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BMC Infect Dis
BMC Infect Dis
BMC Infectious Diseases
1471-2334
BioMed Central London

9806
10.1186/s12879-024-09806-y
Research
Increasing trends of antibiotic resistance in Uganda: analysis of the national antimicrobial resistance surveillance data, 2018–2021
Namubiru Saudah saudahnam@gmail.com

1
Migisha Richard 1
Okello Paul Edward 1
Simbwa Brenda 1
Kabami Zainah 1
Agaba Brian 1
Zalwango Jane Frances 1
Naiga Hellen Nelly 1
Zalwango Marie Gorreti 1
Wanyana Mercy Wendy 1
Monje Fred 1
King Patrick 1
Kawungezi Peter Chris 1
Kiggundu Thomas 1
Ninsiima Mackline 1
Akunzirwe Rebecca 1
Namusosa Rita 2
Mugerwa Ibrahim 2
Winfred Atuhaire D 2
Achola Caroline 2
Najjuka Grace 2
Bulage Lilian 1
Kwesiga Benon 1
Kadobera Daniel 1
Ario Alex Riolexus 1
Nabadda Susan 2
1 Uganda Public Health Fellowship Program, Uganda National Institute of Public Health, Kampala, Uganda
2 https://ror.org/00hy3gq97 grid.415705.2 National Health Laboratory and Diagnostic Services Department, Ministry of Health, Kampala, Uganda
9 9 2024
9 9 2024
2024
24 93025 4 2024
23 8 2024
© The Author(s) 2024
2024
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Background

Continuous monitoring of antimicrobial resistance (AMR) in Uganda involves testing bacterial isolates from clinical samples at national and regional hospitals. Although the National Microbiology Reference Laboratory (NMRL) analyzes these isolates for official AMR surveillance data, there's limited integration into public health planning. To enhance the utilization of NMRL data to better inform drug selection and public health strategies in combating antibiotic resistance, we evaluated the trends and spatial distribution of AMR to common antibiotics used in Uganda.

Methods

We analyzed data from pathogenic bacterial isolates from blood, cerebrospinal, peritoneal, and pleural fluid from AMR surveillance data for 2018–2021. We calculated the proportions of isolates that were resistant to common antimicrobial classes. We used the chi-square test for trends to evaluate changes in AMR resistance over the study period.

Results

Out of 537 isolates with 15 pathogenic bacteria, 478 (89%) were from blood, 34 (6.3%) were from pleural fluid, 21 (4%) were from cerebrospinal fluid, and 4 (0.7%) were from peritoneal fluid. The most common pathogen was Staphylococcus aureus (20.1%), followed by Salmonella species (18.8%). The overall change in resistance over the four years was 63–84% for sulfonamides, fluoroquinolones macrolides (46–76%), phenicols (48–71%), penicillins (42–97%), β-lactamase inhibitors (20–92%), aminoglycosides (17–53%), cephalosporins (8.3–90%), carbapenems (5.3–26%), and glycopeptides (0–20%). There was a fluctuation in resistance of Staphylococcus aureus to methicillin (60%-45%) (using cefoxitin resistance as a surrogate for oxacillin resistance) Among gram-negative organisms, there were increases in resistance to tetracycline (29–78% p < 0.001), ciprofloxacin (17–43%, p = 0.004), ceftriaxone (8–72%, p = 0.003), imipenem (6–26%, p = 0.004), and meropenem (7–18%, p = 0.03).

Conclusion

The study highlights a concerning increase in antibiotic resistance rates over four years, with significant increase in resistance observed across different classes of antibiotics for both gram-positive and gram-negative organisms. This increased antibiotic resistance, particularly to commonly used antibiotics like ceftriaxone and ciprofloxacin, makes adhering to the WHO's Access, Watch, and Reserve (AWaRe) category even more critical. It also emphasizes how important it is to guard against the growing threat of antibiotic resistance by appropriately using medicines, especially those that are marked for "Watch" or "Reserve."

Keywords

Antimicrobial resistance (AMR)
Hospital-acquired infections
Low- to middle-income countries (LMICs)
Multidrug-resistant organisms (MDRO)
Antibiotic stewardship
Gram-positive organisms
Gram-negative organisms
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

Globally, antimicrobial resistance (AMR) is an increasing health concern. AMR is the ability of microorganisms to resist the effects of medicines that were previously used to treat such diseases [1]. Over 700,000 individuals worldwide die every year from illnesses linked to AMR; it is predicted that 10 million deaths will have occurred by 2050, costing the world $100 trillion [2]. In 2019, estimates of invasive infections, particularly antimicrobial-resistant invasive infections, accounted for 5.3 million deaths around the world annually [3]. A significant proportion of these deaths occurs in low- to middle-income countries (LMICs) [4]. While developing countries are battling an accelerated spread of AMR, developed countries are also experiencing the same trend [4].

AMR has increased as a result of the use of broad-spectrum antibiotics to treat invasive infections such as bloodstream [5, 6] and central nervous system (CNS) infections [6]. As a result, treating these infections is becoming more challenging, which increases treatment failures and death [7]. Over-the-counter medication access in developing countries such as Uganda is one of the drivers of AMR in such nations [8]. Indiscriminate usage of antimicrobials exerts increased selection pressure [9] on the bacterial population, resulting in accelerated emergence of AMR [10]. Antibiotic usage has led to the rise of widely multidrug-resistant germs, rendering even the most potent medications useless [11].

The unavailability of reliable data in developing countries such as Uganda makes it difficult to develop efficient methods to monitor and control AMR [12, 13]. Nonetheless, a few studies to date have reported [10, 14] trends of resistance in Uganda. A study that investigated antibiotic resistance in Uganda found that E. coli and K. pneumoniae were carried in the Gastrointestinal tract (GIT) of patients attending outpatient clinics in Kampala and two rural districts [15]. It further showed high rates of resistance to commonly used antibiotics such as ampicillin and cotrimoxazole (sulfamethoxazole and trimethoprim) and relatively low resistance rates to amoxicillin/clavulanate, chloramphenicol, ciprofloxacin, gentamicin, nitrofurantoin and ceftriaxone.

We described the organisms that are isolated from patients’ sterile site samples and their antibiotic resistance trends in Uganda to provide data to inform planning and AMR control interventions.

Methods

Study setting, design, and data source

We conducted a descriptive analysis of Uganda’s national AMR surveillance data, 2018–2021. National surveillance sites for AMR in human health comprise microbiology laboratories of regional referral hospitals, national referral hospitals, and selected institutions of learning. These sites analyze clinical samples (pathogen identification and antimicrobial susceptibility testing (AST)) for routine patient care using clinical and laboratory standards Institute (CLSI). The decision to perform a bacterial culture test is entirely at the discretion of the attending clinician. For quality control purposes, these sites also refer isolates with their relevant identifiers to the National Microbiology Reference Laboratory (NMRL) (at the National Health Laboratory and Diagnostic Services (NHLDS) department of the Ministry of Health) for reidentification and antimicrobial susceptibility retesting. NMRL is accredited by the College of American Pathologists (CAP) against International Organization for Standardization ISO 15189:2012. The accredited microbiology tests include microscopy, culture, identification, and antibiotic susceptibility testing The laboratory used both manual (phenotypic using biochemicals) and automated organism identification methods.The automated methods include Brucker Daltonics Microflex LT Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometer, and the Becton Dickson Phoenix™ automated microbiology system for automated identification and susceptibility testing system. Antimicrobial susceptibility of the isolates was determined by using Kirby-Bauer disk diffusion method according to Clinical and Laboratory Standard Institute (CLSI) guidelines. When necessary, the minimal inhibitory concentrations (MICs) are determined by BD phoenix and the E-Test (Bio Mérieux) according to CLSI guidelines The laboratory is the only microbiology reference laboratory for the whole country.

Inclusion and exclusion criteria

All isolates received from all the surveillance sites as patient samples were reanalyzed by the laboratory. However, only isolates with reidentification results matching primary sites were analyzed in this study.

Study variables

We extracted individual-level data from the NMRL database. Data were extracted as an excel sheet from this electronic database containing patient reports, organism identified, and sentinel sites. Information obtained on each case included patient age, patient sex, year of sample collection, specimen source, hospital in which the sample was drawn, isolated pathogen in the positive culture, and susceptibility results (defined as susceptible, intermediate, or resistant).

Data management and analysis

We stored data in password-protected computers and data was not shared with anyone outside the investigation team. The variables analyzed included age, sex, specimen type (i.e., blood and cerebrospinal fluid), organisms isolated, and antibiotics tested. For Gram positive organisms we analysed resistance to the following organisms clindamycin, erythromycin, gentamycin, oxacillin, penicillin G, cotrimoxazole, cefoxitin, vancomycin and ciprofloxacin. For Gram negative organisms, we analyzed for the resistance to amikacin, ampicillin, amoxicillin-clavulanate, cefotaxime, ceftriaxone, cefoxitin, cefepime, chloramphenicol, ciprofloxacin, gentamycin, imipenem, meropenem, piperacillin, piperacillin-tazobactam, cotrimoxazole and tetracycline. The outcome was the antibiotic resistance of the isolates to common antibiotics. Isolates were classified as either susceptible or resistant to an antimicrobial, and all isolates with intermediate reactions were classified as resistant. Data quality was checked using the completeness of data entries in the Laboratory Information System. Rates of resistance were calculated as a proportion of resistant organisms out of the total number of organisms that were tested for antimicrobial susceptibility per year. Resistance to each antibiotic was analyzed separately, and Microsoft Excel 2016 was used to plot the trends from 2018 to 2021. The chi-square test for trends was used to test the significance of antibiotic resistance trends over time. Epi-Info™ 7- (CDC, Atlanta, USA) statistical package was used for additional statistical analysis. A p value < 0.05 was considered statistically significant.

Ethical considerations

Our study utilized routinely collected aggregated program surveillance data that did not have any personal identifiers. We obtained permission to use the AMR surveillance data from the Division of Health Information, Ministry of Health which has the overall mandate to manage collected and stored health related information. Additionally, the U.S. Centers for Disease Control and Prevention (CDC) Center for Global Health determined our study as non-research whose primary intention was to address public health problems.

Results

Attributes of organisms isolated from sterile sample cultures, Uganda, 2018–2022

Of the 537 isolates analyzed, 188 (52.6%) were from children aged 0–5 years, 67 (18.7%) were from persons aged 6–18 years, 59 (16.2%) were from adults aged 19–45 years, and more than 66 years were from adults. Fifty-five percent of isolates were from females. Over time, 2018 had the highest number of isolates 151 (28.1%), while 2019 had the least number of isolates 93 (17%). Geographically, most isolates were from Mbarara Regional Referral Hospital 186 (48%), while the fewest came from Kiruddu National Referral Hospital and the General Military Hospital, each having 1 (0.3%). A total of 108 (20.1%) isolates were Staphylococcus aureus, followed by Salmonella species, with 101 (18.8%) (Table 1). Table 1 Attributes of organisms isolated from sterile sample cultures, Uganda, 2018–2021

Variable	Number of pathogens (n = 537)	Percent	
Age(years) [n = 357]	
 0–5	188	53	
 6–18	67	19	
 19–45	59	17	
 46–65	26	7	
 66 and above	17	5	
Sex (n = 480)	
 Male	216	45	
 Female	264	55	
Sample type analyzed	
 Blood	478	89	
 Cerebrospinal fluid	21	4	
 Peritoneal fluid	4	1	
 Pleural fluid	34	6	
Year of isolation	
 2018	151	28	
 2019	93	17	
 2020	179	33	
 2021	114	21	
Location of primary testing laboratory	
 Arua RRH	35	7	
 Fort Portal RRH	50	9	
 Hoima RRH	6	1	
 General Military Hospital Bombo	1	0	
 IDI/DMM-Makerere	139	26	
 Jinja RRH	38	7	
 Kabale RRH	24	5	
 Kiruddu NRH	1	0	
 Mbale RRH	17	3	
 Mbarara RRH	217	40	
 Soroti RRH	8	2	
Gram class of organisms	
 Negative	351	65	
 Positive	186	35	
Organisms isolated (n = 534)	
 Acinetobacter spp	22	4	
 Citrobacter spp	11	2	
 Enterobacter spp		5	
Enterococcus spp	
 Escherichia coli	80	15	
 Klebsiella pneumoniae	83	16	
 Proteus spp	9	2	
 Pseudomonas aeruginosa	15	3	
 Salmonella typhii/paratyphii	101	19	
 Staphylococcus aureus	108	20	
 Streptococcus spp	14	3	
 Others	7	1	
RRH Regional Referral Hospital, NRH National Referral Hospital, IDI Infectious Diseases Institute, DMM Department of Medical Microbiology, spp species

Trends of total antimicrobial resistance of gram-positive organisms to common antibiotics, Uganda, 2018–2021

Increasing resistance to ciprofloxacin over the four-year study period was observed (26.2–44.9%; p < 0.01) (Fig. 1).Fig. 1 Trends of antimicrobial resistance rates of gram-positive isolates to common antibiotics isolated in Uganda 2018–2021

There is a general trend of increasing resistance among gram-positive organisms to most antibiotics tested in Uganda. Notably, there were significant increases in resistance to Clindamycin (25%-76%), Erythromycin (50%-76%), and Vancomycin (23–55%) over the years. Gentamycin showed fluctuating resistance rates, while resistance to Penicillin G remained consistently high. Cefoxitin susceptibility testing was only done for Staphylococcus aureus and resistance to it is a surrogate for methicillin resistance staphylococcus aureas(MRSA). See Table 2 below. Table 2 Rates of resistance of gram-positive organisms to common antibiotics, Uganda, 2018–2021

Drug	2018	2019	2020	2021	
N	(%)	N	(%)	N	(%)	N	(%)	
Cefoxitin	3	(60)	4	(25)	15	(38)	9	(47)	
Clindamycin	6	(25)	5	(9)	14	(55)	11	(76)	
Ciprofloxacin	10	(26)	5	(28)	26	(53)	11	(45)	
Erythromycin	12	(50)	8	(46)	46	(67)	29	(76)	
Gentamycin	10	(76)	24	(42)	11	(40)	25	(54)	
Penicillin G	10	(80)	13	(72)	58	(92)	17	(73)	
SXT	19	(63)	16	(79)	22	(71)	14	(78)	
Tetracycline	6	(60)	8	(38)	5	(56)	5	(46)	
Vancomycin	3	(23)	6	(29)	21	(36)	16	(55)	

Trends of total antimicrobial resistance of gram-negative organisms to common antibiotics, Uganda 2018–2021

Overall, the percentage antibiotic resistance of gram-negative organisms increased over the years. Resistance to penicillins (piperacillin and ampicillin) was highest among gram-negative bacteria. Specifically, increases in resistance over time were noted for ceftriaxone (8.3–78.6%; p = 0.003), ciprofloxacin (17.1–42.6%; p-0.004), imipenem (5.7–29.7%; p-0.004), meropenem (5.3–18.4%; p-0.03), and tetracycline (28.6%-81.6%; p-0.02) (Table 3). Table 3 Rates of resistance of gram-negative organisms to common antibiotics, Uganda, 2018–2021

Antibiotic/Year	2018		2019		2020		2021		p-value	
N	(%)	N	(%)	N	(%)	N	(%)	
Amikacin	11	(52.4)	8	(22.9)	12	(20.3)	14	(38.9)	0.63	
Ampicillin	41	(62.1)	35	(89.7)	58	(89.2)	19	(76.0)	0.34	
Amoxiclav	6	(46.2)	23	(54.8)	28	(87.5)	23	(92.0)	0.09	
Cefotaxime	9	(39.1)	20	(60.6)	44	(78.6)	34	(87.2)	0.07	
Ceftriaxone	3	(8.3)	25	(62.5)	66	(78.6)	41	(71.9)	< 0.001	
Cefoxitin	4	(80.0)	8	(25.4)	14	(33.3)	4	(50.0)	0.92	
Cefepime	13	(56.5)	13	(50.0)	48	(71.6)	29	(90.6)	0.16	
Chloramphenicol	28	(48.3)	26	(51.0)	51	(71.6)	26	(48.6)	0.87	
Ciprofloxacin	12	(17.1)	16	(30.2)	48	(5.8)	20	(42.6)	< 0.001	
Gentamycin	15	(75.0)	16	(45.7)	21	(45.6)	14	(70.0)	0.84	
Imipenem	4	(5.7)	3	(8.8)	11	(19.0)	14	(25.9)	< 0.001	
Meropenem	5	(7.2)	2	(5.3)	7	(18.4)	5	(2.5)	0.03	
Piperacillin			20	(76.9)	38	(97.4)	36	(87.8)	0.79	
TPZ	1	(20.0)	4	(57.1)	13	(68.4)	15	(68.2)	0.37	
SXT	60	(62.5)	37	(84.1)	59	(78.7)	31	(75.6)	0.41	
Tetracycline	14	(28.6)	23	(71.6)	18	(81.8)	14	(77.8)	0.02	
TPZ Piperacillin/tazobactam, SXT Cotrimoxazole

Trends of ciprofloxacin resistance by health region, 2018–2021

There was increasing resistance to ciprofloxacin across the health subregions. A significant decline was observed in 2021. There is an increasing resistance that is observed in the Elgon and Busoga subregions over time (Fig. 2).Fig. 2 Trends and spatial distribution of ciprofloxacin resistance by health region, Uganda, 2018–2021

Discussion

We described trends and patterns of antimicrobial resistance of organisms isolated from patients’ sterile site samples in Uganda during 2018–2021 with an emphasis on the antibiotics frequently utilized to treat common infections and the bacterial Gram classification. The rates of resistance to commonly-isolated antibiotics increased significantly over time. The most commonly encountered gram-positive organism was Staphylococcus aureus, while the most common gram-negative organisms were Salmonella typhii/paratyphii species and Escherichia coli. We found an increase in resistance of gram-negative organisms to ciprofloxacin, ceftriaxone, tetracycline, meropenem, and imipenem.

The increase in resistance to these antibiotic classes over time is consistent with a study by Mboowa et al. which found that also found an overall increase in resistance to antibiotics among surgical patients in a national referral hospital from 2014 to 2018 [16].

Over the study period, gram-negative bacteria had an increase in resistance to ciprofloxacin from 26 to 56%. This is similar to the findings from a systematic review that included studies assessing the prevalence of ciprofloxacin-resistant clinical isolates in Ethiopia. It found that 1 in 6 Proteus bacteria (mostly P. mirabilis) and about 1 in 7 Pseudomonas bacteria (mainly P. aeruginosa) were resistant to the ciprofloxacin [17]. A similar study in Ethiopia in 2015 observed that the rates of resistance to ciprofloxacin were high (20–70%) among bacterial organisms isolated from blood, which is a sterile site sample [18]. Currently the global recommendation on management of tuberculosis reserve ciprofloxacin as a second line option for drug resistant tuberculosis [19]. The above findings therefore pose a great threat in the management of drug resistant tuberculosis. Furthermore, as per the Uganda clinical guidelines, ciprofloxacin is the drug of choice for common conditions like enteric fever and a second line option in the management of urinary tract infections. This further complicates the treatment of these common conditions.

Gram- negative organisms had increasing resistance to ceftriaxone, ciprofloxacin, and tetracycline as well as last-resort antibiotics such as meropenem and imipenem. The growing resistance rates to carbapenems are worrisome and may lead to the spread of fatal infections, especially in hospitals [20].

Among gram-positive organisms still, particularly for Staphylococcus aureus, methicillin resistance fluctuated significantly, dropping from 60 to 45% over the study period. Cefoxitin resistance was utilized as a surrogate measure for oxacillin resistance, suggesting methicillin-resistant Staphylococcus aureus (MRSA) prevalence. This shift is relevant because MRSA is a major cause of healthcare-associated infections. A similar observation was seen by a study in Nigeria whuch also found varying MRSA frequencies, underlining the dynamic nature of MRSA resistance trends [21].

We also identified increase in resistance of gram-negative organisms to the antibiotics ceftriaxone (8–79%), tetracycline (28–81%), and imipenem (6–26%). Similar increase in resistance to third generation cephalosporins like ceftriaxone was found by a systematic review in Uganda, 2018 found that there was an overall increase in resistance to cefotaxime (46–49%) and cefuroxime, both of which are third-generation cephalosporins, for which resistance can be used to infer ceftriaxone resistance [22]. In contrast, in this same study, most organisms were susceptible to ciprofloxacin hence recommended in this study as a drug of choice for infections caused by gram-negative organisms. Another study in South Africa conducted over a 12-month from October 2011 to September 2012 at a tertiary facility in Cape Town, South Africa, Carroll et al. [23] found that for healthcare-associated Enterobacteriaceae bloodstream isolates, susceptibility rates were 58.5% to ceftriaxone and 70% to ciprofloxacin.

The observed increasing resistance across various classes of antibiotics for both gram-positive and gram-negative organisms signifies a declining range of effective treatment options. This may pose a direct threat to patient care, leading to prolonged illnesses, heightened severity of infections, and increased mortality rates. The study's identification of significant resistance spikes, particularly against commonly-used antibiotics such as ceftriaxone and ciprofloxacin, emphasizes the need for adherence to the WHO's Access, Watch, and Reserve (AWaRe) classification. Our findings highlight the need to strengthen antibiotic stewardship programs and public awareness campaigns on responsible antibiotic use to safeguard public health and ensure ongoing treatment efficacy.

Limitations

Our study only utilized secondary data from the national AMR surveillance system, which are limited in terms of variables available for analysis. Secondly, the study utilized resistance of only organisms obtained from sterile samples, and thus other organisms from non-sterile samples were excluded, resulting in an under-estimation of the rates of antibiotic resistance in Uganda. Patient-level studies using primary data to determine the factors rates and trends of antibiotic resistance from all patient samples may be beneficial in understanding the trends of antibiotic resistance in Uganda. An analysis with more samples would be more predictive of more significant findings. A longitudinal study of antibiotic prescriptions and its relationship to resistance would be helpful.

Conclusion

The study highlights a concerning increase in antibiotic resistance rates over four years, with significant increase in resistance observed across different classes of antibiotics for both gram-positive and gram-negative organisms. This increased antibiotic resistance, particularly to commonly used antibiotics like ceftriaxone and ciprofloxacin, makes adhering to the WHO's Access, Watch, and Reserve (AWaRe) category even more critical. It also emphasizes how important it is to guard against the growing threat of antibiotic resistance by appropriately using medicines, especially those that are marked for "Watch" or "Reserve."

Abbreviations

AMR Antimicrobial resistance

LIC Low-income country

NMRL National microbiology reference laboratory

RRH Regional Referral Hospital

NRH National Referral Hospital

IDI Infectious diseases institute

MOH Ministry of Health

Acknowledgements

The authors thank the staff of the Uganda Public Health Fellowship Program for the technical support and guidance offered during this study. The authors also extend their appreciation to the Ministry of Health, National Microbiology Reference Laboratory and National Health Laboratory and Diagnostic Centre for the technical support they offered during this study. Finally, we appreciate the microbiology laboratory staff of the different hospitals who ensure reporting of AMR data from their respective hospitals, as well as the team managing the AMR surveillance data at the National Coordination Centre; your hard work enabled the availability of data we used for this analysis.

Authors’ contributions

SNK: participated in the conception, design, analysis, interpretation of the study and wrote the draft manuscript; RH, PEO, BS, ZK, BA, JFZ, HNN, RA, RN, IM and GN reviewed the report, reviewed the drafts of the manuscript for intellectual content and made multiple edits to the draft manuscript; RM, DK, BK POE, LB, ARA and SN reviewed the manuscript to ensure intellectual content and scientific integrity. All authors read and approved the final manuscript.

Funding

The project was supported by the President’s Emergency Plan for AIDS (PEPFAR) through the United States Centers for Disease Control and Prevention Cooperative Agreement number GH001353-01 through Makerere University School of Public Health to the Uganda Public Health Fellowship Program, Ministry of Health. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the US Centers for Diseases Control and Prevention, the Department of Health and Human Services, Makerere University School of Public Health, or the Ministry of Health.

The funders had no role in the study design, data collection, data analysis and decision to publish or preparation of the manuscript.

Availability of data and materials

The datasets upon which our findings are based belong to the Uganda Public Health Fellowship Program. For confidentiality reasons, the datasets are not publicly available. The datasets can be availed upon reasonable request from the corresponding author with permission from the Uganda Public Health Fellowship Program.

Declarations

Ethics approval and consent to participate

Because our study used routinely collected surveillance data reported by health facilities in the National AMR system which were also aggregated with no individual patient identifiers.However, we sought permission to use the data from the Uganda MoH. The US Centers for Disease Control and Prevention (CDC) also provided the non-research determination (NRD) for non-human subjects which is equivalent to a waiver. Data were only accessed by the study team.

Consent to publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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