A multi-seasonal mixed-method point-prevalence study of antibiotic prescription patterns in a tertiary healthcare facility in India

A multi-seasonal mixed-method point-prevalence study of antibiotic prescription patterns in a tertiary healthcare facility in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A multi-seasonal mixed-method point-prevalence study of antibiotic prescription patterns in a tertiary healthcare facility in India Vinay Modgil, Sundeep Sahay, Arunima Mukherjee, Rashmi Surial, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9029871/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Point prevalence surveys (PPS) are a core tool of the WHO Global Action Plan on antimicrobial resistance (AMR), yet their implementation in low- and middle-income countries (LMICs) remains limited by weak prescribing and surveillance systems. We conducted a multiseasonal mixed-method PPS, integrating quantitative prescribing data with qualitative ward observations, and the behavioural and operational factors shaping antibiotic prescribing and use of culture testing in a tertiary-care hospital in northern India. Methods A hospital-wide PPS was conducted across autumn (November 2023), summer (April 2024), monsoon (August 2024), and winter (January 2025), with each phase comprising a two-week data collection episode, following WHO Global-PPS methodology. Data were collected from five inpatient departments (Medicine, Surgery, Obstetrics-Gynaecology, adult, paediatric, and neonatal intensive care units (ICUs) using standardised forms. Quantitative data on indications, routes, and AWaRe categories were supplemented with ward observations and inpatient follow-up to assess culture testing, antibiotic sensitivity test (AST) use, and treatment modifications. Results A total of 1,680 inpatients were surveyed. Ceftriaxone (30–33%), Piperacillin–tazobactam (9–20%), and Doxycycline (8–16%) were the top prescribed antibiotics. Azithromycin use dropped sharply after the first phase. Amikacin (6–9%) and Meropenem prescribing (4–7%) remained low. Empirical prescribing dominated (63–67%), while culture-guided therapy remained ≤ 6%. Over 80% of antibiotics were given parenterally. Watch antibiotics accounted for 46–56% of prescriptions, Access 35–51%, and Reserve ≤ 4%. Prophylactic use ranged from 24–30%, and combination therapy was common in the ICU. Clinical diagnoses showed seasonal variation, with gastrointestinal (20–22%) and chronic conditions (17–23%) most frequent, and respiratory infections (3–9%) peaking in monsoon and winter. Antibiotic modification following AST occurred in only 4.7–6.5% of cases. Qualitative findings highlighted stock-outs of oral antibiotics, delays in culture sampling, and documentation gaps during patient transfers, limited stewardship activities, collectively reinforcing broad-spectrum empirical use. Conclusions This PPS found high empirical and broad-spectrum antibiotic use, limited culture-based prescribing, and systemic gaps hindering stewardship. Strengthening diagnostic access and use, ensuring drug availability, and embedding multidisciplinary stewardship teams with real-time feedback are essential to promote evidence-based prescribing in resource-limited settings. Clinical Pharmacology Antimicrobial stewardship point prevalence survey antibiotic prescription patterns AWaRe framework culture-based practices Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Point prevalence surveys (PPS) are widely used to monitor antimicrobial prescribing practices among hospital inpatients, providing a structured and relatively cost-effective approach to generating standardized data on antibiotic use, particularly in resource-constrained settings (WHO, 2019; Versporten et al., 2018 ). The World Health Organization (WHO) promotes PPS as a core surveillance tool within its Global Action Plan on antimicrobial resistance (AMR), recognising that hospital-level data on antimicrobial use remain limited across many low- and middle-income countries (LMICs), despite their central role in antimicrobial stewardship interventions. While PPS methodologies have been standardised and benchmarked through large surveillance initiatives, including those in high-income settings, their adaptation and application are especially critical within LMIC health systems (Zarb et al., 2011). In many LMICs, routine recording of antimicrobial-related transactional data—including prescribing, dispensing, and consumption remains limited, contributing to poor visibility of antimicrobial use patterns. In settings where electronic surveillance systems are underdeveloped, PPS offers a pragmatic strategy for generating real-time data on antimicrobial use among inpatient departments (IPD) (Holt et al.,2025 ) . Consequently, PPS has been widely implemented across diverse LMIC contexts, including India, Mexico, and other comparable settings (Pauwels et al., 2025 ; Sharma et al., 2022 ). Evidence from PPS conducted across India and other LMICs consistently highlights several dominant prescribing patterns. First, a high proportion of hospitalised patients receive at least one antimicrobial, often exceeding half of all inpatients, reflecting extensive empirical use in tertiary care settings (Bhattacharjee et al., 2025). Second, Watch-category antibiotics, particularly broad-spectrum agents such as third-generation cephalosporins, account for a substantial share of prescriptions, raising concerns regarding resistance selection and stewardship alignment (Moja et al., 2024 ). Third, despite the availability of microbiology services in many tertiary hospitals, culture and antimicrobial susceptibility testing (AST), guided prescribing remains limited, with empirical therapy predominating across wards and clinical indications (Modgil et al., 2025 ). Despite its widespread use, the utility of the WHO PPS framework is constrained by several implementation challenges in LMIC settings, including incomplete documentation, inter-observer variation, and fragmented patient records, all of which can compromise data quality. Systematic reviews further indicate that LMICs bear a disproportionately high burden of healthcare-associated infections, with Gram-negative pathogens dominating and empirical prescribing substantially exceeding that observed in high-income countries (Zumaya-Estrada et al., 2021 ). These findings underscore both the adaptability of PPS and the structural constraints that limit its interpretive value in settings with restricted diagnostic and information infrastructure. Despite these patterns, a central paradox emerges. In outpatient settings, empirical prescribing is often attributed to limited diagnostic access. In contrast, inpatient departments of tertiary hospitals typically have greater access to microbiological capacity, where AST-informed prescribing would be expected to play a more prominent role in guiding rational and targeted therapy. Yet PPS from Indian hospitals consistently demonstrate that empirical antibiotic use continues to dominate even in these inpatient settings. This disconnect between available diagnostic capacity and its limited influence on prescribing decisions highlights the need to better understand how AST is incorporated or not into routine clinical practice (Wojcik et al., 2021 ). In addition, most PPS conducted in India and other LMICs remain single-day, cross-sectional assessments. Such snapshot designs provide limited insight into temporal changes in prescribing behaviour, seasonal variation in infection burden, and how antimicrobial use evolves following treatment initiation, thereby constraining the utility of PPS for stewardship planning and policy development. To address these limitations and enhance the analytical value of PPS in understanding antimicrobial prescribing practices, this study examines two key research questions: (i) how antimicrobial prescribing patterns vary across patient groups, wards, and seasons in a tertiary-care hospital; and (ii) how diagnostic practices, particularly culture testing and AST, shape prescribing decisions over time. The present study implements a four-season, mixed-method PPS in a tertiary-care public hospital in northern India. By integrating quantitative PPS data with qualitative ward-level observations and structured inpatient follow-up across one year, the study moves beyond single-timepoint descriptions to generate context-sensitive insights relevant for strengthening antimicrobial stewardship in Indian public hospitals and comparable LMIC settings. Specifically, the study aims to (i) describe antimicrobial prescribing patterns across wards and patient demographic profiles, (ii) assess seasonal variation in infection profiles and antibiotic use, and (iii) examine the extent to which AST informs prescription review and modification. 2. Materials and Methods 2.1 Study team The study was conducted as part of the EquityAMR project, a four-year collaborative research initiative (2021–2025) between India and Norway that examined AMR through a health equity and health systems lens. Embedding the PPS within the EquityAMR framework enabled the integration of quantitative PPS antimicrobial prescribing data with qualitative ward-level observations to explore how diagnostic access, clinical workflows, and institutional practices shape antimicrobial use and stewardship in public hospital settings. A multidisciplinary research team of five members with training in microbiology, infectious diseases, anthropology, and health informatics participated in the study. All data collectors were trained in the WHO PPS methodology, standardised data extraction procedures, and inter-observer reliability checks prior to data collection. The team visited inpatient wards during each survey phase and was responsible for planning, implementing, and collecting data. 2.2 Study context 2.2.1 Hospital setting This study was conducted at Dr. Rajendra Prasad Government Medical College (RPGMC), Tanda, a tertiary-care public teaching hospital located in Kangra district of Himachal Pradesh, northern India. Situated at approximately 720 metres above sea level in the foothills of the Dhauladhar mountain ranges, the hospital serves as a major referral centre for a predominantly rural and geographically dispersed population across Kangra and neighbouring districts, including Chamba, Mandi, and Hamirpur. Access to healthcare in these regions is shaped by hilly terrain, seasonal road connectivity, and limited availability of specialised services (Fig. 1 ). RPGMC Tanda manages a high inpatient load across medical, surgical, obstetric, and critical care services, with marked seasonal fluctuations in admissions. Medicine and Surgery wards account for the largest proportion of inpatient admissions throughout the year, together contributing more than two-thirds of the hospital’s inpatient load, followed by Obstetrics and Gynaecology, while the Intensive Care Unit (ICU) and Neonatal/Paediatric Intensive Care Units (NICU/PICU) manage a smaller but clinically complex patient population. Monsoon months are associated with increased gastrointestinal and water-borne infections, winter with a higher burden of respiratory illnesses, and summer with dehydration-related, enteric, and trauma cases. These seasonal surges often coincide with constraints in diagnostic capacity, bed availability, and clinical workload, increasing reliance on empirical treatment decisions. This combination of high patient turnover, seasonal variation in disease profiles, and resource limitations makes RPGMC Tanda a relevant setting for examining antimicrobial prescribing practices in a public hospital context. 2.2.2 Capturing multi-seasonal antimicrobial and disease data The site was selected to conduct a multi-seasonal PPS to capture temporal changes in prescribing behaviour and the operational factors influencing antimicrobial use. The study captured four distinct seasonal periods that reflect the climatic and epidemiological patterns of the north-western Himalayan region. Autumn (October–November) is characterised by post-monsoon transition with moderate temperatures (15–25°C) and declining rainfall (India Meteorological Department, 2020 ). Summer (April–June) is marked by warmer temperatures (25–35°C), increased patient inflow due to dehydration, gastrointestinal illnesses, and trauma-related admissions. The monsoon season (July–September) brings heavy rainfall (average annual rainfall exceeding 2,000 mm), high humidity, and an increased burden of waterborne, gastrointestinal, and vector-borne infections. Winter (December–February) is characterized by colder temperatures (0–15°C), particularly in surrounding hilly areas, and an increase in respiratory tract infections and exacerbations of chronic diseases such as chronic respiratory illnesses and cardiovascular conditions. These seasonal variations influence both disease profiles and healthcare utilisation patterns, providing a strong rationale for employing a multi-seasonal PPS design to examine temporal changes in antimicrobial prescribing (Laxminarayan and Chaudhury, 2016 ). 2.3 Study design, data collection tools The study was conducted from 2023 to 2024 and employed a multi-seasonal, mixed-methods PPS design to capture antimicrobial prescribing patterns across various wards in the tertiary hospital (Fig. 2 ). An initial pilot phase was undertaken to adapt the WHO PPS tool to local medical record systems and ward workflows prior to full-scale implementation. A mixed methods approach was adopted, including: i) use of the PPS tool to collect quantitative data on antimicrobial use; ii) qualitative data based on meetings and discussions with treating staff to understand some of the reasons behind the adoption of certain practices. A unique feature of the study design was its iterative nature, where, after each iteration, based on feedback received through self-reflection of the researchers, the tool was incrementally improved. Table 1 Purpose, Key reflections on process, and Modifications made for the following phase. Phases Purpose Key reflection Modification introduced Phase 1 Baseline PPS implementation Incomplete documentation and weak linkage between prescriptions and AST data Refined variable definitions and standardised data abstraction procedures Phase 2 Process stabilisation Limited visibility of AST influence on prescribing decisions Introduced structured inpatient follow-up to track culture testing and treatment modification Phase 3 Contextual observation Prescribing influenced by drug availability, stock-outs, and ward-level practices No modification to PPS tool; contextual observations documented Phase 4 Consolidation Workflow and handover gaps across wards persisted No further modifications; data collection procedures maintained 2.4 Methodology: Selection of wards, patients, and timing Data collection was conducted on pre-designated survey days, selected in advance by the study team in consultation with the hospital Principal Investigator (PI), in accordance with WHO PPS methodology. On each survey day, all admitted patients from five inpatient departments, Medicine, Surgery, Obstetrics and Gynaecology, Neonatal/Paediatric Intensive Care Units (NICU/PICU), and the Intensive Care Unit (ICU), were included. These wards were selected because they contributed to a high volume of clinical samples to the microbiology laboratory and represented areas with substantial antimicrobial utilisation. 2.5 Patient selection Only patients who were already hospitalized in the ward at 08:00 on the day of the survey were included in the survey. Patients admitted to the ward after 08:00 were excluded (Table 1 ). Neonates born on the day of data collection before 8:00 a.m. were also included in the survey. All patients meeting the eligibility criteria were included in the survey, regardless of whether they were receiving antimicrobial treatment or not. All the eligible patients went through the consent approval processes, in either verbal or written form. 2.6 Daycare patients were excluded, such as: Patients undergoing treatment or surgery who were discharged the same day Patients seen in outpatient departments Patients in the emergency room Outpatient dialysis patients Discharged patients who remained as lodgers while waiting for transportation Parents or relatives of admitted children who stayed as lodgers in the ward to nurse them Patients receiving outpatient parenteral antimicrobial therapy (OPAT) 2.7 Antimicrobials selection Only antimicrobials listed in Annex XI (as per WHO guidelines) and administered through oral, parenteral, rectal, or inhalation routes were included in the survey. Table 1 Inclusion criteria and examples of exclusion criteria by the levels of stratification Level Include Exclude Hospital Acute care hospitals Nursing homes, Rehabilitation centers, and Psychiatric centers Ward Acute care inpatient wards Long-term care wards, Emergency departments (except for wards attached to the departments), Day surgery wards, Day care wards (e.g., renal dialysis) Patient Patients hospitalized as inpatients at or before 08:00 Hospitalized after 08:00, Outpatient clinic, Day surgery/day treatment, Emergency room, Outpatient dialysis, discharged patients waiting for transportation, Parents/relatives of admitted children, Outpatient parenteral antimicrobial therapy (OPAT) Antimicrobial WHO-listed antimicrobials, administered orally, parenterally, rectally, or through inhalation. Ongoing treatment at 08:00 Topical antimicrobials, Ophthalmologic antimicrobials, Treatment initiated after 08:00, Treatment discontinued before 08:00. 2.8 Sampling approach The sampling was done in each ward on the day of the survey through the following procedure: 1. On the day of the survey, the researcher prepared a list of all eligible patients according to the inclusion criteria in each ward. 2. From this list, the investigator selected one out of two patients until the end of the list was reached. Given that the hospital has 500-800 total inpatient beds, every second patient from each ward was included in the survey, according to WHO guidelines. 3. If a selected patient was not present in the ward, for example, due to surgery or radiology examination, and his/her patient records and associated documentation were not available at the time of the survey, the investigator either chose to return later during the same day or select the next patient on the list. 2.9 Data collection procedures Quantitative data were collected using the standard WHO PPS guidelines and data collection templates. The study team reviewed patients’ medical records, bedside treatment charts, antimicrobial prescription records, and available microbiology culture and AST reports. Where required, clarifications related to prescription details were obtained from treating clinicians, including ward residents and nursing staff. All quantitative data were recorded using the WHO PPS data collection form. 2.10 Structured patient follow-up and ward-level observations To complement the quantitative PPS data, structured patient-level follow-up and ward-level observations were conducted. This included documenting culture testing practices, availability and review of AST reports, prescription review or modification, and operational factors influencing antimicrobial prescribing. These data were collected using structured field notes and predefined data elements to contextualize prescribing patterns and were not intended as formal qualitative interviews or thematic qualitative analysis. Data were collected using the standard WHO PPS data collection guidelines and template. The study team reviewed patients’ medical record files, bedside treatment charts, prescribed antimicrobial records, and available microbiology culture reports. Where required, clarifications were obtained from treating clinicians, including ward residents and nursing staff, during data extraction. All data were entered into the WHO PPS form at the time of review. Information collected included hospital and ward characteristics, patient demographics, and the presence of at least one antimicrobial prescription on the day of the survey. For patients receiving antimicrobials, detailed prescription-level data were recorded for each agent, including drug name, dose and frequency, route of administration, indication for use, and classification as empirical, prophylactic, or culture-guided therapy. Additional variables included documentation of diagnosis, date of antimicrobial initiation, and classification of infection as community-acquired or hospital-acquired. 2.11 Analysis: Parameters calculated The following parameters were calculated based on the data collected. The number/percentage of patients prescribed antimicrobials and categorization of antimicrobial prescriptions as empiric, prophylactic, or lab-based. The number/percentage of antimicrobials administered by the oral or parenteral route, categorization of antimicrobial prescriptions based on the indication, i.e., medical prophylaxis, surgical prophylaxis, unknown infections, or others. The number/percentage of patients on designated antimicrobials, overall consumption of antimicrobials by class, prevalence of antimicrobial use by ward type, prevalence of broad-spectrum antimicrobial prescribing, AWaRe category, and percentage of antimicrobial prescriptions given by parenteral or oral route. 2.12 Qualitative data for structured patient follow-up As part of the survey, a structured follow-up was conducted to capture longitudinal insights into antimicrobial use, culture testing, and prescribing practices beyond the scope of the cross-sectional quantitative study. Eligible patients included those admitted before 8:00 AM on the survey day, especially those receiving antimicrobials, undergoing catheterization, intubation, or transferred between wards. For each, the day of antibiotic initiation, the basis of prescription (empirical or guided), and the indication were recorded. The team tracked when and what clinical samples (e.g., blood, urine, wound) were sent for microbiological testing, and whether prescriptions were subsequently adjusted based on AST results. Length of hospital stay, antibiotic continuation at discharge, and any outpatient follow-up advice were also documented. Particular attention was given to high-risk groups with variable antibiotic use. Data were collected from patient records, laboratory reports, and ward staff inputs, ensuring anonymization and systematic entry into structured forms for analysis. This follow-up provided critical insights into antibiotic treatment timelines, gaps in culture use, and real-world prescribing behavior to inform antimicrobial stewardship interventions. 2.13 Data Analysis Data analysis comprised three analytical components: (i) patient and ward-level prescribing profiles, (ii) categorization of antimicrobial use, and (iii) assessment of seasonal variation in prescribing. Patient profiles were developed to describe demographic and clinical characteristics of inpatients receiving antimicrobials, including ward type, infection classification, route of administration, and indication for use. This analysis was descriptive and aimed to contextualize prescribing patterns across inpatient settings. Antimicrobial prescriptions were categorized by drug class, AWaRe classification, indication, and basis of prescription (empirical or culture-guided). Frequencies and proportions were used to examine patterns of antimicrobial use across wards and patient groups. Seasonal variation in prescribing was assessed by comparing antimicrobial prevalence, spectrum of use, and AWaRe category distribution across survey phases and seasonal diagnosis patterns using tabular and graphical methods. The quantitative data extracted from WHO PPS forms were entered into Microsoft Excel and analysed using descriptive statistics in accordance with Global-PPS definitions. Data from structured patient follow-up and ward-level observations were analysed using an inductive, interpretive approach. Initial codes included empirical prescribing rationale, delays in culture sampling, non-review of AST reports, drug availability constraints, and documentation practices. These were grouped into broader themes such as diagnostic–prescribing disconnects, operational barriers to culture-guided therapy, and workflow-driven continuation of empirical antibiotics. Triangulation was undertaken by comparing observational themes with quantitative PPS findings across wards and seasons. 3. Results This analysis part is organised to describe patient and ward-level characteristics, overall antimicrobial prescribing patterns, seasonal variation in antimicrobial use across PPS phases, and insights from structured inpatient follow-up and ward-level observations that contextualise prescribing practices. 3.1 Patient and Ward-Level Characteristics Across the four PPS phases, the inpatient population was predominantly adult and consistently drawn from medical and surgical wards, which together accounted for the majority of admissions in each survey round (Table 2 ). While the overall ward mix remained broadly stable, seasonal variations were observed, with a relatively greater contribution of surgical admissions during the summer and autumn, and a higher representation of medical admissions during the monsoon and winter periods. Obstetrics and Gynaecology admissions showed minimal seasonal fluctuation, reflecting routine service utilisation. Critical care units, including ICU and NICU/PICU, constituted a smaller proportion of total inpatients across all phases but represented a clinically complex subgroup. The age profile was dominated by middle-aged and older adults, with paediatric admissions varying across seasons, particularly during the monsoon and winter. Collectively, the observed patient case-mix and ward distribution across PPS phases established a stable yet seasonally differentiated context for interpreting subsequent analyses of antimicrobial prescribing patterns. Table 2 Patient demographic and ward characteristics across four PPS phases. Characteristic PPS1 (Autumn) n (%) PPS2 (Summer) n (%) PPS3 (Monsoon) n (%) PPS4 (Winter) n (%) Number of patients surveyed 510 365 418 387 Ward distribution Medicine 201 (39.4) 152 (41.6) 136 (32.5) 121 (31.0) Surgery 171 (33.5) 130 (35.6) 162 (38.7) 165 (43.0) Obstetrics and Gynaecology 30 (5.8) 36 (9.8) 41 (10.0) 38 (10.0) ICU 33 (6.4) 27 (7.3) 30 (7.1) 33 (8.5) NICU/PICU 75 (14.7) 20 (5.4) 49 (12.0) 30 (8.0) Sex Male 276 (54.0) 186 (50.9) 204 (49.0) 226 (58.0) Female 234 (45.8) 179 (49.1) 214 (51.0) 161 (42.0) Age group (years) 0–19 97 (19.0) 29 (7.9) 73 (17.4) 56 (14.3) 20–39 100 (19.6) 81 (22.1) 105 (25.1) 97 (25.1) 40–59 166 (32.5) 94 (25.7) 129 (30.9) 125 (32.3) ≥ 60 147 (28.8) 147 (40.3) 111 (26.6) 109 (28.2) Table 2 shows that the inpatient population and ward composition remained broadly stable across PPS phases, with Medicine and Surgery accounting for most admissions, alongside modest seasonal variation in ward contribution and age distribution. 3.2 Antimicrobial categorization and prescribing patterns Table 3 summarises antimicrobial exposure and prescribing patterns across the four PPS phases. Antimicrobial exposure remained high across all PPS phases, although a gradual decline in the proportion of patients receiving antibiotics was observed over time (Table 3 ). Empirical prescribing consistently dominated, accounting for approximately two-thirds of prescriptions in every survey round, while AST-guided therapy remained below 6% across all phases. Combination therapy was common, with nearly half of patients receiving two antimicrobials in most PPS rounds. Parenteral administration exceeded 80% throughout the study period, indicating a strong reliance on injectable antibiotics. Prophylactic use constituted a substantial and persistent share of prescribing, particularly in surgical wards. Table 3 Antimicrobial use and prescribing characteristics across four PPS phases Antimicrobial use characteristic PPS1 (Autumn) n (%) PPS2 (Summer) n (%) PPS3 (Monsoon) n (%) PPS4 (Winter) n (%) Patients receiving ≥ 1 antimicrobial 316 (62.0) 201 (55.0) 217 (52.0) 185 (48.0) Patients not receiving antimicrobials 194 (38.0) 164 (45.0) 201 (48.0) 202 (52.0) Number of antimicrobials per patient One antimicrobial 134 (42.4) 70 (34.8) 94 (43.3) 98 (53.0) Two antimicrobials 155 (49.0) 105 (52.0) 97 (45.0) 70 (38.0) Three antimicrobials 27 (9.0) 26 (13.0) 26 (12.0) 17 (9.1) Prescribing intent Empirical therapy 342 (65.0) 232 (64.8) 230 (63.0) 194 (67.0) Definitive (AST-guided) therapy 25 (4.7) 15 (4.1) 22 (6.0) 17 (4.4) Prophylactic use 158 (30.0) 85 (23.7) 104 (28.4) 84 (29.0) Route of administration Parenteral 428 (81.5) 304 (85.0) 322 (88.0) 255 (88.5) Oral 97 (18.5) 54 (15.0) 44 (12.2) 33 (11.5) 3.3 Spectrum and Drug Class of Commonly Prescribed Antibiotics Across all PPS phases, prescribing was dominated by broad-spectrum antibiotics, particularly third-generation cephalosporins and beta-lactam/beta-lactamase inhibitor combinations (Table 4 ). Narrow-spectrum agents contributed a smaller proportion of prescriptions across seasons. Consistent with the AWaRe distribution, most commonly prescribed antibiotics belonged to the Watch category, while Reserve antibiotics were used sparingly. Table 4 AWaRe Classification, Spectrum, and Drug Class of Commonly Prescribed Antibiotics. Antibiotic AWaRe Category Spectrum Drug Class Ceftriaxone Watch Broad-spectrum Third-generation Cephalosporin Azithromycin Access Broad-spectrum Macrolide Metronidazole Access Narrow-spectrum Nitroimidazole Piperacillin-Tazobactam (Pip-Taz) Watch Broad-spectrum Extended-spectrum Penicillin + β-lactamase inhibitor Doxycycline Access Broad-spectrum Tetracycline Cefuroxime Access Broad-spectrum Second-generation Cephalosporin Amikacin Watch Broad-spectrum Aminoglycoside Amoxicillin-Clavulanic Acid (Amoxyclav) Access Broad-spectrum Penicillin + β-lactamase inhibitor Meropenem Reserve Broad-spectrum Carbapenem Vancomycin Watch Narrow-spectrum Glycopeptide Ceftriaxone + Sulbactam Watch Broad-spectrum Third-generation Cephalosporin + β-lactamase inhibitor 3.4 Seasonal variation in antimicrobial prescribing, with contextual diagnosis patterns Figure 3 illustrates seasonal trends in the prescribing of commonly used antibiotics across the four PPS phases. Ceftriaxone remained the most frequently prescribed antibiotic throughout the study period, showing consistently high use across all seasons. In contrast, prescribing of Piperacillin–tazobactam increased progressively across successive PPS phases, while doxycycline uses also demonstrated a steady upward trend over time. Azithromycin prescribing declined sharply after the first PPS phase and remained low thereafter, whereas Amoxicillin–clavulanate and Cefuroxime showed variable seasonal patterns. Amikacin and Meropenem were prescribed less frequently across all phases, with only minor seasonal fluctuations. Notably, Ceftriaxone–sulbactam, a non-recommended fixed-dose combination, continued to be prescribed across all PPS phases. 3.5 AWaRe Classification Across PPS Phase Figure 4 illustrates the distribution of prescribed antibiotics by WHO AWaRe classification across the four PPS phases. Watch-category antibiotics consistently accounted for the largest share of prescriptions in all survey rounds, indicating sustained reliance on broad-spectrum agents. Access antibiotics showed seasonal variation, with relatively higher use during the monsoon phase followed by a decline in winter. Reserve antibiotics remained minimally used throughout the study period, while not-recommended fixed-dose combinations constituted a small but persistent proportion of prescriptions across all PPS seasons. 3.6 Distribution of Prescribed Antibiotics by Ward and ATC Classification Across wards, Ceftriaxone, Piperacillin–tazobactam, and Doxycycline were the most frequently prescribed antibiotics, with clear ward-wise variation. ICU and NICU/PICU settings showed higher use of Piperacillin–tazobactam and amikacin, whereas surgical wards relied more heavily on Cefuroxime and Metronidazole. Prescribing in Obstetrics and Gynaecology more commonly included Amoxicillin–clavulanate and Cefuroxime, reflecting relatively greater oral antibiotic use. Overall, broad-spectrum agents predominated across all wards (Figs. 4 ). ATC-based classification showed that cephalosporins (J01D) were the predominant antibiotic class across all PPS phases, followed by Penicillins (J01C) and Tetracyclines (J01A), with seasonal variation in relative contributions across survey rounds (Fig. 6 ). 3.7 Distribution of Patients Diagnoses Across PPS Phases Across all PPS phases, gastrointestinal disorders (20.6–22.2%) and chronic diseases (17.0–23.5%) were the most frequent diagnoses among surveyed patients, followed by urological/gynaecological (8.0–11.0%) and neurological conditions (7.0–12.3%). Respiratory tract infections reached a maximum in PPS2 (9.6%) and PPS4 (9.0%), while other categories, including post-surgical cases, malignancies, cardiovascular diseases, and bacterial infections, contributed smaller proportions (Fig. 7 ). 3.8 Findings from structured inpatient follow-up on antibiotic use and culture testing Table 5 summarises findings from the structured inpatient follow-up conducted during PPS2–PPS4. The highest proportion of culture samples was sent from the Medicine ward (11–13%), followed by Surgery and ICU. Across all phases, positive cultures accounted for 7.5–10.0% of samples, while sterile cultures constituted 15–18%. Antibiotics were frequently initiated before the availability of AST results (18.6–21.6%), and 8.5–9.6% of prescriptions were continued despite sterile culture findings. Modification of antibiotic therapy based on AST results was observed in only 4.7–6.5% of cases, highlighting the limited integration of AST reports into inpatient prescribing decisions. Table 5 Findings from structured inpatient follow-up on culture use and antibiotic modification. Parameter PPS2 (Summer) PPS3 (Monsoon) PPS4 (Winter) Total patients surveyed 365 418 387 Ward-wise culture sampling Medicine 47 (12.9%) 50 (12.0%) 44 (11.4%) Surgery 19 (5.2%) 21 (5.0%) 18 (4.7%) ICU 15 (4.1%) 18 (4.3%) 20 (5.2%) NICU/PICU 3 (0.8%) 7 (1.7%) 4 (1.0%) Obstetrics & Gynaecology 12 (3.3%) 9 (2.2%) 4 (1.0%) Culture results Positive cultures 34 (9.3%) 42 (10.0%) 29 (7.5%) Sterile cultures 65 (17.8%) 63 (15.1%) 61 (15.8%) Antibiotic prescribing in relation to AST Antibiotics started before AST availability 79 (21.6%) 89 (21.3%) 72 (18.6%) Antibiotics prescribed despite sterile cultures 35 (9.6%) 39 (9.3%) 33 (8.5%) Prescription modified after AST results 22 (6.0%) 27 (6.5%) 18 (4.7%) 3.9 Qualitative Observations: Thematic Insights Ward-level observations conducted across all four PPS phases provided contextual insights into the operational, behavioural, and system-level factors shaping antimicrobial prescribing practices across wards. These observations complemented quantitative PPS findings by highlighting routine clinical workflows, diagnostic practices, and institutional constraints influencing prescribing decisions. The key themes identified are summarised below. 1. Predominance of Empirical Prescribing and Delayed Culture Testing Across wards, empirical antibiotic prescribing was the dominant initial treatment approach, with antimicrobials frequently initiated before the availability of microbiological culture and AST results. In Medicine wards and ICUs, culture samples were typically sent after the first 48–72 hours of admission, while in Surgery wards, cultures were largely limited to overt postoperative infections, most commonly wound or urine samples. Although the ICU demonstrated more systematic microbiological surveillance compared to other wards, antibiotic de-escalation following sterile culture reports was uncommon. Observations suggested that empirical prescribing was shaped by a combination of operational pressures, perceived urgency of clinical decision-making, concerns regarding patient affordability of diagnostics, and limited feedback mechanisms linking microbiology results to prescribing teams. 2. Antibiotic Supply Constraints and Availability-Driven Prescribing Prescribing practices varied across PPS phases in ways that appeared closely linked to antibiotic availability and stock-outs. Oral antibiotics, including azithromycin, were observed to be prescribed less frequently during later PPS phases due to supply constraints, with patients or attendants often instructed to procure medications externally. This contributed to increased reliance on parenteral formulations, particularly ceftriaxone in Medicine wards and cefuroxime in Surgery. Limited availability of narrow-spectrum agents further constrained guideline-aligned prescribing, frequently necessitating the use of broader-spectrum alternatives. The absence of a formal system for advanced communication regarding stock shortages meant that prescribing decisions were often made without awareness of inventory limitations, reinforcing supply-driven patterns of antimicrobial use. 3. Documentation and Communication Gaps During Care Transitions Ward observations revealed gaps in documentation and communication during patient transfers between wards, occasionally resulting in missed antibiotic doses or unclear treatment continuation. These issues were attributed to high patient volumes, heavy nursing workloads, and the absence of standardised handover protocols. Such gaps have implications for patient safety, continuity of care, and the reliability of antimicrobial documentation, with potential downstream effects on stewardship monitoring and quality improvement efforts. 4. ICU-Specific Prescribing and Care Patterns Patients admitted to ICUs represented a distinct subgroup characterised by longer hospital stays, frequent use of multiple concurrent antibiotics, and intensive diagnostic monitoring. Culture samples were sent more regularly from ICU as compared to other wards, sometimes repeatedly for individual patients. Despite this, antibiotics were often continued even in the presence of repeated sterile culture results, reflecting the high perceived risk of undertreatment in critically ill patients. ICU care was marked by frequent ward rounds and close clinical supervision; however, the density of equipment and patient acuity posed challenges to maintaining optimal ward conditions despite regular cleaning schedules. Table 5 Selected Quotes and Observations from Ward-Level Qualitative Data. Theme Ward/Source Quotes Oral antibiotic stock-outs Nurse (Medicine/Surgery) “Most of the oral antibiotics are not in supply, so IVs are given in most cases. If a patient needs oral antibiotics from outside, we write it on a slip, and the attendant brings it from the pharmacy.” Preoperative antibiotic use Doctor (Surgery) “Before operating on any patient, we start with broad-spectrum antibiotics so that during the procedure, the patient doesn’t get an infection. If an infection still occurs, we immediately send a wound culture.” Culture testing practices Doctor (Surgery) “We send wound cultures of patients with skin wounds or urine of catheterized patients. Blood cultures are rarely sent because we mostly deal with onsite infections.” Missed dose after transfer Nurse (Surgery and Medicine) “It is the duty of the previous nurses from the earlier ward to remind us, as we have so many patients to deal with.” (explaining why a ceftriaxone dose was missed during patient transfer) ICU culture testing routine ICU Observation “From the ICU, daily, a sample for culture was sent to the microbiology lab.” Workload during patient transfer Nurse (General comment) “We have so many patients to handle that sometimes small details are missed unless handed over properly.” Ward rounds and workflow Observation (All wards) Four rounds daily: first, third, fourth by JRs; second major round by senior doctors with JRs and 3rd-year students. Whiteboards are updated daily with patient counts and staff details, guiding rounds. Nursing student involvement Observation (All wards) Nursing college students (4–5 per ward) are actively involved in dressing patients, administering medications, and managing IV fluids under the supervision of senior nurses. ICU antibiotic continuation ICU Observation Despite multiple sterile culture results, antibiotics were rarely stopped, reflecting cautious prescribing due to critical patient conditions. Bed doubling in December Observation (Medicine/Surgery) Bed capacity was temporarily doubled in December to manage a seasonal surge, increasing the strain on resources and workload. Post-discharge antibiotic use Observation (Surgery) Postoperative patients usually completed their antibiotic course during admission; they were rarely prescribed antibiotics at discharge. Cleanliness across wards Observation (All wards) Surgery and Medicine wards were observed to be cleaner and better maintained compared to the ICU, NICU, and Gynaecology, despite all wards following a four-times-daily cleaning protocol. 4. Discussion This multi-seasonal point prevalence survey provides a detailed assessment of antimicrobial use in a tertiary-care teaching hospital in northern India, combining prescription-level data with qualitative observations of clinical practice. By integrating quantitative patterns with operational and behavioural insights from wards, the study offers a comprehensive understanding of antimicrobial decision-making in a resource-constrained setting. In our findings, antibiotic use declined from 62% in PPS1 to 48% in PPS4, yet remained higher than benchmark usage levels reported from European hospitals (25–35%), and comparable to rates reported from several LMIC settings including Ghana (65%), Kenya (82%) (Afriyie et al., 2020; Sharma et al., 2015; Plachouras et al., 2018), Benin (64.6%), Vietnam (67.4%), and Nigeria (78.6%) (Ahoyo et al., 2014; Thu et al., 2012; Oduyebo et al., 2017). Earlier studies from India reported antibiotic use prevalence of around 50.3% across tertiary centres [10], while private-sector data showed even higher rates (up to 84%) (Sharma et al., 2015). Although the study did not assess appropriateness, the consistently high prevalence suggests substantial potential to optimize prescribing practices in this setting. Across all PPS rounds, 63–67% of antimicrobial prescriptions were empirical, while definitive therapy guided by culture and susceptibility testing remained very low (4.1–6.5%). These findings align with other Indian PPS studies that demonstrate limited microbiological utilisation ( Bhattacharjee et al., 2024) . Only 10–18% of followed-up patients underwent culture testing, and modifications based on AST occurred in just 4.7–6.5% of cases. Qualitative observations revealed that cultures were often collected on Days 2–3, yielding predominantly sterile results, thereby reducing their relevance to prescribing decisions. Clinicians also cited financial concerns for patients as a barrier to early culture testing, an observation consistent with studies highlighting economic drivers of empirical prescribing in LMIC contexts. Together, these findings suggest that delays in culture collection and limited confidence in microbiology outputs diminish the practical utility of AST in routine inpatient care. Prophylactic antibiotic use accounted for 24–30% of prescriptions, higher than proportions reported in European PPS networks, where medical prophylaxis accounted for 15% and surgical prophylaxis for 6.7% (Van der Meer et al., 2005). In our study, broad-spectrum agents such as Ceftriaxone and Cefuroxime were frequently initiated preoperatively or when infection was suspected, reflecting established norms around preventive coverage. Staff interviews supported these findings, indicating that “broad-spectrum antibiotics are started before operating to prevent infection during exposure.” The AWaRe classification further highlights stewardship priorities. In our study, Watch-group antibiotics, particularly Ceftriaxone, represented the largest share of prescriptions (45–56%) across all PPS rounds. This sustained reliance on Watch-group agents reflects a risk-averse prescribing culture in the absence of timely diagnostic confirmation. This parallels findings from Sub-Saharan Africa and other LMICs where Ceftriaxone is widely used due to broad coverage and convenient dosing (Kiggundu et al., 2022; Abubakar and Salman, 2024). Not-recommended FDCs such as Ceftriaxone–sulbactam accounted for 3.8–5.1% of prescriptions, despite WHO discouragement. Seasonal variations were observed, including increased use of doxycycline and Piperacillin–tazobactam during monsoon and winter. These patterns underscore the necessity of developing local antibiograms to support data-driven empirical therapy and reduce reliance on broad-spectrum agents. Similar trends have been reported from hospitals in Southeast Nigeria and Ghana, though local resistance profiles and access constraints likely influence differences seen across settings (Umeokonkwo et al., 2019; Afriyie et al., 2020). We reported that more than 80% of antibiotics were administered parenterally in all PPS rounds, exceeding the 59.9% parenteral-use rate reported in Ghanaian hospitals (Amponsah et al., 2021) and reflecting trends in several LMIC studies (Hodoșan et al., 2023). Interviews with nurses revealed that stock-outs of oral antibiotics often led clinicians to rely on intravenous formulations, as attendants were asked to purchase oral agents externally. This supply-driven prescribing pattern increases treatment complexity and prolongs intravenous therapy. In our findings, combination therapy was frequent, particularly in ICU settings, where patients often had prolonged stays and underwent repeated culturing. Common combinations included Ceftriaxone with Azithromycin and Doxycycline with Metronidazole. While combination therapy can be clinically justified in severe infections, high rates may also reflect attempts to broaden coverage in the absence of timely diagnostic guidance. European data reported 29.4% combination therapy (Plachouras et al., 2018), whereas studies from France and earlier surveys reported even higher rates (40–42.6%) (Robert et al., 2012; Dodoo et al., 2021). Redundant combinations may arise due to systemic gaps in prescribing practices, involvement of multiple practitioners, and limited understanding of antibiotic spectra, as noted in prior literature (Laxminarayan and Chaudhury, 2016; Kotwani et al., 2010). Our qualitative observations also highlighted workflow challenges such as documentation gaps, missed doses during inter-ward transfers, absence of structured handover protocols, and limited communication of laboratory results, all of which constrain rational antibiotic use. Stock-outs were often not communicated systematically, leaving prescribers unaware of changes in inventory and contributing to reactive prescribing patterns. This study has several limitations. It is a single-centre assessment, and its findings may not be generalizable to other Indian hospitals with different case mixes or diagnostic capacity. The PPS design captures prescribing on one day per season and does not measure appropriateness or clinical outcomes. Documentation inconsistencies may have influenced data accuracy, and delayed culture reporting may have affected AST-guided therapy. Nonetheless, the study’s strengths include its multi-seasonal design, incorporation of follow-up data, and integration of qualitative insights that contextualize quantitative findings. Implications for Antimicrobial Stewardship The findings from this multi-seasonal PPS highlight both quantitative prescribing patterns and qualitative operational challenges that directly inform antimicrobial stewardship priorities. By identifying system-level gaps in diagnostics, procurement, and prescribing behaviour, this study offers practical, evidence-based recommendations for strengthening AMS implementation in resource-constrained tertiary settings: The consistently low proportion of culture-based prescribing despite the availability of culture test facilities signals an urgent need to embed diagnostic testing more coherently into routine clinical workflows. Timely culture collection and feedback mechanisms must be institutionalized to reduce empirical overuse. Empirical reliance on broad-spectrum Watch group antibiotics like ceftriaxone underscores the absence of data-informed prescribing. Creating and disseminating department-wise antibiograms quarterly would help tailor empiric treatment protocols and encourage de-escalation. The dominance of the Watch category and the use of non-recommended FDCs point to gaps in stewardship oversight. Prescribers should be guided toward greater use of Access group antibiotics in line with WHO targets, supported by local evidence and formulary restrictions. Stock-outs of oral antibiotics and lack of supply-chain transparency result in default parenteral prescribing, increasing costs and complexity. Streamlining procurement, ensuring real-time stock visibility, and enabling oral-to-IV switching protocols are crucial interventions. A multidisciplinary AMS team, including infectious disease physicians, microbiologists, and pharmacists, should lead clinical audits, participate in ward rounds, and provide case-based feedback. This participatory model will support both behavior change and system redesign. Regular training on the interpretation of AST results, narrow-spectrum prescribing, and management of specific syndromes should be made mandatory for junior residents and nursing staff to promote long-term practice transformation. 5. Conclusion This multi-seasonal PPS shows sustained high antibiotic use, heavy reliance on empirical broad-spectrum therapy, and limited uptake of culture testing in a tertiary hospital in northern India. The dominance of Watch-group agents, frequent prophylaxis, and workflow constraints highlight the need for stronger diagnostic stewardship and system-level reforms. Implementing routine culture practices, reliable antibiotic supply, department-specific antibiograms, and a multidisciplinary stewardship program will be essential to promote rational, evidence-based prescribing and curb antimicrobial resistance. Declarations Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics statement This study was conducted under the EquityAMR project, a four-year collaborative study (2021U2025) ˝ between Norway and India focused on the analysis of health equities and antimicrobial resistance (AMR) in India. The project obtained permissions from the Norwegian Centre for Research Data (NSD) and the Health Ministry Screening Committee (HMSC), Government of India, prior to data collection. To enable data collection ensuring informed consent, anonymization, and adherence to required ethical guidelines, formal approval was obtained from the HISP India Research Ethics Committee. Memorandums of Understanding and Non-Disclosure Agreements were signed between HISP India and the state hospitals to ensure permissions for data collection and maintain data security and integrity. At the individual level, verbal informed consent was obtained from all patients prior to interviews and collection of life experiences, ensuring complete anonymization of the data. Funding The author(s) declare financial support was received for the research and/or publication of this article. This project is supported by funding received from the Research Council of Norway for the EquityAMR research project. Acknowledgments We acknowledge the HISP India team and the state government and hospital authorities for their invaluable contributions to this research project. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Abubakar, U., and Salman, M. (2024). Antibiotic use among hospitalized patients in Africa: a systematic review of point prevalence studies. J. Racial Ethn. Health Disparities 11(3), 1308–1329. doi: 10.1007/s40615-023-01610-9 Afriyie, D. K., Sefah, I. A., Seaton, R. A., et al. (2020). Antimicrobial point prevalence surveys in two Ghanaian hospitals: opportunities for antimicrobial stewardship. Antimicrob. Resist. Infect. Control 9, 191. doi: 10.1186/s13756-020-00809-3 Ahoyo, A. T., Bankolé, H. S., Adéoti, F. M., et al. (2014). Prevalence of nosocomial infections and anti-infective therapy in Benin: results of the first nationwide survey in 2012. Antimicrob. Resist. Infect. Control 3, 17. doi: 10.1186/2047-2994-3-17 Amponsah, O. K. O., Buabeng, K. O., Owusu-Ofori, A., et al. (2021). Point prevalence survey of antibiotic consumption across three hospitals in Ghana. JAC Antimicrob. Resist. 3(1), dlab008. doi: 10.1093/jacamr/dlab008 Bhattacharjee, S., Aarzoo, et al. (2024). Antimicrobial prescription patterns in tertiary care centres in India: a multicentric point prevalence survey. eClinicalMedicine 82, 103175. doi: 10.1016/j.eclinm.2024.103175 Dodoo, C. C., Orman, E., Alalbila, T., et al. (2021). Antimicrobial prescription pattern in Ho Teaching Hospital, Ghana: seasonal determination using a point prevalence survey. Antibiotics 10(2), 199. doi: 10.3390/antibiotics10020199 Hodoșan, V., Daina, L. G., Zaha, D. C., et al. (2023). Pattern of antibiotic use among hospitalized patients at a level one multidisciplinary care hospital. Healthcare 11(9), 1302. doi: 10.3390/healthcare11091302 Holt, K. E., Carey, M. E., Chandler, C., et al. (2025). Tools and challenges in the use of routine clinical data for antimicrobial resistance surveillance. npj Antimicrob. Resist. 3, 37. doi: 10.1038/s44259-025-00105-3 India Meteorological Department (2020). Climatological normals (1981–2010): Himachal Pradesh . Ministry of Earth Sciences, Government of India. Available at: https://mausam.imd.gov.in Kiggundu, R., Wittenauer, R., Waswa, J. P., et al. (2022). Point prevalence survey of antibiotic use across 13 hospitals in Uganda. Antibiotics 11(2), 199. doi: 10.3390/antibiotics11020199 Kotwani, A., Wattal, C., Katewa, S., Joshi, P. C., and Holloway, K. (2010). Factors influencing primary care physicians to prescribe antibiotics in Delhi, India. Fam. Pract. 27(6), 684–690. doi: 10.1093/fampra/cmq059 Laxminarayan, R., and Chaudhury, R. R. (2016). Antibiotic resistance in India: drivers and opportunities for action. PLoS Med. 13(3), e1001974. doi: 10.1371/journal.pmed.1001974 Moja, L., Zanichelli, V., Mertz, D., et al. (2024). WHO’s essential medicines and AWaRe: recommendations on first- and second-choice antibiotics for empiric treatment of clinical infections. Clin. Microbiol. Infect. 30(Suppl. 2), S1–S51. doi: 10.1016/j.cmi.2024.02.001 Modgil, V., Sahay, S., Taneja, N., et al. (2025). Enhancing access to antimicrobial resistance diagnostics for the marginalised: challenges and opportunities of point-of-care technologies. J. Glob. Antimicrob. Resist. 44, 281–286. doi: 10.1016/j.jgar.2025.06.012 Oduyebo, O. O., et al. (2017). Antimicrobial use and resistance in Nigeria: findings from a point prevalence survey in Lagos. Niger. J. Clin. Pract. 20(9), 1080–1087. doi: 10.4103/1119-3077.181376 Pauwels, I., Versporten, A., Ashiru-Oredope, D., et al. (2025). Implementation of hospital antimicrobial stewardship programmes in low- and middle-income countries: a qualitative study from a multiprofessional perspective in the Global-PPS network. Antimicrob. Resist. Infect. Control 14, 26. doi: 10.1186/s13756-025-01541-6 Plachouras, D., Kärki, T., Hansen, S., et al. (2018). Antimicrobial use in European acute care hospitals: results from the second point prevalence survey of healthcare-associated infections and antimicrobial use, 2016–2017. Euro Surveill. 23(46), 1800393. doi: 10.2807/1560-7917.ES.23.46.1800393 Robert, J., Péan, Y., Varon, E., et al. (2012). Point prevalence survey of antibiotic use in French hospitals in 2009. J. Antimicrob. Chemother. 67(4), 1020–1026. doi: 10.1093/jac/dkr571 Sharma, A., Singh, A., Dar, M. A., et al. (2022). Menace of antimicrobial resistance in LMICs: current surveillance practices and control measures to tackle hostility. J. Infect. Public Health 15(2), 172–181. doi: 10.1016/j.jiph.2021.12.008 Sharma, M., Damlin, A., Pathak, A., and Stålsby Lundborg, C. (2015). Antibiotic prescribing among pediatric inpatients with potential infections in two private sector hospitals in Central India. PLoS ONE 10(11), e0142317. doi: 10.1371/journal.pone.0142317 Thu, T. A., Rahman, M., Coffin, S., et al. (2012). Antibiotic use in Vietnamese hospitals: a multicenter point-prevalence study. Am. J. Infect. Control 40(9), 840–844. doi: 10.1016/j.ajic.2011.10.020 Van der Meer, J. W. M., Gyssens, I. C., and ESAC Project Group (2005). Antimicrobial use in European hospitals: results of the ESAC point-prevalence survey. Clin. Microbiol. Infect. 11(Suppl. 2), 31–38. doi: 10.1111/j.1469-0691.2005.01112.x Versporten, A., Zarb, P., Caniaux, I., et al. (2018). Antimicrobial consumption and resistance in adult hospital inpatients: results of a global point prevalence survey. Lancet Glob. Health 6(6), e619–e629. doi: 10.1016/S2214-109X(18)30186-4 World Health Organization (2019). WHO methodology for point prevalence survey on antimicrobial use in hospitals . Geneva: World Health Organization. Available at: https://www.who.int/publications/i/item/WHO-EMP-IAU-2018.01 Wojcik, G., Ring, N., McCulloch, C., et al. (2021). Understanding the complexities of antibiotic prescribing behaviour in acute hospitals: a systematic review and meta-ethnography. Arch. Public Health 79, 134. doi: 10.1186/s13690-021-00624-1 Zumaya-Estrada, F. A., Alpuche-Aranda, C. M., and Saturno-Hernandez, P. J. (2021). The WHO methodology for point prevalence surveys on antibiotic use in hospitals should be improved: lessons from pilot studies in four Mexican hospitals. Int. J. Infect. Dis. 108, 13–17. doi: 10.1016/j.ijid.2021.04.079 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-9029871","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600629417,"identity":"f3738578-210e-467b-ae7e-0093bd0efc5b","order_by":0,"name":"Vinay Modgil","email":"","orcid":"https://orcid.org/0000-0001-7214-241X","institution":"Society for Health Information Systems Programmes (HISP India), New Delhi, India","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"","lastName":"Modgil","suffix":""},{"id":600629418,"identity":"0bbb3310-a2b7-4543-9031-925d34df91a6","order_by":1,"name":"Sundeep Sahay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJACCRDBz8z44ACUTaQWyWZmAxK1GBxgNiBOufyM3IM3PlTcsTc+zsx4gLHNQo5BuvkAXi0GN/KSLWeceZa47TAzA1CLhDGDzLEE/FokcsykedsOJ5gd5j8A0pLYIJGD34XyM4Ba/v47bG/cDLGFsBaGG0AtjA2HGTcwE6vF4MwbY8ueY88SZ4D8knBOwphNIg2/X+Tbcwxv/Ki5Y8/ff5j5w4eyOjl+ieQD+B0GAVBFIOPZiFGP0DIKRsEoGAWjABsAAIMHQtXPXIfNAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Informatics, University of Oslo, Oslo, Norway","correspondingAuthor":true,"prefix":"","firstName":"Sundeep","middleName":"","lastName":"Sahay","suffix":""},{"id":600629419,"identity":"da28b9e1-4e61-4e40-9dc5-c9192b717fe6","order_by":2,"name":"Arunima Mukherjee","email":"","orcid":"","institution":"Department of Informatics, University of Oslo, Oslo, Norway","correspondingAuthor":false,"prefix":"","firstName":"Arunima","middleName":"","lastName":"Mukherjee","suffix":""},{"id":600629420,"identity":"a0184c6e-ca5d-4b86-832a-08a739274a46","order_by":3,"name":"Rashmi Surial","email":"","orcid":"","institution":"Society for Health Information Systems Programmes (HISP India), New Delhi, India","correspondingAuthor":false,"prefix":"","firstName":"Rashmi","middleName":"","lastName":"Surial","suffix":""},{"id":600629421,"identity":"929cd67b-313b-412f-b043-d7136ab3c9a7","order_by":4,"name":"Oshin Sinha","email":"","orcid":"","institution":"Society for Health Information Systems Programmes (HISP India), New Delhi, India","correspondingAuthor":false,"prefix":"","firstName":"Oshin","middleName":"","lastName":"Sinha","suffix":""},{"id":600629422,"identity":"1635682a-7264-47c2-b1a1-98442d38d884","order_by":5,"name":"Sahil Kumar","email":"","orcid":"","institution":"Society for Health Information Systems Programmes (HISP India), New Delhi, India","correspondingAuthor":false,"prefix":"","firstName":"Sahil","middleName":"","lastName":"Kumar","suffix":""},{"id":600629423,"identity":"76148ee8-50d7-4bec-9b3b-d5cbf79c2003","order_by":6,"name":"Raman Chauhan","email":"","orcid":"","institution":"Dr. Rajendra Prasad Government Medical College (RPGMC), Kangra at Tanda, Himachal Pradesh, India","correspondingAuthor":false,"prefix":"","firstName":"Raman","middleName":"","lastName":"Chauhan","suffix":""},{"id":600629424,"identity":"21fde4d7-337c-40ed-98dd-f73007d13721","order_by":7,"name":"Sunil Kumar Raina","email":"","orcid":"","institution":"Dr. Rajendra Prasad Government Medical College (RPGMC), Kangra at Tanda, Himachal Pradesh, India","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"Kumar","lastName":"Raina","suffix":""}],"badges":[],"createdAt":"2026-03-04 11:49:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9029871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9029871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104404061,"identity":"259a6afe-99d8-410f-ad8b-3c62389f2970","added_by":"auto","created_at":"2026-03-11 12:19:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":413742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographic location of Kangra district and study site (RPGMC, Tanda) in Himachal Pradesh, India.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/2fb5ab37d9a00472a7120d12.png"},{"id":104180318,"identity":"1ae38f52-4f14-42c2-86e9-f27bf5d0feac","added_by":"auto","created_at":"2026-03-08 17:12:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of Survey Phases Across the Study Period.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/67276ed54109bb396780833f.png"},{"id":104404556,"identity":"47caafb6-cfa2-46ed-8fb2-08c44c7466ad","added_by":"auto","created_at":"2026-03-11 12:20:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":168018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in commonly prescribed antibiotics (%) across four seasonal PPS phases.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/2254a0757b2a0264db111ad8.png"},{"id":104180321,"identity":"cc5910cc-41f7-425b-a7b9-f2a0f1dae2df","added_by":"auto","created_at":"2026-03-08 17:12:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal Variations in Access, Watch, Reserve, and Not Recommended Antibiotics.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/ff7c594b0ce8ce5fc001d9f4.png"},{"id":104180319,"identity":"331cf99e-7dd9-4e1f-be41-be48f47b619a","added_by":"auto","created_at":"2026-03-08 17:12:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWard-wise Distribution of Most Commonly Prescribed Antibiotics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/35c8a7ad13fe7bb8df51c8a2.png"},{"id":104180322,"identity":"f60bf87e-a968-4f48-9e27-c99bcc0c570f","added_by":"auto","created_at":"2026-03-08 17:12:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":83891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal Variation in Antibiotic Patterns by ATC Class.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/e423c28bdb2bf0bffaea3f70.png"},{"id":104180323,"identity":"17d8e05d-d563-49e2-ad4e-5638779761d0","added_by":"auto","created_at":"2026-03-08 17:12:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":186740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal Variation in Primary Diagnosis Categories Across PPS Phases.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/dcc840de858cd8db61e66c9a.png"},{"id":104408658,"identity":"c5fe4db3-fad9-49e3-83fd-616a7d502e6c","added_by":"auto","created_at":"2026-03-11 12:42:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3114649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9029871/v1/ffe7fb57-e771-492b-a7ac-d3bdca647557.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA multi-seasonal mixed-method point-prevalence study of antibiotic prescription patterns in a tertiary healthcare facility in India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePoint prevalence surveys (PPS) are widely used to monitor antimicrobial prescribing practices among hospital inpatients, providing a structured and relatively cost-effective approach to generating standardized data on antibiotic use, particularly in resource-constrained settings (WHO, 2019; Versporten et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The World Health Organization (WHO) promotes PPS as a core surveillance tool within its Global Action Plan on antimicrobial resistance (AMR), recognising that hospital-level data on antimicrobial use remain limited across many low- and middle-income countries (LMICs), despite their central role in antimicrobial stewardship interventions. While PPS methodologies have been standardised and benchmarked through large surveillance initiatives, including those in high-income settings, their adaptation and application are especially critical within LMIC health systems (Zarb et al., 2011).\u003c/p\u003e \u003cp\u003eIn many LMICs, routine recording of antimicrobial-related transactional data\u0026mdash;including prescribing, dispensing, and consumption remains limited, contributing to poor visibility of antimicrobial use patterns. In settings where electronic surveillance systems are underdeveloped, PPS offers a pragmatic strategy for generating real-time data on antimicrobial use among inpatient departments (IPD) (Holt et al.,2025\u003cb\u003e)\u003c/b\u003e. Consequently, PPS has been widely implemented across diverse LMIC contexts, including India, Mexico, and other comparable settings (Pauwels et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvidence from PPS conducted across India and other LMICs consistently highlights several dominant prescribing patterns. First, a high proportion of hospitalised patients receive at least one antimicrobial, often exceeding half of all inpatients, reflecting extensive empirical use in tertiary care settings (Bhattacharjee et al., 2025). Second, Watch-category antibiotics, particularly broad-spectrum agents such as third-generation cephalosporins, account for a substantial share of prescriptions, raising concerns regarding resistance selection and stewardship alignment (Moja et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Third, despite the availability of microbiology services in many tertiary hospitals, culture and antimicrobial susceptibility testing (AST), guided prescribing remains limited, with empirical therapy predominating across wards and clinical indications (Modgil et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its widespread use, the utility of the WHO PPS framework is constrained by several implementation challenges in LMIC settings, including incomplete documentation, inter-observer variation, and fragmented patient records, all of which can compromise data quality. Systematic reviews further indicate that LMICs bear a disproportionately high burden of healthcare-associated infections, with Gram-negative pathogens dominating and empirical prescribing substantially exceeding that observed in high-income countries (Zumaya-Estrada et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings underscore both the adaptability of PPS and the structural constraints that limit its interpretive value in settings with restricted diagnostic and information infrastructure.\u003c/p\u003e \u003cp\u003eDespite these patterns, a central paradox emerges. In outpatient settings, empirical prescribing is often attributed to limited diagnostic access. In contrast, inpatient departments of tertiary hospitals typically have greater access to microbiological capacity, where AST-informed prescribing would be expected to play a more prominent role in guiding rational and targeted therapy. Yet PPS from Indian hospitals consistently demonstrate that empirical antibiotic use continues to dominate even in these inpatient settings. This disconnect between available diagnostic capacity and its limited influence on prescribing decisions highlights the need to better understand how AST is incorporated or not into routine clinical practice (Wojcik et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, most PPS conducted in India and other LMICs remain single-day, cross-sectional assessments. Such snapshot designs provide limited insight into temporal changes in prescribing behaviour, seasonal variation in infection burden, and how antimicrobial use evolves following treatment initiation, thereby constraining the utility of PPS for stewardship planning and policy development.\u003c/p\u003e \u003cp\u003eTo address these limitations and enhance the analytical value of PPS in understanding antimicrobial prescribing practices, this study examines two key research questions: (i) how antimicrobial prescribing patterns vary across patient groups, wards, and seasons in a tertiary-care hospital; and (ii) how diagnostic practices, particularly culture testing and AST, shape prescribing decisions over time. The present study implements a four-season, mixed-method PPS in a tertiary-care public hospital in northern India. By integrating quantitative PPS data with qualitative ward-level observations and structured inpatient follow-up across one year, the study moves beyond single-timepoint descriptions to generate context-sensitive insights relevant for strengthening antimicrobial stewardship in Indian public hospitals and comparable LMIC settings. Specifically, the study aims to (i) describe antimicrobial prescribing patterns across wards and patient demographic profiles, (ii) assess seasonal variation in infection profiles and antibiotic use, and (iii) examine the extent to which AST informs prescription review and modification.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study team\u003c/h2\u003e\n \u003cp\u003eThe study was conducted as part of the EquityAMR project, a four-year collaborative research initiative (2021\u0026ndash;2025) between India and Norway that examined AMR through a health equity and health systems lens. Embedding the PPS within the EquityAMR framework enabled the integration of quantitative PPS antimicrobial prescribing data with qualitative ward-level observations to explore how diagnostic access, clinical workflows, and institutional practices shape antimicrobial use and stewardship in public hospital settings. A multidisciplinary research team of five members with training in microbiology, infectious diseases, anthropology, and health informatics participated in the study. All data collectors were trained in the WHO PPS methodology, standardised data extraction procedures, and inter-observer reliability checks prior to data collection. The team visited inpatient wards during each survey phase and was responsible for planning, implementing, and collecting data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Study context\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Hospital setting\u003c/h2\u003e\n \u003cp\u003eThis study was conducted at Dr. Rajendra Prasad Government Medical College (RPGMC), Tanda, a tertiary-care public teaching hospital located in Kangra district of Himachal Pradesh, northern India. Situated at approximately 720 metres above sea level in the foothills of the Dhauladhar mountain ranges, the hospital serves as a major referral centre for a predominantly rural and geographically dispersed population across Kangra and neighbouring districts, including Chamba, Mandi, and Hamirpur. Access to healthcare in these regions is shaped by hilly terrain, seasonal road connectivity, and limited availability of specialised services (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRPGMC Tanda manages a high inpatient load across medical, surgical, obstetric, and critical care services, with marked seasonal fluctuations in admissions. Medicine and Surgery wards account for the largest proportion of inpatient admissions throughout the year, together contributing more than two-thirds of the hospital\u0026rsquo;s inpatient load, followed by Obstetrics and Gynaecology, while the Intensive Care Unit (ICU) and Neonatal/Paediatric Intensive Care Units (NICU/PICU) manage a smaller but clinically complex patient population. Monsoon months are associated with increased gastrointestinal and water-borne infections, winter with a higher burden of respiratory illnesses, and summer with dehydration-related, enteric, and trauma cases. These seasonal surges often coincide with constraints in diagnostic capacity, bed availability, and clinical workload, increasing reliance on empirical treatment decisions. This combination of high patient turnover, seasonal variation in disease profiles, and resource limitations makes RPGMC Tanda a relevant setting for examining antimicrobial prescribing practices in a public hospital context.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Capturing multi-seasonal antimicrobial and disease data\u003c/h2\u003e\n \u003cp\u003eThe site was selected to conduct a multi-seasonal PPS to capture temporal changes in prescribing behaviour and the operational factors influencing antimicrobial use.\u003c/p\u003e\n \u003cp\u003eThe study captured four distinct seasonal periods that reflect the climatic and epidemiological patterns of the north-western Himalayan region. Autumn (October\u0026ndash;November) is characterised by post-monsoon transition with moderate temperatures (15\u0026ndash;25\u0026deg;C) and declining rainfall (India Meteorological Department, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Summer (April\u0026ndash;June) is marked by warmer temperatures (25\u0026ndash;35\u0026deg;C), increased patient inflow due to dehydration, gastrointestinal illnesses, and trauma-related admissions. The monsoon season (July\u0026ndash;September) brings heavy rainfall (average annual rainfall exceeding 2,000 mm), high humidity, and an increased burden of waterborne, gastrointestinal, and vector-borne infections. Winter (December\u0026ndash;February) is characterized by colder temperatures (0\u0026ndash;15\u0026deg;C), particularly in surrounding hilly areas, and an increase in respiratory tract infections and exacerbations of chronic diseases such as chronic respiratory illnesses and cardiovascular conditions. These seasonal variations influence both disease profiles and healthcare utilisation patterns, providing a strong rationale for employing a multi-seasonal PPS design to examine temporal changes in antimicrobial prescribing (Laxminarayan and Chaudhury, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Study design, data collection tools\u003c/h2\u003e\n \u003cp\u003eThe study was conducted from 2023 to 2024 and employed a multi-seasonal, mixed-methods PPS design to capture antimicrobial prescribing patterns across various wards in the tertiary hospital (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). An initial pilot phase was undertaken to adapt the WHO PPS tool to local medical record systems and ward workflows prior to full-scale implementation. A mixed methods approach was adopted, including: i) use of the PPS tool to collect quantitative data on antimicrobial use; ii) qualitative data based on meetings and discussions with treating staff to understand some of the reasons behind the adoption of certain practices.\u003c/p\u003e\n \u003cp\u003eA unique feature of the study design was its iterative nature, where, after each iteration, based on feedback received through self-reflection of the researchers, the tool was incrementally improved.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePurpose, Key reflections on process, and Modifications made for the following phase.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePurpose\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKey reflection\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModification introduced\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\u003ePhase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline PPS implementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncomplete documentation and weak linkage between prescriptions and AST data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefined variable definitions and standardised data abstraction procedures\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcess stabilisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLimited visibility of AST influence on prescribing decisions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntroduced structured inpatient follow-up to track culture testing and treatment modification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContextual observation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrescribing influenced by drug availability, stock-outs, and ward-level practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo modification to PPS tool; contextual observations documented\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhase 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsolidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkflow and handover gaps across wards persisted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo further modifications; data collection procedures maintained\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Methodology: Selection of wards, patients, and timing\u003c/h2\u003e\n \u003cp\u003eData collection was conducted on pre-designated survey days, selected in advance by the study team in consultation with the hospital Principal Investigator (PI), in accordance with WHO PPS methodology. On each survey day, all admitted patients from five inpatient departments, Medicine, Surgery, Obstetrics and Gynaecology, Neonatal/Paediatric Intensive Care Units (NICU/PICU), and the Intensive Care Unit (ICU), were included. These wards were selected because they contributed to a high volume of clinical samples to the microbiology laboratory and represented areas with substantial antimicrobial utilisation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Patient selection\u003c/h2\u003e\n \u003cp\u003eOnly patients who were already hospitalized in the ward at 08:00 on the day of the survey were included in the survey. Patients admitted to the ward after 08:00 were excluded (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Neonates born on the day of data collection before 8:00 a.m. were also included in the survey. All patients meeting the eligibility criteria were included in the survey, regardless of whether they were receiving antimicrobial treatment or not. All the eligible patients went through the consent approval processes, in either verbal or written form.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.6 Daycare patients were excluded, such as:\u003c/strong\u003e\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003ePatients undergoing treatment or surgery who were discharged the same day\u003c/li\u003e\n \u003cli\u003ePatients seen in outpatient departments\u003c/li\u003e\n \u003cli\u003ePatients in the emergency room\u003c/li\u003e\n \u003cli\u003eOutpatient dialysis patients\u003c/li\u003e\n \u003cli\u003eDischarged patients who remained as lodgers while waiting for transportation\u003c/li\u003e\n \u003cli\u003eParents or relatives of admitted children who stayed as lodgers in the ward to nurse them\u003c/li\u003e\n \u003cli\u003ePatients receiving outpatient parenteral antimicrobial therapy (OPAT)\u003c/li\u003e\n \u003c/ol\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Antimicrobials selection\u003c/h2\u003e\n \u003cp\u003eOnly antimicrobials listed in Annex XI (as per WHO guidelines) and administered through oral, parenteral, rectal, or inhalation routes were included in the survey.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInclusion criteria and examples of exclusion criteria by the levels of stratification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInclude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExclude\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\u003e\u003cstrong\u003eHospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute care hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNursing homes, Rehabilitation centers, and Psychiatric centers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWard\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute care inpatient wards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLong-term care wards, Emergency departments (except for wards attached to the departments), Day surgery wards, Day care wards (e.g., renal dialysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatients hospitalized as inpatients at or before 08:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalized after 08:00, Outpatient clinic, Day surgery/day treatment, Emergency room, Outpatient dialysis, discharged patients waiting for transportation, Parents/relatives of admitted children, Outpatient parenteral antimicrobial therapy (OPAT)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntimicrobial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHO-listed antimicrobials, administered orally, parenterally, rectally, or through inhalation. Ongoing treatment at 08:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTopical antimicrobials, Ophthalmologic antimicrobials, Treatment initiated after 08:00, Treatment discontinued before 08:00.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Sampling approach\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eThe sampling was done in each ward on the day of the survey through the following procedure:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1. On the day of the survey, the researcher prepared a list of all eligible patients according to the inclusion criteria in each ward.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2. From this list, the investigator selected one out of two patients until the end of the list was reached. Given that the hospital has 500-800 total inpatient beds, every second patient from each ward was included in the survey, according to WHO guidelines.\u003c/p\u003e\n \u003cp\u003e3. If a selected patient was not present in the ward, for example, due to surgery or radiology examination, and his/her patient records and associated documentation were not available at the time of the survey, the investigator either chose to return later during the same day or select the next patient on the list.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Data collection procedures\u003c/h2\u003e\n \u003cp\u003eQuantitative data were collected using the standard WHO PPS guidelines and data collection templates. The study team reviewed patients\u0026rsquo; medical records, bedside treatment charts, antimicrobial prescription records, and available microbiology culture and AST reports. Where required, clarifications related to prescription details were obtained from treating clinicians, including ward residents and nursing staff. All quantitative data were recorded using the WHO PPS data collection form.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10 Structured patient follow-up and ward-level observations\u003c/h2\u003e\n \u003cp\u003eTo complement the quantitative PPS data, structured patient-level follow-up and ward-level observations were conducted. This included documenting culture testing practices, availability and review of AST reports, prescription review or modification, and operational factors influencing antimicrobial prescribing. These data were collected using structured field notes and predefined data elements to contextualize prescribing patterns and were not intended as formal qualitative interviews or thematic qualitative analysis.\u003c/p\u003e\n \u003cp\u003eData were collected using the standard WHO PPS data collection guidelines and template. The study team reviewed patients\u0026rsquo; medical record files, bedside treatment charts, prescribed antimicrobial records, and available microbiology culture reports. Where required, clarifications were obtained from treating clinicians, including ward residents and nursing staff, during data extraction. All data were entered into the WHO PPS form at the time of review.\u003c/p\u003e\n \u003cp\u003eInformation collected included hospital and ward characteristics, patient demographics, and the presence of at least one antimicrobial prescription on the day of the survey. For patients receiving antimicrobials, detailed prescription-level data were recorded for each agent, including drug name, dose and frequency, route of administration, indication for use, and classification as empirical, prophylactic, or culture-guided therapy. Additional variables included documentation of diagnosis, date of antimicrobial initiation, and classification of infection as community-acquired or hospital-acquired.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.11 Analysis: Parameters calculated\u003c/h2\u003e\n \u003cp\u003eThe following parameters were calculated based on the data collected.\u0026nbsp;\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003eThe number/percentage of patients prescribed antimicrobials and categorization of antimicrobial prescriptions as empiric, prophylactic, or lab-based.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe number/percentage of antimicrobials administered by the oral or parenteral route, categorization of antimicrobial prescriptions based on the indication, i.e., medical prophylaxis, surgical prophylaxis, unknown infections, or others.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe number/percentage of patients on designated antimicrobials, overall consumption of antimicrobials by class, prevalence of antimicrobial use by ward type, prevalence of broad-spectrum antimicrobial prescribing, AWaRe category, and percentage of antimicrobial prescriptions given by parenteral or oral route.\u003c/li\u003e\n \u003c/ol\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e2.12 Qualitative data for structured patient follow-up\u003c/h2\u003e\n \u003cp\u003eAs part of the survey, a structured follow-up was conducted to capture longitudinal insights into antimicrobial use, culture testing, and prescribing practices beyond the scope of the cross-sectional quantitative study. Eligible patients included those admitted before 8:00 AM on the survey day, especially those receiving antimicrobials, undergoing catheterization, intubation, or transferred between wards. For each, the day of antibiotic initiation, the basis of prescription (empirical or guided), and the indication were recorded. The team tracked when and what clinical samples (e.g., blood, urine, wound) were sent for microbiological testing, and whether prescriptions were subsequently adjusted based on AST results. Length of hospital stay, antibiotic continuation at discharge, and any outpatient follow-up advice were also documented. Particular attention was given to high-risk groups with variable antibiotic use. Data were collected from patient records, laboratory reports, and ward staff inputs, ensuring anonymization and systematic entry into structured forms for analysis. This follow-up provided critical insights into antibiotic treatment timelines, gaps in culture use, and real-world prescribing behavior to inform antimicrobial stewardship interventions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e2.13 Data Analysis\u003c/h2\u003e\n \u003cp\u003eData analysis comprised three analytical components: (i) patient and ward-level prescribing profiles, (ii) categorization of antimicrobial use, and (iii) assessment of seasonal variation in prescribing.\u003c/p\u003e\n \u003cp\u003ePatient profiles were developed to describe demographic and clinical characteristics of inpatients receiving antimicrobials, including ward type, infection classification, route of administration, and indication for use. This analysis was descriptive and aimed to contextualize prescribing patterns across inpatient settings.\u003c/p\u003e\n \u003cp\u003eAntimicrobial prescriptions were categorized by drug class, AWaRe classification, indication, and basis of prescription (empirical or culture-guided). Frequencies and proportions were used to examine patterns of antimicrobial use across wards and patient groups.\u003c/p\u003e\n \u003cp\u003eSeasonal variation in prescribing was assessed by comparing antimicrobial prevalence, spectrum of use, and AWaRe category distribution across survey phases and seasonal diagnosis patterns using tabular and graphical methods.\u003c/p\u003e\n \u003cp\u003eThe quantitative data extracted from WHO PPS forms were entered into Microsoft Excel and analysed using descriptive statistics in accordance with Global-PPS definitions.\u003c/p\u003e\n \u003cp\u003eData from structured patient follow-up and ward-level observations were analysed using an inductive, interpretive approach. Initial codes included empirical prescribing rationale, delays in culture sampling, non-review of AST reports, drug availability constraints, and documentation practices. These were grouped into broader themes such as diagnostic\u0026ndash;prescribing disconnects, operational barriers to culture-guided therapy, and workflow-driven continuation of empirical antibiotics. Triangulation was undertaken by comparing observational themes with quantitative PPS findings across wards and seasons.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThis analysis part is organised to describe patient and ward-level characteristics, overall antimicrobial prescribing patterns, seasonal variation in antimicrobial use across PPS phases, and insights from structured inpatient follow-up and ward-level observations that contextualise prescribing practices.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient and Ward-Level Characteristics\u003c/h2\u003e \u003cp\u003eAcross the four PPS phases, the inpatient population was predominantly adult and consistently drawn from medical and surgical wards, which together accounted for the majority of admissions in each survey round (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While the overall ward mix remained broadly stable, seasonal variations were observed, with a relatively greater contribution of surgical admissions during the summer and autumn, and a higher representation of medical admissions during the monsoon and winter periods. Obstetrics and Gynaecology admissions showed minimal seasonal fluctuation, reflecting routine service utilisation. Critical care units, including ICU and NICU/PICU, constituted a smaller proportion of total inpatients across all phases but represented a clinically complex subgroup. The age profile was dominated by middle-aged and older adults, with paediatric admissions varying across seasons, particularly during the monsoon and winter. Collectively, the observed patient case-mix and ward distribution across PPS phases established a stable yet seasonally differentiated context for interpreting subsequent analyses of antimicrobial prescribing patterns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographic and ward characteristics across four PPS phases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPS1 (Autumn) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPS2 (Summer) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPS3 (Monsoon) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPS4 (Winter) n (%)\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\u003eNumber of patients surveyed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWard distribution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedicine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (31.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165 (43.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObstetrics and Gynaecology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (10.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNICU/PICU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e226 (58.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161 (42.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u0026ndash;19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u0026ndash;39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (25.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e40\u0026ndash;59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (32.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109 (28.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the inpatient population and ward composition remained broadly stable across PPS phases, with Medicine and Surgery accounting for most admissions, alongside modest seasonal variation in ward contribution and age distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Antimicrobial categorization and prescribing patterns\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises antimicrobial exposure and prescribing patterns across the four PPS phases. Antimicrobial exposure remained high across all PPS phases, although a gradual decline in the proportion of patients receiving antibiotics was observed over time (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Empirical prescribing consistently dominated, accounting for approximately two-thirds of prescriptions in every survey round, while AST-guided therapy remained below 6% across all phases. Combination therapy was common, with nearly half of patients receiving two antimicrobials in most PPS rounds. Parenteral administration exceeded 80% throughout the study period, indicating a strong reliance on injectable antibiotics. Prophylactic use constituted a substantial and persistent share of prescribing, particularly in surgical wards.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAntimicrobial use and prescribing characteristics across four PPS phases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial use characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPS1 (Autumn) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPS2 (Summer) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPS3 (Monsoon) n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPS4 (Winter) n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients receiving\u0026thinsp;\u0026ge;\u0026thinsp;1 antimicrobial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e316 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e185 (48.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients not receiving antimicrobials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e164 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e202 (52.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of antimicrobials per patient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne antimicrobial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98 (53.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo antimicrobials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70 (38.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree antimicrobials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrescribing intent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpirical therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e194 (67.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinitive (AST-guided) therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProphylactic use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84 (29.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRoute of administration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParenteral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e428 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322 (88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255 (88.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (11.5)\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=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spectrum and Drug Class of Commonly Prescribed Antibiotics\u003c/h2\u003e \u003cp\u003eAcross all PPS phases, prescribing was dominated by broad-spectrum antibiotics, particularly third-generation cephalosporins and beta-lactam/beta-lactamase inhibitor combinations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Narrow-spectrum agents contributed a smaller proportion of prescriptions across seasons. Consistent with the AWaRe distribution, most commonly prescribed antibiotics belonged to the Watch category, while Reserve antibiotics were used sparingly.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAWaRe Classification, Spectrum, and Drug Class of Commonly Prescribed Antibiotics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAWaRe Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpectrum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrug Class\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\u003eCeftriaxone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThird-generation Cephalosporin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAzithromycin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMacrolide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetronidazole\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNarrow-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitroimidazole\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePiperacillin-Tazobactam (Pip-Taz)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtended-spectrum Penicillin\u0026thinsp;+\u0026thinsp;β-lactamase inhibitor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDoxycycline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTetracycline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCefuroxime\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecond-generation Cephalosporin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmikacin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAminoglycoside\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmoxicillin-Clavulanic Acid (Amoxyclav)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePenicillin\u0026thinsp;+\u0026thinsp;β-lactamase inhibitor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeropenem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReserve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarbapenem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVancomycin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNarrow-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlycopeptide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCeftriaxone\u0026thinsp;+\u0026thinsp;Sulbactam\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBroad-spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThird-generation Cephalosporin\u0026thinsp;+\u0026thinsp;β-lactamase inhibitor\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=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Seasonal variation in antimicrobial prescribing, with contextual diagnosis patterns\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates seasonal trends in the prescribing of commonly used antibiotics across the four PPS phases. Ceftriaxone remained the most frequently prescribed antibiotic throughout the study period, showing consistently high use across all seasons. In contrast, prescribing of Piperacillin\u0026ndash;tazobactam increased progressively across successive PPS phases, while doxycycline uses also demonstrated a steady upward trend over time. Azithromycin prescribing declined sharply after the first PPS phase and remained low thereafter, whereas Amoxicillin\u0026ndash;clavulanate and Cefuroxime showed variable seasonal patterns. Amikacin and Meropenem were prescribed less frequently across all phases, with only minor seasonal fluctuations. Notably, Ceftriaxone\u0026ndash;sulbactam, a non-recommended fixed-dose combination, continued to be prescribed across all PPS phases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 AWaRe Classification Across PPS Phase\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the distribution of prescribed antibiotics by WHO AWaRe classification across the four PPS phases. Watch-category antibiotics consistently accounted for the largest share of prescriptions in all survey rounds, indicating sustained reliance on broad-spectrum agents. Access antibiotics showed seasonal variation, with relatively higher use during the monsoon phase followed by a decline in winter. Reserve antibiotics remained minimally used throughout the study period, while not-recommended fixed-dose combinations constituted a small but persistent proportion of prescriptions across all PPS seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Distribution of Prescribed Antibiotics by Ward and ATC Classification\u003c/h2\u003e \u003cp\u003eAcross wards, Ceftriaxone, Piperacillin\u0026ndash;tazobactam, and Doxycycline were the most frequently prescribed antibiotics, with clear ward-wise variation. ICU and NICU/PICU settings showed higher use of Piperacillin\u0026ndash;tazobactam and amikacin, whereas surgical wards relied more heavily on Cefuroxime and Metronidazole. Prescribing in Obstetrics and Gynaecology more commonly included Amoxicillin\u0026ndash;clavulanate and Cefuroxime, reflecting relatively greater oral antibiotic use. Overall, broad-spectrum agents predominated across all wards (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eATC-based classification showed that cephalosporins (J01D) were the predominant antibiotic class across all PPS phases, followed by Penicillins (J01C) and Tetracyclines (J01A), with seasonal variation in relative contributions across survey rounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Distribution of Patients Diagnoses Across PPS Phases\u003c/h2\u003e \u003cp\u003eAcross all PPS phases, gastrointestinal disorders (20.6\u0026ndash;22.2%) and chronic diseases (17.0\u0026ndash;23.5%) were the most frequent diagnoses among surveyed patients, followed by urological/gynaecological (8.0\u0026ndash;11.0%) and neurological conditions (7.0\u0026ndash;12.3%). Respiratory tract infections reached a maximum in PPS2 (9.6%) and PPS4 (9.0%), while other categories, including post-surgical cases, malignancies, cardiovascular diseases, and bacterial infections, contributed smaller proportions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Findings from structured inpatient follow-up on antibiotic use and culture testing\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarises findings from the structured inpatient follow-up conducted during PPS2\u0026ndash;PPS4. The highest proportion of culture samples was sent from the Medicine ward (11\u0026ndash;13%), followed by Surgery and ICU. Across all phases, positive cultures accounted for 7.5\u0026ndash;10.0% of samples, while sterile cultures constituted 15\u0026ndash;18%. Antibiotics were frequently initiated before the availability of AST results (18.6\u0026ndash;21.6%), and 8.5\u0026ndash;9.6% of prescriptions were continued despite sterile culture findings. Modification of antibiotic therapy based on AST results was observed in only 4.7\u0026ndash;6.5% of cases, highlighting the limited integration of AST reports into inpatient prescribing decisions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFindings from structured inpatient follow-up on culture use and antibiotic modification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPS2 (Summer)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPS3 (Monsoon)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPS4 (Winter)\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\u003eTotal patients surveyed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWard-wise culture sampling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICU/PICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetrics \u0026amp; Gynaecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCulture results\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive cultures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSterile cultures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntibiotic prescribing in relation to AST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotics started before AST availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotics prescribed despite sterile cultures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrescription modified after AST results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (4.7%)\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=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Qualitative Observations: Thematic Insights\u003c/h2\u003e \u003cp\u003eWard-level observations conducted across all four PPS phases provided contextual insights into the operational, behavioural, and system-level factors shaping antimicrobial prescribing practices across wards. These observations complemented quantitative PPS findings by highlighting routine clinical workflows, diagnostic practices, and institutional constraints influencing prescribing decisions. The key themes identified are summarised below.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1. Predominance of Empirical Prescribing and Delayed Culture Testing\u003c/h3\u003e\n\u003cp\u003eAcross wards, empirical antibiotic prescribing was the dominant initial treatment approach, with antimicrobials frequently initiated before the availability of microbiological culture and AST results. In Medicine wards and ICUs, culture samples were typically sent after the first 48\u0026ndash;72 hours of admission, while in Surgery wards, cultures were largely limited to overt postoperative infections, most commonly wound or urine samples. Although the ICU demonstrated more systematic microbiological surveillance compared to other wards, antibiotic de-escalation following sterile culture reports was uncommon. Observations suggested that empirical prescribing was shaped by a combination of operational pressures, perceived urgency of clinical decision-making, concerns regarding patient affordability of diagnostics, and limited feedback mechanisms linking microbiology results to prescribing teams.\u003c/p\u003e\n\u003ch3\u003e2. Antibiotic Supply Constraints and Availability-Driven Prescribing\u003c/h3\u003e\n\u003cp\u003ePrescribing practices varied across PPS phases in ways that appeared closely linked to antibiotic availability and stock-outs. Oral antibiotics, including azithromycin, were observed to be prescribed less frequently during later PPS phases due to supply constraints, with patients or attendants often instructed to procure medications externally. This contributed to increased reliance on parenteral formulations, particularly ceftriaxone in Medicine wards and cefuroxime in Surgery. Limited availability of narrow-spectrum agents further constrained guideline-aligned prescribing, frequently necessitating the use of broader-spectrum alternatives. The absence of a formal system for advanced communication regarding stock shortages meant that prescribing decisions were often made without awareness of inventory limitations, reinforcing supply-driven patterns of antimicrobial use.\u003c/p\u003e\n\u003ch3\u003e3. Documentation and Communication Gaps During Care Transitions\u003c/h3\u003e\n\u003cp\u003eWard observations revealed gaps in documentation and communication during patient transfers between wards, occasionally resulting in missed antibiotic doses or unclear treatment continuation. These issues were attributed to high patient volumes, heavy nursing workloads, and the absence of standardised handover protocols. Such gaps have implications for patient safety, continuity of care, and the reliability of antimicrobial documentation, with potential downstream effects on stewardship monitoring and quality improvement efforts.\u003c/p\u003e\n\u003ch3\u003e4. ICU-Specific Prescribing and Care Patterns\u003c/h3\u003e\n\u003cp\u003ePatients admitted to ICUs represented a distinct subgroup characterised by longer hospital stays, frequent use of multiple concurrent antibiotics, and intensive diagnostic monitoring. Culture samples were sent more regularly from ICU as compared to other wards, sometimes repeatedly for individual patients. Despite this, antibiotics were often continued even in the presence of repeated sterile culture results, reflecting the high perceived risk of undertreatment in critically ill patients. ICU care was marked by frequent ward rounds and close clinical supervision; however, the density of equipment and patient acuity posed challenges to maintaining optimal ward conditions despite regular cleaning schedules.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected Quotes and Observations from Ward-Level Qualitative Data.\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\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWard/Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuotes\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\u003eOral antibiotic stock-outs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse (Medicine/Surgery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Most of the oral antibiotics are not in supply, so IVs are given in most cases. If a patient needs oral antibiotics from outside, we write it on a slip, and the attendant brings it from the pharmacy.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreoperative antibiotic use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor (Surgery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;Before operating on any patient, we start with broad-spectrum antibiotics so that during the procedure, the patient doesn\u0026rsquo;t get an infection. If an infection still occurs, we immediately send a wound culture.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCulture testing practices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor (Surgery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;We send wound cultures of patients with skin wounds or urine of catheterized patients. Blood cultures are rarely sent because we mostly deal with onsite infections.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMissed dose after transfer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse (Surgery and Medicine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;It is the duty of the previous nurses from the earlier ward to remind us, as we have so many patients to deal with.\u0026rdquo;\u003c/em\u003e (explaining why a ceftriaxone dose was missed during patient transfer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU culture testing routine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICU Observation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;From the ICU, daily, a sample for culture was sent to the microbiology lab.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorkload during patient transfer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse (General comment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;We have so many patients to handle that sometimes small details are missed unless handed over properly.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWard rounds and workflow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation (All wards)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFour rounds daily: first, third, fourth by JRs; second major round by senior doctors with JRs and 3rd-year students. Whiteboards are updated daily with patient counts and staff details, guiding rounds.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNursing student involvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation (All wards)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNursing college students (4\u0026ndash;5 per ward) are actively involved in dressing patients, administering medications, and managing IV fluids under the supervision of senior nurses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU antibiotic continuation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICU Observation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDespite multiple sterile culture results, antibiotics were rarely stopped, reflecting cautious prescribing due to critical patient conditions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBed doubling in December\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation (Medicine/Surgery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBed capacity was temporarily doubled in December to manage a seasonal surge, increasing the strain on resources and workload.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-discharge antibiotic use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation (Surgery)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePostoperative patients usually completed their antibiotic course during admission; they were rarely prescribed antibiotics at discharge.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCleanliness across wards\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation (All wards)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurgery and Medicine wards were observed to be cleaner and better maintained compared to the ICU, NICU, and Gynaecology, despite all wards following a four-times-daily cleaning protocol.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis multi-seasonal point prevalence survey provides a detailed assessment of antimicrobial use in a tertiary-care teaching hospital in northern India, combining prescription-level data with qualitative observations of clinical practice. By integrating quantitative patterns with operational and behavioural insights from wards, the study offers a comprehensive understanding of antimicrobial decision-making in a resource-constrained setting. In our findings, antibiotic use declined from 62% in PPS1 to 48% in PPS4, yet remained higher than benchmark usage levels reported from European hospitals (25\u0026ndash;35%), and comparable to rates reported from several LMIC settings including Ghana (65%), Kenya (82%) (Afriyie et al., 2020; Sharma et al., 2015; Plachouras et al., 2018), Benin (64.6%), Vietnam (67.4%), and Nigeria (78.6%) (Ahoyo et al., 2014; Thu et al., 2012; Oduyebo et al., 2017). Earlier studies from India reported antibiotic use prevalence of around 50.3% across tertiary centres [10], while private-sector data showed even higher rates (up to 84%) (Sharma et al., 2015). Although the study did not assess appropriateness, the consistently high prevalence suggests substantial potential to optimize prescribing practices in this setting.\u003c/p\u003e\n\u003cp\u003eAcross all PPS rounds, 63\u0026ndash;67% of antimicrobial prescriptions were empirical, while definitive therapy guided by culture and susceptibility testing remained very low (4.1\u0026ndash;6.5%). These findings align with other Indian PPS studies that demonstrate limited microbiological utilisation (\u003cstrong\u003eBhattacharjee et al., 2024)\u003c/strong\u003e. Only 10\u0026ndash;18% of followed-up patients underwent culture testing, and modifications based on AST occurred in just 4.7\u0026ndash;6.5% of cases. Qualitative observations revealed that cultures were often collected on Days 2\u0026ndash;3, yielding predominantly sterile results, thereby reducing their relevance to prescribing decisions. Clinicians also cited financial concerns for patients as a barrier to early culture testing, an observation consistent with studies highlighting economic drivers of empirical prescribing in LMIC contexts. Together, these findings suggest that delays in culture collection and limited confidence in microbiology outputs diminish the practical utility of AST in routine inpatient care. Prophylactic antibiotic use accounted for 24\u0026ndash;30% of prescriptions, higher than proportions reported in European PPS networks, where medical prophylaxis accounted for 15% and surgical prophylaxis for 6.7% (Van der Meer et al., 2005). In our study, broad-spectrum agents such as Ceftriaxone and Cefuroxime were frequently initiated preoperatively or when infection was suspected, reflecting established norms around preventive coverage. Staff interviews supported these findings, indicating that \u0026ldquo;broad-spectrum antibiotics are started before operating to prevent infection during exposure.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe AWaRe classification further highlights stewardship priorities. In our study, Watch-group antibiotics, particularly Ceftriaxone, represented the largest share of prescriptions (45\u0026ndash;56%) across all PPS rounds. This sustained reliance on Watch-group agents reflects a risk-averse prescribing culture in the absence of timely diagnostic confirmation. This parallels findings from Sub-Saharan Africa and other LMICs where Ceftriaxone is widely used due to broad coverage and convenient dosing (Kiggundu et al., 2022; Abubakar and Salman, 2024). Not-recommended FDCs such as Ceftriaxone\u0026ndash;sulbactam accounted for 3.8\u0026ndash;5.1% of prescriptions, despite WHO discouragement. Seasonal variations were observed, including increased use of doxycycline and Piperacillin\u0026ndash;tazobactam during monsoon and winter. These patterns underscore the necessity of developing local antibiograms to support data-driven empirical therapy and reduce reliance on broad-spectrum agents. Similar trends have been reported from hospitals in Southeast Nigeria and Ghana, though local resistance profiles and access constraints likely influence differences seen across settings (Umeokonkwo et al., 2019; Afriyie et al., 2020).\u003c/p\u003e\n\u003cp\u003eWe reported that more than 80% of antibiotics were administered parenterally in all PPS rounds, exceeding the 59.9% parenteral-use rate reported in Ghanaian hospitals (Amponsah et al., 2021) and reflecting trends in several LMIC studies (Hodoșan et al., 2023). Interviews with nurses revealed that stock-outs of oral antibiotics often led clinicians to rely on intravenous formulations, as attendants were asked to purchase oral agents externally. This supply-driven prescribing pattern increases treatment complexity and prolongs intravenous therapy. In our findings, combination therapy was frequent, particularly in ICU settings, where patients often had prolonged stays and underwent repeated culturing. Common combinations included Ceftriaxone with Azithromycin and Doxycycline with Metronidazole. While combination therapy can be clinically justified in severe infections, high rates may also reflect attempts to broaden coverage in the absence of timely diagnostic guidance. European data reported 29.4% combination therapy (Plachouras et al., 2018), whereas studies from France and earlier surveys reported even higher rates (40\u0026ndash;42.6%) (Robert et al., 2012; Dodoo et al., 2021). Redundant combinations may arise due to systemic gaps in prescribing practices, involvement of multiple practitioners, and limited understanding of antibiotic spectra, as noted in prior literature (Laxminarayan and Chaudhury, 2016; Kotwani et al., 2010).\u003c/p\u003e\n\u003cp\u003eOur qualitative observations also highlighted workflow challenges such as documentation gaps, missed doses during inter-ward transfers, absence of structured handover protocols, and limited communication of laboratory results, all of which constrain rational antibiotic use. Stock-outs were often not communicated systematically, leaving prescribers unaware of changes in inventory and contributing to reactive prescribing patterns. This study has several limitations. It is a single-centre assessment, and its findings may not be generalizable to other Indian hospitals with different case mixes or diagnostic capacity. The PPS design captures prescribing on one day per season and does not measure appropriateness or clinical outcomes. Documentation inconsistencies may have influenced data accuracy, and delayed culture reporting may have affected AST-guided therapy. Nonetheless, the study\u0026rsquo;s strengths include its multi-seasonal design, incorporation of follow-up data, and integration of qualitative insights that contextualize quantitative findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Antimicrobial Stewardship\u003cbr\u003e\u003c/strong\u003eThe findings from this multi-seasonal PPS highlight both quantitative prescribing patterns and qualitative operational challenges that directly inform antimicrobial stewardship priorities. By identifying system-level gaps in diagnostics, procurement, and prescribing behaviour, this study offers practical, evidence-based recommendations for strengthening AMS implementation in resource-constrained tertiary settings:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eThe consistently low proportion of culture-based prescribing despite the availability of culture test facilities signals an urgent need to embed diagnostic testing more coherently into routine clinical workflows. Timely culture collection and feedback mechanisms must be institutionalized to reduce empirical overuse.\u003c/li\u003e\n \u003cli\u003eEmpirical reliance on broad-spectrum Watch group antibiotics like ceftriaxone underscores the absence of data-informed prescribing. Creating and disseminating department-wise antibiograms quarterly would help tailor empiric treatment protocols and encourage de-escalation.\u003c/li\u003e\n \u003cli\u003eThe dominance of the Watch category and the use of non-recommended FDCs point to gaps in stewardship oversight. Prescribers should be guided toward greater use of Access group antibiotics in line with WHO targets, supported by local evidence and formulary restrictions.\u003c/li\u003e\n \u003cli\u003eStock-outs of oral antibiotics and lack of supply-chain transparency result in default parenteral prescribing, increasing costs and complexity. Streamlining procurement, ensuring real-time stock visibility, and enabling oral-to-IV switching protocols are crucial interventions.\u003c/li\u003e\n \u003cli\u003eA multidisciplinary AMS team, including infectious disease physicians, microbiologists, and pharmacists, should lead clinical audits, participate in ward rounds, and provide case-based feedback. This participatory model will support both behavior change and system redesign.\u003c/li\u003e\n \u003cli\u003eRegular training on the interpretation of AST results, narrow-spectrum prescribing, and management of specific syndromes should be made mandatory for junior residents and nursing staff to promote long-term practice transformation.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis multi-seasonal PPS shows sustained high antibiotic use, heavy reliance on empirical broad-spectrum therapy, and limited uptake of culture testing in a tertiary hospital in northern India. The dominance of Watch-group agents, frequent prophylaxis, and workflow constraints highlight the need for stronger diagnostic stewardship and system-level reforms. Implementing routine culture practices, reliable antibiotic supply, department-specific antibiograms, and a multidisciplinary stewardship program will be essential to promote rational, evidence-based prescribing and curb antimicrobial resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted under the EquityAMR project, a four-year collaborative study (2021U2025) ˝ between Norway and India focused on the analysis of health equities and antimicrobial resistance (AMR) in India. The project obtained permissions from the Norwegian Centre for Research Data (NSD) and the Health Ministry Screening Committee (HMSC), Government of India, prior to data collection. To enable data collection ensuring informed consent, anonymization, and adherence to required ethical guidelines, formal approval was obtained from the HISP India Research Ethics Committee. Memorandums of Understanding and Non-Disclosure Agreements were signed between HISP India and the state hospitals to ensure permissions for data collection and maintain data security and integrity. At the individual level, verbal informed consent was obtained from all patients prior to interviews and collection of life experiences, ensuring complete anonymization of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The author(s) declare financial support was received for the research and/or publication of this article. This project is supported by funding received from the Research Council of Norway for the EquityAMR research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the HISP India team and the state government and hospital authorities for their invaluable contributions to this research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbubakar, U., and Salman, M. (2024). Antibiotic use among hospitalized patients in Africa: a systematic review of point prevalence studies. \u003cem\u003eJ. Racial Ethn. Health Disparities\u003c/em\u003e 11(3), 1308\u0026ndash;1329.\u003cbr\u003e doi: 10.1007/s40615-023-01610-9\u003c/li\u003e\n\u003cli\u003eAfriyie, D. K., Sefah, I. A., Seaton, R. A., et al. (2020). Antimicrobial point prevalence surveys in two Ghanaian hospitals: opportunities for antimicrobial stewardship. \u003cem\u003eAntimicrob. Resist. Infect. Control\u003c/em\u003e 9, 191.\u003cbr\u003e doi: 10.1186/s13756-020-00809-3\u003c/li\u003e\n\u003cli\u003eAhoyo, A. T., Bankol\u0026eacute;, H. S., Ad\u0026eacute;oti, F. M., et al. (2014). Prevalence of nosocomial infections and anti-infective therapy in Benin: results of the first nationwide survey in 2012. \u003cem\u003eAntimicrob. Resist. Infect. Control\u003c/em\u003e 3, 17.\u003cbr\u003e doi: 10.1186/2047-2994-3-17\u003c/li\u003e\n\u003cli\u003eAmponsah, O. K. O., Buabeng, K. O., Owusu-Ofori, A., et al. (2021). Point prevalence survey of antibiotic consumption across three hospitals in Ghana. \u003cem\u003eJAC Antimicrob. Resist.\u003c/em\u003e 3(1), dlab008.\u003cbr\u003e doi: 10.1093/jacamr/dlab008\u003c/li\u003e\n\u003cli\u003eBhattacharjee, S., Aarzoo, et al. (2024). Antimicrobial prescription patterns in tertiary care centres in India: a multicentric point prevalence survey. \u003cem\u003eeClinicalMedicine\u003c/em\u003e 82, 103175.\u003cbr\u003e doi: 10.1016/j.eclinm.2024.103175\u003c/li\u003e\n\u003cli\u003eDodoo, C. C., Orman, E., Alalbila, T., et al. (2021). Antimicrobial prescription pattern in Ho Teaching Hospital, Ghana: seasonal determination using a point prevalence survey. \u003cem\u003eAntibiotics\u003c/em\u003e 10(2), 199.\u003cbr\u003e doi: 10.3390/antibiotics10020199\u003c/li\u003e\n\u003cli\u003eHodoșan, V., Daina, L. G., Zaha, D. C., et al. (2023). Pattern of antibiotic use among hospitalized patients at a level one multidisciplinary care hospital. \u003cem\u003eHealthcare\u003c/em\u003e 11(9), 1302.\u003cbr\u003e doi: 10.3390/healthcare11091302\u003c/li\u003e\n\u003cli\u003eHolt, K. E., Carey, M. E., Chandler, C., et al. (2025). Tools and challenges in the use of routine clinical data for antimicrobial resistance surveillance. \u003cem\u003enpj Antimicrob. Resist.\u003c/em\u003e 3, 37.\u003cbr\u003e doi: 10.1038/s44259-025-00105-3\u003c/li\u003e\n\u003cli\u003eIndia Meteorological Department (2020). \u003cem\u003eClimatological normals (1981\u0026ndash;2010): Himachal Pradesh\u003c/em\u003e. Ministry of Earth Sciences, Government of India.\u003cbr\u003e Available at: https://mausam.imd.gov.in\u003c/li\u003e\n\u003cli\u003eKiggundu, R., Wittenauer, R., Waswa, J. P., et al. (2022). Point prevalence survey of antibiotic use across 13 hospitals in Uganda. \u003cem\u003eAntibiotics\u003c/em\u003e 11(2), 199.\u003cbr\u003e doi: 10.3390/antibiotics11020199\u003c/li\u003e\n\u003cli\u003eKotwani, A., Wattal, C., Katewa, S., Joshi, P. C., and Holloway, K. (2010). Factors influencing primary care physicians to prescribe antibiotics in Delhi, India. \u003cem\u003eFam. Pract.\u003c/em\u003e 27(6), 684\u0026ndash;690.\u003cbr\u003e doi: 10.1093/fampra/cmq059\u003c/li\u003e\n\u003cli\u003eLaxminarayan, R., and Chaudhury, R. R. (2016). Antibiotic resistance in India: drivers and opportunities for action. \u003cem\u003ePLoS Med.\u003c/em\u003e 13(3), e1001974.\u003cbr\u003e doi: 10.1371/journal.pmed.1001974\u003c/li\u003e\n\u003cli\u003eMoja, L., Zanichelli, V., Mertz, D., et al. (2024). WHO\u0026rsquo;s essential medicines and AWaRe: recommendations on first- and second-choice antibiotics for empiric treatment of clinical infections. \u003cem\u003eClin. Microbiol. Infect.\u003c/em\u003e 30(Suppl. 2), S1\u0026ndash;S51.\u003cbr\u003e doi: 10.1016/j.cmi.2024.02.001\u003c/li\u003e\n\u003cli\u003eModgil, V., Sahay, S., Taneja, N., et al. (2025). Enhancing access to antimicrobial resistance diagnostics for the marginalised: challenges and opportunities of point-of-care technologies. \u003cem\u003eJ. Glob. Antimicrob. Resist.\u003c/em\u003e 44, 281\u0026ndash;286.\u003cbr\u003e doi: 10.1016/j.jgar.2025.06.012\u003c/li\u003e\n\u003cli\u003eOduyebo, O. O., et al. (2017). Antimicrobial use and resistance in Nigeria: findings from a point prevalence survey in Lagos. \u003cem\u003eNiger. J. Clin. Pract.\u003c/em\u003e 20(9), 1080\u0026ndash;1087.\u003cbr\u003e doi: 10.4103/1119-3077.181376\u003c/li\u003e\n\u003cli\u003ePauwels, I., Versporten, A., Ashiru-Oredope, D., et al. (2025). Implementation of hospital antimicrobial stewardship programmes in low- and middle-income countries: a qualitative study from a multiprofessional perspective in the Global-PPS network. \u003cem\u003eAntimicrob. Resist. Infect. Control\u003c/em\u003e 14, 26.\u003cbr\u003e doi: 10.1186/s13756-025-01541-6\u003c/li\u003e\n\u003cli\u003ePlachouras, D., K\u0026auml;rki, T., Hansen, S., et al. (2018). Antimicrobial use in European acute care hospitals: results from the second point prevalence survey of healthcare-associated infections and antimicrobial use, 2016\u0026ndash;2017. \u003cem\u003eEuro Surveill.\u003c/em\u003e 23(46), 1800393.\u003cbr\u003e doi: 10.2807/1560-7917.ES.23.46.1800393\u003c/li\u003e\n\u003cli\u003eRobert, J., P\u0026eacute;an, Y., Varon, E., et al. (2012). Point prevalence survey of antibiotic use in French hospitals in 2009. \u003cem\u003eJ. Antimicrob. Chemother.\u003c/em\u003e 67(4), 1020\u0026ndash;1026.\u003cbr\u003e doi: 10.1093/jac/dkr571\u003c/li\u003e\n\u003cli\u003eSharma, A., Singh, A., Dar, M. A., et al. (2022). Menace of antimicrobial resistance in LMICs: current surveillance practices and control measures to tackle hostility. \u003cem\u003eJ. Infect. Public Health\u003c/em\u003e 15(2), 172\u0026ndash;181.\u003cbr\u003e doi: 10.1016/j.jiph.2021.12.008\u003c/li\u003e\n\u003cli\u003eSharma, M., Damlin, A., Pathak, A., and St\u0026aring;lsby Lundborg, C. (2015). Antibiotic prescribing among pediatric inpatients with potential infections in two private sector hospitals in Central India. \u003cem\u003ePLoS ONE\u003c/em\u003e 10(11), e0142317.\u003cbr\u003e doi: 10.1371/journal.pone.0142317\u003c/li\u003e\n\u003cli\u003eThu, T. A., Rahman, M., Coffin, S., et al. (2012). Antibiotic use in Vietnamese hospitals: a multicenter point-prevalence study. \u003cem\u003eAm. J. Infect. Control\u003c/em\u003e 40(9), 840\u0026ndash;844.\u003cbr\u003e doi: 10.1016/j.ajic.2011.10.020\u003c/li\u003e\n\u003cli\u003eVan der Meer, J. W. M., Gyssens, I. C., and ESAC Project Group (2005). Antimicrobial use in European hospitals: results of the ESAC point-prevalence survey. \u003cem\u003eClin. Microbiol. Infect.\u003c/em\u003e 11(Suppl. 2), 31\u0026ndash;38.\u003cbr\u003e doi: 10.1111/j.1469-0691.2005.01112.x\u003c/li\u003e\n\u003cli\u003eVersporten, A., Zarb, P., Caniaux, I., et al. (2018). Antimicrobial consumption and resistance in adult hospital inpatients: results of a global point prevalence survey. \u003cem\u003eLancet Glob. Health\u003c/em\u003e 6(6), e619\u0026ndash;e629.\u003cbr\u003e doi: 10.1016/S2214-109X(18)30186-4\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (2019). \u003cem\u003eWHO methodology for point prevalence survey on antimicrobial use in hospitals\u003c/em\u003e. Geneva: World Health Organization.\u003cbr\u003e Available at: https://www.who.int/publications/i/item/WHO-EMP-IAU-2018.01\u003c/li\u003e\n\u003cli\u003eWojcik, G., Ring, N., McCulloch, C., et al. (2021). Understanding the complexities of antibiotic prescribing behaviour in acute hospitals: a systematic review and meta-ethnography. \u003cem\u003eArch. Public Health\u003c/em\u003e 79, 134.\u003cbr\u003e doi: 10.1186/s13690-021-00624-1\u003c/li\u003e\n\u003cli\u003eZumaya-Estrada, F. A., Alpuche-Aranda, C. M., and Saturno-Hernandez, P. J. (2021). The WHO methodology for point prevalence surveys on antibiotic use in hospitals should be improved: lessons from pilot studies in four Mexican hospitals. \u003cem\u003eInt. J. Infect. Dis.\u003c/em\u003e 108, 13\u0026ndash;17.\u003cbr\u003e doi: 10.1016/j.ijid.2021.04.079\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Antimicrobial stewardship, point prevalence survey, antibiotic prescription patterns, AWaRe framework, culture-based practices","lastPublishedDoi":"10.21203/rs.3.rs-9029871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9029871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePoint prevalence surveys (PPS) are a core tool of the WHO Global Action Plan on antimicrobial resistance (AMR), yet their implementation in low- and middle-income countries (LMICs) remains limited by weak prescribing and surveillance systems. We conducted a multiseasonal mixed-method PPS, integrating quantitative prescribing data with qualitative ward observations, and the behavioural and operational factors shaping antibiotic prescribing and use of culture testing in a tertiary-care hospital in northern India.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA hospital-wide PPS was conducted across autumn (November 2023), summer (April 2024), monsoon (August 2024), and winter (January 2025), with each phase comprising a two-week data collection episode, following WHO Global-PPS methodology. Data were collected from five inpatient departments (Medicine, Surgery, Obstetrics-Gynaecology, adult, paediatric, and neonatal intensive care units (ICUs) using standardised forms. Quantitative data on indications, routes, and AWaRe categories were supplemented with ward observations and inpatient follow-up to assess culture testing, antibiotic sensitivity test (AST) use, and treatment modifications.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,680 inpatients were surveyed. Ceftriaxone (30\u0026ndash;33%), Piperacillin\u0026ndash;tazobactam (9\u0026ndash;20%), and Doxycycline (8\u0026ndash;16%) were the top prescribed antibiotics. Azithromycin use dropped sharply after the first phase. Amikacin (6\u0026ndash;9%) and Meropenem prescribing (4\u0026ndash;7%) remained low. Empirical prescribing dominated (63\u0026ndash;67%), while culture-guided therapy remained\u0026thinsp;\u0026le;\u0026thinsp;6%. Over 80% of antibiotics were given parenterally. Watch antibiotics accounted for 46\u0026ndash;56% of prescriptions, Access 35\u0026ndash;51%, and Reserve\u0026thinsp;\u0026le;\u0026thinsp;4%. Prophylactic use ranged from 24\u0026ndash;30%, and combination therapy was common in the ICU. Clinical diagnoses showed seasonal variation, with gastrointestinal (20\u0026ndash;22%) and chronic conditions (17\u0026ndash;23%) most frequent, and respiratory infections (3\u0026ndash;9%) peaking in monsoon and winter. Antibiotic modification following AST occurred in only 4.7\u0026ndash;6.5% of cases. Qualitative findings highlighted stock-outs of oral antibiotics, delays in culture sampling, and documentation gaps during patient transfers, limited stewardship activities, collectively reinforcing broad-spectrum empirical use.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis PPS found high empirical and broad-spectrum antibiotic use, limited culture-based prescribing, and systemic gaps hindering stewardship. Strengthening diagnostic access and use, ensuring drug availability, and embedding multidisciplinary stewardship teams with real-time feedback are essential to promote evidence-based prescribing in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"A multi-seasonal mixed-method point-prevalence study of antibiotic prescription patterns in a tertiary healthcare facility in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:12:48","doi":"10.21203/rs.3.rs-9029871/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10642087-4fa4-42d9-abc7-304c85fcba7d","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63917662,"name":"Clinical Pharmacology"}],"tags":[],"updatedAt":"2026-03-08T17:12:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:12:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9029871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9029871","identity":"rs-9029871","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}