The role of an active surveillance strategy of targeting household and neighborhood contacts related to leprosy cases released from treatment in a low-endemic area of China.

OBJECTIVE: Early diagnosis remains the primary goal for leprosy management programs. This study aims to determine whether active surveillance of patients with leprosy and their contact individuals increased identification of latent leprosy cases in the low-endemic areas. METHODS: This cross-sectional survey was carried out between October 2014 and August 2016 in 21 counties throughout Shandong Province. The survey was conducted among patients with leprosy released from treatment (RFT) and their contacts from both household and neighbors. RESULTS: A total of 2,210 RFT patients and 9,742 contacts comprising 7877 household contacts (HHCs), including 5,844 genetic related family members (GRFMs) and 2033 non-genetic related family members and 1,865 contacts living in neighboring houses (neighbor contacts, NCs), were recruited. Among identified individuals, one relapsed and 13 were newly diagnosed, giving a detection rate of 0.12%, corresponding to 120 times the passive case detection rate. Detection rates were similar for HHCs and NCs (0.114% vs. 0.214%, P = 0.287). Analysis of the family history of leprosy patients revealed clustering of newly diagnosed cases and association with residential coordinates of previously-diagnosed multibacillary leprosy cases. CONCLUSION: Active case-finding programs are feasible and contributes to early case detection by tracking HHCs and NCs in low-endemic areas.

Prediction of leprosy in the Chinese population based on a weighted genetic risk score.

Genome wide association studies (GWASs) have revealed multiple genetic variants associated with leprosy in the Chinese population. The aim of our study was to utilize the genetic variants to construct a risk prediction model through a weighted genetic risk score (GRS) in a Chinese set and to further assess the performance of the model in identifying higher-risk contact individuals in an independent set. The highest prediction accuracy, with an area under the curve (AUC) of 0.743 (95% confidence interval (CI): 0.729-0.757), was achieved with a GRS encompassing 25 GWAS variants in a discovery set that included 2,144 people affected by leprosy and 2,671 controls. Individuals in the high-risk group, based on genetic factors (GRS > 28.06), have a 24.65 higher odds ratio (OR) for developing leprosy relative to those in the low-risk group (GRS≤18.17). The model was then applied to a validation set consisting of 1,385 people affected by leprosy and 7,541 individuals in contact with leprosy, which yielded a discriminatory ability with an AUC of 0.707 (95% CI: 0.691-0.723). When a GRS cut-off value of 22.38 was selected with the optimal sensitivity and specificity, it was found that 39.31% of high risk contact individuals should be screened in order to detect leprosy in 64.9% of those people affected by leprosy. In summary, we developed and validated a risk model for the prediction of leprosy that showed good discrimination capabilities, which may help physicians in the identification of patients coming into contact with leprosy and are at a higher-risk of developing this condition.