Incidence of and Factors Associated With Leprosy Among Household Contacts of Patients With Leprosy in Brazil | Dermatology | JAMA Dermatology | JAMA Network
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Figure 1.  Flowchart
Flowchart

CadUnico indicates Cadastro Unico para Programas Sociais; SINAN, Sistema de Informação de Agravos de Notificação.

Figure 2.  Cumulative Incidence of Subsequent Leprosy Cases Among Households of Patients With Leprosy
Cumulative Incidence of Subsequent Leprosy Cases Among Households of Patients With Leprosy
Table 1.  Incidence of Leprosy Among Household Contacts by Geographic Factors in the Total Population and Children Younger Than 15 Years
Incidence of Leprosy Among Household Contacts by Geographic Factors in the Total Population and Children Younger Than 15 Years
Table 2.  Household and Individual Characteristics of the Study Population and Incidence of Subsequent Leprosy Cases Among Household Contacts
Household and Individual Characteristics of the Study Population and Incidence of Subsequent Leprosy Cases Among Household Contacts
Table 3.  Odds Ratios for Detecting Subsequent Leprosy Cases Among Household Contacts for the Total Population and Children Younger Than 15 Years
Odds Ratios for Detecting Subsequent Leprosy Cases Among Household Contacts for the Total Population and Children Younger Than 15 Years
1.
Stolk  WA, Kulik  MC, le Rutte  EA,  et al.  Between-country inequalities in the neglected tropical disease burden in 1990 and 2010, with projections for 2020.   PLoS Negl Trop Dis. 2016;10(5):e0004560. doi:10.1371/journal.pntd.0004560 PubMedGoogle Scholar
2.
Pedrosa  VL, Dias  LC, Galban  E,  et al.  Leprosy among schoolchildren in the Amazon region: a cross-sectional study of active search and possible source of infection by contact tracing.   PLoS Negl Trop Dis. 2018;12(2):e0006261. doi:10.1371/journal.pntd.0006261 PubMedGoogle Scholar
3.
Le  W, Haiqin  J, Danfeng  H,  et al.  Monitoring and detection of leprosy patients in southwest China: a retrospective study, 2010-2014.   Sci Rep. 2018;8(1):11407. doi:10.1038/s41598-018-29753-4 PubMedGoogle ScholarCrossref
4.
Fine  PE, Sterne  JA, Pönnighaus  JM,  et al.  Household and dwelling contact as risk factors for leprosy in northern Malawi.   Am J Epidemiol. 1997;146(1):91-102. doi:10.1093/oxfordjournals.aje.a009195 PubMedGoogle ScholarCrossref
5.
Moet  FJ, Meima  A, Oskam  L, Richardus  JH.  Risk factors for the development of clinical leprosy among contacts, and their relevance for targeted interventions.   Lepr Rev. 2004;75(4):310-326.PubMedGoogle Scholar
6.
Rao  PN.  Global leprosy strategy 2016-2020: issues and concerns.   Indian J Dermatol Venereol Leprol. 2017;83(1):4-6. doi:10.4103/0378-6323.195075 PubMedGoogle ScholarCrossref
7.
Pescarini  JM, Strina  A, Nery  JS,  et al.  Socioeconomic risk markers of leprosy in high-burden countries: a systematic review and meta-analysis.   PLoS Negl Trop Dis. 2018;12(7):e0006622. doi:10.1371/journal.pntd.0006622 PubMedGoogle Scholar
8.
Nery  JS, Ramond  A, Pescarini  JM,  et al.  Socioeconomic determinants of leprosy new case detection in the 100 Million Brazilian Cohort: a population-based linkage study.   Lancet Glob Health. 2019;7(9):e1226-e1236. doi:10.1016/S2214-109X(19)30260-8PubMedGoogle ScholarCrossref
9.
Centro de Integração de Dados e Conhecimentos para a Saúde–Cidacs. Accessed February 14, 2020. http://cidacs.bahia.fiocruz.br/
10.
Brasil. Ministerio de Saúde, Departamento de Estatísticado SUS. Sistema de Informação de Agravos de Notificação–SINAN. Assessed March 10, 2020. https://portalsinan.saude.gov.br/
11.
Ali  MS, Ichihara  MY, Lopes  LC,  et al.  Administrative data linkage in Brazil: potentials for health technology assessment.   Front Pharmacol. 2019;10:984. doi:10.3389/fphar.2019.00984 PubMedGoogle ScholarCrossref
12.
Pita  R, Pinto  C, Sena  S,  et al.  On the accuracy and scalability of probabilistic data linkage over the Brazilian 114 Million Cohort.   IEEE J Biomed Health Inform. 2018;22(2):346-353. doi:10.1109/JBHI.2018.2796941 PubMedGoogle ScholarCrossref
13.
Brasil. Ministério da Saúde. Sistema de Legislação da Saúde. Portaria no 2.556, de 28 de outubro de 2011. Accessed November 25, 2019. http://bvsms.saude.gov.br/bvs/saudelegis/gm/2011/prt2556_28_10_2011.html
14.
Talhari  C, Talhari  S, Penna  GO.  Clinical aspects of leprosy.   Clin Dermatol. 2015;33(1):26-37. doi:10.1016/j.clindermatol.2014.07.002 PubMedGoogle ScholarCrossref
15.
Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância das Doenças Transmissíveis. Diretrizes Para Vigilância, Atenção e Eliminacao da Hanseníase Como Problema de Saúde Pública: Manual Técnico-Operacional. 2016. Accessed November 25, 2019. http://www.credesh.ufu.br/sites/credesh.hc.ufu.br/arquivos/diretrizes-eliminacao-hanseniase-4fev16-web.pdf
16.
Aalen  O.  Nonparametric inference for a family of counting processes.   Ann Stat. 1978;6(4):701-726. doi:10.1214/aos/1176344247 Google ScholarCrossref
17.
Nelson  W.  Theory and applications of hazard plotting for censored failure data.   Technometrics. 1972;14:945-966. doi:10.1080/00401706.1972.10488991 Google ScholarCrossref
18.
Brasil. Ministério da Saúde. Sala de Apoio à Gestão Estratégica. Situação de Saúde. Indicadores de Morbidade. Hanseníase. Accessed November 25, 2019. http://sage.saude.gov.br/#
19.
Kumar  A, Girdhar  A, Girdhar  BK.  Incidence of leprosy in Agra district.   Lepr Rev. 2007;78(2):131-136.PubMedGoogle Scholar
20.
Bobosha  K, Wilson  L, van Meijgaarden  KE,  et al.  T-cell regulation in lepromatous leprosy.   PLoS Negl Trop Dis. 2014;8(4):e2773. doi:10.1371/journal.pntd.0002773 PubMedGoogle Scholar
21.
Richardus  JH, Oskam  L.  Protecting people against leprosy: chemoprophylaxis and immunoprophylaxis.   Clin Dermatol. 2015;33(1):19-25. doi:10.1016/j.clindermatol.2014.07.009 PubMedGoogle ScholarCrossref
22.
Feenstra  SG, Nahar  Q, Pahan  D, Oskam  L, Richardus  JH.  Social contact patterns and leprosy disease: a case-control study in Bangladesh.   Epidemiol Infect. 2013;141(3):573-581. doi:10.1017/S0950268812000969 PubMedGoogle ScholarCrossref
23.
Hegazy  AA, Abdel-Hamid  IA, Ahmed  EF, Hammad  SM, Hawas  SA.  Leprosy in a high-prevalence Egyptian village: epidemiology and risk factors.   Int J Dermatol. 2002;41(10):681-686. doi:10.1046/j.1365-4362.2002.01602.x PubMedGoogle ScholarCrossref
24.
van’t Noordende  AT, Korfage  IJ, Lisam  S, Arif  MA, Kumar  A, van Brakel  WH.  The role of perceptions and knowledge of leprosy in the elimination of leprosy: a baseline study in Fatehpur district, northern India.   PLoS Negl Trop Dis. 2019;13(4):e0007302. doi:10.1371/journal.pntd.0007302 PubMedGoogle Scholar
25.
Lie  HP.  Why is leprosy decreasing in Norway?   Trans R Soc Trop Med Hyg. 1929;22(4):357-366. doi:10.1016/S0035-9203(29)90026-2 Google ScholarCrossref
26.
de Souza  CDF, Rocha  VS, Santos  NF,  et al.  Spatial clustering, social vulnerability and risk of leprosy in an endemic area in Northeast Brazil: an ecological study.   J Eur Acad Dermatol Venereol. 2019;33(8):1581-1590. doi:10.1111/jdv.15596 PubMedGoogle ScholarCrossref
27.
Szklo  M, Nieto  FJ.  Epidemiology: Beyond the Basics. Jones & Bartlett Learning; 2012.
28.
Penna  MLF, Penna  GO, Iglesias  PC, Natal  S, Rodrigues  LC.  Anti-PGL-1 positivity as a risk marker for the development of leprosy among contacts of leprosy cases: systematic review and meta-analysis.   PLoS Negl Trop Dis. 2016;10(5):e0004703. doi:10.1371/journal.pntd.0004703 PubMedGoogle Scholar
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    Original Investigation
    April 15, 2020

    Incidence of and Factors Associated With Leprosy Among Household Contacts of Patients With Leprosy in Brazil

    Author Affiliations
    • 1Centro de Integração de Dados e Conhecimentos para Saúde, Fundação Oswaldo Cruz, Salvador, Brazil
    • 2Instituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, Brazil
    • 3Núcleo de Medicina Tropical, Universidade de Brasília, Brasília, Brazil
    • 4Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    • 5Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
    • 6Health Data Research, London, United Kingdom
    • 7Departamento de Epidemiologia e Bioestatística, Universidade Federal Fluminense, Rio de Janeiro, Brazil
    • 8Escola Fiocruz do Governo, Fiocruz Brasília, Brasília, Brazil
    • 9Escola de Nutrição, Universidade Federal da Bahia, Salvador, Brazil
    JAMA Dermatol. 2020;156(6):640-648. doi:10.1001/jamadermatol.2020.0653
    Key Points

    Question  What are the incidence of and the factors associated with leprosy among household contacts of patients with leprosy in the low-income population of Brazil?

    Findings  In this cohort study of data from the 100 Million Brazilian Cohort, the incidence of leprosy among 42 725 household contacts of patients with leprosy was higher than that in the overall cohort and the incidence recorded in 2017 in Brazil. Detection of leprosy was associated with the clinical characteristics of the primary leprosy case.

    Meaning  The findings suggest that household contacts of patients with previously diagnosed leprosy should be targeted for public health intervention.

    Abstract

    Importance  Despite progress toward reducing global incidence, leprosy control remains a challenge in low- and middle-income countries.

    Objective  To estimate new case detection rates of leprosy among household contacts of patients with previously diagnosed leprosy and to investigate its associated risk factors.

    Design, Setting, and Participants  This population-based cohort study included families registered in the 100 Million Brazilian Cohort linked with nationwide registries of leprosy; data were collected from January 1, 2007, through December 31, 2014. Household contacts of patients with a previous diagnosis of leprosy from each household unit were followed up from the time of detection of the primary case to the time of detection of a subsequent case or until December 31, 2014. Data analysis was performed from May to December 2018.

    Exposures  Clinical characteristics of the primary case and sociodemographic factors of the household contact.

    Main Outcomes and Measures  Incidence of leprosy, estimated as the new case detection rate of leprosy per 100 000 household contacts at risk (person-years at risk). The association between occurrence of a subsequent leprosy case and the exposure risk factors was assessed using multilevel mixed-effects logistic regressions allowing for state- and household-specific random effects.

    Results  Among 42 725 household contacts (22 449 [52.5%] female; mean [SD] age, 22.4 [18.5] years) of 17 876 patients detected with leprosy, the new case detection rate of leprosy was 636.3 (95% CI, 594.4-681.1) per 100 000 person-years at risk overall and 521.9 (95% CI, 466.3-584.1) per 100 000 person-years at risk among children younger than 15 years. Household contacts of patients with multibacillary leprosy had higher odds of developing leprosy (adjusted odds ratio [OR], 1.48; 95% CI, 1.17-1.88), and the odds increased among contacts aged 50 years or older (adjusted OR, 3.11; 95% CI, 2.03-4.76). Leprosy detection was negatively associated with illiterate or preschool educational level (adjusted OR, 0.59; 95% CI, 0.38-0.92). For children, the odds were increased among boys (adjusted OR, 1.70; 95% CI, 1.20-2.42).

    Conclusions and Relevance  The findings in this Brazilian population-based cohort study suggest that the household contacts of patients with leprosy may have increased risk of leprosy, especially in households with existing multibacillary cases and older contacts. Public health interventions, such as contact screening, that specifically target this population appear to be needed.

    Introduction

    Leprosy, which is caused mainly by Mycobacterium leprae, persists in populations in low- and middle-income countries.1 Current evidence suggests that, within these settings, household contacts of existing patients with leprosy are at high risk for developing leprosy.2-4 The increased incidence of leprosy in household contacts is likely associated with a combination of increased exposure to infectious cases (eg, contacts of patients with multibacillary leprosy have a 5- to 10-times greater risk of developing leprosy than the general population4,5) and the sharing of social risk factors within a given family (eg, lower familial income and unfavorable household living conditions).5-8 To enhance understanding of household leprosy transmission, this study used linked data from the 100 Million Brazilian Cohort to estimate the incidence of leprosy among household contacts of patients with leprosy and to compare the odds of leprosy detection among contacts by potential clinical, geographic, and socioeconomic risk factors.

    Methods
    Study Design and Data Source

    In this cohort study, household contacts of patients with leprosy were followed up from January 1, 2007, to December 31, 2014, using geographic and socioeconomic data from the baseline of the 100 Million Brazilian Cohort9 (2001-2015) linked with leprosy records from the Notifiable Diseases Information System (Sistema de Informação de Agravos de Notificação, SINAN-leprosy) (2007-2014).10 Individual records from the 2 data sets were deterministically linked using 5 identifying variables: name, mother’s name, sex, date of birth, and municipality of residence.11 A manual assessment of 10 000 random pairs showed sensitivity of 0.91 (95% CI, 0.90-0.92) and specificity of 0.89 (95% CI, 0.88-0.90).12 The study was approved by the ethics committees of the Universidade de Brasilia, Brazil, the Instituto Gonçalo Muniz (Fiocruz), Salvador, Brazil, and the London School of Hygiene & Tropical Medicine, London, United Kingdom. No personally identifiable information was included in the data set used for analysis; thus, informed consent was waived by the committees. Data analyses were performed from May to December 2018.

    Setting and Participants

    This study included members of the 100 Million Brazilian Cohort enrolled between January 1, 2007, and December 31, 2014, with at least 1 household member aged 15 years or older. We defined the first new leprosy case detected in each household as the primary case and defined individuals residing in the same household with the primary case as household contacts. We excluded individuals belonging to households (1) without at least 1 leprosy case, (2) without at least 1 household contact free of leprosy at the time of detection of the primary case, and (3) in which the primary case was diagnosed before study entry.

    Outcome

    The primary outcome was the detection of subsequent leprosy cases (ie, new leprosy cases detected among household contacts after the primary case) in the overall population and the subgroup of children younger than 15 years. Household contacts were followed up from the detection of the primary case until the detection of a subsequent case or until December 31, 2014. In the subanalysis of children younger than 15 years, children were censored on their 15th birthday.

    Exposures

    Geographic exposures included area of residence (rural or urban), Brazilian region, and residence in a leprosy high-burden priority municipality (ie, defined by the Brazilian Ministry of Health as all capitals, municipalities with new case detection rate of more than 20 per 100 000 inhabitants, and municipalities outside geographical risk areas with 50 new cases and at least 5 cases in children).13

    Socioeconomic and demographic exposures included household conditions (ie, household density, construction material, water supply, waste disposal, and electricity), monthly household per capita income, and individual sociodemographic variables (ie, age, sex, self-identified race/ethnicity, educational level, and work condition). For individuals younger than 18 years, we used the education and employment characteristics of the oldest member of the household as proxy for the household head.

    Clinical exposures included the clinical features of the primary case (ie, operational classification, based on the number of skin and nerve injuries [ie, paucibacillary or multibacillary]); grade of disability at diagnosis, estimated by sensory and motor functions of the eyes, hands, and feet (ie, grade 0, 1, or 2); and reaction episodes, acute inflammatory conditions triggered by disease severity (ie, none, type 1, 2, or 1 + 2).14,15 The operational classification of the primary case and the sex and age of the household contact were considered to be confounders a priori.

    Statistical Analysis

    The incidence of leprosy was estimated as the new case detection rate (hereafter, incidence) per 100 000 household contacts at risk (person-years at risk) overall and within subpopulations (ie, by age group, geographic factors, and clinical characteristics of the primary case). We calculated the cumulative incidence of leprosy by age group (<15 years vs ≥15 years) and according to the clinical classification of the primary case (paucibacillary vs multibacillary) using the Nelson-Aalen estimator.16,17 We estimated the Levin population attributable risk of being exposed to a leprosy case within the household using previous leprosy incidence estimates from the 100 Million Brazilian Cohort as a proxy for the unexposed population.8

    We estimated the crude and adjusted odds ratio (OR) of developing a subsequent leprosy case by the clinical features of the primary case and the socioeconomic and demographic characteristics of the household contact using multilevel mixed-effects logistic regressions allowing for state- and household-specific random effects. Adjusted models were built using a backward selection approach, where we first included all variables with P < .20 in the univariate analysis and removed variables one by one, maintaining those with P < .05 in the final model. We checked all model adjustments. Because of the high missingness of certain variables (eg, reaction type), univariate analyses were performed for all individuals with data for a given covariate, whereas multivariate analyses used a complete case approach excluding individuals with any missing data.

    In sensitivity analyses, we assessed potential residual confounding using a full multilevel mixed-effects logistic model adjusting for all socioeconomic and demographic factors. In addition, to test our assumption that subsequent cases occurring in a short period after the primary case were already infected but had longer incubation periods, we excluded subsequent cases that were detected within 2, 6, and 12 months of the primary case diagnosis date. All analyses were performed using Stata, version 15.1 (StataCorp).

    Results

    The study population included 42 725 household contacts (22 449 [52.5%] female; mean [SD] age, 22.4 [18.5] years) of 17 876 primary cases (Figure 1) followed up for a total of 130 289.3 person-years (median, 2.8 years; interquartile range [IQR], 1.2-4.6 years). We observed 829 subsequent leprosy cases, of which 303 (36.6%) were in children younger than 15 years (Table 1). For both population strata, the detection of subsequent leprosy cases peaked in the first year after detection of the primary case (Figure 2A). The incidence of leprosy among household contacts was 636.3 per 100 000 person-years (95% CI, 594.4-681.1 per 100 000 person-years) overall and 521.9 per 100 000 person-years (95% CI, 466.3-584.1 per 100 000 person-years) among children younger than 15 years. The percentages of cases attributed to exposure inside the household were 97.3% overall and 99.0% among children younger than 15 years. The incidence was broadly consistent across geographic factors (Table 1) and did not vary substantively by socioeconomic factors and living conditions (Table 2).

    In both the total population and children younger than 15 years, the incidence of leprosy was higher among contacts of patients with multibacillary leprosy, grade-2 physical disabilities, or reactions type 1 + 2 (eTable 1 in the Supplement). The incidence among household contacts of patients with multibacillary leprosy was approximately 60% higher than that among household contacts of patients with paucibacillary leprosy, with similar associations over time (Figure 2B and C and eTable 1 in the Supplement).

    After adjusting for sex and age, contacts of patients with multibacillary leprosy had higher odds of having leprosy detected (adjusted OR, 1.48; 95% CI, 1.17-1.88) (Table 3). Contacts aged 50 years or older had more than 3 times the odds of leprosy than children younger than 5 years (adjusted OR, 3.11; 95% CI, 2.03-4.76), and illiterate or preschool-educated contacts had lower leprosy detection compared with individuals attaining high school education (adjusted OR, 0.59; 95% CI, 0.38-0.92). For children younger than 15 years, leprosy detection was also increased among males (adjusted OR, 1.70, 95% CI, 1.20-2.42) (Table 3).

    In the sensitivity analyses, full-adjusted models were similar to the primary analysis (eTable 2 in the Supplement). After exclusion of subsequent cases diagnosed within 2, 6, and 12 months of the primary case, leprosy cases detected later in time were more likely to be associated with being a contact of a patient with multibacillary leprosy and with having a high school or college education (eTable 3 in the Supplement). For children, leprosy cases detected later in time were associated with being a contact of a patient with multibacillary leprosy, being younger (age 0-5 years), and being male (eTable 4 in the Supplement).

    Discussion

    In conducting a nationwide analysis of 42 725 household contacts of leprosy cases from the 100 Million Brazilian Cohort, this investigation provided robust estimates of the incidence of leprosy among household contacts. Among these contacts, leprosy incidence was estimated to be approximately 37-times higher than that in the 100 Million Brazilian Cohort overall (17.1 per 100 000 person-years)8 and 50-times higher than the rate recorded for the general population of Brazil in 2017 (12.9 per 100 000 person-years).18 Furthermore, although household contacts younger than 15 years had a lower detection rate of leprosy than adults, the rate was 100 times higher than in the full population of children from the 100 Million Brazilian Cohort (5.2 per 100 000 person-years).8 Overall, these results were similar to previously reported new case detection rates of 80 per 100 000 person-years,4 364 per 100 000 person-years,3 and 676 per 100 000 person-years19 among household contacts in China, Malawi, and India. Together, these findings suggest that there is a high incidence of leprosy among household contacts compared with individuals with similar low-income status.

    Within the total population, individuals who resided with patients with multibacillary leprosy, were aged 50 years or older, or had attained at least a high school educational level had increased odds of leprosy detection. In contrast, other geographic, socioeconomic, and individual-level characteristics that have previously been shown to be associated with an increased risk of leprosy detection8 were not associated with leprosy detection among household contacts. These findings suggest that the risk associated with living in increased proximity to a primary leprosy case may supersede individual-level and geographic leprosy risk factors for becoming a subsequent leprosy case.

    Higher leprosy rates among household contacts of patients with multibacillary leprosy might be explained by the exposure to relatively higher bacillary load.20,21 Similar to our findings, previous research has reported higher odds of leprosy detection among contacts who are older5,22,23 and male.2 In this study, we found lower leprosy detection among contacts with lower educational levels. However, it is plausible that after a primary leprosy case in the household, contacts with education beyond the preschool level may have had improved leprosy knowledge, increased health-seeking behavior, and/or better access to health services that may have enhanced their case detection rates.24

    Social development has been central to leprosy control historically25 and remains key to reducing leprosy burden in contacts as well as in the general population. In this study, leprosy risk among household contacts was similar across geographic location or socioeconomic conditions of households, which differed from previous studies.8,25,26 However, given that the households affected by leprosy in the 100 Million Brazilian Cohort were more likely to have low-income circumstances,8 the sample in the present study was relatively homogeneously composed of individuals of limited resources, which may have limited our ability to differentiate any health outcomes associated with socioeconomic status.27

    The high proportion of cases associated with exposure to leprosy cases within the household compared with exposure outside of household suggests that household contacts with low-income status may benefit from targeted and effective strategies to prevent transmission, such as strengthening screening of contacts. Although immunotherapy and chemoprophylaxis remain a challenge,28 the dermatoneurological examination of household contacts continues to be the criterion standard approach for mitigating risks to household contacts. In 2017, a total of 78.9% of contacts of patients with leprosy were examined across Brazil.18 Since the Global Leprosy Strategy 2016-2020,15 national guidelines have been expanded for surveillance of social contacts, but their implementation is still restricted because of the stigma associated with the disease and, in some regions, the lack of trained health care professionals. The training of professionals to screen contacts and health education (eg, pamphlets, lectures, and screening campaigns) will continue to be important strategies for detecting leprosy early, reducing stigmatizing disabilities, and preventing subsequent transmission.

    Limitations

    Although this study has provided a unique opportunity to investigate leprosy in a large cohort of household contacts from national health- and administrative-linked databases, it also has limitations. In relying on routinely collected records, the data set had a considerable proportion of missingness for certain variables and also unmeasured confounders, such as health-seeking behavior and proximity to health services. In addition, because the proportion of households of patients with leprosy evaluated in Brazil is still insufficient (<80%)18 and leprosy reporting to the SINAN system is passive, this study may underestimate the true incidence of leprosy among household contacts. Also, because the population of the 100 Million Brazilian Cohort consists of applicants to social programs, the findings may not be generalizable to all household contacts of patients with leprosy in Brazil.

    Conclusions

    The findings suggest that household contacts of patients with leprosy may have increased risk of leprosy, especially in households with existing multibacillary cases and older contacts. Strengthening public health interventions, such as contact screening, along with social interventions that specifically target this population appear to be needed.

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    Article Information

    Accepted for Publication: February 15, 2020.

    Published Online: April 15, 2020. doi:10.1001/jamadermatol.2020.0653

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Teixeira CSS et al. JAMA Dermatology.

    Corresponding Author: Júlia Moreira Pescarini, PhD, Centro de Integração de Dados e Conhecimentos para a Saúde, Rua Mundo, sem número Parque Tecnológico da Bahia, Trobogy, Salvador, Bahia 41745-715, Brazil (juliapescarini@gmail.com).

    Author Contributions: Ms Teixeira and Dr Pescarini contributed equally as first authors. Drs Brickley, Penna, Barreto, and Silva contributed equally as senior authors. Ms Teixeira and Dr Pescarini had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Teixeira, Pescarini, Alves, Nery, Ramond, Smeeth, M. Penna, Rodrigues, Brickley, G. Penna, Barreto, Silva.

    Acquisition, analysis, or interpretation of data: Teixeira, Pescarini, Alves, Nery, Sanchez, Teles, Ichihara, Ramond, Smeeth, Rodrigues, Brickley, Barreto, Silva.

    Drafting of the manuscript: Teixeira, Pescarini, Nery, Sanchez, Ramond, Brickley.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Teixeira, Pescarini, Alves, Sanchez, Teles, Brickley.

    Obtained funding: Nery, Smeeth, Rodrigues, G. Penna, Barreto.

    Administrative, technical, or material support: Ichihara, Brickley.

    Supervision: Pescarini, Nery, Sanchez, Smeeth, M. Penna, Rodrigues, Brickley, Barreto, Silva.

    Conflict of Interest Disclosures: Ms Teixeira reported receiving grants and personal fees from Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa (CONFAP)/Economic and Social Research Council (ESRC)/Medical Research Council (MRC)/Biotechnology and Biological Sciences Research Council (BBSRC)/Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)/Fundação de Apoio à Pesquisa do Distrito Federal (FAPDF) and from the Wellcome Trust; and reported receiving personal fees from Capes during the conduct of the study. Dr Pescarini reported receiving grants and personal fees from CONFAP/ESRC/MRC/BBSRC/CNPq/FAPDF and from the Wellcome Trust during the conduct of the study; and reported receiving grants from the Medical Research Council, the Wellcome Trust, and the Bill and Melinda Gates Foundation. Dr Ichihara reported receiving grants from Ministerio da Ciência, Tecnologia, Inovações e Comunicações (MCTI)/Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)/Ministério da Saúde (MS)/Secretaria de Ciência, Tecnologia e Insumos Estratégicos do Ministério da Saúde (SCTIE)/Departamento de Ciência e Tecnologia (Decit)/Bill & Melinda Gates Foundation during the conduct of the study. Dr Ramond reported receiving grants from MRC during the conduct of the study. Dr G. Penna reported receiving grants from FAPDF during the conduct of the study. Dr Brickley reported receiving grants from the MRC (MR/N017250/1) during the conduct of the study. Dr Barreto reported receiving grants from CONFAP/ESRC/MRC/BBSRC/CNPq/FAPDF and the Wellcome Trust during the conduct of the study. Dr Silva reported receiving grants from CONFAP/ESRC/MRC/BBSRC/CNPq/FAPDF and the Wellcome Trust during the conduct of the study. No other disclosures were reported.

    Funding/Support: This study was funded by grant MR/N017250/1 from the MRC (Dr Rodrigues), grant FAPDF 193.000.008/2016 from CONFAP/ESRC/MRC/BBSRC/CNPq/FAPDF 2015–Neglected Tropical Diseases (Dr G Penna), and grant 202912/Z/16/Z from the Wellcome Trust (Dr Barreto). This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brazil, Finance Code 001.

    Role of the Funder/Sponsor: The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: We thank the Cidacs/Fiocruz team for building the 100 Million Brazilian Cohort and for helping with the development of this study. Everyone working in the data center received personal fees from Wellcome Trust, the Bill and Melinda Gates Foundation, CONFAP/ESRC/MRC/BBSRC/CNPq/FAPDF, or another source.

    References
    1.
    Stolk  WA, Kulik  MC, le Rutte  EA,  et al.  Between-country inequalities in the neglected tropical disease burden in 1990 and 2010, with projections for 2020.   PLoS Negl Trop Dis. 2016;10(5):e0004560. doi:10.1371/journal.pntd.0004560 PubMedGoogle Scholar
    2.
    Pedrosa  VL, Dias  LC, Galban  E,  et al.  Leprosy among schoolchildren in the Amazon region: a cross-sectional study of active search and possible source of infection by contact tracing.   PLoS Negl Trop Dis. 2018;12(2):e0006261. doi:10.1371/journal.pntd.0006261 PubMedGoogle Scholar
    3.
    Le  W, Haiqin  J, Danfeng  H,  et al.  Monitoring and detection of leprosy patients in southwest China: a retrospective study, 2010-2014.   Sci Rep. 2018;8(1):11407. doi:10.1038/s41598-018-29753-4 PubMedGoogle ScholarCrossref
    4.
    Fine  PE, Sterne  JA, Pönnighaus  JM,  et al.  Household and dwelling contact as risk factors for leprosy in northern Malawi.   Am J Epidemiol. 1997;146(1):91-102. doi:10.1093/oxfordjournals.aje.a009195 PubMedGoogle ScholarCrossref
    5.
    Moet  FJ, Meima  A, Oskam  L, Richardus  JH.  Risk factors for the development of clinical leprosy among contacts, and their relevance for targeted interventions.   Lepr Rev. 2004;75(4):310-326.PubMedGoogle Scholar
    6.
    Rao  PN.  Global leprosy strategy 2016-2020: issues and concerns.   Indian J Dermatol Venereol Leprol. 2017;83(1):4-6. doi:10.4103/0378-6323.195075 PubMedGoogle ScholarCrossref
    7.
    Pescarini  JM, Strina  A, Nery  JS,  et al.  Socioeconomic risk markers of leprosy in high-burden countries: a systematic review and meta-analysis.   PLoS Negl Trop Dis. 2018;12(7):e0006622. doi:10.1371/journal.pntd.0006622 PubMedGoogle Scholar
    8.
    Nery  JS, Ramond  A, Pescarini  JM,  et al.  Socioeconomic determinants of leprosy new case detection in the 100 Million Brazilian Cohort: a population-based linkage study.   Lancet Glob Health. 2019;7(9):e1226-e1236. doi:10.1016/S2214-109X(19)30260-8PubMedGoogle ScholarCrossref
    9.
    Centro de Integração de Dados e Conhecimentos para a Saúde–Cidacs. Accessed February 14, 2020. http://cidacs.bahia.fiocruz.br/
    10.
    Brasil. Ministerio de Saúde, Departamento de Estatísticado SUS. Sistema de Informação de Agravos de Notificação–SINAN. Assessed March 10, 2020. https://portalsinan.saude.gov.br/
    11.
    Ali  MS, Ichihara  MY, Lopes  LC,  et al.  Administrative data linkage in Brazil: potentials for health technology assessment.   Front Pharmacol. 2019;10:984. doi:10.3389/fphar.2019.00984 PubMedGoogle ScholarCrossref
    12.
    Pita  R, Pinto  C, Sena  S,  et al.  On the accuracy and scalability of probabilistic data linkage over the Brazilian 114 Million Cohort.   IEEE J Biomed Health Inform. 2018;22(2):346-353. doi:10.1109/JBHI.2018.2796941 PubMedGoogle ScholarCrossref
    13.
    Brasil. Ministério da Saúde. Sistema de Legislação da Saúde. Portaria no 2.556, de 28 de outubro de 2011. Accessed November 25, 2019. http://bvsms.saude.gov.br/bvs/saudelegis/gm/2011/prt2556_28_10_2011.html
    14.
    Talhari  C, Talhari  S, Penna  GO.  Clinical aspects of leprosy.   Clin Dermatol. 2015;33(1):26-37. doi:10.1016/j.clindermatol.2014.07.002 PubMedGoogle ScholarCrossref
    15.
    Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância das Doenças Transmissíveis. Diretrizes Para Vigilância, Atenção e Eliminacao da Hanseníase Como Problema de Saúde Pública: Manual Técnico-Operacional. 2016. Accessed November 25, 2019. http://www.credesh.ufu.br/sites/credesh.hc.ufu.br/arquivos/diretrizes-eliminacao-hanseniase-4fev16-web.pdf
    16.
    Aalen  O.  Nonparametric inference for a family of counting processes.   Ann Stat. 1978;6(4):701-726. doi:10.1214/aos/1176344247 Google ScholarCrossref
    17.
    Nelson  W.  Theory and applications of hazard plotting for censored failure data.   Technometrics. 1972;14:945-966. doi:10.1080/00401706.1972.10488991 Google ScholarCrossref
    18.
    Brasil. Ministério da Saúde. Sala de Apoio à Gestão Estratégica. Situação de Saúde. Indicadores de Morbidade. Hanseníase. Accessed November 25, 2019. http://sage.saude.gov.br/#
    19.
    Kumar  A, Girdhar  A, Girdhar  BK.  Incidence of leprosy in Agra district.   Lepr Rev. 2007;78(2):131-136.PubMedGoogle Scholar
    20.
    Bobosha  K, Wilson  L, van Meijgaarden  KE,  et al.  T-cell regulation in lepromatous leprosy.   PLoS Negl Trop Dis. 2014;8(4):e2773. doi:10.1371/journal.pntd.0002773 PubMedGoogle Scholar
    21.
    Richardus  JH, Oskam  L.  Protecting people against leprosy: chemoprophylaxis and immunoprophylaxis.   Clin Dermatol. 2015;33(1):19-25. doi:10.1016/j.clindermatol.2014.07.009 PubMedGoogle ScholarCrossref
    22.
    Feenstra  SG, Nahar  Q, Pahan  D, Oskam  L, Richardus  JH.  Social contact patterns and leprosy disease: a case-control study in Bangladesh.   Epidemiol Infect. 2013;141(3):573-581. doi:10.1017/S0950268812000969 PubMedGoogle ScholarCrossref
    23.
    Hegazy  AA, Abdel-Hamid  IA, Ahmed  EF, Hammad  SM, Hawas  SA.  Leprosy in a high-prevalence Egyptian village: epidemiology and risk factors.   Int J Dermatol. 2002;41(10):681-686. doi:10.1046/j.1365-4362.2002.01602.x PubMedGoogle ScholarCrossref
    24.
    van’t Noordende  AT, Korfage  IJ, Lisam  S, Arif  MA, Kumar  A, van Brakel  WH.  The role of perceptions and knowledge of leprosy in the elimination of leprosy: a baseline study in Fatehpur district, northern India.   PLoS Negl Trop Dis. 2019;13(4):e0007302. doi:10.1371/journal.pntd.0007302 PubMedGoogle Scholar
    25.
    Lie  HP.  Why is leprosy decreasing in Norway?   Trans R Soc Trop Med Hyg. 1929;22(4):357-366. doi:10.1016/S0035-9203(29)90026-2 Google ScholarCrossref
    26.
    de Souza  CDF, Rocha  VS, Santos  NF,  et al.  Spatial clustering, social vulnerability and risk of leprosy in an endemic area in Northeast Brazil: an ecological study.   J Eur Acad Dermatol Venereol. 2019;33(8):1581-1590. doi:10.1111/jdv.15596 PubMedGoogle ScholarCrossref
    27.
    Szklo  M, Nieto  FJ.  Epidemiology: Beyond the Basics. Jones & Bartlett Learning; 2012.
    28.
    Penna  MLF, Penna  GO, Iglesias  PC, Natal  S, Rodrigues  LC.  Anti-PGL-1 positivity as a risk marker for the development of leprosy among contacts of leprosy cases: systematic review and meta-analysis.   PLoS Negl Trop Dis. 2016;10(5):e0004703. doi:10.1371/journal.pntd.0004703 PubMedGoogle Scholar
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