Designing the GIS predicting regional endemicity in Cambodia

Suguru OKAMI, Naohiko KOHTAKE Keio university

1 Malaria is a global issue.

584,000 5th

A life-threatening disease Estimated malaria caused The world 5th biggest cause of death caused by parasites that are deaths in 2013 in children except neonatal transmitted to people through (Uncertainty range 367,000 – causes.(WHO-UNICEF, 2013) the bites of infected 755,000).(WHO, 2015) mosquitoes.

WHO. Malaria Fact sheet No94, 2015 WHO-UNICEF. Child Health Reference Group, 2013 2 WHO. Trends in reported malaria incidence, 2000-2012 resistance is an emerging threat…

• Delayed parasite clearance in patients taking artemisinin combination therapy has been reported in Greater Mekong Subregions.(Ashley, 2014)

• If resistance to artemisinin develops and spreads to other large geographical areas, the public health consequences could be dire, as no alternative antimalarial medicine is now available.(WHO, 2015)

E.A Ashley et al. N Engl J Med 2014;371:411-23. 3 WHO. Malaria QA on altemisinin resistance, 2015 Ensuring appropriate course of treatment is an effective option for addressing artemisinin resistance.

Issues of drug use in resistant malaria

• Widespread availability of mono- therapy(WHO, 2015) • Counterfeit medicines(Tabernero, 2014)

• Un-regulated use of antimalarials. Therapeutic rate in Pailin Using AS4-DP for 6days Duration of follow up; 42 days 95%CI; 90.9−99.4 75% (95% CI, 49 - 90) with the standard 3-day dihydroartemisinin– piperaquine regimen, reported 1 year before.

WHO. Malaria QA on altemisinin resistance, 2015 P. Tabernero et al. Malaria Journal 2014, 13:139 E.A Ashley et al. N Engl J Med 2014;371:411-23. Leang R et al. Antimicrob Agents Chemother 2013;57:818-26. High proportion of patient with parasitemia on day 3 and the gap from symptom(fever) resolution.

Study site No. of pts Artesunate Artemisinin- Median Duration Gametocytemia on Positive for (mg/kg) Based of Fever Day 0 Parasitemia on Comnination (days) (%) Day3 therapy (%) Pailin 100 4 DP 2.5 (1.5-3.5) 19/100 (19) 65/98 (66) Preah Vihear 120 2 or 4 DP 2.8 (2.0-3.0) 6/120 (5) 21/120 (18)

Ratanakiri 120 2 or 4 DP 2 (2-3) 7/120 (6) 8/119 (7)

Pursat 120 4 DP 3 (3-4) 22/120 (18) 71/118 (60) *DP: dihydroartemisinin-piperaquine Patent gametocytemia was more likely to develop in patients with slower parasite clearance (OR 2.40 95%CI, 1.53-3.76; p<0.001) Resistant malaria have a transmission advantage that may drive the spread of resistance after day 3.

E.A Ashley et al. N Engl J Med 2014;371:411-23. Community-based surveillance is feasible, but highly intensive…(Cox, 2014)

• Systems incorporating existing networks of village malaria workers (VMWs) to monitor day 3-positive P. falciparum cases were piloted*.

• Day 0 : Preparing blood slides Day 0-2 : Administering directly observed therapy (DOT)

Day 3 : Obtaining follow- up slides and transporting them to HC. *In Pailin, Pursat and Battambang province

6 Cox et al. Malaria Journal 2014, 13:282 Pailin and Preah Vihear province where delayed arteminin clearance was observed(Ashley, 2014),(Bosman, 2014)

Preah Vihear

Pailin

7 Creating basal map

*Health center(◆) with 5km surrounding circle area covers most of populated village(●)

Item Notes

Road, DEM, Pol. Open development Cambodia Boundary, Village dist.

Hospital and health Health coverage plan, ministry of health Cambodia, 2005 center location

8 Think about what we can do through health GIS

Measuring the risk in “fine-scale”

For areas not well-covered -Sufficient health resource delivery -Reference for local healthcare staff recruitment

For cross-border transmission -Arrange efficient cross-border screening in high risk area How does GIS work under this situation?

Spread of drug-resistance

Poor quality medicines • Predict areas at risk Inappropriate drug use • Estimate the effectiveness of intervention

Human mobility

Issues in heath resource distribution

10 Objective

• To identify the areas with high malaria endemicity where intensive monitoring and support is needed by creating fine scale risk map using spatial statistical risk modeling.

11 Method

Data collection; Space satellite data Village level Surveillance data etc. eSMR

Modeling and statistics

Plotting the point estimated village level risks and interpolation Change in the estimated prevalence and SMR of malaria in 2010-2013 Incidence (/1,000 pop.) SMR （Empirical bayese estimated） 60 (Empirical bayese estimated) 7

6 50

5 40

4

30

3

20 2

10 1

0 0 EBI_2010 EBI_2011 EBI_2012 EBI_2013 EBSRR_2010 EBSRR_2011 EBSRR_2012 EBSRR_2013

13 Environmental and demographic variables collected Category Items Source Vegetation Normalized difference vegetation MODIS data from LPDAAC index (NDVI) MODIS/Terra Surface Reflectance 8-Day L3 Global 250m V005 Period: 2010 Jan. -2013 Dec. (001 – 361) Hydration Water Index MODIS data from LPDAAC Normalized difference water MODIS/Terra Surface Reflectance 8-Day L3 index (NDWI) Global 500m V005 Period: 2010 Jan– 2013 Dec. (001 – 361) Land Surface Water Index (LSWI) Climate Precipitations(1) WorldClim global climate data Climate Mean temperature(1) WorldClim global climate data Temperature Land surface temperature MODIS data from LPDAAC (Day/Night) MODIS Land Surface Temperature prod. Topology Topology Wetness Index(2) Calculated from ASTER GDEM data suitability P.falciparum and P.vivax Malaria Atlas Project temperature suitability index(3)

Population density Population density WorldPop project Open development Cambodia Land use/cover Global Land cover data(1) 0.5 km MODIS-based Global Land Cover Climatology from USGS (1) Chikodzi D, J Geosciences and Geomatics, 2013 (1):1; 8-14 (2) Cohen J.M. et al, Malaria J, 2010, 9 :328. (3) Gething P.W. et.al, Parasite & Vectors, 2011 (4):92 Distance is related to the fitting of modeled risk.

Correlation between log(EBSMR) and Coefficient of determination for selected environmental variables models 0.78 0.7

0.6 0.76

0.5 0.74

0.4 2 0.72 r R 0.3

0.7 0.2

0.68 0.1

0 0.66 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 Distance (m) Distance (m) NDVI_6 NDVI_mean NDVI_0.4 NDWI Model1 Model2 Model3 LSWI TWI LSWI/TWI Model 1: mean NDVI Model 2: # of months with mean NDVI >= 0.4 Model 3: # of pixels with NDVI >= 0.4 in equal or more than 6 months 15 15 Linear regression model for predicting EBSMR

where adjusted R2 = 0.7326, AIC = 161.6 NDVImean,5000mi: mean normal difference vegetation index in 5000m circular buffer from village NDWI1000m: mean difference water index in 1000m circular buffer TWI1000m: Topological wetness index in 1000m circular buffer Temp: Plasmodium temperature suitability index Pop: Population density (/km2) LLINsuf: Sufficient LLIN coverage

Estimate Std. Error P value

β0 -15.690 4.81 **

β1 8.814 2.074 ***

β2 -22.350 5.584 ***

β3 -1.152 0.655 ***

β4 0.00027 0.000047 .

β5 -0.0057 0.002 **

β6 -0.0426 0.0068 ***

***: P < 0.001 , **: P < 0.01 , . : P< 0.1 16 Bayesian method for risk modeling

mu Sd Rhat*

β0 -15.77790 4.91861 1.00111

β1 8.78043 2.07920 1.00106

β2 -22.41311 5.68282 1.00098

β3 -1.14354 0.65792 1.00088

β4 0.00027 0.00005 1.00144

β5 -0.00566 0.00201 1.00058

β6 -0.04262 0.00697 1.00186 σ 0.50527 0.09195 1.00066

MCMC 10,000 times, burin = 1,000, chain # = 3,000 *Gelman-Rubin statistics

DIC ~ 162.5 17 Observed vs. eSMR*

8 8 *estimated SMR Observed 2010 2011 7 Linear Regression Model 7 Bayese 6 6

5 5

4 4 3 3 2 2 1 1 0 0

8 2012 8 2013 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 0 Mapping results (Pailin, 2010) Mapping results (Preah Vihear, 2010) Mapping results (IDW, Pailin, 2010) Mapping results (Ordinal, kriging, Preah Vihear, 2010) Is this map reliable?

23 Visual feature

From Malaria Atlas Project database(Moyes, 2013) Fine-scale risk map (Preah Vihear province, 2010)

24 Verification of spatial risk model

• Compare with geocoded observed data -Pailin (Médecins Sans Frontières, 2005) -Preah Vihear (Incadona, 2007)

t = 0.59 (N.S.) t = 0.40 (N.S.) r = 0.66 r = 0.67

* t : Welch Two Sample t-test (Predicted vs Observed) r : Spearman's rank correlation 25 Discussion

• Compared with global-scale map -Different interpolation method -Variation and spatial density of source data -Needs for fine scale mapping for malaria elimination(Malaria Elimination Group, 2009) and drug resistance containment(Cox, 2014) • Cross scale prediction -Accessibility for micro data -Managing spuriousness and unreliability for multi-scale prediction -Bayesian Approach(Keil, 2013)(Sturrok, 2014)

26 Next Step

• Improving accuracy of the model -Non-linear model such as general additive model -Bayesian approach for multivariate time series analysis • Present the relationship between risks and containment status

27 Conclusion

• We created a fine scale risk map using surveillance data, remote sensing data and other demo/geographic data sources adjusted by distance from human communities. • For more effective and practical applications, further studies are needed.

28 Reference

1: Wesolowski et al., Science. 338 (6104): 267-270: 2012 2: 2011. Global plan for artemisinin resistance containment, WHO 3: WHO Guidelines for the treatment of malaria – 2nd edition, 2010 4: WHO Fact Sheet No 94 WHO, 2015 5: E.A Ashley et al. N Engl J Med 2014;371:411-23. 6: Malaria in the Greater Mekong Subregion Regional and Country Profiles, WHO, 2010 7: 2014. Status report on artemisinin resistance, WHO Global 8:Leang R et al. Antimicrob Agents Chemother 2013;57:818-26. 9:Cox et al. Malaria Journal 2014, 13:282 10:Patricia T et al. Malaria Journal 2014, 13:139 11: 2011 Moving Towards Malaria EliminationTool for Strengthening Malaria Surveillance in Cambodia 12:Health delivery profile in Cambodia 2012, MOH Cam and WHO 13:Nihei et al. Jpn. J Infect. Dis. 2002;55: 160-66. 14:Hay SI et al. Remote sensing and geographical information systems in epidemiology: San Diego Academic press 2000 15:Cohen J.M. et ak, Malaria J, 2010, 9:328. 16:Chikodzi D, J Geosciences and Geomatics, 2013 (1):1,8-14 17:Gathing P.W. et. Al, Parisite & Vectors, 2011 (4):92 18:Archie CA Clements, Simon I Hay et al. Lancet Infect Dis 2013;13: 709–18

29 Reference

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30 Thank you!

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