Implementing a Healthcare-Associated Bloodstream Infection Surveillance Network in India: a Mixed-Methods Study on the Best Practices, Challenges and Opportunities, 2022

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Vedachalam, Valan A. Siromany, Daniel VanderEnde, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4891610/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2024 Read the published version in Antimicrobial Resistance & Infection Control → Version 1 posted 9 You are reading this latest preprint version 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. Healthcare-associated infection (HAI) surveillance in developing countries Sepsis Surveillance system Patient safety Cross-infection Nosocomial infections Figures Figure 1 Figure 2 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 – 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 – 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, qualitative questions and data collection method, India, 2022 Attribute and operational definition Indicator Data source and data collection method Simplicity : Simplicity of workflow and ease of implementation 1. Ease of collecting data (staff required/ time spent) 2. Proportion of surveillance staff who report applying BSI case definition as easy or very easy 3. Proportion of surveillance staff who report online reporting as easy or very easy 4. Average amount of time spent by surveyed staff in reporting one BSI case in the portal 4. Number of levels of reporting in the system Interview of Project coordinator Onsite observations Survey of Surveillance staff and Data entry operator (DEO) Stability : System’s reliability, availability, and sustainability 1. Proportional of surveillance staff trained in BSI surveillance protocols 2. Proportion of sites where denominator data is collected everyday including holidays 3. Proportion of sites with 24 by 7 laboratory 4. Proportion of sites with LIS 5. Proportion of sites where surveillance staff have access to all positive cultures 6. Proportion of sites who review every positive culture at month-end to capture missing cases 7. Availably of the system since its inception in 2017 8. Proportion of sites with funding support Interview of key stakeholders Survey of surveillance staff and DEO Review of network data Review of site-visit records Acceptability: W illingness of individuals and institutions to participate in the BSI surveillance network Number of hospitals participating in surveillance Interview of Project Coordinator and site representatives FGD of technical advisory team Representativeness : System’s ability to accurately describe BSIs over time Proportion of febrile episodes (in patients admitted in surveillance ICUs) where a blood culture is collected Review of patient case files during on-site visits Data quality: C ompleteness and validity of the captured data Data validity: Proportion of CSF with case definition applied correctly Data completeness: Proportion of CSF with 100% mandatory fields filled Record review of CRF during site visits Review of site-visit records Timeliness : System’s ability to detect BSI cases and outbreaks in timely fashion 1. Proportional of febrile episodes (in patients admitted in surveillance ICUs) where a blood culture is collected within 24 hours of the febrile episode 2. Proportion of sites reporting BSI data within 10 days of the reporting month 3. Proportion of quarterly reports submitted by the network within one month of the reporting quarter 4. Proportion of ICU physicians who received monthly feedback on their ICU’s BSI rate 4. Proportion of BSI outbreaks detected and controlled while still ongoing Review of ICU patient case files Review of HAI network database Review of quaterly reports Survey of physicians Interview of site representatives Sensitivity : System’s ability to detect BSI cases and outbreaks correctly 1. Proportional of BSI cases reported among all BSI cases detected during review of laboratory records 2. Monitoring trends of BSI 3. Number of early warning signals generated in the last one year (Jan-Dec 2021) and last one month (Dec 2021) Review of patient case files and laboratory records during on-site visits Review of network database Positive Predictive Value : Probability that a detected BSI case is true case of BSI Proportion of true BSI cases among all BSI cases reported Method for confirming true cases Review of CRF during on-site visits Usefulness 1. Monitor network-based BSI trends over time 2. Number of BSI outbreaks detected using surveillance data 3. Proportion of sites using data to improve IPC practices at their ICUs Review of network database Survey of physicians Open-ended questions Probes Data Source and data collection method 1. How well is this system capturing BSIs? 2. Is the data generated by this system used? 3. Do you feel participation in BSI surveillance benefits the patient or your centre? How? 4. Do clinicians get feedback about their BSI rates 5. Is your feedback linked to IPC activities and QI initiatives- Could you give us a few examples Interview of Project Coordinator and site representatives FGD of technical advisory team What are some of the successes of this network according to you? -Do you think it has led to QI initiatives/ any IPC practice change/ outbreaks detected/ capacity building of your IPC team Interview of Project Coordinator and site representatives FGD of technical advisory team What are some of the challenges? -Areas where you wish things could be better- network level/facility level Interview of Project Coordinator and site representatives and FGD Data collection Data was collected from both the network level and tertiary-care hospitals, which is the reporting level used in document reviews, 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 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. 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-hour 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 hours. 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 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. 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 2 ). Table 2 Evaluation results of HAI Network’s BSI Surveillance system attributes, India, 2022 Attribute Indicator Evidence collected Assessment Overall evaluation Simplicity Ease of collecting data • Minimum 2 full-time surveillance staff • 80% staff spends > 2 hours or more every day in collecting the 23 variables and the lab confirmation for case confirmation from paper-based reports Time-consuming Simple Ease of applying case definition • 15/20 (75%) staff rate it as very easy or easy Easy Ease of online reporting • 18/20 (90%) rate it as very easy or easy • 10–15 minutes to submit one CRF, described as “user-friendly” Easy Levels of reporting • Two a) Local (hospital administration), and b) National (AIIMS, New Delhi) Easy Stability Reliability • All 40 staff surveyed are trained in protocol • All 21 sites reviewed collected denominator data on all days including weekends/holidays • All 21 sites reviewed have access to a 24 by 7 working laboratory • 16/21 sites reviewed have access to all positive cultures, required for classifying BSI type • 5/21 sites reviewed reported not having LIS/ HMIS, reported using manual registers • 15/21 (71%) sites review every positive culture at month-end to capture missing cases Reliable Stable if funding is available Availability • Available from 2017 including during second wave of COVID-19 in India • Decreased reporting to 84/131 (64%) in ICUs and surveillance stopped in 22/39 (56%) sites seen briefly during April 2020 Available Sustainability • 26/39 (67%) of the sites are funded by US CDC • Reporting decreased to 63/131 (48%) in the quarter 4, 2021 when funding was interrupted Funding stability a concern Acceptability Willingness of stakeholders to participate • Started with 20 ICUs in 2017 and has increased to 131 ICUs in 2019 • Hospital administration of all 10 hospitals interviewed accepts this system is required to control multi-drug resistant pathogens High Acceptable Proportion of physicians accepting feedback from the surveillance system • 90% physicians surveyed starting a QI initiative in their ICU based on the feedback received from surveillance Representativeness Population representative of the participating hospital • 32/58 (55%) of the febrile episodes reviewed had their blood cultured Not representative Not representative Data Quality Data validity • 280/284 (98%) of CRFs reviewed have correctly applied the case definition Valid Data quality is good Data completeness • 259/284 (91%) of CRFs have data filled in each data va without any missing details Complete Timeliness Blood collection • 27/61 (44%) of febrile episodes reviewed were cultured (blood collection) < 24 hrs of fever Not timely Blood culture collection and feedback to ICU physicians is not timely Reporting to network • 36/39 (92%) sites reported data within 10 days of the reporting month Timely Dissemination to key stakeholders • 6/10 (60%) ICU physicians surveyed reported getting consistent monthly feedback • 13/14 (93%) quarterly reports shared by AIIMS with all key stakeholders within one month of the reporting quarter Not timely Timely Detection of BSI outbreaks • Sites not comfortable in sharing information regarding outbreaks detected in their hospitals, hence this information could not be captured • One outbreak of Burkholderia cepacia detected in the network using surveillance data and controlled (retrieved from published data) ------ Sensitivity True cases detected • 26/26 (100%) cases reviewed during site visit were correctly identified Sensitive Sensitive Monitoring trends • System can identify BSI trends from May 2017 to December 2021 Sensitive Early warning signals generated • System has generated 684 ICU specific BSI rate alerts from May 2017 to December 2021 • Alerts are generated when ICU specific BSI rates are > 20 for that month Sensitive Positive Predictive Value Proportion of true cases with confirmed BSI • 26/26 (100%) cases reported as BSI for the months of November 2021 to March 2022 were reviewed and found to be true cases Good Good PPV Usefulness Monitoring trends Detecting outbreaks Improving IPC • Quarterly trend analysis done for every quarter from Jan 2018 to Dec 2018 • Three outbreaks detected including one outbreak of Burkholderia cepacia using surveillance data and controlled (retrieved from published data) • 12/39 (31%) sites have completed/ongoing QI projects to improve BSI rates • 7/10 (70%) physicians surveyed reported having increased adherence to recommended Central Line insertion and maintenance practices following feedback from the system Useful Useful 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. 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 hours 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 hours 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 14 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. Qualitative results Semi-structured interviews and FGD: At the network level, we were able to conduct 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 were able to conduct ten interviews (one per site, with one to two staff participating in each interview). 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 (Box 1 and Table 3 . Table 3 Themes, codes and representative quotes obtained from interviews, HAI Network’s BSI Surveillance evaluation, India, 2022 Themes Codes Quotes Developed a resource-appropriate case definition Simple easy to implement case-definition Applicable to Indian hospitals Sensitive case definition “The team developed a simple case definition, clear SOP, which helped us train sites well” #ProgramCoordinator “I’m pretty confident that in a given unit, more than 80–90% of the BSIs were being captured, pre-COVID-19 .” #TechnicalTeam01 “Modified case definition is sensitive in capturing true CLABSIs: “when you do these focussed QI projects, their CLABSI rates go down significantly, which tells us there’s probably not a huge definitional gap where we are finding BSI and classifying them as CLABSI when they are really secondary BSI” #TechnicalTeam02 Established a representative network-based surveillance to detect trends and investigate outbreaks Implementing a surveillance program across different states Representative of different patient populations within ICUs Representation of public and private hospitals Geographically representative Outbreaks detected “(we have been successful in helping) how to implement a surveillance and prevention program that can be implemented consistently across a network of 39 + hospitals in a relatively well-resourced setting, it’s a huge success.” #TechnicalTeam01 “Good representation of patient population- medical, surgical, paediatric, neonatal and oncology” #TechnicalTeam01 “When it comes to tertiary care hospitals in the country, we are fairly representative as we have geographical diversity, also a mix of private and public hospitals” #TechnicalTeam02 “We have used data from this surveillance to investigate HAI outbreaks, and control them in time” #TechnicalTeam03 Ensured regular ongoing IPC trainings with Quality Improvement (QI) projects Shifted to online training Quick dissemination of COVID-19 IPC measures in network Every infection is accounted for Targeted recommendation to avoid each infection “This IPC training and surveillance helped us in correct handling of COVID-19 patients during the pandemic” #ICN02” “With QI initiatives, I can say that, first we were just recording and showing the rates in the HICC (hospital infection control committee) meeting, now we are asking questions for every BSI reported and suggesting ways to avoid that infection.” #ResearchFellow06 Limited human resources Limited staff allotted to surveillance Staff attrition “Limited staff allotted to surveillance, so we are not expanding surveillance to all ICUs within the hospital” #Microbiologist03 “Difficult to retain trained staff once funding ends, impacts our data collection” #Microbiologist05 “Staff turnover impacts timeliness of data upload” #Microbiologist02 Lack of digitalization of medical and laboratory records Patient tracking outside of ICU is difficult Tracking different culture reports is difficult End-of month validation difficult Deciphering handwritten notes is challenging “Tracking samples from other body sites is difficult as no common book in ICU which has a list of all cultures sent for that day….it takes a lot of man-hours” #ICN03 “Accessing and deciphering handwritten notes from manual registers is time-consuming and inefficient” #ICN01. “Monthly validation of all blood cultures is not always done, difficult in centres with no LIS” # DEO (02) Variable blood culturing practices Lack of availability of culture bottles Staff shortage in ICU Patient unable to pay for blood cultures Clinician’s judgement “Blood culture is paid, in our hospital social service organizations help poor patients but these are not present in all hospitals” #Microbiologist01 (private hospital) “In our hospital, blood culture is free, but sometimes patients must buy supplies’# Microbiologist03 (public hospital) “This is a tertiary care hospital, patients have already taken a lot of antibiotics and come in very sick, so we directly start on antibiotics empirically, only when patient does not respond, we send cultures.” #Microbiologist01 Inconsistent information sharing and data use Staff not analysing data Analyzed data not shared with physicians Physicians may not accept recommendations “Even though they have lot of AST data, they (sites) are not analysing this data for antibiograms, nor converting it into information and then sharing it (with their physicians)” # TechnicalTeam02 “Physicians many times do not accept the results of the data, or the IPC measures suggested possibly due to a difference in surveillance and clinical definitions” #TechnicalTeam03 “there’s a kind of hierarchy and defensiveness that can exist between the microbiology department and the clinical side, that’s been a bottleneck to the data actually being used.” # TechnicalTeam02 Funding and sustainability Funding affects stability. Further expansion of network depends on funding. Institutionalization of IPC staff helps stability “Difficult to retain trained staff once funding ends, impacts our data collection” #Microbiologist06 “If we have allocated budget either internal or external, we plan to expand the network by creating regional trainers and including tier 2 (secondary care) hospitals in the network.” #ProgramCoordinator “Hospitals with dedicated IPC staff continued surveillance despite drop in funding” #TechnicalTeam03 Impact of the COVID pandemic Disrupted regular trainings and expansion Staff attrition Reduced reporting units Limited access of staff to COVID ICUs “We are confident that in a given unit, more than 80–90% of the BSIs were being captured, pre-COVID-19 pandemic #TechnicalTeam01 “During the COVID-19 pandemic nobody was willing to support any intervention to improve CLABSI (infection rates) as there was staff shortage, now is not the right time, they said” #ReseachFellow05 Awareness and acceptance of BSI surveillance among participating sites Surveillance is useful Sites eager to join network Increase in blood cultures received after joining surveillance “For a public hospital, where we have so much patient load, because of this surveillance, we are able to focus on areas to improve,” #Microbiologist02 “They (ICU physicians) have come to realise that without IPC, (BSI) rates cannot be reduced” #Microbiologist03 “Number of blood culture samples have increased significantly in the last 2 years after our trainings” #ICN02 Box 1. Description of Themes from Qualitative Analysis, BSI Surveillance Evaluation, India, 2022 1. Developed a context specific resource-appropriate case definition. The program coordinator stated that the technical team had developed a simple to understand case-definition and clear SOP applicable in Indian context which helped train the sites quickly. Site staff reported the case definition as easy to apply. 2. Established surveillance through a network-based approach. Technical advisers stated that it was challenging initially to bring hospitals from different states on a common platform to agree on a common case definition and protocols, but they succeeded by starting with a small number of committed sites and gradually enrolling more sites to establish a network that consistently reported BSI trends. They considered doing so in a limited timeframe of five years, a success. 3. Regular IPC trainings and use of QI to improve IPC Sites reported the network gave them regular virtual trainings which kept them up to date during the early days of the pandemic with the evolving IPC guidelines. Staff at one of sites which implemented a QI project described the regular ongoing IPC trainings as very helpful in understanding and using data for improving patient safety. 4. Awareness and acceptance of BSI surveillance among participating sites Microbiology staff reported increase in the number of blood cultures received in their laboratory for pathogen identification after their hospital joined the network and believe this surveillance can help prioritize IPC measures in public-funded resource-limited settings. The program coordinator stated that willingness to join this network is high as hospitals are actively soliciting opportunity to participate. 5. Limited human resources Funded public hospitals reported that limited staff allotted to surveillance was the reason for not expanding surveillance to all ICUs within the hospital. They also faced challenges in retaining trained staff once funding ended, impacting data collection and reporting. Non-funded public hospitals (with institutional IPC staff performing surveillance activities) also reported staff shortages. However, they did not report any interruption in surveillance activities. In response to this, the technical advisors stated that the network’s capacity to provide resources for staffing may be limited and it is ultimately up to individual hospitals to allocate additional staff for surveillance and IPC. 6. Lack of digitalization of medical and laboratory records Sites without a Health Management Information System (HMIS) reported challenges in tracking patients outside of ICUs using paper-based forms. Sites without a Laboratory Information System (LIS) reported challenges in tracking multiple positive cultures for a single patient, and challenges in performing monthly validation of all reported BSI cases. They described accessing and deciphering handwritten notes in laboratory notebooks (that maintain a list of positive cultures and patient details) as difficult and time-consuming. 7. Variable blood culturing practices Staff (both at private and public hospitals) reported that patient’s blood culture was collected based on the treating physician’s decision and not according to the clinical standard of care mentioned in the surveillance protocols (at-least two blood cultures drawn for bacterial cultures when a patient has a febrile episode). Treating physicians reported culturing blood only when their patient does not respond to their empiric antibiotic therapy due to high cost of cultures. Public hospital staff (both funded and non-funded) reported several other reasons affecting culture collection including lack of availability of culture bottles, staff shortage in ICUs/ lab and inability of the patient to pay for blood cultures where these services were paid. 8. Inconsistent information sharing and data use. The technical advisers stated many challenges to using data for action at the facility level by the site staff including a lack of training, the analysed data not being shared with physicians, and physicians not accepting the results of the aggregated surveillance data (in few sites) as the clinical and surveillance case definitions varied. 9. Funding and sustainability According to the project coordinator, the sustainability and expansion of this system depended on securing sustainable funding. Staff at one of the sites visited stated its difficult to retain trained staff once project funding ends and this will impact their data collection and reporting. 10. Impact of the COVID-19 Pandemic on the surveillance According to the technical advisors, during the early months of the COVID pandemic, the network transitioned from physical to virtual training. Interviewed staff reported that during the pandemic, IPC and best practice updates provided by the network coordinators and CDC team were received in a timely manner and applied. According to the program coordinator, surveillance stopped in many sites between March 2020-April 2020 as staff were reassigned for COVID duties. The loss of trained staff, along with the designation of many ICUs as COVID-19 ICUs with limited access to surveillance staff, forced sites to reduce the number of reporting units. Mixed-methods integration We consolidated the quantitative attributes, their indicators, and the qualitative themes under best practices, challenges, and opportunities (Table 4 ). 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 4 Integration of qualitative themes and quantitative indicators, HAI Network’s BSI surveillance evaluation, India, 2022 Domain Qualitative themes Corresponding quantitative indicator result Best practices Developed a resource-appropriate case definition Simplicity: easy to apply case definition Established a network-based surveillance to detect BSI trends and outbreaks Stability: All 21 (100%) sites checked had access to 24 by 7 lab facility Sensitivity and PPV: Checked events had 100% PPV and 100% sensitivity Sensitivity: Sensitive in detecting BSI trends from May 2017 to Dec 2021 One outbreak of Burkholderia cepacia detected in the network using surveillance data Ensured regular ongoing IPC trainings with Quality Improvement (QI) projects Acceptability: 90% physicians surveyed starting a QI initiative in their ICU based on the feedback received from surveillance Usefulness: 70% reported the feedback and trainings affecting care in the ICU by improving documentation of, and increasing adherence to, recommended central-line practices, 31% sites implemented one or more QI measures to decrease BSI rates Challenges Limited human resources Lack of digitalization of medical and laboratory records Stability: 76% sites had access to all positive cultures, required for classifying BSI type, rest 24% did not have LIS, recorded lab results in manual registers Stability: 71% sites capture missing cases at end of month Simplicity: 80% of surveyed surveillance staff reported spending two hours or more per day collecting data from paper-based reports Variable blood culturing practices Representativeness: 55% had their blood cultured with 44% cultured within 24 hours of a febrile episode Survey: 50% physicians reported culturing 80% of the febrile patients Timeliness: 44% of the febrile episodes reviewed had blood cultured within 24 hours Inconsistent information sharing and data use Timeliness: 6/10 (60%) ICU physicians reported getting consistent monthly feedback Funding and sustainability Stability: reporting ICUs decreased to 63/131 (48%) and reporting sites to 30/39 (77%) during quarter 4, 2021 when funding was interrupted Impact of the COVID-19 pandemic Stability: Surveillance stopped in 22/39 (56%) sites during March-April 2020 as staff were absorbed in COVID-19 duties Opportunities Awareness and acceptance of BSI surveillance among participating sites Acceptability: Acceptable among stakeholders at national and site level In all domains, the evidence from surveys, interviews, and document reviews aligned with each other except in blood culturing practices. While the surveyed physician reported culturing 80% of febrile patients, document review 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 getting together 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 – 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 – 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. Declarations Ethical approval and consent to participate We conducted this study as part of the monitoring and evaluation of a national public health surveillance project titled “Capacity Building and Strengthening of Hospital Infection Control to Detect and Prevent Antimicrobial Resistance in India”. The project received ethical approval (IEC/NP-386/10.09.2015) from the Institutional Ethics Committee, All India Institute of Medical Sciences (AIIMS), New Delhi, and approval from the Health Ministry Screening Committee (HMSC), India. We obtained permission from the HAI surveillance network’s program coordinators before reaching out to network sites, and study participants provided consent via email for interviews, FGD, and surveys. Consent for publication Not applicable Availability of data and materials The datasets supporting the conclusions of this study are included within this published article (and its additional files). Competing interests The authors declare that they have no competing interests to declare relevant to this article's content. Funding No funding was received to conduct this study or to assist with the preparation of this manuscript. Authors contribution SKV, VAS, DV, PMal, and TD conceived the study design. SKV acquired on-field data. KW and PMath approved the acquisition of data. VAS and PMath supervised the study. SKV, VAS, DV, PMal, AV and TD analyzed the data and interpreted the results. SKV wrote the original manuscript text and prepared the figures and tables. SKV, VAS, DV, PMal, AV, TD, KW and PMath revised and edited the manuscript. All authors reviewed the manuscript, approved the submitted version, and agreed to be personally accountable for the manuscript. Acknowledgments We acknowledge all project staff and PI of the “Capacity Building and Strengthening of Hospital Infection Control to Detect and Prevent Antimicrobial Resistance in India” supported by the U.S. Centers for Disease Control and Prevention, Global Health Security Agenda cooperative agreement 1U2GGH001869 (2016-2021) & NU2HGH000088-01-00 (2021-22) including Mr. Sharad Srivastava, Statistician, Dr. Rasna Parveen, Scientist C, Mr. Naresh, Field Investigator, Mr. Pawan Kashik and Infection Control Nurses at AIIMS, New Delhi. We acknowledge the support of Dr Camilla Rodriguez, Head of Department, Microbiology, PD Hinduja Hospital, Mumbai, Ms. Julliah Chelliah, Senior Research Fellow, Dr Veena Kumari, Head of Department, Microbiology, NIMHANS, Bangalore and their Infection Control Nurses and Dr Rajni Gaind, Head of Department, Microbiology, Safdarjung Hospital, New Delhi, Dr Rushika Saksena, and team. We acknowledge the support of Mathew Hudson, EIS Officer, DHQP, CDC Atlanta, USA, and Ms. Dorothy Southern, Scientific Writing Advisor, SAFETYNET. Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. CDC or the U.S. Department of Health and Human Services. References Report on the Burden of Endemic Health Care-Associated Infection Worldwide. World Health Organisation; 2011. Available from: https://www.who.int/publications/i/item/report-on-the-burden-of-endemic-health-care-associated-infection-worldwide. 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Health-care-associated bloodstream and urinary tract infections in a network of hospitals in India: a multicentre, hospital-based, prospective surveillance study. Lancet Global Health. 2022; doi: 10.1016/S2214-109X(22)00274-1 Healthcare-associated Infection Surveillance India, HAIS-India. http://www.haisindia.com . Accessed 2 January 2022 Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008; doi: 10.1016/j.ajic.2008.03.002 Surveillance For Healthcare-associated Infections (HAI) in Intensive Care Units; Standard Operating Procedures. Prepared by AIIMS, New Delhi; CDC, India and ICMR, New Delhi. Published November 2018:pg-23. https://www.haisindia.com/upload/fileuploads/1543398274_SOP%20updated%20November%202018.pdf . Accessed 2 January 2022 Center for Diseases Control and Prevention (CDC). MMWR updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group, (July 2001). https://www.cdc.gov/mmwr/preview/mmwrhtml/rr5013a1.htm . Accessed 20 October 2021 Fomda B, Velayudhan A, Siromany VA, Bashir G, Nazir S, Ali A, et al. An outbreak of Burkholderia cepacia bloodstream infections in a tertiary-care facility in northern India detected by a healthcare-associated infection surveillance network. Infect Control Hosp Epidemiol. 2022; doi: 10.1017/ice.2022.111. Epub ahead of print. PMID: 35670040. Wagenaar BH, Hirschhorn LR, Henley C, Gremu A, Sindano N, Chilengi R; AHI PHIT Partnership Collaborative. Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia. BMC Health Serv Res. 2017 Dec 21;17(Suppl 3):830. doi: 10.1186/s12913-017-2661-x. PMID: 29297319; PMCID: PMC5763308. Odhus CO, Kapanga RR, Oele E (2024) Barriers to and enablers of quality improvement in primary health care in low- and middle-income countries: A systematic review. PLOS Glob Public Health. 2024; doi.org/10.1371/journal.pgph.0002756 Gandra S, Alvarez-Uria G, Turner P, Joshi J, Limmathurotsakul D, van Doorn HR.2020.Antimicrobial Resistance Surveillance in Low- and Middle-Income Countries: Progress and Challenges in Eight South Asian and Southeast Asian Countries. Clin Microbiol Rev. 2020; doi.org/10.1128/cmr.00048-19 Iskandar, K., Molinier, L., Hallit, S. et al. Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture. Antimicrob Resist Infect Control . 2021; doi.org/10.1186/s13756-021-00931-w Global strategy on infection prevention and control. Geneva: World Health Organization; 2023. Licence: CC BY-NC-SA 3.0 IGO. Available from: https://cdn.who.int/media/docs/default-source/gsipc/who_ipc_global-strategy-for-ipc.pdf?sfvrsn=ebdd8376_4. Accessed 3 December 2023 Supriadi IR, Haanappel CP, Saptawati L, Widodo NH, Sitohang G, Usman Y et al. Infection prevention and control in Indonesian hospitals: identification of strengths, gaps, and challenges. Antimicrob Resist Infect Control. 2023; doi: 10.1186/s13756-023-01211-5. PMID: 36732802; PMCID: PMC9894741. Aghdassi SJS, Hansen S, Bischoff P, Behnke M, Gastmeier P. A national survey on the implementation of key infection prevention and control structures in German hospitals: results from 736 hospitals conducting the WHO Infection Prevention and Control Assessment Framework (IPCAF). Antimicrob Resist Infect Control. 2019; doi: 10.1186/s13756-019-0532-4. PMID: 31080588; PMCID: PMC6505265. Azak E, Sertcelik A, Ersoz G, Celebi G, Eser F, Batirel A et al. THIRG, Turkish Hospital Infection Research Group. Evaluation of the implementation of WHO infection prevention and control core components in Turkish health care facilities: results from a WHO infection prevention and control assessment framework (IPCAF)-based survey. Antimicrob Resist Infect Control. 2023; doi: 10.1186/s13756-023-01208-0. PMID: 36782267; PMCID: PMC9923650. Harun MGD, Anwar MMU, Sumon SA, Hassan MZ, Haque T, Mah-E-Muneer S et al. Infection prevention and control in tertiary care hospitals of Bangladesh: results from WHO infection prevention and control assessment framework (IPCAF). Antimicrob Resist Infect Control. 2022; doi: 10.1186/s13756-022-01161-4. PMID: 36203207; PMCID: PMC9535892. Global report on infection prevention and control. Geneva: World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO. Available from: https://iris.who.int/bitstream/handle/10665/354489/9789240051164-eng.pdf?sequence=1. Accessed 3 September 2023 Maaike S M van Mourik, Eli N Perencevich, Petra Gastmeier, Marc J M Bonten, Designing Surveillance of Healthcare-Associated Infections in the Era of Automation and Reporting Mandates, Clinical Infectious Diseases . 2018; doi: https://doi.org/10.1093/cid/cix835 Atreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ; Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control. Am J Infect Control. 2008; doi: 10.1016/j.ajic.2008.01.002. PMID: 18374211; PMCID: PMC7115272. Raupach-Rosin, H., Duddeck, A., Gehrlich, M. et al. Deficits in knowledge, attitude, and practice towards blood culture sampling: results of a nationwide mixed-methods study among inpatient care physicians in Germany. Infection . 2017; doi: https://doi.org/10.1007/s15010-017-0990-7 Dräger S, Giehl C, Søgaard KK, Egli A, de Roche M, Huber LC, Osthoff M. Do we need blood culture stewardship programs? A quality control study and survey to assess the appropriateness of blood culture collection and the knowledge and attitudes among physicians in Swiss hospitals. Eur J Intern Med. 2022; doi: 10.1016/j.ejim.2022.04.028. Epub 2022 Jun 14. PMID: 35715280. Yalçinkaya R, Öz FN, Erdoğan G, Kaman A, Aydın Teke T, Yaşar Durmuş S et al. Turkish pediatric residents' knowledge, perceptions, and practices of blood culture sampling. Arch Pediatr. 2021; doi: 10.1016/j.arcped.2021.02.013. Epub 2021 Mar 9. PMID: 33707101. Gebretekle GB, Mariam DH, Mac S, et al. Cost– utility analysis of antimicrobial stewardship programme at a tertiary teaching hospital in Ethiopia. BMJ Open 2021; doi:10.1136/ bmjopen-2020-047515 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2024 Read the published version in Antimicrobial Resistance & Infection Control → Version 1 posted Editorial decision: Revision requested 24 Oct, 2024 Reviews received at journal 14 Oct, 2024 Reviews received at journal 16 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers invited by journal 29 Aug, 2024 Editor assigned by journal 11 Aug, 2024 Submission checks completed at journal 11 Aug, 2024 First submitted to journal 10 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4891610","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349145493,"identity":"bb217609-5aaa-440e-8b30-ec3bb3d311b6","order_by":0,"name":"Srividya K. 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Bloodstream infections (BSIs) are among the most common HAIs in low- and middle-income countries (LMIC), prolong hospital stays, and increase mortality rates (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). 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 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). 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 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). 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 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). There is limited information available from LMICs on the impact of these HAI surveillance systems and the implementation challenges faced.\u003c/p\u003e \u003cp\u003eIn 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) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) to document BSI trends (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The BSI surveillance was implemented in intensive-care units (ICUs) in selected tertiary-care hospitals across the country.\u003c/p\u003e \u003cp\u003eAfter five years, we evaluated BSI surveillance in India\u0026rsquo;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.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, population, and period:\u003c/h2\u003e \u003cp\u003eWe 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOperational definitions\u003c/h2\u003e \u003cp\u003eHealthcare-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\u0026rsquo;s National Healthcare Safety Network (NHSN) case definition (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe followed the updated CDC Morbidity and Mortality Weekly Report (MMWR) guidelines 2001 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHAI Network\u0026rsquo;s BSI Surveillance system attributes, qualitative questions and data collection method, India, 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttribute and operational definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData source and data collection method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSimplicity\u003c/b\u003e: Simplicity of workflow and ease of implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Ease of collecting data (staff required/ time spent)\u003c/p\u003e \u003cp\u003e2. Proportion of surveillance staff who report applying BSI case definition as easy or very easy\u003c/p\u003e \u003cp\u003e3. Proportion of surveillance staff who report online reporting as easy or very easy\u003c/p\u003e \u003cp\u003e4. Average amount of time spent by surveyed staff in reporting one BSI case in the portal\u003c/p\u003e \u003cp\u003e4. Number of levels of reporting in the system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterview of Project coordinator\u003c/p\u003e \u003cp\u003eOnsite observations\u003c/p\u003e \u003cp\u003eSurvey of Surveillance staff and Data entry operator (DEO)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStability\u003c/b\u003e: System\u0026rsquo;s reliability, availability, and sustainability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Proportional of surveillance staff trained in BSI surveillance protocols\u003c/p\u003e \u003cp\u003e2. Proportion of sites where denominator data is collected everyday including holidays\u003c/p\u003e \u003cp\u003e3. Proportion of sites with 24 by 7 laboratory\u003c/p\u003e \u003cp\u003e4. Proportion of sites with LIS\u003c/p\u003e \u003cp\u003e5. Proportion of sites where surveillance staff have access to all positive cultures\u003c/p\u003e \u003cp\u003e6. Proportion of sites who review every positive culture at month-end to capture missing cases\u003c/p\u003e \u003cp\u003e7. Availably of the system since its inception in 2017\u003c/p\u003e \u003cp\u003e8. Proportion of sites with funding support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterview of key stakeholders\u003c/p\u003e \u003cp\u003eSurvey of surveillance staff and DEO\u003c/p\u003e \u003cp\u003eReview of network data\u003c/p\u003e \u003cp\u003eReview of site-visit records\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcceptability: W\u003c/b\u003eillingness of individuals and institutions to participate in the BSI surveillance network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of hospitals participating in surveillance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterview of Project Coordinator and site representatives\u003c/p\u003e \u003cp\u003eFGD of technical advisory team\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRepresentativeness\u003c/b\u003e: System\u0026rsquo;s ability to accurately describe BSIs over time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of febrile episodes (in patients admitted in surveillance ICUs) where a blood culture is collected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReview of patient case files during on-site visits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eData quality: C\u003c/b\u003eompleteness and validity of the captured data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData validity: Proportion of CSF with case definition applied correctly\u003c/p\u003e \u003cp\u003eData completeness: Proportion of CSF with 100% mandatory fields filled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecord review of CRF during site visits\u003c/p\u003e \u003cp\u003eReview of site-visit records\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTimeliness\u003c/b\u003e: System\u0026rsquo;s ability to detect BSI cases and outbreaks in timely fashion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Proportional of febrile episodes (in patients admitted in surveillance ICUs) where a blood culture is collected within 24 hours of the febrile episode\u003c/p\u003e \u003cp\u003e2. Proportion of sites reporting BSI data within 10 days of the reporting month\u003c/p\u003e \u003cp\u003e3. Proportion of quarterly reports submitted by the network within one month of the reporting quarter\u003c/p\u003e \u003cp\u003e4. Proportion of ICU physicians who received monthly feedback on their ICU\u0026rsquo;s BSI rate\u003c/p\u003e \u003cp\u003e4. Proportion of BSI outbreaks detected and controlled while still ongoing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReview of ICU patient case files\u003c/p\u003e \u003cp\u003eReview of HAI network database\u003c/p\u003e \u003cp\u003eReview of quaterly reports\u003c/p\u003e \u003cp\u003eSurvey of physicians\u003c/p\u003e \u003cp\u003eInterview of site representatives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSensitivity\u003c/b\u003e: System\u0026rsquo;s ability to detect BSI cases and outbreaks correctly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Proportional of BSI cases reported among all BSI cases detected during review of laboratory records\u003c/p\u003e \u003cp\u003e2. Monitoring trends of BSI\u003c/p\u003e \u003cp\u003e3. Number of early warning signals generated in the last one year (Jan-Dec 2021) and last one month (Dec 2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReview of patient case files and laboratory records during on-site visits\u003c/p\u003e \u003cp\u003eReview of network database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePositive Predictive Value\u003c/b\u003e: Probability that a detected BSI case is true case of BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProportion of true BSI cases among all BSI cases reported\u003c/p\u003e \u003cp\u003eMethod for confirming true cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReview of CRF during on-site visits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUsefulness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Monitor network-based BSI trends over time\u003c/p\u003e \u003cp\u003e2. Number of BSI outbreaks detected using surveillance data\u003c/p\u003e \u003cp\u003e3. Proportion of sites using data to improve IPC practices at their ICUs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReview of network database\u003c/p\u003e \u003cp\u003eSurvey of physicians\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOpen-ended questions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProbes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eData Source and data collection method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. How well is this system capturing BSIs?\u003c/p\u003e \u003cp\u003e2. Is the data generated by this system used?\u003c/p\u003e \u003cp\u003e3. Do you feel participation in BSI surveillance benefits the patient or your centre? How?\u003c/p\u003e \u003cp\u003e4. Do clinicians get feedback about their BSI rates\u003c/p\u003e \u003cp\u003e5. Is your feedback linked to IPC activities and QI initiatives- Could you give us a few examples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterview of Project Coordinator and site representatives\u003c/p\u003e \u003cp\u003eFGD of technical advisory team\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhat are some of the successes of this network according to you?\u003c/p\u003e \u003cp\u003e-Do you think it has led to QI initiatives/ any IPC practice change/ outbreaks detected/ capacity building of your IPC team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterview of Project Coordinator and site representatives\u003c/p\u003e \u003cp\u003eFGD of technical advisory team\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhat are some of the challenges?\u003c/p\u003e \u003cp\u003e-Areas where you wish things could be better- network level/facility level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterview of Project Coordinator and site representatives and FGD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData was collected from both the network level and tertiary-care hospitals, which is the reporting level used in document reviews, data extraction from the network database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://orcid.org/0009-0007-0188-6420\" target=\"_blank\"\u003ewww.haisindia.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.haisindia.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), surveys, semi-structured interviews, focus group discussions, and on-site visits. We developed structured questionnaires for interviews at the network level and 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.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNetwork level\u003c/strong\u003e \u003cp\u003eWe 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.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e 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-hour 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.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReporting Unit level\u003c/strong\u003e \u003cp\u003eWe 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.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWe 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 hours. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analyses\u003c/h2\u003e \u003cp\u003eAfter manually coding the transcribed qualitative data from interviews, we performed a thematic analysis. Quantitative data from monitoring indicators, surveys and document reviews 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDescription of the system\u003c/h2\u003e\n \u003cp\u003eThe 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. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This patient\u0026rsquo;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\u0026rsquo;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.\u003c/p\u003e\n \u003cp\u003eThe network received funding support from the US CDC under a cooperative agreement between 2017\u0026ndash;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\u0026rsquo;s internal budget.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvaluation of the system:\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eQuantitative results\u003c/h2\u003e\n \u003cp\u003eAt 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 \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvaluation results of HAI Network\u0026rsquo;s BSI Surveillance system attributes, India, 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttribute\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvidence collected\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall evaluation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSimplicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEase of collecting data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Minimum 2 full-time surveillance staff\u003c/p\u003e\n \u003cp\u003e\u0026bull; 80% staff spends\u0026thinsp;\u0026gt;\u0026thinsp;2 hours or more every day in collecting the 23 variables and the lab confirmation for case confirmation from paper-based reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime-consuming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSimple\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEase of applying case definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 15/20 (75%) staff rate it as very easy or easy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEasy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEase of online reporting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 18/20 (90%) rate it as very easy or easy\u003c/p\u003e\n \u003cp\u003e\u0026bull; 10\u0026ndash;15 minutes to submit one CRF, described as \u0026ldquo;user-friendly\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEasy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevels of reporting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Two a) Local (hospital administration), and b) National (AIIMS, New Delhi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEasy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eStability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReliability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; All 40 staff surveyed are trained in protocol\u003c/p\u003e\n \u003cp\u003e\u0026bull; All 21 sites reviewed collected denominator data on all days including weekends/holidays\u003c/p\u003e\n \u003cp\u003e\u0026bull; All 21 sites reviewed have access to a 24 by 7 working laboratory\u003c/p\u003e\n \u003cp\u003e\u0026bull; 16/21 sites reviewed have access to all positive cultures, required for classifying BSI type\u003c/p\u003e\n \u003cp\u003e\u0026bull; 5/21 sites reviewed reported not having LIS/ HMIS, reported using manual registers\u003c/p\u003e\n \u003cp\u003e\u0026bull; 15/21 (71%) sites review every positive culture at month-end to capture missing cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReliable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eStable if funding is available\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvailability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Available from 2017 including during second wave of COVID-19 in India\u003c/p\u003e\n \u003cp\u003e\u0026bull; Decreased reporting to 84/131 (64%) in ICUs and surveillance stopped in 22/39 (56%) sites seen briefly during April 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvailable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 26/39 (67%) of the sites are funded by US CDC\u003c/p\u003e\n \u003cp\u003e\u0026bull; Reporting decreased to 63/131 (48%) in the quarter 4, 2021 when funding was interrupted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFunding stability a concern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAcceptability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWillingness of stakeholders to participate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Started with 20 ICUs in 2017 and has increased to 131 ICUs in 2019\u003c/p\u003e\n \u003cp\u003e\u0026bull; Hospital administration of all 10 hospitals interviewed accepts this system is required to control multi-drug resistant pathogens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of physicians accepting feedback from the surveillance system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 90% physicians surveyed starting a QI initiative in their ICU based on the feedback received from surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRepresentativeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation representative of the participating hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 32/58 (55%) of the febrile episodes reviewed had their blood cultured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot representative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot representative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eData Quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData validity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 280/284 (98%) of CRFs reviewed have correctly applied the case definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eData quality is good\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData completeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 259/284 (91%) of CRFs have data filled in each data va without any missing details\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplete\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eTimeliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 27/61 (44%) of febrile episodes reviewed were cultured (blood collection)\u0026thinsp;\u0026lt;\u0026thinsp;24 hrs of fever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot timely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eBlood culture collection and feedback to ICU physicians is not timely\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReporting to network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 36/39 (92%) sites reported data within 10 days of the reporting month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTimely\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDissemination to key stakeholders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 6/10 (60%) ICU physicians surveyed reported getting consistent monthly feedback\u003c/p\u003e\n \u003cp\u003e\u0026bull; 13/14 (93%) quarterly reports shared by AIIMS with all key stakeholders within one month of the reporting quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot timely\u003c/p\u003e\n \u003cp\u003eTimely\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDetection of BSI outbreaks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Sites not comfortable in sharing information regarding outbreaks detected in their hospitals, hence this information could not be captured\u003c/p\u003e\n \u003cp\u003e\u0026bull; One outbreak of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e detected in the network using surveillance data and controlled (retrieved from published data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e------\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue cases detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 26/26 (100%) cases reviewed during site visit were correctly identified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonitoring trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; System can identify BSI trends from May 2017 to December 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarly warning signals generated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; System has generated 684 ICU specific BSI rate alerts from May 2017 to December 2021\u003c/p\u003e\n \u003cp\u003e\u0026bull; Alerts are generated when ICU specific BSI rates are \u0026gt;\u0026thinsp;20 for that month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive Predictive Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of true cases with confirmed BSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; 26/26 (100%) cases reported as BSI for the months of November 2021 to March 2022 were reviewed and found to be true cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood PPV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonitoring trends\u003c/p\u003e\n \u003cp\u003eDetecting outbreaks\u003c/p\u003e\n \u003cp\u003eImproving IPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull; Quarterly trend analysis done for every quarter from Jan 2018 to Dec 2018\u003c/p\u003e\n \u003cp\u003e\u0026bull; Three outbreaks detected including one outbreak of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e using surveillance data and controlled (retrieved from published data)\u003c/p\u003e\n \u003cp\u003e\u0026bull; 12/39 (31%) sites have completed/ongoing QI projects to improve BSI rates\u003c/p\u003e\n \u003cp\u003e\u0026bull; 7/10 (70%) physicians surveyed reported having increased adherence to recommended Central Line insertion and maintenance practices following feedback from the system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUseful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUseful\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSimplicity\u003c/h2\u003e\n \u003cp\u003eAmong the surveyed staff, 83% rated the modified NHSN case definitions as easy to apply and found the online reporting platform user-friendly.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStability\u003c/h2\u003e\n \u003cp\u003eThe 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. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). 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.\u003c/p\u003e\n \u003cp\u003eAmong 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).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eRepresentativeness\u003c/h2\u003e\n \u003cp\u003eAmong the 135 ICU patient case files reviewed, blood culture was collected within 24 hours 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 hours 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eData quality\u003c/h2\u003e\n \u003cp\u003eAmong the 284 CRFs reviewed, 91% had complete data, and 98% had correctly applied the BSI case definition.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eTimeliness\u003c/h2\u003e\n \u003cp\u003eFrom 2017, the network submitted 14 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity\u003c/h2\u003e\n \u003cp\u003eAmong 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003ePositive Predictive Value\u003c/h2\u003e\n \u003cp\u003eAll 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%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eUsefulness\u003c/h2\u003e\n \u003cp\u003eUsing 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 \u003cem\u003eBurkholderia cepacia\u003c/em\u003e, were detected and controlled (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e). 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eQualitative results\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003eSemi-structured interviews and FGD:\u003c/h2\u003e\n \u003cp\u003eAt the network level, we were able to conduct 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 were able to conduct ten interviews (one per site, with one to two staff participating in each interview). 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 (Box 1 and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThemes, codes and representative quotes obtained from interviews, HAI Network\u0026rsquo;s BSI Surveillance evaluation, India, 2022\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThemes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCodes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuotes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloped a resource-appropriate case definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple easy to implement case-definition\u003c/p\u003e\n \u003cp\u003eApplicable to Indian hospitals\u003c/p\u003e\n \u003cp\u003eSensitive case definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;The team developed a simple case definition, clear SOP, which helped us train sites well\u0026rdquo; #ProgramCoordinator\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;I\u0026rsquo;m pretty confident that in a given unit, more than 80\u0026ndash;90% of the BSIs were being captured, pre-COVID-19 .\u0026rdquo; #TechnicalTeam01\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Modified case definition is sensitive in capturing true CLABSIs: \u0026ldquo;when you do these focussed QI projects, their CLABSI rates go down significantly, which tells us there\u0026rsquo;s probably not a huge definitional gap where we are finding BSI and classifying them as CLABSI when they are really secondary BSI\u0026rdquo; #TechnicalTeam02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstablished a representative network-based surveillance to detect trends and investigate outbreaks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImplementing a surveillance program across different states\u003c/p\u003e\n \u003cp\u003eRepresentative of different patient populations within ICUs\u003c/p\u003e\n \u003cp\u003eRepresentation of public and private hospitals\u003c/p\u003e\n \u003cp\u003eGeographically representative\u003c/p\u003e\n \u003cp\u003eOutbreaks detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;(we have been successful in helping) how to implement a surveillance and prevention program that can be implemented consistently across a network of 39\u0026thinsp;+\u0026thinsp;hospitals in a relatively well-resourced setting, it\u0026rsquo;s a huge success.\u0026rdquo; #TechnicalTeam01\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Good representation of patient population- medical, surgical, paediatric, neonatal and oncology\u0026rdquo; #TechnicalTeam01\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;When it comes to tertiary care hospitals in the country, we are fairly representative as we have geographical diversity, also a mix of private and public hospitals\u0026rdquo; #TechnicalTeam02\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;We have used data from this surveillance to investigate HAI outbreaks, and control them in time\u0026rdquo; #TechnicalTeam03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnsured regular ongoing IPC trainings with Quality Improvement (QI) projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShifted to online training\u003c/p\u003e\n \u003cp\u003eQuick dissemination of COVID-19 IPC measures in network\u003c/p\u003e\n \u003cp\u003eEvery infection is accounted for\u003c/p\u003e\n \u003cp\u003eTargeted recommendation to avoid each infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;This IPC training and surveillance helped us in correct handling of COVID-19 patients during the pandemic\u0026rdquo; #ICN02\u0026rdquo;\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;With QI initiatives, I can say that, first we were just recording and showing the rates in the HICC (hospital infection control committee) meeting, now we are asking questions for every BSI reported and suggesting ways to avoid that infection.\u0026rdquo; #ResearchFellow06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited human resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited staff allotted to surveillance\u003c/p\u003e\n \u003cp\u003eStaff attrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Limited staff allotted to surveillance, so we are not expanding surveillance to all ICUs within the hospital\u0026rdquo; #Microbiologist03\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Difficult to retain trained staff once funding ends, impacts our data collection\u0026rdquo; #Microbiologist05\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Staff turnover impacts timeliness of data upload\u0026rdquo; #Microbiologist02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of digitalization of medical and laboratory records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient tracking outside of ICU is difficult\u003c/p\u003e\n \u003cp\u003eTracking different culture reports is difficult\u003c/p\u003e\n \u003cp\u003eEnd-of month validation difficult\u003c/p\u003e\n \u003cp\u003eDeciphering handwritten notes is challenging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Tracking samples from other body sites is difficult as no common book in ICU which has a list of all cultures sent for that day\u0026hellip;.it takes a lot of man-hours\u0026rdquo; #ICN03\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Accessing and deciphering handwritten notes from manual registers is time-consuming and inefficient\u0026rdquo; #ICN01.\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Monthly validation of all blood cultures is not always done, difficult in centres with no LIS\u0026rdquo; # DEO (02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable blood culturing practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of availability of culture bottles\u003c/p\u003e\n \u003cp\u003eStaff shortage in ICU\u003c/p\u003e\n \u003cp\u003ePatient unable to pay for blood cultures\u003c/p\u003e\n \u003cp\u003eClinician\u0026rsquo;s judgement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Blood culture is paid, in our hospital social service organizations help poor patients but these are not present in all hospitals\u0026rdquo; #Microbiologist01 (private hospital)\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;In our hospital, blood culture is free, but sometimes patients must buy supplies\u0026rsquo;# Microbiologist03 (public hospital)\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;This is a tertiary care hospital, patients have already taken a lot of antibiotics and come in very sick, so we directly start on antibiotics empirically, only when patient does not respond, we send cultures.\u0026rdquo; #Microbiologist01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInconsistent information sharing and data use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStaff not analysing data\u003c/p\u003e\n \u003cp\u003eAnalyzed data not shared with physicians\u003c/p\u003e\n \u003cp\u003ePhysicians may not accept recommendations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Even though they have lot of AST data, they (sites) are not analysing this data for antibiograms, nor converting it into information and then sharing it (with their physicians)\u0026rdquo; # TechnicalTeam02\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Physicians many times do not accept the results of the data, or the IPC measures suggested possibly due to a difference in surveillance and clinical definitions\u0026rdquo; #TechnicalTeam03\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;there\u0026rsquo;s a kind of hierarchy and defensiveness that can exist between the microbiology department and the clinical side, that\u0026rsquo;s been a bottleneck to the data actually being used.\u0026rdquo; # TechnicalTeam02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFunding and sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFunding affects stability.\u003c/p\u003e\n \u003cp\u003eFurther expansion of network depends on funding.\u003c/p\u003e\n \u003cp\u003eInstitutionalization of IPC staff helps stability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Difficult to retain trained staff once funding ends, impacts our data collection\u0026rdquo; #Microbiologist06\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;If we have allocated budget either internal or external, we plan to expand the network by creating regional trainers and including tier 2 (secondary care) hospitals in the network.\u0026rdquo; #ProgramCoordinator\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Hospitals with dedicated IPC staff continued surveillance despite drop in funding\u0026rdquo; #TechnicalTeam03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImpact of the COVID pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisrupted regular trainings and expansion\u003c/p\u003e\n \u003cp\u003eStaff attrition\u003c/p\u003e\n \u003cp\u003eReduced reporting units\u003c/p\u003e\n \u003cp\u003eLimited access of staff to COVID ICUs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;We are confident that in a given unit, more than 80\u0026ndash;90% of the BSIs were being captured, pre-COVID-19 pandemic #TechnicalTeam01\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;During the COVID-19 pandemic nobody was willing to support any intervention to improve CLABSI (infection rates) as there was staff shortage, now is not the right time, they said\u0026rdquo; #ReseachFellow05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness and acceptance of BSI surveillance among participating sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurveillance is useful\u003c/p\u003e\n \u003cp\u003eSites eager to join network\u003c/p\u003e\n \u003cp\u003eIncrease in blood cultures received after joining surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;For a public hospital, where we have so much patient load, because of this surveillance, we are able to focus on areas to improve,\u0026rdquo; #Microbiologist02\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;They (ICU physicians) have come to realise that without IPC, (BSI) rates cannot be reduced\u0026rdquo; #Microbiologist03\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Number of blood culture samples have increased significantly in the last 2 years after our trainings\u0026rdquo; #ICN02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eBox 1. Description of Themes from Qualitative Analysis, BSI Surveillance Evaluation, India, 2022\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. \u003cstrong\u003eDeveloped a context specific resource-appropriate case definition.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe program coordinator stated that the technical team had developed a simple to understand case-definition and clear SOP applicable in Indian context which helped train the sites quickly. Site staff reported the case definition as easy to apply.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e2. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eEstablished surveillance through a network-based approach.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTechnical advisers stated that it was challenging initially to bring hospitals from different states on a common platform to agree on a common case definition and protocols, but they succeeded by starting with a small number of committed sites and gradually enrolling more sites to establish a network that consistently reported BSI trends. They considered doing so in a limited timeframe of five years, a success.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e3. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eRegular IPC trainings and use of QI to improve IPC\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSites reported the network gave them regular virtual trainings which kept them up to date during the early days of the pandemic with the evolving IPC guidelines. Staff at one of sites which implemented a QI project described the regular ongoing IPC trainings as very helpful in understanding and using data for improving patient safety.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e4. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAwareness and acceptance of BSI surveillance among participating sites\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMicrobiology staff reported increase in the number of blood cultures received in their laboratory for pathogen identification after their hospital joined the network and believe this surveillance can help prioritize IPC measures in public-funded resource-limited settings. The program coordinator stated that willingness to join this network is high as hospitals are actively soliciting opportunity to participate.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e5. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLimited human resources\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFunded public hospitals reported that limited staff allotted to surveillance was the reason for not expanding surveillance to all ICUs within the hospital. They also faced challenges in retaining trained staff once funding ended, impacting data collection and reporting. Non-funded public hospitals (with institutional IPC staff performing surveillance activities) also reported staff shortages. However, they did not report any interruption in surveillance activities. In response to this, the technical advisors stated that the network\u0026rsquo;s capacity to provide resources for staffing may be limited and it is ultimately up to individual hospitals to allocate additional staff for surveillance and IPC.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e6. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLack of digitalization of medical and laboratory records\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSites without a Health Management Information System (HMIS) reported challenges in tracking patients outside of ICUs using paper-based forms. Sites without a Laboratory Information System (LIS) reported challenges in tracking multiple positive cultures for a single patient, and challenges in performing monthly validation of all reported BSI cases. They described accessing and deciphering handwritten notes in laboratory notebooks (that maintain a list of positive cultures and patient details) as difficult and time-consuming.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e7. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eVariable blood culturing practices\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStaff (both at private and public hospitals) reported that patient\u0026rsquo;s blood culture was collected based on the treating physician\u0026rsquo;s decision and not according to the clinical standard of care mentioned in the surveillance protocols (at-least two blood cultures drawn for bacterial cultures when a patient has a febrile episode). Treating physicians reported culturing blood only when their patient does not respond to their empiric antibiotic therapy due to high cost of cultures. Public hospital staff (both funded and non-funded) reported several other reasons affecting culture collection including lack of availability of culture bottles, staff shortage in ICUs/ lab and inability of the patient to pay for blood cultures where these services were paid.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e8. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eInconsistent information sharing and data use.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe technical advisers stated many challenges to using data for action at the facility level by the site staff including a lack of training, the analysed data not being shared with physicians, and physicians not accepting the results of the aggregated surveillance data (in few sites) as the clinical and surveillance case definitions varied.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e9. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eFunding and sustainability\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAccording to the project coordinator, the sustainability and expansion of this system depended on securing sustainable funding. Staff at one of the sites visited stated its difficult to retain trained staff once project funding ends and this will impact their data collection and reporting.\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e10. \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eImpact of the COVID-19 Pandemic on the surveillance\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAccording to the technical advisors, during the early months of the COVID pandemic, the network transitioned from physical to virtual training. Interviewed staff reported that during the pandemic, IPC and best practice updates provided by the network coordinators and CDC team were received in a timely manner and applied. According to the program coordinator, surveillance stopped in many sites between March 2020-April 2020 as staff were reassigned for COVID duties. The loss of trained staff, along with the designation of many ICUs as COVID-19 ICUs with limited access to surveillance staff, forced sites to reduce the number of reporting units.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMixed-methods integration\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe consolidated the quantitative attributes, their indicators, and the qualitative themes under best practices, challenges, and opportunities (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). 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.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntegration of qualitative themes and quantitative indicators, HAI Network\u0026rsquo;s BSI surveillance evaluation, India, 2022\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDomain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQualitative themes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorresponding quantitative indicator result\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBest practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeveloped a resource-appropriate case definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimplicity: easy to apply case definition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstablished a network-based surveillance to detect BSI trends and outbreaks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStability: All 21 (100%) sites checked had access to 24 by 7 lab facility\u003c/p\u003e\n \u003cp\u003eSensitivity and PPV: Checked events had 100% PPV and 100% sensitivity\u003c/p\u003e\n \u003cp\u003eSensitivity: Sensitive in detecting BSI trends from May 2017 to Dec 2021\u003c/p\u003e\n \u003cp\u003eOne outbreak of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e detected in the network using surveillance data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnsured regular ongoing IPC trainings with Quality Improvement (QI) projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptability: 90% physicians surveyed starting a QI initiative in their ICU based on the feedback received from surveillance\u003c/p\u003e\n \u003cp\u003eUsefulness: 70% reported the feedback and trainings affecting care in the ICU by improving documentation of, and increasing adherence to, recommended central-line practices, 31% sites implemented one or more QI measures to decrease BSI rates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChallenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited human resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of digitalization of medical and laboratory records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStability: 76% sites had access to all positive cultures, required for classifying BSI type, rest 24% did not have LIS, recorded lab results in manual registers\u003c/p\u003e\n \u003cp\u003eStability: 71% sites capture missing cases at end of month\u003c/p\u003e\n \u003cp\u003eSimplicity: 80% of surveyed surveillance staff reported spending two hours or more per day collecting data from paper-based reports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable blood culturing practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRepresentativeness: 55% had their blood cultured with 44% cultured within 24 hours of a febrile episode\u003c/p\u003e\n \u003cp\u003eSurvey: 50% physicians reported culturing 80% of the febrile patients\u003c/p\u003e\n \u003cp\u003eTimeliness: 44% of the febrile episodes reviewed had blood cultured within 24 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInconsistent information sharing and data use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTimeliness: 6/10 (60%) ICU physicians reported getting consistent monthly feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFunding and sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStability: reporting ICUs decreased to 63/131 (48%) and reporting sites to 30/39 (77%) during quarter 4, 2021 when funding was interrupted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImpact of the COVID-19 pandemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStability: Surveillance stopped in 22/39 (56%) sites during March-April 2020 as staff were absorbed in COVID-19 duties\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpportunities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness and acceptance of BSI surveillance among participating sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptability: Acceptable among stakeholders at national and site level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn all domains, the evidence from surveys, interviews, and document reviews aligned with each other except in blood culturing practices. While the surveyed physician reported culturing 80% of febrile patients, document review indicated a figure of 44%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur 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.\u003c/p\u003e \u003cp\u003eThe 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 getting together hospitals with varying capacities and from different Indian states on a common platform. They have adapted CDC\u0026rsquo;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 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eOur 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 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and aligns with WHO guidance to allot dedicated funding to build IPC programs with capacity to conduct HAI surveillance (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur 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 (\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). 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\u0026rsquo;s Global IPC report (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). 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 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur 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 (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). 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 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral 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.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAn 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted this study as part of the monitoring and evaluation of a national public health surveillance project titled “Capacity Building and Strengthening of Hospital Infection Control to Detect and Prevent Antimicrobial Resistance in India”. The project received ethical approval (IEC/NP-386/10.09.2015) from the Institutional Ethics Committee, All India Institute of Medical Sciences (AIIMS), New Delhi, and approval from the Health Ministry Screening Committee (HMSC), India. We obtained permission from the HAI surveillance network’s program coordinators before reaching out to network sites, and study participants provided consent via email for interviews, FGD, and surveys.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this study are included within this published article (and its additional files).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests to declare relevant to this article's content.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funding was received to conduct this study or to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthors contribution\u003c/h2\u003e\n\u003cp\u003eSKV, VAS, DV, PMal, and TD conceived the study design. SKV acquired on-field data. KW and PMath approved the acquisition of data. VAS and PMath supervised the study. SKV, VAS, DV, PMal, AV and TD analyzed the data and interpreted the results. SKV wrote the original manuscript text and prepared the figures and tables. SKV, VAS, DV, PMal, AV, TD, KW and PMath revised and edited the manuscript. \u0026nbsp;All authors reviewed the manuscript, approved the submitted version, and agreed to be personally accountable for the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge all project staff and PI of the “Capacity Building and Strengthening of Hospital Infection Control to Detect and Prevent Antimicrobial Resistance in India” supported by the U.S. Centers for Disease Control and Prevention, Global Health Security Agenda cooperative agreement 1U2GGH001869 (2016-2021) \u0026amp; NU2HGH000088-01-00 (2021-22)\u0026nbsp;including Mr. Sharad Srivastava, Statistician, Dr. Rasna Parveen, Scientist C, Mr. Naresh, Field Investigator, Mr. Pawan Kashik and Infection Control Nurses at AIIMS, New Delhi.\u0026nbsp;We acknowledge the support of\u0026nbsp;Dr Camilla Rodriguez, Head of Department, Microbiology, PD Hinduja Hospital, Mumbai, Ms. Julliah Chelliah, Senior Research Fellow, \u0026nbsp;Dr Veena Kumari, Head of Department, Microbiology, NIMHANS, Bangalore and their Infection Control Nurses and Dr Rajni Gaind, Head of Department, Microbiology, Safdarjung Hospital, New Delhi, Dr Rushika Saksena, and team.\u0026nbsp;We acknowledge the support of Mathew Hudson, EIS Officer, DHQP, CDC Atlanta, USA, and Ms. Dorothy Southern, Scientific Writing Advisor, SAFETYNET.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. CDC or the U.S. Department of Health and Human Services.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eReport on the Burden of Endemic Health Care-Associated Infection Worldwide. 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Clin Microbiol Rev. 2020; doi.org/10.1128/cmr.00048-19\u003c/li\u003e\n \u003cli\u003eIskandar, K., Molinier, L., Hallit, S. \u003cem\u003eet al.\u003c/em\u003e Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture. \u003cem\u003eAntimicrob Resist Infect Control\u003c/em\u003e. 2021; doi.org/10.1186/s13756-021-00931-w\u003c/li\u003e\n \u003cli\u003eGlobal strategy on infection prevention and control. Geneva: World Health Organization; 2023. Licence: CC BY-NC-SA 3.0 IGO. Available from: https://cdn.who.int/media/docs/default-source/gsipc/who_ipc_global-strategy-for-ipc.pdf?sfvrsn=ebdd8376_4. Accessed 3 December 2023\u003c/li\u003e\n \u003cli\u003eSupriadi IR, Haanappel CP, Saptawati L, Widodo NH, Sitohang G, Usman Y et al. Infection prevention and control in Indonesian hospitals: identification of strengths, gaps, and challenges. Antimicrob Resist Infect Control. 2023; doi: 10.1186/s13756-023-01211-5. PMID: 36732802; PMCID: PMC9894741.\u003c/li\u003e\n \u003cli\u003eAghdassi SJS, Hansen S, Bischoff P, Behnke M, Gastmeier P. A national survey on the implementation of key infection prevention and control structures in German hospitals: results from 736 hospitals conducting the WHO Infection Prevention and Control Assessment Framework (IPCAF). Antimicrob Resist Infect Control. 2019; doi: 10.1186/s13756-019-0532-4. PMID: 31080588; PMCID: PMC6505265.\u003c/li\u003e\n \u003cli\u003eAzak E, Sertcelik A, Ersoz G, Celebi G, Eser F, Batirel A et al. THIRG, Turkish Hospital Infection Research Group. Evaluation of the implementation of WHO infection prevention and control core components in Turkish health care facilities: results from a WHO infection prevention and control assessment framework (IPCAF)-based survey. Antimicrob Resist Infect Control. 2023; doi: 10.1186/s13756-023-01208-0. PMID: 36782267; PMCID: PMC9923650.\u003c/li\u003e\n \u003cli\u003eHarun MGD, Anwar MMU, Sumon SA, Hassan MZ, Haque T, Mah-E-Muneer S et al. Infection prevention and control in tertiary care hospitals of Bangladesh: results from WHO infection prevention and control assessment framework (IPCAF). Antimicrob Resist Infect Control. 2022; doi: 10.1186/s13756-022-01161-4. PMID: 36203207; PMCID: PMC9535892.\u003c/li\u003e\n \u003cli\u003eGlobal report on infection prevention and control. Geneva: World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO. Available from: https://iris.who.int/bitstream/handle/10665/354489/9789240051164-eng.pdf?sequence=1. Accessed 3 September 2023\u003c/li\u003e\n \u003cli\u003eMaaike S M van Mourik, Eli N Perencevich, Petra Gastmeier, Marc J M Bonten, Designing Surveillance of Healthcare-Associated Infections in the Era of Automation and Reporting Mandates, \u003cem\u003eClinical Infectious Diseases\u003c/em\u003e. 2018; doi: https://doi.org/10.1093/cid/cix835\u003c/li\u003e\n \u003cli\u003eAtreja A, Gordon SM, Pollock DA, Olmsted RN, Brennan PJ; Healthcare Infection Control Practices Advisory Committee. Opportunities and challenges in utilizing electronic health records for infection surveillance, prevention, and control. Am J Infect Control. 2008; doi: 10.1016/j.ajic.2008.01.002. PMID: 18374211; PMCID: PMC7115272.\u003c/li\u003e\n \u003cli\u003eRaupach-Rosin, H., Duddeck, A., Gehrlich, M. \u003cem\u003eet al.\u003c/em\u003e Deficits in knowledge, attitude, and practice towards blood culture sampling: results of a nationwide mixed-methods study among inpatient care physicians in Germany. \u003cem\u003eInfection\u003c/em\u003e. 2017; doi: https://doi.org/10.1007/s15010-017-0990-7\u003c/li\u003e\n \u003cli\u003eDr\u0026auml;ger S, Giehl C, S\u0026oslash;gaard KK, Egli A, de Roche M, Huber LC, Osthoff M. Do we need blood culture stewardship programs? A quality control study and survey to assess the appropriateness of blood culture collection and the knowledge and attitudes among physicians in Swiss hospitals. Eur J Intern Med. 2022; doi: 10.1016/j.ejim.2022.04.028. Epub 2022 Jun 14. PMID: 35715280.\u003c/li\u003e\n \u003cli\u003eYal\u0026ccedil;inkaya R, \u0026Ouml;z FN, Erdoğan G, Kaman A, Aydın Teke T, Yaşar Durmuş S et al. Turkish pediatric residents\u0026apos; knowledge, perceptions, and practices of blood culture sampling. Arch Pediatr. 2021; doi: 10.1016/j.arcped.2021.02.013. Epub 2021 Mar 9. PMID: 33707101.\u003c/li\u003e\n \u003cli\u003eGebretekle GB, Mariam DH, Mac S, et al. Cost\u0026ndash; utility analysis of antimicrobial stewardship programme at a tertiary teaching hospital in Ethiopia. BMJ Open 2021; doi:10.1136/ bmjopen-2020-047515\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"antimicrobial-resistance-and-infection-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aric","sideBox":"Learn more about [Antimicrobial Resistance and Infection Control](http://aricjournal.biomedcentral.com/)","snPcode":"13756","submissionUrl":"https://submission.nature.com/new-submission/13756/3","title":"Antimicrobial Resistance \u0026 Infection Control","twitterHandle":"@ARICJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Healthcare-associated infection (HAI) surveillance in developing countries, Sepsis, Surveillance system, Patient safety, Cross-infection, Nosocomial infections","lastPublishedDoi":"10.21203/rs.3.rs-4891610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4891610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHealthcare-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\u0026rsquo;s ability to detect BSI to identify best practices, challenges, and opportunities in its implementation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003e 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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eImplementing 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.\u003c/p\u003e","manuscriptTitle":"Implementing a Healthcare-Associated Bloodstream Infection Surveillance Network in India: a Mixed-Methods Study on the Best Practices, Challenges and Opportunities, 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-08 18:45:32","doi":"10.21203/rs.3.rs-4891610/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-24T09:27:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-14T18:52:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T03:32:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128262233359281645317812594465673276855","date":"2024-09-03T20:37:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232133538625285208082738227662939019676","date":"2024-08-30T10:05:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-29T06:19:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-12T01:40:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-12T01:39:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Antimicrobial Resistance \u0026 Infection Control","date":"2024-08-10T11:45:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"antimicrobial-resistance-and-infection-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aric","sideBox":"Learn more about [Antimicrobial Resistance and Infection Control](http://aricjournal.biomedcentral.com/)","snPcode":"13756","submissionUrl":"https://submission.nature.com/new-submission/13756/3","title":"Antimicrobial Resistance \u0026 Infection Control","twitterHandle":"@ARICJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"57dd4e09-5a3c-4691-be02-3640e8ded258","owner":[],"postedDate":"September 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:07:20+00:00","versionOfRecord":{"articleIdentity":"rs-4891610","link":"https://doi.org/10.1186/s13756-024-01501-6","journal":{"identity":"antimicrobial-resistance-and-infection-control","isVorOnly":false,"title":"Antimicrobial Resistance \u0026 Infection Control"},"publishedOn":"2024-12-02 15:58:03","publishedOnDateReadable":"December 2nd, 2024"},"versionCreatedAt":"2024-09-08 18:45:32","video":"","vorDoi":"10.1186/s13756-024-01501-6","vorDoiUrl":"https://doi.org/10.1186/s13756-024-01501-6","workflowStages":[]},"version":"v1","identity":"rs-4891610","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4891610","identity":"rs-4891610","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}