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. 2025 Mar 25;5(3):e0003995. doi: 10.1371/journal.pgph.0003995

Epidemiology of Burkholderia pseudomallei, Streptococcus suis, Salmonella spp., Shigella spp. and Vibrio spp. infections in 111 hospitals in Thailand, 2022

Charuttaporn Jitpeera 1, Somkid Kripattanapong 1, Preeyarach Klaytong 2, Chalida Rangsiwutisak 2, Prapass Wannapinij 2, Pawinee Doungngern 1, Papassorn Pinyopornpanish 3, Panida Chamawan 4, Voranadda Srisuphan 4, Krittiya Tuamsuwan 4, Phairam Boonyarit 4, Orapan Sripichai 5, Soawapak Hinjoy 6, John Stelling 7, Paul Turner 2,8,9, Wichan Bhunyakitikorn 1, Sopon Iamsirithaworn 10, Direk Limmathurotsakul 2,9,11,*
Editor: Megan Elizabeth Carey12
PMCID: PMC11936208  PMID: 40132043

Abstract

The information on notifiable diseases in low- and middle-income countries is often incomplete, limiting our understanding of their epidemiology. Our study addresses this knowledge gap by analyzing microbiology laboratory and hospital admission data from 111 of 127 public referral hospitals in Thailand, excluding Bangkok, from January to December 2022. We evaluated factors associated with the incidence of notifiable bacterial diseases (NBDs) caused by 11 pathogens; including Brucella spp., Burkholderia pseudomallei, Corynebacterium diphtheriae, Neisseria gonorrhoeae, Neisseria meningitidis, non-typhoidal Salmonella spp. (NTS), Salmonella enterica serovar Paratyphi, Salmonella enterica serovar Typhi, Shigella spp., Streptococcus suis, and Vibrio spp.. We used multivariable Poisson random-effects regression models. Additionally, we compared their yearly incidence rates in 2022 with those from 2012-2015 in hospitals where paired data were available. In 2022, the NBD associated with the highest total number of deaths was B. pseudomallei (4,407 patients; 1,219 deaths) infection, followed by NTS (4,501 patients; 461 deaths), S. suis (867 patients, 134 deaths) and Vibrio spp. (809 patients, 122 deaths) infection. The incidence rates of B. pseudomallei, S. suis and Vibrio spp. infections were highest in the northeast, upper central and west, respectively. The incidence rate of NTS infection was generally high across all geographical regions. The yearly incidence rates of B. pseudomallei and S. suis infections in 2022 were higher than those between 2012-2015, while those of fecal-oral transmitted NBDs including NTS infection, typhoid, shigellosis and vibriosis were lower. Overall, B. pseudomallei and S. suis infections are emerging and associated with a very high number of deaths in Thailand. Although the incidence of NTS infection and vibriosis are decreasing, they are still associated with a high number of cases and deaths. Specific public health interventions are warranted.

Introduction

Timely, reliable and complete information regarding notifiable diseases is essential for disease control and prevention, and enhancing our understanding of their epidemiology [1–3]. To achieve timeliness and completeness in data reporting, many high-income and upper-middle-income countries have strengthened their national surveillance systems by modernizing tools, technology and strategies [4–6]. These include automatic electronic laboratory-based reporting of notifiable diseases [5–7]. However, most low and middle-income countries (LMICs) have a shortage of resources, still use conventional or semi-automated data reporting systems, and do not automatically integrate laboratory data into their surveillance systems [4,8]. Therefore, the data available in the national surveillance systems of LMICs are still largely incomplete [9–11] and our understanding of their epidemiology remains limited.

In Thailand, the national surveillance systems monitors 13 dangerous communicable diseases and 57 notifiable diseases, overseen by the Department of Disease Control (DDC), Ministry of Public Health (MoPH) [12]. Of 13 dangerous communicable diseases, 11 were viral infections (e.g., Ebola infection) and two were bacterial infections (Yersinia pestis infection and extensively drug-resistant Mycobacterium tuberculosis). Immediate reporting of any suspected cases of these 13 diseases is required. For the 57 notifiable diseases, the reporting systems can be semi-automatic, utilizing the electronic data of final diagnosis based on the International Classification of Diseases, 10th revision (ICD-10) recorded in the hospital information systems (HIS). However, the ICD-10 is reliable only in few conditions [13,14] and the surveillance systems do not utilize microbiology laboratory data. The ICD-10 is unreliable for notifiable bacterial diseases (NBDs) because many disease diagnoses recorded by physicians are incomplete, ICD-10 coders enter codes based solely on the diagnoses recorded by physicians, and coders tend to code only diagnoses that result in reimbursement [13,14]. Recent studies showed that many public hospitals, which have microbiology laboratories, do not report most cases and deaths following culture-confirmed NBDs to the national surveillance systems [11,15]. The incomplete data hinders efforts to improve awareness, control, prevention, and our understanding of NBDs in the country [16].

To overcome limitations in LMICs, we developed the AMASS (AutoMated tool for Antimicrobial resistance Surveillance System), an offline application that enables hospitals to automatically analyse and generate standardized antimicrobial resistance (AMR) surveillance reports from routine microbiology and hospital data [17]. The AMASS version 1.0 was released on 1st February 2019 and tested in seven hospitals in seven countries [17]. We conceptualized that the AMASS could additionally analyse and generate summary reports for multiple NBDs [18]. The AMASS version 2.0 (AMASSv2.0) was released on 16th May 2022 and tested using data of 49 public hospitals in Thailand from 2012 to 2015 [15]. We demonstrate that national statistics on NBDs in LMICs could be improved by integrating information from readily available databases [15]. In 2023, collaborating with Health Administration Division, MoPH Thailand, we implemented AMASSv2.0 in 127 public hospitals in Thailand using the data from 2022 [19]. We recently reported the epidemiology of AMR bloodstream infections in 111 hospitals [20].

Here we aimed to evaluate the epidemiology of multiple NBDs in 111 hospitals in Thailand using the data from 2022. We also examined the trends of each NBD by comparing the data from 2022 with the data from 2012 to 2015.

Methods

Study setting

In 2022, Thailand had a population of 66.1 million, consisted of 77 provinces, and covered 513,120 km2. The health systems in each province were integrated into 12 groups of provinces, known as health regions, plus the capital Bangkok as health region 13 (Fig 1), using the concept of decentralization [21]. The Health Administration Division, Ministry of Public Health (MoPH) Thailand, supervised 127 public referral hospitals in health regions 1 to 12. These included 35 advanced-level referral hospital (i.e., level A, with a bed size of about 500-1,200 beds), 55 standard-level referral hospital (i.e., level S, with a bed size of about 300 to 500) and 37 mid-level referral hospital (i.e., level M1, with a bed size of about 180-300) [22]. All level A and S hospitals, and most of level M1 hospitals were equipped with a microbiology laboratory capable of performing bacterial culture using standard methodologies for bacterial identification and susceptibility testing provided by the Department of Medical Sciences, MoPH, Thailand [23]. Public referral hospitals in Bangkok (health region 13) were managed under Department of Medical Services, MoPH (i.e., not under the Health Administration Division, MoPH). Therefore, public referral hospitals in Bangkok were not included, and their summary data were neither generated nor accessed in the study.

Fig 1. Health regions in Thailand.

Fig 1

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National Health Security Office (NHSO) Region 1 is in Chiang Mai, 2 in Phitsanulok, 3 in Nakhonsawan, 4 in Saraburi, 5 in Ratchaburi, 6 in Rayong, 7 in Khon Kaen, 8 in Udon Thani, 9 in Nakonratchasima, 10 in Ubonratchathani, 11 in Suratthani, 12 in Songkhla and 13 in Bangkok. Map created by the authors using STATA version 14.2 (StataCorp, College Station, TX) and a base layer map from GADM (https://gadm.org/download_country.html) (term of use: https://gadm.org/license.html).

From 16 December 2022 to 30 June 2023, on behalf of the Health Administration Division, MoPH, we invited and trained 127 public referral hospitals in health regions 1 to 12 to utilize the AMASS with their own microbiology and hospital admission data files via four online meetings, five face-to-face meetings and on-line support [20]. Subsequently, the hospitals that completed utilization of the AMASS submitted summary data generated by the tool to the MoPH [20]. Data were accessed for research purposes from 15 December 2023 to 31 January 2024.

Study design

We conducted a retrospective study evaluating epidemiology of selected NBDs diagnosed by culture using microbiology laboratory and hospital admission data from 2022. We also compared the yearly incidence rates of each NBD in 2022 with those from 2012-2015, using paired data from 49 public referral hospitals that were previously published [15].

The NBDs under evaluation included infections caused by 11 pathogens; Brucella spp., Burkholderia pseudomallei, Corynebacterium diphtheriae, Neisseria gonorrhoeae, Neisseria meningitidis, Non-typhoidal Salmonella spp., Salmonella enterica serovar Paratyphi, Salmonella enterica serovar Typhi, Shigella spp., Streptococcus suis, and Vibrio spp. infections. NBD cases were defined as having any clinical specimen (including blood, respiratory tract specimens, urine, stool, cerebrospinal fluid, genital swabs and other specimens) culture positive for a pathogen. The AMASS merged microbiology laboratory and hospital admission data using the hospital number (i.e., the patient identifier) present in both data files [15,20]. Then, the AMASS deduplicated the data and reported total number of inpatients with a clinical specimen culture positive for a pathogen during the evaluation period. Mortality was defined using the discharge summary (in the hospital admission data) which was routinely completed by the attending physician and reported to the MoPH. For each NBD, in case that a patient was admitted with that NBD more than once during the evaluation period, the mortality outcome of the first admission was presented. This approach was taken to avoid overestimating mortality due to other causes (e.g., healthcare-associated infections and road accidents) that might be associated with the subsequent admissions. Some patients with certain NBDs (e.g., B. pseudomallei infection) may need several months of treatment to achieve culture negativity.

Statistical analysis

Data were summarized with medians and interquartile ranges (IQR) for continuous measures, and proportions for discrete measures. IQRs are presented in terms of 25th and 75th percentiles. Continuous variables and proportions were compared between groups using Kruskal Wallis tests and Chi-square tests, respectively.

For NBDs with more than 100 cases in the year 2022, we evaluated factors associated with the incidence rate of NBDs per 100,000 population using multivariable Poisson random-effects regression models [24]. Our summary data from each hospital could be considered nested within its each province. The Poisson random-effects models are appropriate for count outcomes and allow the analysis to partition the total variation in the outcome into between-cluster variation and between-individual variation [24]. The magnitude of the effect of clustering provides a measure of the general contextual effect. The total number of NBD cases in each province was calculated by summing the number of NBD cases from all hospitals located in the same province. We assumed that the distribution of province-specific random effects was normal. Factors evaluated included health region, Gross Provincial Product (GPP), pig density and poultry density. Data of GPP in 2021 [25] were used as a proxy for the size of the economy in each province. Pig density and poultry density (per square meters) were estimated by using the total number of pigs and poultry in each province in Thailand in 2022, divided by the total area of each province [26].

Additionally, we compared the yearly incidence rates of each NBD in 2022 with the those between 2012-2015 in hospitals where paired data were available. The previous study [15] and the period between 2012-2015 were chosen as the comparator because it was the only study that collected both microbiology and hospital admission data and used the same analytical approach for the 11 NBDs. Specifically, AMASS was also previously utilized to merge both data files, deduplicate the data, and report total number of inpatients with a clinical specimen culture positive for a pathogen during the evaluation period [15]. Multivariable Poisson random-effects regression models were utilized to evaluate the change of yearly incidence rate per 100,000 population between the time periods.

We also compared the total number of cases and deaths of each NBD diagnosed by culture in 2022 in our study with those of relevant notifiable diseases reported to the national surveillance systems of Thailand in 2022 [27]. We used STATA (version 14.2; College Station, Texas) for all analyses (S1 Text).

Ethics

Ethical permission for this study was obtained from the Institute for the Committee of the Faculty of Tropical Medicine, Mahidol University (TMEC 23-085). Individual patient consent was not sought as this was a retrospective study, and the Ethical and Scientific Review Committees approved the process.

Results

Baseline characteristics

Of 127 public referral hospitals, 116 (91%) used the AMASS to analyze their microbiology and hospital admission data files, and submitted the summary AMR and NBD data from 2022 to the MoPH. Four hospitals had incomplete microbiology data, and one hospital had incomplete hospital admission data. These hospitals were removed from the analysis. Therefore, a total of 111 hospitals were included in the final analysis.

Of all public referral hospitals in Thailand, 100% of Level A hospitals (35/35), 89% of Level S hospitals (49/55) and 73% of Level M1 hospitals (27/37) were included in this study. Data were available from 74 of 77 provinces (96%) in Thailand, all provinces except Mae Hong Son, Nakorn Nayok and Bangkok.

A total of 46 hospitals in 42 provinces had paired data from 2022 and 2012-2015 (Fig S1). The 46 hospitals were located in all 12 health regions. There was no difference in proportions of health regions among hospitals with and without paired data (p=0.31). Nineteen hospitals were Level A (41%), 20 Level S (44%) and 7 Level M1 (15%). There was borderline evidence showing that the proportion of Level M1 among hospitals with paired data was lower than those without paired-data (15% [7/46] vs. 31% [20/65], p=0.079).

Brucella spp.

In 2022, there were 11 cases with culture-confirmed Brucella spp. infection (Fig 2A) and 1 of them died (in-hospital mortality 9%). Among provinces where paired data were available, the yearly incidence rate in 2022 did not differ from that between 2012-2015 (p=0.15).

Fig 2. Incidence rate of cases with notifiable bacterial diseases diagnosed by culture per 100,000 population in 2022 in Thailand.

Fig 2

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A. Brucella spp. infection, B. Burkholderia pseudomallei infection, C. Corynebacterium diphtheriae infection, D. Neisseria gonorrhoeae infection, E. Neisseria meningitidis infection, F. Non-typhoidal Salmonella spp. infection, G. Salmonella enterica serovar Paratyphi infection, H. Salmonella enterica serovar Typhi infection, I. Shigella spp. infection, J. Streptococcus suis infection, and K. Vibrio spp. infection. Map created by the authors using STATA version 14.2 (StataCorp, College Station, TX) and a base layer map from GADM (https://gadm.org/download_country.html) (term of use: https://gadm.org/license.html).

B. pseudomallei

In 2022, there were 4,407 cases with culture-confirmed B. pseudomallei infection and 1,219 of them died (in-hospital mortality 27.7%). The incidence rate (Fig 2B) was highest in the northeast (health regions 7, 8, 9 and 10), followed by the upper central (health region 3), north (health region 1 and 2), east (health region 6) and south (health region 11 and 12). The incidence rate was lowest in the lower central (health region 4) and west (health region 5). In the multivariable models, health region and poultry density (adjust incidence rate ratio [aIRR] 1.31, 95%CI 1.02-1.69, p=0.04) were independently associated with the incidence of B. pseudomallei infection per 100,000 population (Table S1). GPP and pig density were not independently associated with the incidence rate (both p>0.20)

Among provinces where paired data were available, the yearly incidence rate of B. pseudomallei infection in 2022 was higher than that between 2012-2015 by 58% (aIRR 1.58, 95%CI 1.49-1.68, p<0.001).

C. diphtheriae

In 2022, there were 10 cases with culture-confirmed C. diphtheriae infection (Fig 2C) and 1 of them died (in-hospital mortality 10%). Among provinces where paired data were available, the yearly incidence rate in 2022 did not differ from that between 2012-2015 (p=0.29).

N. gonorrhoeae

In 2022, there were 25 cases with culture-confirmed N. gonorrhoeae infection (Fig 2D) and no deaths following the infections were observed. Among provinces where paired data were available, the yearly incidence rate in 2022 did not differ from that between 2012-2015 (p=0.33).

N. meningitidis

In 2022, there were 9 cases with culture-confirmed N. meningitidis infection (Fig 2E) and 2 died (in-hospital mortality 22%). Among provinces where paired data were available, the yearly incidence rate in 2022 did not differ from that between 2012-2015 (p=0.55).

Non-typhoidal Salmonella spp. (NTS)

In 2022, there were 4,501 cases with culture-confirmed NTS infection (Fig 2F) and 461 died (in-hospital mortality 10.2%). In the multivariable models, health region, GPP, pig density and poultry density were not associated with the incidence rate (all p>0.20, Table S2).

Among provinces where paired data were available, the yearly incidence rate of NTS cases in 2022 was lower than that between 2012-2015 by 37% (aIRR 0.63, 95%CI 0.60-0.67, p<0.001).

Salmonella enterica serovar Paratyphi

In 2022, there were 30 cases with culture-confirmed Salmonella enterica serovar Paratyphi infection (Fig 2G) and 4 died (in-hospital mortality 13%). Among provinces where paired data were available, the yearly incidence rate in 2022 did not differ from that between 2012-2015 (p=0.39).

Salmonella enterica serovar Typhi

In 2022, there were 32 cases with culture-confirmed Salmonella enterica serovar Typhi infection (Fig 2H) and 6 died (in-hospital mortality 19%).

Among provinces where paired data were available, the yearly incidence rate of Salmonella enterica serovar Typhi infection in 2022 was lower than that between 2012-2015 by 83% (aIRR 0.17, 95%CI 0.07-0.41, p<0.001).

Shigella spp.

In 2022, there were 68 cases with culture-confirmed Shigella spp. infection (Fig 2I) and 4 died (in-hospital mortality 6%).

Among provinces where paired data were available, the yearly incidence rate of Shigella spp. infection in 2022 was lower than that between 2012-2015 by 78% (aIRR 0.22, 95%CI 0.14-0.36, p<0.001).

S. suis

In 2022, there were 867 cases with culture-confirmed S. suis infection and 134 of them died (in-hospital mortality 15.5%). The incidence rate (Fig 2J) was highest in the upper central (health regions 3), followed by the north (health regions 1 and 2) and northeast (health regions 7, 8, 9 and 10). The incidence rate was lowest in the south (health regions 11 and 12) and lower central (health region 4). In the multivariable models, health region was associated with the incidence of S. suis infection per 100,000 population (Table S3). GPP, pig density and poultry density were not associated with the incidence rate (all p>0.10).

Among provinces where paired data were available, the yearly incidence rate of S. suis infection in 2022 was higher than that between 2012-2015 by 172% (aIRR 2.72, 95%CI 2.29-3.24, p<0.001).

Vibrio spp.

In 2022, there were 809 cases with culture-confirmed Vibrio spp. infection and 122 of them died (in-hospital mortality 15.1%). The incidence rate (Fig 2K) was highest in the west (health region 5), followed by the east (health region 6). The incidence rate was lowest in the central (health region 3 and 4) and upper north (health region 1). In the multivariable models, health region was associated with the incidence of Vibrio spp. infection per 100,000 population (Table S4). GPP, pig density and poultry density were not independently associated with the incidence rate (all p>0.10).

Among provinces where paired data were available, the yearly incidence rate of Vibrio spp. infection in 2022 was lower than that between 2012-2015 by 25% (aIRR 0.75, 95%CI 0.66-0.85, p<0.001).

Comparison with the national surveillance systems

In 2022, the total number of cases with B. pseudomallei infection diagnosed by culture (4,407 cases) was higher than that reported to the national surveillance systems (3,357 cases, a 1.3-fold difference, Table 1). The total number of deaths following B. pseudomallei infection diagnosed by culture (1,129 deaths) was higher than that reported to the national surveillance systems (157 deaths, a 7.2-fold difference). The total number of cases with S. suis infection diagnosed by culture (867 cases) was also higher than those reported to the national surveillance systems (383 cases, a 2.3-fold difference). The total number of deaths following S. suis infection diagnosed by culture (134 deaths) was also higher than that reported to the national surveillance systems (10 deaths, a 13.4-fold difference). The total number of deaths following fecal-oral transmitted NBDs diagnosed by culture (including 461 for non-typhoidal salmonella, 122 for vibriosis, 4 for paratyphoid, 6 for typhoid and 4 for shigellosis) were also higher than those reported to the national surveillance systems (0 deaths for all relevant fecal-oral transmitted NBDs).

Table 1. Total number of cases and deaths following selected notifiable bacterial diseases (NBDs) diagnosed by culture in 111 hospitals compared with total number of cases and deaths with relevant notifiable diseases reported to the national surveillance systems (NSS) in Thailand in 2022.

Infections* This study NSS
Cases Deaths Cases Deaths
Brucella spp. infection 11 1 15 0
Burkholderia pseudomallei infection 4,407 1,219 3,573 157
Corynebacterium diphtheriae infection 10 1 0 0
Neisseria gonorrhoeae infection 25 0 6,915 0
Neisseria meningitidis infection 9 2 19 3
Non-typhoidal Salmonella spp. infection 4,501 461 72,439 0
Salmonella enterica serovar Paratyphi infection 30 4 91 0
Salmonella enterica serovar Typhi infection 32 6 754 0
Shigella spp. infection 68 4 377 0
Streptococcus suis infection 867 134 383 10
Vibrio spp. infection 809 122 4 0
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*

In this study, cases were defined as an inpatient with a clinical specimen culture positive for a pathogen during the evaluation period, while the NSS included reports of suspected, probable and confirmed cases using a wide range of case definitions of each notifiable disease (S2 Text).

Discussion

Our findings provide evidence that the incidences of melioidosis and S. suis infections in Thailand in 2022 are increasing compared to the data from 2012-2015 and associated with a high number of deaths. We also show that fecal-oral transmitted NBDs including non-typhoidal salmonellosis, typhoid, shigellosis and vibriosis are still present and associated with deaths, but their incidence rates are decreasing compared to the data from 2012-2015. This study highlights the potential advantage of utilization of routine microbiology and hospital admission data from hospitals. The local and timely data of NBDs can supplement and monitor the performance of the national surveillance systems. The accurate data can consequently identify diseases and areas with high burden, improve public health interventions, and prioritize resource allocation.

Underreporting of cases and deaths following B. pseudomallei, S. suis and other infections to the national surveillance systems is consistent with the previous finding [11,16]. It is likely that many healthcare workers responsible for reporting to the national surveillance systems are still unaware that all cases and deaths following culture-positive NBDs must be reported [16] and that ICD-10 of those culture-confirmed cases in the hospitals’ health information systems were largely inaccurate [13,14]. Additionally, their perceptions of their workload and the belief that reporting cases is not used for outbreak response may contribute to underreporting [28]. Further studies on underreporting and on automating reporting systems utilizing laboratory data from healthcare facilities are required.

The finding that there were more than 1,200 deaths following melioidosis in 2022 is alarming. This finding is consistent with a previous modelling study predicting that the total number of deaths following melioidosis could range from 1,259 to 6,678 in Thailand if all patients were tested with bacterial culture and data were reported nationwide [29]. Melioidosis is usually acquired by skin inoculation, ingestion or inhalation of B. pseudomallei in soil and water. Preventive measures include wearing appropriate protective clothing and footwear when contacting with soil or water, consuming bottled or boiled water, and ensuring that the domestic water supply is not contaminated with B. pseudomallei [30–32]. The increased incidence rate of melioidosis could be associated with the increasing incidence of diabetes (the major risk factor of melioidosis) and improvements in diagnostic stewardship (i.e., utilization of culture) and bacterial identification in public hospitals in Thailand over time [16,33,34]. The observed increase was not due to the differences in the geographical regions evaluated in 2022 compared to 2012-2015, as we evaluated only provinces with paired data. The difference in the incidence by regions in Thailand is consistent with previous studies [29]. The association between poultry density and melioidosis has never been observed, and could be due to a collinearity with residual confounding factors (e.g., agricultural occupation). Further studies and actions to reduce the burden of melioidosis in Thailand are urgently needed [35].

Similarly, the increase in S. suis infection is alarming. Risk factors for S. suis infection include consuming undercooked pork contaminated with S. suis and exposure to pigs or pork. The increased incidence could be associated with an increase in consumption of undercooked pork products [36–38] and an increase in infected meat in the market [39]. The latter is a concern following the news of the large illegal pork imported to Thailand since 2021 after the shortage of domestic pork due to the outbreak of African swine fever in Thailand [40]. The increased incidence rate of S. suis infection could partially be associated with improvements in diagnostic stewardship and bacterial identification in public hospitals in Thailand over time. Nonetheless, these factors alone cannot explain the nearly three-fold increase in S. suis infection in the country. The DDC will utilize the data to additionally implement and enforce behavioral interventions, education and food biosafety in the country [38]. These interventions include educating at-risk populations, such as pig farmers, and increasing public disease awareness and prevention efforts through public health officers and village health volunteers. Additionally, food biosafety measures will be enforced in collaboration with relevant stakeholders, emphasizing proper handling and processing of pork at abattoir, market and consumer levels to prevent contamination and transmission.

There are currently no licensed vaccines for melioidosis, S. suis infection and shigellosis. Vaccination for salmonella and vibriosis are not widely available and are seldom used in Thailand.

The striking decrease in incidence rates of Salmonella enterica serovar Typhi and Shigella spp. infections (83% and 78% decrease, respectively) could be due to improvement in clean water supply, sanitation, regulatory environment, food safety, and related health intervention programs over time [41,42]. Improvement in health-seeking behaviour and access to healthcare under the universal coverage have likely contributed to the decrease [43,44]. Although the incidence rates of NTS and Vibrio spp. infection minimally decrease (37% and 25% decrease), these pathogens still cause substantial public health problems across the regions. The higher incidence rate of NTS infection compared to typhoid is consistent with those observed in other LMICs [45]. Persistence of virulent or drug-resistant strains among susceptible population including children and people with co-morbidities may be associated with the slow reduction of the NTS [45] and Vibrio spp. infections [46]. The MoPH and related stakeholders should maintain and strengthen the public health interventions to decrease incidences of these infections further. Further studies and measures to reduce the burden of NTS and Vibrio spp. infections are also required.

Our study and approach have several strengths. First, our study included most of the public referral hospitals in the country. Second, hospital admission data used in the analysis (including hospital number, admission date, discharge date and in-hospital mortality) were reliable, as these data were recorded electronically and required for retrospective hospital payment nationwide [47,48]. Third, we utilized microbiology laboratory data, which is highly specific to the diagnosis of NBDs. Fourth, the method for collecting data between 2012 and 2015 was similar to those used in 2022 [15], utilizing microbiology and hospital admission data and AMASS. Fifth, although our approach is semi-automatic, this approach is easy to scale up in LMICs because the AMASS programme is open-access, highly-compatible, and user-friendly without the need for data experts with adequate skills in statistical software [17,19].

Our study and approach have several limitations. First, our approach included only inpatients in the public referral hospitals. Therefore, our estimates did not include patients who did not require hospitalization, and those who were hospitalized in private, military or university hospitals. According to data from the Healthcare Accreditation Institute, Thailand [49], the public referral hospitals included in this study represented 56.6% (120/212) of hospitals with at least 180 beds, 66.4% (81/122) of hospitals with at least 300 beds and 70% (42/60) of hospitals with at least 500 beds in Thailand. Second, our approach is not applicable in settings where microbiology and hospital admission data are not computerized. Third, our approach focused on bacterial culture results. Therefore, the findings could be influenced by diagnostic stewardship and, capability and expertise of the microbiology laboratories. Methods used for bacterial culture and identification may differ by region and over time. For example, some hospitals might have implemented new bacterial identification automated systems in 2022, reducing the likelihood of misidentification of S. suis (as Streptococcus spp.) and B. pseudomallei (as contaminants or Pseudomonas spp.). Fourth, our approach utilizes summary data and could not evaluate individual-level clinical data and microbiology laboratory data in details. Fifth, the AMASS did not generate reports for patients who were culture positive for more than one pathogen. Such patients would be counted separately for each pathogen. Sixth, the AMASS did not generate reports for number of admissions among patients who were culture positive for pathogens under evaluation. Seventh, the all-cause mortality could be higher than in-hospital mortality because a preference to die at home is high in some regions in Thailand [11].

In conclusion, the burden of melioidosis and S. suis infections is increasing in Thailand. Their incidence rates are higher in some regions than in others. Although the incidence rates of NTS infection and vibriosis are decreasing, they are still associated with a high number of cases and deaths. Specific public health interventions to reduce the burden of melioidosis, S. suis infection, NTS infection and vibriosis are urgently required.

Supporting information

S1 Table. Factors associated with the incidence of cases with culture-confirmed Burkholderia pseudomallei infection per 100,000 population in 74 provinces in Thailand, 2022.

(Word)

pgph.0003995.s001.docx (40KB, docx)
S2 Table. Factors associated with the incidence of cases with culture-confirmed non-typhoidal Salmonella spp. (NTS) infection per 100,000 population in 74 provinces in Thailand, 2022 .

(Word)

pgph.0003995.s002.docx (39.3KB, docx)
S3 Table. Factors associated with the incidence of cases with culture-confirmed S. suis infection per 100,000 population in 74 provinces in Thailand, 2022.

(Word)

pgph.0003995.s003.docx (39.8KB, docx)
S4 Table. Factors associated with the incidence of cases with culture-confirmed Vibrio spp. infection per 100,000 population in 74 provinces in Thailand, 2022.

(Word)

pgph.0003995.s004.docx (39.4KB, docx)
S5 Table. Total number of cases following selected notifiable bacterial diseases (NBDs) diagnosed by culture in 2022 compared with the yearly incidence between 2012-2015 in 49 hospitals where paired data were available.

(Word)

pgph.0003995.s005.docx (37.6KB, docx)
S1 Text. Stata code for fitting the multivariable Poisson regression models.

(Word)

pgph.0003995.s006.docx (37.1KB, docx)
S2 Text. Example of reporting criteria of relevant notifiable diseases in Thailand.

(Word)

pgph.0003995.s007.docx (37.4KB, docx)
S1 Fig. Map of 42 provinces that had paired data from 2022 and 2012-2015.

Map created by the authors using STATA version 14.2 (StataCorp, College Station, TX) and a base layer map from GADM (https://gadm.org/download_country.html) (term of use: https://gadm.org/license.html)

(Image)

pgph.0003995.s008.tif (4.3MB, tif)

Acknowledgments

We gratefully acknowledge the laboratory team and IT team of all hospitals for their participation and support.

Data Availability

The hospital-level summary data used for the study are open-access and available at https://doi.org/10.6084/m9.figshare.25907494

Funding Statement

This research was funded in whole, or in part, by the Wellcome Trust (Grant 224681/Z/21/Z to DL). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.White A, Tillman A, Hedberg C, Bruce B, Batz M, Seys S. Foodborne illness outbreaks reported to national surveillance, United States, 2009-2018. Emerg Infect Dis. 2022;28(6):1117–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Nash K, Lai J, Sandhu K, Chandan JS, Shantikumar S, Ogunlayi F, et al. Impact of national COVID-19 restrictions on incidence of notifiable communicable diseases in England: an interrupted time series analysis. BMC Public Health. 2022;22(1):2318. doi: 10.1186/s12889-022-14796-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Wei F, Sheng W, Wu X, Yin K, Lan J, Huang Y, et al. Incidence of anogenital warts in Liuzhou, south China: a comparison of data from a prospective study and from the national surveillance system. Emerg Microbes Infect. 2017;6(12):e113. doi: 10.1038/emi.2017.100 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Jayatilleke K. Challenges in implementing surveillance tools of high-income countries (HICs) in low middle income countries (LMICs). Curr Treat Options Infect Dis. 2020;12(3):191–201. doi: 10.1007/s40506-020-00229-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Vlieg WL, Fanoy EB, van Asten L, Liu X, Yang J, Pilot E, et al. Comparing national infectious disease surveillance systems: China and the Netherlands. BMC Public Health. 2017;17(1):415. doi: 10.1186/s12889-017-4319-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Panackal AA, M’ikanatha NM, Tsui FC, McMahon J, Wagner MM, Dixon BW, et al. Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerg Infect Dis. 2002;8(7):685–91. doi: 10.3201/eid0807.010493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Nguyen TQ, Thorpe L, Makki HA, Mostashari F. Benefits and barriers to electronic laboratory results reporting for notifiable diseases: the New York city department of health and mental hygiene experience. Am J Public Health. 2007;97(Suppl 1):S142-5. doi: 10.2105/AJPH.2006.098996 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Sahal N, Reintjes R, Aro AR. Review article: communicable diseases surveillance lessons learned from developed and developing countries: literature review. Scand J Public Health. 2009;37(2):187–200. doi: 10.1177/1403494808101179 [DOI] [PubMed] [Google Scholar]
  • 9.Benson FG, Musekiwa A, Blumberg L, Rispel LC. Comparing laboratory surveillance with the notifiable diseases surveillance system in South Africa. Int J Infect Dis. 2017;59:141–7. doi: 10.1016/j.ijid.2017.03.007 [DOI] [PubMed] [Google Scholar]
  • 10.Chandrasekar K, Mahesan S, Bath PA. Notifiable disease surveillance in Sri Lanka and the United Kingdom: a comparative study. Sri Lanka J Bio-Medical Informatics. 2013;4(1):14. doi: 10.4038/sljbmi.v4i1.5190 [DOI] [Google Scholar]
  • 11.Hantrakun V, Kongyu S, Klaytong P, Rongsumlee S, Day NPJ, Peacock SJ, et al. Clinical epidemiology of 7126 melioidosis patients in Thailand and the implications for a national notifiable diseases surveillance system. Open Forum Infect Dis. 2019;6(12):ofz498. doi: 10.1093/ofid/ofz498 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Department of Disease Control, Ministry of Public Health, Thailand. Communicable Disease Act B.E. 2558 (A.D. 2015). Available from: https://ddc.moph.go.th/law_gcd_eng.php [Google Scholar]
  • 13.Rattanaumpawan P, Wongkamhla T, Thamlikitkul V. Accuracy of ICD-10 coding system for identifying comorbidities and infectious conditions using data from a Thai university hospital administrative database. J Med Assoc Thai. 2016;99(4):368–73. [PubMed] [Google Scholar]
  • 14.Sukanya C. Validity of principal diagnoses in discharge summaries and ICD-10 coding assessments based on national health data of Thailand. Healthc Inform Res. 2017;23(4):293–303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Lim C, Hantrakun V, Klaytong P, Rangsiwutisak C, Tangwangvivat R, Phiancharoen C, et al. Frequency and mortality rate following antimicrobial-resistant bloodstream infections in tertiary-care hospitals compared with secondary-care hospitals. PLoS One. 2024;19(5):e0303132. doi: 10.1371/journal.pone.0303132 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Hinjoy S, Hantrakun V, Kongyu S, Kaewrakmuk J, Wangrangsimakul T, Jitsuronk S, et al. Melioidosis in Thailand: present and future. Trop Med Infect Dis. 2018;3(2):38. doi: 10.3390/tropicalmed3020038 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Lim C, Miliya T, Chansamouth V, Aung MT, Karkey A, Teparrukkul P, et al. Automating the generation of antimicrobial resistance surveillance reports: proof-of-concept study involving seven hospitals in seven countries. J Med Internet Res. 2020;22(10):e19762. doi: 10.2196/19762 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Lim C, Klaytong P, Hantrakun V, Rangsiwutisak C, Phiancharoen C, Tangwangvivat R. Automating the generation of notifiable bacterial disease reports: proof-of-concept study and implementation in six hospitals in Thailand. Am J Trop Med Hyg. 2024;111(1):151–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Srisuphan V, Klaytong P, Rangsiwutisak C, Tuamsuwan K, Boonyarit P, Limmathurotsakul D. Local and timely antimicrobial resistance data for local and national actions: the early implementation of an automated tool for data analysis at local hospital level in Thailand. JAC Antimicrob Resist. 2023;5(4):dlad088. doi: 10.1093/jacamr/dlad088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Tuamsuwan K, Chamawan P, Boonyarit P, Srisuphan V, Klaytong P, Rangsiwutisak C, et al. Frequency of antimicrobial-resistant bloodstream infections in 111 hospitals in Thailand, 2022. J Infect. 2024;89(4):106249. doi: 10.1016/j.jinf.2024.106249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Jongudomsuk P, Srithamrongsawat S, Patcharanarumol W, Limwattananon S, Pannarunothai S, Vapatanavong P. The Kingdom of Thailand health system review. 2015. Available from: https://apo.who.int/docs/librariesprovider15/publications/hits/thailand_health_systems_review_th.pdf [Google Scholar]
  • 22.Ministry of Public Health, Thailand. List of Healthcare Facilities Under Health Administration Division, Ministry of Public Health, Thailand. 2023. Available form: http://dmsic.moph.go.th/index/detail/9188
  • 23.Department of Medical Science, Ministry of Public Health, Thailand. Laboratory manual for emerging diseases for regional and general hospitals. 2021. Available from: https://www3.dmsc.moph.go.th/download/files/dmsc_recurrence.pdf
  • 24.Austin P, Stryhn H, Leckie G, Merlo J. Measures of clustering and heterogeneity in multilevel poisson regression analyses of rates/count data. Stat Med. 2018;37(4):572–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.National Statistical Office, Ministry of Digital Economy and Society, Thailand. Statistical yearbook Thailand 2023. 2023. Available form: https://www.nso.go.th/public/e-book/Statistical-Yearbook/SYB-2023
  • 26.Information and Communication Technology Center, Department of Livestock Development, Ministry of Agriculture and Cooperatives, Thailand. Information of Animal-Raising Farmers at the Provincial Level. 2022. Available from: https://ict.dld.go.th/webnew/index.php/th/service-ict/report/396-report-thailand-livestock/reportservey2565/1711-province-2565
  • 27.Department of Disease Control, Ministry of Public Health, Thailand. National Disease Surveillance. 2023. Available from: http://doe.moph.go.th/surdata/
  • 28.Benson FG, Levin J, Rispel LC. Health care providers’ compliance with the notifiable diseases surveillance system in South Africa. PLoS One. 2018;13(4):e0195194. doi: 10.1371/journal.pone.0195194 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Limmathurotsakul D, Golding N, Dance DAB, Messina JP, Pigott DM, Moyes CL, et al. Predicted global distribution of Burkholderia pseudomallei and burden of melioidosis. Nat Microbiol. 2016;115008. doi: 10.1038/nmicrobiol.2015.8 [DOI] [PubMed] [Google Scholar]
  • 30.Suntornsut P, Teparrukkul P, Wongsuvan G, Chaowagul W, Michie S, Day NPJ, et al. Effectiveness of a multifaceted prevention programme for melioidosis in diabetics (PREMEL): a stepped-wedge cluster-randomised controlled trial. PLoS Negl Trop Dis. 2021;15(6):e0009060. doi: 10.1371/journal.pntd.0009060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Tantirat P, Chantarawichian Y, Taweewigyakarn P, Kripattanapong S, Jitpeera C, Doungngern P, et al. Melioidosis in patients with COVID-19 exposed to contaminated tap Water, Thailand, 2021. Emerg Infect Dis. 2024;30(4):791–4. doi: 10.3201/eid3004.231476 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.McRobb E, Kaestli M, Mayo M, Price EP, Sarovich DS, Godoy D, et al. Melioidosis from contaminated bore water and successful UV sterilization. Am J Trop Med Hyg. 2013;89(2):367–8. doi: 10.4269/ajtmh.13-0101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Greer RC, Wangrangsimakul T, Amornchai P, Wuthiekanun V, Laongnualpanich A, Dance DAB, et al. Misidentification of Burkholderia pseudomallei as Acinetobacter species in northern Thailand. Trans R Soc Trop Med Hyg. 2019;113(1):48–51. doi: 10.1093/trstmh/try108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Mahikul W, White LJ, Poovorawan K, Soonthornworasiri N, Sukontamarn P, Chanthavilay P, et al. Modelling population dynamics and seasonal movement to assess and predict the burden of melioidosis. PLoS Negl Trop Dis. 2019;13(5):e0007380. doi: 10.1371/journal.pntd.0007380 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Department of Disease Control, Ministry of Public Health, Thailand. Guideline of melioidosis. 2021. Available from: http://klb.ddc.moph.go.th/dataentry/handbook/form/129
  • 36.Takamatsu D, Wongsawan K, Osaki M, Nishino H, Ishiji T, Tharavichitkul P, et al. Streptococcus suis in humans, Thailand. Emerg Infect Dis. 2008;14(1):181–3. doi: 10.3201/eid1401.070568 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Takeuchi D, Kerdsin A, Akeda Y, Chiranairadul P, Loetthong P, Tanburawong N, et al. Impact of a food safety campaign on Streptococcus suis infection in humans in Thailand. Am J Trop Med Hyg. 2017;96(6):1370–7. doi: 10.4269/ajtmh.16-0456 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Kerdsin A, Segura M, Fittipaldi N, et al. Sociocultural factors influencing human Streptococcus suis disease in Southeast Asia. Foods. 2022;11(9). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Pappas G. Socio-economic, industrial and cultural parameters of pig-borne infections. Clin Microbiol Infect. 2013;19(7):605–10. doi: 10.1111/1469-0691.12262 [DOI] [PubMed] [Google Scholar]
  • 40.Bangkok Post. Fighting pork smugglers. 2023. Available from: https://www.bangkokpost.com/opinion/opinion/2667934/fighting-pork-smugglers
  • 41.Boonsoong B, Sangpradub N, Barbour MT, Simachaya W. An implementation plan for using biological indicators to improve assessment of water quality in Thailand. Environ Monit Assess. 2010;165(1–4):205–15. doi: 10.1007/s10661-009-0939-0 [DOI] [PubMed] [Google Scholar]
  • 42.Techasaensiri C, Radhakrishnan A, Als D, Thisyakorn U. Typhoidal salmonella trends in Thailand. Am J Trop Med Hyg. 2018;99(3_Suppl):64–71. doi: 10.4269/ajtmh.18-0046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Makoge V, Maat H, Vaandrager L, Koelen M. Health-seeking behaviour towards poverty-related disease (PRDs): a qualitative study of people living in camps and on campuses in Cameroon. PLoS Negl Trop Dis. 2017;11(1):e0005218. doi: 10.1371/journal.pntd.0005218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Tangcharoensathien V, Witthayapipopsakul W, Panichkriangkrai W, Patcharanarumol W, Mills A. Health systems development in Thailand: a solid platform for successful implementation of universal health coverage. Lancet. 2018;391(10126):1205–23. [DOI] [PubMed] [Google Scholar]
  • 45.Balasubramanian R, Im J, Lee J-S, Jeon HJ, Mogeni OD, Kim JH, et al. The global burden and epidemiology of invasive non-typhoidal Salmonella infections. Hum Vaccin Immunother. 2019;15(6):1421–6. doi: 10.1080/21645515.2018.1504717 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Yano Y, Hamano K, Satomi M, Tsutsui I, Ban M, Aue-umneoy D. Prevalence and antimicrobial susceptibility of vibrio species related to food safety isolated from shrimp cultured at inland ponds in Thailand. Food Control. 2014;38:30–6. doi: 10.1016/j.foodcont.2013.09.019 [DOI] [Google Scholar]
  • 47.Annear PL, Kwon S, Lorenzoni L, Duckett S, Huntington D, Langenbrunner JC, et al. Pathways to DRG-based hospital payment systems in Japan, Korea, and Thailand. Health Policy. 2018;122(7):707–13. doi: 10.1016/j.healthpol.2018.04.013 [DOI] [PubMed] [Google Scholar]
  • 48.Pongpirul K, Walker D, Winch P, Robinson C. A qualitative study of DRG coding practice in hospitals under the Thai universal coverage scheme. BMC Health Serv Res. 2011;11(1):71. doi: 10.1186/1472-6963-11-71 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Healthcare Accreditation Institute, Thailand. Resource data: general data of hospitals. 2022. Available from: https://data.go.th/dataset/hospital [Google Scholar]
PLOS Glob Public Health. doi: 10.1371/journal.pgph.0003995.r002

Decision Letter 0

Megan Elizabeth Carey

10 Oct 2024

PGPH-D-24-02039

Epidemiology of Burkholderia pseudomallei, Streptococcus suis, Salmonella spp., Shigella spp. and Vibrio spp. infections in 111 hospitals in Thailand, 2022

PLOS Global Public Health

Dear Dr. Limmathurotsakul,

Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The reviewers have shared major and minor comments to be addressed in the updated manuscript. In particular, additional context should be provided about the study that was conducted from 2012-2015 and several of the pathogen-specific findings from that study, including how incidence rates were calculated and what any reported changes might be attributable to. How are findings from these two studies and study periods comparable? Please also explain why there are no data included from Bangkok in this analysis. 

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Reviewer #1

Overall comments

Jitpeera et al. analyse the incidence of 11 different bacterial pathogens from data they have collected from the majority of Thailand’s hospitals in 2022, and then compare incidence from some of these regions with paired data from 2012-2015. In-hospital mortality and associations with local agricultural animal density, economic data and the health region are also investigated. The scale of the data and observed trends are valuable to the field and overall the data is well presented and described, and conclusions are appropriately supported by the data. However, several points need to be clarified and some recommendations for additional data presentation are included below. While the discussion raises good points, this section would benefit from expanding slightly to put more details into context, particularly for readers not familiar with these bacterial pathogens. Unable to comment on the validity of the statistical analysis.

Major comments

1. Line 47. At some point in the manuscript (methods?) the authors should explain why there is no data from Bangkok.

2. Line 58. “The incident rate of NTS infection was not associated with geographical region.” This sentence is not accurate as written, and should be rephrased to make it clear that NTS incidence does vary by region, perhaps by as much as 10-20 times (from the data shown in Fig 2F). I believe the authors meant to say that NTS incidence was generally high across all regions of Thailand, and there is no clear epicentre of infection.

3. Line 59 – 61. The authors highlight that the incidence fecal-oral transmitted NBDs was lower and that of B. pseudomallei and S. suis was higher. The route of transmission of the later should be mentioned (perhaps in the discussion).

4. Line 78-81. It should be made clear whether any of the 13 dangerous communicable diseases are bacterial and if so what they are.

5. Line 84: It should be noted whether the ICD-10 is reliable for any of the 11 bacterial diseases described in the manuscript. The following sentence stating that microbiology lab data is not used would suggest that they are not, but this should be made clear.

6. Line 119-121 & Line 139 - 142: Some more specifics on how each bacterial disease was cultured and identified is warranted. Ref 23 handbook link is broken, but is dated as 2012; has there been any update to procedures/methods since? It should be made clear whether the methods for collecting data differ between the earlier timepoint and the later. This is particularly important for Burholderia and S. suis which have seen large increases in incidence over the period.

7. While culture is the gold standard for the diagnosis of many of these pathogens, the authors should note in the discussion if there are any limitations with using solely culture as a readout for specific pathogens that may bias the data. This is very briefly mentioned in the discussion at line 344-346, but more detail should be included in terms of how cultured methods may vary between regions if know.

8. Were any patients culture positive for more than one pathogen? Regardless of answer this should be stated if known.

9. Line 146-147: How many patients had multiple admissions? Explain why mortality data was based only on the first admission.

10. Line 165-166. It should be noted what biases the paired data introduce in the analysis. Eg. what regions/ hospital types were less/more represented in the 2012-2015 dataset relative to that of the 2022 dataset.

11. Line 196: Which regions are included in the 2012-2015 dataset? Can this be displayed on a map in a figure?

12. Line 288: This paragraph compares the collected data with that of another dataset from the national surveillance system, and concludes that there are significant differences between the two datasets in terms of incidence and mortality. The argument is made in the discussion in lines 305 -306 that “local data can supplement and monitor the performance of the national surveillance systems”. However, it is not explained why such a disparity exists between the two datasets, and it is not obvious to the reader why; this needs to be clearly explained in the discussion.

13. Line 320: specify “increase from 2012-2015 to 2022”. The authors should note, as they have done for burkholderia, whether the increase observed for S. suis could have been caused by changes in diagnostic procedures/methods etc.

14. Line 327: specify the “fecal-oral transmitted diseases”. Transmission of burkholderia and S. suis should be discussed as a point of difference.

15. Line 328-330: This sentence is very general. Be specific about what interventions could decrease incidence and why.

16. Line 351: In the concluding paragraph only melioidosis and S. suis are mentioned as requiring specific public health inventions. While it is true that these have increased dramatically and have the highest associated mortality, the data shows that NTS and cholera, despite decreasing over the same period, continue to have high incidence in Thailand and high mortality. Therefore NTS and cholera should be highlighted as continuing to have high burden in the country and requiring specific public health interventions.

17. The comparison is between a multiyear timeframe (2012-2015) and a single year. Was there anything different or special about the 2022 year (eg. climate) that may have lead to a changes in disease transmission? Have any vaccination programs/antimicrobial treatment/general healthcare programs for any of these pathogens been introduced since 2015 which may have affected disease incidence?

18. Is there patient meta data available that could help explain changes in incidences and affected populations eg. age, gender, comorbidities, coinfections with more than one NBD?

Figures/Tables:

1. Figure 2:

a. The colours at the high end of incidence are similar and hard to differentiate. Recommend adapting the colours (perhaps to a 2 or 3 colour system?) to make the difference incidence levels easier to distinguish.

b. The size of the numbers in the colour legend should be increased to a more legible level.

c. Fig legend should note the pathogen for each of the numbered graphs.

2. The authors compare paired data from 2022 with that of 2012-2015. It should be made clear what regions/provinces are included in the this temporal comparison. Perhaps a map highlighting the regions/provinces incorporated? A geographical figure similar to Fig 2 that shows the % or fold change in incidence of each NBD during this period may work. Or could be a column in a data table.

3. Recommend that the authors include a table summarising all data for easy comparison between pathogens eg. total patients, total deaths, % deaths, % change from 2012-2015 data.

Minor comments

1. Line 135: Should be 11 pathogens, not 12.

2. Ref 23 url does not open.

3. Line 201: “the time periods” should be specified.

4. Line 225: “none died” should be rephrased to “no associated mortality” or similar.

5. Line 289-290. Make clear that the second figures in the brackets are from national surveillance and quantify the difference eg. in fold or %

6. Line 300-301: Make clear that increase is relative to 2012-2015 data.

7. Line 310. Sentence not grammatically correct, rephrase.

8. Line 316-317: “spurious” is not the right word here.

9. Line 332-33: Quantify the % of referral hospitals in the country included in the data.

Reviewer #2

The study presents a thorough analysis of notifiable bacterial diseases (NBDs) in Thailand, providing valuable insights into emerging epidemiological trends. By leveraging comprehensive microbiology and hospital admission data from a large network of public referral hospitals, the authors address a significant knowledge gap regarding the incidence and distribution of NBDs in low- and middle-income countries. The use of multivariable Poisson random-effects regression models and the comparison with historical data (2012-2015) strengthen the robustness of the findings. However, there are some major and minor comments and areas that warrant further consideration for clarification and improvement.

Major comments

  1. Please summarize the study that was performed in 2012-2015 as this is used as comparator. Why was this period chosen as a comperator? Please explain why chosen for these eleven bacterial diseases. And where these already notifiable pathogens in 2012 for example? 

At the moment, I do not understand what you are comparing your results to and how to interpretate the major increases and decreases in incidence levels. For example, if the regions where melioidosis is known to be highly endemic were not included in the 2012-2015 study, but are now included in the 2022 study due to its larger scope, this obviously explains the increase in incidence. Without this information (or adjustment for this fact) in the analysis, it’s not possible to interpretate the incidence increases or decreases as mentioned below under a to f. It may be true that there’s an increase in awareness and subsequent identification, but then my main question remains: is the incidence increasing or is the detection increasing? The authors should be more critical about this distinction. 

  1. Line 218. The authors state that the incidence of B. pseudomallei increased by 58% in 2022 compared to 2012-2015. Can the authors explain this major increase? The reasons mentioned in the discussion might be true (line 318-320), but please incorporate the suggestions above.  

  2. Line 242. The authors state that the incidence of NTS decreased by 37% in 2022 compared to 2012-2015. Can the authors explain this major decrease? Please elaborate on this decrease in the discussion. 

  3. Line 254. The authors state that the incidence of Salmonella enterica serovar Typhi decreased by 83% in 2022 compared to 2012-2015. Can the authors explain this major decrease? Please elaborate on this decrease in the discussion.

  4. Line 262. The authors state that the incidence of Shigella decreased by 78% in 2022 compared to 2012-2015. Can the authors explain this major decrease? Please elaborate on this decrease in the discussion.

  5. Line 275. The authors state that the incidence of S. suis increased by 172% in 2022 compared to 2012-2015. Can the authors explain this major increase? The reasons mentioned in the discussion might be true (line 324-328), but please incorporate the suggestions above.  

  6. Line 288. The authors state that the incidence of Vibrio decreased by 25% in 2022 compared to 2012-2015. Can the authors explain this major decrease? Please elaborate on this decrease in the discussion. 

  1. Is the use of multivariable Poisson random-effects regression models appropriate for this type of data and analysis? Please explain why this model was chosen.

  2. The study relies on hospital discharge summaries to define mortality. How confident are you in the reliability and consistency of these summaries across different hospitals and regions? Given the possibility of discrepancies in reporting. 

  3. Why was Bangkok excluded from the analysis?

Minor comments

  1. Line 188. The authors state that 91% used the AMASS to acquire data, but how was the other 9% of the data obtained? Please elaborate. 

  2. Table 1. Suggestion to place the total number of cases and deaths in columns next to each other. This is easier for the reader to comprehend as it immediately reads as a mortality rate. Also, suggest to use the same nomenclature for the disease/causative agent. So, column 1 and column 3 should be combined to a single column. If there are discrepancies between the definition used in the AMASS and NSS, state so in the methods or supplemental files. 

  3. Line 331-332. The authors mention that measures were taken over time that resulted in a decrease in fecal-oral transmitted diseases. The information is very brief.

The decrease in fecal-oral transmitted diseases such as typhoid and shigellosis is attributed to improvements in sanitation and clean water supply. Could other factors, such as changes in healthcare-seeking behavior, vaccination campaigns, or environmental factors, play a role in this decline? How did you isolate the specific impact of water and sanitation improvements from these other potential influences?  Please give more information on the context of the interventions. Readers should not have to go back and forth to other publications to identify this information, especially since it is considered critical information by the authors as it explains decreases in incidence levels. 

  1. The study highlights regional differences in the incidence of B. pseudomallei and S. suis infections. Could these regional differences be influenced by varying healthcare access, diagnostic capacity, or reporting practices across provinces? How did the study address such potential confounding factors to ensure that the differences are truly epidemiological rather than artefacts of the healthcare system?

  2. The study calls for public health interventions to address the increasing burden of melioidosis and S. suis infections. Given the complexity of these diseases, what specific interventions would be both effective and feasible in Thailand's current public health landscape? How would you prioritize resource allocation, and what are the potential barriers to implementing these interventions across diverse regions?

  3. While the AMASS program is scalable and user-friendly, how generalizable is the study’s approach to other low- and middle-income countries (LMICs) with different healthcare infrastructures, disease profiles, and data management capabilities? Could the reliance on computerized microbiology and hospital data present a challenge for broader adoption in less digitized health systems?

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Timothy A Scott

Reviewer #2: No

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PLOS Glob Public Health. doi: 10.1371/journal.pgph.0003995.r004

Decision Letter 1

Megan Elizabeth Carey

1 Jan 2025

PGPH-D-24-02039R1

Epidemiology of Burkholderia pseudomallei, Streptococcus suis, Salmonella spp., Shigella spp. and Vibrio spp. infections in 111 hospitals in Thailand, 2022

PLOS Global Public Health

Dear Dr. Limmathurotsakul,

Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 31 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at globalpubhealth@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Megan Elizabeth Carey

Academic Editor

PLOS Global Public Health

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Many thanks for addressing the major review comments from the editors. A few very minor edits still need to be made:

-line 80 - suggest replacing "information" with data

-line 86 - suggest replacing "tuberculosis" with "Mycobacterium tuberculosis" for consistency

-line 90-91 - point about notifiable bacterial diseases warrants additional explanation

-lines 130-2 - thanks for addressing the question about why Bangkok wasn't included, but some additional explanation for why would be welcome (e.g. data could not be accessed, and/or data are captured differently and therefore aren't comparable, etc.)

-line 189 - what is "the same analytical approach"? some additional specificity would be helpful

-line 353 - "underreporting *of* cases and deaths..."

-line 402 - decrease in... numbers? incidence?...of Salmonella enterica serovar Typhi and Shigella spp. infections

-line 403 - decreases*

-line 407 - rates*

-lines 408-410 - what is meant by "burden" - incidence? kind of an imprecise term, could say that these pathogens cause substantial public health problems, or similar

-line 441 - methods *used for*...

-line 442 - some hospitals might have *implemented* ...?

-line 452 - "Although the incidence *rates* of..."

-should be "vibrioses" rather than "fibrosis"

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I don't know

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Global Public Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Minor: The new ref 23 link is broken

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Timothy A Scott

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLOS Glob Public Health. doi: 10.1371/journal.pgph.0003995.r006

Decision Letter 2

Megan Elizabeth Carey

16 Jan 2025

Epidemiology of Burkholderia pseudomallei, Streptococcus suis, Salmonella spp., Shigella spp. and Vibrio spp. infections in 111 hospitals in Thailand, 2022

PGPH-D-24-02039R2

Dear Limmathurotsakul,

We are pleased to inform you that your manuscript 'Epidemiology of Burkholderia pseudomallei, Streptococcus suis, Salmonella spp., Shigella spp. and Vibrio spp. infections in 111 hospitals in Thailand, 2022' has been provisionally accepted for publication in PLOS Global Public Health.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Global Public Health.

Best regards,

Megan Elizabeth Carey

Academic Editor

PLOS Global Public Health

***********************************************************

Reviewer Comments (if any, and for reference):

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Factors associated with the incidence of cases with culture-confirmed Burkholderia pseudomallei infection per 100,000 population in 74 provinces in Thailand, 2022.

    (Word)

    pgph.0003995.s001.docx (40KB, docx)
    S2 Table. Factors associated with the incidence of cases with culture-confirmed non-typhoidal Salmonella spp. (NTS) infection per 100,000 population in 74 provinces in Thailand, 2022 .

    (Word)

    pgph.0003995.s002.docx (39.3KB, docx)
    S3 Table. Factors associated with the incidence of cases with culture-confirmed S. suis infection per 100,000 population in 74 provinces in Thailand, 2022.

    (Word)

    pgph.0003995.s003.docx (39.8KB, docx)
    S4 Table. Factors associated with the incidence of cases with culture-confirmed Vibrio spp. infection per 100,000 population in 74 provinces in Thailand, 2022.

    (Word)

    pgph.0003995.s004.docx (39.4KB, docx)
    S5 Table. Total number of cases following selected notifiable bacterial diseases (NBDs) diagnosed by culture in 2022 compared with the yearly incidence between 2012-2015 in 49 hospitals where paired data were available.

    (Word)

    pgph.0003995.s005.docx (37.6KB, docx)
    S1 Text. Stata code for fitting the multivariable Poisson regression models.

    (Word)

    pgph.0003995.s006.docx (37.1KB, docx)
    S2 Text. Example of reporting criteria of relevant notifiable diseases in Thailand.

    (Word)

    pgph.0003995.s007.docx (37.4KB, docx)
    S1 Fig. Map of 42 provinces that had paired data from 2022 and 2012-2015.

    Map created by the authors using STATA version 14.2 (StataCorp, College Station, TX) and a base layer map from GADM (https://gadm.org/download_country.html) (term of use: https://gadm.org/license.html)

    (Image)

    pgph.0003995.s008.tif (4.3MB, tif)
    Attachment

    Submitted filename: response_to_reviewers 2011 11 03.docx

    pgph.0003995.s009.docx (39KB, docx)
    Attachment

    Submitted filename: response_to_reviewers 2025 01 06.docx

    pgph.0003995.s010.docx (20.9KB, docx)

    Data Availability Statement

    The hospital-level summary data used for the study are open-access and available at https://doi.org/10.6084/m9.figshare.25907494

    Articles from PLOS Global Public Health are provided here courtesy of PLOS

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