Commentary: Strategic surveillance—key to attainment of leprosy control and elimination goals at the local level

Nina Marano and Brian Plikaytis

Meningitis and Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Road Mailstop C-O9, Atlanta GA 30333, USA. E-mail: nbm8{at}cdc.gov

Currently the prevalence of leprosy in six countries is still above the level of 1 per 10 000 population, defined in 1991 by the World Health Assembly as the target prevalence level for attainment of leprosy elimination.1 Together, India, Brazil, Madagascar, Mozambique, United Republic of Tanzania, and Nepal represent approximately 90% of the global leprosy burden, and new cases continue to be detected with about 620 672 registered in 2003.2 Of these, approximately 20% are at risk of developing nerve function impairment and subsequent disability, the most serious consequence of leprosy both to the patient, their family and community.3 In February 2003, the WHO Technical Advisory Group on Elimination of Leprosy met to evaluate the intensified strategy for global leprosy elimination. Panel members noted that while many leprosy-endemic countries have achieved elimination at the national level, India and Brazil are unlikely to attain a prevalence rate below 1 per 10 000 population within the next 2 years. The panel observed that most countries successful in achieving national elimination goals have developed strategies for reaching elimination goals at regional and local levels.4

As countries focus ongoing and renewed efforts for leprosy elimination and control at regional and local levels, those with pockets of high endemnicity face particular challenges. Techniques for case detection that are rapid, practical, accurate, and cost effective are needed to effectively monitor progress of control and elimination strategies. Lot quality assurance sampling (LQAS) is a quality control technique developed in industry in the 1920s for products manufactured on a factory assembly line, whereby a small number of units from a particular lot are randomly selected for examination.5

If the number of defective units in the small sample exceeds a certain predetermined number, the entire lot is rejected. In public health, LQAS has been used to assess immunization coverage, neonatal tetanus mortality, response to antimalarial treatment, and use of oral rehydration therapy.6 To employ LQAS for assessment of public health programmes, a critical value d is calculated based on an estimate of disease prevalence and represents the maximum allowable number of cases with the disease of interest per sample population group. If the number of cases in the sample population exceeds the critical value d, the prevalence rate of disease in the larger population from which the sample was derived is considered to exceed the upper limits of the original prevalence estimate, indicating that further intervention is needed.

The objective of Gupte et al. is to validate the applicability of LQAS in monitoring the progress of leprosy elimination in Tamil Nadu, a highly endemic state in India.7 In this and previous papers, the authors utilize cluster sampling (CS) techniques to determine whether the prevalence of leprosy exceeds a maximum allowable number.8,9 CS is frequently employed in surveys; primary considerations to employ CS over simple random sampling (SRS) include the prohibitive cost of SRS and difficulty or impossibility of constructing a sampling frame for the population. Also, the population may be so widely distributed geographically that sampling naturally occurring units such as villages and households may prove to be the most economical method for estimating disease prevalence. CS is simpler to perform than SRS because there are fewer sampling units (clusters) to identify and they are more easily selected and characterized. Comparing variances derived from CS and SRS, investigators are able to compute a design effect which is used to inflate sample size calculations to accommodate the clustered sampling design. We believe this technique is appropriate to monitor the progress of leprosy elimination programmes, but that it is important to consider the impact of decreased precision (increased variance) that accompanies CS. In the present study, Gupte et al., calculated within- and between-cluster variances and determined that the design effect for their sample was a combined 1.06. Consequently, the authors did not experience a penalty for implementing a cluster sample and did not need to increase their sample sizes.

To further monitor progress in leprosy elimination and control, consideration should be given to incorporating surveillance methods that are capable of predicting the future needs of the particular population at risk. Clinical prediction rules have been previously described that could be used for early identification of patients at greatest risk for subsequent development of nerve function impairment (NFI).3 Based on simple tests of sensory function, patients would be assigned as having a low, intermediate, or high risk of future development of NFI. Application of these prediction rules could serve as a guide to determine the appropriate length of time to conduct surveillance after initial patient registration. With this information, programme managers can more effectively allocate precious resources for elimination and control programmes in high endemnicity areas.

The importance of assessment for NFI in leprosy patients is corroborated by Richardus et al.10 The authors provide data from a prospective cohort study of 2664 new leprosy patients followed up to 5 years for the development of NFI. The occurrence of NFI in leprosy patients has previously been demonstrated in numerous studies; however, this study is notable for the length of the follow-up period, 3 years beyond completion of multidrug therapy (MDT). The authors conclude that multibacillary patients with longstanding NFI at first registration have a high likelihood of developing new NFI within, but not beyond 2 years. This demonstrates the importance of conducting surveillance among high-risk patients for at least 2 years. In this cohort of patients, the majority of NFI cases involved sensory or motor impairment without skin signs of reversal reaction or erythema nodosum leprosum (ENL), also known as silent neuropathy, highlighting the importance of creating early awareness among patients and their families to take steps to prevent further disability.11

In summary, it has been said that:

leprosy is one of the few infectious diseases that meet the demanding criteria for elimination, namely, practical and simple diagnostic tools, the availability of an effective intervention to reduce its transmission and a single significant reservoir of infection—humans.12

To maintain the momentum and progress being made towards leprosy elimination, as the prevalence of leprosy decreases, countries will need to employ more sensitive strategies to detect and manage cases. Gupte and Richardson, while addressing different aspects of leprosy management, have both provided effective strategies for surveillance to evaluate progress in leprosy elimination in regions where endemnicity remains high.


    References
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 References
 
1 World Health Organization. Leprosy Resolution 44.9. Forty-fourth World Health Assembly, 1991.

2 World Health Organization. Global Leprosy Situation in 2003. Geneva: WHO, 2003.

3 Croft RP, Nicholls PG, Steyerberg EW, Richardus JH, Cairns W, Smith S. A clinical prediction rule for nerve-function impairment in leprosy patients. Lancet 2000;355:1603–06.[CrossRef][ISI][Medline]

4 World Health Organization. Conclusions and Recommendations of the 5th Meeting of the WHO Technical Advisory Group on Elimination of Leprosy. Yangon, Myanmar, 2003.

5 Reinke WA. Applicability of industrial sampling techniques to epidemiologic investigations: examination of an under utilized resource. Am J Epidemiol 1991;134:1222–31.[Abstract]

6 Robertson SE, Anker M, Roisin AJ, Macklai N, Engstrom K, LaForce M. The lot quality technique: a global review of applications in the assessment of health services and disease surveillance. World Health Stat Q Rep 1997;50:199–209.

7 Gupte MD, Murthy BN, Mahmood K, Meeralakshmi S, Nagaraju B, Prabhakran R. Application of lot quality assurance sampling for leprosy elimination monitoring-examination of some critical factors. Int J Epidemiol 2004;33:344–48.[CrossRef][ISI][Medline]

8 Gupte MD, Narasimhamurthy B. Lot quality assurance sampling (LQAS) for monitoring a leprosy elimination program. Int J Leprosy 1999;67:143–49.[ISI]

9 Murthy BN, Subbiah SM, Boopathi K, Ramakrishnan R, Gupte MD. Lot quality assurance sampling (LQAS) for monitoring leprosy elimination in an endemic district in Tamilnadu. Ind J Leprosy 2001;73:111–19.

10 Richardus JH, Nicholls PG, Croft RP, Withington SG, Smith WCS. Incidence of acute nerve function impairment and reactions in leprosy: a prospective cohort analysis after 5 years of follow-up. Int J Epidemiol 2004;33:337–43.[CrossRef][ISI][Medline]

11 Van Brakel WH, Khawas IB. Silent neuropathy in leprosy: an epidemiological description. Leprosy Rev 1994;65:350–60.[ISI][Medline]

12 Neira M. Disease Elimination and Eradication: Lessons Learnt from Leprosy. State of the Art Lecture, International Leprosy Congress, Bahia, Brazil, 2002.





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