Post-Caesarean Wound Infections: Incidence, Bacterial Profiles, Antimicrobial Susceptibility Patterns and Associated Factors in Public Hospitals, Southern Ethiopia

Post-Caesarean Wound Infections: Incidence, Bacterial Profiles, Antimicrobial Susceptibility Patterns and Associated Factors in Public Hospitals, Southern Ethiopia | 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 Article Post-Caesarean Wound Infections: Incidence, Bacterial Profiles, Antimicrobial Susceptibility Patterns and Associated Factors in Public Hospitals, Southern Ethiopia Teshome Kebede, Aseer Manilal, Mohammed Seid, Mheret Tesfaye, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3113435/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 Post-caesarean wound infections are a maternal health concern associated with increased morbidity and extended hospital stays, mainly caused by drug-resistant pathogens. A prospective cross-sectional study was undertaken in the title public hospitals among pregnant women who had undergone Caesarean section (CS). All women were followed up for 30 days, and those who developed a clinically infected wound (i.e., 204) were included in the bacteriological analysis. A pre-tested questionnaire was used to collect the data. Wound samples were collected to identify bacteria as per the microbiological guidelines. Antimicrobial susceptibility profiles were determined by the Kirby–Bauer disk diffusion method. Of the 204 samples, 85.78% (175/204) were culture-positive, yielding 203 bacteria. Staphylococcus aureus predominantly caused wound infections (n = 65, 32.01%), followed by Klebsiella pneumoniae (n = 54, 26.6%). Gram-negative bacilli were highly resistant to piperacillin, ceftriaxone, cefotaxime, and co-trimoxazole (> 85%), whereas Gram-positive cocci were highly resistant to penicillin and tetracycline (> 90%). Overall, 70.44% (n = 143) of isolates were multidrug-resistant. Parity [P = 0.01, AOR: 4.4, (CI: 1.40, 13.87)], previous CS [P = 0.0, AOR: 6.3, (CI: 3.10, 13.01)], diabetes mellitus [P = 0.05, AOR: 3.2, (CI: 2.1, 5.8)], and emergency CS [P = 0.05, AOR: 2.07, (CI: 1.06, 2.63)] were significantly associated with post-caesarean wound infections. Biological sciences/Microbiology Health sciences/Diseases drug resistance Caesarean-section surgical site infection associated factors Arba Minch Figures Figure 1 1. Introduction Despite the advances in infection prevention and control practices, post-caesarean wound infections (PSWI) remain a major public health problem affecting millions yearly. Caesarean-sections (CS) carry a five to twenty-fold increased risk of infection compared to vaginal delivery [1]. According to the Centers for Disease Control and Prevention (CDC), surgical site infections (SSIs) are the second most common complication after CS, and their incidence range from 3 to 15% [2, 3]. The prevalence and incidence rates of PSWI vary widely concerning procedures, type of hospitals, surgeons who perform the procedure, patient characteristics, and geographic locations [4]. The rates of PSWI are of great concern to low-income countries in Africa [5]. A review on the burden of surgical site infection following CS in sub-Saharan Africa recently described that the rate of surgical site infections was 10.21% [6]. Similarly, various studies performed in Ethiopia too estimated that the incidence of PSWI ranges from 6.8 to 13.7%, reflecting variations among different geographical settings and time domains [7]. The PSWI can be of the nature of a relatively trivial wound discharge with no other complications to life-threatening sepsis; complications include bacteraemia, fever, endometritis, urinary tract infection, and other serious multisystem infections [8]. It is also the third most frequently reported type of non-device-associated healthcare-associated infections (HAI), especially in sub-Saharan Africa, leading to morbidity, mortality, prolonged hospital stays, escalating costs, higher hospital readmission rates, and jeopardized health outcomes [3,5]. Post-cesarean wound infections account for up to 3% of mortality in women undergoing CS deliveries [9]. Nevertheless, independent risk factors are not well documented in the literature; several host and intrapartum-related factors, pregnancy, and procedure-related factors are associated with the risk of developing PSWI [10]. Microbiological evidence is of utmost importance in diagnosing and managing PSWI. In most cases, the causative pathogens originate from endogenous flora of patients’ skin, mucous membranes, or hollow viscera. The most commonly isolated bacterial pathogens are S. aureus , Enterobacteriaceae, coagulase-negative Staphylococci (CoNs), Enterococci , and P. aeruginosa [11]. Successful treatment of these infections depends on accurately identifying etiological agents and their antibiogram profiles. However, in the last decade, PSWI has further been complicated by an increasing prevalence of multidrug-resistant (MDR) organisms [12]. A scan of the literature indicated that in Ethiopia, only limited data exist which describe the epidemiology of PSWI, the causative organisms, or the incidence of antimicrobial drug resistance, and often, there is no information linked to the post-discharge surveillance programs. Furthermore, risk factors linked to PSWI, too, showed wider variations in several Ethiopian studies [13–17]. In addition, the incidence of infections may vary, depending on the surgeon, hospital, surgical procedures applied, and patient. Therefore, the investigation of incidence, aetiological profiles, and antimicrobial susceptibility patterns of isolates from PSWI are of urgent health priority for patients, clinicians, and policymakers, hence the present study. 2. Materials and Methods 2.1. Study Area and Period A multi-centre prospective cross-sectional study was conducted from 1st February to 31st July 2021 in selected government hospitals in three zones of the Southern Nations, Nationalities, and Peoples' Region, Ethiopia. According to the Regional Health Bureau’s annual report, there are 21 Government Hospitals, 703 Health Centers, and 3,835 Health Posts [18]. Most health institutions in the selected administrative regions are relatively new and not well-equipped to perform CS. Nevertheless, the capacity of the health facilities to perform surgeries was considered as an inclusion criterion. Therefore, one hospital from each zone was selected for the study setting, such as Arba Minch General Hospital (AMGH) from Gamo Zone, Gidole Primary Hospital (GPH) from Derashe, and Wolita Sodo University Teaching Hospital (WSUTH) from Wolita Zone. Arba Minch General Hospital is the biggest health institution among the three and provides health services to residents of the Gamo Zone. The hospital serves over 1.5 million by providing preventive, curative, and rehabilitative care in outpatients, inpatients, pharmacy, and laboratory departments. As per the 2020 records, a total of 3815 pregnant women attended the gynaecology and obstetrics departments to give birth, and there were a total of 2772 normal deliveries and 1043 CS. Wolaita Sodo University Teaching Hospital is situated in the Sodo Town of Wolaita Zone. In 2020, a total of 8,463 pregnant women came to the gynaecology and obstetrics department to give birth, and there were a total of 6,379 normal births and 2,084 CS. Gidole Primary Hospital serves more than 200,000 through preventive, curative, and rehabilitative care in outpatients, inpatients, pharmacy, and laboratory departments. According to the records, in 2020, 1792 pregnant women attended the gynaecology and obstetrics department to give birth, and there were 1,332 normal deliveries and 460 cases of CS in 2020. 2.2. Study design and eligibility criteria A prospective cross-sectional study was conducted among all pregnant women who underwent CS in any of the three title hospitals during the study period as per the inclusion criteria such as 1. women (all reproductive age-women (i.e., 15–49 years)) underwent CS during hospitalization or within 30 days post-discharge survey period, 2. those who were willing to participate in the study, 3. women with at least one of the following signs and symptoms of the surgical site, 4. infection that is clinically confirmed by the surgeon/physician was selected (drainage of pus, foul odour coming from the wound, fever/ chills, hot to touch, redness, pain, or tenderness), 5. only the first procedure was considered eligible for inclusion for patients who underwent multiple surgical procedures during the study period. The exclusion criteria were: 1. those who have been seriously ill or are unable to respond well, 2. women who were on antibiotic treatment (except Surgical antibiotic prophylaxis- SAP) during preoperative hospital stay (up to one week before the commencement of the study period), 3. patients who are undergoing pre-surgery, 4. patients who failed to attend the follow-up 30 days after the day of operation (surgery). 2.3. Sample size calculation The required sample size was calculated by using a single population proportion formula by fixing a prevalence of culture-positive PSWI rate of 8.5% from a recent bacteriological study conducted elsewhere [19]. After considering a confidence interval of 95% (z = 1.96) and 5% of marginal error (d = 0.05), the initial sample size was estimated to be 196, and by computing a 10% non-response rate, the final sample size was consolidated as 206 (n). 2.4. Sample allocation and sampling techniques Women who attended any of the three specified hospitals underwent CS during the study period were assessed for eligibility, and those who developed signs and symptoms of wound infection within 30 days of surgery were included in the bacteriological analysis. The selection of study participants from three hospitals was made by proportional allocation corresponding to each institution, i.e., the final sample size proportionately attained for each hospital was 65 (AMGH), 114 (WSUTH), and 25 (GPH). Study participants were included consecutively until the final sample size was achieved. 2.5. Data collection tools and methods A pre-tested, semi-structured questionnaire and observational checklist/data extraction form were used for data collection at the time of enrollment and also during follow-up. The data collection tool was adapted from prior studies and modified based on the contextual situation. The questionnaire contains three parts; Part I (socio-demographic characteristics), Part II (obstetric & clinical information), and Part III (surgical procedure). Data collection methods include interviews, chart reviews, physical examinations, and bacteriological sample analyses. Informed consent was obtained from all subjects and/or their legal guardian(s). All methods were performed in accordance with the relevant guidelines and regulations. Trained research assistants (B.Sc. Midwifery nurses) collected information from all study participants. Socio-demographic and behavioural data, including patients' age, level of education, residence, occupation, and smoking habit, were solicited through face-to-face interviews using a pre-tested. Patients’ obstetric/clinical data: Semi-structured questionnaires/ data abstraction forms were used to extract data from the patient's case notes; parity, premature rupture of membrane, duration of labour, previous CS, previous hospitalization within six months of the commencement of the study, and referral status. Surgical procedure-related parameters included surgical hand scrub, preoperative skin preparation, antibiotic prophylaxis, type of surgery and incision, skin enclosure, and kind of anaesthesia. 2.6. Case definition of post-cesarean wound infections Data from the clinical wound examination and bacteriological analysis of samples were pooled. Patients were allocated to the surgical site infection group based on meeting any one of the following criteria established by the CDC for SSIs: Briefly, a superficial surgical site infection is defined as one that occurs within 30 days of the surgical procedure and affects only the skin or subcutaneous tissue of the incision, and has at least one of the following signs or symptoms of infection: purulent discharge from the superficial incision wound, pain or tenderness, localized swelling, redness or heat, and the superficial incision is deliberately opened by a surgeon [2]. 2.7. Clinical Examination Post-caesarean wound infections were assessed for infection by directly examining the wound in the hospital and during follow-up visits after discharge by a surgeon, nurse and/or gynaecologist. The wound was examined with due emphasis on signs and symptoms consistent with PSWI according to the clinical criteria for developing surgical site infection set by CDC. 2.8. Microbiological analysis of wound samples During the 6-month study period, we collected wound swabs from 206 women who developed the infection, as two clinically suspected wound samples were excluded based on the exclusion criteria (no discharge). The sample was collected using a sterile swab stick from each participant by gently rubbing in the infected site (Levine or Z technique) and immediately placed into sterile test tubes prefilled with Amie’s transport media. And transported by following standard transportation procedures to the Medical Microbiology and Parasitology Laboratory, Department of Medical Laboratory Sciences, for further processing and stored aerobically at 4–8°C in case of delay. All specimens were separately inoculated respectively onto a set of culture media such as blood agar plate, mannitol salt agar, and MacConkey agar (Oxoid, Ltd., England) and were incubated aerobically at 37°C for 24 hours. The isolates were identified as per the standard bacteriological procedure [20]. 2.9. Antimicrobial susceptibility testing Antimicrobial susceptibility testing was performed on Mueller-Hinton agar (Oxoid, UK) using the Kirby-Bauer disk diffusion method according to Clinical and Laboratory Standards Institute (CLSI) guidelines [21]. For Gram-positive cocci (GPC), antibiotics such as penicillin (10µg), ciprofloxacin (5µg), erythromycin (15µg), clindamycin (10µg), chloramphenicol (30µg), cefoxitin (30µg), gentamicin (10µg), sulfamethoxazole-trimethoprim (co-trimoxazole) (1.25/23.75 µg), tetracycline (30µg) and vancomycin (30µg) were used, whereas, for Gram-negative bacilli (GNB), piperacillin (100µg), ciprofloxacin (5µg), chloramphenicol (30µg), amikacin (30µg), ceftriaxone (30µg), cefoxitin (30µg), cefotaxime (30µg), cefopime (30µg), meropenem (10µg), gentamicin (30µg) and sulfamethoxazole-trimethoprim (1.25/23.75 µg). The selection of antibiotics was based on the guidelines set by CLSI, 2019. The presence of methicillin-resistant S. aureus (MRSA), extended-spectrum beta-lactamases producers (ESBL), and carbapenem-resistant Enterobacterales (CRE) was detected as per the standard procedures [21]. Multi-drug resistance in this study was extrapolated as the resistance of at least three or more groups of antibiotics tested [22]. 2.10. Quality Controls To assure the quality of the data, a pre-test was done on 5% of the total sample before the actual work; one day of training was given to data collectors. Data were checked for completeness, accuracy, clarity, and consistency by the principal investigator daily; the standard operating procedure for each operation was strictly followed. Expiry date of media and reagents and quality control parameters were checked as per CLSI guidelines. All culture media were prepared following the instruction of the manufacturer, and the sterility of the culture media was tested by incubating 5% of each batch at 35–37°C overnight (for evaluation of possible contamination). After preparation, all culture plates and antibiotic disks were stored at the recommended refrigeration temperature (2–8°C). Quality of media, antibiotic disks, as well as the performance of a person carrying the tests, was controlled by reference strains: P. aeruginosa (ATCC 27853), E. coli (ATCC 25922), K. pneumoniae (ATCC 700603) for ESBL, S. aureus (ATCC 25923), and S. aureus (ATCC 29213) for MRSA. During quality control of ESBL screening and phenotypic confirmatory tests, we simultaneously tested the non-ESBL-producing organism, such as E. coli (ATCC 25922), and an ESBL-producing organism, K. pneumoniae (ATCC 700603). All the reference strains were procured from Ethiopian Public Health Institute. 2.11. Statistical Analyses Data were checked, cleaned, and coded for completeness, entered by Epi-Data version 4.4.3.1, and exported to Statistical Package for Social Sciences (SPSS) version 25 (IBM Corporation, Armonk, NY, USA). Socio-demographic and clinical factors were described by using descriptive statistics like frequency and percentage. Inferential statistics such as bivariable and multivariable binary logistic regression analyses were done to determine the strength of association among independent and outcome variables. Initially, the data were subjected to a series of bivariable analyses, and those with a cut-off point of P-value ≤ 0.25 were processed further by multivariable analysis. The Hosmer-Lemeshow goodness fit test checked the fitness of the model. Adjusted odds ratio (AOR) and 95% confidence interval (CI) were used to determine the strength of association; P-values ≤ 0.05 in the multivariable analysis were considered statistically significant. 2.12. Ethical Considerations Ethical approval for the work was provided by the Institutional Review Board of Arba Minch University, College of Medicine and Health Sciences, and the Department of Medical Laboratory Science (IRB 1045/21). 3. Results 3.1. Socio-demographic characteristics This study was conducted over a span of 6 months, between 1st February to 31st July 2021, involving three hospitals (AMGH, WSUTH & GPH). The age of women who developed post-cesarean wound infections ranged from 17 to 41, with a mean value of 26.74 ± 5.04. The majority of women, i.e., 121 (59.3%) affected, were within the age category of 20–39. One hundred and eight patients (52.9%) were rural residents; 36.8% (n = 75) of them had their education up to the first cycle only; homemakers/unemployed correspond to 47.1%. Of the 204 PSWI, 106(51.96%) were identified at readmission to the hospital, while 9% were diagnosed post-discharge (Table 1 ). Table 1 Socio-demographic and behavioural characteristics of women suspected of post-caesarean wound infections in three public hospitals, southern Ethiopia, 2021 (n = 204) Age Variable and category Study settings AMGH n (%) WSUTH N (%) GPH n (%) Total 204 (%) 39 0(0) 3(60) 2(40) 5(2.5) Residence Urban 28(29.2) 59(61.5) 9(9.4) 96(47.1) Rural 37(34.) 55(50.9) 16(14.8) 108(52.9) Educational Level Informal education 14(21.5) 29(25.4) 5(20) 48(23.5) First Cycle 26(40) 41(36) 8(32) 75(36.8) Second cycle 9(13.8) 17(14.9) 7(28) 33(16.2) High School 4(6.2) 12(10.5) 2(8) 18(8.8) Higher 12(18.5) 15(13.2) 3(12) 30(14.7) Occupation Employer 11(16.9) 26(22.8) 5(20) 42(20.6) Student 9(13.8) 19(16.7) 2(8) 30(14.7) Labourer 5(7.7) 4(3.5) 3(12) 12(5.9) Homemaker/unemployed 28(43.1) 54(47.4) 14(56) 96(47.1) Merchant 12(18.5) 11(9.6) 1(4) 24(11.8) Smoking during pregnancy Yes 0(0) 6(5.3) 1(4) 7(3.4) No 65(100) 108(94.7) 24(96) 97(96.6) 3.2. Obstetrics, clinical, and surgical procedure-related characteristics In this study, 35% (n = 71) of participants were referred from other facilities, while 65% had no history of referrals. More than half of them were multipara 109(53.4%). More than fifty percent (n = 111) of the subjects had a history of a previous CS. One hundred and seven patients (52%) had a membrane rupture before CS. Sixty-two mothers (30.4%) had one or more co-morbidities, namely HIV, 8 (3.9%), diabetic mellitus, 5 (2.4%), hypertension, 12 (5.8%), and anaemia, 37 (18.1%) (Table 2 ) with respect to factors related to the surgical procedure, the most commonly used type of anaesthesia was spinal, 83% (n = 170). Surgical antibiotic prophylaxis was carried out in 97.1% (n = 198) of the patients. The number of women with an interrupted type of skin closure was 178 (87.3%). Further data are mentioned in Table 2 . Table 2 Obstetrics, clinical and surgical procedure-related characteristics of women suspected of post-caesarean wound infections stratified by study settings, southern Ethiopia, 2021(n = 204) Variables and category Frequency and percentage in study setting Total n (%) AMGH WSUTH GPH n = 65 n(%) n = 114 n(%) n = 25 n(%) n = 204 n(%) Referred from other health facilities Yes 21(32.3) 38(33.3) 12(48) 71(35) No 44(67.3) 76(66.7) 13(52) 133(65) Parity status Nullipara 3(4.6) 20(17.5) 3(12) 26(12.7) Primipara 35(53.8) 25(21.9) 9(36) 69(33.8) Multipara 27(41.5) 69(60.5) 13(52) 109(53.4) HIV status Positive 3(4.6) 3 (2.6) 2 (8) 8(3.9) Negative 62(05.4) 111 (97.4) 23 (92) 196(96.1) Hypertension Yes 0(0.0) 9(7.9) 3(25) 12(5.88) No 65(100) 105(92.1) 22(75) 192(94) Diabetes mellitus Yes 3(4.7) 0(0.0) 2(8) 5(2.5%) No 62(95.3) 114(100) 23(92) 199(97.5) Anemia Yes 10(15.4) 23(20.2) 4(16) 37(18.1) No 55(84.6) 91(79.8) 21(84) 167(81.9) Variable and category Study setting Total AMGH WSUTH GPH n (%) n (%) N (%) Duration of labour 24hrs 5(7.7) 8(7) 2(8) 15(7.4) PROM Yes 22(33.8) 71(62.2) 14(56) 107(52) No 43(66.2) 43(37.8) 11(44) 97(48) Previous CS Yes 29(44.6) 65(57) 17(68) 111(54.4) No 36(55.4) 49(43) 8(32) 93(45.6) Previous hospitalization Yes 9(16.07) 18(12.5) 4(16) 31(15.2) No 56(83.93) 96(87.5) 21(84) 173(84.2) Surgical procedure-related characteristics Preoperative skin preparation Aqueous betadine 8(12.3%) 6(5.3%) 6(23%) 20(10.8%) Chlorhexidine–alcohol 57(87.7%) 108(94.7%) 19(76.9%) 184(89,2%) Surgical hand scrub Normal soap and water 5(7.7%) 27(23%) 12(46.2%) 44(21.6%) Medicated soap and water 60(92.3%) 87(77%) 13(53.8%) 160(78.4%) Type of surgery Elective 1(1.5) 25(21.9) 13(52) 39(19) Emergency 64(98.5) 89(78.1) 12(48) 165(81) Type of incision Transverse 52(80) 80(70.2) 21(84) 153(75) Vertical 13(20) 34(29.8) 4(16) 51(25) Type of anaesthesia Spinal 65(100) 96(84.2) 9(36) 170(83) General 0(0.0) 18(15.8) 16(64) 34(17) Skin enclosure Interrupted sutures 64(98.5) 92(81.4) 22(84.6) 178(87.3) Continuous 1(1.5) 22(18.6) 3(15.4) 26(12.7) SAP Yes 65(100) 110(96.5) 23(92) 198(97.1) No 0(0) 4(3.5) 2(8) 6(2.9) 3.3. Incidence and profile of bacterial post-caesarean wound infections Results of the bacteriological analysis showed that out of 204 wound specimens processed, 175 (85.8%) were found to be aerobic culture-positive. Gram-negative and Gram-positive bacteria were detected in 57.8 (n = 118) and 41.6% (n = 85), respectively. Among the three hospitals studied, patients who attended WSUTH had the highest bacterial isolation rate of 62%. Of all culture-confirmed post-caesarean wound infections, mono-bacterial infections were observed in most (72%) cases. Gram-negative bacilli, 57.8% (n = 118), were isolated more frequently (Table 3 ). Of the total of nine distinct species of bacteria isolated, S. aureus, K. pneumoniae , and P. aeruginosa were the three most predominant isolates corresponding to 32.01, 26.6, and 14.7%, respectively (Fig. 1 ). Table 3 Bacterial profile and patterns of infections (mono-bacterial vs. bi-bacterial) among women clinically suspected of post-caesarean wounds in the three public hospitals, southern Ethiopia, 2021 Profile of bacteria Isolated No. of isolates n = 203 n(%) Study setting AMGH n(%) WSUTH n(%) GPH n(%) Gram-negative 118 (58.1) 34(28.8) 70(59.3) 14(11.8) K. pneumoniae 54(26.6) 15(27.7) 33(61.1) 6(11.1) P. aeruginosa 30(14.7) 9(30) 16(53.3) 5(16.6) E. coli 24(11.8) 7(29.1) 14(58.3) 3(12.5) Proteus sp. 4(1.97) 1(25) 3(75) 0 Enterobacter sp. 2(0.9) 0 2(100) 0 A. baumannii 4(1.97) 2(50) 2(50) 0 Gram-positive 85(41.8) 23(27) 54(63.5) 8(9.4) S. aureus 65(32.01) 17(26.1) 43(66.1) 5(7.6) CoNs 17(8.4) 5(29.4) 9(52.9) 3(17.6) E. faecium 3(1.48) 1(33.3) 2(66.6) 0 Mono-bacterial isolates 147(72.4) 39(26.5) 94(63.9) 14(9.5) Bi-bacterial isolates (n = 28 cases) 56(27.5) 18(32.1) 30(53.5) 8 (14.2) P. aeruginosa and S. aureus (n = 12 cases) 24(42.9) 8(33.3) 14(58.3) 2(8.3) E. coli and S. aureus (n = 3 cases) 6(10.7) 2(33.3) 4(66.6) 0 K. pneumoniae and S. aureus (n = 8 cases) 16(28.5) 4(25) 10(62.5) 2(12.5) P. aeruginosa and CoNs (n = 5 cases) 10(17.9) 4(40) 2(20) 4(40) Of the culture-confirmed cases of PSWI, bi-bacterial infections were detected only in 28 out of 175 specimens; no cases of Gram-positive and Gram-positive co-infections were recorded. Predominant bi-bacterial infections involved S. aureus and P. aeruginosa co-infections (12 out of 28 cases). Furthermore, K. pneumoniae and S. aureus co-infections have been reported in 8 out of 28 cases; however, co-infection with P. aeruginosa / CoNs and E. coli / S. aureus occurred in five and three cases, respectively. The rate of bi-bacterial infections varied in all hospitals (Table 3 ). 3.4. Antibiotic susceptibility profiles 3.4.1. Antimicrobial susceptibility profiles of Gram-negative bacilli The susceptibility profiles of GNB (n = 118) against eleven antibiotics are presented in Table 4 as susceptible and resistant. The intermediate results were also considered resistant for the purpose of statistical analysis. In the present study, isolates of GNB showed a wider range of variations in terms of their susceptibility (10.2 to 77.9%) as well as resistance (22.2 to 89.8%) profiles. The GNB showed resistance to cefotaxime, 106 (89.8%), cotrimoxazole, 104 (88.2%), ceftriaxone, 103 (87.3%), and piperacillin, 102 (86.4%). However, most GNB showed a relatively higher level of susceptibility to amikacin, gentamicin, and meropenem, i.e., 92/118 (77.9%), 77/118 (65.2%), and 71/118 (60.2%), respectively (Table 4 ). The predominant isolate of GNB, K. pneumoniae , demonstrated a high level of resistance against cefotaxime, 48/54 (88.9%), piperacillin, 45/54 (83.4%), co-trimoxazole and ceftriaxone, each 44/54 (81.5%) and a lower level of resistance to chloramphenicol, 13/54 (24.1%). Among other members of Enterobacteriaceae, E. coli showed the highest level of resistance to piperacillin, 22/24 (91.7%), followed by co-trimoxazole, cefotaxime, and ceftriaxone, 21/24 (87.5% resistance to each), and was highly susceptible to amikacin, 21/24(87.5%) and ciprofloxacin, 19/24(79%). Isolates of P. aeruginosa exhibited resistance to piperacillin, 27/30 (90%), and cefotaxime, 28/30 (93.4%). On the other hand, isolates were highly susceptible to amikacin, 26/30 (86.6%), and ciprofloxacin, 25/30 (83%). Table 4 Antimicrobial susceptibility profiles of Gram-negative bacilli of women suspected of post-caesarean wound infections, southern Ethiopia, 2021(n = 204) List of antibiotics tested AST Isolated Gram-negative bacilli from post-CS wound infections n(%) Total n(118) K. pneumoniae (n = 54) P. aeruginosa (n = 30) E. coli (n = 24) Proteu s sp. (n = 4) Enterobacter sp. (n = 2) A. baumannii n = 4) PIP S 9(16.6) 3(10) 2(8.3) 2(50) 0(0.0) 0(0.0) 16(13.6) R 45(83.4) 27(90) 22(91.7) 2(50) 2(100) 4(100) 102(86.4) CPR S 32(59.2) 25(83) 19(79) 3(75) 2(100) 1(25) 82(69.5) R 22(40.8) 5(17) 5(21) 1(25) 0(0.0) 3(75) 36(30.5) MER S 36(66.6) 13(43) 15(62.5) 3(75) 1(50) 3(75) 71(60.2) R 18(33.4) 17(57) 9(37.5) 1(25) 1(50) 1(25) 47(39.8) GEN S 35(64.8) 19(63) 17(70.8) 4(100) 0(0.0) 2(50) 77(65.2) R 19(35.2) 11(37) 7(29.2) 0(0) 2(100) 2(50) 41(34.8) AMK S 38(70.4) 26(86.6) 21(87.5) 3(75) 1(50) 3(75) 92(77.9) R 16(29.6) 4(13.4) 3(12.5) 1(25) 1(50) 1(25) 26(22.1) SXT S 10(18.5) NA 3(12.5) 1(25) 0(0) NA 14(11.8) R 44(81.5) NA 21(87.5) 3(75) 2(100) NA 104(88.2) CFP S 30(55.5) 18(60) 16(66.7) 4(100) 2(100) 1(25) 71(60.2) R 24(44.5) 12(40) 8(33.3) 0(0) 0(0.0) 3(75) 47(39.8) CHL S 41(75.9) NA 16(66.7) 2(40) 2(100) NA 61(51.7) R 13(24.1) NA 8(33.3) 3(75) 0(0.0) NA 57(48.3) CTR S 10(18.5) NA 3(12.5) 1(25) 0(0.0) 1(25) 15(12.7) R 44(81.5) NA 21(87.5) 3(75) 2(100) 3(75) 103(87.3) CXT S 25(46.2) NA 6(25) 3(75) 1(50) 2(50) 37(31.4) R 29(53.8) NA 18(75) 1(25) 1(50) 2(50) 81(68.6) CTX S 6(11.1) 2(6.6) 3(12.5) 1(25) 0(0.0) NA 12(10.2) R 48(88.9) 28(93.4) 21(87.5) 3(75) 2(100) NA 106(89.8) AST: Antibiotics susceptibility, R: resistance, S: susceptible, CPR: ciprofloxacin, CTR: ceftriaxone, CHL: chloramphenicol, CXT: cefoxitin, AMK: amikacin, CTX: cefotaxime, PIP: piperacillin, CFP: cefepime, MER: meropenem, GEN: gentamicin, SXT: sulfamethoxazole-trimethoprim. NA: not applicable 3.4.2. Antimicrobial susceptibility profiles of Gram-positive cocci The susceptibility profiles of GPC (n = 85) against ten antibiotics isolated from the PSWI are presented in Table 5 as susceptible and resistant. In contrast, the intermediate results were also considered resistant for the purpose of statistical analysis, as mentioned earlier. Table 5 Antimicrobial susceptibility profiles of Gram-positive cocci among women with post-caesarean wound infections, southern Ethiopia, 2021(n = 204) List of antibiotics AST Isolated Gram-positive bacteria from post-CS wound infections n(%) Total n(%) S. aureus (n═65) CoNs (n═17) E. faecium (n═3) PEN S 3(4.6) 2(11.7) 0(0) 5(5.8) R 62(95.4) 15(88.3) 3(100) 80(94.2) TET S 4(6.1) 4(23.5) NA 8(9.8) R 61(93.9) 13(76.5) NA 74(90.2) CPR S 41(63) 12(70.5) 2(66.7) 55(65) R 24(37) 5(29.5) 1(33.3) 30(35) GEN S 49(75.3) 15(88.2) NA 64(75.3) R 16(24.7) 2(11.8) NA 18(24.7) CHL S 47(72.3) 12(70.5) 2(66.7) 61(71.7) R 18(27.7) 5(29.5) 1(33.3) 24(28.3) CLD S 42(65) 10(58) NA 52(63.4) R 23(35) 7(42) NA 30(36.6) ERY S 51(78.5) 11(64.7) 2(66.7) 64(75.3) R 14(21.5) 6(35.3) 1(33.3) 21(24.7) SXT S 10(15.3) 3(17.6) NA 13(15.8) R 55(84.7) 14(82.4) NA 69(84.2) CXT S 34(52.3) 10(58.8) NA 44(53.6) R 31(47.7) 7(41.2) NA 38(46.4) VAN S NA NA 1(33.3) 1(33.3) R NA NA 2(66.7) 2(66.7) CoNs: Coagulase-negative S. aureus , AST: Antibiotics susceptibility Test, R: resistance, S: susceptible, PEN: penicillin, CPR: ciprofloxacin, ERY: erythromycin, CLN: clindamycin, CHL: chloramphenicol, CXT: cefoxitin, GEN: gentamicin, SXT: sulfamethoxazole-trimethoprim, TET: tetracycline, VAN: vancomycin. NA: not applicable Similar to that of GNB isolates, a wider range of variations concerning resistance (24.7 to 94.2%), as well as susceptibility (5.8 to 75.3%) profiles, were also observed in this case too. Gram-positive cocci were highly resistant to penicillin, 80 (94.2%), tetracycline, 74 (90.2%), and co-trimoxazole, 69 (84.2%). On the other hand, isolates were highly susceptible to gentamicin, 64 (75.5%), erythromycin, 64 (75.3%), and chloramphenicol, 61(71.7%). Analysis of species-specific resistance rates indicated that most of the isolates of S. aureus were resistant to penicillin, 62/65 (95.4%), tetracycline, 61/65 (93.9%), and co-trimoxazole, 55/65 (84.7%). On the other hand, 78.5, 75.3 and 72.3% of isolates of S. aureus were susceptible to erythromycin, gentamicin, and chloramphenicol, respectively. Among the 65 isolates of S. aureus , 31 showed a zone of inhibition of ≤ 21mm (14.5 to 20 mm) in the cefoxitin disk diffusion test and were extrapolated as methicillin-resistant S. aureus. The percentage of MRSA among S. aureus was found to be 31/65 (47.7%), while the remaining 34 (52.3%) were methicillin-sensitive S. aureus (MSSA). In the current study, isolates of CoNs were resistant to penicillin, 15/17 (88.3%), cotrimoxazole, 14/17 (82.4%), and tetracycline, 13/17 (76.5%). Likewise, 7/17 (41.2%) were found to be methicillin-resistant. On the other hand, isolates of CoNs were susceptible to gentamicin, 15/17 (88.2%), chloramphenicol, 12/17 (70.5%), and ciprofloxacin, 12/17 (70.5%). Invariably, all the isolates of E. faecium in this study were 100% resistant to penicillin, and two of them were found to be VRE. 3.4.3. Multi-Drug Resistance profiles In this study, MDR is inferred as the resistance to three or more groups of antibiotics tested. Out of the 203 total bacterial isolates, 143 were found to be MDR (i.e., 70.44%), of which 86/118 (72.88%) belong to the Gram-negative group. The MDR GPC comprise 46/65 (70.7%) of S. aureus and 10/17 (58.8%) of CoNs. Among the GNB, MDR types comprise 4/4 (100%) of A. baumannii , 37/54 (68.5%) of K. pneumoniae , and 24/30 (80%) of P. aeruginosa (Table 6 ). Besides, in this study, ESKAPE pathogens were detected in different proportions, such as S. aureus (n = 46) followed by K. pneumoniae (n = 37), P. aeruginosa (n = 24), A. baumannii (n = 4), Enterobacter sp. (n = 2) and E. faecium (n = 1). Table 6 Multidrug resistance profiles of Gram-positive and Gram-negative bacterial isolates from women with post-caesarean wound infections, southern Ethiopia, 2021(n = 204) Gram-positive cocci Classes of antibiotics n(%) MDR ≥R3 n(%) R3 R4 R5 and above S. aureus (n = 65) 17(26.2) 17(26.2) 12(18.5) 46(70.7) CoNs (n = 17) 4(23.5) 5(29.4) 1(5.8) 10(58.8) E. faecium (n = 3) 1(33.3) 0(0) 0(0) 1(33.3) Total 22(25.8) 22(25.8) 13(15.3) 57(67.05) Gram-negative bacilli K. pneumoniae (n = 54) 7(12.96) 18(33.3) 12(22.2) 37(68.5) P. aeruginosa (n = 30) 11(36.6) 12(40) 1(3.33) 24(80) E. coli (n = 24) 4(16.6) 7(29.1) 5(20.83) 16(66.6) Proteus sp. (n = 4) 0(0) 2(50) 1(25) 3(75) Enterobacter sp.(n = 2) 1(50) 1(50) 0(0) 2(100) A.baumannii (n = 4) 2(50) 2(50) 0(0) 4(100) Total = 118 25(21.1) 42(35.5) 19(16) 86(72.8) Total Gram-positive cocci & Gram-negative bacilli (203 ) 143(70.44) n : number of isolates, R3 : resistant to three antibiotics, R4 : resistant to four antibiotics, R5 : resistant to five or more antibiotics 3.4.4. ESBL and Carbapenemase-producing GNB All isolates showing resistance to the indicator, cephalosporin class (cefotaxime or ceftriaxone and cefepime) were suspected to be ESBL producers, and the meropenem-resistant isolates were suspected to be carbapenemase producers. In the present study, 85 and 47 isolates were suspected of ESBL and carbapenemase production, respectively. Out of 85 and 47 ESBL and carbapenemase-producing suspected isolates, 22 (18.64%) and 8 (6.77%) were phenotypically confirmed for ESBL and carbapenemase productions, respectively. The most common ESBL producer was E.coli , 7/24(29.16), followed by K. pneumoniae , 13/54(24.1%). In the case of carbapenemase producers, isolates of K. pneumoniae , 4(7.40%), and P. aeruginosa , 3( 10 ) were predominant (Table 7 ). Table 7 Phenotypically suspected and confirmed ESBL and carbapenemase-producing GNB producers from women with post-caesarean wound infections, southern Ethiopia, 2021(n = 204). GNB ESBL and Carbapenemase-producing GNB n(%) ESBL suspected ESBL confirmed Carbapenemase suspected Carbapenemase confirmed n (5) K. pneumoniae n = 54 44 13 (24.1) 18 4(7.40) P. aeruginosa n = 30 12 2(6.66) 17 3(10) E. coli n = 24 21 7(29.16) 9 1(4.16) Proteus sp. n = 4 3 0(0) 1 0 Enterobacter sp.n = 2 2 0(0) 1 0 A.baumannii n = 4 3 0(0) 1 0 Total n = 118 85(72.02) 22 (18.64) 47(39.83) 8(6.77) 3.5. Factors associated with post-caesarean wound infections Various factors were analyzed to find the possible association of PSWI among the participants. In bivariable logistic regression analysis, only 11 variables were found to be statistically significant, such as patients aged 30–39 (P = 0.097), informal education (P = 0.19), educational level, first cycle (P = 0.02), smoking (P = 0.13), occupation (student), (P = 0.05), parity (primipara), (P = 0.032), previous CS, (P = 0.00), diabetes mellitus (P = 0.04), anaesthesia (P = 0.21), emergency type of surgery (P = 0.20) and vertical type of incision (P = 0.19), interrupted type of skin closure (P = 0.25). In multivariable logistic regression analysis, only four variables showed an independent association, i.e., parity [P = 0.01, AOR: 4.4, (CI: 1.40, 13.87)], previous CS [P = 0.00, AOR: 6.3, (CI: 3.10, 13.01)], diabetes mellitus [P = 0.05, AOR: 3.2, (CI: 2.1, 5.8)], and emergency type of surgery [P = 0.052, AOR: 2.07, (CI: 1.06, 2.63)] (Table 8 ). Table 8: Bivariable and multivariable logistic regression analyses of factors associated with women with post-caesarean wound infections, southern Ethiopia, 2021(n=204) Variable category Wound culture Bivariable analysis Multivariable analysis Culture positive n(%) Culture negative n(%) COR (95%CI) P-value AOR (95%CI) P-value Maternal Age 39 1(50) 1(50) 0.78(0.044-14.02) 0.870 Educational status Informal education 29(47.8) 24(52.2) 1.72(0.75-3.93) 0.19* 1.39(0.38-5.08) 0.613 First cycle 26(59.1) 18(40.9) 3.69(1.17-6.34) 0.02* 2.36(0.67-8.28) 0.180 Second cycle 13(38.2) 21(61.8) 1.16(0.47-2.88) 0.74 0.68(0.18-2.49) 0.566 High school 14(45.2) 17(54.8) 1.55(0.61-3.89) 0.35 1.18(0.29-4.72) 0.807 Higher 17(34.7) 32(65.3) 1 Residence Urban 47(49.0) 49(51.0) 1 Rural 45(41.7) 63(58.3) 0.74(0.42-1.29) 0.297 Smoking Yes 1(14.3) 6(85.7) 0.19(0.02-1.60) 0.13* 0.14(0.01-1.4) 0.097 No 91(46.2) 106(53.8) 1 Occupation Employer 14(33.3) 28(66.7) 1 Student 17(56.7) 13(43.3) 2,61(0.99-6.87) 0.05* 2.10(0.53-8.2) 0.285 Laborer 5(41.7) 7(58.3) 1.42(0.38-5.32) 0.59 1.15(0.2-6.4) 0.871 Homemaker 48(50.0) 48(50.0) 2.00(0.93-4.26) 0.72 1.4(0.4-4.9) 0.549 Merchant 8(33.3) 16(66.7) 1.00(0.34-2.89) 1 0.5(0.1-2.5) 0.465 Parity Nullipara 7(26.9) 19(73.1) 1 Primipara 35(50.7) 34(49.3) 2.8(1.09-6.93) 0.032* 4.4(1.40-13.87) 0.011** Multipara 39(35.8) 70(64.2%) 1.5(0.48-4.65) 0.490 2.90(0.74-11.39) 0.126 Previous CS Yes 71(60.2) 47(39.8) 4.7(2.53-8.65) 0.00* 6.3(3.10-13.01) 0.000** No 21(24.4) 65(75.6) 1 Previous hospitalization Yes 16(51.6) 15(48.4) 1.4(0.633-2.92) 0.430 No 76(43.6) 97(56.4) 1 Diabetic Mellitus Yes 3(60.0) 2(40) 1.85(0.30-11.33) 0.04* 3.2( (2.8–5.8) 0.05** No 89(44.7%) 110(55.3) 1 Hypertension Yes 7(58.3) 5(41.7) 1.76(0.54-5.74) 0.348 No 85(44.3) 107(55.7) 1 HIV status Yes 3(37.5) 5(62.5) 0.72(0.16-3.10) 0.661 No 89(45.4) 107(54.6) 1 Referral status Yes 33(46.5) 38(53.5) 1.08(0.61-1.94) 0.772 No 59(44.4) 74(55.6) 1 PROM Yes 50(46.7) 57(53.3) 1.10(0.66-1.20) 0.623 No 42(43.3) 55(56.7) 1 Duration of labour 24hrs 6(40) 9(60) 0.79(0.27-2.33) 0.681 Anaemia Yes 31(83.8) 6(16.2) 0.82(0.45-3.22) 0.700 No 144(86.2) 23(13.8) 1 Surgical hand scrub Normal soap and water 18(42.9) 24(57.1) 0.89(0.57-2.25) 0.719 Medicated soap and water 74(46.0) 88(54.0) 1 Preoperative skin preparation Aqueous betadine 12(54.5) 10(45.5) 1 Chlorhexidine–alcohol 80(44.0) 102(56.0) 065(0.26-1.59) 0.348 Type of anaesthesia General 12(35.3) 22(64.7) 0.51(0.28-1.31) 0.211* 0.51(0.20-1.27) 0.151 Spinal 80(47.1) 75(52.9) 1 Surgical antibiotic prophylaxis Yes 89(44.9) 109(55.1) 1 No 3(50.0) 3(50.0) 1.22(0.24-6.21) 0.807 Type of surgery Elective 14(35.9) 25(64.1) 1 Emergency 78(47.3) 87(52.7) 1.60(0.77-3.29) 0.201* 2.07(1.06-2.63) 0.052** Type of incision Transverse 73(47.7) 80(52.3) 1 Vertical 19(37.3) 32(62.7) 0.65(0.34-1.24) 0.195* 0.99(0.43-2.26) 0.988 Skin enclosure Interrupted 83(46.6) 95(53.4) 1.65(0.69-3.90) 0.254* 0.89(0.37-4.73) 0.586 Continuous 9(34.6) 17(65.4) 1 Note: *Statistically significant at P≤0.25, ** statistically significant at P≤0.05, AOR: Adjusted odds ratio, COR: Crude odds ratio, 1: reference group, CI: Confidence interval 4. Discussions The overall rate of culture-confirmed post-caesarean wound infections observed in the current study was 85.78% and is consistent with the reports from Kuwait (75.7%) [23], Nepal (74.75%) [24] and Uganda (85%) [25]. However, it was slightly higher than the results of earlier studies reported from Nigeria (92.2%) [19] and Cameroon (96%) [26]. These differences in culture positivity could be due to the variations in wound sampling procedures, processing, and culturing techniques applied (i.e., molecular techniques). Most post-caesarean wound infections were mono-bacterial (147 out of 175 cases), and the rest were bi-bacterial. This finding is similar to the well-documented data found in the literature showing a preponderance of mono-bacterial infections in wound infections [24]. However, in a recent study done in Ukraine, 56.1% (192/342) reported that post-caesarean wound infections are bi-bacterial [27]. Regarding bi-bacterial infections, the most common agents involved are P. aeruginosa and S. aureus (42.9%) (12 out of 28 cases), warranting effective combination drugs. Gram-negative bacilli were the predominant and leading causes of post-caesarean wound infections in the study area, resulting in 58.12% of infections, which agrees with a couple of studies done in Nigeria and Cameroon [19, 26]. This finding confirms the prevailing hypothesis that there has been a shift in causative agents from Gram-positive to Gram-negative variety [28]. Poor knowledge of patients’ personal hygiene, the high environmental burden of GNB, and insufficient infection control practices might play an important role in the development of SSIs [28]. Staphylococcus aureus is the most common organism isolated in post-caesarean wound infections, accounting for 32% of cases. Coagulase-negative staphylococci, E. faecium , and K. pneumoniae were other commonly isolated organisms, and this supports the claims made in a couple of studies done in Cameroon and Tanzania [26, 29]. The observed predominance of S. aureus and K. pneumoniae further support the hypothesis that the source of post-caesarean wound infections may be endogenous, i.e., the flora of the skin/mucosa of the perineum, genital tract, nose, mouth, and intestines [30]. It may further invade the patient during surgical or instrumental manipulations. This resembles the results of several studies conducted worldwide, in which the most common bacteria were S. aureus [26, 29, 31]. Isolation of P. aeruginosa and A . baumannii from infected wounds in this study revealed that the source of infection is exogenous and could be attributed to the contamination originating from health professionals who directly handled the incision, surgical instruments and materials and the surgery room environment [32]. Antibiotic resistance is one of the most significant and most urgent cross-border public health threats. Bacteria are becoming increasingly resistant to existing antibiotics, and pathogens associated with post-caesarean wound infections are no exception. Knowing the local susceptibilities and prevalence of antibiotic resistance is helpful in eradicating it and thus should be the guiding principle while choosing a treatment regimen. Resistance shown by the GPC was severe in the case of the penicillin class of antimicrobials (94.2%), followed by tetracycline (90.2%) and co-trimoxazole (84.2%). We envisaged that this is likely to be correlated with the long-term use of the above-mentioned antibiotics in the study area. This was consistent with the results of an earlier study done elsewhere [33]. On the other hand, more than seventy percent of GPC were susceptible to gentamicin, erythromycin, and chloramphenicol, indicating the possibility of using these drugs to manage wound infection. Notably, 95.4% of isolates of S. aureus had demonstrated resistance to penicillin, whereas 93.9% of them were resistant to tetracycline, limiting their empirical usage in the study area. This is more or less similar to an earlier trend reported in a couple of studies done in Rwanda (100% resistant to penicillin only) [33] and Nigeria (83.3% resistant to tetracycline only) [34]. At the same time, approximately 75% of isolates of S. aureus exhibited susceptibility to erythromycin, chloramphenicol, and gentamicin. The second most predominant Gram-positive is CoNs, of which 88.3, 82.4, and 76.5% showed resistance to each of the three antibiotics tested, such as penicillin, co-trimoxazole, and tetracycline, respectively, and this is similar to previous research done in Rwanda (100% resistance to penicillin and 82% to co-trimoxazole) [33]. Our results also revealed that, invariably, 100% of isolates of E . faecium are resistant to penicillin, which is comparable to the value of previous research done in Uganda (93.3% resistance) [25]. In the same study [25], the authors reported that E. faecium was susceptible to chloramphenicol (60%) and ciprofloxacin (80%), and this was more or less similar to the results observed in our study. Regarding the susceptibility profiles of GNB, maximum resistance (i.e., more than 80%) was observed against antibiotics such as piperacillin, co-trimoxazole, and the cephalosporin class, like cefotaxime and ceftriaxone. The trend of resistance observed currently is similar to the patterns observed in two previous studies conducted in Uganda and Rwanda [25, 33]. At the same time, a study done in Tanzania reported that GNB were highly susceptible to ceftriaxone (78%) [29]. The disparity observed in the resistance patterns of isolates to first-line antimicrobial agents could be due to their overuse in the study settings, especially with the common usage of beta-lactam antibiotics without prescriptions for the treatment of many clinical syndromes. From the overall results, it can be deduced that ceftriaxone and cefotaxime have only a twenty percent probability of being used as prophylactic or empirical therapy for post-caesarean wound infections, particularly in the study area. Therefore, the influence of prophylaxis could be an important factor leading to the low susceptibility to cephalosporin drugs in this study. Another interesting factor is that 70% of GNB were susceptible to amikacin and ciprofloxacin, suggesting the possibility of using these drugs. This result was, by and large, equivalent to the results of previous studies done in Nigeria and Rwanda [19, 33]. Over 80% of the isolates of K. pneumoniae were resistant to four antibiotics such as ceftriaxone, cefotaxime, co-trimoxazole, and piperacillin. However, they were highly susceptible to amikacin and chloramphenicol (more than 70%). This scenario is comparable to the picture obtained from past studies done in Rwanda (100 and 75% resistance to ceftriaxone and co-trimoxazole, respectively, and 100% susceptibility to amikacin) and Cameroon (100% resistance to ceftriaxone and 100% susceptibility to amikacin) [26, 33]. Furthermore, it was found that almost 80% of the isolates of E. coli were susceptible to ciprofloxacin and amikacin, and this was by and large similar to the data obtained from a couple of studies done in Nigeria (70% susceptibility to ciprofloxacin) and Rwanda (100 and 67% susceptibility to amikacin and ciprofloxacin respectively) [19, 33]. However, 87% of them exhibited resistance to ceftriaxone, cefotaxime, piperacillin, and co-trimoxazole, and this was more or less similar to the results reported from Uganda (100% resistance against each co-trimoxazole and ceftriaxone), Rwanda (100% resistance against each co-trimoxazole and ceftriaxone) [25, 33]. Ninety percent of isolates of P. aeruginosa were found to be resistant to a couple of antibiotics, such as piperacillin and cefotaxime, in this study. On the other hand, 80% of them were susceptible to amikacin and ciprofloxacin, and this is in accordance with the results of a study done in Rwanda (100% susceptibility to each ciprofloxacin and amikacin) [33]. In the present study, MDR was observed in 70.4% (n = 143) of the isolates, which is higher than the results reported in a couple of studies conducted in Nigeria, 45% [12], and Kuwait (37.5%) [23]. Fluctuations in the extent of resistance may be due to the differences in sample size, study design and prescription pattern, antibiotic therapy, and the epidemiology of causative organisms at different locations. The most threatening and common MDR pathogens were grouped under the acronym ‘ESKAPE.’ In this study, we have observed the presence of ESKAPE pathogens in different proportions, such as S. aureus (n = 46) followed by K. pneumoniae (n = 37), P. aeruginosa (n = 24), A. baumannii (n = 4), Enterobacter sp. (n = 2) and E . faecium (n = 1). A dismaying finding of our study is that bacterial isolates comprise MRSA, ESBL, CRE, and vancomycin-resistant Enterococci, which the WHO enlists as antibiotic-resistant priority pathogens. To mention a few, 22 (18.64%) GNBs were identified as ESBL producers, which more or less resembles the results of a previous study conducted in Ukraine. In that study, the overall proportion of ESBL production among Enterobacteriaceae was 18.3% [27]. However, our results are quite contrary to a study done in Tanzania that described merely 2 (13%) of the enteric GNB as ESBL producers [29]. A recent study done in Uganda revealed that 10 (91%) of the 11 E. coli and 19 (48%) of the 40 Klebsiella species are ESBL producers [25]. The extent of SSI by ESBL producers found in our study was also similar to that found in other African countries [35]. The emergence of CRE has become a major public health conundrum with significant implications in the case of surgical patients [36]. The overall percentage of carbapenemase producers in our study was 8(6.77%). Carbapenemase production was detected in 15 (38%) of the 40 ceftazidime-resistant Klebsiella species as per a study done in Uganda [25]. A study conducted in Ukraine also identified carbapenem resistance in 7.3% of P. aeruginosa isolates [27]. The prevalence of post-cesarean wound infections with respect to MRSA was 47.7% (n = 31) and was higher than the value reported in a couple of studies done in Ukraine (13.9%) [27] and Ghana (9.8%) [31]. On the other hand, a study conducted in Uganda (9/10, 91%) reported a high rate of MRSA in post-cesarean wound infections [25]. Likewise, a study done in Nepal (17/29, 58%) reported a high rate of MRSA in post-cesarean delivery infection [24]. On the other hand, a study done in Tanzania reported that 16.7% (n = 1) of isolates were MRSA [29]. Therefore, the higher MRSA rate observed in the present study could be due to the frequent use of these drugs, especially the third-generation cephalosporins, in hospitals as part of empirical emergency therapy; however, more studies are needed to clarify this aspect. Another important aspect is that three scores (n = 60) of the Enterococci isolates are proved to be vancomycin-resistant. Moreover, antibiotic resistance can be passively transferred to the new borne from the mother, and hence extreme care must be taken in the judicious selection of drugs for treatment. In light of these results, an active antibiotic stewardship program, together with an evidence-based antibiotic policy, is extremely important for our study settings. Knowledge of risk factors associated with surgical site infection is essential to develop targeted prevention strategies and reduce the risk of infection. It has been revealed that post-cesarean wound infections in the currently studied settings are influenced by socio-demographic as well as clinical and obstetric factors and surgery-related components. Specifically, our results showed that diabetic mellitus, parity, history of CS, and emergency CS were significantly associated with post-cesarean wound infections and were independent predictors. For instance, it was found that primiparous participants were found to be 4.4 times more prone to developing post-cesarean wound infections. A literature scan indicated that a previous study done in Egypt reported the association of parity with SSIs [24]. The results of this study are contradictory to the outcome of a couple of studies done in other cities of Ethiopia, which reported that parity is not associated with SSIs [14, 37]. In our study, participants who underwent an emergency type of surgery were 2.07 times more prone to CS wound infection. This is in accordance with the results of a couple of studies done in Ghana and Egypt [31, 38]. This could be linked to the fact that patients undergoing emergency CS are generally at higher risk of contracting infections due to insufficient preparation time owing to the threat to the mother or the fetus [39]. In this study, participants with diabetic mellitus were 3.2 times more prone than their peers to develop bacterial post-cesarean wound infections. A recent study done in Gondar, Ethiopia, reported that mothers with diabetes mellitus were 6.02 times more likely to develop surgical site infections than their non-diabetic counterparts [17]. Likewise, a study done in another city in Ethiopia (Assela) reported that diabetic mellitus (OR = 3.7, 95% CI 1.112–12.519) is significantly associated with CS [40]. A study done in Egypt, too, reported the association between diabetes and SSIs [38]. An increased risk of infections due to diabetes has long been attributed to physiological alterations precipitated by inadequate long-term glucose control [41]. The risk of contracting post-cesarean wound infections in participants with diabetes was also significantly associated with perioperative hyperglycemia [42]. Limitations of this study include the shorter study duration, study design, and sampling technique. In addition, the molecular identification of species and antibiotic-resistant genes of the bacterial isolates was not carried out due to the lack of infrastructure. 5. Conclusions The observed incidence rate of wound infections after caesarean delivery is well-nigh comparable to earlier reported rates, especially in sub-Saharan Africa. Most post-caesarean wound infections are mono-bacterial and are caused by both Gram-positive and Gram-negative aerobic bacteria such as S. aureus and K. pneumoniae . Notably, Staphylococcus aureus remains the main microorganism responsible for post-caesarean wound infections. Multiple drug resistance (inclusive of ESKAPE pathogens) observed among the bacterial isolates was 70.4% (n = 143). An alarming finding is that bacterial isolates, including MRSA, VRE, ESBL, and CRE, which are enlisted as critical and high-priority pathogens by WHO, are detected. With respect to the antimicrobial susceptibility results, gentamicin, amikacin, and ciprofloxacin were the most effective drugs for GNB. In contrast, gentamicin, erythromycin, and chloramphenicol were the most effective drugs for GPC. Based on the inferential statistics, four risk factors were identified to reduce high-risk of contracting post-caesarean wound infections (patients in primipara, history of CS, diabetes mellitus, and emergency CS). Declarations Author Contributions TK, AM and MS conceived this study. TK performed the experiments. TK, AM, MS, AI and MAE analyzed the data. AM, MS, MT, DT, AA, AZ GK, KK, AA jointly supervised the study and provided oversight during the manuscript editing process. AM, MS, AI, and MAE performed writing—original draft. All authors contributed to the article and approved the submitted version. Funding No specific fund was received for this study. Data availability All the data used and/or analyzed in this study are presented, and data will be available from the corresponding author upon reasonable request. Acknowledgment The authors would like to thank the College of Medicine and Health Sciences, Arba Minch University, and Arba Minch General Hospital. The authors (MAE and AI) would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2023R182), King Saud University, Riyadh, Saudi Arabia. References Conroy K, Koenig AF, Yu YH, Courtney A, Lee HJ, Norwitz ER. Infectious morbidity after cesarean delivery: 10 strategies to reduce risk. Rev. Obstet. Gynecol. 5 (2):69-77 (2012). Opøien H, Valbø A, Grinde-Andersen A, Walberg M. Postcesarean Surgical Site Infections According to CDC Standards: Rates and Risk Factors: A Prospective Cohort Study. Obstetric Anesthesia Digest. 28 (2):107-8 (2008). Saeed KB, Greene RA, Corcoran P, O'Neill SM. Incidence of surgical site infection following caesarean section: a systematic review and meta-analysis protocol. BMJ Open 7 (1):e013037 (2017). doi: 10.1136/bmjopen-2016-013037. Alfouzan W, Al Fadhli M, Abdo N, Alali W, Dhar R. Surgical site infection following cesarean section in a general hospital in Kuwait: trends and risk factors. Epidemiol. Infect. 147:e287(2019). doi: 10.1017/S0950268819001675. Sway A, Nthumba P, Solomkin J, Tarchini G, Gibbs R, Ren Y, Wanyoro A. Burden of surgical site infection following cesarean section in sub-Saharan Africa: a narrative review. Int. J. Womens Health. 11:309-318 (2019). https://doi.org/10.2147/IJWH.S182362 Mekonnen AG, Mittiku YM. Surgical site infection and its association with rupture of membrane following cesarean section in Africa: a systematic review and meta-analysis of published studies. Matern. Health Neonatal Perinatal. 7 (1):2. (2021). doi: 10.1186/s40748-020-00122-2 Getaneh T, Negesse A, Dessie G. Prevalence of surgical site infection and its associated factors after cesarean section in Ethiopia: systematic review and meta-analysis. BMC Pregnancy Childbirth 20 (1):311 (2020). doi: 10.1186/s12884-020-03005-8. Kawakita T, Landy HJ. Surgical site infections after cesarean delivery: epidemiology, prevention and treatment. Matern Health Neonatal Perinatal. 3 :12 (2017). doi: 10.1186/s40748-017-0051-3. Gomaa K, Abdelraheim AR, El Gelany S. et al. Incidence, risk factors and management of post cesarean section surgical site infection (SSI) in a tertiary hospital in Egypt: a five year retrospective study. BMC Pregnancy Childbirth. 21, 634 (2021). https://doi.org/10.1186/s12884-021-04054-3 Childs C, Sandy-Hodgetts K, Broad C, Cooper R, Manresa M, Verdú-Soriano J. Risk, Prevention and Management of Complications After Vaginal and Caesarean Section Birth. J. Wound Care 29(Sup11a):S1-S48 (2020). doi: 10.12968/jowc.2020.29.Sup11a.S1. De D, Saxena S, Mehta G, Yadav R, Dutta R. Risk Factor Analysis and Microbial Etiology of Surgical Site Infections following Lower Segment Caesarean Section. Int J Anti 2013, Article ID 283025 (2013). https://doi.org/10.1155/2013/283025 Olukitibi T, Adebolu T. Antibiogram of Bacteria Isolated from Post-Operative Wounds of Mothers who Underwent Caesarean Section at the Mother and Child Hospital, Akure, Ondo State. Asian J. Res. Med. Pharm. Sci. 2 (3): 1-9(2017). Dessu S, Samuel S, Gebremeskel F, Basazin A, Tariku Z, Markos M. Determinants of post cesarean section surgical site infection at public hospitals in Dire Dawa administration, Eastern Ethiopia: Case control study. PloS One 16 (4):e0250174 (2021). Bizuayew H, Abebe H, Mullu G, Bewuket L, Tsega D, Alemye T. Post-cesarean section surgical site infection and associated factors in East Gojjam zone primary hospitals, Amhara region, North West Ethiopia, 2020. PloS One 16 (12):e0261951 (2021). Ayala D, Tolossa T, Markos J, Yilma MT. Magnitude and factors associated with surgical site infection among mothers underwent cesarean delivery in Nekemte town public hospitals, western Ethiopia. Plos One 16 (4):e0250736 (2021). Alemye T, Oljira L, Fekadu G, Mengesha MM. Post cesarean section surgical site infection and associated factors among women who delivered in public hospitals in Harar city, Eastern Ethiopia: A hospital-based analytic cross-sectional study. PloS One 16 (6):e0253194 (2021). Ali O, Kassahun D, Rade BK, Atnafu A. Maternal factors are important predictors for surgical site infection following cesarean section in Northwest Ethiopian. Clin. Epidemiology Glob. Health 13 :100936 (2022). Endriyas M, Mekonnen E, Dana T, Daka K, Misganaw T, Ayele S, Shiferaw M, Tessema T, Getachew T. Burden of NCDs in SNNP region, Ethiopia: a retrospective study. BMC Health Serv. Res. 18 (1):520 (2018). doi: 10.1186/s12913-018-3298-0. Njoku CO, Njoku AN. Microbiological Pattern of Surgical Site Infection Following Caesarean Section at the University of Calabar Teaching Hospital. Open Access Maced. J. Med. Sci. 7 (9):1430-1435 (2019). doi: 10.3889/oamjms.2019.286. Collee JG, Marmion BP, Fraser AG, Simmons A. Mackie T. Mackie & McCartney Practical Medical Microbiology: Editors: 14th Edition. Elsevier, A division of Reed Elsevier India Private Limited, (2012). Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing, 29th ed CLSI supplement M100 Clinical and Laboratory Standards Institute, (2019). Magiorakos AP. et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: An international expert proposal for interim standard definitions for acquired resistance. Clin. Microbiol. Infect. 18 (3), 268–281 (2012). Alfouzan W, Al Fadhli M, Abdo N, Alali W, Dhar R. Surgical site infection following cesarean section in a general hospital in Kuwait: trends and risk factors. Epidemiol. Infect. 147 :e287 (2019). doi: 10.1017/S0950268819001675. Chhetry M, Subedi S, Ghimire S, Lamichhane S, Banerjee B, Singh GK. Antibiotic sensitivity in post cesarean surgical site infection at a tertiary care centre in Eastern Nepal. J. Lumbini Med. Coll. 4 (2):55-9 (2016). Wekesa YN, Namusoke F, Sekikubo M, Mango DW, Bwanga F. Ceftriaxone- and ceftazidime-resistant Klebsiella species, Escherichia coli , and methicillin-resistant Staphylococcus aureus dominate caesarean surgical site infections at Mulago Hospital, Kampala, Uganda. SAGE Open Med . 8 :2050312120970719 (2020). doi: 10.1177/2050312120970719. Essome H. Ebongue Co, Doumbe J, Fougang Me, Boten M and Adiogo D.Bacteriological and Antibiotic Resistance Profile of Isolated Organisms of Post-Caesarean Suppuration in the Department of Gynaecology and Obstetrics of Laquintinie Hospital, Douala, Cameroon GSL. J. Public Health Epidemiol . 1 :108 (2018). Salmanov AG, Vitiuk AD, Ishchak OM, Insarova KS, Chyrva SL, Kuzomenska ML, Golianovsky OV. Surgical Site Infection After Cesarean Section In Ukraine: Results A Multicenter Study. Wiad Lek (2021) 74 (4):934-939. Deka S, Kalita D, Mahanta P, Baruah D. High Prevalence of Antibiotic-Resistant Gram-Negative Bacteria Causing Surgical Site Infection in a Tertiary Care Hospital of Northeast India. Cureus 12 (12):e12208 (2020). doi: 10.7759/cureus.12208. Mpogoro FJ, Mshana SE, Mirambo MM, Kidenya BR, Gumodoka B, Imirzalioglu C. Incidence and predictors of surgical site infections following caesarean sections at Bugando Medical Centre, Mwanza, Tanzania. Antimicrob. Resist. Infect. Control 3 :25 (2014). doi: 10.1186/2047-2994-3-25. Ward HR, Jennings OG, Potgieter P, Lombard CJ. Do plastic adhesive drapes prevent post caesarean wound infection? J. Hosp. Infect. 47 (3):230-4 (2001). doi: 10.1053/jhin.2000.0843. Onuzo CN, Sefogah PE, Nuamah MA, Ntumy M, Osei MM, Nkyekyer K. Surgical site infections following caesarean sections in the largest teaching hospital in Ghana. Infect. Prev. Pract. 4 (2):100203(2022). doi: 10.1016/j.infpip.2022.100203. Kirby JP, Mazuski JE. Prevention of surgical site infection. Surg. Clin. North Am. 89 (2):365-89(2009). doi: 10.1016/j.suc.2009.01.001. Velin L, Umutesi G, Riviello R, Muwanguzi M, Bebell LM, Yankurije M, Faktor K, Nkurunziza T, Rukundo G, de Dieu Gatete J, Emil I, Hedt-Gauthier BL, Kateera F. Surgical Site Infections and Antimicrobial Resistance After Cesarean Section Delivery in Rural Rwanda. Ann. Glob. Health 87 (1):77 (2021). doi: 10.5334/aogh.3413. Andrew EF, Friday UN, Andrew EK, Isaiah LN, Silas EE, Unah UV. Prevalence and antimicrobial susceptibility profile of bacterial isolates from infected caesarean sites in three federal capital territory hospitals, Abuja, Nigeria. Am. J. Biomed. Life Sci. 6 (4):90-5(2018). Lai PS, Bebell LM, Meney C, Valeri L, White MC. Epidemiology of antibiotic-resistant wound infections from six countries in Africa. BMJ Glob. Health 2 (Suppl 4):e000475 (2018). doi: 10.1136/bmjgh-2017-000475. Mora-Guzmán I, Rubio-Perez I, Maqueda González R, Domingo Garcia D, Martín-Pérez E. Surgical site infection by carbapenemase-producing Enterobacteriaceae. A challenge for today's surgeons. Cir. Esp . (Engl Ed) 98 (6):342-349(2020). English, Spanish. doi: 10.1016/j.ciresp.2019.11.006. Wodajo S, Belayneh M, Gebremedhin S. Magnitude and Factors Associated With Post-Cesarean Surgical Site Infection at Hawassa University Teaching and Referral Hospital, Southern Ethiopia: A Cross-sectional Study. Ethiop. J. Health Sci. 27 (3):283-290 (2017). doi: 10.4314/ejhs.v27i3.10. Gomaa K, Abdelraheim AR, El Gelany S, Khalifa EM, Yousef AM, Hassan H. Incidence, risk factors and management of post cesarean section surgical site infection (SSI) in a tertiary hospital in Egypt: a five year retrospective study. BMC Pregnancy Childbirth 21 (1):634 (2021). doi: 10.1186/s12884-021-04054-3. Killian CA, Graffunder EM, Vinciguerra TJ, Venezia RA. Risk factors for surgical-site infections following cesarean section. Infect. Control Hosp. Epidemiol. 22 (10):613-7(2001). doi: 10.1086/501831. Mamo T, Abebe TW, Chichiabellu TY, Anjulo AA. Risk factors for surgical site infections in obstetrics: a retrospective study in an Ethiopian referral hospital. Patient Saf. Surg. 11: 24 (2017). doi: 10.1186/s13037-017-0138-9. Zhang Y, Zheng Q-J, Wang S, Zeng S-X, Zhang Y-P, Bai X-J, et al. Diabetes mellitus is associated with increased risk of surgical site infections: A meta-analysis of prospective cohort studies. Am. J. Infect. Control 43 (8):810-5(2015). Bellusse GC, Ribeiro JC, de Freitas ICM, Galvão CM. Effect of perioperative hyperglycemia on surgical site infection in abdominal surgery: A prospective cohort study. Am. J. Infect. Control 48 (7):781-5 (2020). Additional Declarations No competing interests reported. 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-3113435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":223683374,"identity":"b6ff3dec-79b6-400e-a3bc-f14b6959600d","order_by":0,"name":"Teshome Kebede","email":"","orcid":"","institution":"Arba Minch General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Teshome","middleName":"","lastName":"Kebede","suffix":""},{"id":223683375,"identity":"66d98204-df45-420e-9e09-18432b397438","order_by":1,"name":"Aseer Manilal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDADNnYGxgMfIAxitTAzMBycAWUQCYAqD/NAGXgBf//hg48rc+zy+JiZHxy2+bVNno+ZgfHDxxzcWiRupCUbnt2WXMzGzGZwOLfvtmEbMwOz5MxteKy5wWMm2biNORGoEqil5zYjkMHGzItHi/z5899/Nm6rB2ph/3DYsue2PUEtBgdy2Bgbtx0GauExOMzw43YiQS2GN9KMgQ47DtJScLC34XZyGzNjM16/yJ0//PBj47bqxPnt7Rsf/Phz23Z+e/PBDx/xeR8FMLaByQZi1YPAH1IUj4JRMApGwUgBABTwUQXN0oB4AAAAAElFTkSuQmCC","orcid":"","institution":"Arba Minch University, Arba Minch","correspondingAuthor":true,"prefix":"","firstName":"Aseer","middleName":"","lastName":"Manilal","suffix":""},{"id":223683376,"identity":"a5afdc07-fc2c-45a1-8bc9-c472b0af50a7","order_by":2,"name":"Mohammed Seid","email":"","orcid":"","institution":"Arba Minch University, Arba Minch","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Seid","suffix":""},{"id":223683377,"identity":"a288a3da-c1bf-4006-b37b-86c43f99c149","order_by":3,"name":"Mheret Tesfaye","email":"","orcid":"","institution":"Ethiopian Public Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Mheret","middleName":"","lastName":"Tesfaye","suffix":""},{"id":223683378,"identity":"fcf216b1-c152-416c-9e1a-ec5d1a386142","order_by":4,"name":"Dereje Tolessa","email":"","orcid":"","institution":"Arba Minch University, Arba Minch","correspondingAuthor":false,"prefix":"","firstName":"Dereje","middleName":"","lastName":"Tolessa","suffix":""},{"id":223683379,"identity":"64864683-072a-47fc-a0b4-bafaffcd979c","order_by":5,"name":"Addis Akiilu","email":"","orcid":"","institution":"Arba Minch University, Arba Minch","correspondingAuthor":false,"prefix":"","firstName":"Addis","middleName":"","lastName":"Akiilu","suffix":""},{"id":223683380,"identity":"a3884d45-49f0-405e-8d1f-71dc68beb560","order_by":6,"name":"Abdurezak Zakir","email":"","orcid":"","institution":"Arba Minch University, Arba Minch","correspondingAuthor":false,"prefix":"","firstName":"Abdurezak","middleName":"","lastName":"Zakir","suffix":""},{"id":223683381,"identity":"41648acf-4d90-46c5-ace6-a2aca4492717","order_by":7,"name":"Gebere Keyta","email":"","orcid":"","institution":"Arba Minch University, Arba Minch","correspondingAuthor":false,"prefix":"","firstName":"Gebere","middleName":"","lastName":"Keyta","suffix":""},{"id":223683382,"identity":"3cfe89d2-301f-44da-9109-f5503a6578cd","order_by":8,"name":"Kebede Kulyta","email":"","orcid":"","institution":"Arba Minch College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kebede","middleName":"","lastName":"Kulyta","suffix":""},{"id":223683383,"identity":"411e2de3-6e9c-475c-96e8-4c64faacb6d3","order_by":9,"name":"Mohamed A. El-Sheikh","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"A.","lastName":"El-Sheikh","suffix":""},{"id":223683384,"identity":"53387d22-beef-4b5b-80c5-e5d0f10f5975","order_by":10,"name":"Akbar Idhayadhulla","email":"","orcid":"","institution":"Nehru Memorial College (Affiliated to Bharathidasan University)","correspondingAuthor":false,"prefix":"","firstName":"Akbar","middleName":"","lastName":"Idhayadhulla","suffix":""}],"badges":[],"createdAt":"2023-06-27 04:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3113435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3113435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41250099,"identity":"85fe0b26-6c77-4cef-a08c-37e62525bf0a","added_by":"auto","created_at":"2023-08-08 14:57:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57111,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial profile and percentage of post-caesarean wound infections at three public hospitals in southern Ethiopia (total isolates; n=203)\u003c/p\u003e","description":"","filename":"FIGURE1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3113435/v1/6348e61e9ba1a25744ca0f90.jpg"},{"id":43201667,"identity":"a166680d-2b24-4a38-b868-936793d91a2c","added_by":"auto","created_at":"2023-09-15 15:52:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1028234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3113435/v1/7de47242-e735-4548-8567-514cef5ab59e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Post-Caesarean Wound Infections: Incidence, Bacterial Profiles, Antimicrobial Susceptibility Patterns and Associated Factors in Public Hospitals, Southern Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDespite the advances in infection prevention and control practices, post-caesarean wound infections (PSWI) remain a major public health problem affecting millions yearly. Caesarean-sections (CS) carry a five to twenty-fold increased risk of infection compared to vaginal delivery [1]. According to the Centers for Disease Control and Prevention (CDC), surgical site infections (SSIs) are the second most common complication after CS, and their incidence range from 3 to 15% [2, 3]. The prevalence and incidence rates of PSWI vary widely concerning procedures, type of hospitals, surgeons who perform the procedure, patient characteristics, and geographic locations [4]. The rates of PSWI are of great concern to low-income countries in Africa [5]. A review on the burden of surgical site infection following CS in sub-Saharan Africa recently described that the rate of surgical site infections was 10.21% [6]. Similarly, various studies performed in Ethiopia too estimated that the incidence of PSWI ranges from 6.8 to 13.7%, reflecting variations among different geographical settings and time domains [7].\u003c/p\u003e \u003cp\u003eThe PSWI can be of the nature of a relatively trivial wound discharge with no other complications to life-threatening sepsis; complications include bacteraemia, fever, endometritis, urinary tract infection, and other serious multisystem infections [8]. It is also the third most frequently reported type of non-device-associated healthcare-associated infections (HAI), especially in sub-Saharan Africa, leading to morbidity, mortality, prolonged hospital stays, escalating costs, higher hospital readmission rates, and jeopardized health outcomes [3,5]. Post-cesarean wound infections account for up to 3% of mortality in women undergoing CS deliveries [9]. Nevertheless, independent risk factors are not well documented in the literature; several host and intrapartum-related factors, pregnancy, and procedure-related factors are associated with the risk of developing PSWI [10].\u003c/p\u003e \u003cp\u003eMicrobiological evidence is of utmost importance in diagnosing and managing PSWI. In most cases, the causative pathogens originate from endogenous flora of patients\u0026rsquo; skin, mucous membranes, or hollow viscera. The most commonly isolated bacterial pathogens are \u003cem\u003eS. aureus\u003c/em\u003e, Enterobacteriaceae, coagulase-negative \u003cem\u003eStaphylococci\u003c/em\u003e (CoNs), \u003cem\u003eEnterococci\u003c/em\u003e, and \u003cem\u003eP. aeruginosa\u003c/em\u003e [11]. Successful treatment of these infections depends on accurately identifying etiological agents and their antibiogram profiles. However, in the last decade, PSWI has further been complicated by an increasing prevalence of multidrug-resistant (MDR) organisms [12].\u003c/p\u003e \u003cp\u003eA scan of the literature indicated that in Ethiopia, only limited data exist which describe the epidemiology of PSWI, the causative organisms, or the incidence of antimicrobial drug resistance, and often, there is no information linked to the post-discharge surveillance programs. Furthermore, risk factors linked to PSWI, too, showed wider variations in several Ethiopian studies [13\u0026ndash;17]. In addition, the incidence of infections may vary, depending on the surgeon, hospital, surgical procedures applied, and patient. Therefore, the investigation of incidence, aetiological profiles, and antimicrobial susceptibility patterns of isolates from PSWI are of urgent health priority for patients, clinicians, and policymakers, hence the present study.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area and Period\u003c/h2\u003e \u003cp\u003eA multi-centre prospective cross-sectional study was conducted from 1st February to 31st July 2021 in selected government hospitals in three zones of the Southern Nations, Nationalities, and Peoples' Region, Ethiopia. According to the Regional Health Bureau\u0026rsquo;s annual report, there are 21 Government Hospitals, 703 Health Centers, and 3,835 Health Posts [18]. Most health institutions in the selected administrative regions are relatively new and not well-equipped to perform CS. Nevertheless, the capacity of the health facilities to perform surgeries was considered as an inclusion criterion. Therefore, one hospital from each zone was selected for the study setting, such as Arba Minch General Hospital (AMGH) from Gamo Zone, Gidole Primary Hospital (GPH) from Derashe, and Wolita Sodo University Teaching Hospital (WSUTH) from Wolita Zone. Arba Minch General Hospital is the biggest health institution among the three and provides health services to residents of the Gamo Zone. The hospital serves over 1.5\u0026nbsp;million by providing preventive, curative, and rehabilitative care in outpatients, inpatients, pharmacy, and laboratory departments. As per the 2020 records, a total of 3815 pregnant women attended the gynaecology and obstetrics departments to give birth, and there were a total of 2772 normal deliveries and 1043 CS.\u003c/p\u003e \u003cp\u003eWolaita Sodo University Teaching Hospital is situated in the Sodo Town of Wolaita Zone. In 2020, a total of 8,463 pregnant women came to the gynaecology and obstetrics department to give birth, and there were a total of 6,379 normal births and 2,084 CS. Gidole Primary Hospital serves more than 200,000 through preventive, curative, and rehabilitative care in outpatients, inpatients, pharmacy, and laboratory departments. According to the records, in 2020, 1792 pregnant women attended the gynaecology and obstetrics department to give birth, and there were 1,332 normal deliveries and 460 cases of CS in 2020.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study design and eligibility criteria\u003c/h2\u003e \u003cp\u003eA prospective cross-sectional study was conducted among all pregnant women who underwent CS in any of the three title hospitals during the study period as per the inclusion criteria such as 1. women (all reproductive age-women (i.e., 15\u0026ndash;49 years)) underwent CS during hospitalization or within 30 days post-discharge survey period, 2. those who were willing to participate in the study, 3. women with at least one of the following signs and symptoms of the surgical site, 4. infection that is clinically confirmed by the surgeon/physician was selected (drainage of pus, foul odour coming from the wound, fever/ chills, hot to touch, redness, pain, or tenderness), 5. only the first procedure was considered eligible for inclusion for patients who underwent multiple surgical procedures during the study period. The exclusion criteria were: 1. those who have been seriously ill or are unable to respond well, 2. women who were on antibiotic treatment (except Surgical antibiotic prophylaxis- SAP) during preoperative hospital stay (up to one week before the commencement of the study period), 3. patients who are undergoing pre-surgery, 4. patients who failed to attend the follow-up 30 days after the day of operation (surgery).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample size calculation\u003c/h2\u003e \u003cp\u003eThe required sample size was calculated by using a single population proportion formula by fixing a prevalence of culture-positive PSWI rate of 8.5% from a recent bacteriological study conducted elsewhere [19]. After considering a confidence interval of 95% (z\u0026thinsp;=\u0026thinsp;1.96) and 5% of marginal error (d\u0026thinsp;=\u0026thinsp;0.05), the initial sample size was estimated to be 196, and by computing a 10% non-response rate, the final sample size was consolidated as 206 (n).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Sample allocation and sampling techniques\u003c/h2\u003e \u003cp\u003eWomen who attended any of the three specified hospitals underwent CS during the study period were assessed for eligibility, and those who developed signs and symptoms of wound infection within 30 days of surgery were included in the bacteriological analysis. The selection of study participants from three hospitals was made by proportional allocation corresponding to each institution, i.e., the final sample size proportionately attained for each hospital was 65 (AMGH), 114 (WSUTH), and 25 (GPH). Study participants were included consecutively until the final sample size was achieved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data collection tools and methods\u003c/h2\u003e \u003cp\u003eA pre-tested, semi-structured questionnaire and observational checklist/data extraction form were used for data collection at the time of enrollment and also during follow-up. The data collection tool was adapted from prior studies and modified based on the contextual situation. The questionnaire contains three parts; Part I (socio-demographic characteristics), Part II (obstetric \u0026amp; clinical information), and Part III (surgical procedure). Data collection methods include interviews, chart reviews, physical examinations, and bacteriological sample analyses. Informed consent was obtained from all subjects and/or their legal guardian(s). All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003eTrained research assistants (B.Sc. Midwifery nurses) collected information from all study participants. Socio-demographic and behavioural data, including patients' age, level of education, residence, occupation, and smoking habit, were solicited through face-to-face interviews using a pre-tested. Patients\u0026rsquo; obstetric/clinical data: Semi-structured questionnaires/ data abstraction forms were used to extract data from the patient's case notes; parity, premature rupture of membrane, duration of labour, previous CS, previous hospitalization within six months of the commencement of the study, and referral status. Surgical procedure-related parameters included surgical hand scrub, preoperative skin preparation, antibiotic prophylaxis, type of surgery and incision, skin enclosure, and kind of anaesthesia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Case definition of post-cesarean wound infections\u003c/h2\u003e \u003cp\u003eData from the clinical wound examination and bacteriological analysis of samples were pooled. Patients were allocated to the surgical site infection group based on meeting any one of the following criteria established by the CDC for SSIs: Briefly, a superficial surgical site infection is defined as one that occurs within 30 days of the surgical procedure and affects only the skin or subcutaneous tissue of the incision, and has at least one of the following signs or symptoms of infection: purulent discharge from the superficial incision wound, pain or tenderness, localized swelling, redness or heat, and the superficial incision is deliberately opened by a surgeon [2].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Clinical Examination\u003c/h2\u003e \u003cp\u003ePost-caesarean wound infections were assessed for infection by directly examining the wound in the hospital and during follow-up visits after discharge by a surgeon, nurse and/or gynaecologist. The wound was examined with due emphasis on signs and symptoms consistent with PSWI according to the clinical criteria for developing surgical site infection set by CDC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Microbiological analysis of wound samples\u003c/h2\u003e \u003cp\u003eDuring the 6-month study period, we collected wound swabs from 206 women who developed the infection, as two clinically suspected wound samples were excluded based on the exclusion criteria (no discharge). The sample was collected using a sterile swab stick from each participant by gently rubbing in the infected site (Levine or Z technique) and immediately placed into sterile test tubes prefilled with Amie\u0026rsquo;s transport media. And transported by following standard transportation procedures to the Medical Microbiology and Parasitology Laboratory, Department of Medical Laboratory Sciences, for further processing and stored aerobically at 4\u0026ndash;8\u0026deg;C in case of delay. All specimens were separately inoculated respectively onto a set of culture media such as blood agar plate, mannitol salt agar, and MacConkey agar (Oxoid, Ltd., England) and were incubated aerobically at 37\u0026deg;C for 24 hours. The isolates were identified as per the standard bacteriological procedure [20].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Antimicrobial susceptibility testing\u003c/h2\u003e \u003cp\u003e Antimicrobial susceptibility testing was performed on Mueller-Hinton agar (Oxoid, UK) using the Kirby-Bauer disk diffusion method according to Clinical and Laboratory Standards Institute (CLSI) guidelines [21]. For Gram-positive cocci (GPC), antibiotics such as penicillin (10\u0026micro;g), ciprofloxacin (5\u0026micro;g), erythromycin (15\u0026micro;g), clindamycin (10\u0026micro;g), chloramphenicol (30\u0026micro;g), cefoxitin (30\u0026micro;g), gentamicin (10\u0026micro;g), sulfamethoxazole-trimethoprim (co-trimoxazole) (1.25/23.75 \u0026micro;g), tetracycline (30\u0026micro;g) and vancomycin (30\u0026micro;g) were used, whereas, for Gram-negative bacilli (GNB), piperacillin (100\u0026micro;g), ciprofloxacin (5\u0026micro;g), chloramphenicol (30\u0026micro;g), amikacin (30\u0026micro;g), ceftriaxone (30\u0026micro;g), cefoxitin (30\u0026micro;g), cefotaxime (30\u0026micro;g), cefopime (30\u0026micro;g), meropenem (10\u0026micro;g), gentamicin (30\u0026micro;g) and sulfamethoxazole-trimethoprim (1.25/23.75 \u0026micro;g). The selection of antibiotics was based on the guidelines set by CLSI, 2019. The presence of methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA), extended-spectrum beta-lactamases producers (ESBL), and carbapenem-resistant Enterobacterales (CRE) was detected as per the standard procedures [21]. Multi-drug resistance in this study was extrapolated as the resistance of at least three or more groups of antibiotics tested [22].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Quality Controls\u003c/h2\u003e \u003cp\u003eTo assure the quality of the data, a pre-test was done on 5% of the total sample before the actual work; one day of training was given to data collectors. Data were checked for completeness, accuracy, clarity, and consistency by the principal investigator daily; the standard operating procedure for each operation was strictly followed. Expiry date of media and reagents and quality control parameters were checked as per CLSI guidelines. All culture media were prepared following the instruction of the manufacturer, and the sterility of the culture media was tested by incubating 5% of each batch at 35\u0026ndash;37\u0026deg;C overnight (for evaluation of possible contamination). After preparation, all culture plates and antibiotic disks were stored at the recommended refrigeration temperature (2\u0026ndash;8\u0026deg;C). Quality of media, antibiotic disks, as well as the performance of a person carrying the tests, was controlled by reference strains: \u003cem\u003eP. aeruginosa\u003c/em\u003e (ATCC 27853), \u003cem\u003eE. coli\u003c/em\u003e (ATCC 25922), \u003cem\u003eK. pneumoniae\u003c/em\u003e (ATCC 700603) for ESBL, \u003cem\u003eS. aureus\u003c/em\u003e (ATCC 25923), and \u003cem\u003eS. aureus\u003c/em\u003e (ATCC 29213) for MRSA. During quality control of ESBL screening and phenotypic confirmatory tests, we simultaneously tested the non-ESBL-producing organism, such as \u003cem\u003eE. coli\u003c/em\u003e (ATCC 25922), and an ESBL-producing organism, \u003cem\u003eK. pneumoniae\u003c/em\u003e (ATCC 700603). All the reference strains were procured from Ethiopian Public Health Institute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Statistical Analyses\u003c/h2\u003e \u003cp\u003eData were checked, cleaned, and coded for completeness, entered by Epi-Data version 4.4.3.1, and exported to Statistical Package for Social Sciences (SPSS) version 25 (IBM Corporation, Armonk, NY, USA). Socio-demographic and clinical factors were described by using descriptive statistics like frequency and percentage. Inferential statistics such as bivariable and multivariable binary logistic regression analyses were done to determine the strength of association among independent and outcome variables. Initially, the data were subjected to a series of bivariable analyses, and those with a cut-off point of P-value\u0026thinsp;\u0026le;\u0026thinsp;0.25 were processed further by multivariable analysis. The Hosmer-Lemeshow goodness fit test checked the fitness of the model. Adjusted odds ratio (AOR) and 95% confidence interval (CI) were used to determine the strength of association; P-values\u0026thinsp;\u0026le;\u0026thinsp;0.05 in the multivariable analysis were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Ethical Considerations\u003c/h2\u003e \u003cp\u003eEthical approval for the work was provided by the Institutional Review Board of Arba Minch University, College of Medicine and Health Sciences, and the Department of Medical Laboratory Science (IRB 1045/21).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Socio-demographic characteristics\u003c/h2\u003e\n \u003cp\u003eThis study was conducted over a span of 6 months, between 1st February to 31st July 2021, involving three hospitals (AMGH, WSUTH \u0026amp; GPH). The age of women who developed post-cesarean wound infections ranged from 17 to 41, with a mean value of 26.74\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04. The majority of women, i.e., 121 (59.3%) affected, were within the age category of 20\u0026ndash;39. One hundred and eight patients (52.9%) were rural residents; 36.8% (n\u0026thinsp;=\u0026thinsp;75) of them had their education up to the first cycle only; homemakers/unemployed correspond to 47.1%. Of the 204 PSWI, 106(51.96%) were identified at readmission to the hospital, while 9% were diagnosed post-discharge (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \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\u003eSocio-demographic and behavioural characteristics of women suspected of post-caesarean wound infections in three public hospitals, southern Ethiopia, 2021 (n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable and category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eStudy settings\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMGH\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWSUTH\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGPH\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e204 (%)\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\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6(24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e74(61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121(59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31(58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e59(61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(34.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e55(50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e29(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41(36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17(14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15(13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26(22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLabourer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHomemaker/unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e54(47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMerchant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11(9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking during pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24(96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97(96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Obstetrics, clinical, and surgical procedure-related characteristics\u003c/h2\u003e\n \u003cp\u003eIn this study, 35% (n\u0026thinsp;=\u0026thinsp;71) of participants were referred from other facilities, while 65% had no history of referrals. More than half of them were multipara 109(53.4%). More than fifty percent (n\u0026thinsp;=\u0026thinsp;111) of the subjects had a history of a previous CS. One hundred and seven patients (52%) had a membrane rupture before CS. Sixty-two mothers (30.4%) had one or more co-morbidities, namely HIV, 8 (3.9%), diabetic mellitus, 5 (2.4%), hypertension, 12 (5.8%), and anaemia, 37 (18.1%) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) with respect to factors related to the surgical procedure, the most commonly used type of anaesthesia was spinal, 83% (n\u0026thinsp;=\u0026thinsp;170). Surgical antibiotic prophylaxis was carried out in 97.1% (n\u0026thinsp;=\u0026thinsp;198) of the patients. The number of women with an interrupted type of skin closure was 178 (87.3%). Further data are mentioned in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eObstetrics, clinical and surgical procedure-related characteristics of women suspected of post-caesarean wound infections stratified by study settings, southern Ethiopia, 2021(n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVariables and category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFrequency and percentage in study setting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMGH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWSUTH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGPH\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;65\u003c/p\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;114\u003c/p\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;204\u003c/p\u003e\n \u003cp\u003en(%)\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\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferred from other health facilities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12(48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133(65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNullipara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimipara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9(36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultipara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109(53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(05.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196(96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105(92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192(94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23(92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199(97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21(84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167(81.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable and category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy setting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAMGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWSUTH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of labour\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;24hrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106(93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e189(92.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;24hrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePROM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e107(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e97(48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious CS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e111(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e93(45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(16.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(83.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e173(84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical procedure-related characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative skin preparation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous betadine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20(10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChlorhexidine\u0026ndash;alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(87.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e184(89,2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical hand scrub\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal soap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(46.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e44(21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicated soap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(92.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87(77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e160(78.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of surgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39(19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e165(81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of incision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e153(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of anaesthesia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e170(83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e34(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkin enclosure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterrupted sutures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e178(87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26(12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110(96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e198(97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Incidence and profile of bacterial post-caesarean wound infections\u003c/h2\u003e\n \u003cp\u003eResults of the bacteriological analysis showed that out of 204 wound specimens processed, 175 (85.8%) were found to be aerobic culture-positive. Gram-negative and Gram-positive bacteria were detected in 57.8 (n\u0026thinsp;=\u0026thinsp;118) and 41.6% (n\u0026thinsp;=\u0026thinsp;85), respectively. Among the three hospitals studied, patients who attended WSUTH had the highest bacterial isolation rate of 62%. Of all culture-confirmed post-caesarean wound infections, mono-bacterial infections were observed in most (72%) cases. Gram-negative bacilli, 57.8% (n\u0026thinsp;=\u0026thinsp;118), were isolated more frequently (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Of the total of nine distinct species of bacteria isolated, \u003cem\u003eS. aureus, K. pneumoniae\u003c/em\u003e, and \u003cem\u003eP. aeruginosa\u003c/em\u003e were the three most predominant isolates corresponding to 32.01, 26.6, and 14.7%, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBacterial profile and patterns of infections (mono-bacterial vs. bi-bacterial) among women clinically suspected of post-caesarean wounds in the three public hospitals, southern Ethiopia, 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eProfile of bacteria\u003c/p\u003e\n \u003cp\u003eIsolated\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003eisolates\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;203\u003c/p\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eStudy setting\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMGH\u003c/p\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWSUTH\u003c/p\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGPH\u003c/p\u003e\n \u003cp\u003en(%)\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\u003eGram-negative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProteus\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eA. baumannii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGram-positive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85(41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(32.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. faecium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMono-bacterial isolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147(72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBi-bacterial isolates\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28 cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;12 cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3 cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;8 cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e and CoNs\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5 cases)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(40)\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 \u003cp\u003eOf the culture-confirmed cases of PSWI, bi-bacterial infections were detected only in 28 out of 175 specimens; no cases of Gram-positive and Gram-positive co-infections were recorded. Predominant bi-bacterial infections involved \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eP. aeruginosa\u003c/em\u003e co-infections (12 out of 28 cases). Furthermore, \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e co-infections have been reported in 8 out of 28 cases; however, co-infection with \u003cem\u003eP. aeruginosa\u003c/em\u003e / CoNs and \u003cem\u003eE. coli\u003c/em\u003e / \u003cem\u003eS. aureus\u003c/em\u003e occurred in five and three cases, respectively. The rate of bi-bacterial infections varied in all hospitals (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Antibiotic susceptibility profiles\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1. Antimicrobial susceptibility profiles of Gram-negative bacilli\u003c/h2\u003e\n \u003cp\u003eThe susceptibility profiles of GNB (n\u0026thinsp;=\u0026thinsp;118) against eleven antibiotics are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e as susceptible and resistant. The intermediate results were also considered resistant for the purpose of statistical analysis. In the present study, isolates of GNB showed a wider range of variations in terms of their susceptibility (10.2 to 77.9%) as well as resistance (22.2 to 89.8%) profiles. The GNB showed resistance to cefotaxime, 106 (89.8%), cotrimoxazole, 104 (88.2%), ceftriaxone, 103 (87.3%), and piperacillin, 102 (86.4%). However, most GNB showed a relatively higher level of susceptibility to amikacin, gentamicin, and meropenem, i.e., 92/118 (77.9%), 77/118 (65.2%), and 71/118 (60.2%), respectively (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The predominant isolate of GNB, \u003cem\u003eK. pneumoniae\u003c/em\u003e, demonstrated a high level of resistance against cefotaxime, 48/54 (88.9%), piperacillin, 45/54 (83.4%), co-trimoxazole and ceftriaxone, each 44/54 (81.5%) and a lower level of resistance to chloramphenicol, 13/54 (24.1%). Among other members of Enterobacteriaceae, \u003cem\u003eE. coli\u003c/em\u003e showed the highest level of resistance to piperacillin, 22/24 (91.7%), followed by co-trimoxazole, cefotaxime, and ceftriaxone, 21/24 (87.5% resistance to each), and was highly susceptible to amikacin, 21/24(87.5%) and ciprofloxacin, 19/24(79%). Isolates of \u003cem\u003eP. aeruginosa\u003c/em\u003e exhibited resistance to piperacillin, 27/30 (90%), and cefotaxime, 28/30 (93.4%). On the other hand, isolates were highly susceptible to amikacin, 26/30 (86.6%), and ciprofloxacin, 25/30 (83%).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAntimicrobial susceptibility profiles of Gram-negative bacilli of women suspected of post-caesarean wound infections, southern Ethiopia, 2021(n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eList of antibiotics tested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eIsolated Gram-negative bacilli from post-CS wound infections n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003en(118)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProteu\u003c/em\u003es sp. (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter\u003c/em\u003e sp. (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eA. baumannii\u003c/em\u003e n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(59.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77(65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAMK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSXT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104(88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103(87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCXT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81(68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCTX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106(89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eAST: Antibiotics susceptibility, R: resistance, S: susceptible, CPR: ciprofloxacin, CTR: ceftriaxone, CHL: chloramphenicol, CXT: cefoxitin, AMK: amikacin, CTX: cefotaxime, PIP: piperacillin, CFP: cefepime, MER: meropenem, GEN: gentamicin, SXT: sulfamethoxazole-trimethoprim. NA: not applicable\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2. Antimicrobial susceptibility profiles of Gram-positive cocci\u003c/h2\u003e\n \u003cp\u003eThe susceptibility profiles of GPC (n\u0026thinsp;=\u0026thinsp;85) against ten antibiotics isolated from the PSWI are presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e as susceptible and resistant. In contrast, the intermediate results were also considered resistant for the purpose of statistical analysis, as mentioned earlier.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAntimicrobial susceptibility profiles of Gram-positive cocci among women with post-caesarean wound infections, southern Ethiopia, 2021(n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eList of antibiotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIsolated Gram-positive bacteria from post-CS wound infections n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e (n═65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoNs\u003c/p\u003e\n \u003cp\u003e(n═17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. faecium\u003c/em\u003e (n═3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(90.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSXT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCXT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eCoNs: Coagulase-negative \u003cem\u003eS. aureus\u003c/em\u003e, AST: Antibiotics susceptibility Test, R: resistance, S: susceptible, PEN: penicillin, CPR: ciprofloxacin, ERY: erythromycin, CLN: clindamycin, CHL: chloramphenicol, CXT: cefoxitin, GEN: gentamicin, SXT: sulfamethoxazole-trimethoprim, TET: tetracycline, VAN: vancomycin. NA: not applicable\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eSimilar to that of GNB isolates, a wider range of variations concerning resistance (24.7 to 94.2%), as well as susceptibility (5.8 to 75.3%) profiles, were also observed in this case too. Gram-positive cocci were highly resistant to penicillin, 80 (94.2%), tetracycline, 74 (90.2%), and co-trimoxazole, 69 (84.2%). On the other hand, isolates were highly susceptible to gentamicin, 64 (75.5%), erythromycin, 64 (75.3%), and chloramphenicol, 61(71.7%). Analysis of species-specific resistance rates indicated that most of the isolates of \u003cem\u003eS. aureus\u003c/em\u003e were resistant to penicillin, 62/65 (95.4%), tetracycline, 61/65 (93.9%), and co-trimoxazole, 55/65 (84.7%). On the other hand, 78.5, 75.3 and 72.3% of isolates of \u003cem\u003eS. aureus\u003c/em\u003e were susceptible to erythromycin, gentamicin, and chloramphenicol, respectively. Among the 65 isolates of \u003cem\u003eS. aureus\u003c/em\u003e, 31 showed a zone of inhibition of \u0026le;\u0026thinsp;21mm (14.5 to 20 mm) in the cefoxitin disk diffusion test and were extrapolated as methicillin-resistant \u003cem\u003eS. aureus.\u003c/em\u003e The percentage of MRSA among \u003cem\u003eS. aureus\u003c/em\u003e was found to be 31/65 (47.7%), while the remaining 34 (52.3%) were methicillin-sensitive \u003cem\u003eS. aureus\u003c/em\u003e (MSSA).\u003c/p\u003e\n \u003cp\u003eIn the current study, isolates of CoNs were resistant to penicillin, 15/17 (88.3%), cotrimoxazole, 14/17 (82.4%), and tetracycline, 13/17 (76.5%). Likewise, 7/17 (41.2%) were found to be methicillin-resistant. On the other hand, isolates of CoNs were susceptible to gentamicin, 15/17 (88.2%), chloramphenicol, 12/17 (70.5%), and ciprofloxacin, 12/17 (70.5%). Invariably, all the isolates of \u003cem\u003eE. faecium\u003c/em\u003e in this study were 100% resistant to penicillin, and two of them were found to be VRE.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.3. Multi-Drug Resistance profiles\u003c/h2\u003e\n \u003cp\u003eIn this study, MDR is inferred as the resistance to three or more groups of antibiotics tested. Out of the 203 total bacterial isolates, 143 were found to be MDR (i.e., 70.44%), of which 86/118 (72.88%) belong to the Gram-negative group. The MDR GPC comprise 46/65 (70.7%) of \u003cem\u003eS. aureus\u003c/em\u003e and 10/17 (58.8%) of CoNs. Among the GNB, MDR types comprise 4/4 (100%) of \u003cem\u003eA. baumannii\u003c/em\u003e, 37/54 (68.5%) of \u003cem\u003eK. pneumoniae\u003c/em\u003e, and 24/30 (80%) of \u003cem\u003eP. aeruginosa\u003c/em\u003e (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Besides, in this study, ESKAPE pathogens were detected in different proportions, such as \u003cem\u003eS. aureus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;46) followed by \u003cem\u003eK. pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;37), \u003cem\u003eP. aeruginosa\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;24), \u003cem\u003eA. baumannii\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eEnterobacter\u003c/em\u003e sp. (n\u0026thinsp;=\u0026thinsp;2) and \u003cem\u003eE. faecium\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultidrug resistance profiles of Gram-positive and Gram-negative bacterial isolates from women with post-caesarean wound infections, southern Ethiopia, 2021(n\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGram-positive cocci\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eClasses of antibiotics n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMDR\u003c/p\u003e\n \u003cp\u003e\u0026ge;R3 n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR5 and above\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\u003cem\u003eS. aureus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(70.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoNs (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. faecium\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(67.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGram-negative bacilli\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eK. pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(12.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e (n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(20.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProteus\u003c/em\u003e sp. (n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter\u003c/em\u003e sp.(n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eA.baumannii\u003c/em\u003e (n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u0026thinsp;=\u0026thinsp;118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Gram-positive cocci \u0026amp; Gram-negative bacilli (203\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143(70.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003en\u003c/strong\u003e: number of isolates, \u003cstrong\u003eR3\u003c/strong\u003e: resistant to three antibiotics, \u003cstrong\u003eR4\u003c/strong\u003e: resistant to four antibiotics, \u003cstrong\u003eR5\u003c/strong\u003e: resistant to five or more antibiotics\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.4. ESBL and Carbapenemase-producing GNB\u003c/h2\u003e\n \u003cp\u003eAll isolates showing resistance to the indicator, cephalosporin class (cefotaxime or ceftriaxone and cefepime) were suspected to be ESBL producers, and the meropenem-resistant isolates were suspected to be carbapenemase producers. In the present study, 85 and 47 isolates were suspected of ESBL and carbapenemase production, respectively. Out of 85 and 47 ESBL and carbapenemase-producing suspected isolates, 22 (18.64%) and 8 (6.77%) were phenotypically confirmed for ESBL and carbapenemase productions, respectively. The most common ESBL producer was \u003cem\u003eE.coli\u003c/em\u003e, 7/24(29.16), followed by \u003cem\u003eK. pneumoniae\u003c/em\u003e, 13/54(24.1%). In the case of carbapenemase producers, isolates of \u003cem\u003eK. pneumoniae\u003c/em\u003e, 4(7.40%), and \u003cem\u003eP. aeruginosa\u003c/em\u003e, 3(\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e) were predominant (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhenotypically suspected and confirmed ESBL and carbapenemase-producing GNB producers from women with post-caesarean wound infections, southern Ethiopia, 2021(n\u0026thinsp;=\u0026thinsp;204).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGNB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eESBL and Carbapenemase-producing GNB n(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eESBL suspected\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eESBL confirmed\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbapenemase suspected\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarbapenemase confirmed n (5)\u003c/strong\u003e\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\u003cem\u003eK. pneumoniae\u003c/em\u003e n\u0026thinsp;=\u0026thinsp;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e n\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(6.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(29.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eProteus\u003c/em\u003e sp. n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter\u003c/em\u003e sp.n\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eA.baumannii\u003c/em\u003e n\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal n\u0026thinsp;=\u0026thinsp;118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e85(72.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22 (18.64)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e47(39.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8(6.77)\u003c/strong\u003e\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\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Factors associated with post-caesarean wound infections\u003c/h2\u003e\n \u003cp\u003eVarious factors were analyzed to find the possible association of PSWI among the participants. In bivariable logistic regression analysis, only 11 variables were found to be statistically significant, such as patients aged 30\u0026ndash;39 (P\u0026thinsp;=\u0026thinsp;0.097), informal education (P\u0026thinsp;=\u0026thinsp;0.19), educational level, first cycle (P\u0026thinsp;=\u0026thinsp;0.02), smoking (P\u0026thinsp;=\u0026thinsp;0.13), occupation (student), (P\u0026thinsp;=\u0026thinsp;0.05), parity (primipara), (P\u0026thinsp;=\u0026thinsp;0.032), previous CS, (P\u0026thinsp;=\u0026thinsp;0.00), diabetes mellitus (P\u0026thinsp;=\u0026thinsp;0.04), anaesthesia (P\u0026thinsp;=\u0026thinsp;0.21), emergency type of surgery (P\u0026thinsp;=\u0026thinsp;0.20) and vertical type of incision (P\u0026thinsp;=\u0026thinsp;0.19), interrupted type of skin closure (P\u0026thinsp;=\u0026thinsp;0.25). In multivariable logistic regression analysis, only four variables showed an independent association, i.e., parity [P\u0026thinsp;=\u0026thinsp;0.01, AOR: 4.4, (CI: 1.40, 13.87)], previous CS [P\u0026thinsp;=\u0026thinsp;0.00, AOR: 6.3, (CI: 3.10, 13.01)], diabetes mellitus [P\u0026thinsp;=\u0026thinsp;0.05, AOR: 3.2, (CI: 2.1, 5.8)], and emergency type of surgery [P\u0026thinsp;=\u0026thinsp;0.052, AOR: 2.07, (CI: 1.06, 2.63)] (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTable 8:\u0026nbsp;Bivariable and multivariable logistic regression analyses of factors associated with women with post-caesarean wound infections, southern Ethiopia, 2021(n=204)\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVariable category\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.92%\" valign=\"top\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Wound culture \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.64%\" valign=\"top\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" valign=\"top\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Multivariable analysis \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.27906976744186%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCulture positive n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCulture negative\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.58139534883721%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.953488372093023%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.953488372093023%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Age\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e14(56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e11(44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003e20-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e65(45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e77(54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e0.66(0.28-1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e23(65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e12(34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e0.40(0.14-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.097*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e1(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e0.78(0.044-14.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.52%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eInformal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e29(47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e24(52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.96%\" valign=\"top\"\u003e\n \u003cp\u003e1.72(0.75-3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.19*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e1.39(0.38-5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eFirst cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e26(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e18(40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.96%\" valign=\"top\"\u003e\n \u003cp\u003e3.69(1.17-6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e2.36(0.67-8.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eSecond cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e13(38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e21(61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.96%\" valign=\"top\"\u003e\n \u003cp\u003e1.16(0.47-2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e0.68(0.18-2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e14(45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e17(54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.96%\" valign=\"top\"\u003e\n \u003cp\u003e1.55(0.61-3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e1.18(0.29-4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eHigher\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e17(34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e32(65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.96%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e47(49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e49(51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.96%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.32%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.6%\" valign=\"top\"\u003e\n \u003cp\u003e45(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e63(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.96%\" valign=\"top\"\u003e\n \u003cp\u003e0.74(0.42-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.32%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e1(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e6(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e0.19(0.02-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.13*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.32%\" valign=\"top\"\u003e\n \u003cp\u003e0.14(0.01-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e91(46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e106(53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.32%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eEmployer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e14(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e28(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.64%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eStudent\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e17(56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e13(43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e2,61(0.99-6.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.64%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28%\" valign=\"top\"\u003e\n \u003cp\u003e2.10(0.53-8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eLaborer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e7(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.42(0.38-5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.64%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28%\" valign=\"top\"\u003e\n \u003cp\u003e1.15(0.2-6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eHomemaker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e48(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e48(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e2.00(0.93-4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.64%\" valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28%\" valign=\"top\"\u003e\n \u003cp\u003e1.4(0.4-4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003eMerchant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.44%\" valign=\"top\"\u003e\n \u003cp\u003e8(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.48%\" valign=\"top\"\u003e\n \u003cp\u003e16(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\" valign=\"top\"\u003e\n \u003cp\u003e1.00(0.34-2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.64%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28%\" valign=\"top\"\u003e\n \u003cp\u003e0.5(0.1-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.6%\" valign=\"top\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNullipara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e7(26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e19(73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003ePrimipara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e35(50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e34(49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e2.8(1.09-6.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e4.4(1.40-13.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eMultipara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e39(35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e70(64.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1.5(0.48-4.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e2.90(0.74-11.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious CS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e71(60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e47(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e4.7(2.53-8.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e6.3(3.10-13.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e21(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e65(75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e16(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e15(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1.4(0.633-2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e76(43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e97(56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetic Mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e3(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e2(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1.85(0.30-11.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e3.2( (2.8\u0026ndash;5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e89(44.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e110(55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e7(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e5(41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1.76(0.54-5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e85(44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e107(55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e3(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e5(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e0.72(0.16-3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e89(45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e107(54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferral status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e33(46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e38(53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1.08(0.61-1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e59(44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e74(55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ePROM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e50(46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e57(53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1.10(0.66-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e42(43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e55(56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of labour\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;24hrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e86(45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e103(54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;24hrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e6(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e9(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e0.79(0.27-2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnaemia\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e31(83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e6(16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e0.82(0.45-3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.699115044247787%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.274336283185841%\" valign=\"top\"\u003e\n \u003cp\u003e144(86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.389380530973451%\" valign=\"top\"\u003e\n \u003cp\u003e23(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.123893805309734%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.849557522123893%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.929203539823009%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.734513274336283%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical hand scrub\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eNormal soap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e18(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e24(57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e0.89(0.57-2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eMedicated soap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e74(46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e88(54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative skin preparation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eAqueous betadine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e12(54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e10(45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eChlorhexidine\u0026ndash;alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e80(44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e102(56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e065(0.26-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of anaesthesia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e12(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e22(64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e0.51(0.28-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e0.211*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e0.51(0.20-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eSpinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e80(47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e75(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"top\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical antibiotic prophylaxis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e89(44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e109(55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e3(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e3(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1.22(0.24-6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of\u003cem\u003e\u0026nbsp;\u003c/em\u003esurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eElective\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e14(35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e25(64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eEmergency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e78(47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e87(52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1.60(0.77-3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.201*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e2.07(1.06-2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.052**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of incision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eTransverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e73(47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e80(52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eVertical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e19(37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e32(62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e0.65(0.34-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.195*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e0.99(0.43-2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkin enclosure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eInterrupted\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e83(46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e95(53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1.65(0.69-3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.254*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e0.89(0.37-4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.249617151607964%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.872894333843798%\" valign=\"top\"\u003e\n \u003cp\u003e9(34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e17(65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241960183767228%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.03522205206738%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10719754977029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eNote: *Statistically significant at P\u0026le;0.25, ** statistically significant at P\u0026le;0.05, AOR: Adjusted odds ratio, COR: Crude odds ratio, 1: reference group, CI: Confidence interval\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThe overall rate of culture-confirmed post-caesarean wound infections observed in the current study was 85.78% and is consistent with the reports from Kuwait (75.7%) [23], Nepal (74.75%) [24] and Uganda (85%) [25]. However, it was slightly higher than the results of earlier studies reported from Nigeria (92.2%) [19] and Cameroon (96%) [26]. These differences in culture positivity could be due to the variations in wound sampling procedures, processing, and culturing techniques applied (i.e., molecular techniques).\u003c/p\u003e \u003cp\u003eMost post-caesarean wound infections were mono-bacterial (147 out of 175 cases), and the rest were bi-bacterial. This finding is similar to the well-documented data found in the literature showing a preponderance of mono-bacterial infections in wound infections [24]. However, in a recent study done in Ukraine, 56.1% (192/342) reported that post-caesarean wound infections are bi-bacterial [27]. Regarding bi-bacterial infections, the most common agents involved are \u003cem\u003eP. aeruginosa\u003c/em\u003e and \u003cem\u003eS. aureus\u003c/em\u003e (42.9%) (12 out of 28 cases), warranting effective combination drugs.\u003c/p\u003e \u003cp\u003eGram-negative bacilli were the predominant and leading causes of post-caesarean wound infections in the study area, resulting in 58.12% of infections, which agrees with a couple of studies done in Nigeria and Cameroon [19, 26]. This finding confirms the prevailing hypothesis that there has been a shift in causative agents from Gram-positive to Gram-negative variety [28]. Poor knowledge of patients\u0026rsquo; personal hygiene, the high environmental burden of GNB, and insufficient infection control practices might play an important role in the development of SSIs [28].\u003c/p\u003e \u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e is the most common organism isolated in post-caesarean wound infections, accounting for 32% of cases. Coagulase-negative staphylococci, \u003cem\u003eE. faecium\u003c/em\u003e, and \u003cem\u003eK. pneumoniae\u003c/em\u003e were other commonly isolated organisms, and this supports the claims made in a couple of studies done in Cameroon and Tanzania [26, 29]. The observed predominance of \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eK. pneumoniae\u003c/em\u003e further support the hypothesis that the source of post-caesarean wound infections may be endogenous, i.e., the flora of the skin/mucosa of the perineum, genital tract, nose, mouth, and intestines [30]. It may further invade the patient during surgical or instrumental manipulations. This resembles the results of several studies conducted worldwide, in which the most common bacteria were \u003cem\u003eS. aureus\u003c/em\u003e [26, 29, 31]. Isolation of \u003cem\u003eP. aeruginosa\u003c/em\u003e and \u003cem\u003eA\u003c/em\u003e. \u003cem\u003ebaumannii\u003c/em\u003e from infected wounds in this study revealed that the source of infection is exogenous and could be attributed to the contamination originating from health professionals who directly handled the incision, surgical instruments and materials and the surgery room environment [32].\u003c/p\u003e \u003cp\u003eAntibiotic resistance is one of the most significant and most urgent cross-border public health threats. Bacteria are becoming increasingly resistant to existing antibiotics, and pathogens associated with post-caesarean wound infections are no exception. Knowing the local susceptibilities and prevalence of antibiotic resistance is helpful in eradicating it and thus should be the guiding principle while choosing a treatment regimen. Resistance shown by the GPC was severe in the case of the penicillin class of antimicrobials (94.2%), followed by tetracycline (90.2%) and co-trimoxazole (84.2%). We envisaged that this is likely to be correlated with the long-term use of the above-mentioned antibiotics in the study area. This was consistent with the results of an earlier study done elsewhere [33]. On the other hand, more than seventy percent of GPC were susceptible to gentamicin, erythromycin, and chloramphenicol, indicating the possibility of using these drugs to manage wound infection.\u003c/p\u003e \u003cp\u003eNotably, 95.4% of isolates of \u003cem\u003eS. aureus\u003c/em\u003e had demonstrated resistance to penicillin, whereas 93.9% of them were resistant to tetracycline, limiting their empirical usage in the study area. This is more or less similar to an earlier trend reported in a couple of studies done in Rwanda (100% resistant to penicillin only) [33] and Nigeria (83.3% resistant to tetracycline only) [34]. At the same time, approximately 75% of isolates of \u003cem\u003eS. aureus\u003c/em\u003e exhibited susceptibility to erythromycin, chloramphenicol, and gentamicin.\u003c/p\u003e \u003cp\u003eThe second most predominant Gram-positive is CoNs, of which 88.3, 82.4, and 76.5% showed resistance to each of the three antibiotics tested, such as penicillin, co-trimoxazole, and tetracycline, respectively, and this is similar to previous research done in Rwanda (100% resistance to penicillin and 82% to co-trimoxazole) [33]. Our results also revealed that, invariably, 100% of isolates of \u003cem\u003eE\u003c/em\u003e. \u003cem\u003efaecium\u003c/em\u003e are resistant to penicillin, which is comparable to the value of previous research done in Uganda (93.3% resistance) [25]. In the same study [25], the authors reported that \u003cem\u003eE. faecium\u003c/em\u003e was susceptible to chloramphenicol (60%) and ciprofloxacin (80%), and this was more or less similar to the results observed in our study.\u003c/p\u003e \u003cp\u003eRegarding the susceptibility profiles of GNB, maximum resistance (i.e., more than 80%) was observed against antibiotics such as piperacillin, co-trimoxazole, and the cephalosporin class, like cefotaxime and ceftriaxone. The trend of resistance observed currently is similar to the patterns observed in two previous studies conducted in Uganda and Rwanda [25, 33]. At the same time, a study done in Tanzania reported that GNB were highly susceptible to ceftriaxone (78%) [29]. The disparity observed in the resistance patterns of isolates to first-line antimicrobial agents could be due to their overuse in the study settings, especially with the common usage of beta-lactam antibiotics without prescriptions for the treatment of many clinical syndromes. From the overall results, it can be deduced that ceftriaxone and cefotaxime have only a twenty percent probability of being used as prophylactic or empirical therapy for post-caesarean wound infections, particularly in the study area. Therefore, the influence of prophylaxis could be an important factor leading to the low susceptibility to cephalosporin drugs in this study. Another interesting factor is that 70% of GNB were susceptible to amikacin and ciprofloxacin, suggesting the possibility of using these drugs. This result was, by and large, equivalent to the results of previous studies done in Nigeria and Rwanda [19, 33].\u003c/p\u003e \u003cp\u003eOver 80% of the isolates of \u003cem\u003eK. pneumoniae\u003c/em\u003e were resistant to four antibiotics such as ceftriaxone, cefotaxime, co-trimoxazole, and piperacillin. However, they were highly susceptible to amikacin and chloramphenicol (more than 70%). This scenario is comparable to the picture obtained from past studies done in Rwanda (100 and 75% resistance to ceftriaxone and co-trimoxazole, respectively, and 100% susceptibility to amikacin) and Cameroon (100% resistance to ceftriaxone and 100% susceptibility to amikacin) [26, 33]. Furthermore, it was found that almost 80% of the isolates of \u003cem\u003eE. coli\u003c/em\u003e were susceptible to ciprofloxacin and amikacin, and this was by and large similar to the data obtained from a couple of studies done in Nigeria (70% susceptibility to ciprofloxacin) and Rwanda (100 and 67% susceptibility to amikacin and ciprofloxacin respectively) [19, 33]. However, 87% of them exhibited resistance to ceftriaxone, cefotaxime, piperacillin, and co-trimoxazole, and this was more or less similar to the results reported from Uganda (100% resistance against each co-trimoxazole and ceftriaxone), Rwanda (100% resistance against each co-trimoxazole and ceftriaxone) [25, 33].\u003c/p\u003e \u003cp\u003eNinety percent of isolates of \u003cem\u003eP. aeruginosa\u003c/em\u003e were found to be resistant to a couple of antibiotics, such as piperacillin and cefotaxime, in this study. On the other hand, 80% of them were susceptible to amikacin and ciprofloxacin, and this is in accordance with the results of a study done in Rwanda (100% susceptibility to each ciprofloxacin and amikacin) [33].\u003c/p\u003e \u003cp\u003eIn the present study, MDR was observed in 70.4% (n\u0026thinsp;=\u0026thinsp;143) of the isolates, which is higher than the results reported in a couple of studies conducted in Nigeria, 45% [12], and Kuwait (37.5%) [23]. Fluctuations in the extent of resistance may be due to the differences in sample size, study design and prescription pattern, antibiotic therapy, and the epidemiology of causative organisms at different locations. The most threatening and common MDR pathogens were grouped under the acronym \u0026lsquo;ESKAPE.\u0026rsquo; In this study, we have observed the presence of ESKAPE pathogens in different proportions, such as \u003cem\u003eS. aureus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;46) followed by \u003cem\u003eK. pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;37), \u003cem\u003eP. aeruginosa\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;24), \u003cem\u003eA. baumannii\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eEnterobacter\u003c/em\u003e sp. (n\u0026thinsp;=\u0026thinsp;2) and \u003cem\u003eE\u003c/em\u003e. \u003cem\u003efaecium\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eA dismaying finding of our study is that bacterial isolates comprise MRSA, ESBL, CRE, and vancomycin-resistant Enterococci, which the WHO enlists as antibiotic-resistant priority pathogens. To mention a few, 22 (18.64%) GNBs were identified as ESBL producers, which more or less resembles the results of a previous study conducted in Ukraine. In that study, the overall proportion of ESBL production among Enterobacteriaceae was 18.3% [27]. However, our results are quite contrary to a study done in Tanzania that described merely 2 (13%) of the enteric GNB as ESBL producers [29]. A recent study done in Uganda revealed that 10 (91%) of the 11 \u003cem\u003eE. coli\u003c/em\u003e and 19 (48%) of the 40 \u003cem\u003eKlebsiella\u003c/em\u003e species are ESBL producers [25]. The extent of SSI by ESBL producers found in our study was also similar to that found in other African countries [35].\u003c/p\u003e \u003cp\u003eThe emergence of CRE has become a major public health conundrum with significant implications in the case of surgical patients [36]. The overall percentage of carbapenemase producers in our study was 8(6.77%). Carbapenemase production was detected in 15 (38%) of the 40 ceftazidime-resistant \u003cem\u003eKlebsiella\u003c/em\u003e species as per a study done in Uganda [25]. A study conducted in Ukraine also identified carbapenem resistance in 7.3% of \u003cem\u003eP. aeruginosa\u003c/em\u003e isolates [27].\u003c/p\u003e \u003cp\u003eThe prevalence of post-cesarean wound infections with respect to MRSA was 47.7% (n\u0026thinsp;=\u0026thinsp;31) and was higher than the value reported in a couple of studies done in Ukraine (13.9%) [27] and Ghana (9.8%) [31]. On the other hand, a study conducted in Uganda (9/10, 91%) reported a high rate of MRSA in post-cesarean wound infections [25]. Likewise, a study done in Nepal (17/29, 58%) reported a high rate of MRSA in post-cesarean delivery infection [24]. On the other hand, a study done in Tanzania reported that 16.7% (n\u0026thinsp;=\u0026thinsp;1) of isolates were MRSA [29]. Therefore, the higher MRSA rate observed in the present study could be due to the frequent use of these drugs, especially the third-generation cephalosporins, in hospitals as part of empirical emergency therapy; however, more studies are needed to clarify this aspect. Another important aspect is that three scores (n\u0026thinsp;=\u0026thinsp;60) of the \u003cem\u003eEnterococci\u003c/em\u003e isolates are proved to be vancomycin-resistant. Moreover, antibiotic resistance can be passively transferred to the new borne from the mother, and hence extreme care must be taken in the judicious selection of drugs for treatment. In light of these results, an active antibiotic stewardship program, together with an evidence-based antibiotic policy, is extremely important for our study settings.\u003c/p\u003e \u003cp\u003eKnowledge of risk factors associated with surgical site infection is essential to develop targeted prevention strategies and reduce the risk of infection. It has been revealed that post-cesarean wound infections in the currently studied settings are influenced by socio-demographic as well as clinical and obstetric factors and surgery-related components. Specifically, our results showed that diabetic mellitus, parity, history of CS, and emergency CS were significantly associated with post-cesarean wound infections and were independent predictors. For instance, it was found that primiparous participants were found to be 4.4 times more prone to developing post-cesarean wound infections. A literature scan indicated that a previous study done in Egypt reported the association of parity with SSIs [24]. The results of this study are contradictory to the outcome of a couple of studies done in other cities of Ethiopia, which reported that parity is not associated with SSIs [14, 37].\u003c/p\u003e \u003cp\u003eIn our study, participants who underwent an emergency type of surgery were 2.07 times more prone to CS wound infection. This is in accordance with the results of a couple of studies done in Ghana and Egypt [31, 38]. This could be linked to the fact that patients undergoing emergency CS are generally at higher risk of contracting infections due to insufficient preparation time owing to the threat to the mother or the fetus [39].\u003c/p\u003e \u003cp\u003eIn this study, participants with diabetic mellitus were 3.2 times more prone than their peers to develop bacterial post-cesarean wound infections. A recent study done in Gondar, Ethiopia, reported that mothers with diabetes mellitus were 6.02 times more likely to develop surgical site infections than their non-diabetic counterparts [17]. Likewise, a study done in another city in Ethiopia (Assela) reported that diabetic mellitus (OR\u0026thinsp;=\u0026thinsp;3.7, 95% CI 1.112\u0026ndash;12.519) is significantly associated with CS [40]. A study done in Egypt, too, reported the association between diabetes and SSIs [38]. An increased risk of infections due to diabetes has long been attributed to physiological alterations precipitated by inadequate long-term glucose control [41]. The risk of contracting post-cesarean wound infections in participants with diabetes was also significantly associated with perioperative hyperglycemia [42].\u003c/p\u003e \u003cp\u003eLimitations of this study include the shorter study duration, study design, and sampling technique. In addition, the molecular identification of species and antibiotic-resistant genes of the bacterial isolates was not carried out due to the lack of infrastructure.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe observed incidence rate of wound infections after caesarean delivery is well-nigh comparable to earlier reported rates, especially in sub-Saharan Africa. Most post-caesarean wound infections are mono-bacterial and are caused by both Gram-positive and Gram-negative aerobic bacteria such as \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eK. pneumoniae\u003c/em\u003e. Notably, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e remains the main microorganism responsible for post-caesarean wound infections. Multiple drug resistance (inclusive of ESKAPE pathogens) observed among the bacterial isolates was 70.4% (n\u0026thinsp;=\u0026thinsp;143). An alarming finding is that bacterial isolates, including MRSA, VRE, ESBL, and CRE, which are enlisted as critical and high-priority pathogens by WHO, are detected. With respect to the antimicrobial susceptibility results, gentamicin, amikacin, and ciprofloxacin were the most effective drugs for GNB. In contrast, gentamicin, erythromycin, and chloramphenicol were the most effective drugs for GPC. Based on the inferential statistics, four risk factors were identified to reduce high-risk of contracting post-caesarean wound infections (patients in primipara, history of CS, diabetes mellitus, and emergency CS).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;TK, AM and MS conceived this study. TK performed the experiments. TK, AM, MS, AI and MAE analyzed the data. AM, MS, MT, DT, AA, AZ GK, KK, AA jointly supervised the study and provided oversight during the manuscript editing process. AM, MS, AI, and MAE performed writing—original draft. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific fund was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used and/or analyzed in this study are presented, and data will be available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the College of Medicine and Health Sciences, Arba Minch University, and Arba Minch General Hospital. The authors \u0026nbsp;(MAE and AI) would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2023R182), King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eConroy K, Koenig AF, Yu YH, Courtney A, Lee HJ, Norwitz ER. Infectious morbidity after cesarean delivery: 10 strategies to reduce risk. \u003cem\u003eRev. Obstet. Gynecol.\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e(2):69-77 (2012).\u003c/li\u003e\n\u003cli\u003eOp\u0026oslash;ien H, Valb\u0026oslash; A, Grinde-Andersen A, Walberg M. Postcesarean Surgical Site Infections According to CDC Standards: Rates and Risk Factors: A Prospective Cohort Study. \u003cem\u003eObstetric Anesthesia Digest.\u003c/em\u003e\u003cstrong\u003e28\u003c/strong\u003e(2):107-8 (2008).\u003c/li\u003e\n\u003cli\u003eSaeed KB, Greene RA, Corcoran P, O\u0026apos;Neill SM. 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Risk factors for surgical site infections in obstetrics: a retrospective study in an Ethiopian referral hospital. \u003cem\u003ePatient Saf. Surg.\u003c/em\u003e 11: \u003cstrong\u003e24 \u003c/strong\u003e(2017). doi: 10.1186/s13037-017-0138-9. \u003c/li\u003e\n\u003cli\u003eZhang Y, Zheng Q-J, Wang S, Zeng S-X, Zhang Y-P, Bai X-J, et al. Diabetes mellitus is associated with increased risk of surgical site infections: A meta-analysis of prospective cohort studies. \u003cem\u003eAm. J. Infect. Control\u003c/em\u003e\u003cstrong\u003e43\u003c/strong\u003e(8):810-5(2015).\u003c/li\u003e\n\u003cli\u003eBellusse GC, Ribeiro JC, de Freitas ICM, Galv\u0026atilde;o CM. Effect of perioperative hyperglycemia on surgical site infection in abdominal surgery: A prospective cohort study. \u003cem\u003eAm. J. Infect. Control\u003c/em\u003e\u003cstrong\u003e48\u003c/strong\u003e(7):781-5 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"drug resistance, Caesarean-section, surgical site infection, associated factors, Arba Minch","lastPublishedDoi":"10.21203/rs.3.rs-3113435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3113435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePost-caesarean wound infections are a maternal health concern associated with increased morbidity and extended hospital stays, mainly caused by drug-resistant pathogens. A prospective cross-sectional study was undertaken in the title public hospitals among pregnant women who had undergone Caesarean section (CS). All women were followed up for 30 days, and those who developed a clinically infected wound (i.e., 204) were included in the bacteriological analysis. A pre-tested questionnaire was used to collect the data. Wound samples were collected to identify bacteria as per the microbiological guidelines. Antimicrobial susceptibility profiles were determined by the Kirby\u0026ndash;Bauer disk diffusion method. Of the 204 samples, 85.78% (175/204) were culture-positive, yielding 203 bacteria. \u003cem\u003eStaphylococcus aureus\u003c/em\u003e predominantly caused wound infections (n\u0026thinsp;=\u0026thinsp;65, 32.01%), followed by \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;54, 26.6%). Gram-negative bacilli were highly resistant to piperacillin, ceftriaxone, cefotaxime, and co-trimoxazole (\u0026gt;\u0026thinsp;85%), whereas Gram-positive cocci were highly resistant to penicillin and tetracycline (\u0026gt;\u0026thinsp;90%). Overall, 70.44% (n\u0026thinsp;=\u0026thinsp;143) of isolates were multidrug-resistant. Parity [P\u0026thinsp;=\u0026thinsp;0.01, AOR: 4.4, (CI: 1.40, 13.87)], previous CS [P\u0026thinsp;=\u0026thinsp;0.0, AOR: 6.3, (CI: 3.10, 13.01)], diabetes mellitus [P\u0026thinsp;=\u0026thinsp;0.05, AOR: 3.2, (CI: 2.1, 5.8)], and emergency CS [P\u0026thinsp;=\u0026thinsp;0.05, AOR: 2.07, (CI: 1.06, 2.63)] were significantly associated with post-caesarean wound infections.\u003c/p\u003e","manuscriptTitle":"Post-Caesarean Wound Infections: Incidence, Bacterial Profiles, Antimicrobial Susceptibility Patterns and Associated Factors in Public Hospitals, Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-08 14:57:22","doi":"10.21203/rs.3.rs-3113435/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":"ab9a7078-899e-484f-8a1b-b596a8e74c95","owner":[],"postedDate":"August 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23770421,"name":"Biological sciences/Microbiology"},{"id":23770422,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2023-09-15T15:44:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-08 14:57:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3113435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3113435","identity":"rs-3113435","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}