{"paper_id":"d4f5d77d-bad4-4365-8424-8d4273b097d2","body_text":"1 \n \nMan uscri pt title: Context -specific  impact of antimi crobial stewardsh ip on antibiotic use 1 \nand antibiotic resistance in hospitals in a lower-middle income coun try - results from an 2 \nimplemen tation study with a contr olled interrupted time series desig n in Vietna m  3 \n 4 \nAuthors: Le Quyn h Trang 1 , Vu Tie n Viet Du ng 1 , Le Min h Q uang 2 , Ng uyen Thi Thu 5 \nHuyen 2 , Vu Hai Vinh 2 , Chau Minh Duc 3 , Vo Thi Hoang Du ng Em 3 , Nguyen Thi Cam 6 \nTu 1 , Truong Anh Quan 1 , Nguyen H ong Khanh 1 , Le Nguyen Minh Hoa 4 , Thomas 7 \nKestema n 1 , Elizabet h Dodds Ashley 5 , Deverick J. Anderson 5 , Hugo C Turner 6 , 8 \nPha m Ngoc Thach 4 , Ben S Cooper 7 , Marc Choisy 7,8 , H Rogier van Doorn 1,7 , V u Thi 9 \nLan H uong 1  10 \n 11 \n1 Oxford University Clinical Research Unit, Bau Hamle t, Ki m Chung Wa rd, Dong Anh 12 \nDistrict, Vie t Nam  13 \n2 Viet Tiep Hospital, 1 Nha Th uong,  Cat Dai, Le Chan , Hai P hong, Viet Nam  14 \n3 Dong Thap Hospital, 144 Mai Van  Khai, My Tan, Cao Lan h City, Đon g Thap 15 \n4 National Hospital for Tropical Dis eases, Bau Hamle t, Ki m Chung Wa rd, Dong Anh 16 \nDistrict, Ha Noi, Vie t Nam  17 \n5 Duke Antimi crobial Stewardship Outrea ch Network, D uke Center fo r Antimicrobial 18 \nStewardship and Infection Preven ti on, Duke University, D urha m, NC 27710, United 19 \nStat es 20 \n6  MRC Centre for Global Infectious Disease Analysis, School of Public Health , I mperial 21 \nCollege London, London , UK.    22 \n7 Centre for Tropical Medicine and Global Health , Nuffield Departm en t of Medicine, 23 \nUniversity of Oxford, Oxford, UK  24 \n8 Oxford University Clinical Research Unit, Ho Chi Minh city, Vie tna m  25 \nCor respo ndence to V u Thi Lan Hu ong: huongvtl@o ucru .org   26 \n 27 \n28 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n2 \n \nAbstract  29 \n 30 \nIntroduction :  31 \nHigh-quality evidence regarding the impact of anti microbial stewardship (AMS) is 32 \nlimited in Asia. In this study, we aimed to determine the effe cts of a pharmacist-led 33 \nprospective audit with feedback intervention , as part of an AMS programme following 34 \nnational guidelines, in two provincial-level general hospitals in Vietna m, a lower-middle-35 \nincome coun try.  36 \n 37 \nMethods:  38 \nWe performed controlled interrupt ed time-series analyses to evaluate  the impact of an 39 \nAMS interven tion on antibiotic use  in days of  therapy per 1000 patie nt-days, antibiotic 40 \nnon-susceptibility percentage and patient out comes . In each hospital , four wards 41 \nreceived the interven tion and four wards acted as controls. Pre-interv ention periods 42 \nbegan in January 2019 and contin ued to May 2020 (Hospital 1) and July 2020 (Hospital 43 \n2), followed by a 12-month post-in tervention period.  44 \n 45 \nResults:  46 \nIn Hospital 1, th e interven tion was associated with a reduction in the level of antibiotic 47 \nuse (95.9, 95% CI [10. 9, 180 .8]), alt hough there was no evidence for a change in trend 48 \n(0.9[-3.6 , 5.4]) . In con trast, in Hospital 2, there was no evidence for a change in either 49 \nlevel (6.3[-83.7 , 96.3]) or trend (-2. 1[-4.8 , 0.6]). I n Hospital 1, we observed a decreasing 50 \ntrend in antibiotic non-suscep tibility among hospital-acquired Escherichia coli  to 51 \naminoglycosides (odd s ratio: 0.87[0.78, 0 .97]), but incre asing for Pseudomonas 52 \naeruginosa  to carbapenems (1.1 1[1.00, 1 .22]) and Acinet obacte r  spp. to aminoglycosides 53 \n(1.07[1.00 , 1.27]) . In Hospital 2, evi dence indicated decreasing trends in Acinetobact er  54 \nspp. to carbapenems (0.96[0.88 , 1. 00]), ciprofloxacin (0.93[0 .85, 1 .00]), and piperacillin-55 \ntazobactam (0 .94[0.7 8, 1.0 0]), but i ncreasing for P. aeruginosa  to ami noglycosides 56 \n(1.07[1.00 , 1.20] , ciprofloxacin (1.4 5[1.18, 1 .77]), and cef tazidime (1.0 3[1.00, 1 .19]). We 57 \ndid not find evidence that the in te rvention was associated with chan ges in mortality or 58 \nhospitalisation costs. 59 \n 60 \nConclusion:  61 \nThe impact of AM S varied between the two hospitals, highlighting c ontex t-specific 62 \nimplemen tation ch allenges and th e necessity to monitor changes in antibiotic resistance 63 \nover time to tailor interven tions th at respond to local resi stance epidemiology.  64 \n65 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n3 \n \nKey words  66 \nAntimicrobial stewardship 67 \nImpact  68 \nAsia 69 \nVietna m  70 \nAntibiotic use  71 \nAntimicrobial resistance  72 \nMortality 73 \nHospitalization cost  74 \n 75 \n76 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n4 \n \nWHAT IS ALREADY KN OWN ON THIS TOPIC  77 \n• Previous systematic reviews and recent interrup ted time series (ITS) studies have 78 \nshown large variations in the impact of antimicrobial stewardship (AMS) on 79 \nhospital antibiotic use and resistance. Few previous ITS studies of AMS have used 80 \na control in their analysis. 81 \n 82 \nWHAT THIS STUDY ADDS  83 \n• Using a strong quasi-experimental  study design with a control group , we 84 \ngenerated empirical evidence on t he multi- facet ed effects of an AMS  intervention 85 \non antibiotic use and resistance in hospitals in a middle-income coun try in Asia. 86 \n• The impleme ntation of prospectiv e audit and feedback in the conte x t of 87 \nestablished AMS programmes following the national guidelines had different 88 \neffects on total an tibiotic use and antibiotic non-suscep tibility propo rtions 89 \namong com mon hospital-acquired  pathogens found in Vietna m.  90 \n 91 \nHOW THIS STUDY MIGHT AFF EC T RES EAR CH, PRACTI CE O R POLI CY  92 \n• This study contributes evidence from a strong study design on the e ffects of AMS 93 \nimplemen tation in hospitals in a m iddle-income coun try in Asia, which can inform 94 \nfuture AM S programmes in similar settings.  95 \n• It highlights the need to mo nitor t he emergence and spread of antibiotic 96 \nresistance in hospital settings in Asia to support development and design of 97 \nnovel interventio ns for AMS and other programmes to respond to th e fast-98 \nchanging resistance profiles of bac terial pathogens in hospitals. 99 \n 100 \n 101 \n  102 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n5 \n \nIntroduction  103 \nWidespread use of antibiotics in humans, food production ani mals and spillover into the 104 \nenvironmen t has accelerated t he e mergence and trans mission of dru g-resistant 105 \nbacteria 1 . Anti microbial stewardship (AMS) interventions are designed to target 106 \ninappropriate antibiotic use to reduce selective pressure on resistant bacteria 2 . A recen t 107 \nmeta -analysis of the global impact of AMS programmes reported that such 108 \ninterventions were associated with  an estimated me an reduction in t he proportion of 109 \npatients receiving antibiotic prescriptions by 10% (95% CI: [4%, 15%])  and a mean rate 110 \nratio of 0.72 (95%CI: [0. 56, 0. 92]) in the consump tion rate measured b y defined daily  111 \ndoses per 100  patient-days 3 . Impo rtantly, AM S programmes need to monitor the i mpact 112 \non mortality to ensure interve ntio ns do not harm patients as well as impact on antibiotic 113 \nuse and resistance. Unfortu nately , such evidence for hospital inpatien ts is currently 114 \ninsufficient 4 5 . A meta -analysis including 221 studies across  34 countries by Davey et al 115 \nfound that mortality risks were sim ilar between intervention and con t rol groups 4 .   116 \n 117 \nThe two study designs conside red appropriate for evaluating the impact of AMS 118 \ninterventions are randomised cont rolled trials and  quasi-experimenta l studies (non-119 \nrandomised controlled trials, controlled before-and-after designs, and interrupted time 120 \nseries) 4 . Recent evidence of t he eff ect of AMS interv entions on resistance outco mes 121 \ncomes from studies with weak desi gns. For example , between 2012 a nd 2017, only 8 of 122 \n26 studies used interrupted time series, and none included a control group in their 123 \nanalysis 5 . In recent studies, only on e study in Canada used a control g roup and 124 \ndemonstrated th e impact of a com prehensive AMS programme with a sustained 125 \nreduction of hospital-acquired anti biotic-resistant organisms 6 . Large heterogeneity in 126 \nstudy designs, AMS interventions, and how resistance was evaluated (denominators of 127 \noutcom e measures), along with un controlled contex t-specific confo u nding factors, have 128 \ncontributed to variations in the re ported impact of AMS programme s on resistance 5 .  129 \n 130 \nIn this study, we aimed to evaluat e  the impact of an AM S interven tio n in two provincial 131 \ngeneral hospitals (Hospital 1 and Hospital 2) in Vietna m, a lower-mi ddle-income coun try 132 \nin Asia. W e hypothesised that t he AMS interven tion would reduce an tibiotic use without 133 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n6 \n \nnegatively impacting patien t outc omes, and reduce the proportions of patients carrying 134 \nresistant organisms for clinically i mportant bac terial species. Vietna m developed its first 135 \nnational action plan for controlling  antimicrobial resistance and initiat ed discussions  on 136 \nAMS impleme nta tion in a local hospital network in 2013 7 . Since t hen , several guidelines 137 \nfor antibiotic treat ment a nd AMS implemen tation in hospitals have b een issued and 138 \nlocal hospitals have started their AMS programmes following guideli nes from the 139 \nMinistry of Health (MoH) 8 . We pre viously reported on our implemen tation research to 140 \nassess the feasibility of  AMS interv entions in these two provincial hospitals in 141 \ncollaboration with the Duke An tim icrobial Stewardship Outreach Ne t work 9 . The 142 \ntheoretical fra mework for AMS implementa tion in this research was based on the 143 \nassumption tha t hospitals are complex adaptive systems and that AMS tea ms could 144 \nleverage their unique charact eristics and interconnec tions to develop a locally feasi ble 145 \nand sustainable programme. Prosp ective audit and feedback (PAF) w as chosen as the 146 \ncore AMS interven tion implem ent ed at these two hospitals based on  evidence from a 147 \nprevious systematic review on effective behaviour change in terventio ns for antibiotic 148 \nprescribing in hos pitals 4 .  149 \n 150 \nMethods 151 \nStudy setting and population 152 \nThe study was implemented in two  provincial hospitals in Vietnam: H ospital 1 (1000 153 \nbeds) and Hospital 2 (2000 be ds). We selected 8/26 clinical wards in Hospital 1 and 8/27 154 \nclinical wards in Hospital 2, equally  divided between interven tion and  control groups. 155 \nWard selection and assignment we re described previously 9 . Briefly, w ards were selected 156 \nbased on two criteria: (1) higher-than-average antibiotic use in th e ho spital based on 157 \npharmacy-reported data and (2) willingness of the ward head to pa rticipate. All 158 \ninpatients in the study wards during evaluation periods were include d. Allocations of the 159 \nwards in the two hospitals were  si milar in terms of clinical specialties, with four 160 \nintervention versus con trol ward p airs, as shown in Figure 1. Detailed characteristics of 161 \nthese two hospitals are presented in Table 1S (Supplemen tary Data).  162 \n 163 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n7 \n \nBefore the interven tion, an AM S te am was formally established at the two hospitals with 164 \nthe implem enta tion of the pre-au t horisation policy (which required  d octors to obtain 165 \napproval from the head of the clinical ward and a d irector board re pr esentative before 166 \nusing antibiotics on the restricted antibiotic list) following the 2016 national AMS 167 \nguidelines 10 . Infectio n prevention and control (IPC) guidance from th e MoH has been 168 \nissued since 2009 ( 18/2009/TT-BYT) with documents and training provided by the MoH 169 \nto the hospitals since then 11 . The 2 016 AMS guidelines included the role of IPC staff in 170 \nthe AMS co mmi ttee and described the set of responsibilities in implementi ng protocols 171 \nfor the isolation of patients with m ultidrug-resistant organisms, alon g with basic IPC 172 \nmeasures such as hand hygiene, u se of personal p rotective equipme nt, sterilisation of 173 \nmedical equipment , enh anced mo nitoring, and outbreak investigatio ns. Although t here 174 \nwere cases of COVID-19 in 2020 in  some specific areas of Vietnam, th e COVID -19 175 \npandemic did not affect th e two h ospitals until late April 2021 in Hos pital 1 and July 176 \n2021 in Hospital 2, towards the en d of the intervention periods 12 .   177 \n 178 \nInterventions 179 \nAt the beginning of the project , an  AMS team was established to collect baseline data 180 \nfor assessments of needs, gaps, str engths, and weaknesses to inform planning of the 181 \nintervention 9 . As part of the PA F a ctivity, clinical pharma cists made weekly visits to the 182 \nintervention wards to review antibiotic prescriptions for patients and provide 183 \nrecomme ndations for improveme nt where needed. During t his intervention period, both 184 \nhospitals still maintained the pre-a uthorisation policy and routine hospital-level IPC 185 \nactivities in all clinical wards a s usu al, including the study wards. Figure 1S outlines the 186 \ntimeline of project activi ties implement ed before, during, and after t he interven tion 187 \nperiod, when PAF started on 01 Ju ne 2020 in Hospital 1 and 29 July 2020 in Hospital 2.  188 \n 189 \nDuring the one-year int ervention period, the PAF ac tivity at Hospital  1 was led  by two 190 \nclinical pharmacists who conducte d a total of 1,890 PAF reviews, whil e Hospital 2 191 \nassigned four clinical pharmacists to conducted a total of 1,628 PAF r eviews 9 . 82 192 \nrecomme ndations were made am ong the reviews at Hospital 1 (75% were accepted by 193 \nthe treati ng doctors), and 128 were made at Hospital 2 (33% accept e d) . Common 194 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n8 \n \nrecomme ndations included de-esc alation of antibiotics, microbiology and additional 195 \ntesting, m edication switches, a nd documen tation of an tibiotic indications in the charts. 196 \nRecom mendations were com muni cated through face -to- face discussions, notes 197 \nattach ed to patient medical charts,  or phone calls. AMS teams also prepared a monthly 198 \nsummary PA F report for each inter vention ward in both hospitals, and clinical 199 \npharmacists presented this report in ward meetings and attended routine clinical ward 200 \nrounds.  201 \n 202 \nIn addition to PAF , doctors in the interven tion wards also participated in evaluation 203 \nactivities at baseline and during the intervention period conducted b y the AMS tea m to 204 \ninform planning and monitoring, i ncluding retrospective review of antibiotic 205 \nprescriptions 13  and repeated surve ys on knowledge, attitude, and practices (KAP) related 206 \nto antibiotic use, AMR , and AMS ; K AP surveys were also completed b y doctors in the 207 \ncontrol wards (Figure 1S, Supplem entary Data) . Training was delivere d by experts from 208 \nnational universities for medicine a nd pharmacy and hospitals for tro pical diseases, 209 \nfocusing on antibiotic treat men t f or common infec tions, surgical prophylaxis, antibiotics, 210 \nand the use of microbiological tests and interpretation of microbiology results. 211 \n 212 \nPatient and public involment 213 \nPatien ts and/or the public were not involved in the design, or conduct, or reporting, or 214 \ndissemination plans of this researc h.  215 \n 216 \nOutcome measures  217 \nPrimary outco mes:  218 \n• Antibiotic use:  The primary outco m e is the amount of an tibiotic use in Days of 219 \nTherapy (DOT) per 1000 patient-d ays on a weekly  time interval. Raw patient-lev el 220 \nantibiotic prescription data from hospital information systems (HI S) were 221 \nextract ed together with patien t administrative , diagnosis di scharge o utcom es and 222 \nbed day information. From t hese data, we calcula ted the an tibiotic use indicators 223 \noverall and by Anatomical Therapeutic Chemical (ATC) classification of chemical 224 \ntherapeutic subgroup defined by the World Health Organization ( W HO) . Pa tients 225 \ncould move between study wards; each patient was coun ted only once under 226 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n9 \n \neach grouping (and antibiotics used by the patient on a specific day were 227 \ncounted for the ward where the pa tient stayed on tha t day). We also described 228 \nantibiotic use before and after the start of the AMS in terventio n by calculating 229 \nthe proportions of patients admitt ed to each study ward who used  at least one 230 \nantibiotic, and proportions of all used antibiotics by AWaRe (Access, Watc h, 231 \nReserve, and Ot her) groups (2021 version) defined by WHO 14 .  232 \n 233 \n• Antibiotic no n-susceptibility among hospital-acquired isolates:  Microbi ology data 234 \nwere deduplicated, i.e. if a patient had several specimens collected within 30-235 \ndays, then the duplicate results we re excluded . Hospital-acquired isolates were 236 \ndefined as those from specimens sampled at least 48 hours after hospital 237 \nadmission (counted for the first positive sample of the same specime n type and 238 \nbacterium only). Hospital-acquired  isolates were identified and analy s ed for the 239 \nchange in the non -susceptibility proportion after interven tion. We me asured the 240 \nproportions of antibiotic non-susceptibility in five common organism s identified 241 \nin routine clinical investigations (specimens from all bodily sites, excluding 242 \nspecimens for screening purposes) : Escherichia coli, Klebsiella  spp ., Ac inet obacte r 243 \nspp ., Pseudomo nas aeruginosa, an d Staphylococcus  aureus . Raw data for antibiotic 244 \nsusceptibility testing results were e xtracted from the WH ONET dat abase of each 245 \nhospital and interpreted using the AMR R package 15  (interpretation u sing CLSI 246 \nguidelines 2023). Non-susceptibility proportions were calculated as the ratio of 247 \nthe nu mber of non-susceptible isolates to the nu mber of tested isolates for a 248 \nspecific organism (isolates were de duplicated by patient and specimen type). We 249 \nreported microbiology data follow ing the recomm endations of the MICRO 250 \nframework (Supplemen tary Data) f or the following pathogen-drug combinations 251 \nwhich were considered relevant in the local epidemiological context :  252 \n• E.coli and Klebsiella spp.: third-generation cephalosporin (ceftriaxone or 253 \nceftazidime) , a minoglycoside (gentamicin and one of amikacin or tob ramycin) , 254 \nfluoroquinolone (ciprofloxacin),  ca rbapenem (one of ertapene m, imi penem , 255 \nmeropenem , or doripenem);  256 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n10 \n \n• P. ae ruginosa and Acinetobac ter  sp p.: third-genera tion cephalosporin 257 \n(ceftazidime), a minoglycoside (one of amikacin or tobramycin), 258 \nfluoroquinolone (ciprofloxacin), ca rbapenem (one of imipene m, m eropenem, 259 \nor doripenem), piperacillin-tazobactam , aztreona m , colistin ;  260 \n• S. aureus:  Methicillin-resistan t (MR SA) (oxacillin or cefoxitin).  261 \nSecondary outco mes:  262 \n• In-hospital mortali ty:  In- hospital m ortality per 1000 admitted patients   on a 263 \nweekly time interval, including pati ents who died in hospital and thos e who were 264 \ndischarged to die at home. 265 \n• Cost of hospitalisation : Includes all costs incurred during each hospit al admission 266 \nas recorded in the medical record of each patient af ter hospital discharge. This is 267 \ndirect medical costs (including all types of costs: drugs, medical servi ces, 268 \nprocedures, consumables, tes ts, be d and room services) pa id to the hospital, 269 \neither by the patient ou t of pocket  or by a  third-party payer ( such as health 270 \ninsurance). Direc t non- medical costs and indirect costs were not included. All 271 \ncosts were converted from Vietn a m Dong to US Dollar in 2021 value s, with costs 272 \nincurred in 2019 and 2020 adjusted to the equivalent values in 2021 using Gross 273 \nDomestic Product deflation rates 16 . 274 \n 275 \nFor antibiotic use, mor tality, and h ospitalisation costs, before-inteve ntion tim e series 276 \nwere available from 1 Jan 2019 to 1 Jun 2021 in Hospital 1 and from 1 Jan 2019 to 29 Jul 277 \n2021 in Hospital 2. Antibiotic non - susceptibility data were available f rom 1 Jan 2014 to 278 \n31 Dec 2021 in Hospital 1, excep t f or S. aureus  from 1 Jan 2017 to 31 Dec 2021 and from 279 \n1 Dec 2017 to 31 Dec 2021 in Hos pital 2. 280 \n 281 \nStatistical methods 282 \nAll analyses were conducted separ ately for each hospital. The proportion of patients 283 \nwith any antibiotic use during their hospital stay, DOT per 1000 patient-days, D OT 284 \npercentage by AWaRe classificatio n, in-hospital mor tality per 1000 admitted patien ts, 285 \nand cost of hospitalisation were su mmarised by pre- and post-intervention periods for 286 \nall study ward s and for each ward  pair. Out come variables were visualised and 287 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n11 \n \ndecomposed to examine pote ntial  patterns, trends, and seasonality. Based on the 288 \navailable observations and perio di c patterns, t he aggregate unit , i.e. week or month, for 289 \neach outco me was determined .   290 \n 291 \nAn interrupted tim e series (ITS) design was used  to compare longitudinal changes in the 292 \npost-interven tion period to a hypo thetical scenario in which t he inter vention did not 293 \noccur. W e then perfor med a contr olled interrupted time series (CITS) analysis that 294 \nincorporated both control and inte rvention groups into an ITS model.  Segmen ted 295 \nregression models we re used to es timate the effe cts of the int erventi on for both ITS and 296 \nCITS, which are shown in the suppl emen tary data. Specifically, a ntibiotic non-297 \nsusceptibility was modelled us ing l ogistic regress ion, while other out comes were 298 \nmodelled using linear regres sions. 299 \n 300 \nSegmen ted regression model assu mptions were checked by examini ng the residuals, 301 \nparticularly temporal correlation using ACF/PACF plots and Ljung-Box test. For linear 302 \nsegmented regression, an Autoreg ressive Integrated Moving Average (ARIMA) model 303 \nwas added to the regres sion model to adjust for non-stationarity, au t ocorrelation, and 304 \nseasonality. ARIMA model selectio n was performed using an automated process in the R 305 \nfunction au to.ari ma().  306 \n 307 \nFor logistic regres sion, the models were first run without any lags of antibiotic non-308 \nsusceptibility proportions. In case of auto-correlation in t he simulate d residuals, the 309 \nmodels were re-run afte having added lags of antibiotic non-suscepti bility propo rtion as 310 \nsuggested by the ACF plots. In case of presence of overdisper sion, the logistic 311 \nregression models we re re-run replacing the binomial distribution by a quasibinomial. 312 \nFinally, for all models with less tha n 10 events (antibiotic use or antib iotic non-313 \nsusceptibility) or non-events per co-variable, the models were re-run  with L 2  314 \nregularization (ridge) in order to a void biases  and large confidence i ntervals for the 315 \nparameter estima tes. Th e optimal value of the shrinkage parameter was searched by 316 \ncross-validation as implemented by the cv.glmn et() func tion of the gl mnet R package . 317 \n(See additional information in t he methods provided in Supplementa ry Data).  318 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n12 \n \n All analyses were conducted in R (v.4.3 .1; R Core Team 2022) .  319 \n 320 \nThis study report follow s the criteria described for  nonrandomised evaluations of 321 \nbehavioral and public health interv entions 17  (see TREND S tate men t Checklist in  322 \nSupplemen tary Data) .  323 \n 324 \nResults  325 \nFigure 1 illustrates the inpatient ad missions to the eight study wards i n each hospital, 326 \nboth prior to and following  the implementa tion of the AM S programme. I n total , there 327 \nwere 45,623 patients in the in terve ntion group and 51,444 patients in  the control group 328 \nin Hospital 1, while Hospital 2 reported 18,842 and 25,401 patie nts, r espectively. A 329 \nsummary of patient de mographic characteristics by study group, war d pair and hos pital 330 \nis available in Table 2S (Supplementary Data).  331 \n 332 \nIn Hospital 1, th e most com mon di agnoses in the intervention group included 333 \ninjury/poisoning (18.3%), gastroint estinal (13.6%) , infectious (12 .5%), and respiratory 334 \n(10.9%) . In the co ntrol group, the predominant diagnoses were respi ratory (29.3%), 335 \ninfectious (14.8 %), digestive (13.4 %), cardiovascular (9.8%) , and genitourinary (8.0%). I n 336 \nHospital 2's intervention group, th e leading diagnoses  were injury/p oisoning (38.3%), 337 \ncardiovascular (12.2%), gastrointes tinal (9.5%) , and respiratory (8.2%).  In the control 338 \ngroup, the most frequen t diagnoses were oncological (29.7%), gastrointestinal (26.2 %), 339 \ninfectious (14.4 %), and respiratory (9.1%) (Table 3S, Suppleme ntary D ata).  340 \n 341 \nAntibiotic use 342 \nProportion of patients with any antibiotic use 343 \nHigher proportions of patients with any antibiotic use were observed  in the interven tion 344 \ngroup compared to the control group across most diagnostic categories in Hospital 1, 345 \nexcept for infec tious, genito- urinar y, and respiratory d iagnoses durin g both periods. 346 \nConversely, in Hospital 2, the proportions of antibiotic use were lowe r for circulatory, 347 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n13 \n \ngastrointestinal, and not -elsewher e-classified diagnoses in the interv ention group (Table 348 \n3S, Supplemen tary Data) .  349 \n 350 \nThe proportion of patients with any antibiotic use decreased slightly i n the post-351 \nintervention period for both interv ention and control group wards in Hospital 1, with 352 \nrespective absolute reductions of 1.3% (from 71 .4% to 70 .1%) and 0. 6% (from 67.0 % to 353 \n66.4%) (Table 1). In con trast, Hospital 2 experienced an absolute reduction in antibiotic 354 \nuse in intervention wards of 4.4% ( from 68.3% to 63.9%) and an abso lute increase of 355 \n4.3% (from 72 .0% to 76 .3%) in con trol wards (Tab le 1). 356 \n 357 \nProportion of AWaRe antibiotic classification 358 \nThere was an increase in the proportion of Access antibiotics from 11.3% to 19 .1% in th e 359 \nintervention group and from 14.4 % to 19.7% in the con trol group at Hospital 1. 360 \nConversely, the use of Access antibiotics fell at Hospital 2, declining from 21.9% to 361 \n14.9% in t he interven tion group and from 21.0% to 15. 6% in the co nt rol group. 362 \nAdditionally, there was a rise in th e use of other antibiotic categories that were classified 363 \nas “Not recommended” or unclassified by the WH O in the A WaRe cla ssifications version 364 \n2021 (including cefoperazone/ beta-lacta mase inhibitor and ticarcillin/ beta-lacta mase 365 \ninhibitor), increasing from 16.9% t o 26.2% and from 16.9 % to 22. 4% ,  respectively (Table 366 \n1). We observed decreases in the use of certain subgroups, accompanied by increases in 367 \nothers, depending on the available antibiotic agents at each hospital , with more 368 \npronounced changes noted at Hospital 2 compared to Hospital 1 (Table 2S, 369 \nSupplemen tary Data) . In the in terv ention groups, there were decreases in the use of 370 \nsecond-generation cephalosporins (from 13.8% to 5 .2%) and glycopeptides (from 3.7% 371 \nto 2.3%) at Hospital 1, and in penic illin/beta-lacta mase inhibitors (from 27.9 % to 24.6 %), 372 \nfourth-gen eration cephalosporins (from 5.6% to 2 .5%) , and fluoroquinolones (from 373 \n22.6% to 16 .5%) at Hospital 2.374 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n14 \n \nTable 1. Study participants, antibiot ic use, mortality, costs of hospitaliz ation and antibio tic 375 \nnon -susceptibility among hospital- acquired isolates before and af te r t he start of AMS 376 \ninte rv en tion  377 \nStudy varia ble s  Hos pita l 1 Hos pita l 2 \nInterven tion  Control  Interven tion  Control  \nBefore After Before After Before After Before After \nNumber of patie nt s          \nAll s tu dy wards 27,341 18,282 30,245 21,199 10,994 7,848 15,403 9,998 \nICU pair  15,569 10,644 8,359 6,126 2,256 1,444 2,799 2,125 \nSurg ical pa ir 4,979 3,462 2,081 1,574 3,700 2,730 4,575 3,190 \nInternal pair 1 3,130 2,954 13,146 8,933 3,472 2,322 4,289 2,917 \nInternal pair 2 6,602 3,418 9,074 6,156 1,806 1,450 4,220 2,040 \nAntib ioti c use ( % )          \nAll s tu dy wards 71.4 70.1 67.0 66.4 68.3 63.9 72.0 76.3 \nICU pair  71.1 63.9 52.0 49.5 93.7 91.6 88.6 94.3 \nSurg ical pa ir 79.1 77.5 92.0 90.0 82.5 80.7 88.7 89.8 \nInternal pair 1 79.2 77.1 80.7 82.5 37.4 26.8 50.1 50.1 \nInternal pair 2 49.2 57.9 49.7 47.8 68.2 65.7 63.5 72.6 \nDOT1000           \nAll s tu dy wards 804 875 907 934 551 490 361 427 \nICU pair  957 968 964 945 979 861 941 1178 \nSurg ical pa ir 866 778 1361 1497 587 531 591 693 \nInternal pair 1 1100 1219 978 1016 215 190 117 122 \nInternal pair 2 491 571 635 604 561 527 472 581 \nAWaRe group (% DOT) for all study wards \nAcces s  11.3 19.1 14.4 19.7 21.9 14.0 21.0 15.6 \nWatch  88.1 80.0 84.6 78.9 60.1 58.7 58.6 57.8 \nReserve 0.4 0.5 0.7 0.9 1.0 1.1 3.5 4.2 \nOther* 0.1 0.4 0.3 0.4 16.9 26.2 16.9 22.4 \nMortal ity §          \nAll s tu dy wards 6.7 9.6 45.2 58.8 43.8 41.7 92.3 81.5 \nICU pair  5.0 8.2 160.2 199.8 194.1 213.3 368.3 382.1 \nSurg ical pa ir 1.8 0.6 2.4 1.3 6.5 3.3 7.4 11.6 \nInternal pair 1 27.2 28.1 0.5 0.4 7.8 4.7 36.4 19.5 \nInternal pair 2 2.0 2.0 16.2 16.6 8.9 6.9 26.1 27.0 \nHos pita liza tion co st #          \nAll s tu dy wards 183  \n(69-263) \n192  \n(82-277) \n97  \n(54-198) \n106  \n(61-207) \n491  \n(240-1027) \n470  \n(249-922) \n402  \n(193-835) \n520  \n(265-979) \nICU pair  233  \n(186-324) \n237  \n(186-323) \n206  \n(112-382) \n206  \n(111-382) \n1575  \n(829-2571) \n1453  \n(771-2341) \n1575  \n(829-2571) \n1210  \n(599-2127) \nSurg ical pa ir 146  \n(81-310) \n142  \n(78-279) \n176  \n(82-387) \n178  \n(82-410) \n521  \n(275-1003) \n507  \n(266-908) \n434  \n(237-760) \n560  \n(326-909) \nInternal pair 1 145  \n(82-250) \n150  \n(80-264) \n67  \n(41-106) \n79  \n(48-121) \n291  \n(165-494) \n284  \n(166-454) \n300  \n(187-529) \n287  \n(180-521) \nInternal pair 2 49  \n(35-71) \n58  \n(42-87) \n116  \n(65-234) \n114  \n(66-222) \n508  \n(256-880) \n519  \n(282-812) \n255  \n(123-504) \n357  \n(198-633) \nAntib ioti c non -s us cept ibi lity amo ng ho spi tal ac quire d iso late s ∏  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n15 \n \nStudy varia ble s  Hos pita l 1 Hos pita l 2 \nInterven tion  Control  Interven tion  Control  \nBefore After Before After Before After Before After \nE. coli          \nAmin og lyc os ides  242/657 \n(37) \n65/145 \n(45) \n395/806 \n(49) \n104/176  \n(59) \n12/48 \n(25) \n3/14 \n(21) \n27/74 \n(36) \n7/26 \n(27) \nCarbapenem s  78/657 \n(12) \n23/145 \n(16) \n216/806 \n(27) \n54/176 \n(31) \n14/144 \n(10) \n0/14 \n(0) \n5/74 \n(7) \n2/26 \n(8) \nCi prof lo xaci n  335/476 \n(70) \n122/144 \n(85) \n515/590 \n(87) \n157/167 \n(94) \n91/113 \n(81) \n27/34 \n(79) \n44/55 \n(80) \n16/18 \n(89) \n3 rd  genenerati on  \ncephal os pori ns  \n311/657 \n(47) \n123/145 \n(85) \n508/806  \n(63) \n166/176 \n(94) \n89/144 \n(62) \n27/45 \n(60) \n43/74 \n(58) \n13/26 \n(50) \nKlebsiella spp .          \nA m i no gl y co si d e s 33/141 \n(23) \n26/49 \n(53) \n106/455 \n(23) \n73/96 \n(76) \n150/249 \n(60) \n31/67 \n(46) \n51/119 \n(43) \n21/41 \n(51) \nCarbapenem s  32/141 \n(23) \n23/49 \n(47) \n95/455 \n(21) \n68/96 \n(71) \n130/249 \n(52) \n21/67 \n(31) \n50/119 \n(42) \n19/41 \n(46) \nCi prof lo xaci n  46/105  \n(44) \n34/45 \n(76) \n154/268 \n57 \n81/96 \n(84) \n166/205 \n(81) \n35/56 \n(62) \n69/91 \n(76) \n23/30 \n(77) \n3 rd  genenerati on  \ncephal os pori ns  \n60/141 \n(43) \n43/49 \n(88) \n164/455  \n(36) \n86/96 \n(90) \n174/249 \n(70) \n36/67 \n(54) \n70/119 \n(59) \n23/41 \n(56) \nP. aeruginosa          \nAmin og lyc os ides  15/50 \n(30) \n11/20 \n(55) \n69/150 \n(46) \n35/41 \n(85) \n206/348 \n(59) \n44/87 \n(51) \n45/92 \n(49) \n13/22 \n(59) \nCarbapenem s  21/50  \n(42) \n5/20 \n(25) \n74/150 \n(49) \n28/41 \n(68) \n174/348 \n(50) \n42/87 \n(48) \n49/92 \n(53) \n12/22 \n(55) \nCi prof lo xaci n  22/42 \n(52) \n13/20 \n(65) \n63/109 \n(58) \n28/40 \n(70) \n193/293 \n(66) \n46/75 \n(61) \n47/83 \n(57) \n12/19 \n(63) \nCeftaz idi me 12/43 \n(28) \n8/20 \n(40) \n53/108 \n(49) \n25/40 \n(62) \n91/316 \n(29) \n14/61 \n(23) \n44/86 \n(51) \n4/15 \n(27) \nPiperaci ll in-  \ntazobactam  \n11/43 \n(26) \n6/20 \n(30) \n38/110 \n(35) \n20/41 \n(49) \n30/309 \n(10) \n7/77 \n(9) \n6/83 \n(7) \n1/20 \n(5) \nAcinetobacter spp.          \nAmin og lyc os ides  120/140 \n(86) \n50/60 \n(83) \n172/310 \n(55) \n65/70 \n(93) \n244/310 \n(79) \n88/103 \n(85) \n153/198 \n(77) \n47/60 \n(78) \nCarbapenem s  78/140 \n(56) \n39/60 \n(65) \n153/310 \n(49) \n49/70 \n(70) \n251/310 \n(81) \n93/103 \n(90) \n165/198 \n(83) \n52/60 \n(87) \nCi prof lo xaci n  118/124 \n(95) \n57/58 \n(98) \n162/179 \n(91) \n60/67 \n(90) \n245/282 \n(87) \n90/97 \n(93) \n160/177 \n(90) \n51/56 \n(91) \nCeftaz idi me 112/122 \n(92) \n53/58 \n(91) \n153/183 \n(84) \n63/69 \n(91) \n267/289 \n(92) \n84/87 \n(97) \n160/179 \n(89) \n \n50/54 \n(93) \nPiperaci ll in-  \ntazobactam  \n102/124 \n(82) \n44/60 \n(73) \n151/183 \n(83) \n52/70 \n(74) \n219/254 \n(86) \n89/93 \n(96) \n142/156 \n(91) \n51/54 \n(94) \nS. aureus         \nMRSA  74/85 \n(87) \n50/57 \n(88) \n210/278 \n(76) \n88/112 \n(79) \n71/93 \n(76) \n19/25 \n(76) \n60/79 \n(76) \n11/12 \n(92) \n 378 \n*  Antimic robials classified und er this g roup followin g WH O AW AR E’s 2021 v ersion in clude: cefo per azon e and  bet a-lact am ase 379 \ninhibitor, ticar cillin and beta-l act am ase inhibitor.  380 \n§ In-hospital mortality w as c alcul ated as those with o utcome re cord ed as “deat h” and  “going home  to di e” out of the p atients 381 \nadmitted  to the cor respondin g study  wa rds ( exp ress ed as p er 1000 admissions).  382 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n16 \n \n# Costs are presen ted in US Dollar media n (inte rqu a rtile r ange)  per  da y of hospit alization, c alcul ated fo r individual pa tients, and  with 383 \ncosts in 2019 an d 2020 adjust ed to th e costs of 202 1 using GDP defl ation r ates.  384 \n∏  Antibiotic non-susc eptibility d ata ar e pr esented  as  the pro portion of n umbe r of non-susc eptible isolat es over all isolates 385 \n(per cent age ). 386 \n 387 \n 388 \nBased on the frequency of observa tions for each outcom e, we deter mined to apply a 389 \nweekly interval for antibiotic use, mortality, and hospitalisation costs , while adopting a 390 \nmonthly int erval for antibiotic non -susceptibility, given that the nu m ber of antibiotic 391 \nnon-susceptible isolates is relatively low.  392 \n 393 \nDays of antibiotic therapy per 1000 patient-days (DOT1000) 394 \nFrom the su mmary report, t he ove rall DOT1000 increased in the post -interven tion 395 \nperiod for b oth the interven tion a nd control groups in Hospital 1. In contrast, Hospital 2 396 \nexperienced a decline in the post-i nterven tion period for the interven tion group, while 397 \nthe control group showed an increase (Table 1; for comprehensive details of numerators 398 \nand denominators, refer to Table 2 S, S upplemen tary Data) .   399 \n 400 \nITS/CITS r esults for D OT1000 in the  inter ve ntio n group  401 \nITS/CIT S models provided evidenc e of consistent changes in D OT100 0 for Hospital 1 402 \nregarding overall antibiotic use an d certain subgroups. Specifically, t he interven tion was 403 \nassociated with an immediate (i.e . l evel) reduction in overall antibiotic use by 95.9 (CITS 404 \n95% CI: [10.9 , 180.8] ; ITS : 93. 3 [0.7, 186.0]) and a reduction in the D OT 1000 level of 405 \nglycopeptide antibacterials by 52.7 (CITS [38.0, 67 .5]; IT S: 60 .2 [40.6 , 7 9.8]). In con trast, 406 \nthe D OT1000 level of beta-lac tam ase resistant penicillins increased by 7.3 (CITS [0.2, 407 \n14.3]; IT S: 8 .4 [2.7 , 14.0]) . The slope of the DOT1000 for second-gen eration 408 \ncephalosporins indicates a long-term effect , with an average decreas e of 1.3 (CITS [0.5 , 409 \n2.0]; IT S: 1 .9 [1.1 , 2.8]) for each additional week, reflecting a declining long-term tre nd 410 \nover time. Conversely, th e slope of the DOT1 000 for penicillin/beta-la ctamase inhibitors 411 \nincreased by 1.9 (CITS [0.9, 2.9] ; IT S: 1. 6 [1.0, 2 .2]). The slopes of aminoglycosides, 412 \npolymyxins, and imidazole derivatives increased in ITS models by 0.4 (ITS [0.1 , 0.7]), 0 .1 413 \n(ITS [0.03 , 0.2]), a nd 0.5 (ITS 95 % CI: [0.1 , 0.9]), respectively , while the slope of 414 \nglycopeptide antibacterials DOT10 00 decreased in the CITS model by 0.6 (CITS [0.1 , 1.0]) 415 \n(Figure 2; Figure 4S , Table 11S, Su pplementary Data) .  416 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n17 \n \n 417 \nFor the ward pair subg roup analysi s, the interve ntion was associated with a decrease in 418 \noverall antibiotic DOT1000 levels in the trau matology ward by 213.0 (ITS [74.8 , 351.3]) , 419 \nan increase in DOT1000 levels in t he general medicine ward by 127.9 (ITS [6.2, 249 .6]), 420 \nand a decreasing slope  in DOT100 0 for the respiratory/musculoskeletal system ward by 421 \n3.1 (CITS [1.1 , 5.0]) (Figure 5S , Table 11S, S upplemen tary Data).  422 \n 423 \nIn Hospital 2, th e results from ITS and CITS were inconsistent , with some evidence of 424 \nchanges observed only in s pecific antibiotic subgroups. Notably, ther e was strong 425 \nevidence indicating tha t the slope of aminoglycosides DOT1000 decreased by 0.6 (CITS 426 \n[0.4, 0. 8]), whereas the D OT1000 f or penicillin/beta-lacta mase inhibit ors exhibited a 427 \nsignificant reduction only in the CI TS model by 3.7 (CITS [1.7 , 5.6]), a nd the slope of 428 \ncarbapenem D OT1000 showed a significant increasing effec t only in the ITS model by 429 \n0.3 (ITS [0. 1, 0. 5]). In t he ward pair analysis, only the high-quality general medicine ward 430 \ndemonstrated an increase in D OT1 000 by 87.6 (ITS [16.0, 159 .2]) (Figu re 2; Figure 5S, 431 \nTable 11S, S upplemen tary Data).  432 \nAntibiotic non-susceptibility  433 \nDescriptive statistics, enco mpassing the frequency and percentage of  non-susceptibility 434 \nto antibiotics among hospital-acq uired isolates, as well a s the intervention and control 435 \nITS/CIT S models, are presented for the interven tion group (Table 1; Figure 3&4). A 436 \ncomprehensive report of the descriptive statistics relating to other an tibiotic non-437 \nsusceptibility and the ITS/CITS mo dels conducted on the control group is provi ded in 438 \nSupplemen tary Data .  439 \nE. coli 440 \nAmong hospital-acquired E. coli is olates, the percen tages of non-sus ceptible isolates 441 \nwere observed to be higher  during  the post-interve ntion period in Hospital 1, whereas 442 \nthe opposite trend was noted in Hospital 2 (Table 1; Figure 3; Table 1 0S, Supplemen tary 443 \nData). However, in t he CITS model s, the AMS in terventio n resulted in a slope re duction 444 \nfor aminoglycoside non-susceptibility (odds  ratio [OR] 95%CI: 0.87 [0 . 78, 0.97]) in 445 \nHospital 1. In Hospital 2, the level decreased for aminoglycosides  (0.81 [0.35, 1.0 0]) and 446 \nthird-generation cephalosporins non-susceptibility (0.42 [ 0.15, 1 .00]) , but increased for 447 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n18 \n \ncarbapenems non-suscep tibility (2.21 [1.00, 8.3 1]; no evidence of cha nges in the slopes 448 \n(Figure 2; Table 12S, Suppleme ntar y Data).  449 \nKlebsiella spp. 450 \nThe percentages of Klebsiella spp. non-susceptible to antibiotics ex hi bited patterns 451 \nsimilar to those observed in E. coli,  characterised by a higher percentage of non-452 \nsusceptible isolates in the post-intervention phase for Hospital 1, an d a lower 453 \npercentage for Hospital 2 (see Table 1; Figure 3; Table 10S, Suppleme ntary Data). 454 \nAnalysis using ITS/CITS models showed consistent changes only in the levels of non-455 \nsusceptibility, with a decrease in the level of non-susceptibility to carbapenems (CITS 456 \n0.77 [0.33 , 1.00]) and ciprofloxacin (0.50 [0.17 , 1.00]) in Hospital 1. Co nversely, in 457 \nHospital 2, the in terventio n was as sociated with a decreased level of non-susceptibility 458 \nto carbapenems in the IT S model ( ITS 0.27[0 .10 , 0.71]) but increased in the CITS model 459 \n(1.06 [1.00 , 1.95]), and increased in  both models for ciprofloxacin (ITS 2.01 [ 1.00, 4 .18], 460 \nCITS 1.36 [1.0 0, 2. 50]) (Figure 2; Ta ble 12S, Supple ment ary Data).  461 \nP. aeruginosa 462 \nSimilar trends were observed in the percentages of non-susceptible P. ae ruginosa  463 \nisolates across b oth hospitals (Tabl e 1; Figure 3; Table 10S , Supplem e ntary Data). I n 464 \nHospital 1, the results from t he ITS /CITS analyses indicated an increase in the slope of 465 \nnon-susceptibility to carbapenems  in the CITS model (1.11 [1. 00, 1 .22 ]), and a decrease 466 \nfor aminoglycosides but in the ITS model only (0.98 [0.80, 1 .00]). Con versely, Hospital 2 467 \nexhibited mixed results between b oth models in the slopes of non-susceptibility to 468 \nceftazidime (CITS 1 .03 [1.00 , 1.19] ; ITS 0.84 [0 .58, 1 .13]), a minoglycosides (CITS 1.07 469 \n[1.00, 1 .20]; IT S: 0 .73 [0.58 , 0.88]) , and ciprofloxacin (CITS 1.45 [1.18 , 1. 77]; ITS : 0.63 [0 .49, 470 \n0.78]) (Figure 2; Table 12S , Supple ment ary Data).  471 \nAcinetobacter spp. 472 \nThe percentages of non-susceptibi lity showed varying  patterns in the  descriptive 473 \nanalysis. Notably, the non-suscep tibility percentages were heterogeneous in Hospital 1, 474 \nwhereas all antibiotic non-suscepti bility percentages were elevated in Hospital 2 (Table 475 \n1; Figure 3; Table 10S , Supple ment ary Data). The CITS models indicated an increase in 476 \nthe level (11.93 [1.00 , 94. 25] and slope of non-susceptibility to amino glycosides  (1.07 477 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n19 \n \n[1.00, 1 .27]), and in the level of no n-susceptibility to piperacillin tazobactam (1. 23 [1.00 , 478 \n2.52]) in Hospital 1. In con trast, a r eduction was noted in the slope of non-susceptibility 479 \nto carbapenems (CITS 0. 96 [0.88 , 1.00]), ciprofloxacin (CITS 0 .93 [0.85 ,  1.00]) and 480 \npiperacillin tazobactam (CITS 0.94 [0.78, 1 .00]) in Hospital 2 (Figure 2;  Table 12S, 481 \nSupplemen tary Data) .  482 \nMRSA 483 \nThe percentages of MRSA rem aine d stable during the post-interventi on period in both 484 \nhospitals (Table 1; Figure 3; Table 10S&1 2S , Supplem entary Dat a), ex cept for the 485 \ndecreasing slope only  observed  in the IT S model of Hospital 2 (0.44 [0.26, 1 .00]) (Figure 486 \n2; Table 12S, Suppleme ntary Data) .   487 \n  488 \nIn-hospital mortality and costs 489 \nThere was a non-significant decrea se in the level of mortality with inc reasing slope in 490 \nHospital 1 intervention wards overall, while increasing levels and s lop es were observed  491 \nin Hospital 2 (Table 2S; Table 13S, Supplemen tary Data) . The IT S/CITS  models for 492 \nmortality show inconsistent results  for the specific wards of  both hospitals (Figure 4; 493 \nTable 13S, S upplemen tary Data), w ith insignificant changes reported in all CITS models.  494 \n 495 \nConsidering the costs for monthly activities of the AMS team (coordi nator/pharmacists) 496 \nand on-site training as regular activities as part of the AMS programme at each hospital , 497 \nthe one-year costs for AMS imple ment ation were approximately 2,8 09 USD for Hospital 498 \n1 and 2,283 USD for Hospital 2 (Ta ble 1S, Supple mentary Da ta).  499 \n 500 \nRegarding hospitalization costs, in both ITS and CITS models, we observed consistent 501 \ndecreasing levels and s lopes overa ll and across most study wards in both hospitals. 502 \nParticularly, t he cost of hospitalisation in the interven tion surgical ward decreased in 503 \nslope by 5.5 (CITS [3.1, 8.0]) US Dol lar in Hospital 1 (Figure 4; Table 14 S, S upplemen tary 504 \nData).  505 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n20 \n \nDiscussion  506 \nWe exa mined the i mpact of a PA F intervention on a ntibiotic use and antibiotic non-507 \nsusceptibility for multiple pathogen-drug pairs among common hos pital-acquired 508 \nbacterial isolates. This approach acknowledged the potential “squeezing the balloon” 509 \nphenomeno n in AMS programmes 18 , wherein limiting th e use of specific antibiotics may 510 \nlead to counteracti ng and uninten ded changes in the use of other antibiotics and drug 511 \nresistance mecha nisms. Our findin gs support this as sumption, with o bserved decreases 512 \nin some antibiotic groups but increases in others, reflecting the co m plexity of antibiotic 513 \nuse and resistance in provincial ho spitals with high antibiotic consum ption.  514 \n 515 \nThe observed impact models varied between the two hospitals, refle cting the con tex t-516 \nspecific nature of the imple men tat ion. In Hospital 1, IT S and CITS mo dels provi de 517 \nevidence tha t overall antibiotic consumption im mediately decreased in the interven tion 518 \ngroup with consistent immediat e and long-term impac ts on antibioti c non-susceptibility 519 \nfor hospital-acquired E. coli. In contrast, th e impact of AM S on antibi otic use was more 520 \nlimited in Hospital 2, with some ev idence of positive long-term effec t s on non-521 \nsusceptibility of Acinetobacte r spp. These heterogeneous findings and the lack of 522 \nsignificant interven tion effec ts in various subgroups  or outcome indicators were likely 523 \ninfluenced by confounders, such a s differences in communi ty antibiotic pressure, patient 524 \ncharacteristics, baseline resistance profiles, resource availability (number of clinical 525 \npharmacists dedicated to the PAF activity), hospital cult ure and staff engagemen t, and 526 \nthe behaviour and collaboration of doctors and pharmacists towards the AMS 527 \nintervention , which could have i m pacted both the int ervention a nd the antibiotic use 528 \noutcom es. Furt hermore , the ef fect s on antibiotic consumption likely contributed to th e 529 \ninconsistent impac ts on antibiotic non-susceptibility.  530 \n 531 \nOne of th e inclusion criteria for selecting the wards was the willingness of the ward head 532 \nto participate. This may have helpe d increase the collaboration of doctors in the 533 \nintervention wards and their comp liance with the reco mme ndations of the AMS te am in 534 \nthe PA F activity , and thus possibly limits the generalizability of the study. However, t he 535 \nPAF ac tivity reached only a small number of patients during the impl emen tation period 536 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n21 \n \n(1,890/45,6 23 or 4.1% in Hospital 1, and 1,628/18 ,842 or 8.6% in Hospital 2). This 537 \nsuggests the change in practice following the PAF activi ty is likely  to be from the 538 \nsystems level rather than th e individual patient level, particularly the i mpact on reducing 539 \noverall antibiotic use in Hospital 1. The presence of clinical pharmacists on the clinical 540 \nwards to regularl y audit antibiotic prescriptions may have increased the compliance of 541 \ndoctors to prescription guidelines  in general and in the surgical ward  (traumatology) in 542 \nparticular, poten tially providing a  generalizable evidence for the impact of this non-543 \nrestrictive AMS interve ntion for ot her settings.  544 \n 545 \nFor mortality, t he results of our CITS models indicate tha t AMS imple ment ation at t he 546 \nhospital-wide level could improve antibiotic use without ca using negative consequences 547 \non patient out comes. T he increasing trends in mortality were found in ITS models for 548 \nboth interven tion and control ICUs, but not in th e CITS models, highli ghting the 549 \nimportance of using a control 19  when evaluati ng impact of interve nt ions. Further 550 \nexamina tion revealed increased post-interven tion mort ality for most diagnoses among 551 \nICU patients in both hospitals, part icularly for di seases of the respirat ory sy stem and 552 \nabnormal clinical and laboratory fi ndings (Table 15S, Supplemen tary Data). Non etheless, 553 \nfurther study is needed to fully inv estigate the impac t of AMS in ICU patients, 554 \nconsidering the differential effects that might happen to patien ts of different diagnoses. 555 \nThe limited nu mber of data points in our current datasets does not provide sufficient 556 \npower to identify the changes in specific clinical diagnoses over time in ICUs. 557 \n 558 \nGenerally, AMS programm es in Vietnam ese hospitals must follow national guidelines, 559 \nwhich require forming an AMS co mmit tee , assigning roles, developi ng hospital-specific 560 \npolicies, and implementing restrict ed antibiotic lists 10 20 . Most hospitals responded  to 561 \nthe nation al guidelines by quickl y convening com mit tees, while specific actions and 562 \ninterventions for moni toring and improving antibiotic use and resistance remained 563 \nlimited due to poor leadership co mmit men t, lack of dedicated staff , and weak IT 564 \ncapacity and lab resources 8 . Many hospitals established pre-authorization systems for 565 \nrestricted antibiotics to ensure co mpliance with natio nal guidelines, often tied to social 566 \nhealth insurance rei mbursemen t, p articularly for expensive and potentially overused 567 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n22 \n \nmedical services 21 . In this cont ext , clinical pharmacist-led audits of an tibiotic 568 \nprescriptions, with constructive fee dback to doctors, offer a proactive intervention to 569 \nenhance collaboration a mong heal thcare professionals. Our feasibility study showed 570 \nthat t his approach could be integr ated into routine clinical pharma cy work 9 . The 571 \nintervention involved reviewing ac tive antibiotic prescriptions during pharmacist visits, 572 \nproviding recommendations for inappropriate practices, and assessin g changes in total 573 \nantibiotic use and specific groups. We measured cha nges in both tot al antibiotic use 574 \nand specific groups to as sess the i nterven tion’s impact. I ncreased collaboration among 575 \nprescribing doctors, clinical pharm acists, microbiologists, and infectio us disease 576 \nspecialists helped optimise antibio tic use for individual patients 18 .  577 \n 578 \nLiterat ure supports the causal relationships between antibiotic use in hospitals and 579 \nresistance prevalence among hosp ital-acquired isolates antibiotics 22 . Mixed results from 580 \nITS and CITS models in our study s uggest the presence of history bia s and other events 581 \nimpacting the co ntrol group 19 . Pre vious ITS studies, often lacking co ntrol groups, 582 \nreported mixed results as shown i n a systematic review of studies by  2018 5  as well as  in 583 \nmore recent studies in the U S 23-26 , Spain 27-29 , Germa ny 30 , Japan 31 , Brazil 32 , Korea 33 , and 584 \nChina 34 . To our knowledge, only one observational study in a 627-bed hospital in 585 \nCanada used communi ty-acquired  isolates as a control time series 6 . T his study 586 \ndemonstrated AM S impact in redu cing the incidence of hospital-acq uired multidrug-587 \nresistant organisms by 12.6%. How ever, this approach could not acco unt for the 588 \nconcurrent in terven tions, such as I PC or other programmes within hospital settings, that 589 \ncould affect hospital-acquired resistance.   590 \n 591 \nThe significant strength of our stu dy is the inclusion of a control gro up, allowing to 592 \naccount for conc urrent eve nts, suc h as the COVI D-19 pande mic and I PC measures. In 593 \nparticular, AMS ac tivities and staff atten tion to optimal an tibiotic prescribing may have 594 \nbeen negatively affected in t he last few weeks of the intervention per iod in Hospital 1 595 \ndue to the early effects of the four th wave of the CO VID -19 pandemi c in Vietna m in 596 \nApril – May 2021 12 . We assumed such non-in terven tion even ts had broadly similar  597 \neffects on both th e interven tion an d control groups. Along with the s trengths of 598 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n23 \n \nemploying an implement ation research design as reported previousl y 9 , we 599 \ndemonstrated th e practicality of u sing a participatory action researc h approach to 600 \nevaluate a healt hcare interve ntion that could not be et hically assesse d through 601 \ntraditional randomised controlled trials 35 . We also showed that beha viour change 602 \ninterventions , such as prospective audit and feedback, can be safely integrated into 603 \nroutine clinical practice withou t ca using negative consequences on patient ou tcomes in 604 \nresource-limited settings like Vietn am, u nder existing national leadership and guidance.  605 \n 606 \nLimita tions include the relatively s hort timefra me for the pre- and post-interven tion 607 \nperiods, which restricted our abilit y to fully capture seasonal and hist orical trends in 608 \nantibiotic use, especially when ther e were frequent shortages and stock-outs of 609 \nantibiotics, as well as changes in d rug bidding  cycles and health insurance policies that 610 \ncould affect prescribing practices i n each hospital. There was an incre asing trend in the 611 \nuse of fixed-dose combinations in Hospital 2 which could raise concerns about 612 \nsubstitution effec ts as a result of the AMS interve ntion . Fu ture studie s with longer pre- 613 \nand post-interven tion periods will  help investigate in-depth t hese specific changes over 614 \ntime to inform the design of interventions. Despite effor ts to extrac t data for a longer  615 \npre-interven tion period, we were unable to use data before 2019 for analysing antibiotic 616 \nand clinical outcomes in the co ntr ol and intervention groups due to changes in the H IS . 617 \nHowever, since t hese system-level changes are likely to have had simi lar effects on both 618 \nthe interve ntion and con trol group s, we expect the esti mat es under the controlled 619 \nanalyses (CITS models) to be relati vely robust to such system-level changes. Longer ti me 620 \nseries for antibiotic non-susceptibility data were available from labor atory systems, 621 \nthough missing patient identifiers impacted model performanc e. Nev ertheless, our 622 \nanalysis used more data points than the mini mu m suggested for interrupted time-series 623 \nfrom a simulation-based power calculation 36 . Another limit ation , intri nsic to our 624 \nimplemen tation study design, is that there can be a “spillover effect”, i.e. an uni ntended 625 \nimpact of the in terven tion on the prescribing p ractices of the contro l wards. This may 626 \nhave diluted our estimated impac t  of the interven tion on antibiotic u se in the 627 \nintervention group in the CIT S mo dels.  628 \n 629 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n24 \n \nIn addition, microbiology data quality (which could be affected by wa rd-specific 630 \nvariations in specimen collection p ractices and testing frequency), t h e absence of 631 \nmolecular data on resistance me ch anisms and potential misclassificat ion of hospital-632 \nacquired infections limits our ability to link antibiotic use changes to resistance 633 \ndynamics. Finally, we were not able to collect more granular data for cost analyses as 634 \nonly the total cost of hospitalization was available in the cost data extracted from HI S. I n 635 \norder to collect more detailed costs, we are now conducting an additional study on a 636 \nspecific patient population and will use these data to inform our upc oming cost analyses 637 \nfor the AMS programme .  638 \n 639 \nOur study provides initial evidence on the complex eff ects of AMS int erventions on 640 \nantibiotic use and resistance, and on how quickly changes in antibiotic use lead to 641 \nreverse resistance in a particular or ganism 37 . These findings could be generalised to 642 \nsimilar settings with limited resources in Asia. A combination of strat egies, including 643 \ndrug discovery, res istance monitor ing and novel interventions, is nec essary to respond  644 \nto current resistance pheno types and to anticipate th e evolution of a ntibiotic resistance 645 \nin hospital settings. This is particul arly challenging for hospitals  in lo w- and middle-646 \nincome coun tries, which have li mit ed resources, where infectious diseases are prevalent, 647 \nantibiotic use is high, and environment al reservoirs accelerate resistance spread. 648 \nSurveillance data on t he mec hanis ms of emergence and trans mission of antibiotic 649 \nresistance within and between hos pital reservoirs, along w ith extend ed evaluation 650 \nperiods, are needed to further clarify the impact of AMS in terven tions and antibiotic use, 651 \nand to inform more effec tive, large -scale control and policy measures across hospitals 38 .  652 \n 653 \nIn conclusion, this study highlights the interdependen t changes in an tibiotic use and 654 \nantibiotic resistance driven by AMS programmes, e mploying pharmacist-led prospective 655 \nreview of antibiotic prescriptions a nd feedback to doctors in provinci al-level hospitals in 656 \nVietna m . Our findings confirm th at  developing and strengthening the  surveillance of 657 \nantibiotic resistance, alongside AMS impleme nta tion and IPC measur es, is essential to 658 \nmonitor and respond to resistance dynamics in hospitals effectively. These efforts will 659 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n25 \n \nhelp to ensure that in terventions k eep pace with the rapid evolution of antibiotic 660 \nresistance.   661 \n662 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n26 \n \nAcknowledgements  663 \nWe acknowledge the support and collaboration of the Medical Servic es Administration 664 \nof the Viet Na m Ministry of Healt h  and the World Health Organizatio n Office in Vie t 665 \nNam during the planning of this study.  666 \n 667 \nFu nding  668 \nPfizer Independent Gra nts for Lear ning  & Change (IGLC) provided pr oject funding, 669 \nadministered through The Join t Commission; H. V.T .L was supported by the National 670 \nInstitu te for Healt h Research (NI H R) (using the UK’s Official Development Assistance 671 \n(ODA) Fu nding) and Wellcome (Gr ant Referen ce Number : 216367/Z/ 19/Z) under the 672 \nNIHR -Wellco me P artnership for Global Health Research . HCT acknow ledges funding 673 \nfrom the MRC Centre for Global In fectious Disease Analysis (reference MR/R015600/1), 674 \njointly funded by the UK Medical Research Council (MRC) and the UK  Foreign, 675 \nCommonwealt h & Develop ment O ffice (FCDO) , under the MRC/ FCD O Concordat 676 \nagreement and is also part of the EDCTP2 program supported by the European Union. 677 \nThe views expressed are those of t he authors and not necessarily tho se of Wellcome, the 678 \nNIHR or the Depar tme nt of Heal th  and Social Care. 679 \n 680 \nCompeti ng inte rests  681 \nNone declared.  682 \n 683 \nCont ri but ors  684 \nVTL H , HRvD , EDA and DJA obtain e d funding and contributed to all aspects of the study 685 \ndesign.  VTL H and HRvD had over all responsibi lity for the study. LMQ, NTT H , V HV , CMD, 686 \nVTH DE, PNT supervised and coord inated the runni ng of the study with support from 687 \nEDA, NTCT, TA Q , LNM H and NH K. VTL H , L QT and VTTD an alysed the data with 688 \nsupervision and input from MC, BC, TK, and HCT . VT LH was responsible for the drafting 689 \nof the man uscript. All authors gave approval for the final version of t he manuscript .  690 \n 691 \nPatient co nsent fo r p ublic atio n  692 \nNot required for this research. 693 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n27 \n \nEthics app roval  694 \nThe study protocol was app roved by the Oxford University Tropical Research Ethics 695 \nCommit tee (O xTREC Referen ce 52 6-19), and the E thics Commi ttee of  National Hospital 696 \nof Tropical Diseases (08/HĐĐĐ-N ĐT 31 May 2019). The conduct of t his study conformed 697 \nto the principles embodied in the Declaration of Helsinki.  698 \n 699 \nORCID IDs  700 \nLe Q uynh Trang 0009-0001 -9758- 2873 701 \nVu Hai Vinh 0000-00 01-6130 -7864  702 \nElizabeth Dodds Ashley 0000-0002-4213-6104  703 \nDeverick J. Anderson 0000-0001 -6 882-5496  704 \nBen S. Cooper 0000-0002-9445 -72 17  705 \nMarc Choisy 0000-0002-5187-639 0 706 \nH. Rogier van Doorn 0000-0002 -9 807-1821  707 \nVu Thi La n Huong 0000 -0002-957 9-5576  708 \n 709 \nData availability  710 \nDe-identified data may be obtaine d from the hospitals participating in this study when a 711 \ndata sharing agreement is in place.  712 \n713 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n28 \n \nList of figures:  714 \nFigure 1. Pa tien t locatio n befo re an d after th e AMS implementa tion sta rt at Hospital 1 (Jan 715 \n2019 – May 2021) and Hospital 2 (J an 2019 – Jul 2021). There wer e 4 pairs in each 716 \nHospital: 1) ICU pair: surgical  ICU versus inter nal ICU (both hospitals); 2) Surgical  pair: 717 \ntraumatology ve rsus nephro-ur olog y (Hospital  1) and traumatology versus 718 \ngastroe nt erology (Hospital 2); 3) Internal pair 1: respirat ory ve rsus paediatrics (Hosp ital 1); 719 \ngen eral inte rnal medicine (high-quality services) versus infectious diseases (Hospital  2); 720 \nand 4) Inte rnal pair 2: infec tious dis eases versus gen eral int ernal medic ine (Hospital 1); 721 \ngen eral inte rnal medicine (n ormal services) ve rsus oncology (Hospital 2). n: numbe r of 722 \npatient .  723 \n 724 \n 725 \nFigure 2.  Results of ITS and CITS models for antibio tic use and antibiot ic non-susceptibility 726 \namong the hospital-acquired common pathoge ns identifi ed from rou tine microbiology in 727 \nthe int er ve ntio n group at two hospitals. 1  Odds ratio betwe en non -susceptibility vs 728 \nsusceptibility. Bold val ues: statistically s ignificant estimate ; Gre en shades: decr easing 729 \ntre nds; Red shades: incr easing tre n ds; Dark shades: consiste nt significant r esults for 730 \nITS/CITS models. Pip-tazobactam: Piperacillin-tazobac tam.  731 \n 732 \nFigure 3. P ropo rtio n of antibio tic n on-susceptibility for main pathoge n -drug pairs in 733 \ninte rv en tion and co ntr ol groups bef ore and af ter the start o f AMS int er ve ntio n at two 734 \nhospitals; H1: Hospital 1; H2: Hosp i tal 2; I: In te rv en tion ; C: C on trol ; MR SA: Methicillin-735 \nresistant Staphylococcus aureus; Those columns with * present r esults for hospital acquired 736 \nisolates, the r emaining columns are  for all isolates in the correspo ndin g group.  737 \n 738 \n 739 \nFigure 4. Results of ITS and CITS models for in-hospital mortality and cost of 740 \nhospitalization in the int erv en tio n group at two hospitals. Bold values: statistically  741 \nsignificant estimate ; Gre e n shades: decreasing tr e nds; Red shades: incr easing tre nds.  742 \n 743 \n744 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n29 \n \nRefere nces  745 \n1. Ahmad N, Joji RM, Shahid M. Evolution and implementation of One Health to control the 746 \ndissemination of antibiotic-resistant bacteria and resistance genes: A review. Front Cell 747 \nInfect Microbiol  2022;12:1065796. doi: 10.3389/fcimb.2022.1065796 [published Online 748 \nFirst: 2023/02/03] 749 \n2. Monnier AA, Schouten J, Le Maréchal M, et al. Quality indicators for responsible antibiotic 750 \nuse in the inpatient setting: a systematic review followed by an international 751 \nmultidisciplinary consensus procedure. J Antimicrob Chemother  2018;73(suppl_6):vi30-752 \nvi39. doi: 10.1093/jac/dky116 753 \n3. Zay Ya K, Win PTN, Bielicki J, et al. Association Between Antimicrobial Stewardship Programs 754 \nand Antibiotic Use Globally: A Systematic Review and Meta-Analysis. JAMA Netw Open  755 \n2023;6(2):e2253806. doi: 10.1001/jamanetworkopen.2022.53806 [published Online 756 \nFirst: 20230201] 757 \n4. Davey P, Marwick CA, Scott CL, et al. Interventions to improve antibiotic prescribing practices 758 \nfor hospitals inpatents. Cochrane Database Syst Rev  2017;2: CD003543 doi: 759 \n10.1002/14651858.CD003543.pub4. 760 \n5. Bertollo LG, Lutkemeyer DS, Levin AS. Are antimicrobial stewardship programs effective 761 \nstrategies for preventing antibiotic resistance? A systematic review. Am J Infect Control  762 \n2018;46(7):824-36. doi: 10.1016/j.ajic.2018.01.002 [published Online First: 2018/02/24] 763 \n6. Peragine C, Walker SAN, Simor A, et al. Impact of a Comprehensive Antimicrobial 764 \nStewardship Program on Institutional Burden of Antimicrobial Resistance: A 14-Year 765 \nControlled Interrupted Time-series Study. Clinical Infectious Diseases  2020;71(11):2897-766 \n904. doi: 10.1093/cid/ciz1183 767 \n7. Wertheim H, Chandna A, Phu V, et al. Providing impetus, tools and guidance to strengthen 768 \nnational capacity for antimicrobial stewardship in Viet Nam. PLoS Med  769 \n2013;10(5):e1001429. doi: 10.1371/journal.pmed.1001429 770 \n8. Huong VTL, Ngan TTD, Thao HP, et al. Improving antimicrobial use through antimicrobial 771 \nstewardship in a lower-middle income setting: a mixed-methods study in a network of 772 \nacute-care hospitals in Viet Nam. J Glob Antimicrob Resist  2021;27:212-21. doi: 773 \n10.1016/j.jgar.2021.09.006 [published Online First: 2021/10/04] 774 \n9. Huong VTL, Ngan TTD, Thao HP, et al. Assessing feasibility of establishing antimicrobial 775 \nstewardship programmes in two provincial-level hospitals in Vietnam: an 776 \nimplementation research study. BMJ Open 2021;11(10):e053343. doi: 777 \n10.1136/bmjopen-2021-053343 [published Online First: 2021/10/03] 778 \n10. Viet Nam Ministry of Health. Quy/i(Qt đ/i(Qnh v/i(Q vi/i(Qc ban hành tài li/i(Qu \"H/i(Q/i(Qng d/i(Qn th/i(Qc hi/i(Qn 779 \nqu/i(Qn lý s/i(Q d/i(Qng kháng sinh trong b/i(Qnh vi/i(Qn\" [Decision on the issuance of the document 780 \n\"Guideline on implementing antimicrobial stewardship in hospitals\"]. Hanoi: Viet Nam 781 \nMinistry of Health, 2016. 782 \n11. Lien LTQ, Chuc NTK, Hoa NQ, et al. Knowledge and self-reported practices of infection 783 \ncontrol among various occupational groups in a rural and an urban hospital in Vietnam. 784 \nScientific Reports  2018;8(1):5119. doi: 10.1038/s41598-018-23462-8 785 \n12. Nguyen T\nP, Wong ZS, Wang L, et al. Rapid impact assessments of COVID-19 control 786 \nmeasures against the Delta variant and short-term projections of new confirmed cases 787 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n30 \n \nin Vietnam. J Glob Health  2021;11:03118. doi: 10.7189/jogh.11.03118 [published Online 788 \nFirst: 2022/01/07] 789 \n13. Ngan TTD, Quan TA, Quang LM, et al. Review of antibiotic prescriptions as part of 790 \nantimicrobial stewardship programmes: results from a pilot implementation at two 791 \nprovincial-level hospitals in Viet Nam. JAC-Antimicrobial Resistance  2023;5(1):dlac144. 792 \ndoi: 10.1093/jacamr/dlac144 793 \n14. Sharland M, Zanichelli V, Ombajo LA, et al. The WHO essential medicines list AWaRe book: 794 \nfrom a list to a quality improvement system. Clinical Microbiology and Infection  795 \n2022;28(12):1533-35. doi: https://doi.org/10.1016/j.cmi.2022.08.009  796 \n15. Berends MS, Luz CF, Friedrich AW, et al. AMR: An R Package for Working with Antimicrobial 797 \nResistance Data. Journal of Statistical Software  2022;104(3):1 - 31. doi: 798 \n10.18637/jss.v104.i03 799 \n16. Turner HC, Lauer JA, Tran BX, et al. Adjusting for Inflation and Currency Changes Within 800 \nHealth Economic Studies. Value in Health  2019;22(9):1026-32. doi: 801 \n10.1016/j.jval.2019.03.021 802 \n17. Des Jarlais DC, Lyles C, Crepaz N. Improving the reporting quality of nonrandomized 803 \nevaluations of behavioral and public health interventions: the TREND statement. Am J 804 \nPublic Health  2004;94(3):361-6. doi: 10.2105/ajph.94.3.361 [published Online First: 805 \n2004/03/05] 806 \n18. Burke JP. Antibiotic Resistance—Squeezing the Balloon? JAMA  1998;280(14):1270-71. doi: 807 \n10.1001/jama.280.14.1270 808 \n19. Lopez Bernal J, Cummins S, Gasparrini A. The use of controls in interrupted time series 809 \nstudies of public health interventions. International Journal of Epidemiology  810 \n2018;47(6):2082-93. doi: 10.1093/ije/dyy135 811 \n20. Viet Nam Ministry of Health. Quy/i(Qt đ/i(Qnh v/i(Q vi/i(Qc ban hành tài li/i(Qu \"H/i(Q/i(Qng d/i(Qn th/i(Qc hi/i(Qn 812 \nqu/i(Qn lý s/i(Q d/i(Qng kháng sinh trong b/i(Qnh vi/i(Qn\" [Decision on the issuance of the document 813 \n\"Guideline on implementing antimicrobial stewardship in hospitals\"] Numbered 814 \n5631/QD-BYT. In: Health Mo, ed. Ha Noi: Ministry of Health, 2020. 815 \n21. Ha NT, Anh NQ, Van Toan P, et al. Health Insurance Reimbursement to Hosptials in Vietnam: 816 \nPolicy Implementation Results and Challenges. Health Serv Insights  817 \n2021;14:11786329211010126. doi: 10.1177/11786329211010126 [published Online 818 \nFirst: 2021/04/30] 819 \n22. McGowan JE, Jr. Antimicrobial resistance in hospital organisms and its relation to antibiotic 820 \nuse. Rev Infect Dis  1983;5(6):1033-48. doi: 10.1093/clinids/5.6.1033 [published Online 821 \nFirst: 1983/11/01] 822 \n23. Zeana C, Palmieri FE, Gupta V, et al. Association between fluoroquinolone utilization rates 823 \nand susceptibilities of gram-negative bacilli: Results from an 8-year intervention by an 824 \nantibiotic stewardship program in an inner-city United States hospital. Sci Prog  825 \n2021;104(2):368504211011876. doi: 10.1177/00368504211011876 [published Online 826 \nFirst: 2021/04/29] 827 \n24. Belsky B, Minson Q. Short- and long-term impact of a multifaceted approach targeting 828 \nfluoroquinolone use in a community hospital: an interrupted time-series analysis. Int J 829 \nClin Pharm  2022;44(3):741-48. doi: 10.1007/s11096-022-01405-8 [published Online 830 \nFirst: 2022/04/23] 831 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n31 \n \n25. Hecker MT, Son AH, Murphy NN, et al. Impact of syndrome-specific antimicrobial 832 \nstewardship interventions on use of and resistance to fluoroquinolones: An interrupted 833 \ntime series analysis. Am J Infect Control  2019;47(8):869-75. doi: 834 \n10.1016/j.ajic.2019.01.026 [published Online First: 2019/03/10] 835 \n26. Kinnear CL, Patel TS, Young CL, et al. Impact of an Antimicrobial Stewardship Intervention 836 \non Within- and Between-Patient Daptomycin Resistance Evolution in Vancomycin-837 \nResistant Enterococcus faecium. Antimicrob Agents Chemother  2019;63(4) doi: 838 \n10.1128/aac.01800-18 [published Online First: 2019/02/06] 839 \n27. Álvarez-Marín R, López-Cerero L, Guerrero-Sánchez F, et al. Do specific antimicrobial 840 \nstewardship interventions have an impact on carbapenem resistance in Gram-negative 841 \nbacilli? A multicentre quasi-experimental ecological study: time-trend analysis and 842 \ncharacterization of carbapenemases. Journal of Antimicrobial Chemotherapy  843 \n2021;76(7):1928-36. doi: 10.1093/jac/dkab073 844 \n28. López-Viñau T, Peñalva G, García-Martínez L, et al. Impact of an Antimicrobial Stewardship 845 \nProgram on the Incidence of Carbapenem Resistant Gram-Negative Bacilli: An 846 \nInterrupted Time-Series Analysis. Antibiotics (Basel)  2021;10(5) doi: 847 \n10.3390/antibiotics10050586 [published Online First: 2021/06/03] 848 \n29. Rodríguez-Baño J, Pérez-Moreno MA, Peñalva G, et al. Outcomes of the PIRASOA 849 \nprogramme, an antimicrobial stewardship programme implemented in hospitals of the 850 \nPublic Health System of Andalusia, Spain: an ecologic study of time-trend analysis. Clin 851 \nMicrobiol Infect  2020;26(3):358-65. doi: 10.1016/j.cmi.2019.07.009 [published Online 852 \nFirst: 2019/07/20] 853 \n30. Schönherr SG, Ranft D, Lippmann N, et al. Changes in antibiotic consumption, AMR and 854 \nClostridioides difficile infections in a large tertiary-care center following the 855 \nimplementation of institution-specific guidelines for antimicrobial therapy: A nine-year 856 \ninterrupted time series study. PLoS One  2021;16(10):e0258690. doi: 857 \n10.1371/journal.pone.0258690 [published Online First: 2021/10/15] 858 \n31. Akazawa T, Kusama Y, Fukuda H, et al. Eight-Year Experience of Antimicrobial Stewardship 859 \nProgram and the Trend of Carbapenem Use at a Tertiary Acute-Care Hospital in Japan-860 \nThe Impact of Postprescription Review and Feedback. Open Forum Infect Dis  861 \n2019;6(10):ofz389. doi: 10.1093/ofid/ofz389 [published Online First: 2019/10/30] 862 \n32. Zequinao T, Gasparetto J, Oliveira DDS, et al. A broad-spectrum beta-lactam-sparing 863 \nstewardship program in a middle-income country public hospital: antibiotic use and 864 \nexpenditure outcomes and antimicrobial susceptibility profiles. Braz J Infect Dis  865 \n2020;24(3):221-30. doi: 10.1016/j.bjid.2020.05.005 [published Online First: 2020/06/07] 866 \n33. Kim YC, Kim EJ, Heo JY, et al. Impact of an Infectious Disease Specialist on an Antimicrobial 867 \nStewardship Program at a Resource-Limited, Non-Academic Community Hospital in 868 \nKorea. J Clin Med  2019;8(9) doi: 10.3390/jcm8091293 [published Online First: 869 \n2019/08/28] 870 \n34. Wang H, Wang H, Yu X, et al. Impact of antimicrobial stewardship managed by clinical 871 \npharmacists on antibiotic use and drug resistance in a Chinese hospital, 2010-2016: a 872 \nretrospe\nctive observational study. BMJ Open 2019;9(8):e026072. doi: 873 \n10.1136/bmjopen-2018-026072 [published Online First: 2019/08/05] 874 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n32 \n \n35. Leykum LK, Pugh Ja Fau - Lanham HJ, Lanham Hj Fau - Harmon J, et al. Implementation 875 \nresearch design: integrating participatory action research into randomized controlled 876 \ntrials. Implement Sci  2009;4(69) doi: doi: 10.1186/1748-5908-4-69 877 \n36. Zhang F, Wagner AK, Ross-Degnan D. Simulation-based power calculation for designing 878 \ninterrupted time series analyses of health policy interventions. Journal of Clinical 879 \nEpidemiology  2011;64(11):1252-61. doi: 10.1016/j.jclinepi.2011.02.007 880 \n37. Baym M, Stone LK, Kishony R. Multidrug evolutionary strategies to reverse antibiotic 881 \nresistance. Science  2016;351(6268):aad3292. doi: 10.1126/science.aad3292 [published 882 \nOnline First: 2016/01/02] 883 \n38. Bonomo RA, Perez F, Hujer AM, et al. The Real Crisis in Antimicrobial Resistance: Failure to 884 \nAnticipate and Respond. Clinical Infectious Diseases  2024:ciad758. doi: 885 \n10.1093/cid/ciad758 886 \n 887 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted August 27, 2025. ; https://doi.org/10.1101/2025.08.25.25334341doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}