1 KIT (Koninklijk Instituut voor de Tropen/Royal Tropical Institute), KIT Biomedical Research, Amsterdam, The Netherlands
2 Department of Microbiology, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
3 Department of Dermatology, Academic Medical Center, Amsterdam, The Netherlands
Correspondence: Dr L Oskam, Koninklijk Instituut voor de Tropen, KIT Biomedical Research, Meibergdreef 39, 1105 AZ Amsterdam, The Netherlands. E-mail: l.oskam{at}kit.nl
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
Methods Clinical data and blood samples for anti-M. leprae ELISA were collected during a cross-sectional survey on five Indonesian islands highly endemic for leprosy. A geographic information system (GIS) was used to define contacts of patients. We investigated spatial clustering of patients and seropositive people and used logistic regression to determine risk factors for seropositivity.
Results Of the 3986 people examined for leprosy, 3271 gave blood. Seroprevalence varied between islands (1.78.7%) and correlated significantly with leprosy prevalence. Five clusters of patients and two clusters of seropositives were detected. In multivariate analysis, seropositivity significantly differed by leprosy status, age, sex, and island. Serological status of patients appeared to be the best discriminator of contact groups with higher seroprevalence: contacts of seropositive patients had an adjusted odds ratio (aOR) of 1.75 (95% CI 0.9223.31). This increased seroprevalence was strongest for contact groups living 75 m of two seropositive patients (aOR = 3.07; 95% CI 1.745.42).
Conclusions In this highly endemic area for leprosy, not only household contacts of seropositive patients, but also people living in the vicinity of a seropositive patient were more likely to harbour antibodies against M. leprae. Through measuring the serological status of patients and using a broader definition of contacts, higher risk groups can be more specifically identified.
Accepted 1 June 2004
Leprosy is an infectious disease caused by Mycobacterium leprae and is endemic in many developing countries. The World Health Organization (WHO) has adopted the goal of eliminating leprosy as a public health problem by the year 2005, defined as reducing the national prevalence below 1/10 000.1 Until now, the prevalence decreased mainly due to the introduction and subsequent shortening of multidrug treatment (MDT). Leprosy control strategies are designed to stop transmission through early case detection and treatment with MDT, but do not seem to have the desired effect. The number of new cases719 330 in 20002did not decline over the last 15 years,3 indicating that transmission is continuing at the same level.
Leprosy manifests itself as a disease spectrum, which for treatment purposes has been divided into two forms: multibacillary (MB) and paucibacillary (PB) leprosy. Not every leprosy patient is equally effective in transmitting the disease;4 it is generally accepted that untreated MB patients are the most important source of transmission,46 but patient characteristics other than classification, such as bacterial index (BI) or seropositivity, may be important as well. There is a weakly positive correlation between BI and antibody levels to M. leprae.7 Once it is known which patients are most efficiently transmitting leprosy and which contact groups are most at risk of becoming infected, intervention strategies like prophylactic treatment can be better targeted at specific high-risk groups.
For control strategies it is necessary to have a clear definition of contacts. The 'stone-in-the-pond' concept, originally developed for tuberculosis, was used for leprosy describing transmission in concentric circles around a patient. This study indicated that not only household contacts but also neighbours and social contacts have an increased risk of developing leprosy.4
In endemic populations infection can be detected by the presence of elevated titres of IgM antibodies against M. leprae-specific phenolic glycolipid-I (PGL-I).810 To assess the transmission potential of different types of patients, we compared the seroprevalence in contacts of these patients with non-contacts. Studies investigating the seroprevalence among contacts of leprosy patients have shown variable results. Some studies did find an increased seroprevalence among contacts9,11,12 and others did not.8,13,14
Here we studied clustering of seropositives and leprosy patients and identified risk factors for infection using seropositivity as a marker. We also investigated which disease characteristics of leprosy patients (classification, BI or serology) determine transmission most. We used a geographic information system (GIS) to combine spatial, clinical, and demographic data. GIS is a powerful tool for examining spatial patterns15 and, apart from surveillance studies, has never been used in leprosy research before.
![]() |
Methods |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
During a cross-sectional study in June/July 2000 the population was clinically examined for leprosy. The diagnosis was based on the WHO classification.16,17 Patients with one lesion were classified as PB1 and with 25 lesions as PB25. Patients with >5 five lesions and/or with a positive BI in at least one of three skin smears were classified as MB. Simultaneously, we collected venous blood of the population aged >5 years. Serum was separated by centrifugation on the same day and kept frozen until use.
ELISA
The presence of IgM antibodies to M. leprae PGL-I was measured using an enzyme-linked immunosorbent assay (ELISA) as described previously18 using the natural trisaccharide moiety of PGL-I linked to bovine serum albumin (NT-P-BSA). Pre-coated plates were used. Serum was diluted 1:500 and tested in duplo. The optical density at 450 nm (OD) of each serum was calculated by subtracting the OD value of BSA coated wells from that of NT-P-BSA-coated wells. A positive reference serum on each plate was used to minimize plate-to-plate variation. When this serum reached an OD value of 0.6, the colour reactions of the entire plate were stopped. The cut-off value for seropositivity was set at 0.200.
For quality control 10% of the samples were randomly chosen and re-tested with the same protocol in a different laboratory. These results did not differ significantly from the main results.
Preparation of maps
Longitudes and latitudes of approximately every fifth house were measured using a hand-held Global Positioning System (GPS, Garmin, Kansas USA). In Arcview 3.2 (Esri, California USA) the remaining houses were situated between the geo-referenced houses using detailed hand-drawn maps.
Contact definition
People were only classified as 'contact' when the contact had lasted for a minimum of 6 months and had ended not longer than 6 months prior to the survey as determined through a registration survey about 6 months prior to the examination.
Types of contacts were determined by two factors, namely (1) the type of index patient based on his/her classification, serological status and BI, and (2) the distance between the houses of the contact and the index patient. The distance to an index patient was determined with buffers: circles with a radius of 0, 25, 50, 75, 100, 125, or 150 m around the patient. Household contacts (circle radius = 0) were defined as people who shared a house with a patient. Buffer 1 contacts were defined as people who lived within a radius of 25 metres around a patient, buffer 2 contacts between 2650 m, and so forth up to buffer 6 contacts (126150 m).
Data analyses
The leprosy prevalence was defined as the proportion of leprosy patients registered for therapy at the end of the survey (July 2000) over the examined population at that time. The seroprevalence was defined as the proportion of seropositives over the population screened for antibodies (excluding patients).
Logistic regression was used to determine independent factors associated with a positive ELISA result. Factors associated with a positive ELISA result in univariate analyses (P < 0.15) were selected for multivariate analyses. For the final model we tested for statistically significant (P < 0.05) interactions between factors and for confounding.
To investigate clustering of patients and of seropositive people the Kulldorff spatial scan statistic was used in Satscan version 2.1.19 Clustering occurs when the probability of having leprosy/being seropositive is not randomly distributed, but concentrated on certain parts of the islands. Houses were used as census areas. We used purely spatial analyses. The P-value was obtained from a likelihood ratio test based on Monte Carlo simulation with 9999 replicates. The Satscan was performed per island using the patients as cases and the examined people without leprosy as controls. Satscan was once performed using all patients and once using only PB25 and MB. For the detection of clusters of seropositive people we used the seropositives as cases and the seronegatives as controls. This was done once including and once excluding seropositive patients.
![]() |
Results |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
Table 2 shows all the detected clusters and Figure 1 shows the clusters on Sapuka as an example. The Satscan analyses identified three significant clusters of leprosy patients varying in size from one to five houses: two on Sapuka (both P = 0.014) and one on Kembanglemari (P = 0.016). Furthermore, two clusters were found on Sailus with a P < 0.10. These five clusters included 20 patients, of which 13 were MB patients. Repeating the same analysis, but now excluding the PB1 patients, did not change the overall result, but the two clusters on Sailus became significant.
|
|
Table 3 shows risk factors for leprosy seropositivity among patients and non-patients. In univariate analyses the variables status, age, island, and household size (seroprevalence higher for houses with more than nine inhabitants) were significantly related with seropositivity. All, except household size, remained statistically significant in the multivariate analysis. The variable sex, after adjustment for the other factors, appeared to relate significantly with seropositivity. Women were more likely to be seropositive than men (adjusted odds ratio [aOR] = 1.59, 95% CI 1.062.40). The prevalence of seropositivity decreased with age. Those aged 414 and 1529 years had an aOR of 2.55 (95% CI 1.046.25) and 2.80 (95% CI 1.176.73), respectively, compared with people aged 4559 years. Seroprevalence was significantly higher on Kembanglemari (aOR = 3.13, 95% CI 1.755.60) and Pelokang (aOR: 2.17, 95% CI 1.144.13) compared with the island Sapuka. No significant interaction existed between the variables.
|
|
Performing the analysis on household level with seropositivity defined as at least one person in the house being seropositive did not change the results.
![]() |
Discussion |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
We detected five significant clusters of leprosy patients (especially PB2-5 and MB patients) which included 22% of all patients and 32% of the MB patients. Of the RFT patients, 27% were living in a cluster of leprosy patients. All detected clusters of leprosy patients were small in size, varying from one to five houses, indicating that close contact is important in the transmission of leprosy. Clustering can also be caused by a tendency of family members with high genetic susceptibility to live together24 or a common underlying factor like low socio-economic status in certain neighbourhoods.25 However, there are no specific neighbourhoods for rich or poor people in our study area.
We used seroprevalence in the community as a marker for prevalence of infection.26 Follow-up studies showed that seropositive contacts run an increased risk of developing leprosy, especially MB leprosy.8 It is well known that mainly MB patients produce antibodies against M. leprae. Based on this one can argue that seropositivity in contacts is a marker for incubating multibacillary infection and not infection with M. leprae in general. Since there is no 'gold standard' for measuring M. leprae infection, it is difficult to indicate sensitivity and specificity of the assay used. Speculations have been made in other studies about the possibility of cross-reactivity due to environmental mycobacteria, which would lower the specificity of the test,27 but this hypothesis has never been substantiated.
Several studies investigated the seroprevalence among contacts of leprosy patients, but with different and often contradicting outcomes which can be attributed to different endemicities, methodologies, and classification criteria.
In this study we related seropositivity among contacts with the type of index patient based on their classification, serological status and BI. Seropositivity among contacts was most closely related to the serological status of the index patient. Thus, the serological status of a patient seems to be a better indicator for the transmission potential than the BI. This supports earlier theories that antibodies to PGL-I may be a better reflection of total bacterial load in the body than the BI of a local skin smear.30,31 It also indicates that knowledge of the serological status of a patient is important in order to assess the transmission potential.
Based on earlier findings by Van Beers et al.4 we expanded the concept of contact from household contacts to people living in neighbouring houses. Social contacts (in mosques, schools or other meeting places) were not investigated. Since the definition of neighbours can be very subjective, especially when houses are not ordered in straight lines, we standardized this by using a buffer concept prepared in a GIS, consisting of circles with a fixed radius. We studied the significance of the distance between the houses of the contact and the seropositive patient(s) for the seropositivity of the contact.
We found that seroprevalence is higher among people living in close proximity to seropositive patients (75 m). It appeared that this increased seroprevalence mainly counted for those contacts living in the vicinity of two seropositive patients, maybe due to increased opportunities to acquire infection. There were five pairs of seropositive patients living on the islands; contact groups of only two of these five pairs showed this increased seroprevalence (data not shown). The fact that not all the pairs of seropositive patients were equally efficient at transmitting infection suggests that other factors apart from distance (such as social behaviour and/or duration of disease) may be important. In our study area we found that this increased seroprevalence seemed to be limited to a buffer of 075 m around two seropositive patients. However, this maximum distance of 75 m may depend on the average distance between houses and could be different for different epidemiological and/or sociological settings. Also, GPS, map preparation and the choice of radius of the buffers may influence the outcome and can cause imprecision.
Seropositive non-contacts could also be a contact of an undetected leprosy patient. Even though the coverage of our study was high (84%), among the 16% not screened one would still expect another 17 patients.
In conclusion, we showed that living in the vicinity of two seropositive patients increases the risk of harbouring antibodies to M. leprae. Thus it may be important for a more accurate estimation of transmission potential to measure the serological status of all patients with newly developed simple tools such as the ML-Flow test32 and to include a broader definition of contacts than only household contacts in the contact survey of each new patient. People at risk should be followed carefully or be the subject of intervention strategies such as prophylactic treatment to prevent new leprosy patients arising.
KEY MESSAGES
|
![]() |
Acknowledgments |
---|
We are grateful to Hj Rabiah, Head of the Health Centre Liukang Tangaya Pangkep for her enthusiastic support before and during the fieldwork. Furthermore we want to thank Amril, Arief, Sufrianti, and Syahuni of the local health centre and Mr Mus Jebaru of UNHAS for their participation in the fieldwork. We thank Mr Romi Usman and Mr Marwani of UNHAS and Lara Siebeling and Sanne Wouterse (medical students of AMC/University of Amsterdam) for their participation in the fieldwork and testing the serum samples. We thank all the inhabitants of the five islands for their co-operation.
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() ![]() |
---|
2 World Health Organization. LeprosyGlobal situation. Wkly Epidemiol Rec 2002;1:18.
3 Lockwood DNJ. Leprosy eliminationa virtual phenomenon or a reality? BMJ 2002;324:151618.
4 Van Beers SM, Hatta M, Klatser PR. Patient contact is the major determinant in incident leprosy: implication for future control. Int J Lepr Other Mycobact Dis 1999;67:11928.[ISI][Medline]
5 Anonymous. Report of the International Leprosy Association Technical Forum. Paris, France, 2228 February 2002. Int J Lepr Other Mycobact Dis 2002;70:S1S62.[Medline]
6 Fine PEM, Sterne JAC, Pönnighaus JM et al. Household and dwelling contact as risk factors for leprosy in Northern Malawi. Am J Epidemiol 1997;146:91102.[Abstract]
7 Roche PW, Britton WJ, Failbus SS, Williams D, Pradhan HM, Theuvenet WJ. Operational value of serological measurements in multibacillary leprosy patients: clinical and bacteriological correlates on antibody responses. Int J Lepr Other Mycobact Dis 1990;58:48090.[ISI][Medline]
8 Cunanan A, Chan GP, Douglas JT. Risk of development of leprosy among culion contacts. Int J Lepr Other Mycobact Dis 1998;66:78A.
9 Cellona RV, Walsh GP, Fajardo TT et al. Cross-sectional assessment of ELISA reactivitity in leprosy patients, contacts and normal population using the semisynthetic antigen natural disaccharide octyl bovine serum albumin (ND-O-BSA) in Cebu, The Philippines. Int J Lepr Other Mycobact Dis 1993;61:19298.[ISI][Medline]
10 Gonzalez-Abreu E, Mora N, Perez M, Pereira M, Perez J, Gonzalez AB. Serodiagnosis of leprosy in patients' contacts by enzyme-linked immunosorbent assay. Lepr Rev 1990;61:14550.[ISI][Medline]
11 Hatta M, van Beers SM, Madjid B, Djumadi A, de Wit MYL, Klatser PR. Distribution and persistence of Mycobacterium leprae nasal carriage among a population in which leprosy is endemic in Indonesia. Trans R Soc Trop Med Hyg 1995;89:38185.[CrossRef][ISI][Medline]
12 Chanteau S, Cartel JL, Roux J, Plichart R, Bach MA. Comparison of synthetic antigens for detecting antibodies to phenolic glycolipid I in patients with leprosy and their household contacts. J Infect Dis 1988;157:77076.[ISI][Medline]
13 Van Beers SM, Izumi S, Madjid B, Maeda Y, Day R, Klatser PR. An epidemiological study of leprosy infection by serology and polymerase chain reaction. Int J Lepr Other Mycobact Dis 1994;62:19.[ISI][Medline]
14 Bagshawe AF, Garsia RJ, Baumgart K, Astbury L. IgM serum antibodies to phenolic glycolipid-I and clinical leprosy: two years' observation in a community with hyperendemic leprosy. Int J Lepr Other Mycobact Dis 1990;58:2530.[ISI][Medline]
15 Clarke KC, McLafferty SL, Tempalski BJ. On epidemiology and geographic information systems: a review and discussion of future directions. Emerg Infect Dis 1996;2:8592.[ISI][Medline]
16 Bakker MI, Hatta M, Kwenang A, Klatser PR, Oskam L. Epidemiology of leprosy of five isolated islands in the Flores Sea, Indonesia. Trop Med Int Health 2002;7:78087.[CrossRef][ISI][Medline]
17 World Health Organization. WHO Expert Committee on leprosy. WHO Technical Report Series 1998;874:143.[ISI][Medline]
18 Bührer-Sekula S, Sarno EN, Oskam L et al. Use of ML dipstick as a tool to classify leprosy patients. Int J Lepr Other Mycobact Dis 2000;68:45663.[ISI][Medline]
19 Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med 1995;14:799810.[ISI][Medline]
20 Bührer SS, Smits HL, Gussenhoven GC, van Ingen CW, Klatser PR. A simple dipstick assay for the detection of antibodies to phenolic glycolipid-I of Mycobacterium leprae. Am J Trop Med Hyg 1998;58:13336.
21 Hussain R, Jamil S, Kifayet A et al. Quantitation of IgM antibodies to the M. leprae synthetic dissacharide can predict early bacterial multiplication in leprosy. Int J Lepr Other Mycobact Dis 1990;58:491502.[ISI][Medline]
22 Saad MHF, Medeiros MA, Gallo MEN, Gontijo PP, Fonseca LS. IgM immunoglobulins reacting with the phenolic glycolipid-1 antigen from mycobacterium leprae in sera of leprosy patients and their contacts. Mem Inst Oswaldo Cruz 1990;85:19194.[ISI][Medline]
23 Van Beers S, Hatta M and Klatser PR. Seroprevalence rates of antibodies to phenolic glycolipid-I among school children as an indicator of leprosy endemicity. Int J Lepr Other Mycobact Dis 1999;67:24349.[ISI][Medline]
24 Fitness J, Tosh K, Hill AV. Genetics of susceptibility to leprosy. Genes Immun 2002;3:44153.[CrossRef][ISI][Medline]
25 Ponnighaus JM, Fine PEM, Sterne JAC, Malema SS, Bliss L, Wilson RJ. Extended schooling and good housing conditions are associated with reduced risk of leprosy in rural Malawi. Int J Lepr Other Mycobact Dis 1994;62:34552.[ISI][Medline]
26 Menzel S, Harboe M, Bergsvik H, Brennan PJ. Antibodies to a synthetic analog of phenolic glycolipid-I of Mycobacterium leprae in healthy household contacts of patients with leprosy. Int J Lepr Other Mycobact Dis 1987;55:61725.[ISI][Medline]
27 Fine PEM, Ponnighaus JM, Burgess P, Clarkson JA, Draper CC. Seroepidemiological studies of leprosy in Northern Malawi based on an Enzyme-linked Immunosorbent Assay using synthetic glycoconjugate antigen. Int J Lepr Other Mycobact Dis 1988;56:24354.[ISI][Medline]
28 Soebono H, Klatser PR. A seroepidemiological study of leprosy in high- and low-endemic Indonesian villages. Int J Lepr Other Mycobact Dis 1991;59:41625.[ISI][Medline]
29 Agis F, Schlich P, Cartel JL, Guidi C, Bach MA. Use of anti-M leprae phenolic glycolipid-I antibody detection for early diagnosis and prognosis of leprosy. Int J Lepr Other Mycobact Dis 1988;56:52736.[ISI][Medline]
30 Douglas JT, Hirsch DS, Fajardo TT et al. Evaluation of Mycobacterium leprae antigens in the serological monitoring of a clofazimine-based chemotherapy study of dapsone resistant lepromatous leprosy patients in Cebu, Philippines. Lepr Rev 1989;60:819.[ISI][Medline]
31 Klatser PR, de Wit MYL, Fajardo TT et al. Evaluation of Mycobacterium leprae antigens in the monitoring of a dapsone-based chemotherapy of previously untreated lepromatous patients in Cebu, Philippines. Lepr Rev 1989;60:17886.[ISI][Medline]
32 Buhrer-Sekula S, Smits HL, Gussenhoven GC et al. Simple and fast lateral flow test for classification of leprosy patients and identification of contacts with high risk of developing leprosy. J Clin Microbiol 2003;41:199195.
|