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printed by www.postersession.com Age-targeted control strategies for schistosomiasis– Age-targeted control strategies for schistosomiasis– associated morbidity and childhood developmental associated morbidity and childhood developmental impairment impairment David Gurarie 1 and Charles H. King 2 Math. Department; Center for Global Health and Diseases, Case University, Cleveland, OH Schistosomiasis has multiple adverse effects, including long-term chronic disease, and retardation of 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 control strategies, and shows the potential for a substantial reduction of both early (developmental) and late-term morbidity. ABSTRACT RESULTS SUMMARY AND CONCLUSIONS 267 267 4 4 Premises: Stationary human populations and transmission environment Age-dependent (behavioral) risk factors Genetic risk factors. 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; D L ;D H – accumulated disease (for low/high risk groups); H,h – developmental index (weight, height, etc.) for normal and delayed growth, =h/H - disability fraction ( – ‘normal state’). All variables are functions of age a, and time t. transmission and therapy-control drives stable (endemic) equilibrium state, that obeys integro-differential equations: Parameters: – per capita force of infection (depends on community-wide transmission and snail infection) a – age-dependent contact rates (determine worm establishment and snail contamination). - 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 (w) – infectious inhibition function (0<<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. 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 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. 1.Medley GF, Bundy DA, Am J Trop Med Hyg 1996;55:149-58. 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. 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. CHRONIC DISEASE FORMATION Treatm ent ; (1) ; ; a a a a L L L a a a H H H a a a w w D w D D w D The results below combine mathematical analysis / solutions of (1)-(2), and numeric codes implemented in Wolfram Mathematica 5. Fig.3: Age-specific worm burden (dashed) and chronic damage (solid) with 4 possible disease resolution rates: linear case (left), nonlinear case (right) 0 10 20 30 40 50 60 0 1 2 3 4 Age d e t a l u m u c c A e g a m a d 1 0.2 0.1 0.05 0 10 20 30 40 50 60 0 1 2 3 4 5 6 Age d e t a l u m u c c A e g a m a d 1 0.2 0.1 0.05 10 20 30 40 50 60 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Worm burden 10 20 30 40 50 60 0.2 0.4 0.6 0.8 1 1.2 Low risk morbidity 10 20 30 40 50 60 1 2 3 4 5 6 High risk morbidity 0 5 10 15 20 25 30 1 2 3 4 5 6 Year of first treatment x a M y t i d i b r o m 20 % efficacy 90 % efficacy Low risk 0 5 10 15 20 25 30 1 2 3 4 5 6 Year of first treatment x a M y t i d i b r o m 20 % efficacy 90 % efficacy Low risk Fig. 6: The US median and 3rd percentile (NCHS) growth curves vs. Kenyan S. haematobium data (orange dots), and the best-fit DE solutions. EARLY GROWTH RETARDATION Infection + chronic disease Treatm ent ; (2) 1 1 a a a a a w w ga w wra Infection + early development 0 5 10 15 20 10 20 30 40 50 60 70 A ge t h g i e W ,g k Bestfit D E US m edian US 3rd percentile 0 5 10 15 20 60 80 100 120 140 160 180 A ge t h g i e H ,m c Bestfit D E US m edian US 3rd percentile 0 5 10 15 20 0.7 0.75 0.8 0.85 0.9 0.95 1 A ge t h g i e W t i c i f e d 0 5 10 15 20 0.9 0.92 0.94 0.96 0.98 1 A ge t h g i e H t i c i f e d 0 5 10 15 20 0.7 0.75 0.8 0.85 0.9 0.95 1 A ge t h g i e W t i c i f e d 0 5 10 15 20 0 5 10 15 20 0.9 0.92 0.94 0.96 0.98 1 A ge t h g i e H t i c i f e d 0.9 0.92 0.94 0.96 0.98 1 A ge t h g i e H t i c i f e d 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) Fig. 1: Fig. 1: Typical age-prevalence, infection intensity, and chronic disease formation in S.haematobium-endemic areas Fig. 2: Fig. 2: Abnormal growth curve of male children and teen-age boys in an S.haematobium-endemic area Methods MODELS: TRANSMISSION-DISEASE-DEVELOPMENT
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Transcript of Printed by Age-targeted control strategies for schistosomiasis–associated morbidity and childhood...
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www.postersession.com
Age-targeted control strategies for schistosomiasis–associated Age-targeted control strategies for schistosomiasis–associated
morbidity and childhood developmental impairmentmorbidity and childhood developmental impairmentDavid Gurarie1 and Charles H. King2
1Math. Department; 2Center for Global Health and Diseases, Case University, Cleveland, OH
Schistosomiasis has multiple adverse effects, including long-term chronic disease, and retardation of 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.
ABSTRACT RESULTS
SUMMARY AND CONCLUSIONS
2674 2674
Premises:Stationary human populations and transmission environmentAge-dependent (behavioral) risk factors Genetic risk factors. 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, =h/H - disability fraction (– ‘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:
Parameters: – per capita force of infection (depends on community-wide
transmission and snail infection) a – age-dependent contact rates (determine worm establishment and
snail contamination). - 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 (w) – infectious inhibition function (0<<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.
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.1.Medley GF, Bundy DA, Am J Trop Med Hyg 1996;55:149-58.2.Chan MS, Guyatt HL, Bundy DA., Medley GF, Am J Trop Med Hyg 1996;55:52–623.Gurarie D, King CH, Parasitology 2005;130:49-65
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.
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.
CHRONIC DISEASE FORMATION
Treatment ;
(1) ;
;
a a a a
L L La a a
H H Ha a a
w w
D w D
D w D
The results below combine mathematical analysis / solutions of (1)-(2), and numeric codes implemented in Wolfram Mathematica 5.
Fig.3: Age-specific worm burden (dashed) and chronic damage (solid) with 4 possible disease resolution rates: linear case (left), nonlinear case (right)
0 10 20 30 40 50 600
1
2
3
4
Age
detalumuccAegamad
1
0.2
0.1
0.05
0 10 20 30 40 50 600
1
2
3
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5
6
Age
detalumuccAegamad
1
0.2
0.1
0.05
10 20 30 40 50 60
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Worm burden
10 20 30 40 50 60
0.2
0.4
0.6
0.8
1
1.2
Low risk morbidity
10 20 30 40 50 60
1
2
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6
High risk morbidity
0 5 10 15 20 25 30
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Year of first treatment
xaMytidibrom
20% efficacy 90% efficacy
Low risk
0 5 10 15 20 25 30
1
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3
4
5
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Year of first treatment
xaMytidibrom
20% efficacy
90% efficacy
Low risk
Fig. 6: The US median and 3rd percentile (NCHS) growth curves vs. Kenyan S. haematobium data (orange dots), and the best-fit DE solutions.
EARLY GROWTH RETARDATION
Infection + chronic disease
Treatment ;(2)
1 1
a a a a
a
w w
g a w w r a
Infection + early development
0 5 10 15 20
10
20
30
40
50
60
70
Age
thgieW
,gk
Best fit DEUS medianUS 3rd percentile
0 5 10 15 20
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180Age
thgieH
,mc
Best fit DEUS medianUS 3rd percentile
0 5 10 15 20
0.7
0.75
0.8
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0.95
1Age
thgieW
t icifed
0 5 10 15 200.9
0.92
0.94
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1Age
thgieH
ti cifed
0 5 10 15 20
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thgieW
t icifed
0 5 10 15 200 5 10 15 200.9
0.92
0.94
0.96
0.98
1Age
thgieH
ti cifed
0.9
0.92
0.94
0.96
0.98
1Age
thgieH
ti cifed
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)
Fig. 1: Fig. 1: Typical age-prevalence, infection intensity, and chronic disease formation in S.haematobium-endemic areas
Fig. 2: Fig. 2: Abnormal growth curve of male children and teen-age boys in an S.haematobium-endemic area
Methods
MODELS: TRANSMISSION-DISEASE-DEVELOPMENT
