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Dispatch
Anthroponotic Cutaneous Leishmaniasis,
Kabul, Afghanistan
Richard Reithinger,*† Mohammad Mohsen,† Khoksar Aadil,† Majeed Sidiqi,†
Panna Erasmus,† and Paul G. Coleman*
*London School of Hygiene & Tropical Medicine, London, U.K.; †HealthNet
International, Peshawar, Pakistan
Suggested citation for this article: Reithinger
R, Mohsen M, Aadil K, Sidiqi M, Erasmus P, Coleman PG. Anthroponotic
cutaneous leishmaniasis, Kabul, Afghanistan. Emerg Infect Dis [serial
online] 2003 Jun [date cited]. Available from: URL: http://www.cdc.gov/ncidod/EID/vol9no6/03-0026.htm
A prevalence survey
in Kabul City showed that 2.7% and 21.9% of persons have active leishmaniasis
lesions or scars, respectively. Incidence of disease was estimated to
be 2.9% (29 cases/1,000 persons per year; 95% confidence interval 0.018
to 0.031). Disease was associated with age and gender; logistic regression
analyses showed significant clustering of cases.
In Afghanistan, the majority of leishmaniasis cases are caused by Leishmania
tropica (1), which is transmitted anthroponotically
(i.e., humans are the reservoir) by the sandfly (Phlebotomus sergenti)
(2). Anthroponotic cutaneous leishmaniasis (ACL) is generally
characterized by large and/or multiple cutaneous lesions with a variable
tendency to self-cure (3). Because of sandfly exposure,
most lesions occur on the face, often leading to severe stigmatization
in affected persons (e.g., women with lesions are often deemed unsuitable
for marriage or raising children). Anecdotal reports suggest that because
of the political instability and destruction of the housing and health
infrastructure during the Mojahedin and Taliban regimes, L. tropica
cases have increased during the past decade, with current World Health
Organization (WHO) estimates of 200,000 ACL cases in Kabul alone (4).
Also, because of several factors (the mass migration of L. tropica–infected
[and infectious] Afghan refugees; the sporadic treatment of ACL cases
by WHO and nongovernmental organizations; and the virtual absence of vector-control
strategies), L. tropica has spread to areas where ACL was previously
nonendemic (e.g., northwestern Pakistan) (5).
The Study
Before developing an ACL-control strategy, we conducted a cluster-based,
house-to-house survey in Kabul City between July and September 2001 to
collect data on the extent of ACL. The leishmaniasis transmission season
is between April and October. Each of the city’s 14 districts was divided
into random clusters according to the district’s population size, for
a total of 90 sample clusters; 30 neighboring households were surveyed
in each cluster, with the first household selected at random. A team of
medical staff diagnosed disease in household members on the basis of presence
or absence of ACL lesions or scars, number of lesions, and date of lesion
onset; members were interviewed to collect demographic data such as gender
and age. Because of logistic constraints, parasitologic diagnosis of ACL
lesions (e.g., microscopic examination or parasite culture) was not carried
out. However, in Afghanistan, ACL-like skin lesions from other causes
are rare, and our experience suggests that clinical diagnosis has a sensitivity
and specificity of >80% and >90%, respectively (Reithinger et al.,
unpub. data). Written approval to conduct the study was obtained from
the Afghan Ministry of Public Health. Informed consent was obtained from
study participants; all persons with active cases surveyed were offered
free antileishmanial treatment at the HealthNet International clinic.
Of 26,892 persons surveyed, 726 (2.7%) and 5,900 (21.9%) had active leishmaniasis
lesions or scars, respectively. Of those persons with ACL lesions, the
mean lesion number was 2.4 (range 1–50) and the mean lesion duration (to
survey date) was 9.1 months (range 0.1–96). A total of 26,887 observations,
with full disease and demographic records, from 2,683 households from
the 90 sample clusters, were used in logistic regression analyses with
a binary outcome variable (ACL lesion or scar).
Four variables were created to assess the distribution of leishmaniasis
cases: the prevalence of active lesions in other members of the same household,
the prevalence of scars in other members of the same household, the prevalence
of active lesions in the nearest neighbor households, and the prevalence
of scars in the nearest neighbor households. The nearest neighbor households
were defined in terms of the survey protocol. In a given sample cluster,
the nearest neighbor to household 1 was household 2; the nearest neighbors
to household 2 were households 1 and 3; and so forth. The logistic regressions
with robust standard errors (i.e., clustering of households to control
for within-household correlations) were also adjusted for age (continuous
to year of age at last birthday), gender, and sample cluster (categorical
90 levels). All analyses were conducted in STATA 7 (Stata Corporation,
College Station, TX).
The regression analyses show that female patients are at significantly
higher odds of having leishmaniasis lesions or scars (Table).
Odds of disease were associated with age: elderly people are at slightly
greater risk of having active lesions; and elderly persons are less likely
to have leishmaniasis scars (the drop in scar prevalence in persons >12
years was significant, odds ratio: 0.994 (95% confidence interval [CI]
0.991 to 0.997), p<0.001). The analyses showed significant clustering
of ACL within and between households (Table). A person’s
probability of having an active lesion was increased greatly if other
lesions appeared on persons in the same household (Figure,
A) and also (but less so) if other scars occurred on persons in the
same household. A person’s probability of having a scar was greatly increased
if persons with active lesions or scars were present in the same household
(Figure, B). Finally, a person’s probability of
having a scar was greatly increased with the presence of persons with
active lesions or scars in neighboring households (Figure,
C). No significant clustering of ACL lesions occurred between households;
however, the sample size was small for analyses. Overall, these findings
are consistent with clustering of ACL transmission, including transmission
in areas where previous transmission has occurred (because of association
with scar prevalence). When maximum likelihood methods (6)
are used, the average annual force of ACL infection (λ) was estimated
to be 0.029 per year (29 cases/1,000 persons per year; 95% CI 0.028 to
0.031) over the past 12 years (Figure, D).
Conclusions
Currently six clinics provide leishmaniasis diagnostic and treatment
services in Kabul; an estimated 20% of the total 67,500 patients (based
on the observed prevalence of 2.7% and a total 2.5 million population
for Kabul) are diagnosed and treated. Whether this fact alone could explain
the extent and duration of the leishmaniasis epidemic in Kabul is uncertain.
Our analyses show that persons are at high risk for active ACL when a
high proportion of persons with ACL scars are in the same or neighboring
households. The likely explanation for this finding is that sandfly distribution
and abundance are patchy but stable over time. The high prevalence of
persons with active ACL in Kabul and the comparatively high ACL incidence
show that ACL-control strategies (e.g., increasing the number of clinics
providing treatment facilities or providing personal protection methods
against sandflies) should be conducted soon. We demonstrate that a blanket-coverage
ACL-control strategy is not necessary: transmission of this disease is
focalized, and interventions (e.g., household insecticide spraying, insecticide-impregnated
bednets or chaddars) (5) targeting households with a
high proportion of persons with leishmaniasis lesions or scars or city
districts containing a high number of high transmission clusters should
have a major impact on transmission in Kabul.
The international donor community often considers ACL to be of peripheral
importance (e.g., the disease was not included in the basic package of
health services for Afghanistan) (7) because this disease
has no impact on death rates and patient treatment costs (usually U.S.$15–200
[8]) are not recovered. Failure to implement a control
strategy for this disease will likely lead to an increase in its impact
and social stigmatization and represent further problems for a health
infrastructure already crippled by 20 years of war.
Acknowledgments
We are grateful for the logistic support of the Afghan
Ministry of Public Health and HealthNet International Khair Khana clinic
staff in conducting the survey. We thank Rupert Quinnell for comments
on the manuscript.
HealthNet International Malaria and Leishmaniasis Control
Program Afghanistan is supported by the European Union, the Gesellschaft
für Technische Zusammenarbeit, the Norwegian Afghanistan Committee,
the Dutch Government, Thermosurgeries Inc., the United Nations Aid Mission
to Afghanistan, and the World Health Organization.
Dr. Reithinger is a program manager for HealthNet International,
a Dutch nongovernmental organization that has been working on malaria
and leishmaniasis control in Afghanistan for 10 years. His interests
are the diagnosis, epidemiology, prevention, and control of infectious
diseases, especially leishmaniasis, Chagas disease, and malaria.
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| Table.
Within- and between-household clustering of anthroponotic cutaneous
leishmaniasis lesions and scars, Kabul City, Afghanistana |
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Explanatory variables
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Outcome variableb
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Lesion
OR (CI)
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Scar
OR (CI)
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Within-household prevalence
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Prevalence of lesions in other household members
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132.3 (67.3 to 259.8)
p<0.001
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3.728 (2.799 to 4.964)
p<0.001
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Prevalence of scars in other household members
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1.988 (1.500 to 2.635)
p<0.001
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48.24 (41.79 to 55.68)
p<0.001
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| Between-household prevalence |
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Prevalence of lesions in nearest neighbor households
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2.323 (0.984 to 5.486)
NS
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1.585 (1.036 to 1.353)
p<0.05
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Prevalence of scars in nearest neighbor households
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1.376 (0.957 to 1.980)
NS
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1.184 (1.036 to 1.353)
p<0.05
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| Other |
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Age
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1.005 (1.000 to 1.009)
p<0.05
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1.013 (1.011 to 1.015)
p<0.001
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Sex (female relative to male)
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1.383 (1.177 to 1.626)
p<0.001
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1.186 (1.108 to 1.270)
p<0.001
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Sampling areac
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Chi square=213.6, d.f.=89
p<0.001
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Chi square=330.8, d.f.=89
p<0.001
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Overall model fit
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Pseudo R2 = 11.89%
p<0.0001
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Pseudo R2=22.72
p<0.0001
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| aAbbreviations
used: OR, odds ratio; CI, 95% confidence interval; d.f., degrees of
freedom; NS, not significant. |
| bThe statistical
model controlled for age (in years), sex, and sampling area (a total
of 90 areas), while the standard errors were adjusted for sampling
of persons at the household level. |
| cThe overall significance
of the variable is shown rather than the ORs for the 90 different
sampling areas. |
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