, 12].

We followed the updated CDC Morbidity and Mortality Weekly Report (MMWR) guidelines 2001 [13] to evaluate BSI surveillance on the following attributes: simplicity, stability, acceptability, representativeness, data quality, timeliness, sensitivity, positive predictive value, and usefulness. We developed operational definitions and monitoring indicators for each of these attributes and created interview and survey questions to score the indicators (Table 1).

Table 1 HAI Network’s BSI Surveillance system attributes, indicators and data collection method, India, 2022

Data collection

Data was collected from both the network level and tertiary-care hospitals, which is the reporting level used in document reviews. Data was collected by data extraction from the network database (www.haisindia.com), surveys, semi-structured interviews, focus group discussions, and on-site visits. We developed structured questionnaires for interviews at the network level and the reporting level. We created three separate online surveys targeted at three groups of reporting-level staff involved in BSI surveillance: surveillance staff who validated each BSI case from the intensive care unit (ICU), data entry operators (DEO) who reported each case to the network database from a paper-based case report form (CRF), and ICU physicians (Tables 1 and 2).

Table 2 Qualitative questions and data collection method used, HAI Network’s BSI Surveillance system evaluation, India, 2022

Network level: We identified key stakeholders who had participated in developing and implementing the HAI surveillance network or were actively overseeing its operations and included them purposively. They included the project coordinator, statistician, and research fellow of the HAI surveillance program placed at AIIMS New Delhi and the technical advisers for the HAI surveillance program from the US CDC. We collected qualitative data from them using semi-structured interviews (network coordinators) and focus group discussions (FGD) (technical advisors) to evaluate the simplicity, stability, acceptability, usefulness, funding and organization of the surveillance system; and to document best practices, opportunities, and challenges during implementation.

We reviewed monthly reporting pattern of reporting units, and time of submission of quarterly reports to evaluate timeliness. We examined reports from routine site visits to document the presence of a 24-h laboratory and access of surveillance staff to all positive culture reports (blood, urine, sputum, pus, etc.), the percentage of laboratories having a laboratory information system (LIS) and monthly reporting pattern of units from 2017 to 2021 to evaluate system stability. We checked CRFs submitted by sites from October-December 2021 to evaluate data quality.

Reporting Unit level: We invited the principal investigators of all the sites (reporting units) enrolled in the network to participate in the evaluation and included the sites who volunteered to participate. We collected data using semi-structured interviews with key stakeholders of these sites to evaluate acceptability and usefulness and to document opportunities and challenges during implementation. Stakeholders who participated in interviews were asked to suggest at least one surveillance staff, DEO, and ICU physician from their site to receive the survey.

We included all suggested site staff and shared the surveys via email or WhatsApp Messenger. Each person could respond only once on the survey link provided. Questions included ease of applying the BSI case definition, the time required for data collection, ease of submitting data online to evaluate simplicity, and whether surveillance feedback was received monthly and used (only for physicians) to assess timeliness, acceptability, and usefulness. Given the BSI case definition required a positive blood culture, understanding blood culture ordering practices in eligible patients at surveillance sites was important to contextualize representativeness. Eligible patients were those admitted for more than two calendar days in a surveillance ICU, had a febrile episode and a potential BSI. During the on-site visits, we reviewed ICU patient files for the two months preceding the visit to look for febrile episodes. For each febrile episode, we searched for a blood culture entry in the corresponding laboratory records and if it was performed within 24 h. This information was used to calculate the percentage of febrile episodes that were cultured to investigate the blood culture ordering practices. We reviewed the list of positive blood cultures (sensitivity) and physical copies of CRF to assess the correct application of the case definition (positive predictive value) from October 2021 to December 2021.

Data analyses

After manually coding the transcribed qualitative data from interviews, we performed a thematic analysis. Quantitative data from monitoring indicators, surveys, and document reviews were calculated using Microsoft Excel, and are reported as counts and percentages. We combined qualitative and quantitative data to create a mixed methods interpretation, which we presented under the domains of best practices, opportunities, and challenges.

Results

Description of the system

The HAI surveillance network started reporting in May 2017 with 20 sites (66 ICUs) and increased to 39 sites (29 public and 10 private) with 131 surveillance units (ICUs) across 22 of the 36 states and union territories of India as of December 2021. Reporting ICUs included 26/131 (20%) medical, 19/131(19%) neonatal, 16/131 (12%) pediatric medical, 14/131 (11%) surgical, 10/131 (8%) COVID-19 ICUs, and others. At the reporting ICU, the BSI event data flow starts once a patient admitted to one of the surveillance units has a positive blood culture (Fig. 1). This patient’s case details are checked to see if they fit the BSI case definition. If yes, then a case report form (CRF) is generated by the surveillance staff, and it is uploaded into the HAIS web portal by the data entry operator after validation by the site principal investigator (PI). Using the BSI case numbers and the denominator data from the respective sites, facility and network level rates are generated and communicated to all stakeholders. To identify concerning BSI trends and outbreaks, the network database’s early warning signal generates an alert to users automatically when an ICU-specific BSI rate exceeds 20 per 1,000 patient days in that reporting unit/ ICU.

Fig. 1

Data flow in HAI Network’s Bloodstream Infection Surveillance (BSI), India, 2022

The network received funding support from the US CDC under a cooperative agreement between 2017–2022 technically coordinated by ICMR. The CDC funding was provided to AIIMS, and AIIMS distributed the funds to the funded sites to hire surveillance staff depending on the units under surveillance. Sites that are not funded by the AIIMS-CDC projects and are part of the surveillance network as voluntary participants receive technical support and reporting platform access. They use internal funds to hire surveillance staff or use existing staff for surveillance activities. Material resources for data collection and any additional human resources required for surveillance expansion are financed through the site’s internal budget.

Evaluation of the system: Quantitative results

At the network level, we reviewed 21 site visit reports, 14 quarterly reports, data reported to the network database from 1st May 2017 to 31st December 2021, and 284 CRF. Ten hospitals agreed to participate in our evaluation. At the reporting level, surveys were distributed to 20 surveillance staff (two from each of the 10 sites), 20 DEOs (two from each of the 10 sites), and 20 physicians (two from each of the 10 sites). Among these, all the surveillance staff, all DEOs and ten physicians responded. Surveillance staff who responded to the surveys included infection control nurses (ICN), laboratory technicians, and research fellows (RF). We visited four (two funded and two non-funded) sites. We reviewed 135 ICU patients clinical case files, 72 positive blood culture reports (reported during the evaluation period) and 26 CRFs (reported during the evaluation period) from six surveillance ICUs in these four sites to evaluate system attributes (Table 3).

Table 3 Evaluation results of HAI Network’s BSI Surveillance system attributes, India, 2022

Simplicity

Among the surveyed staff, 83% rated the modified NHSN case definitions as easy to apply and found the online reporting platform user-friendly.

Stability

The network functioned throughout these five years (May 2017 to December 2021) with variable reporting. The number of reporting ICUs dropped from 125/131 (95%) in February 2020 to 84/131 (64%) in April 2020 coinciding with the start of the COVID-19 pandemic. Reporting gradually increased to 100% in March 2021 before decreasing again in April 2021 during the second wave of COVID-19. The reporting increased till August 2021(128/131, 98%) before decreasing again to 63/ 131 (48%) in December 2021 when external funding for this project was interrupted (Fig. 2). Despite reduced project funding, 25% of the hospitals continued to provide data to the system with their own dedicated infection prevention and control (IPC) staff.

Fig. 2

Reporting pattern of HAI Network ICUs reporting BSIs, May 2017 to December 2021

Among the 21 site visit reports reviewed, 76% of sites reported that surveillance staff had access to all positive cultures (cultures taken from other body sites). All sites which reported challenges in surveillance staff accessing all positive culture reports were public hospitals. These hospitals used manual registers for recording and reporting laboratory results and did not have a Laboratory Information System (LIS) or Hospital Management Information System (HMIS).

Representativeness

Among the 135 ICU patient case files reviewed, blood culture was collected within 24 h in 27/61 (44%) febrile episodes identified in these patient files. Two of these hospitals cultured blood based on patient symptoms (with 44% of patients being cultured within 24 h of a febrile episode in both hospitals), and the other two hospitals cultured patients twice a week irrespective of patient symptoms (26% and 62% of patients having a febrile episode being cultured in each hospital respectively). Eight physicians reported sending paired blood cultures from each febrile patient, while five physicians reported culturing up to 80% of febrile patients in their ICU.

Data quality

Among the 284 CRFs reviewed, 91% had complete data, and 98% had correctly applied the BSI case definition.

Timeliness

From 2017, the network submitted 13/14 (93%) quarterly HAI surveillance reports to the Ministry of Health and Family Welfare within one month of the reporting quarter. Of the ten ICU physicians surveyed, 60% reported receiving consistent monthly feedback on BSI rates from their ICUs.

Sensitivity

Among 72 positive blood cultures reports reviewed, 26 positive blood cultures and their corresponding patient case files met the BSI case definition criteria and all 26 were correctly reported as BSIs by the sites to the network database. The 14 quarterly reports reported on pooled network trends mapped for each quarter. The system generated 684 ICU-specific BSI rate alerts from May 2017 to December 2021.

Positive Predictive Value

All 26 CRFs reported from site-level surveillance staff to the network database during site visits met the BSI case definition, with a positive predictive value (PPV) of 100%.

Usefulness

Using data from this network, 12 of the 39 (31%) participating sites had implemented targeted IPC measures to reduce their BSI rates. Three major healthcare-associated BSI outbreaks, including an outbreak caused by Burkholderia cepacia, were detected and controlled [14]. Among surveyed physicians, 70% stated that surveillance data feedback positively impacted care in the ICU by improving documentation and increasing adherence to recommended central-line practices.

Evaluation of the system: Qualitative results

At the network level, we conducted three interviews (the program coordinator, one statistician, and one research fellow of the HAI surveillance program) and one FGD (with three technical advisers from the US CDC). At the site level, we conducted ten interviews (one per site, with one to two staff participating in each). The interviewees included six microbiologists, four ICNs, six RFs, and two DEOs. The qualitative analysis from the interviews yielded ten themes related to implementing the surveillance (Tables 4 and 5).

Table 4 Summary of Themes from Qualitative Analysis, BSI Surveillance Evaluation, India, 2022

Table 5 Themes, codes and representative quotes obtained from interviews, HAI Network’s BSI Surveillance evaluation, India, 2022

Mixed-methods integration: We consolidated the quantitative attributes, their indicators, and the qualitative themes under best practices, challenges, and opportunities (Table 6). Best practices encompassed developing case definitions suitable for the available resources in a diverse health system, establishing network-based surveillance, and IPC training of surveillance staff. Challenges identified included limited human resources, lack of digitalization, variable blood culturing practices, inconsistent information sharing, funding, and the COVID-19 pandemic. Opportunities highlighted the awareness and acceptance of BSI surveillance among participating sites.

Table 6 Integration of qualitative themes and quantitative indicators, HAI Network’s BSI surveillance evaluation, India, 2022

In all domains, the evidence from surveys, interviews, and document reviews aligned with each other except in blood culturing practices. While the surveyed physicians reported culturing 80% of febrile patients, document reviews indicated a figure of 44%.

Discussion

Our evaluation demonstrates that implementing a standardized BSI surveillance among a diverse resourced network across India has been successful, with lessons learned for other countries interested in initiating similar HAI surveillance networks. The BSI surveillance is simple, acceptable, and sensitive in reporting trends. but there are challenges to sustainability due to limited human resources, lack of digitalization of medical records, variable blood culture practices, limited information sharing among key stakeholders, and funding.

The BSI surveillance conducted by the HAI surveillance network has achieved many successes since its inception. The team has established network-level surveillance of BSI for India by assembling hospitals with varying capacities and from different Indian states on a common platform. They have adapted CDC’s NHSN case definitions for resource-limited settings and trained network sites using a common modified case definition that can track trends at the facility, subnational, and national levels. The surveillance established is an active, prospective surveillance with higher specificity and sensitivity than passive or retrospective surveillance. Beyond detecting BSI rates, this study shows that sites are willing to use surveillance data to improve IPC processes and reduce BSI rates if provided human resources and training. This is a best practice to adopt and is consistent with other studies [15, 16]. While not a primary purpose of the network, interviewed staff felt they benefited from the efficient and timely dissemination of IPC information and guidelines during the COVID-19 pandemic. The use of such networks can be leveraged to quickly disseminate and amplify information in epidemics and pandemics.

Our study highlights the importance of stable, dedicated funding to the stability of a surveillance network, including the impact on staff retention, institutional knowledge, and data reporting. Unreliable funding also limited expansion of surveillance to other intensive care units (ICUs) within these hospitals. We found that external funding partially mitigated the shortage of human resources in funded public hospitals in the short-term. It should be noted that relying solely on external funding may serve as an initial step to initiate work and pilot a surveillance program. Sustainable long-term solutions to address resource limitations should be sought, as demonstrated by funding challenges faced by antimicrobial resistance surveillance programs in LMICs [17, 18] and aligns with WHO guidance to allot dedicated funding to build IPC programs with capacity to conduct HAI surveillance [19].

Our study's findings regarding the impact of a shortage of trained staff on data collection, data use, and surveillance expansion are consistent with previous research conducted in both low- and high-resource settings. These studies have consistently identified inadequate staffing as a common barrier to performing essential IPC activities [20,21,22,23]. Our study also showed that the lack of sufficient supplies specific to blood culture and the lack of digital medical records, issues unique to public hospitals, compromise data quality and increase the time required for surveillance activities. Specifically, the challenges highlighted in our study at the facility level align with challenges in IPC core component 6 (monitoring/audit of IPC practices and feedback), and 7 (workload, staffing and bed occupancy) reported in the WHO’s Global IPC report [24]. Considering these findings, and the disruption seen with turnover of staff, we believe that appointing full-time infection control professionals in both public and private hospitals, along with allocating adequate material resources, implementing a robust supply chain management system and digitalization of medical and laboratory records in public hospitals, are fundamental to establishing a successful HAI surveillance program as reported in previous research [25, 26].

Our study highlights the presence of inconsistent culturing practices during febrile episodes and a lack of agreement between actual and reported febrile patients among physicians, which is not exclusive to low-resource settings. Similar deficits in blood culture ordering and adherence to guidelines have also been observed among inpatient care physicians in high-resource settings [27,28,29]. The underlying reasons for these variations in culturing practices remain unclear but should be studied to provide ways to enhance the detection of BSIs and improve the representativeness of the surveillance system. Contrary to physician opinions in our study suggesting that conducting cultures is too costly, studies conducted in low-resource settings demonstrates investing in laboratory capacity and culturing practices can result in cost savings despite greater upfront investments and lead to improved health outcomes by reducing inappropriate antibiotic use [30].

Several limitations were identified in our study. The participating sites joined the study voluntarily, which might have introduced a potential selection bias as these sites may have had a more favorable opinion towards the network. The onsite visits were conducted in four network hospitals, and blood culture ordering practices documented in these hospitals might not represent the entire network.

Conclusion

An active, prospective BSI surveillance, utilizing a common definition, is feasible in a low-resource settings. Prioritizing allocation of dedicated personnel for surveillance, training them to use data for action, digitalizing medical records, improving blood culturing practices, establishing systematic feedback mechanisms to share data with treating physicians, and long-term funding commitment from policymakers are crucial to make HAI surveillance networks sustainable.