Laboratory-based versus population-based surveillance of antimicrobial resistance to inform empirical treatment for suspected urinary tract infection in Indonesia.

Surveillance of antimicrobial resistance (AMR) enables monitoring of trends in AMR prevalence. WHO recommends laboratory-based surveillance to obtain actionable AMR data at local or national level. However, laboratory-based surveillance may lead to overestimation of the prevalence of AMR due to bias. The objective of this study is to assess the difference in resistance prevalence between laboratory-based and population-based surveillance (PBS) among uropathogens in Indonesia. We included all urine samples submitted to the laboratory growing Escherichia coli and Klebsiella pneumoniae in the laboratory-based surveillance. Population-based surveillance data were collected in a cross-sectional survey of AMR in E. coli and K. pneumoniae isolated from urine samples among consecutive patients with symptoms of UTI, attending outpatient clinics and hospital wards. Data were collected between 1 April 2014 until 31 May 2015. The difference in percentage resistance (95% confidence intervals) between laboratory- and population-based surveillance was calculated for relevant antibiotics. A difference larger than +/- 5 percent points was defined as a biased result, precluding laboratory-based surveillance for guiding empirical treatment. We observed high prevalence of AMR ranging between 63.1% (piperacillin-tazobactam) and 85% (ceftriaxone) in laboratory-based surveillance and 41.3% (piperacillin-tazobactam) and 74.2% (ceftriaxone) in population-based surveillance, except for amikacin and meropenem (5.7%/9.8%; 10.8%/5.9%; [laboratory-/population-based surveillance], respectively). Laboratory-based surveillance yielded significantly higher AMR prevalence estimates than population-based surveillance. This difference was much larger when comparing surveillance data from outpatients than from inpatients. All point estimates of the difference between the two surveillance systems were larger than 5 percent points, except for amikacin and meropenem. Laboratory-based AMR surveillance of uropathogens, is not adequate to guide empirical treatment for community-based settings in Indonesia.

Background

Surveillance of antimicrobial resistance (AMR) enables monitoring of trends in AMR prevalence and is an important tool in the fight against the increasing threat of AMR globally. Surveillance of AMR is needed to inform policy-makers, regulators, and clinicians in support of recommendations for (inter)national policy and local antimicrobial stewardship activities in health facilities, to ultimately reduce AMR associated mortality and morbidity. Low- and middle-income countries (LMIC) are affected disproportionally by the emergence of AMR due to weak national and local policies, lack of quality diagnostic and surveillance capacity, and lack of antibiotic stewardship programs [1].

The WHO Global Action Plan on Antimicrobial Resistance recognizes surveillance as one of its five pillars of action [2]. In WHO’s Global Antimicrobial resistance Surveillance System (GLASS), laboratory-based surveillance of AMR is recognized as a priority for the development of strategies to contain antibiotic resistance, and for assessment of the impact of interventions [3]. Laboratory-based surveillance with linkage to patient information is considered as the most efficient and feasible surveillance approach because the data are generated by microbiology laboratories that routinely identify and determine the susceptibility of bacteria isolated from clinical specimens submitted to the laboratory. Population-based surveillance, which is based on surveillance of individuals in a defined population who present with signs and symptoms that meet a clinical case definition, provides more precise data about the burden of AMR in this population. However, population-based surveillance is often considered too laborious and may require resources and capacity that are not available where patients present with symptoms [3]. Whilst laboratory-based surveillance can be used to provide information on local AMR prevalence with the aim to guide the empirical treatment choices, results of laboratory-based surveillance may be biased because of the potential barriers to and selection processes for submission of clinical specimens to laboratories for culture and susceptibility testing, particularly in resource-constrained settings such as in LMIC [3,4]. This bias may result in laboratory-based surveillance results being skewed towards higher prevalence of AMR. Previous studies have assessed the potential sources of bias in laboratory-based surveillance, but studies that assess the actual difference in prevalence estimates between laboratory-based AMR surveillance and population-based surveillance in LMIC are lacking [5].

Indonesia is a lower-middle income country with the world’s 4th largest population, where almost all bacteriology laboratories in tertiary hospitals and district laboratories carry out antimicrobial susceptibility testing (AST). Several hospitals report cumulative AST reports every six months, but the AST data are not linked to patient information. Moreover, AST reports are not aggregated at national level, due to difficulties in networking of hospitals, district laboratories and research centers [1,6]. We previously performed a population-based survey of AMR in Escherichia coli and Klebsiella pneumoniae isolated from patients with symptoms of urinary tract infection (UTI) in Indonesia [7]. Comparing these results to routine laboratory results obtained in the same setting and period allowed us to assess the magnitude of bias of laboratory-based surveillance.

Materials and methods

Study design

We compared two surveillance approaches performed in an overlapping time frame in a tertiary referral hospital and in outpatients clinics in Medan. The hospital services the city of Medan as well as the provinces North Sumatera, Aceh, West Sumatera and Riau on the island of Sumatra. We collected laboratory-based AMR surveillance data from 1 April 2014 until 31 May 2015 to coincide with data collected through population-based AMR surveillance in the same time period. Laboratory data were collected retrospectively from the computer-based laboratory records, consisting of routine microbiological investigations on all clinical urine specimens received both from inpatients and outpatients, with a positive culture that yielded Escherichia coli and/or Klebsiella pneumoniae and their AST results. From patients with multiple positive urine cultures, only the first culture result was included in the study [3,8]. During the surveillance period, systematic screening cultures of urine, for example as part of outbreak management or detection of asymptomatic carriage of (multi-drug resistant) microorganisms, was not performed in the hospital.

Population-based AMR surveillance data were collected in a cross-sectional survey of AMR in E. coli and K. pneumoniae isolated from urine samples from patients suspected of a UTI, carried out from 1 April 2014 until 31 May 2015, as described previously [7]. In brief, consecutive patients attending four public and private outpatient clinics of urology and obstetrics/gynaecology, or all patients who were admitted to the internal medicine-, surgery-, obstetrics/gynaecology-, or neurology wards, were actively screened for the presence of symptoms of UTI, according to CDC definitions [9]. Inpatients were screened for these symptoms on a daily basis. Laboratory procedures were carried out following CLSI guidelines [10].

We included only E. coli and K. pneumoniae in this study, since those pathogens are the most commonly observed uropathogens, as also recommended in WHO’s GLASS [3]. Bacteria which were not identified as E. coli or K. pneumoniae, we classified as “other” and not included in the direct comparison of the two surveillance strategies.

Laboratory procedures

Laboratory-based surveillance

Routine microbiological investigations on all clinical urine specimens received, were performed following CLSI guidelines and using in-house standard operating procedures of the hospital microbiology laboratory in Medan [8]. All urine specimens were cultured on blood agar and MacConkey Agar. Any growth on those agar plates was identified to detect uropathogens, using the Vitek2 Compact platform (Biomerieux, France). Uropathogens showing growth of 105 colony forming units (CFUs)/ml or greater were submitted to antimicrobial susceptibility testing (AST) (Vitek AST GN-N317, & GN-N100, Biomerieux) using the same instrument. Escherichia coli ATCC 25922 was used as quality control strain for identification and AST [11].

Population-based surveillance

From all included patients, a urine specimen was collected for urinary dipstick analysis. All urine specimens with a positive dipstick test result (positive leukocyte esterase and/or nitrite reaction) were cultured and suspected colonies with growth of 103 CFU/ml or greater and identified to be E. coli or K. pneumoniae using standard biochemical tests (IMVIC), were submitted to AST using disk diffusion method according to CLSI guidelines [11]. Quality controls (QC) were included for media preparation and QC for susceptibility testing were performed on a weekly basis according to CLSI guidelines, using reference strains E. coli ATCC25922, E. coli ATCC 35218 and K. pneumoniae ATCC 700603 [11].

Antimicrobial susceptibility tests

The antimicrobial drugs tested routinely in the microbiology laboratory as well as included in the population-based study, were amoxicillin-clavulanic acid, amikacin, ceftazidime, ceftriaxone, levofloxacin, meropenem and piperacillin-tazobactam [3]. AST results were interpreted as susceptible, intermediate or resistant according to breakpoints from CLSI document M100-S22 for both automated and manual AST [11]. An intermediate test result was considered resistant.

Data analysis

All datasets were collected and available as electronic files, capturing basic information on patient characteristics (inpatient, outpatient), bacteria isolated, antimicrobial agents tested and inhibitory zone diameter or MIC. The data extraction procedure from the Vitek 2 Compact System was done according to the manufacturer instruction. Isolates were determined susceptible or resistant using CLSI 2012 breakpoints for both laboratory- and population-based surveillance approaches since these were the breakpoints used during the study time period [9,11]. Data were analyzed using Stata 12.1 (Stata Corp, TX, USA).

We determined the percentage points difference in prevalence estimation between the two surveillance approaches and calculated the 95% confidence interval (CI) of this difference, for each antibiotic tested. We arbitrarily considered bias to be present if the point estimate of the difference between the two surveillance approaches was larger than +/- 5 percentage points, on the basis of clinical relevance for empirical treatment guidelines[12].

We first assessed the difference in prevalence estimates between laboratory-based surveillance and population-based surveillance for all isolates. Subsequently we stratified this analysis by inpatient and outpatient settings. We performed a sensitivity analysis for these comparisons with a unified definition of a positive culture as ≥ 105 CFUs/ml of a given pathogen present after growth on MacConkey agar, for both surveillance approaches.

Ethical approval

This study was approved by the University of Sumatera Utara Faculty of Medicine Ethics Committee, H. Adam Malik General Hospital Research Committee, Universitas Padjadjaran Faculty of Medicine Ethics Committee (286/KOMET/FK USU/ 2013).

Results

A total of 896 isolates were collected during laboratory-based surveillance of which 474 isolates were E. coli or K. pneumoniae. Meanwhile, a total number of 645 isolates was collected during the population-based surveillance, of which 508 E. coli or K. pneumoniae (Table 1).

Table 1

Frequency of Escherichia coli and Klebsiella pneumoniae culture as observed during laboratory- and population-based surveillance from outpatients and inpatients.

	Outpatients				Inpatients
	Laboratory-based N = 227		Population-based N = 339		Laboratory-based N = 669		Population-based N = 306
	n	%	n	%	n	%	n	%
E. coli	124	54.6	221	65.2	189	28.3	199	65.0
K. pneumoniae	33	14.5	40	11.8	128	19.1	48	15.7
Other	70	30.8	78	23.0	352	52.6	59	19.3

N = total number of isolates identified; n = total number of isolates per species.

High prevalence of AMR was observed ranging between 61.7% (piperacillin-tazobactam) and 86.1% (ceftriaxone) in laboratory-based surveillance, and 41.3% (piperacillin-tazobactam) and 74.2% (ceftriaxone) in population-based surveillance. Only for amikacin (6.4% and 9.8% for laboratory-based surveillance and population-based surveillance, respectively), and meropenem (10.9% and 5.9% for laboratory-based surveillance and population-based surveillance, respectively) prevalence estimates were below or around 10% (S1 Table).

Laboratory-based surveillance yielded substantially higher AMR prevalence estimates than population-based surveillance (Fig 1A). This difference was larger when comparing laboratory-based surveillance with population-based surveillance isolates from outpatients than from inpatients (Fig 1B and 1C, S2 and S3 Tables). All point estimates of the difference between the two surveillance approaches were larger than 5 percentage points in the overall analysis and in the outpatient comparison, except for amikacin and meropenem.

Fig 1

Difference in prevalence estimates between laboratory- and population-based surveillance.

(A) Total (inpatient & outpatient setting) (B) Inpatients; (C) Outpatients; L>P = Laboratory-based surveillance prevalence estimate of resistance higher than population-based surveillance estimate. Bullets: percentage point difference between laboratory- and population-based surveillance. Horizontal lines: confidence interval for the difference.

A sensitivity analysis with a unified definition of culture positivity, showed smaller differences in prevalence estimates in the combined inpatient and outpatient analysis and for inpatients only, but showed still marked differences in prevalence estimates for the outpatient population (S4–S6 Tables, S1 Fig).

Discussion

Both laboratory-based and population-based surveillance approaches showed strikingly high prevalence estimates of AMR for most antibiotics tested, except for amikacin and meropenem. The difference between laboratory-based surveillance estimates and population-based estimates was much larger for outpatients than for inpatients. These differences indicate that laboratory-based AMR prevalence data are not suitable to guide empirical treatment decisions, especially in the outpatient setting.

As recommended by WHO, Indonesia has adopted a National Action Plan to combat AMR in 2017, which includes enhanced surveillance and networking in order to obtain national representative AMR surveillance data to inform guidelines [1]. WHO recommends laboratory-based surveillance with linkage to patient data, as an initial step towards national surveillance since this is considered the most feasible surveillance approach [3]. However, here we show that laboratory-based surveillance is likely to suffer from serious bias, as has been suggested previously [4,13]. Whilst laboratory-based surveillance depends on a clinician’s decision to submit a sample for culture and susceptibility testing based on clinical experience or guidelines, potentially leading to differences in case ascertainment and sampling bias, population-based surveillance typically includes all patients who fulfill predefined case definition and inclusion criteria. In LMIC settings where access to diagnostics is often limited due to a range of potential constraints, such selection process may be even more pronounced. Despite Indonesia’s progress towards universal health coverage [14], financial constraints may create barriers to bacterial culture and susceptibility testing limiting microbiological diagnostics to those patients with severe or recurrent infections, who often have been pretreated with antibiotics, or to those with insurance coverage that includes diagnostic microbiology, which is often limited to in-patients. This type of selection processes may explain the differences observed between laboratory-based and population-based surveillance in outpatients in the current study. Aggregating laboratory-based AMR surveillance data of UTI outpatients and inpatients has previously been shown to lead to potential overestimation of the prevalence of AMR in outpatients in a high-income setting [4].

We defined the difference between the prevalence estimates as indicating relevant bias at five percent point or more, based on clinical relevance. Whilst this definition is arbitrary, for the outpatient population the difference between laboratory-based and population-based resistance prevalence estimates was much larger than 5 percent points for most antibiotics analyzed, even when taking into account the uncertainty indicated by the relatively large 95% CIs. We performed a sensitivity analysis which considered differences in the definition of a positive culture result as a potential source of the observed differences between laboratory-based and population-based surveillance, as also described in previous studies [4,13]. Given that only culture results that are considered clinically relevant lead to a susceptibility test result, such difference between definition of a positive culture may contribute to the differences in prevalence estimates. Indeed, after adjusting the definition of culture positivity in the population-based surveillance to the definition applied in laboratory-based surveillance, a reduction in the difference in prevalence estimates between the two surveillance approaches was observed for almost all antibiotics, however with similar overall conclusions compared to the primary analysis. In this study, we focused our analysis on E. coli and K. pneumoniae since these are the most common pathogens in UTI and the priority pathogens for surveillance as recommended by WHO-GLASS [3]. However, surveillance of other pathogens may be useful in this setting, in particular for inpatients.

Bias related to differences in gender and age distribution between laboratory-based and population-based surveillance cannot be excluded. Data on gender distribution were not available for the laboratory-based surveillance. Out of 860 samples included in the laboratory-based surveillance, 36 (4%) were submitted from paediatric departments. Only adult patients (age ≥ 18 years) were included in the population-based surveillance. Taken together, a difference in age distribution (paediatric vs adult) is unlikely to explain the differences between laboratory-based and population-based surveillance in this study.

The results of our study indicate that laboratory-based surveillance of uropathogens, in particular when aggregating data of outpatients and inpatients, is likely to overestimate AMR prevalence for outpatient settings. Such overestimation could lead to unwarranted early switch to second line empirical antibiotic treatment in outpatients, which is often more costly and can lead to early emergence of resistance against these second line treatments. Population-based surveillance is more labor intensive and time consuming than laboratory-based surveillance. Alternative strategies should be studied and employed to overcome these drawbacks of conventional population-based surveillance. We have recently shown that using a Lot Quality Assurance Sampling (LQAS) approach is one such alternative strategy for population-based AMR surveillance. Instead of assessment of a prevalence estimate with corresponding confidence intervals, a LQAS-based surveillance approach classifies the prevalence to be above a pre-defined threshold determined on the basis of clinical criteria and guidelines [15]. The study showed that LQAS-based surveillance of the prevalence of AMR provided the opportunity to obtain locally relevant estimation in a timely and affordable manner that can be repeated for monitoring purposes [16]. Other surveillance approaches that provide unbiased population-based AMR prevalence estimates may provide similar solutions and need to be explored.

Our study had some limitations. In the absence of surveillance of nosocomial transmission, we were not informed about potential clonal transmission of urinary pathogens on the hospital wards. However, the routine hospital surveillance report did not show increasing trend of resistance during the study periods, indicating that clonal transmission or hospital outbreaks are unlikely to have affected the results. A second limitation is that the laboratory-based surveillance data were obtained from a single hospital, in contrast to the population based surveillance. However, all data were analysed in the same accredited reference laboratory. Thirdly, different AST methods were used during the two surveillance approaches. Since both methods were performed according to CLSI guidelines using the same breakpoints, this difference is unlikely to affect the surveillance outcomes. Finally, QC performance was done under different protocols. The population-based surveillance was carried out as a research surveillance project with more stringent application of QC procedures, whilst during laboratory-based surveillance standard QC procedures were in place. However, these differences in QC protocols are unlikely to explain the observed differences in resistance prevalence across all antibiotics studied given the overall direction of high prevalence of resistance, which was similar across the two surveillance approaches.

In conclusion, laboratory-based AMR surveillance of uropathogens, which typically includes a majority of samples from hospital-associated patients, is not adequate to guide empirical treatment for outpatient settings, in Indonesia. Alternative surveillance strategies are needed that provide timely and affordable population-based AMR prevalence data, to inform local and population directed empirical treatment guidelines.

Difference in prevalence estimates between laboratory- and population-based surveillance, unified for definition of culture positivity (≥ 105 CFU/ml).

(A) Total (inpatient & outpatient setting) (B) Inpatients; (C) Outpatients; L>P = Laboratory-based surveillance prevalence estimate of resistance higher than population-based surveillance estimate. Bullets: percentage difference between laboratory- and population-based surveillance. Horizontal lines: confidence interval for the difference between the two prevalence estimates. Vertical dotted lines indicate to definition of bias (+/-5 percent point difference in prevalence estimate).

(DOCX)

Click here for additional data file.

Difference in resistance prevalence among uropathogens between laboratory-based surveillance and population-based surveillance; combined inpatient and outpatient settings.

Abbrev: n, number of isolates; R, number of resistance isolates; %R, resistance percentage; L, Laboratory-based data; P, Population-based data; %D, Percentage point difference; B, Bias; Y, Yes; N, No; CI, Confidence Interval; lb, lower boundaries; ub, upper boundaries; AMC, Amoxicillin Clavulanic–Acid; AK, Amikacin; CAZ, Ceftazidime; CRO, Ceftriaxone; LVX, Levofloxacin; MEM, Meropenem; TZP, Piperacillin Tazobactam.

(DOCX)

Click here for additional data file.

Difference in resistance prevalence among uropathogens between laboratory-based surveillance and population-based surveillance; inpatient setting.

Abbrev: n, number of isolates; R, number of resistance isolates; %R, resistance percentage; L, Laboratory-based data; P, Population-based data; %D, Percentage point difference; B, Bias; Y, Yes; N, No; CI, Confidence Interval; lb, lower boundaries; ub, upper boundaries; AMC, Amoxicillin Clavulanic–Acid; AK, Amikacin; CAZ, Ceftazidime; CRO, Ceftriaxone; LVX, Levofloxacin; MEM, Meropenem; TZP, Piperacillin Tazobactam.

(DOCX)

Click here for additional data file.

Difference in resistance prevalence among uropathogens between laboratory-based surveillance and population-based surveillance; outpatient setting.

Abbrev: n, number of isolates; R, number of resistance isolates; %R, resistance percentage; L, Laboratory-based data; P, Population-based data; %D, Percentage point difference; B, Bias; Y, Yes; N, No; CI, Confidence Interval; lb, lower boundaries; ub, upper boundaries; AMC, Amoxicillin Clavulanic–Acid; AK, Amikacin; CAZ, Ceftazidime; CRO, Ceftriaxone; LVX, Levofloxacin; MEM, Meropenem; TZP, Piperacillin Tazobactam.

(DOCX)

Click here for additional data file.

Difference in resistance prevalence among uropathogens between laboratory-based surveillance and population-based surveillance; unified for definition of culture positivity (≥ 105 CFU/ml); combined inpatient and outpatient settings.

Abbrev: n, number of isolates; R, number of resistance isolates; %R, resistance percentage; L, Laboratory-based data; P, Population-based data; %D, Percentage point difference; B, Bias; Y, Yes; N, No; CI, Confidence Interval; lb, lower boundaries; ub, upper boundaries; AMC, Amoxicillin Clavulanic–Acid; AK, Amikacin; CAZ, Ceftazidime; CRO, Ceftriaxone; LVX, Levofloxacin; MEM, Meropenem; TZP, Piperacillin Tazobactam.

(DOCX)

Click here for additional data file.

Difference in resistance prevalence among uropathogens between laboratory-based surveillance and population-based surveillance; unified for definition of culture positivity (≥ 105 CFU/ml); inpatient setting.

Abbrev: n, number of isolates; R, number of resistance isolates; %R, resistance percentage; L, Laboratory-based data; P, Population-based data; %D, Percentage point difference; B, Bias; Y, Yes; N, No; CI, Confidence Interval; lb, lower boundaries; ub, upper boundaries; AMC, Amoxicillin Clavulanic–Acid; AK, Amikacin; CAZ, Ceftazidime; CRO, Ceftriaxone; LVX, Levofloxacin; MEM, Meropenem; TZP, Piperacillin Tazobactam.

(DOCX)

Click here for additional data file.

Difference in resistance prevalence among uropathogens between Laboratory-based surveillance and Population-based surveillance; unified for definition of culture positivity (≥ 105 CFU/ml); outpatient setting.

Abbrev: n, number of isolates; R, number of resistance isolates; %R, resistance percentage; L, Laboratory-based data; P, Population-based data; %D, Percentage point difference; B, Bias; Y, Yes; N, No; CI, Confidence Interval; lb, lower boundaries; ub, upper boundaries; AMC, Amoxicillin Clavulanic–Acid; AK, Amikacin; CAZ, Ceftazidime; CRO, Ceftriaxone; LVX, Levofloxacin; MEM, Meropenem; TZP, Piperacillin Tazobactam.

(DOCX)

Click here for additional data file.

12 Nov 2019

PONE-D-19-19644

Laboratory-based versus population-based surveillance of antimicrobial resistance to inform empirical treatment for suspected urinary tract infection in Indonesia

PLOS ONE

Dear Prof. Schultsz,

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The manuscript has been assessed by two reviewers; their comments are available below.

The reviewers find the work of relevance but have raised some comments that need attention in a revision. The reviewers recommend that the relationship to the study reported in your earlier publication in J Antimicrob Chemother. 2017;72: 1469–77 is described in greater detail and in particular, that you clarify any overlap in sample populations between the two studies. The reviewers recommend that the analysis of additional pathogens is included, or if that is not possible, that this is clearly acknowledged as a limitation.

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Overview:

The study assessed the difference in antibiotic resistance between laboratory based surveillance and population based surveillance in uropathogens in Indonesia. They focused the microbiology work on Escherichia coli and Klebsiella pneumoniae as these are the main burden of UTIs. The population based surveillance involved collection through a cross sectional survey from people with symptoms corresponding to UTI attending clinics and hospitals as outpatients. Data was collected between the 1st of April 2014 and the 31st of May 2015. Some of the main results were that there was a higher prevalence of resistance to Piperacillin-tazobactam (63.1%) and ceftriaxone (85.2%) in the laboratory based surveillance strains compared to the population based surveillance at 41.3% and 74.2% respectively.

Overall comments:

This is a very relevant study which addresses many questions being raised at the moment to understand the bias of results reported in hospital microbiology laboratories. A community based study such as this will help to solve many questions on bias for antibiotic resistance.

Specific queries:

• Although I note this study was previously published and referenced in the current publication [1], I believe there needs to be more description about the study subjects. In particular I would like to understand how the researchers were able to determine the patients who were in patients in the population based study. Particularly as the major difference between the laboratory and population based studies occurred in the outpatients when the population based inpatients were removed. Further explanation about this is necessary in the text.

• Please further explain the following sentence in your discussion: “A reduction in resistance was observed when only clinically relevant culture results were reported in the laboratory”, what does this mean?

• It would be useful to have a map of the area depicting where the hospital is based and where the population based work took place

• What is the age and gender distribution of your patients? Could there be a difference in your results due to either of these variables?

• The major difference seems to be between outpatient samples and whether the results are derived from the laboratory or the population based work, although numbers of strains are low in these groups for Klebsiella pneumoniae at 33 and 40 strains respectively). I would like to see more discussion on your thoughts as to why this is the case.

• Is there a difference in prescribing of antibiotics in those patients who are included in the population based study compared to the laboratory results, might this impact on the results that you have reported?

Reference

1. Sugianli, A.K., et al., Antimicrobial resistance in uropathogens and appropriateness of empirical treatment: a population-based surveillance study in Indonesia. J Antimicrob Chemother, 2017. 72(5): p. 1469-1477.

Reviewer #2: The authors have assessed laboratory-based surveillance versus population-based surveillance for studying the prevalence of antimicrobial resistance in UTI in Indonesia. The data presented is sound and the methodology as well as the analysis are supporting the outcome of this paper. I have a few minor suggestions that may improve the paper further:

MINOR:

- In the background, the authors address the bias in the selection process that may occur in laboratory based surveillance. I would suggest adding some clarification on the reasoning behind this bias.

- Is the population-based surveillance data presented in this manuscript exactly as the same data that was previously published in J Antimicrob Chemother. 2017;72: 1469–77?

If so, I would suggest including a clarification to further highlight this point. I also would like to encourage the authors to visit the permission requirements of JAC to make sure that the reuse of the published data is in alignment with the journal's policy. This can be found in https://academic.oup.com/jac/pages/General_Instructions#Permissions

MAJOR:

- The authors have clearly addressed the possible bias that may occur from laboratory-based surveillance, alongside the selection bias on E. coli and K. pneumoniae to represent uropathogens, as suggested by GLASS. When conducting this study, the authors have decided to apply to same bias and only select for E. coli and K. pneumoniae in the population based study. Is it possible to include the analysis of the other pathogens isolated in the results? If not possible, I think it would be important to address this limitation and highlight the need to test the value of population-based surveillance on a wider range of uropathogens.

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Reviewer #1: Yes: Catrin E Moore

Reviewer #2: Yes: Hosam Zowawi

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9 Jan 2020

Reviewer #1: Overview:

The study assessed the difference in antibiotic resistance between laboratory based surveillance and population based surveillance in uropathogens in Indonesia. They focused the microbiology work on Escherichia coli and Klebsiella pneumoniae as these are the main burden of UTIs. The population based surveillance involved collection through a cross sectional survey from people with symptoms corresponding to UTI attending clinics and hospitals as outpatients. Data was collected between the 1st of April 2014 and the 31st of May 2015. Some of the main results were that there was a higher prevalence of resistance to Piperacillin-tazobactam (63.1%) and ceftriaxone (85.2%) in the laboratory based surveillance strains compared to the population based surveillance at 41.3% and 74.2% respectively.

Overall comments:

This is a very relevant study which addresses many questions being raised at the moment to understand the bias of results reported in hospital microbiology laboratories. A community based study such as this will help to solve many questions on bias for antibiotic resistance.

We thank the reviewer for the positive comments.

Specific queries:

• Although I note this study was previously published and referenced in the current publication [1], I believe there needs to be more description about the study subjects. In particular I would like to understand how the researchers were able to determine the patients who were in patients in the population based study. Particularly as the major difference between the laboratory and population based studies occurred in the outpatients when the population based inpatients were removed. Further explanation about this is necessary in the text.

We have described the inclusion of inpatients in the Methods section:

“Population-based AMR surveillance data were collected in a cross-sectional survey of AMR in E. coli and K. pneumoniae isolated from urine samples from patients suspected of a UTI, carried out from 1 April 2014 until 31 May 2015, as described previously. In brief, consecutive patients attending four public and private outpatient clinics of urology and obstetrics/gynaecology, or who were admitted to the internal medicine-, surgery-, obstetrics/gynaecology-, or neurology wards, were actively screened for the presence of symptoms of UTI, according to CDC definitions.”

In other words, the population of inpatients included in the population-based study consisted of those who were admitted to the internal medicine-, surgery-, obstetrics/gynaecology-, or neurology wards at the time of sampling and had symptoms of UTI. Admitted patients were screened on a daily basis for these symptoms. We have added more details regarding the inclusion procedure of inpatients to the Methods section.

• Please further explain the following sentence in your discussion: “A reduction in resistance was observed when only clinically relevant culture results were reported in the laboratory”, what does this mean?

We are not clear which sentence the reviewer is referring to. The sentence the reviewer is quoting is not in our manuscript.

• It would be useful to have a map of the area depicting where the hospital is based and where the population based work took place

All study sites are in the city of Medan. The Adam Malik hospital is a tertiary referral hospital which services the city of Medan as well as the provinces North Sumatera, Aceh, West Sumatera and Riau on the island of Sumatra. We have provided this information which we believe is more helpful than a city map of Medan.

• What is the age and gender distribution of your patients? Could there be a difference in your results due to either of these variables?

Data on gender and age distribution were not available for the laboratory-based surveillance. However, out of 860 samples, 36 (4%) were from paediatric departments. Only adult patients (≥ 18 years) were included in the population-based surveillance. Taken together, the difference in gross age distribution (paediatric vs adult) is unlikely to explain the differences between laboratory-based and population-based surveillance. We have added this information to the Discussion.

• The major difference seems to be between outpatient samples and whether the results are derived from the laboratory or the population based work, although numbers of strains are low in these groups for Klebsiella pneumoniae at 33 and 40 strains respectively). I would like to see more discussion on your thoughts as to why this is the case.

As indicated in the Discussion, the major difference in AMR estimates between the two surveillance approaches is explained by the fact that in laboratory-based surveillance prevalence estimates for outpatients are markedly higher than estimates in the population-based surveillance. We have elaborated on the potential causes of this difference in the Discussion and consider sampling bias in the laboratory-based surveillance (clinician’s decision to submit a sample to the laboratory vs systematic inclusion), laboratory practice (definition of a positive culture), as well as differences in age distribution (see comment above). We focused our analysis on E. coli and K. pneumoniae since these are the most common pathogens in UTI and since these are the priority pathogens for surveillance as recommended by WHO-GLASS. We analysed these two pathogens together and did not perform a separate analysis for the K. pneumoniae isolates since this is compatible with clinical practice where during prescription of empirical therapy the causative pathogen is unknown but likely to be E. coli and/or K. pneumoniae. We have modified the Discussion to include the latter consideration.

• Is there a difference in prescribing of antibiotics in those patients who are included in the population based study compared to the laboratory results, might this impact on the results that you have reported?

The primary objective of our study was to assess the difference in AMR prevalence estimates between laboratory-based and population-based surveillance. We did not specifically focus on the antibiotic pre-treatment or prescriptions. Indeed, differences in antibiotic pre-treatment may explain some of the bias which we observe in laboratory-based surveillance because patients for whom samples have been submitted may have been pre-treated more frequently. We have added this to the description of the bias in laboratory-based surveillance in the Discussion.

Reviewer #2: The authors have assessed laboratory-based surveillance versus population-based surveillance for studying the prevalence of antimicrobial resistance in UTI in Indonesia. The data presented is sound and the methodology as well as the analysis are supporting the outcome of this paper. I have a few minor suggestions that may improve the paper further:

We thank the reviewer for the positive comments.

MINOR:

- In the background, the authors address the bias in the selection process that may occur in laboratory based surveillance. I would suggest adding some clarification on the reasoning behind this bias.

We added some examples of potential bias in laboratory-based surveillance to clarify.

- Is the population-based surveillance data presented in this manuscript exactly as the same data that was previously published in J Antimicrob Chemother. 2017;72: 1469–77?

If so, I would suggest including a clarification to further highlight this point. I also would like to encourage the authors to visit the permission requirements of JAC to make sure that the reuse of the published data is in alignment with the journal's policy. This can be found in https://academic.oup.com/jac/pages/General_Instructions#Permissions

The data are not exactly the same; we used a limited data set, i.e. only one of two study areas from our previous study that matched with the laboratory-based surveillance data which we added to this study. We checked the JAC requirements.

MAJOR:

- The authors have clearly addressed the possible bias that may occur from laboratory-based surveillance, alongside the selection bias on E. coli and K. pneumoniae to represent uropathogens, as suggested by GLASS. When conducting this study, the authors have decided to apply to same bias and only select for E. coli and K. pneumoniae in the population based study. Is it possible to include the analysis of the other pathogens isolated in the results? If not possible, I think it would be important to address this limitation and highlight the need to test the value of population-based surveillance on a wider range of uropathogens.

We focused our analysis on E. coli and K. pneumoniae since these are the most common pathogens in UTI and since these are the priority pathogens for surveillance as recommended by WHO-GLASS. We therefore do not consider our focus as a limitation of the study. We don’t have additional information for other pathogens isolated for the population-based surveillance. However, we agree that for inpatients in particular, surveillance of other pathogens may be helpful. We added this to the Discussion.

Authors’ note added to the review:

- We have corrected the denominators in S4-6 Tables and S2 Figure. These corrections do not affect the results.

- We have added the URL at which the study data and associated code book can be accessed.

3 Mar 2020

Laboratory-based versus population-based surveillance of antimicrobial resistance to inform empirical treatment for suspected urinary tract infection in Indonesia

PONE-D-19-19644R1

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Reviewer #1: Yes: Dr Catrin Moore

13 Mar 2020

PONE-D-19-19644R1

Laboratory-based versus population-based surveillance of antimicrobial resistance to inform empirical treatment for suspected urinary tract infection in Indonesia

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