Antimicrobial resistance is a global problem. Each year, an estimated 23,000 deaths in the United States and 25,000 deaths in the European Union are extra deaths caused by bacteria resistant to antibiotics. People in low- and middle-income countries are also using more antibiotics, in part because of rising incomes, lower costs of antibiotics, and a lack of control of antimicrobial usage in the hospitals and over-the-counter sales of the drugs. These factors are thought to be driving the development and spread of bacteria that are resistant to multiple antibiotics in countries such as China, India, Indonesia and Thailand. However, a lack of information makes it difficult to estimate the size of the problem and, then, to track how antimicrobial resistance and multi-drug resistance is changing over time in these and other low- and middle-income countries.

Now, by integrating routinely collected data from a range of databases, Lim, Takahashi et al. estimate that around an extra 19,000 deaths are caused by multi-drug resistant bacteria in Thailand each year. Thailand has a population of about 70 million, and so, per capita, this estimate is about 3 to 5 times larger than those for the United States and European Union (which have a populations of about 300 million and 500 million, respectively). Lim, Takahashi et al. also show that more of the bacteria collected from patients are resistant to multiple antimicrobial drugs and that the burden of antimicrobial resistance in Thailand is worsening over time.

These findings suggest that more studies with a systematic approach need to be done in other low- and middle-income countries, especially in countries where microbiological laboratories are readily available and routinely used. Further work is also needed to identify where resources and attentions are most needed to effectively fight against antimicrobial resistance in low- and middle-income countries.

The emergence of antimicrobial resistance (AMR) is of major medical concern, particularly in low- and middle-income countries (LMICs) (World Health Organization, 2014; Laxminarayan et al., 2013). In LMICs, antibiotic use is increasing with rising incomes, affordable antimicrobials and the lack of stewardship in hospital and poor control of over-the-counter sales. This is driving the emergence and spread of multidrug-resistant (MDR) pathogens in community and hospital settings. Hospital data from LMICs suggest that the cumulative incidence of community-acquired Extended-Spectrum Beta-Lactamase (ESBL) producing Escherichia coli and Klebsiella pneumoniae infections are increasing over time (Kanoksil et al., 2013; Ansari et al., 2015). A recent report from the International Nosocomial Infection Control Consortium (INICC) also showed that the prevalence of AMR organisms causing hospital-acquired infections (HAI) in ICUs in LMICs is much higher than those in the United States (US) (Rosenthal et al., 2014).

Attributable mortality, generally defined as the difference in mortality between those with and without the condition of interest, is an important parameter used to estimate the burden of AMR. In the US, it is estimated that mortality from infection attributable to AMR is 6.5%, (Roberts et al., 2009) leading to an estimate of 23,000 deaths attributable to AMR each year (Center for Disease Controls and Prevention and U.S. Department of Health and Human Services, 2013). In the European Union, it is estimated that the number of deaths attributable to selected antibiotic-resistant bacteria is about 25,000 each year (European Centre for Disease Prevention and Control and European Medicines Agency, 2009). There is limited information on mortality attributable to AMR in LMICs. The mortality attributable to ventilator-associated pneumonia in ICUs in Colombia, Peru, and Argentina is estimated to be 17%, 25%, and 35%, respectively, and is associated with a high percentage of AMR organisms (Moreno et al., 2006; Cuellar et al., 2008; Rosenthal et al., 2003). The mortality attributable to ESBL and methicillin-resistance Staphylococcus aureus (MRSA) is estimated to be 27% and 34% in neonatal sepsis in Tanzania, respectively, (Kayange et al., 2010) which has been used to postulate an estimate that 58,319 deaths could be attributable to ESBL and MRSA in India alone (Laxminarayan et al., 2013). In an effort to harmonize the surveillance systems of AMR, a joint initiative between the European Centre for Disease Prevention and Control (ECDC) and the Centres for Disease Prevention and Control (CDC) have developed standard definitions of multidrug-resistance (MDR) (Magiorakos et al., 2012).

We recently combined large data sets from multiple sources including microbiology databases, hospital admission databases, and the national death registry from a sample of ten public hospitals in northeast Thailand from 2004 to 2010 (Kanoksil et al., 2013; Hongsuwan et al., 2014). We defined community-acquired bacteraemia (CAB) as the isolation of a pathogenic bacterium from blood taken in the first 2 days of admission and without a hospital stay in the 30 days prior to admission, healthcare-associated bacteraemia (HCAB) as the isolation of a pathogenic bacterium from blood taken in the first 2 days of admission and with a hospital stay within 30 days prior to the admission, and hospital-acquired bacteraemia (HAB) as the isolation of a pathogenic bacterium from blood taken after the first 2 days of admission (Kanoksil et al., 2013; Hongsuwan et al., 2014). We reported an increase in the incidence of CAB, HCAB and HAB over the study period, and that bacteraemia was associated with high case fatality rates (37.5%, 41.8% and 45.5%, respectively) (Kanoksil et al., 2013; Hongsuwan et al., 2014). Here, we apply ECDC/CDC standard definitions of MDR to this large data set to evaluate the prevalence, trends, and mortality attributable to MDR bacteria isolated from the blood. We then estimate the number of deaths attributable to MDR in Thailand nationwide.

We contacted all 20 provincial hospitals in Northeast Thailand to participate in the study. All provincial hospitals were equipped with all basic medical specialties and intensive care units (ICUs). Agreement was obtained from 15 (75%) hospitals, of which ten had hospital databases and microbiological laboratory databases as electronic files in a readily accessible format (Kanoksil et al., 2013; Hongsuwan et al., 2014). Of these ten hospitals, nine had databases of antimicrobial susceptibility testing results as electronic files for the study (Figure 1). The median bed number for the nine hospitals included in the analysis was 450 beds (range 300 to 1000 beds). Of these, three had data available for the period 2004–2010, two between 2007 and 2010, three between 2008 and 2010 and one between 2009 and 2010. Overall, 1,803,506 admission records from 1,255,571 patients were evaluated. A total of 20,803 (1.2%) admission records had at least one blood culture positive for pathogenic organisms during admission. Of 10,022 patients with first episodes of bacteraemia caused by S. aureus, Enterococcus spp, E. coli, K. pneumoniae, P. aeruginosa and Acinetobacter spp., 226 patients (2%) were excluded because the causative organisms were tested for susceptibility to fewer than three antimicrobial categories. Therefore, a total of 9796 first episodes of bacteraemia caused by S. aureus (n = 1881), Enterococcus spp (n = 342), E. coli (n = 4279), K. pneumoniae (n = 1661), P aeruginosa (n = 568), and Acinetobacter spp. (n = 1065) were evaluated in the analysis. The proportion of bacteria being MDR was highest in HAB and lowest in CAB for all organisms (all p<0.001 except for Enterococcus spp., Table 1).

Figure 1

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Staphylococcus aureus

Of CAB, HCAB and HAB caused by S. aureus, 8%, 28%, and 50% were caused by MDR S. aureus, respectively (p<0.001). Almost all MDR S. aureus were MRSA (92% [357/389], Table 2). We did not observe a trend in the proportion of S. aureus bacteraemia being caused by MRSA (Figure 2). Vancomycin non-susceptible S. aureus was found in <1% of tested isolates (6/1380).

Figure 2

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Table 2

Antibiotic category	Antibiotic agents	CAB (n = 1176 patients)	HCAB (n = 259 patients)	HAB (n = 446 patients)	p values
Aminoglycosides	Gentamicin	24/484 (5%)	16/84 (19%)	66/151 (44%)	<0.001
Ansamycins	Rifampin	2/129 (2%)	1/19 (5%)	0/38 (0%)	0.37
Anti-MRSA cephalosporins	Ceftaroline	NA	NA	NA	-
Cefamycins	Oxacillin *	80/1145 (7%)	67/247 (27%)	210/441 (48%)	<0.001
Fluoroquinolones	Ciprofloxacin	3/45 (7%)	2/8 (25%)	4/10 (40%)	0.01
	Moxifloxacin	NA	NA	NA	-
Folate pathway inhibitors	Trimethoprim-sulphamethoxazole	99/1139 (9%)	57/251 (23%)	185/438 (42%)	<0.001
Fucidanes	Fusidic acid	33/618 (5%)	4/170 (2%)	12/291 (4%)	0.26
Glycopeptides	Vancomycin †	4/833 (0.5%)	0/190 (0%)	2/357 (1%)	0.86
	Teicoplanin	2/66 (3%)	1/17 (6%)	0/17 (0%)	0.72
	Telavancin	NA	NA	NA	-
Glycylcyclines	Tigecycline	NA	NA	NA	-
Lincosamides	Clindamycin	118/1147 (10%)	77/251 (31%)	202/438 (46%)	<0.001
Lipopeptides	Daptomycin	NA	NA	NA	-
Macrolides	Erythromycin	138/1116 (12%)	76/240 (32%)	222/429 (52%)	<0.001
Oxazolidinones	Linezolid	0/81 (0%)	0/16 (0%)	0/32 (0%)	-
Phenicols	Chloramphenicol	6/86 (7%)	4/24 (17%)	2/14 (14%)	0.21
Phosphonic acids	Fosfomycin	14/361 (4%)	10/66 (15%)	24/141 (17%)	<0.001
Streptogramins	Quinupristin-dalfopristin	NA	NA	NA	-
Tetracyclines	Tetracycline	NA	NA	NA	-
	Doxycycline	NA	NA	NA	-
	Minocycline	NA	NA	NA	-
MDR		94/1176 (8%)	73/259 (28%)	222/446 (50%)	<0.001

1. NOTE: Data are number of isolates demonstrating non-susceptible to the antimicrobial over the total number of isolates tested (%). CAB = Community-acquired bacteraemia, HCAB = Healthcare-associated bacteraemia, HAB = Hospital-acquired bacteraemia, and NA = Not available. The first isolate of each patient was used. MDR (one or more of these have to apply): (i) an MRSA is always considered MDR by virtue of being an MRSA (ii) non-susceptible to ≥1 agent in ≥3 antimicrobial categories.

2. * Defined by using a 30 μg cefoxitin disc and an inhibition zone diameter of <21 mm.

3. † Defined by using a 30 μg vancomycin disc and an inhibition zone diameter of <15 mm.

Escherichia coli

Of CAB, HCAB and HAB caused by E. coli, 35%, 58% and 63% were caused by MDR E. coli, respectively (p<0.001). Of E. coli causing CAB, 79% (2246/2843), 16% (501/3076), 24% (728/3000), 58% (1738/3007), and 17% (559/3346) were non-susceptible to commonly-used antimicrobials for community-acquired infections such as ampicillin, cefotaxime, ciprofloxacin, trimethoprim-sulphamethoxazole, and gentamicin, respectively (Table 4). From 2004 to 2010, the proportions of community-acquired E. coli bacteraemia being caused by E. coli non-susceptible to extended-spectrum cephalosporins rose from 5% (9/169) to 23% (186/815) (p=0.04) (Figure 3). The proportions of healthcare-associated and hospital-acquired E. coli bacteraemia being caused by E. coli non-susceptible to extended-spectrum cephalosporins were high (44% [204/465] and 52% [190/368], respectively), but a significant trend over time was not observed (p=0.18 and p=0.63, respectively). Carbapenem non-susceptible E. coli was found in <1% of tested isolates (12/3838).

Figure 3

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Table 3

Antibiotic category	Antibiotic agents	CAB (n = 176 patients)	HCAB (n = 49 patients)	HAB (n = 117 patients)	p values
Aminoglycosides	Gentamicin (high level)	35/153 (23%)	24/45 (53%)	63/101 (62%)	<0.001
Streptomycin	Streptomycin (high level)	NA	NA	NA	-
Carbapenems*	Imipenem	NA	NA	NA	-
	Meropenem	1/1 (100%)	NA	3/5 (60%)	>0.99
	Doripenem	NA	NA	NA	-
Fluoroquinolones	Ciprofloxacin	37/44 (84%)	9/10 (90%)	31/37 (84%)	>0.99
	Levofloxacin	5/18 (28%)	1/6 (17%)	11/15 (73%)	0.01
	Moxifloxacin	NA	NA	NA	-
Glycopeptides	Vancomycin	9/176 (5%)	0/49 (0%)	6/113 (5%)	0.27
	Teicoplanin	0/11 (0%)	0/4 (0%)	0/10 (0%)	-
Glycylcyclines	Tigecycline	NA	NA	NA	-
Lipopeptides	Daptomycin	NA	NA	NA	-
Oxazolidinones	Linezolid	0/8 (0%)	0/2 (0%)	0/4 (0%)	-
Penicillins	Ampicillin	20/134 (15%)	6/37 (16%)	34/81 (42%)	<0.001
Streptogramins*	Quinupristin-dalfopristin	NA	NA	NA	-
Tetracycline	Doxycycline	NA	NA	NA	-
	Minocycline	NA	NA	NA	-
MDR		0/176 (0%)	0/49 (0%)	4/117 (3%)	0.02

1. NOTE: Data are number of isolates demonstrating non-susceptible to the antimicrobial over the total number of isolates tested (%). CAB = Community-acquired bacteraemia, HCAB = Healthcare-associated bacteraemia, HAB = Hospital-acquired bacteraemia, and NA = Not available. The first isolate of each patient was used. MDR: non-susceptible to ≥1 agent in ≥3 antimicrobial categories.

2. *Intrinsic resistance in E. faecium against carbapenems and in E. faecalis against streptogramins. When a species has intrinsic resistance to an antimicrobial category, that category is removed prior to applying the criteria for the MDR definition and is not counted when calculating the number of categories to which the bacterial isolate is non-susceptible.

Table 4

Antibiotic category	Antibiotic agents	CAB (n = 3382 patients)	HCAB (n = 494 patients)	HAB (n = 403 patients)	p values
Aminoglycosides	Gentamicin	559/3346 (17%)	166/484 (34%)	178/398 (45%)	<0.001
	Tobramycin	NA	NA	NA	-
	Amikacin	72/2685 (3%)	26/397 (7%)	32/326 (10%)	<0.001
	Netilmicin	68/1394 (5%)	25/259 (10%)	42/254 (17%)	<0.001
Anti-MRSA cephalosporins	Ceftaroline	NA	NA	NA	-
Antipseudomonal penicillins + β lactamase inhibitors	Ticarcillin-clauvanic acid	NA	NA	NA	-
	Piperacillin-tazobactam	23/511 (5%)	10/103 (10%)	15/89 (17%)	<0.001
Carbapenems	Ertapenem	4/1325 (<1%)	1/235 (<1%)	4/205 (2%)	0.02
	Imipenem	3/2449 (<1%)	0/386 (0%)	3/344 (1%)	0.04
	Meropenem	0/1988 (0%)	1/314 (<1%)	1/244 (<1%)	0.05
Non-extended spectrum cephalosporins	Cefazolin	468/1095 (43%)	115/174 (66%)	80/102 (78%)	<0.001
	Cefuroxime	219/1438 (15%)	96/226 (42%)	102/202 (50%)	<0.001
Extended-spectrum cephalosporins	Cefotaxime	501/3076 (16%)	199/455 (44%)	185/361 (51%)	<0.001
	Ceftazidime	392/3020 (13%)	165/446 (37%)	164/351 (47%)	<0.001
	Cefepime	30/293 (10%)	12/42 (29%)	18/53 (34%)	<0.001
Cephamycins	Cefoxitin	36/1200 (3%)	16/215 (7%)	16/195 (8%)	<0.001
	Cefotetan	NA	NA	NA	-
Fluoroquinolones	Ciprofloxacin	728/3000 (24%)	221/452 (49%)	171/384 (45%)	<0.001
Folate pathway inhibitors	Trimethoprim-sulphamethoxazole	1738/3007 (58%)	294/442 (67%)	225/350 (64%)	<0.001
Glycylcyclines	Tigecycline	0/7 (0%)	NA	0/1 (0%)	-
Monobactams	Aztreonam	NA	NA	NA	-
Penicillins	Ampicillin	2246/2843 (79%)	371/420 (88%)	301/342 (88%)	<0.001
Penicillins + β lactamase inhibitors	Amoxicillin-clavulanic acid	790/3074 (26%)	191/463 (41%)	158/373 (42%)	<0.001
	Ampicillin-sulbactam	83/296 (28%)	18/48 (38%)	12/25 (48%)	0.06
Phenicols	Chloramphenicol	14/63 (22%)	1/4 (25%)	3/5 (60%)	0.14
Phosphonic acids	Fosfomycin	NA	NA	NA	-
Polymyxins	Colistin*	2/34 (6%)	0/6 (0%)	1/6 (17%)	0.61
MDR		1177/3382 (35%)	288/494 (58%)	252/403 (63%)	<0.001

1. NOTE: Data are number of isolates demonstrating non-susceptible to the antimicrobial over the total number of isolates tested (%). CAB = Community-acquired bacteraemia, HCAB = Healthcare-associated bacteraemia, HAB = Hospital-acquired bacteraemia, and NA = Not available. The first isolate of each patient was used. MDR: non-susceptible to ≥1 agent in ≥3 antimicrobial categories.

2. *Defined by using an inhibition zone of <11 mm.

Klebsiella pneumoniae

Of CAB, HCAB and HAB caused by K. pneumoniae, 14%, 36%, and 66% were caused by MDR K. pneumoniae, respectively (p<0.001). Of K. pneumoniae causing CAB, 16% (146/902), 16% (143/894), 23% (198/876), and 9% (94/999) were non-susceptible to cefotaxime, ciprofloxacin, trimethoprim-sulphamethoxazole and gentamicin, respectively (Table 5). From 2004 to 2010, the proportions of community-acquired K. pneumoniae bacteraemia being caused by K. pneumoniae non-susceptible to extended-spectrum cephalosporins rose from 12% (6/50) to 24% (64/263) (p=0.04) (Figure 4). The proportions of healthcare-associated and hospital-acquired K. pneumoniae bacteraemia being caused by K. pneumoniae non-susceptible to extended-spectrum cephalosporins were also high (40% [71/177] and 71% [304/431], respectively), but a significant trend over time was not observed (p=0.16 and p=0.35, respectively). Carbapenem non-susceptible K. pneumoniae was found in <1% of tested isolates (11/1555).

Figure 4

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Table 5

Antibiotic category	Antibiotic agents	CAB (n = 1010 patients)	HCAB (n = 196 patients)	HAB (n = 455 patients)	p values
Aminoglycosides	Gentamicin	94/999 (9%)	53/193 (27%)	265/444 (60%)	<0.001
	Tobramycin	NA	NA	NA	-
	Amikacin	17/815 (2%)	12/157 (8%)	109/398 (27%)	<0.001
	Netilmicin	20/450 (4%)	23/112 (21%)	124/320 (39%)	<0.001
Anti-MRSA cephalosporins	Ceftaroline	NA	NA	NA	-
Antipseudomonal penicillins + β lactamase inhibitors	Ticarcillin-clauvanic acid	NA	NA	NA	-
	Piperacillin-tazobactam	24/166 (14%)	14/32 (44%)	73/121 (60%)	<0.001
Carbapenems	Ertapenem	2/432 (0%)	1/100 (1%)	5/264 (2%)	0.17
	Imipenem	1/778 (0%)	1/164 (1%)	2/408 (0%)	0.24
	Meropenem	0/583 (0%)	1/113 (1%)	2/317 (1%)	0.10
Non-extended spectrum cephalosporins	Cefazolin	76/319 (24%)	30/60 (50%)	101/127 (80%)	<0.001
	Cefuroxime	81/478 (17%)	35/98 (36%)	161/231 (70%)	<0.001
Extended-spectrum cephalosporins	Cefotaxime	146/902 (16%)	71/173 (41%)	298/424 (70%)	<0.001
	Ceftazidime	124/927 (13%)	63/176 (36%)	295/430 (69%)	<0.001
	Cefepime	5/100 (5%)	8/22 (36%)	25/51 (49%)	<0.001
Cephamycins	Cefoxitin	15/396 (4%)	10/95 (11%)	14/230 (6%)	0.03
	Cefotetan	NA	NA	NA	-
Fluoroquinolones	Ciprofloxacin	143/894 (16%)	66/176 (38%)	187/430 (43%)	<0.001
Folate pathway inhibitors	Trimethoprim-sulphamethoxazole	198/876 (23%)	69/171 (40%)	219/407 (54%)	<0.001
Glycylcyclines	Tigecycline	NA	NA	NA	-
Monobactams	Aztreonam	NA	NA	NA	-
Penicillins + β lactamase inhibitors	Amoxicillin-clavulanic acid	131/945 (14%)	68/183 (37%)	291/443 (66%)	<0.001
	Ampicillin-sulbactam	20/105 (19%)	9/17 (53%)	23/38 (61%)	<0.001
Phenicols	Chloramphenicol	4/19 (21%)	0/2 (0%)	0/3 (0%)	>0.99
Phosphonic acids	Fosfomycin	NA	NA	NA	-
Polymyxins	Colistin *	0/6 (0%)	0/2 (0%)	0/5 (0%)	-
MDR		146/1010 (14%)	71/196 (36%)	301/455 (66%)	<0.001

1. NOTE: Data are number of isolates demonstrating non-susceptible to the antimicrobial over the total number of isolates tested (%). CAB = Community-acquired bacteraemia, HCAB = Healthcare-associated bacteraemia, HAB = Hospital-acquired bacteraemia, and NA = Not available. The first isolate of each patient was used. MDR: non-susceptible to ≥1 agent in ≥3 antimicrobial categories.

2. * Defined by using an inhibition zone of <11 mm.

Pseudomonas aeruginosa

Of CAB, HCAB and HAB caused by P. aeruginosa, 5%, 10%, and 25% were caused by MDR P. aeruginosa, respectively (p<0.001). Of P. aeruginosa causing HAB, 38% (68/179), 27% (48/177), 23% (39/169) and 26% (42/164) were non-susceptible to commonly-used antimicrobials for HAI such as ceftazidime, amikacin, ciprofloxacin and carbapenems, respectively (Table 6). We did not observe a trend in the proportions of P. aeruginosa being caused by P. aeruginosa that were non-susceptible to any specific antibiotic group (Figure 5).

Figure 5

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Table 6

Antibiotic category	Antibiotic agents	CAB (n = 286 patients)	HCAB (n = 103 patients)	HAB (n = 179 patients)	p values
Aminoglycosides	Gentamicin	29/235 (12%)	13/88 (15%)	60/140 (43%)	<0.001
	Tobramycin	NA	NA	NA	-
	Amikacin	27/284 (10%)	13/100 (13%)	48/177 (27%)	<0.001
	Netilmicin	8/155 (5%)	5/67 (7%)	34/120 (28%)	<0.001
Antipseudomonal carbapenems	Imipenem	14/238 (6%)	6/86 (7%)	37/154 (24%)	<0.001
	Meropenem	9/163 (6%)	8/73 (11%)	24/125 (19%)	0.001
	Doripenem	2/17 (12%)	0/3 (0%)	2/2 (100%)	0.04
Antipseudomonal cephalosporins	Ceftazidime	29/280 (10%)	16/103 (16%)	68/179 (38%)	<0.001
	Cefepime	2/36 (6%)	2/18 (11%)	10/28 (36%)	0.01
Antipseudomonal fluoroquinolones	Ciprofloxacin	24/275 (9%)	12/101 (12%)	39/169 (23%)	<0.001
	Levofloxacin	0/1 (0%)	1/1 (100%)	1/1 (100%)	>0.99
Antipseudomonal penicillins + β lactamase inhibitors	Ticarcillin-clauvanic acid	NA	NA	NA	-
	Piperacillin-tazobactam	8/85 (9%)	6/38 (16%)	8/46 (17%)	0.37
Monobactams	Aztreonam	NA	NA	NA	-
Phosphonic acids	Fosfomycin	1/1 (100%)	NA	NA	-
Polymyxins	Colistin	0/7 (0%)	0/3 (0%)	1/7 (14%)	>0.99
	Polymyxin B	NA	NA	NA	-
MDR		13/286 (5%)	10/103 (10%)	45/179 (25%)	<0.001

1. NOTE: Data are number of isolates demonstrating non-susceptible to the antimicrobial over the total number of isolates tested (%). CAB = Community-acquired bacteraemia, HCAB = Healthcare-associated bacteraemia, HAB = Hospital-acquired bacteraemia, and NA = Not available. The first isolate of each patient was used. MDR: non-susceptible to ≥1 agent in ≥3 antimicrobial categories.

Acinetobacter species

Of CAB, HCAB and HAB caused by Acinetobacter spp., 28%, 50%, and 75% were caused by MDR Acinetobacter spp., respectively (p<0.001). Of Acinetobacter spp. causing HAB, 75% (377/500), 63% (310/495), 67% (322/481) and 64% (315/490) were non-susceptible to ceftazidime, amikacin, ciprofloxacin and carbapenems, respectively (Table 7). There was borderline evidence that the proportion of hospital-acquired Acinetobacter spp. bacteraemia being caused by Acinetobacter spp. non-susceptible to carbapenem rose from 49% (19/39) in 2004 to 65% (70/108) in 2010 (p=0.10) (Figure 6). Non-susceptibility to colistin was observed in 3% of tested isolates (2/63).

Figure 6

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Table 7

Antibiotic category	Antibiotic agents	CAB (n = 449 patients)	HCAB (n = 115 patients)	HAB (n = 501 patients)	p values
Aminoglycosides	Gentamicin	112/390 (29%)	45/105 (43%)	310/455 (68%)	<0.001
	Tobramycin	NA	NA	NA	-
	Amikacin	123/442 (28%)	45/112 (40%)	310/495 (63%)	<0.001
	Netilmicin	44/203 (22%)	24/64 (38%)	224/381 (59%)	<0.001
Antipseudomonal carbapenems	Imipenem	87/397 (22%)	37/102 (36%)	293/459 (64%)	<0.001
	Meropenem	65/284 (23%)	32/81 (40%)	229/348 (66%)	<0.001
	Doripenem	16/45 (36%)	9/10 (90%)	6/7 (86%)	0.001
Antipseudomonal fluoroquinolones	Ciprofloxacin	84/413 (20%)	53/106 (50%)	322/481 (67%)	<0.001
	Levofloxacin	2/5 (40%)	2/2 (100%)	8/9 (89%)	0.11
Antipseudomonal penicillins + β lactamase inhibitors	Ticarcillin- clauvanic acid	NA	NA	NA	-
	Piperacillin-tazobactam	22/98 (22%)	13/28 (46%)	74/106 (70%)	<0.001
Extended-spectrum cephalosporins	Cefotaxime	242/291 (83%)	89/94 (95%)	407/420 (97%)	<0.001
	Ceftazidime	133/448 (30%)	61/114 (54%)	377/500 (75%)	<0.001
	Cefepime	18/53 (34%)	10/22 (45%)	95/133 (71%)	<0.001
Folate pathway inhibitor	Trimethopri-sulphamethoxazole	119/356 (33%)	55/99 (56%)	333/435 (77%)	<0.001
Penicillins + β lactamase inhibitors	Ampicillin-sulbactam	43/134 (32%)	16/29 (55%)	79/115 (69%)	<0.001
Polymyxins	Colistin *	2/16 (13%)	0/14 (0%)	0/33 (0%)	0.11
	Polymyxin B	NA	NA	NA	-
Tetracyclines	Tetracycline	NA	NA	NA	-
	Doxycycline	NA	NA	NA	-
	Minocycline	NA	NA	NA	-
MDR		125/449 (28%)	58/115 (50%)	374/501 (75%)	<0.001

1. NOTE: Data are number of isolates demonstrating non-susceptible to the antimicrobial over the total number of isolates tested (%). CAB = Community-acquired bacteraemia, HCAB = Healthcare-associated bacteraemia, HAB = Hospital-acquired bacteraemia, and NA = Not available. The first isolate of each patient was used. MDR: non-susceptible to ≥1 agent in ≥3 antimicrobial categories.

2. * Defined by using an inhibition zone of <11 mm.

Mortality attributable to MDR

The 30-day mortality in patients with CAB, HCAB and HAB caused by MDR bacteria was 35% (549/1555), 49% (247/500), and 53% (640/1198), compared with 32% (1595/4924), 37% (264/716), and 42% (383/903) in CAB, HCAB, and HAB caused by non-MDR bacteria, respectively (Figure 7). In the final multivariable logistic regression model, gender, age group, year of admission and time to bacteraemia (for HAB) were included (Supplementary file 2).

Figure 7

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Figure 7—source data 1

Mortality in patients with MDR and non-MDR bacteraemia in Northeast Thailand.

https://doi.org/10.7554/eLife.18082.017

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If excess mortality in patients infected with MDR bacteria after adjusting for confounding factors in the final multivariable model is assumed to be caused by MDR, the mortality attributable to MDR was 7% (95%CI 4% to 10%, p<0.001) in CAB, 15% (95%CI 5% to 24%, p<0.001) in HCAB and 15% (95%CI 2% to 27%, p<0.001) in HAB (Figure 7). Heterogeneity between different organisms was clearly observed in HAB (p<0.001), and borderline evidence of heterogeneity was observed in HCAB (p=0.09). The heterogeneity observed in HCAB and HAB was largely caused by MDR Acinetobacter spp. (Figure 7B and C). Mortality attributed to MDR was highest for hospital-acquired MDR Acinetobacter bacteraemia (41%).

Using our estimated mortality attributed to MDR bacteraemia (Figure 7C) and national statistics of HAI caused by MDR bacteria, we further estimated that 19,122 of 45,209 (43%) deaths in patients with HAI due to MDR bacteria in Thailand in 2010 represented excess mortality caused by MDR (Table 8). All parameters used to estimate the number of excess deaths in Thailand are shown in Supplementary file 2.

Table 8

Pathogens	No of patients*	Estimated mortality (%)†	Estimated mortality if the infections were caused by non-MDR organisms (%)†, ‡	Estimated excess mortality caused by MDR (%)†, ‡
MDR Staphylococcus aureus	18,725	8262 (44%)	5463 (29%)	2799 (15%)
MDR Escherichia coli	11,116	2163 (19%)	1566 (14%)	597 (5%)
MDR Klebsiella pneumoniae	15,239	5267 (35%)	4979 (33%)	288 (2%)
MDR Pseudomonas aeruginosa	6118	3966 (65%)	3696 (60%)	270 (4%)
MDR Acinetobacter spp	36,553	25,551 (70%)	10,383 (28%)	15,168 (41%)
Total	87,751	45,209 (52%)	26,087 (30%)	19,122 (22%)

1. *Cumulative incidence of antimicrobial resistant HAI in Thailand 2010 estimated by Pumart et al. (2012).

2. ^†All parameters used to estimate the mortality and excess mortality are shown in Supplementary file 2.

3. ^‡Excess mortality caused by MDR (mortality attributable to MDR) was defined as the difference in mortality of patients with MDR infection and their mortality if they were infected with non-MDR infections.

This study presents detailed antimicrobial susceptibility data on common pathogenic bacteria, the association of MDR with infection acquisition (community-acquired, healthcare-associated and hospital-acquired), and excess mortality from MDR in a developing country. Our estimate of excess deaths caused by MDR in HAI patients in Thailand (19,122 deaths per year in a country of about 66 million population in 2010) is large compared to those estimated in USA (23,000 death per year in a country of 316 million population in 2013) (Center for Disease Controls and Prevention and U.S. Department of Health and Human Services, 2013) and the European Union (25,000 deaths per year in EU of about 500 million population in 2007) (European Centre for Disease Prevention and Control and European Medicines Agency, 2009). Our study highlights the need for public health officials and international health organizations to improve systems to track and reduce the burden of AMR in LMICs. Our estimated mortality for those with MDR HAI (45,209, Table 2) is higher than those previously published by Pumart et al. (38,481) (Pumart et al., 2012), probably because we used 30-day mortality rather than in-hospital mortality.

Acinetobacter spp. is increasingly recognized as an important cause of HAI, (Munoz-Price and Weinstein, 2008; Peleg and Hooper, 2010) and our study confirms the importance of this species as a leading cause of hospital-acquired MDR infection in a developing tropical country (Hongsuwan et al., 2014; Nhu et al., 2014). The high mortality observed in MDR Acinetobacter spp. bacteraemia is because treatment options are limited and those available are associated with toxicity (Fishbain and Peleg, 2010). The high proportions of S. aureus, E. coli and K. pneumoniae bactaeremia being caused by MRSA and E. coli and K. pneumoniae non-susceptible to extended-spectrum cephalosporins, respectively, are consistent with previous reports from other tropical countries (Moreno et al., 2006; Cuellar et al., 2008; Rosenthal et al., 2003). The rising proportions of community-acquired E. coli and K. pneumoniae bacteraemia being caused by E. coli and K. pneumoniae non-susceptible to extended-spectrum cephalosporins, and the rising proportion of hospital-acquired Acinetobacter bacteraemia being causing Acinetobacter non-susceptible to carbapenem suggest that the burden of AMR in Thailand is deteriorating over time.

A limitation of this study is that more complete clinical data were not available. Mortality attributable to MDR could be overestimated if MDR infection was associated with more severely ill patients in ICUs. However, our estimated attributable mortality is comparable to the previous reports. For example, our estimated mortality attributable to MDR Acinetobacter bacteraemia (40.6%) is comparable to the mortality attributable to imipenem resistant Acinetobacter bacteraemia reported by Kwon et al. in Korea (41.1%), which was adjusted by severity of illness (Kwon et al., 2007; Falagas and Rafailidis, 2007). In addition, data on hospitalization in other hospitals not participating in the study (for example, a smaller community hospital or a private hospital in the province) were not available, which could have resulted in a misclassification of CAB, HCAB and HAB in some cases. We also note that data on attributable mortality from different countries is difficult to compare because of the differing study designs. For example, our mortality outcome is the overall 30-day mortality, including both directly and indirectly contributed to MDR, while an EU study only considered directly attributable deaths (European Centre for Disease Prevention and Control and European Medicines Agency, 2009). The p values for trends were generated by the stratification method; therefore, the analysis was not biased towards the increasing availability of the hospital data over the study period. Nonetheless, the trends could be affected by an increasing use of blood culture, changes in antimicrobial agents tested for susceptibility, and greater standardization of laboratory methodologies over time (Opartkiattikul and Bejrachandra, 2002). It is likely that the burdens of MDR similar to that observed in our study are present in many secondary and tertiary hospitals in tropical LMICs, particularly where extended-spectrum cephalosporins and carbapenem are widely used. Nonetheless, resources for diagnostics, methodologies used in the laboratories, and study designs need to be carefully considered when performing a comparison between different settings.

Despite the increasing global focus on AMR in LMICs, considerable gaps remain in our understanding of the scale of the problem. We have demonstrated that the integration of information from readily available routinely collected databases can provide valuable information on the trends and mortality attributable to AMR in Thailand. The methodology used in our study could be applied to explore the burden of AMR in other LMICs where microbiological facilities and hospital admission database are available.

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Author details

1. Cherry Lim

   Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

   Contribution

   CL, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Emi Takahashi

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-2555-6980

2. Emi Takahashi

   Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

   Contribution

   ET, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Cherry Lim

   Competing interests

   The authors declare that no competing interests exist.

3. Maliwan Hongsuwan

   Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

   Contribution

   MH, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

4. Vanaporn Wuthiekanun

   Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

   Contribution

   VW, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Visanu Thamlikitkul

   Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

   Contribution

   VT, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Soawapak Hinjoy

   Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand

   Contribution

   SH, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Nicholas PJ Day

   1. Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
   2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

   Contribution

   NPJD, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-2309-1171

8. Sharon J Peacock

   1. Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
   2. London School of Hygiene and Tropical Medicine, London, United Kingdom
   3. University of Cambridge, Addenbrooke’s Hospital, Cambridge, United Kingdom

   Contribution

   SJP, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-1718-2782

9. Direk Limmathurotsakul

   1. Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
   2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
   3. Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

   Contribution

   DL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   direk@tropmedres.ac

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-7240-5320

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