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Estimating Leptospirosis Incidence Using Hospital-Based
Surveillance and a Population-Based Health Care
Utilization Survey in Tanzania
Holly M. Biggs
1
, Julian T. Hertz
1
, O. Michael Munishi
2
, Renee L. Galloway
3
, Florian Marks
4
,
Wilbrod Saganda
5
, Venance P. Maro
2,6
, John A. Crump
1,2,6,7
*
1Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America, 2Kilimanjaro Christian
Medical Centre, Moshi, United Republic of Tanzania, 3Centers for Disease Control and Prevention, Bacterial Special Pathogens Branch, Atlanta, Georgia, United States of
America, 4International Vaccine Institute, Seoul, South Korea, 5Mawenzi Regional Hospital, Moshi, United Republic of Tanzania, 6Kilimanjaro Christian Medical University
College, Moshi, United Republic of Tanzania, 7Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
Abstract
Background:
The incidence of leptospirosis, a neglected zoonotic disease, is uncertain in Tanzania and much of sub-Saharan
Africa, resulting in scarce data on which to prioritize resources for public health interventions and disease control. In this
study, we estimate the incidence of leptospirosis in two districts in the Kilimanjaro Region of Tanzania.
Methodology/Principal Findings:
We conducted a population-based household health care utilization survey in two
districts in the Kilimanjaro Region of Tanzania and identified leptospirosis cases at two hospital-based fever sentinel
surveillance sites in the Kilimanjaro Region. We used multipliers derived from the health care utilization survey and case
numbers from hospital-based surveillance to calculate the incidence of leptospirosis. A total of 810 households were
enrolled in the health care utilization survey and multipliers were derived based on responses to questions about health
care seeking in the event of febrile illness. Of patients enrolled in fever surveillance over a 1 year period and residing in the 2
districts, 42 (7.14%) of 588 met the case definition for confirmed or probable leptospirosis. After applying multipliers to
account for hospital selection, test sensitivity, and study enrollment, we estimated the overall incidence of leptospirosis
ranges from 75–102 cases per 100,000 persons annually.
Conclusions/Significance:
We calculated a high incidence of leptospirosis in two districts in the Kilimanjaro Region of
Tanzania, where leptospirosis incidence was previously unknown. Multiplier methods, such as used in this study, may be a
feasible method of improving availability of incidence estimates for neglected diseases, such as leptospirosis, in resource
constrained settings.
Citation: Biggs HM, Hertz JT, Munishi OM, Galloway RL, Marks F, et al. (2013) Estimating Leptospirosis Incidence Using Hospital-Based Surveillance and a
Population-Based Health Care Utilization Survey in Tanzania. PLoS Negl Trop Dis 7(12): e2589. doi:10.1371/journal.pntd.0002589
Editor: Guilherme S. Ribeiro, Institute of Collective Health, Federal University of Bahia, Brazil
Received April 13, 2013; Accepted October 31, 2013; Published December 5, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This research was supported by an International Studies on AIDS Associated Co-infections (ISAAC) award, a United States National Institutes of Health
(NIH) funded program (U01 AI062563). This work was funded in part by the Typhoid Fever Surveillance in sub-Saharan Africa Program (TSAP) grant OPPGH5231
and US National Institutes of Health grant R01TW009237 as part of the joint NIH-NSF Ecology of Infectious Disease program and the UK Economic and Social
Research Council and Biotechnology and Biological Sciences Research Council. This publication was made possible with help from the Duke University Center for
AIDS Research (CFAR), an NIH funded program (2P30 AI064518). Authors received support from the NIH Fogarty International Center AIDS International Training
and Research Program D43 PA-03-018 (VPM, JAC), NIAID-AI007392 (HMB), and the Duke Clinical Trials Unit and Clinical Research Sites U01 AI069484 (JAC, VPM).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: john.crump@duke.edu
Introduction
Incidence estimates of infectious diseases are crucial for
determining burden of disease and prioritizing resources for
disease control. However, these estimates are often unavailable in
resource constrained settings, resulting in scarce data on which to
base recommendations for public health interventions. Active
population-based surveillance, using door-to-door visits in the
community, is an ideal method for measuring infectious disease
incidence, but active surveillance is limited in many areas due to its
requisite investment of time and resources. Previous studies have
described methods for extrapolating data from hospital based
surveillance and population-based surveys of health care seeking
behavior to estimate disease incidence in a population [1–5]. This
method has facilitated disease incidence estimates in populations
in resource constrained settings where these data were previously
unavailable.
The incidence of leptospirosis, a neglected, poverty-associated
zoonosis found worldwide, is uncertain in sub-Saharan Africa [6].
Several studies in sub-Saharan African countries have shown that
leptospirosis may comprise a substantial proportion of acute febrile
illness [7–11]. However, population-based incidence estimates are
lacking with the exception of studies from the Seychelles showing a
high annual incidence of 60–101 cases per 100,000 persons
PLOS Neglected Tropical Diseases | www.plosntds.org 1 December 2013 | Volume 7 | Issue 12 | e2589
[12,13]. The lack of data is likely the consequence of limited access
to laboratories with leptospirosis diagnostic capability, low
clinician awareness of the disease, often nonspecific clinical
features of leptospirosis, and lack of surveillance infrastructure.
As a result, in sub-Saharan Africa public health measures for
leptospirosis prevention and control have not been prioritized, and
leptospirosis remains a neglected cause of febrile illness. In this
study, we estimate leptospirosis incidence in two districts in the
Kilimanjaro Region of Tanzania using data from hospital based
surveillance and multipliers derived from a population-based
household health care utilization survey.
Methods
Study site
This study was conducted in the Kilimanjaro Region in
northern Tanzania. The household survey was done in 2 districts
in the Kilimanjaro Region, Moshi Rural (population 401,369) and
Moshi Urban (population 143,799) (Figure 1). Febrile illness
surveillance was conducted at 2 hospitals in Moshi, Kilimanjaro
Christian Medical Centre (KCMC) and Mawenzi Regional
Hospital (MRH). These hospitals serve as major providers of care
for residents of Moshi Urban and Moshi Rural. KCMC and
MRH are located a diagonal distance of 3.5 km apart and a
driving distance of approximately 5.5 km apart. KCMC is a 458
bed tertiary referral hospital that serves several regions in northern
Tanzania, and MRH is a 300 bed regional hospital that serves the
Kilimanjaro Region.
Health care utilization survey
Household selection. Thirty (66.67%) of the 45 wards in
Moshi Urban and Moshi Rural Districts were randomly selected
in a population-weighted fashion, including 22 wards from Moshi
Rural and 8 wards from Moshi Urban. Ward-level population
data were taken from the 2002 Tanzanian National Census [14].
A starting point in each selected ward was chosen arbitrarily while
touring the ward on foot by a member of the study team who was
not previously familiar with the area. A direction was similarly
chosen, and the first 27 households along that direction from the
starting point were included in the survey.
Design and administration of the survey. The health care
utilization survey was conducted from June 13, 2011 through July
22, 2011. After obtaining informed consent, members of the study
team administered the survey to heads of the 27 selected
households in each ward. The standardized survey included
questions about demographics, socioeconomic status and health
care seeking behavior. Two distinct sets of questions related to
health care seeking behavior in the event of fever were asked.
Heads of household were queried ‘what is the name of the health
care facility with an inpatient ward where you/your family would
go if you/your family had fever?’ Choices included all 8 hospitals
serving Moshi Urban and Moshi Rural Districts, including MRH
and KCMC, and ‘other’ with a free text field. For this question,
one response per household was recorded without any age
grouping. Heads of household were also asked about health care
seeking behavior for household members in particular age groups
(,1 year, 1 to ,5 years, 5 to ,15 years and $15 years).
Respondents ranked their top 3 choices for response to ‘fever,’
‘elevated body temperature ,3 days,’ and ‘elevated body
temperature $3 days.’ Options included ‘treatment at home,’
‘traditional healer,’ ‘private clinic/health center,’ ‘government
clinic/health center,’ ‘nothing,’ ‘MRH,’ and ‘other hospital’ with a
pick list including the 7 other hospitals serving Moshi Urban and
Moshi Rural Districts as well an option for ‘other’ unspecified
hospital.
Fever surveillance
As part of a comprehensive study of the etiology of febrile illness
in northern Tanzania, adult and pediatric inpatients at KCMC
and adult inpatients at MRH were prospectively enrolled from
September 17, 2007 through August 31, 2008. Methods and
results have been previously described [15–17]. Patients admitted
to the adult medicine wards, aged $13 years, were eligible to
participate if they had an oral temperature of $38.0uC and had
been admitted for ,24 hours. Pediatric inpatients, aged $2
months to ,13 years, were eligible if they had a history of fever in
the past 48 hours, an axillary temperature of $37.5uC or a rectal
temperature of $38.0uC and had been admitted for ,24 hours.
Demographic information, including the participant’s district and
village of residence, was collected. Participants were asked whether
they had been referred from another inpatient hospital. Acute
serum was drawn and archived, and all participants were asked to
return 4–6 weeks after enrollment to submit a convalescent serum
sample. Acute and convalescent serum samples were sent to the
United States Centers for Disease Control and Prevention (CDC)
for serologic analysis for leptospirosis.
Laboratory methods. Leptospirosis laboratory diagnosis was
made using the standard microscopic agglutination test (MAT)
performed at the CDC using a panel of antigens from 20 different
Leptospira serovars representing 17 serogroups [18]. Live leptospiral
cell suspensions were incubated with serially diluted serum
specimens. Resulting agglutination titers were read using darkfield
microscopy. The reported titer was the highest dilution of serum
that agglutinated at least 50% of the cells for each serovar tested
[18].
The serogroups (serovars) included in the antigen panel were
Australis (L. interrogans serovar Australis, L. interrogans serovar
Bratislava), Autumnalis (L. interrogans serovar Autumnalis), Ballum
(L. borgpetersenii serovar Ballum), Bataviae (L. interrogans serovar
Bataviae), Canicola (L. interrogans serovar Canicola), Celledoni (L.
weilii serovar Celledoni), Cynopteri (L. kirschneri serovar Cynopteri),
Djasiman (L. interrogans serovar Djasiman), Grippotyphosa (L.
kirschneri serovar Grippotyphosa), Hebdomadis (L. santarosai serovar
Borincana), Icterohaemorrhagiae (L. interrogans serovar Mankarso,
L. interrogans Icterohaemorrhagiae), Javanica (L. borgpetersenii
serovar Javanica), Mini (L. santarosai serovar Georgia), Pomona
(L. interrogans serovar Pomona), Pyrogenes (L. interrogans serovar
Author Summary
Leptospirosis is a zoonotic infection that occurs worldwide
and is caused by a spirochete, Leptospira spp. The
incidence of leptospirosis is unknown in most of sub-
Saharan Africa, including Tanzania. Incidence estimates are
important in prioritizing resources for disease prevention
and control. In this study, we calculated leptospirosis
incidence in 2 districts in the Kilimanjaro Region of
Tanzania using a multiplier method. We used responses
from a population-based survey that asked where partic-
ipants and their household members would seek health
care in the event of fever along with the number of
leptospirosis cases found at 2 hospitals under surveillance
to calculate estimated incidence. We calculated a high
incidence of leptospirosis in the study area that was
previously unrecognized. This has important implications
for prioritizing further research and consideration of public
health control measures for leptospirosis in Tanzania.
Estimating Leptospirosis Incidence in Tanzania
PLOS Neglected Tropical Diseases | www.plosntds.org 2 December 2013 | Volume 7 | Issue 12 | e2589
Pyrogenes, L. santarosai serovar Alexi), Sejroe (L. interrogans serovar
Wolffi), and Tarassovi (L. borgpetersenii serovar Tarassovi).
Study definitions. Confirmed leptospirosis was defined as a
$4-fold rise in the agglutination titer between acute and
convalescent serum samples [19]. Probable leptospirosis was
defined as any single reciprocal MAT titer $800 among those
not meeting the definition for confirmed leptospirosis [20–22].
Incidence calculations
Incidence was estimated with the use of multipliers derived from
the health care utilization survey and fever surveillance. Multipli-
ers account for leptospirosis cases that were potentially missed in
the stages of reporting (Figure 2) and are the multiplicative inverse
of the relevant proportions.
We calculated the ‘KCMC multiplier’ and ‘MRH multiplier’ to
account for health care seeking preferences and cases potentially
missed due to selection of health care providers or options not
under surveillance. In order to evaluate the sensitivity of our
estimates, the ‘KCMC multiplier’ and ‘MRH multiplier’ were
derived based on head of household responses to the 2 distinct
survey questions: ‘What is the name of the health care facility with
an inpatient ward where you/your family would go if you/your
family had fever?’ and ‘What will you do if a member of this
household in x age group has elevated body temperature for $3
days?.’ We selected the first and second choice responses to
‘elevated body temperature for $3 days’ as most representative of
where patients sufficiently ill to warrant hospital admission would
seek care. If the head of household’s first choice for care in the
event of ‘elevated body temperature for $3 days’ was KCMC and
Figure 1. Africa, Tanzania and Kilimanjaro Region. Moshi Rural District shown in dark gray and Moshi Urban District in black in the Kilimanjaro
Region inset.
doi:10.1371/journal.pntd.0002589.g001
Figure 2. Surveillance pyramid. Multipliers were applied to account for incomplete assessments at various levels of the surveillance pyramid.
doi:10.1371/journal.pntd.0002589.g002
Estimating Leptospirosis Incidence in Tanzania
PLOS Neglected Tropical Diseases | www.plosntds.org 3 December 2013 | Volume 7 | Issue 12 | e2589
second choice was MRH, then we only counted the first choice.
Since KCMC is a tertiary referral hospital, patients who would
elect to present to KCMC first would be unlikely to subsequently
be seen at MRH for the same illness. In addition, we calculated a
‘referral adjustment’ to adjust for patients transferred to KCMC
from another inpatient hospital given that transfer may not reflect
a patient’s preference of health care facility.
We calculated an ‘enrollment multiplier’ to account for patients
who were eligible but not enrolled in fever surveillance for any
reason. We calculated a ‘time multiplier’ to account for fever
surveillance enrollment 5 (71.43%) of 7 days of the week. We also
calculated a ‘paired sera multiplier’ to account for patients in fever
surveillance that did not have acute and convalescent serum
samples tested, and therefore could not meet criteria for confirmed
leptospirosis. This multiplier was applied to the incidence estimates
involving only confirmed cases. We calculated a ‘MAT sensitivity
multiplier’ to account for the sensitivity of the diagnostic test.
MAT sensitivity on paired sera was estimated to approach 100%,
while sensitivity on acute serum only was estimated at 48.7%, and
sensitivity on convalescent serum only was estimated at 93.8%
[23]. Case numbers were also adjusted to account for MAT
specificity of approximately 97.3% [23]. Multiplier derivations
based on study results are presented in detail in the results.
Incidence was calculated by age group as follows: age 0 to ,5
years, age 5 to ,15 years, and age $15 years. We used the 2002
Tanzania National Census, the most recent population data
available for Tanzania, to determine the population of Moshi
Urban and Moshi Rural for the specified age groups [14].
Statistical analysis
Data were entered using the Cardiff Teleform system (Cardiff,
Inc., Vista, CA, USA) into an Access database (Microsoft Corp,
Redmond, WA). Incidence calculations were done using Microsoft
Excel 2010 (Microsoft Corp. Redmond, WA) spreadsheets. Other
analyses were performed using STATA, version 10.1 (STATA-
Corp, College Station, TX) and Epi Info 7, version 7.1.2.0 (CDC,
Atlanta, GA). Pearson’s chi-square was used to compare the health
care utilization study population with the census population. All p
values are 2 sided and evaluated for statistical significance at the
0.05 significance level.
Research ethics
This study was approved by the KCMC Research Ethics
Committee, the Tanzania National Institutes for Medical
Research National Research Ethics Coordinating Committee,
and the Institutional Review Boards of Duke University Medical
Center, the CDC, and the International Vaccine Institute. All
study participants provided written informed consent.
Results
Health care utilization survey
In the health care utilization study, 810 households were
enrolled; no selected household refused participation. Responses
represented a total of 3919 household members. Table 1 shows the
demographics of the health care utilization study population
compared to the general census population of the two districts.
All households had at least one member aged $15 years; 361
had $1 member aged 5 years to ,15 years; 156 had $1 child
aged 1 year to ,5 years; and 42 had $1 infant ,1 year.
Responses for the ,1 year and 1 to ,5 years age groups were
combined for analysis due to the small number of responses in the
,1 year age group as well as to more closely match the age
intervals of Tanzania census data. After combining these age
groups, the ,1 to 5 years age group had 198 responses; 16
households had members of both age groups for which responses
to both questions were included. Aside from this exception, a
response for a given age group was counted only once per
household, regardless of the number of household members in that
age group.
Multiplier derivation. The proportion of heads of house-
hold choosing MRH or KCMC for management of ‘elevated body
temperature for $3 days’ and as the ‘health care facility with an
inpatient ward where you/your family would go for fever?’ with
resultant multipliers are shown in Table 2.
Fever surveillance
A total of 870 inpatients were enrolled at KCMC and MRH.
Participant characteristics have been described elsewhere [15–17].
Residence in Moshi Urban or Moshi Rural Districts was reported
by 588 (67.59%) participants. Of those residing in the study area,
315 (53.57%) of 588 had paired sera tested, 222 (37.76%) had only
acute serum tested, and 28 (4.76%) had only convalescent serum
tested. Of those with paired sera tested, 23 (7.30%) of 315 met the
case definition for confirmed leptospirosis. Of those with $1
serum sample tested, and not classified as confirmed leptospirosis,
19 (3.51%) of 542 met the definition of probable leptospirosis.
Case numbers by age group and enrollment site are shown in
Table 3.
Multiplier derivation. Of patients screened for fever
surveillance, 1,310 were eligible for enrollment and 870
(66.41%) were enrolled, resulting in an overall ‘enrollment
multiplier’ of 1.51. Enrollment multipliers by age group are
shown in Table 4.
Of leptospirosis cases from Moshi Urban and Moshi Rural
Districts, all had either paired sera or acute serum only tested; no
case was defined based on convalescent serum alone. Therefore,
MAT sensitivity multipliers of 1.00 and 2.05, respectively, were
applied. Case numbers were also adjusted for MAT specificity by
multiplying by 0.93. Of enrollees from Moshi Urban and Moshi
Rural Districts, 316 (53.8%) of 588 had paired sera, resulting in a
‘paired sera multiplier’ of 1.86.
Of participants from Moshi Urban and Moshi Rural Districts
admitted at KCMC, 100 (32.5%) of 308 infants and children and
20 (22.5%) of 89 adults reported transfer from another inpatient
hospital. Crude case numbers for children and adults at KCMC
were therefore adjusted by multiplying by 0.68 and 0.78,
respectively.
Incidence calculations
Incidence calculations using the 2 distinct fever-related ques-
tions as well as different leptospirosis case definitions are shown in
Table 3. Leptospirosis incidence in Moshi Urban and Moshi Rural
Districts by age group is estimated as follows: 0 to ,5 years, 175–
288 cases per 100,000 persons/year; 5 to ,15 years, 149–161
cases per 100,000 persons/year; $15 years, 33–59 cases per
100,000 persons/year. Overall annual leptospirosis incidence in
Moshi Urban and Moshi Rural is estimated at 97 to 102 cases per
100,000 persons based on responses to ‘elevated body temperature
$3 days’ and 75 to 85 cases per 100,000 persons based on
responses to ‘health care facility with an inpatient ward where
you/your family would go if you/your family had fever?’. Our best
estimate of overall incidence, including the most comprehensive
use of data derived from the question about ‘elevated body
temperature $3 days’ and using a leptospirosis case definition
including both confirmed and probable cases, is 102 cases per
100,000 persons/year.
Estimating Leptospirosis Incidence in Tanzania
PLOS Neglected Tropical Diseases | www.plosntds.org 4 December 2013 | Volume 7 | Issue 12 | e2589
Discussion
Our leptospirosis incidence estimate in two districts in the
Kilimanjaro Region of Tanzania, among the first estimates from
sub-Saharan Africa, shows a high annual incidence ranging
between 75 and 102cases per 100,000 persons. This likely
underestimates the total incidence of leptospirosis as it represents
hospitalized cases and does not account for milder cases seeking
care in outpatient settings or at home. Our leptospirosis incidence
estimate is comparable to estimates of Salmonella Typhi incidence
in Pemba, Tanzania in 2009–2010 [5]. Despite an incidence
comparable to more recognized infections in this area, such as
Salmonella Typhi, leptospirosis currently remains a neglected and
underdiagnosed cause of febrile illness in Tanzania [17].
Other studies and groups such as the World Health Organi-
zation (WHO) Leptospirosis Burden Epidemiology Reference
Group (LERG), which aim to assess worldwide leptospirosis
incidence and burden of disease [24], have encountered scarce
data from Africa on which to base assessments [6,25,26]. Although
leptospirosis is known to occur in sub-Saharan Africa, the limited
data available are based mostly on serologic surveys or outbreaks,
from which incidence cannot be reliably calculated. To our
knowledge, the only other published leptospirosis incidence
estimate from Africa is from the Seychelles, where incidence in
1995–1996 was estimated at 101 cases per 100,000 persons [13].
This estimate is consistent with our estimate of incidence in
northern Tanzania.
Our data show the highest incidence of leptospirosis in children
in the age groups 0 to ,5 years and 5 to ,15 years, with a
substantially lower incidence in those age $15 years. This age
distribution differs from data reported elsewhere, in which adults
are more commonly affected [26,27]. The age distribution
reported here could have a number of possible explanations.
First, because we enrolled only hospitalized cases, it is possible that
the threshold for hospital admission is lower in children than
adults, which would result in an over-representation of cases
among younger age groups. A lower admission threshold for
children has been proposed in another study that compared
leptospirosis clinical presentations between children and adults
[28]Conversely, it is also possible that children were hospitalized
more often than adults because they had more severe leptospirosis
manifestations, but this explanation would be inconsistent with
evidence from other studies [28,29]. Additionally, it is possible that
in northern Tanzania, children may have similar or greater
exposure to risk factors for leptospirosis compared to adults.
Rather than occupation-related exposures seen in some areas, risk
could be conferred primarily through widespread environmental
contamination, with exposures occurring during daily activities
such as bathing in contaminated water sources or walking barefoot
in mud. This could result in frequent exposures to Leptospira early
Table 1. Demographics of health care utilization study population compared to Tanzania 2002 Population and Housing Census,
Moshi Urban and Moshi Rural Districts, combined.
HCUS study population
n = 3,119 no.(%)
Census population*
n = 545,168 no.(%) P value
Age group (years)
0to,5 225 (7.2) 68,680 (12.6) ,0.001
5to,15 685 (22.0) 150,218 (27.6) ,0.001
$15 2,209 (70.8) 326,270 (59.8) ,0.001
Sex
Female 1,690/3066 (55.1) 282,252 (51.8) ,0.001
Male 1,376/3066 (44.9) 262,916 (48.2)
*Ref 7.
doi:10.1371/journal.pntd.0002589.t001
Table 2. Reported health care seeking behavior among household survey respondents and calculated multipliers, Moshi Urban
and Moshi Rural Districts, Tanzania, 2011.
Age group (years) No. responses No. selecting health facility KCMC multiplier`MRH multiplier1
KCMC* no.(%) MRH{no.(%)
‘Elevated body temperature for 3 or more days’
0to,5 198 17(8.59) 11.65
5to,15 361 10(2.77) 36.10
$15 810 35(4.32) 290(35.80) 23.14 2.79
‘Health care facility with an inpatient ward where you/your family would go for fever?’
810 50(6.17) 313(38.64) 16.20 2.59
KCMC = Kilimanjaro Christian Medical Centre, MRH = Mawenzi Regional Hospital.
*Number of respondents who chose KCMC as their first or second choice for health care in response to respective questions.
{
Number of respondents who chose MRH as their first or second choice for health care in response to respective questions.
`
Inverse of proportion of respondents who select KCMC for care in response to respective questions.
1
Inverse of proportion of respondents who select MRH for care in response to respective questions.
doi:10.1371/journal.pntd.0002589.t002
Estimating Leptospirosis Incidence in Tanzania
PLOS Neglected Tropical Diseases | www.plosntds.org 5 December 2013 | Volume 7 | Issue 12 | e2589
in life. It is not known whether naturally acquired protective
immunity may occur in endemic settings and what role this may
play in age-related risk [30]. Further research is needed to explore
the epidemiology and age-related risks for leptospirosis in sub-
Saharan Africa.
The incidence estimate that we calculated represents our best
effort to contribute leptospirosis incidence data for sub-Saharan
Africa, where data are scarce, but we recognize a number of
limitations. While the best way to estimate the incidence of febrile
illnesses is by active surveillance in a well characterized population
using conventional standard diagnostic methods, this approach has
substantial logistic and cost barriers in resource limited settings.
The multiplier method we employed has been described and
applied as a means of estimating febrile illness incidence in areas
where resources and infrastructure for active population-based
surveillance are limited [1,4,5]. Multiplier methods are also well
accepted by surveillance networks that attempt to approximate
incidence in the face of incomplete assessments at various steps in
the surveillance pyramid [31]. However, multiple assumptions are
made in the derivation and application of multipliers. We assumed
that those who presented to the referral hospitals under
surveillance were representative of those who presented to other
hospitals within the two districts. Surveillance sites are best chosen
in light of findings of the health care utilization survey [2].
However, our health care utilization survey was performed after
selection of our fever surveillance sites. We also assumed that there
was no difference between patients who were enrolled and those
who were eligible and not enrolled.
In the health care utilization survey study population, we did
observe differences in age group distribution and sex compared to
the general census population for the study districts, indicating that
our HCUS study population was not entirely representative of the
general population Although we hoped to minimize this with our
selection methods, this is not entirely unexpected given the large
size of the census population relative to our sample size.
Additionally, multiple responses from a single household were
analyzed if the household contained members in more than one
designated age group. This methodology has the potential to
introduce design effect given that the head of household may be
likely to respond similarly to health care seeking questions for
different age group members within a single household. In order to
explore effects of our multiplier choices, we evaluated the effect of
using more than one leptospirosis case definition and the use of
different health care utilization survey questions, including a
question that was answered only once for a given household. This
provided a range of plausible incidence estimates, from which we
selected the estimate we believed was most representative and
inclusive of available data.
We also recognize that our study, conducted over 1 year in 2
districts may not be generalizable across years or to other areas in
Tanzania or Africa. Leptospirosis transmission varies based on
climatic conditions such as rainfall, temperature, humidity and
other climatic factors [32,33]. Our surveillance continued for a full
year, thus avoiding bias from seasonal fluctuations in incidence,
but incidence could be heterogeneous across years. Additionally,
differences in climate, elevation and other leptospirosis risk factors
such as population and livestock density, water and sanitation, and
types of housing may limit generalizability across other areas in
Tanzania or Africa.
Conclusions
We calculated a high incidence of leptospirosis in 2 districts of
the Kilimanjaro Region in Tanzania. Despite its high incidence,
leptospirosis remains an under-recognized cause of febrile illness in
Table 3. Confirmed and probable leptospirosis cases, Moshi Urban and Moshi Rural Districts residents, Kilimanjaro Christian Medical Center (KCMC) and Mawenzi Regional
Hospital (MRH), Tanzania 2007–2008.
Age group
(years) KCMC MRH
Confirmed cases* no. Probable cases no.
Probable cases,
acute serum only{no.
Probable cases,
paired serum* no.
Confirmed
cases* no.
Probable
cases no.
Probable cases, acute
serum only{no.
Probable cases,
paired serum* no.
0to,57 1 0 1
5to,153303
$15 1 2 1 1 12 13 10 3
Total 11 6 1 5 12 13 10 3
KCMC = Kilimanjaro Christian Medical Centre, MRH = Mawenzi Regional Hospital.
*Confirmed or probable cases based on testing of paired sera. For adjusted case calculations these cases were multiplied by a ‘MAT sensitivity multiplier’ of 1.00.
{
Probable cases based on testing of acute serum only. For adjusted case calculations these cases were multiplied by a ‘MAT sensitivity multiplier’ of 2.05.
doi:10.1371/journal.pntd.0002589.t003
Estimating Leptospirosis Incidence in Tanzania
PLOS Neglected Tropical Diseases | www.plosntds.org 6 December 2013 | Volume 7 | Issue 12 | e2589
Table 4. Leptospirosis incidence estimates, Moshi Urban and Moshi Rural Districts, Tanzania 2007–2008.
Age group
(years) KCMC MRH
KCMC and
MRH
adjusted
cases`no.
Time
multiplier
Enrollment
multiplier
Paired sera
multiplier1
Annual
estimated
cases no. Population
Annual incidence
(per 100,000)#
Crude
cases no.
Adjusted
cases* no.
Crude
cases no.
Adjusted
cases{no.
‘Elevated body temperature for 3 or more days’; confirmed and probable leptospirosis cases
0to,5 8 61 61 1.4 1.41 NA 120 68,680 175
5to,15 6 143 143 1.4 1.21 NA 242 150,218 161
$15 3 71 25 96 84 1.4 1.65 NA 194 326,270 59
Overall 556 545,168 102
‘Elevated body temperature for 3 or more days’; confirmed leptospirosis cases
0to,5 7 54 54 1.4 1.41 1.86 198 68,680 288
5to,15 3 71 71 1.4 1.21 1.86 224 150,218 149
$15 1 18 12 32 25 1.4 1.65 1.86 107 326,270 33
Overall 529 545,168 97
‘Health care facility with an inpatient ward where you/your family would go for fever’; confirmed and probable leptospirosis cases"
17 193 1.4 1.51 NA 408 545,168 75
‘Health care facility with an inpatient ward where you/your family would go for fever’; confirmed leptospirosis cases"
11 118 1.4 1.51 1.86 464 545,168 85
KCMC = Kilimanjaro Christian Medical Centre, MRH = Mawenzi Regional Hospital, MAT = microscopic agglutination test.
*Cases adjusted by ‘MAT sensitivity multiplier,’ MAT specificity, ‘KCMC multiplier,’ and KCMC referral adjustment.
{
Cases adjusted by ‘MAT sensitivity multiplier,’ MAT specificity and ‘MRH multiplier’.
`
No. for the $15 years age group represents the mean of the KCMC adjusted case no. and MRH adjusted case no. No. for the 0 to ,5 years and 5 to ,15 years age groups equal the KCMC adjusted case no. since fever surveillance
was not conducted in these groups at MRH.
1
‘Paired sera multiplier’ applied to estimates using confirmed cases only.
"
Only KCMC data used for this estimate since MRH data represents a limited age range.
#
(Annual estimated cases/population) *100,000.
doi:10.1371/journal.pntd.0002589.t004
Estimating Leptospirosis Incidence in Tanzania
PLOS Neglected Tropical Diseases | www.plosntds.org 7 December 2013 | Volume 7 | Issue 12 | e2589
Africa, resulting in a lack of resources dedicated to defining risk
factors and implementing public health control measures. The
high estimated incidence underscores the importance of prioritiz-
ing further research into the epidemiology of leptospirosis in
Africa. There is an urgent need for rapid diagnostic tests that are
sensitive and specific as well as improved surveillance in order to
better assess leptospirosis case fatality and disease burden across
different types of health care facilities in Africa. An approach
similar to ours, using health facility based surveillance and health
care utilization surveys, is a practical method that may be feasible
across multiple sites with limited resources to improve leptospirosis
incidence data.
Supporting Information
Checklist S1 STROBE checklist.
(DOC)
Acknowledgments
We thank Ahaz T. Kulanga for providing administrative support to this
study; Pilli M. Chambo, Beata V. Kyara, Beatus A. Massawe, Anna D.
Mtei, Godfrey S. Mushi, Lillian E. Ngowi, Flora M. Nkya, and Winfrida
H. Shirima for reviewing and enrolling study participants; Gertrude I.
Kessy, Janeth U. Kimaro, Bona K. Shirima,and Edward Singo for
managing participant follow-up; and Evaline M. Ndosi and Enock J. Kessy
for their assistance in data entry. We are grateful to the leadership,
clinicians, and patients of KCMC and MRH for their contributions to this
research. We thank Andrew Weinhold for providing the images used in
Figure 1.
Disclaimers
The findings and conclusions in this report are those of the authors and
do not necessarily represent the official position of the Centers for Disease
Control and Prevention.
Author Contributions
Conceived and designed the experiments: JAC FM. Performed the
experiments: JAC WS VPM JTH OMM. Analyzed the data: HMB JTH
JAC. Contributed reagents/materials/analysis tools: RLG. Wrote the
paper: HMB JAC JTH OMM RLG FM WS VPM. Supervised laboratory
testing for leptospirosis: RLG.
References
1. Crump JA, Youssef FG, Luby SP, Wasfy MO, Rangel JM, et al. (2003)
Estimating the incidence of typhoid fever and other febrile illnesses in developing
countries. Emerg Infect Dis 9: 539–544.
2. Srikantiah P, Girgis FY, Luby SP, Jennings G, Wasfy MO, et al. (2006)
Population-based surveillance of typhoid fever in Egypt. Am J Trop Med Hyg
74: 114–119.
3. Gargouri N, Walke H, Belbeisi A, Hadadin A, Salah S, et al. (2009) Estimated
burden of human Salmonella,Shigella,andBrucella infections in Jordan, 2003–
2004. Foodborne Pathog Dis 6: 481–486.
4. Paul RC, Rahman M, Gurley ES, Hossain MJ, Diorditsa S, et al. (2011) A novel
low-cost approach to estimate the incidence of Japanese encephalitis in the
catchment area of three hospitals in Bangladesh. Am J Trop Med Hyg 85: 379–
385.
5. Thriemer K, Ley B, Ame S, von Seidlein L, Pak GD, et al. (2012) The burden of
invasive bacterial infections in Pemba, Zanzibar. PLoS One 7: e30350.
6. Pappas G, Papadimitriou P, Siozopoulou V, Christou L, Akritidis N (2008) The
globalization of leptospirosis: worldwide incidence trends. Int J Infect Dis 12:
351–357.
7. Yimer E KS, Messele T, Wolday D, Newayeselassie B, Gessese N, et al. (2004)
Human leptospirosis in Ethiopia: a pilot study in Wonji. EthiopJHealth Dev 18.
8. de Geus A, Kranendonk O, Bohlander HJ (1969) Clinical leptospirosis in Kwale
District, Coast Province, Kenya. East Afr Med J 46: 491–496.
9. de Geus A, Wolff JW, Timmer VE (1977) Clinical leptospirosis in Kenya (1): a
clinical study in Kwale District, Cost Province. East Afr Med J 54: 115–124.
10. Le Bras J, Guyer B, Sulzer C, Mailloux M (1977) [Anademic focus of
leptospirosis at Fondem (U.R. of Cameroon)]. Bull Soc Pathol Exot Filiales 70:
569–583.
11. Crump JA MA, Nicholson WL, Massung RF, Stoddard RA, Galloway RL, et al.
(2013) Etiology of severe non-malaria febrile illness in northern Tanzania: a
prospective cohort study. PLoS Negl Trop Dis 7: e2324.
12. Pinn TG (1992) Leptospirosis in the Seychelles. Med J Aust 156: 163–167.
13. Yersin C, Bovet P, Merien F, Wong T, Panowsky J, et al. (1998) Human
leptospirosis in the Seychelles (Indian Ocean): a popu lation-based study.
Am J Trop Med Hyg 59: 933–940.
14. Tanzania National Bureau of Statistics Village Statistics, 2002 Population and
Housing Census.
15. Crump JA, Ramadhani HO, Morrissey AB, Saganda W, Mwako MS, et al.
(2011) Invasive bacterial and fungal infections among hospitalized HIV-infected
and HIV-uninfected adults and adolescents in northern Tanzania. Clin Infect
Dis 52: 341–348.
16. Crump JA, Ramadhani HO, Morrissey AB, Msuya LJ, Yang LY, et al. (2011)
Invasive bacterial and fungal infections among hospitalized HIV-infected and
HIV-uninfected children and infants in northern Tanzania. Trop Med Int
Health 16: 830–837.
17. Biggs HM, Bui DM, Galloway RL, Stoddard RA, Shadomy SV, et al. (2011)
Leptospirosis among hospitalized febrile patients in northern Tanzania.
Am J Trop Med Hyg 85: 275–281.
18. Dikken H, Kmety E (1978) Serological typing methods of leptospires. In: Bergan
T, Norris JR, editors. Methods in Microbiology. London: Academic Press.
pp.259–307.
19. Centers for Disease Control and Prevention (1997) Case definitions for infectious
conditions under public health surveillance MMWR. Recomm Rep 46: 1–55.
20. Levett PN (2001) Leptospirosis. Clin Microbiol Rev 14: 296–326.
21. Faine S (1982) Guidelines for the control of leptospirosis. Geneva: World Health
Organization.
22. World Health Organization (2003) Human leptospirosis: guidance for diagnosis,
surveillance and control. Geneva: World Health Organization.
23. Bajani MD, Ashford DA, Bragg SL, Woods CW, Aye T, et al. (2003) Evaluation
of four commercially available rapid serologic tests for diagnosis of leptospirosis.
J Clin Microbiol 41: 803–809.
24. Abela-Ridder B, Sikkema R, Hartskeerl RA (2010) Estimating the burden of
human leptospirosis. Int J Antimicrob Agents 36 Suppl 1: S5–7.
25. World Health Organization (2006) Informal Consultation on Global Burden of
Leptospirosis: Methods of Assessment. Geneva: World Health Organization.
26. World Health Organization (2011) Report of the Second Meeting of the
Leptospirosis Burden Epidemiology Reference Group. Geneva: World Health
Organization.
27. Everard CO, Hayes RJ, Edwards CN (1989) Leptospiral infection in school-
children from Trinidad and Barbados. Epidemiol Infect 103: 143–156.
28. Spichler A, Athanazio DA, Vilaca P, Seguro A, Vinetz J, et al. (2012)
Comparative analysis of severe pediatric and adult leptospirosis in Sao Paulo,
Brazil. Am J Trop Med Hyg 86: 306–308.
29. Lopes AA, Costa E, Costa YA, Sacramento E, de Oliveira Junior AR, et al.
(2004) Comparative study of the in-hospital case-fatality rate of leptospirosis
between pediatric and adult patients of different age groups. Rev Inst Med Trop
Sao Paulo 46: 19–24.
30. Tuero I, Vinetz JM, Klimpel GR (2010) Lack of demonstrable memory T cell
responses in humans who have spontaneously recovered from leptospirosis in the
Peruvian Amazon. J Infect Dis 201: 420–427.
31. Voetsch AC, Van Gilder TJ, Angulo FJ, Farley MM, Shallow S, et al. (2004)
FoodNet estimate of the burden of illness caused by nontyphoidal Salmonella
infections in the United States. Clin Infect Dis 38 Suppl 3: S127–134.
32. Desvars A, Jego S, Chiroleu F, Bourhy P, Cardinale E, et al. (201 1) Seasonality
of human leptospirosis in Reunion Island (Indian Ocean) and its association with
meteorological data. PLoS One 6: e20377.
33. Lau CL, Smythe LD, Craig SB, Weinstein P (2010) Climate change, flooding,
urbanisation and leptospirosis: fuelling the fire? Trans R Soc Trop Med Hyg
104: 631–638.
Estimating Leptospirosis Incidence in Tanzania
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