ABSTRACT

1. Age-targeted control strategies for schistosomiasis–associated morbidity and childhood developmental impairment David Gurarie1 and Charles H. King2 1Math. Department; 2Center for Global Health and Diseases, Case University, Cleveland, OH 2674 RESULTS ABSTRACT MODELS: TRANSMISSION-DISEASE-DEVELOPMENT Premises: Stationary human populations and transmissionenvironment Age-dependent (behavioral) risk factors Genetic riskfactors. Variability in immune response can affect infection levels, early development, and the accumulation/ resolution of chronic disease. Accordingly, populations are subdivided into low- and high-risk disease-development cohorts Child development accounts for natural growth, its inhibition by infection, and the potential for therapeutic remediation Age-targeted treatment strategies with complete or partial coverage Model Variables: w – (mean) burden; DL;DH – accumulated disease (for low/high risk groups); H,h – developmental index (weight, height, etc.) for normal and delayed growth, d=h/H - disability fraction (d=1– ‘normal state’). All variables are functions of age a, and time t.Stationary transmission and therapy-control drives the system to a stable (endemic) equilibrium state, that obeys integro-differential equations: Schistosomiasis has multiple adverse effects, including long-term chronic disease, and retardationof juvenile growth and development. W.H.O. advocates control strategy by periodic drug treatment of affected populations, focusing on school-age children as the highest risk group for infection. Such control programs have already began, but important questions remain: I) Given the nature of infection, its associated diseases, and the typical patterns of program participation, what are the optimal strategies for drug delivery to minimize community burden of disease in a resource-limited setting? II) What effect could drug treatment have on improving early childhood development? We address these problems by mathematical modeling that accounts for transmission in age-structured populations, the typical development of acute and chronic diseases, the long term effect of treatment on chronic disease, as well as the impact on early childhood development and growth retardation. Our analysis identifies such optimal control strategies, and shows the potential for a substantial reduction of both early (developmental) and late-term morbidity. Fig.3: Age-specific worm burden (dashed) and chronic damage (solid) with 4 possible disease resolution rates: linear case (left), nonlinearcase (right) Fig.4: Worm burden (left) and chronic disease prevalence (center/right) for the 3 treatment cohorts of case I (shades of gray) vs. a completely untreated population (dashed). The low/high risk groups differ by their resolution rates. Infection + chronic disease CHRONIC DISEASE FORMATION Infection + early development Fig.5:Long-term chronic damage as a function of varying the initial treatment age of strategies II-III (including decreasing adherence), at two different cover levels of the first cohort: 80% of eligible population (left), and 50% (right). Black curves are high-risk, gray - low-risk morbidity groups. Two ‘high risk’ curves on each plot compare the results of risk screening tests at each of two sensitivity levels. • Parameters: • – per capita force of infection (depends on community-wide transmission and snail infection) ha – age-dependent contact rates (determine worm establishment and snail contamination). • r,n - disease accumulation and resolution, can be linear or nonlinear function of w,D. • g(a), r(a) – natural/ remedial growth rates, based on US (NCHS) data • f(w) – infectious inhibition function (0<f<1) • Treatment: • Population is subdivided into treatment cohorts (with different protocols). We consider two scenarios: (A) blind selection of treatment cohorts, where both risk groups enter in proportion to their population fractions; (B) Prescreening to select high-risk individuals for more intense treatment (Fig. 6). • Treatment strategies and adherence for blind selection: • (I) Follow three realistic limited treatment cohorts: a. 60% of population treated at ages 6 and 12; b. 20% treated at age 6 only, c. remaining 20% go untreated • (II) Field compliance levels for multiple treatment: 70% covered by first treatment go to second, 60% of those to a third one. We allow 2-year gap between sessions (recommended by WHO) and let timing of initial treatment vary from 1 to 30 years of age. • Programs with risk screening and stratified treatment delivery: • (III) will apportion the treated/untreated fractions among risk groups based on their predicted risk. We maintain the same overall coverage rates as case (II), but a larger number of the high-risk fraction is treated, depending on efficacy of screening. Fig. 6: The US median and 3rd percentile (NCHS) growth curves vs. Kenyan S. haematobium data (orange dots), and the best-fit DE solutions. Fig. 1: Typical age-prevalence, infection intensity, and chronic disease formation in S.haematobium-endemic areas EARLY GROWTH RETARDATION Fig. 7: The possible effect of ‘near optimal’ treatment regimen on reversing the height/weight deficiencies associated with infection at 3 different program efficacies: 20%,50%,90% (orange dots – untreated Kenyan data) SUMMARY AND CONCLUSIONS • Mathematical models allow us to estimate the effects of age-targeted treatments on both late-term disease formation and early growth retardation. We find optimal strategies (initial age, regimen) that yield significant reductions of both. These can apply to identified high-risk groups or the general population. Pre-screening for risk produces little effect (over a lifespan) with high initial coverage, but grows in significance at lower participation/adherence levels. • Medley GF, Bundy DA, Am J Trop Med Hyg 1996;55:149-58. • Chan MS, Guyatt HL, Bundy DA., Medley GF, Am J Trop Med Hyg 1996;55:52–62 • Gurarie D, King CH, Parasitology 2005;130:49-65 Methods The results below combine mathematical analysis / solutions of (1)-(2), and numeric codes implemented in Wolfram Mathematica 5. Fig. 2: Abnormal growth curve of male children and teen-age boys in an S.haematobium-endemic area • We studied potential impact of area-wide control strategies on spatially-distributed endemic schistosome infection, using a distributed Macdonald-type model. An optimal age-targeted strategy was determined for a range of values r. Focal control fails to achieve the desired impact, due to linked environment. • Future work (in progress) will extend the above models to include immunity, chronic morbidity, and seasonally varying snail ecology. • Macdonald, G. (1965). Trans R Soc Trop Med Hyg59, 489-506. • D. Gurarie, C. King, Parasitology, 2005 (to appear) • We studied potential impact of area-wide control strategies on spatially-distributed endemic schistosome infection, using a distributed Macdonald-type model. An optimal age-targeted strategy was determined for a range of values r. Focal control fails to achieve the desired impact, due to linked environment. • Future work (in progress) will extend the above models to include immunity, chronic morbidity, and seasonally varying snail ecology. • Macdonald, G. (1965). Trans R Soc Trop Med Hyg59, 489-506. • D. Gurarie, C. King, Parasitology, 2005 (to appear) • We studied potential impact of area-wide control strategies on spatially-distributed endemic schistosome infection, using a distributed Macdonald-type model. An optimal age-targeted strategy was determined for a range of values r. Focal control fails to achieve the desired impact, due to linked environment. • Future work (in progress) will extend the above models to include immunity, chronic morbidity, and seasonally varying snail ecology. • Macdonald, G. (1965). Trans R Soc Trop Med Hyg59, 489-506. • D. Gurarie, C. King, Parasitology, 2005 (to appear)