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Case–Case Analysis Using 7 Years of Travelers' Diarrhea Surveillance Data: Preventive and Travel Medicine Applications in Cusco, Peru

Mary Carol JenningsDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Preventive Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

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Drake H. TilleyBacteriology Department, Naval Medical Research Unit No. 6, Lima, Peru.

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Sarah-Blythe BallardDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Parasitology Department, Naval Medical Research Unit No. 6, Lima, Peru.
Asociación Benéfica PRISMA, Lima, Peru.

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Miguel VillanuevaHematology Department, Hospital Universitario de la Princesa, Madrid, Spain.

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Fernando Maldonado CostaDepartment of Epidemiology, Royal Tropical Institute, Amsterdam, The Netherlands.

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Martha LopezDepartment of Internal Medicine, University of Texas Medical Branch, Galveston, Texas.
Collaborative Research Center, Universidad Peruana Cayetano Heredia, Cusco, Peru.

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Hannah E. SteinbergDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

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C. Giannina LunaVirology Department, Naval Medical Research Unit No. 6, Lima, Peru.

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Rina MezaBacteriology Department, Naval Medical Research Unit No. 6, Lima, Peru.

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Maria E. SilvaVirology Department, Naval Medical Research Unit No. 6, Lima, Peru.

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Robert H. GilmanDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

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Mark P. SimonsBacteriology Department, Naval Medical Research Unit No. 6, Lima, Peru.

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Ryan C. MavesBacteriology Department, Naval Medical Research Unit No. 6, Lima, Peru.
Division of Infectious Diseases, Naval Medical Center San Diego, San Diego, California.

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Miguel M. CabadaDepartment of Internal Medicine, University of Texas Medical Branch, Galveston, Texas.
Collaborative Research Center, Universidad Peruana Cayetano Heredia, Cusco, Peru.

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In Cusco, Peru, and South America in general, there is a dearth of travelers' diarrhea (TD) data concerning the clinical features associated with enteropathogen-specific infections and destination-specific risk behaviors. Understanding these factors would allow travel medicine providers to tailor interventions to patients' risk profiles and travel destination. To characterize TD etiology, evaluate region-specific TD risk factors, and examine relationships between preventive recommendations and risk-taking behaviors among medium- to long-term travelers' from high-income countries, we conducted this case–case analysis using 7 years of prospective surveillance data from adult travelers' presenting with TD to a physician in Cusco. At the time of enrollment, participants provided a stool sample and answered survey questions about demographics, risk behaviors, and the clinical features of illness. Stool samples were tested for norovirus (NV), bacteria, and parasites using conventional methods. Data obtained were then analyzed using case–case methods. NV (14%), enterotoxigenic Escherichia coli (11%), and Campylobacter (9%), notably ciprofloxacin-resistant Campylobacter, were the most frequently identified pathogens among adults with TD. Coinfection with multiple enteropathogens occurred in 5% of cases. NV caused severe disease relative to other TD-associated pathogens identified, confining over 90% of infected individuals to bed. Destination-specific risk factors include consumption of the local beverage “chicha,” which was associated with Cryptosporidium infection. Preventive interventions, such as vaccines, directed against these pathogens could significantly reduce the burden of TD.

Introduction

Cusco, Peru, is a popular travel destination in the Andes mountain range that is famous for its archaeological remains, including Machu Picchu. Previous studies have identified Cusco as a destination associated with a high risk of acute travelers' diarrhea (TD), with U.S. travelers at higher risk than those from other countries (adjusted odds ratio [OR] = 1.28; 95% confidence interval [CI] = 1.09–1.50).1

However, in Cusco, and South America in general, the specific etiologies of TD have not been well evaluated, and the clinical features associated with pathogen-specific infections, along with destination-specific risk behaviors, remain largely uncharacterized. Understanding these factors would allow travel medicine providers to tailor interventions to patients' risk profiles and travel destination.2

Anticipatory guidance and interventions (e.g., immunizations and prophylaxis) aim to decrease the incidence of travel-related injury and illness. Previous work shows that 94% of travelers to Cusco seek some sort of pretravel preventive guidance.3

However, North American travelers to Peru are less likely than western European travelers to seek pretravel preventive medical advice from a travel medicine practitioner (37% versus 45.8%, P < 0.01) and from a health-care professional (52.0% versus 67.1%, P < 0.01).3,4

Further, current health-related guidelines and dietary precautions have little evidence of effect on the incidence of TD.57

Although most patients with TD experience spontaneous symptom resolution, some may experience symptoms for weeks,8 and up to half of travelers change travel plans as a result of symptoms.5

Of note, immunocompromised patients from high-income countries are traveling at increasing rates, and their increased risk for developing disease highlights the importance of providing effective preventive recommendations and measures for such complex patients.9 The development of subsequent irritable bowel syndrome has become an increasingly recognized risk in the past decade,10 reported by up to 1/5 of returning travelers with TD.5

Thus, the benefits of reducing TD incidence extend beyond the travel period.

Traditionally, TD has been difficult to characterize because most data are derived from sentinel surveillance systems, which often lack denominator data and asymptomatic controls for comparison. A case–case approach allows a comparison of symptom severity between pathogens identified in the stool, although it lacks mechanisms to question if a particular pathogen present in the stool is causally related to diarrheal disease. A case–case approach allows restricted and refined analysis of some unique exposures associated with different pathogens detected through a surveillance system.1113

Although the approach reduces selection and recall bias relative to case–control formats14 by ensuring that both case and “control” subjects were all affected by similar disease, the design does remain vulnerable to other forms of selection bias, that is, greater selection of infected travelers with severe symptoms compared with milder symptoms. To describe the etiology of medically attended TD, evaluate regional risk factors, and examine relationships between preventive recommendations and risk-taking behaviors among medium- to long-term travelers from high-income countries, we conducted this case–case analysis of 7 years of prospective surveillance data from adults presenting to a physician in Cusco with TD.

Materials and Methods

Ethics statement.

This study was approved by the institutional review boards of Naval Medical Research Unit No. 6 and Universidad Peruana Cayetano Heredia. Participants provided written informed consent at the time of enrollment.

Study population.

Between June 2003 and July 2010, we prospectively enrolled Spanish language students presenting consecutively with acute diarrhea to the Amauta Spanish School's physician. Male and female students, older than 18 years, from countries with low-to-moderate risk of TD, and seeking free medical attention were included.15

We defined diarrhea as three or more loose or watery stools, or two loose or watery stools accompanied by nausea, vomiting, abdominal cramping/pain, or tenesmus, within a 24-hour period.16,17

To reduce confounding by chronic or recently treated diarrhea, students who could not provide a stool sample at the time of presentation, had taken antimicrobial medications within the week prior to presentation, reported chronic diarrhea, or did not speak English were excluded.

Survey administration, sample collection, pathogen identification, and antimicrobial resistance testing.

Using a standardized form, a trained health-care worker surveyed enrollees to determine age, gender, country of birth, country of residence, lodging, and length of stay in Cusco, and Peru, in addition to TD risk factors, such as history of previous episodes, comorbidities, preventive measures, location and type of foods ingested, and clinical features of the presenting episode. Nonrecommended foods were defined using standard lists of unsafe foods.2

We collected survey responses from individual cases while waiting for laboratory results to further reduce information and recall bias. The collection of individual-level data permitted us to control for potential confounding.

Participants provided fresh stool samples for analysis. Upon collection, specimens were kept fresh in preservative-free containers and transported in Cary Blair media to a central reference laboratory (ServiSalud) in Cusco city for bacteria and parasite analyses. On arrival, stool specimens went through a microscopic examination looking for ova and parasites using saline wet preparations to detect protozoa and helminth infections. The Ritchie method18 was used to identify Giardia spp. Cary Blair specimens were cultured for Campylobacter, Escherichia coli, Salmonella, Shigella, Aeromonas, and Plesiomonas using conventional microbiologic techniques.1820

We performed polymerase chain reaction (PCR) on five lactose-fermenting colonies morphologically resembling E. coli to identify heat labile and stable enterotoxigenic (ETEC), enteropathogenic (EPEC), and enteroaggregative (EAEC) E. coli.21

Antibiotic susceptibility testing was performed using the disk diffusion method according to the Clinical and Laboratory Standards Institute (CLSI) guidelines.22

The isolates were tested against the most commonly used antimicrobial agents: ampicillin, amoxicillin–clavulanic acid, cefazolin, ceftriaxone, erythromycin, azithromycin, nalidixic acid, ciprofloxacin, gentamicin, sulfamethoxazole–trimethoprim, and tetracycline.22

In the case of azithromycin, because of the absence of an established breakpoint, minimal inhibitory concentration was also determined by the agar dilution method according to CLSI guidelines22 on all isolates with an inhibitory halo < 15 mm.23

We combined intermediate and resistant isolates for analysis purposes. To determine Enterobacteriaceae susceptibility given lack of breakpoints published in CSLI, our laboratory used Staphylococcus aureus for quality control, and then established a resistance cutoff of 16 mm24 with a susceptible strain of E. coli 25922.22,2528

After preparing fresh specimens for parasite identification, the remaining stool was frozen and sent to Naval Medical Research Unit No. 6 (NAMRU-6) in Lima, Peru, for additional parasite testing using microscopy and commercial enzyme-linked immunosorbent assay (ELISA) kits for Entamoeba histolytica (TechLabs, Blacksburg, VA), Giardia lamblia (BioTech Trading Partners), and Cryptosporidium (BioTech Trading Partners, Encinitas, CA). Cryptosporidium-positive results were confirmed by PCR. Samples positive by microscopy but negative by ELISA were considered positive; in any further cases of discrepancy between results of pathogen identification at ServiSalud and NAMRU-6, NAMRU-6 results were considered confirmatory. A suspension of each stored stool specimen (10% w/v) was prepared in phosphate-buffered saline and RNA extracted using the Qiagen QIAmp Viral RNA Kit (Valencia, CA) in accordance with the manufacturer's instructions. Viral RNA was tested for Norovirus (NV) GI and GII by real-time PCR.29

A sample was considered positive if the negative control did not exhibit fluorescent curves. The threshold cycle for the sample was at least 37 for NV GI and 39 for GII.

Statistical analysis.

Statistical analysis was performed using Stata version 13.0 (StataCorp LP, College Station, TX). By convention, P values < 0.05 were considered statistically significant. Exploratory analysis of participant demographics, diarrhea risk factors, and clinical features of illness was performed using the two-tailed Student's t test to compare continuous variable means for normally distributed variables, Wilcoxon rank-sum tests for nonparametric continuous variables, and Fisher's exact test to compare proportions. We used dichotomous variables to characterize any versus no exposure to individual diarrhea risk factors. After excluding individuals coinfected with more than one pathogen, we performed a case–case analysis to examine the association of single-pathogen infections with dietary risk factors, according to the methods of Wilson and others.30

It is standard practice in a case–case analyses to take the most prevalent or most frequently notified cause as the referent or comparison group.30

We selected ETEC as the case–case analysis reference group because it is the most commonly implicated pathogen worldwide,2 has the added benefit of being the most frequently identified cause of TD in Latin America, and has well characterized risk factors and clinical feature profiles.17

We considered age and gender to be confounders a priori,13,14 and controlled for them using multiple logistic regression. Exploratory analysis did not identify additional potential confounding factors.

Results

Demographics.

During the 7-year passive surveillance period, 230 adults (66% female) aged 18–76 (median = 24, interquartile range [IQR] = 7) years with diarrhea were enrolled in the study (Table 1). Participants were permanent residents of 17 countries, 75% were European, 21.3% North American, and 3.5% Australian. The mean length of stay in Cusco was 45 days (median = 28, range = 1–548, IQR = 52). The diarrhea incidence among cases was 2.2 episodes per 90 person-days (230/9,417.5) in Cusco.

Table 1

Demographic characteristics, protective behaviors, and risk factor frequencies for participants

Variable Mean or frequency Percent* Observations Missing (%) Standard deviation
Demographic factors
 Age, years 26.9   230 0 9.6
 Gender (female) 152 66.1 230 0  
 Length of stay in Cusco, days 44.85   210 8.7 60.7
 Season (summer/rainy) 100 43.5 230 0  
 Country of Residence     230 0  
  Netherlands 53 23.0      
  United Kingdom 44 19.1      
  United States 40 17.4      
  Europe, other 36 15.7      
  Switzerland 22 9.6      
  Germany 20 8.7      
  Canada 9 3.9      
  Australia 6 2.6      
Protective factors against TD
 Received advice 176 76.5 230 0  
 Followed all recommendations     195 15.2  
  Never 6 3.1      
  Sometimes 99 50.8      
  Always 90 46.2      
 Took medications to prevent TD (yes) 55 23.9 230 0  
  If yes, what medications?     50 2.2  
   Antibiotics 16 32.0      
   Other 32 64.0      
   Bismuth 2 4.0      
Risk factors
 Location of majority of meals in past week     230 0  
  Restaurant 108 47.0      
  Other 57 24.8      
  Friend's home 35 15.2      
  Hotel 30 13.0      

TD = travelers' diarrhea.

Percent of number of observations for each variable.

Austria, Belgium, Czech Republic, Denmark, Finland, France, Italy, Israel, Turkey, Spain, and Sweden.

Reported diarrhea risk factors.

During the week before presenting with diarrhea, 47% of participants ate the majority of meals in a restaurant, 15.2% in a friend's home, 13% in a hotel, and 24.8% in an “other” location (Table 1). All participants reported consuming at least one of the following risky food items time during the week before illness: fruit juice (86%), cheese (78%), raw green vegetables (72%), cold sauces (52%), milk (46%), ice (40%), reheated buffet foods (38%), ice cream (33%), unpeeled fruit (29%), street vendor food (28%), the local drink chicha (25%), and tap water (14%) (Table 2).

Table 2

Signs, symptoms, stool characteristics, and effect of daily activities among those positive for given pathogens, or negative for all pathogens

  Total Pathogen
No Pathogen EAEC ETEC Campylobacter Cryptosporidium Giardia Norovirus Shigella
n % n % n % n % n % n % n % n % n %
Signs and symptoms
 Fatigue 156 68 79 63 8 89 18 72 15 75 5 71 5 56 24 75 11 79
 Gas or bloating 145 63 79 63 2 22 16 64 11 55 6 86 8 89 18 56 11 79
 Nausea 113 49 58 46 6 67 11 44 11 55 5 71 2 22 15 47 9 64
 Vomiting 79 34 38 30 5 56 9 36 6 30 3 43 3 33 16 50 3 21
 Fever 96 42 45 36 6 67 8 32 15 75 1 14 3 33 13 41 10 71
 Abdominal cramping/pain 174 76 88 70 9 100 21 84 14 70 7 100 7 78 27 84 12 86
 Loss of appetite 158 69 79 63 7 78 15 60 16 80 6 86 8 89 24 75 12 86
 Loss of weight 80 35 44 35 2 22 10 40 7 35 5 71 6 67 11 34 2 14
Characteristics of stool
 Loose 114 50 72 58 2 22 10 40 7 35 5 71 4 44 12 38 7 50
 Bloody 23 10 8 6 1 11 4 16 4 20 0 0 2 22 3 9 4 29
 Watery 193 84 99 79 8 89 23 92 19 95 7 100 7 78 31 97 10 71
 Mucus 41 18 21 17 1 11 5 20 4 20 1 14 0 0 5 16 5 36
Dietary risks (one or more exposures in the past week)
 Raw greens/vegetables/salad 160 72 89 74 5 56 18 75 15 75 5 71 6 67 21 68 10 77
 Fruit juice 193 86 104 87 8 89 24 96 15 75 6 86 7 78 29 91 11 85
 Cold sauce 114 52 62 52 3 33 13 52 9 47 3 43 4 44 18 58 7 54
 Cheese 175 78 93 77 7 78 21 84 14 70 6 86 6 67 24 75 13 100
 Tap water 30 14 17 15 0 0 2 8 4 20 1 14 0 0 5 16 1 8
 Milk 104 46 63 52 2 22 9 36 9 45 1 14 4 44 14 45 6 46
 Ice with drinks 89 40 52 43 4 44 10 40 7 37 4 57 1 11 11 34 4 31
 Ice cream 74 33 35 29 2 22 12 48 8 44 3 43 1 11 10 31 5 39
 Reheated buffet food 83 38 49 43 4 44 6 24 8 42 5 71 5 56 5 17 6 46
 Chicha 54 25 30 26 4 50 3 12 4 21 4 57 0 0 5 16 3 23
 Street vendor food 63 28 44 37 1 11 7 28 5 25 1 14 0 0 3 9 3 21
 Unpeeled fruit 64 29 37 31 3 18 3 12 4 21 1 14 2 22 9 29 4 31
Effect on daily activities
 No effect 41 18 23 18 1 11 3 12 2 10 2 29 1 11 4 13 0 0
 Stay in bed 143 62 66 53 5 56 17 68 17 85 4 57 5 56 29 91 10 71
 Change itinerary 30 13 16 13 2 22 2 8 3 15 1 14 1 11 2 6 4 29
 Miss tour 52 23 27 22 3 33 3 12 7 35 1 14 1 11 5 16 5 36
 See physician 120 52 60 48 5 56 15 60 10 50 2 29 5 56 18 56 11 79
 Hospitalized 2 1 1 1 0 0 0 0 0 0 1 14 0 0 0 0 0 0

EAEC = enteroaggregative Escherichia coli; ETEC = enterotoxigenic E. coli.

Approximately two-thirds (176/230) of study participants reported receiving advice to prevent diarrhea. These individuals reported following this guidance all (46%), some (51%), or none (3%) of the time. Among those who reported always following recommendations, 93% (84/90) reported consuming three or more of the abovementioned foods linked to TD. Among these three stratified risk groups, the majority consumed fruit juice (84%, 86%, and 83%), cheese (73%, 83%, and 83%), or raw green vegetables (58%, 81%, and 83%), and the majority (84%) of the group that reported always following recommendations also consumed at least three foods linked to TD. In addition, 55/230 (24%) study participants took medications to prevent diarrhea, at some time prior to 1 week before enrollment. Of these individuals, 50 provided information on the type of medication used: 32% had taken antibiotics, 4% took bismuth preparations, and 64% took “other” medications.

Pathogen identification.

A pathogen was identified in 45% of 230 cases (Table 3). NV was identified in 14% of samples (6 GI, 26 GII), ETEC in 11%, Campylobacter in 9%, Shigella in 6%, EAEC in 4%, Giardia in 4%, and Cryptosporidium in 3%. No EPEC, E. histolytica, Salmonella, Aeromonas, or Plesiomonas were detected. Coinfections with more than one pathogen were identified in 12 (5%) participants. Coinfections were most frequent among cases infected with the parasites Giardia (44%, 4/9) and Cryptosporidium (43%, 3/7). Coinfections were also found in individuals infected with ETEC (28%, 7/25 ETEC cases) in combination with NV (19%, 6/32 NV cases), EAEC (11%, 1/9 EAEC cases), and Campylobacter (10%, 2/20 Campylobacter cases). NV and ETEC were most frequently identified together (N = 6 coinfected cases), followed by Cryptosporidium and Giardia (N = 2 coinfected cases). Coinfections with the following pathogens were each observed once: EAEC and NV, ETEC and Giardia, Campylobacter and Cryptosporidium, and Campylobacter and Giardia. Giardia was a coinfecting agent in the greatest number of pathogen combinations (three other pathogens). No Shigella cases were coinfected. Table 4 shows bacterial antimicrobial resistance patterns, and Table 5 shows the relationship between pathogen prevalence and seasonality.

Table 3

Pathogen counts and frequencies for 230 total samples

  Positive Percentage of 230 Negative No. of samples Total reporting Tested* (%)
Norovirus 32 13.9 141 63 167 72.6
 Norovirus GI 6          
 Norovirus GII 26          
ETEC 25 10.9 157 48 182 79.1
 St+ 16          
 Lt+ 5          
 Lt+/St+ 4          
Campylobacter 20 8.7 201 9 221 96.1
jejuni 16          
coli 3          
 Other species 1          
Shigella 14 6.1 215 1 229 99.6
flexneri 4          
sonnei 9          
 Other species 1          
EAEC 9 3.9 45 176 54 23.5
Giardia 9 3.9 218 3 227 98.7
Cryptosporidium 7 3.0 217 6 224 97.4
EPEC 0 0.0 182 48 182 79.1
Entamoeba histolytica 0 0.0 227 3 227 98.7
Salmonella 0 0.0 229 1 229 99.6
Total cases       0 230 100.0
Any pathogen 104 45.2        
No pathogen 125 54.3        

EAEC = enteroaggregative Escherichia coli; EPEC = enteropathogenic E. coli.

Percentage of samples tested for specific pathogen.

Subcategories shown are included in the total pathogen counts given.

Table 4

Antibiogram by isolate and percent susceptibility

  n AM AMC CF CRO E AZM NA CIP GM SXT TE
Campylobacter coli 3 33 100 0 33 33 100 33 33 100 0 100
Campylobacter jejuni 16 56 100 38 13 88 100 25 25 100 0 44
Enteroaggregative Escherichia coli 9 56 89 67 89 89 67 100 100 100 78 78
Enterotoxigenic E. coli* 27 59 89 63 100 4 78 67 93 96 59 59
Shigella flexneri (Group B) 4 50 75 25 100 0 100 100 100 100 25 0
Shigella sonnei (Group D) 9 0 22 67 100 0 56 89 100 100 0 0

AM = ampicillin; AMC = amoxicillin/clavulanic acid; AZM = azithromycin; CF = cefalotin; CIP = ciprofloxacin; CRO = ceftriaxone; E = erythromycin; GM = gentamycin; NA = nalidixic acid; SXT = sulfamethoxazole/trimethoprim; TE = tetracycline.

Two different resistance patterns were detected in two of the 27 samples and included in the table (N = 27).

Table 5

Season of presentation by pathogen cases*

  Summer/rainy Winter/dry P value
Pathogen-specific cases Total cases Pathogen as % total cases Pathogen-specific cases Total cases Pathogen as % total cases
EAEC 6 82 7.3 3 100 3.0 0.072
ETEC 18 82 22.0 7 100 7.0 0.005
Campylobacter 5 95 5.3 15 126 11.9 0.102
Cryptosporidium 2 98 2.0 5 126 4.0 0.472
Giardia 5 100 5.0 4 127 3.1 0.512
Norovirus GI 3 72 4.2 3 95 3.2 1.000
Norovirus GII 17 72 23.6 9 95 9.5 0.017
Norovirus 20 72 27.8 12 95 12.6 0.017
Shigella 6 100 6.0 8 129 6.2 1.000
No pathogen 46 100 46.0 79 129 61.2 0.024
Overall 100     130      

EAEC = enteroaggregative Escherichia coli; ETEC = enterotoxigenic E. coli.

Summer/rainy season refers to participation in study during November through April, and winter/dry season during May through October.

Fisher's exact, two-sided χ2 of the difference in proportion of season for each pathogen outcome. P values < 0.05 are shown in bold.

TD clinical features and severity.

Gastroenteritis episodes were most frequently associated with watery diarrhea (84%), abdominal cramping/pain (76%), loss of appetite (69%), fatigue (68%), and gas/bloating (63%) (Table 2). Subjective fever was reported in 42% of TD cases, most frequently with Campylobacter (75%), Shigella (71%), and EAEC (67%). Thirty-four percent of cases reported vomiting. Fifty-six percent of those with EAEC and 50% of those with NV (13/26 GII, 3/6 GI) vomited. Ten percent reported bloody diarrhea, most often with Shigella (36%), Giardia (22%), Campylobacter (20%), ETEC (16%), and NV GII (9%); no NV GI case had bloody diarrhea. These statistics include two individuals who were coinfected with NV GII and ETEC, along with one individual coinfected with Campylobacter and Giardia. Of the 230 cases, two were hospitalized, 62% stayed in bed, 52% saw a physician, 23% missed a scheduled tour, 13% made a change in travel itinerary, and 18% reported no effect on daily activities. One hospitalized case had no pathogen identified; one was positive for Cryptosporidium. Both were females aged 23 who presented in the winter and reported consuming raw green vegetables, fruit juice, and cheese. Neither had chronic gastritis. The case with no pathogen was from Holland, stayed in Cusco for 2 weeks in the winter, and reported she always followed preventive recommendations. The Cryptosporidium case was from Belgium, stayed in Cusco for 2 months in the winter, gave no answer regarding following recommendations, and reported consuming chicha. Ninety-one percent of norovirus-associated diarrhea cases were confined to bed. ETEC and Shigella cases had the largest proportion of doctors' visits, 60% and 79%, respectively.

Case–case analysis.

In the unadjusted case–case analysis of diarrhea risk factors, individuals with and without ETEC ate at similar locations and had similar food exposures during the week before illness. However, individuals infected with NV GII were significantly less likely to have consumed reheated buffet food (OR = 0.10, 95% CI = 0.01–0.91), and individuals infected with NV overall were significantly less likely to have consumed food from street vendors (OR = 0.11, 95% CI = 0.01–0.88) relative to those with ETEC, although the buffet food relationship did not persist when the NV strains were combined. Chicha consumption was significantly associated with Cryptosporidium (OR = 5.44, 95% CI = 1.1–27.3) and EAEC (OR = 7.33, 95% CI = 1.17–46.1) infections relative to infection with ETEC. Individuals with no pathogen detected were significantly less likely to have consumed ice cream in the past week relative to ETEC (OR = 0.40, 95% CI = 0.17–0.94). Being a permanent resident of the United States or staying in Cusco longer than 1 week were not significantly associated with any pathogen relative to ETEC (data not shown). After adjusting for the potential confounders of age and gender, the statistical significance, OR magnitude, and CI size of these associations remained unchanged (Table 6).

Table 6

Reported dietary risk factor exposure and ORs for different enteric diseases*

  n Reported exposure to risk factor Crude OR Adjusted OR
Yes No Reporting risk (%) Unknown§ (%) OR 95% CI P value OR 95% CI P value
Majority of past week's meals consumed in friend's home, restaurant, or “other,” compared with hotel
Campylobacter 64 55 9 85.9 0.0 0.30 0.07–1.27 0.10 0.31 0.07–1.43 0.13
Cryptosporidium 76 66 10 86.8 0.0 ***     ***    
 EAEC 34 30 4 88.2 0.0 1.09 0.1–12.1 0.94 1.04 0.07–15.1 0.98
Giardia 78 67 11 85.9 0.0 1.17 0.13–10.5 0.89 1.65 0.14–19.4 0.69
 Norovirus GI 70 59 11 84.3 0.0 0.93 0.1–8.79 0.95 0.78 0.08–7.66 0.83
 Norovirus GII 76 66 10 86.8 0.0 ***     ***    
 Norovirus 82 71 11 86.6 0.0 5.43 0.66–44.9 0.12 5.42 0.59–49.4 0.13
Shigella 72 62 10 86.1 0.0 ***     ***    
 No pathogen detected 151 129 22 85.4 0.0 0.73 0.2–2.67 0.63 0.76 0.21–2.83 0.69
 ETEC 182 159 23 87.4 0.0 1.0 (ref)     1.0 (ref)    
Consumed raw green vegetables or salads in past week
Campylobacter 61 46 15 75.4 4.7 0.97 0.28–3.34 0.96 0.12 0.32–4.69 0.77
Cryptosporidium 73 54 19 74.0 3.9 0.87 0.15–4.89 0.87 0.67 0.11–3.91 0.65
 EAEC 33 23 10 69.7 2.9 0.42 0.08–2.08 0.29 0.44 0.08–2.43 0.34
Giardia 75 55 20 73.3 3.8 0.57 0.12–2.63 0.47 0.29 0.05–1.71 0.17
 Norovirus GI 68 52 16 76.5 2.9 ***     ***    
 Norovirus GII 73 52 21 71.2 3.9 0.44 0.15–1.32 0.14 0.33 0.09–1.18 0.09
 Norovirus 79 58 21 73.4 3.7 0.67 0.24–1.92 0.46 0.56 0.17–1.78 0.32
Shigella 69 52 17 75.4 4.2 1.11 0.27–4.62 0.89 1.10 0.25–4.9 0.90
 No pathogen detected 146 107 39 73.3 3.3 1.08 0.41–2.83 0.87 1.07 0.41–2.82 0.89
 ETEC 177 126 51 71.2 2.7 1.0 (ref)     1.0 (ref)    
Consumed fruit juice in past week
Campylobacter 62 53 9 85.5 3.1 0.32 0.08–1.34 0.12 0.26 0.06–1.21 0.09
Cryptosporidium 74 63 11 85.1 2.6 1.05 0.11–9.7 0.96 1.12 0.11–11.8 0.92
 EAEC 34 32 2 94.1 0.0 0.33 0.02–5.97 0.46 0.28 0.01–8.64 0.47
Giardia 76 65 11 85.5 2.6 0.46 0.08–2.63 0.38 0.66 0.1–4.55 0.67
 Norovirus GI 69 58 11 84.1 1.4 0.33 0.05–2.1 0.24 0.23 0.03–1.81 0.16
 Norovirus GII 75 65 10 86.7 1.3 3.72 0.44–31.4 0.23 9.11 0.86–95.8 0.07
 Norovirus 81 69 12 85.2 1.2 1.50 0.37–6.08 0.57 2.55 0.53–12.2 0.24
Shigella 70 60 10 85.7 2.8 0.90 0.17–4.83 0.90 0.87 0.15–4.94 0.87
 No pathogen detected 146 128 18 87.7 3.3 0.54 0.12–2.52 0.43 0.55 0.12–2.56 0.44
 ETEC 178 157 21 88.2 2.2 1.0 (ref)     1.0 (ref)    
Consumed cold sauce in the past week
Campylobacter 60 31 29 51.7 6.3 0.78 0.26–2.31 0.65 0.85 0.28–2.59 0.78
Cryptosporidium 72 38 34 52.8 5.3 0.64 0.13–3.1 0.58 0.59 0.12–2.95 0.52
 EAEC 34 16 18 47.1 0.0 0.46 0.09–2.27 0.34 0.38 0.07–2.18 0.28
Giardia 74 39 35 52.7 5.1 0.50 0.11–2.27 0.37 0.35 0.07–1.86 0.22
 Norovirus GI 67 32 35 47.8 4.3 0.52 0.09–3.03 0.47 0.54 0.09–3.28 0.50
 Norovirus GII 72 38 34 52.8 5.3 2.43 0.8–7.35 0.12 1.77 0.52–6.03 0.36
 Norovirus 78 40 38 51.3 4.9 1.68 0.64–4.41 0.29 1.29 0.44–3.74 0.64
Shigella 68 37 31 54.4 5.6 0.97 0.29–3.27 0.96 0.98 0.28–3.34 0.97
 No pathogen detected 145 75 70 51.7 4.0 1.09 0.47–2.54 0.85 1.11 0.47–2.6 0.82
 ETEC 177 90 87 50.8 2.7 1.0 (ref)     1.0 (ref)    
Consumed cheese in the past week
Campylobacter 62 50 12 80.6 3.1 0.39 0.11–1.41 0.15 0.39 0.1–1.46 0.16
Cryptosporidium 74 61 13 82.4 2.6 1.31 0.14–11.9 0.81 1.24 0.13–12.3 0.85
 EAEC 34 28 6 82.4 0.0 0.67 0.1–4.46 0.68 0.66 0.09–4.98 0.69
Giardia 76 62 14 81.6 2.6 0.32 0.07–1.55 0.16 0.45 0.07–2.67 0.38
 Norovirus GI 69 57 12 82.6 1.4 1.06 0.11–9.97 0.96 0.94 0.1–9.16 0.96
 Norovirus GII 75 59 16 78.7 1.3 0.52 0.16–1.68 0.27 0.65 0.18–2.38 0.52
 Norovirus 81 64 17 79.0 1.2 0.60 0.2–1.82 0.37 0.71 0.22–2.32 0.57
Shigella 70 58 12 82.9 2.8 ***     ***    
 No pathogen detected 147 115 32 78.2 2.6 0.60 0.19–1.9 0.39 0.60 0.19–1.91 0.39
 ETEC 179 137 42 76.5 1.6 1.0 (ref)     1.0 (ref)    
Consumed tap water in the past week
Campylobacter 61 8 53 13.1 4.7 2.32 0.51–10.4 0.28 2.22 0.49–10.2 0.30
Cryptosporidium 73 9 64 12.3 3.9 1.21 0.13–11.4 0.87 0.64 0.16–17.1 0.68
 EAEC 33 2 31 6.1 2.9 ***     ***    
Giardia 75 8 67 10.7 3.8 ***     ***    
 Norovirus GI 68 6 62 8.8 2.9 ***     ***    
 Norovirus GII 73 9 64 12.3 3.9 2.61 0.62–11 0.19 3.00 0.58–15.5 0.19
 Norovirus 79 9 70 11.4 3.7 1.87 0.46–7.65 0.39 1.97 0.42–9.18 0.39
Shigella 69 8 61 11.6 4.2 0.58 0.07–5.2 0.63 0.59 0.07–5.26 0.63
 No pathogen detected 142 20 122 14.1 6.0 1.25 0.34–4.63 0.74 1.27 0.34–4.75 0.72
 ETEC 173 23 150 13.3 4.9 1.0 (ref)     1.0 (ref)    
Consumed milk in the past week
Campylobacter 63 26 37 41.3 1.6 1.25 0.43–3.66 0.68 1.26 0.42–3.74 0.68
Cryptosporidium 75 31 44 41.3 1.3 0.21 0.02–1.85 0.16 0.17 0.02–1.58 0.12
 EAEC 34 11 23 32.4 0.0 0.51 0.09–2.98 0.45 0.44 0.07–2.71 0.37
Giardia 77 33 44 42.9 1.3 1.38 0.32–5.98 0.67 1.38 0.28–6.69 0.69
 Norovirus GI 69 28 41 40.6 1.4 1.52 0.28–8.14 0.63 1.42 0.26–7.84 0.68
 Norovirus GII 74 32 42 43.2 2.6 1.25 0.44–3.57 0.67 1.38 0.42–4.49 0.59
 Norovirus 80 35 45 43.8 2.4 1.28 0.5–3.32 0.61 1.42 0.5–4.01 0.51
Shigella 71 30 41 42.3 1.4 1.21 0.36–4.07 0.75 1.20 0.36–4.06 0.77
 No pathogen detected 148 72 76 48.6 2.0 2.02 0.83–4.88 0.12 2.11 0.87–5.14 0.10
 ETEC 178 83 95 46.6 2.2 1.0 (ref)     1.0 (ref)    
Consumed ice with drinks in the past week
Campylobacter 61 23 38 37.7 4.7 0.95 0.31–2.91 0.93 1.05 0.33–3.29 0.94
Cryptosporidium 73 29 44 39.7 3.9 2.19 0.45–10.6 0.33 1.98 0.39–9.95 0.41
 EAEC 34 14 20 41.2 0.0 1.20 0.26–5.59 0.82 0.94 0.18–4.93 0.94
Giardia 75 28 47 37.3 3.8 0.21 0.03–1.82 0.16 0.13 0.01–1.32 0.08
 Norovirus GI 68 25 43 36.8 2.9 0.32 0.04–2.88 0.31 0.30 0.03–2.75 0.29
 Norovirus GII 74 27 47 36.5 2.6 0.92 0.31–2.67 0.87 0.88 0.28–2.8 0.83
 Norovirus 80 28 52 35.0 2.4 0.76 0.28–2.05 0.58 0.73 0.25–2.08 0.55
Shigella 69 27 42 39.1 4.2 0.64 0.18–2.32 0.50 0.63 0.17–2.32 0.49
 No pathogen detected 148 63 85 42.6 2.0 1.01 0.43–2.39 0.98 1.00 0.42–2.42 0.99
 ETEC 180 71 109 39.4 1.1 1.0 (ref)     1.0 (ref)    
Consumed ice cream in the past week
Campylobacter 61 26 35 42.6 4.7 1.11 0.37–3.37 0.85 1.23 0.39–3.88 0.72
Cryptosporidium 73 30 43 41.1 3.9 1.08 0.22–5.24 0.92 0.95 0.19–4.82 0.95
 EAEC 34 14 20 41.2 0.0 0.31 0.05–1.79 0.19 0.33 0.05–2.14 0.25
Giardia 75 28 47 37.3 3.8 0.21 0.03–1.82 0.16 0.15 0.02–1.51 0.11
 Norovirus GI 67 23 44 34.3 4.3 0.95 0.16–5.63 0.96 0.93 0.15–5.57 0.94
 Norovirus GII 73 25 48 34.2 3.9 0.77 0.25–2.33 0.64 0.58 0.16–2.03 0.39
 Norovirus 79 27 52 34.2 3.7 0.80 0.29–2.17 0.66 0.71 0.24–2.08 0.53
Shigella 69 28 41 40.6 4.2 0.90 0.26–3.09 0.86 0.90 0.26–3.1 0.86
 No pathogen detected 149 48 101 32.2 1.3 0.40 0.17–0.94 0.04 0.40 0.17–0.96 0.04
 ETEC 180 57 123 31.7 1.1 1.0 (ref)     1.0 (ref)  
Consumed food from a reheated buffet in the past week
Campylobacter 61 21 40 34.4 4.7 1.62 0.53–4.98 0.40 1.80 0.57–5.69 0.32
Cryptosporidium 73 27 46 37.0 3.9 5.00 0.9–27.9 0.07 4.91 0.86–27.9 0.07
 EAEC 34 10 24 29.4 0.0 2.53 0.51–12.6 0.26 3.93 0.63–24.7 0.14
Giardia 75 29 46 38.7 3.8 2.99 0.66–13.6 0.16 3.07 0.61–15.5 0.17
 Norovirus GI 68 24 44 35.3 2.9 1.95 0.36–10.5 0.44 1.96 0.36–10.8 0.44
 Norovirus GII 72 21 51 29.2 5.3 0.10 0.01–0.91 0.03 0.07 0.01–0.71 0.02
 Norovirus 78 24 54 30.8 4.9 0.34 0.1–1.14 0.08 0.36 0.1–1.29 0.12
Shigella 69 25 44 36.2 4.2 1.67 0.49–5.67 0.41 1.67 0.49–5.71 0.41
 No pathogen detected 141 55 86 39.0 6.6 2.48 0.93–6.62 0.07 2.47 0.92–6.64 0.07
 ETEC 171 64 107 37.4 6.0 1.0 (ref)     1.0 (ref)    
Consumed chicha in the past week
Campylobacter 61 11 50 18.0 4.7 1.33 0.34–5.24 0.68 1.43 0.35–5.77 0.62
Cryptosporidium 73 17 56 23.3 3.9 5.44 1.08–27.3 0.04 5.78 1.08–30.9 0.04
 EAEC 33 7 26 21.2 2.9 7.33 1.17–46.1 0.03 9.26 1.08–79.2 0.04
Giardia 75 14 61 18.7 3.8 ***     ***    
 Norovirus GI 67 13 54 19.4 4.3 1.04 0.11–10.2 0.97 1.08 0.11–10.9 0.95
 Norovirus GII 74 15 59 20.3 2.6 0.98 0.27–3.52 0.97 0.98 0.26–3.71 0.97
 Norovirus 79 16 63 20.3 3.7 0.98 0.3–3.19 0.97 0.89 0.26–3.04 0.85
Shigella 69 14 55 20.3 4.2 1.23 0.29–5.23 0.78 1.23 0.29–5.25 0.78
 No pathogen detected 142 34 108 23.9 6.0 1.92 0.61–6.02 0.26 1.91 0.61–6.02 0.27
 ETEC 172 42 130 24.4 5.5 1.0 (ref)     1.0 (ref)    
Consumed street vendor food in the past week
Campylobacter 63 17 46 27.0 1.6 0.86 0.26–2.89 0.81 0.89 0.26–3.08 0.86
Cryptosporidium 75 21 54 28.0 1.3 0.40 0.05–3.54 0.41 0.37 0.04–3.33 0.37
 EAEC 34 8 26 23.5 0.0 0.32 0.03–3.06 0.32 0.27 0.02–2.96 0.28
Giardia 77 20 57 26.0 1.3 ***     ***    
 Norovirus GI 70 17 53 24.3 0.0 ***     ***    
 Norovirus GII 76 16 60 21.1 0.0 0.14 0.02–1.17 0.07 0.14 0.02–1.21 0.07
 Norovirus 82 16 66 19.5 0.0 0.11 0.01–0.88 0.04 0.11 0.01–0.87 0.04
Shigella 71 20 51 28.2 1.4 0.64 0.16–2.6 0.53 0.67 0.16–2.78 0.58
 No pathogen detected 146 52 94 35.6 3.3 1.30 0.52–3.24 0.57 1.36 0.54–3.4 0.52
 ETEC 178 49 129 27.5 2.2 1.0 (ref)     1.0 (ref)    
Consumed unpeeled fruit in the past week
Campylobacter 61 12 49 19.7 4.7 1.13 0.3–4.35 0.86 1.24 0.31–4.92 0.76
Cryptosporidium 73 17 56 23.3 3.9 0.52 0.06–4.66 0.56 0.47 0.05–4.28 0.50
 EAEC 34 6 28 17.6 0.0 3.67 0.58–23 0.17 2.85 0.43–18.9 0.28
Giardia 75 18 57 24.0 3.8 1.06 0.2–5.79 0.94 0.75 0.12–4.68 0.76
 Norovirus GI 68 15 53 22.1 2.9 1.89 0.31–11.4 0.49 1.68 0.27–10.5 0.58
 Norovirus GII 73 20 53 27.4 3.9 1.84 0.6–5.65 0.29 1.89 0.55–6.43 0.31
 Norovirus 79 22 57 27.8 3.7 1.77 0.63–4.96 0.27 1.82 0.61–5.43 0.28
Shigella 69 16 53 23.2 4.2 1.63 0.43–6.22 0.48 1.67 0.42–6.68 0.47
 No pathogen detected 146 41 105 28.1 3.3 2.45 0.79–7.62 0.12 2.44 0.78–7.63 0.12
 ETEC 177 50 127 28.2 2.7 1.0 (ref)     1.0 (ref)    

CI = confidence interval; EAEC = enteroaggregative Escherichia coli; ETEC = enterotoxigenic E. coli; OR = odds ratio.

List-wise deletion was used to handle missing variables, and the asterisk (***) indicates that exposure to a risk factor was a perfect predictor of the outcome.

Adjusted for age and gender.

The number of cases of both ETEC and the respective pathogen (or cases without pathogen identified, as relevant) included in each case–case variable.

“Unknown” calculated with denominator of number of observations for each outcome variable.

Discussion

This study is one of the first to evaluate TD among medium- to long-term adult travelers to South America. The 28-day median length of stay reported by participants exceeds the 5-day median stay in Cusco reported previously by travelers1 and also the 17-day median stay in a study of global travelers with TD.31

Thus, these results may be generalizable to backpackers, international students, individuals involved in volunteer/service programs, and military populations deployed to this region.

We most frequently identified NV (14%), ETEC (11%), and Campylobacter (9%) in participants with TD, which differs from the results of a recent review identifying ETEC (33%), NV (15%), and EAEC (13%) as the top three TD pathogens in Latin America.17

Although the lack of reported CIs in this comprehensive review does not allow us to comment on the level of significance of this observation, this suggestion of a lower prevalence of ETEC could be due to the inclusion of more severe diarrhea cases among longer-term travelers, along with the absence of NV detection methods in previous TD studies. While cholera vaccination provides some protection against ETEC,32 we did not systematically assess this exposure. Of two participants who volunteered vaccination status, ETEC was missing for one and negative for the other, which would not appreciably impact ETEC prevalence. The study methodology was limited by a lack of capability to perform molecular testing for the full range of viral pathogens. For example, we might expect to detect Sapovirus among adult travelers to Cusco,33 and data on Rotavirus and Astrovirus could also provide new information if included in future analyses. A recent systematic review of TD from 1973 to 2009 only included one study evaluating NV among travelers to Latin America, with a prevalence of 17% among students traveling to Mexico.17

Globally, NV is the most common cause of all varieties of diarrhea, accounting for double the number of cases as ETEC,34 and diarrheagenic E. coli and NV are commonly identified among Latin American travelers with TD.35

Coinfection in this study was common, with half of coinfected TD cases positive for both NV and ETEC, and almost half of coinfected cases testing positive for Giardia. NV and ETEC were implicated together in a 2008 gastroenteritis outbreak among U.S. Navy personnel visiting Lima,36 and both generally indicate a high level of fecal contamination in the environment. In coinfected travelers, NV is believed to contribute to disease, although detection may be attributable to prolonged viral shedding following a previous symptomatic or asymptomatic NV infection.17,37,38

Giardia was also frequently detected in coinfected cases in our study, making it worthwhile to note that a recent debate in the literature emphasizes the commensal gut bacteria role that Giardia may play for citizens of countries where it is endemic.3941

Excluding participants with chronic diarrhea could have underestimated the contribution of parasites to diarrhea, while excluding those with recent antibiotic use could have impacted the prevalence of antimicrobial resistant pathogens. The exclusion of individuals who could not provide a stool sample at the time of presentation may have biased this study toward the evaluation of cases with particularly severe disease, which assists in the identification of pathogens against which preventive measures might be most impactful. Our population experienced a high rate (62%) of bed confinement, similar to rates reported in military members with TD (52%).31

Compared with 4% bed confinement rates among adult Peruvians with NV-associated diarrhea,42 NV-infected travelers with TD reported 91% bed confinement, suggesting that preventive measures, such as vaccines, directed against this pathogen could significantly reduce the burden of disease. Given TD's mission aborting potential for military troops,43 in whom incapacitation could pose a further threat to life,33,42 recommendations to military members operating in this geographic area should take NV-associated risks into account.

This study has several travel medicine implications. First, the prevalence of ciprofloxacin-resistant Campylobacter in our study reinforces the existing recommendations to empirically treat severe TD with azithromycin.44

Our data also suggest that individuals who reported always following preventive recommendations took relatively fewer risks; however, these individuals still engaged in risky behaviors, indicating a possible gap in traveler receipt or understanding of preventive recommendations, or reflecting the difficulty of full compliance with all travel recommendations. In addition to supporting the use of pretravel interventions, such as vaccines, this may indicate that pretravel guidance is not sufficiently region or country specific, as noted by other authors.31

Case–case analysis identified ice cream consumption as a risk factor for ETEC compared with cases with no pathogen identified, which, depending on method of milk preparation, could be consistent with the literature showing that ETEC is associated with unpasteurized dairy products.45

For instance, the “queso fresco” and other cheeses typical of the Andean diet are often unpasteurized. Case–case analysis identified buffet food and street vendor food as risk factors for ETEC compared with NV GII, as well as an association between chicha with both Cryptosporidium and EAEC, which, despite wide CIs, should be considered for future travel medicine recommendations.

Given the case–case methodology, study limitations include the binomial outcome requirement for statistical analysis and the conservative estimates of association it provides given that most TD pathogens share risk factors.30

In conclusion, NV was the most frequently identified pathogen among medium- to long-term adult travelers from high-income countries presenting to a physician in Cusco with TD. ETEC and Campylobacter, notably ciprofloxacin-resistant Campylobacter, were also frequently identified, and coinfection with multiple enteropathogens was common. NV caused severe disease relative to other TD-associated pathogens identified, confining over 90% of infected individuals to bed. Destination-specific risk factors include consumption of the local beverage chicha, which was associated with Cryptosporidium infection.

ACKNOWLEDGMENTS

We thank Julio Ventocillo and Andres G. Lescano of Naval Medical Research Unit No. 6 for their assistance with parasitology diagnostics.

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Author Notes

* Address correspondence to Mary Carol Jennings or Sarah-Blythe Ballard, International Health Department, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205. E-mails: marycaroljennings@jhu.edu or sballar3@jhu.edu

Financial support: This work was financially supported by the U.S. Department of Defense Global Emerging Infections Surveillance and Response System (GEIS) [work no. 847705 82000 25GB B0016].

Copyright statement: Several authors of this manuscript are employees of the U.S. Government. This work was prepared as part of their duties. Title 17 U.S.C. § 105 provides that “Copyright protection under this title is not available for any work of the United States Government.” Title 17 U.S.C. § 101 defines a U.S. Government work as a work prepared by a military service member or employee of the U.S. Government as part of that person's official duties.

Authors' addresses: Mary Carol Jennings, Preventive Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mail: marycaroljennings@jhu.edu. Drake H. Tilley, Rina Meza, and Mark P. Simons, Bacteriology Department, Naval Medical Research Unit No. 6, Lima, Peru, E-mails: drake.tilley@med.navy.mil, rina.meza@med.navy.mil, and mark.p.simons.mil@mail.mil. Sarah-Blythe Ballard, Parasitology Department, Naval Medical Research Unit No. 6, Lima, Peru, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, and Asociación Benéfica PRISMA, Lima, Peru, E-mail: sballar3@jhu.edu. Miguel Villanueva, Hematology Department, Hospital Universitario de la Princesa, Madrid, Spain, E-mail: mvillanuevaf@gmail.com. Fernando Maldonado Costa, Department of Epidemiology, Royal Tropical Institute, Amsterdam, The Netherlands, E-mail: maldofunk@gmail.com. Martha Lopez, Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, and Collaborative Research Center, Universidad Peruana Cayetano Heredia, Cusco, Peru, E-mail: martlop2000@gmail.com. Hannah E. Steinberg and Robert H. Gilman, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mails: hsteinberg@jhsph.edu and gilmanbob@gmail.com. C. Giannina Luna and Maria E. Silva, Virology Department, Naval Medical Research Unit No. 6, Lima, Peru, E-mails: giannina.luna@med.navy.mil and marita.silva@med.navy.mil. Ryan C. Maves, Division of Infectious Diseases, Naval Medical Center San Diego, San Diego, CA, and Bacteriology Department, Naval Medical Research Unit No. 6, Lima, Peru, E-mail: ryan.maves@med.navy.mil. Miguel M. Cabada, Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, and Collaborative Research Center, Universidad Peruana Cayetano Heredia, Cusco, Peru, E-mail: micabada@utmb.edu.

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