Development of a questionnaire to determine the case detection delay of leprosy: A mixed-methods cultural validation study

Bruijne, Naomi D de; Urgesa, Kedir; Aseffa, Abraham; Bobosha, Kidist; Schoenmakers, Anne; Wijk, Robin van; Hambridge, Thomas; Waltz, Mitzi; Kasang, Christa; Mieras, Liesbeth

doi:10.1371/journal.pntd.0010038

Cited by 13 publications

(35 citation statements)

References 35 publications

(56 reference statements)

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“…The picture set helped to avoid the use of medical jargon [ 58 ]. As advised by de Bruijne et al (2022), annexes (calendar, photos) should be adapted and validated when the CDD questionnaire is used in other geographical regions or cultural contexts [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, study results were collected from the patient perspective only. For readability reasons, when mentioned “symptoms” in the further text of this article, this should be read as “signs and symptoms”, reflecting both objective and subjective disease manifestations [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“…A structured questionnaire was adopted from our previous work conducted in East Hararghe Zone, Ethiopia [ 33 ]. This questionnaire was designed as part of the PEP4LEP project, a research project in which integrated skin screening interventions for leprosy are tested, together with the administration of chemoprophylaxis (single-dose rifampicin post-exposure prophylaxis, SDR-PEP) to contacts of leprosy patients [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…This questionnaire was designed as part of the PEP4LEP project, a research project in which integrated skin screening interventions for leprosy are tested, together with the administration of chemoprophylaxis (single-dose rifampicin post-exposure prophylaxis, SDR-PEP) to contacts of leprosy patients [ 36 ]. The questionnaire comprised: (1) patient socio-demographic characteristics; (2) questions to determine the delay in case detection; (3) questions to identify the reasons for delay in case detection; (4) an annex with a picture set to recognize leprosy symptoms and; (5) an annex with a local calendar to translate the patient’s indication of time to a number of months [ 33 ]. The questionnaire was prepared in English and translated into the local language (Afan Oromo) and back translated into English by an independent English teacher whose mother tongue was Afan Oromo.…”

Section: Methodsmentioning

confidence: 99%

“…Leprosy associated disability is categorized according to the WHO leprosy disability grading system. The delay in case detection was defined as the period between the first symptoms of the disease and the diagnosis of leprosy calculated in total number of months [29][30][31][32][33][34].…”

Section: Operational Definitionsmentioning

confidence: 99%

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Prolonged delays in leprosy case detection in a leprosy hot spot setting in Eastern Ethiopia

et al. 2022

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Background Leprosy or Hansen’s disease is known to cause disability and disfigurement. A delay in case detection among leprosy patients can lead to severe outcomes. In Ethiopia, the disability rates caused by leprosy among new cases are relatively high compared to other endemic countries. This suggests the existence of hidden leprosy cases in the community and a delay in timely detection. To reduce disability rates, it is crucial to identify the factors associated with this delay. This study aimed to determine the extent of delay in case detection among leprosy cases in Eastern Ethiopia. Methods This health-institution based cross-sectional explorative study was conducted in January and February 2019 among 100 leprosy patients diagnosed ≤6 months prior to inclusion. A structured questionnaire was used to collect data, including the initial onset of symptoms, and the reasons for delayed diagnosis. Descriptive statistics, including percentages and medians, were used to describe the case detection delay. Logistic regression analysis was carried out to evaluate the predictors of delay in case detection of >12 months. Findings The median age of patients was 35 years, with a range of 7 to 72 years. The majority were male (80%) and rural residents (90%). The median delay in case detection was 12 months (interquartile range 10–36 months) among the included patients. The mean delay in case detection was 22 months, with a maximum delay of 96 months. The overall prevalence of disability among the study population was 42% (12% grade I and 30% grade II). Fear of stigma (p = 0.018) and experiencing painless symptoms (p = 0.018) were highly associated with a delay in case detection of >12 months. Conclusions Being afraid of stigma and having painless symptoms, which are often misinterpreted as non-alarming at the onset of the disease, were associated with a delay in case detection. This study showed the need to increase knowledge on early symptoms of leprosy among affected communities. Furthermore, it is important to support initiatives that reduce leprosy related stigma and promote health worker training in leprosy control activities.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Operational Definitionsmentioning

confidence: 99%

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Prolonged delays in leprosy case detection in a leprosy hot spot setting in Eastern Ethiopia

et al. 2022

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Measuring leprosy case detection delay and associated factors in Indonesia: a community-based study

Dharmawan,

Korfage,

Abqari

et al. 2023

BMC Infect Dis

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Background Leprosy is a public health burden in Indonesia with a high number of new cases every year and a high proportion of disability among new cases. Case detection delay (CDD) can contribute to ongoing transmission and increased disability chances among leprosy patients. This study aimed to establish the CDD of leprosy and the factors associated with detection delay in Indonesia. Method Community-based study with a cross-sectional design. Data were collected through interviews about sociodemographic and behavioral factors, anticipated stigma, and duration of CDD. Leprosy classification and case detection methods were obtained from health service records. A random sample was taken of 126 leprosy patients registered between 1st October 2020 and 31st March 2022 in the Tegal regency in the Central Java Province. Data were analysed by descriptive and analytical statistics using multiple linear regression. Results The mean CDD, patient delay, and health system delay were 13.0 months, 9.7 months, and 3.2 months, respectively. Factors associated with longer CDD are younger age (below 35 years), male, found through passive case detection, and not having a family member with leprosy. Factors associated with longer patient delay were being younger (below 35 years), being male, not having a family member with leprosy, and anticipated stigma of leprosy. It was not possible to reliably identify factors associated with health system delay. Conclusion CDD in leprosy should be reduced in Indonesia. The Indonesian National Leprosy Control Program (NLCP) is advised to adopt an integrated intervention programme combining active case detection with targeted health education to reduce CDD and thereby preventing disabilities in people affected by leprosy.

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Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach

et al. 2023

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Background Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type. Methods Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors. Results For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: −1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14–2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08–2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review. Conclusions The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs. Graphical Abstract

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Development of a questionnaire to determine the case detection delay of leprosy: A mixed-methods cultural validation study

Cited by 13 publications

References 35 publications

Prolonged delays in leprosy case detection in a leprosy hot spot setting in Eastern Ethiopia

Prolonged delays in leprosy case detection in a leprosy hot spot setting in Eastern Ethiopia

Measuring leprosy case detection delay and associated factors in Indonesia: a community-based study

Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach

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