Vector Control and Surveillance for Malaria & Other Vector-Borne

Diseases

Lessons Learned

Gene Brantly

Malaria/VBD Program Elements

Integrated Vector Management– IVM policy and capacity-building– Larval control methods

Surveillance– Malaria stratification and forecasting– Regional collaboration

Overview

Malaria/VBD Program Locations

Africa – Eritrea, Ghana, Kenya, Mozambique, Tanzania, Uganda, Zambia, and regional

ANE – Nepal and South Asia regional

LAC -- regional

Overview

Malaria Vector Control Methods

Insecticide-treated bed nets

Background

Indoor spraying

Malaria Vector Control Methods

Background

Malaria Vector Control Methods

Larval control

Background

Malaria Vector Control Methods

Larval control

Background

Integrated Vector Management

DefinitionIVM is the targeted, selective use of one or more

vector control methods appropriate to local environmental and epidemiological conditions

IVM Program– Planning– Operations– M&E

Background

Key Roles for EHP

Support IVM initiatives in WHODetermine appropriate settings for malaria vector control methods– Urban and rural environments– Emphasis on larval control

Integrated Vector Management

WHO IVM Initiatives

1999 – IVM starting in Africa, led by WHO/AFRO– Roll Back Malaria focused only on ITNs

Integrated Vector Management

WHO IVM Initiatives

1999 – IVM starting in Africa, led by WHO/AFRO– Roll Back Malaria focused only on ITNs

2004– “IVM Partnership” active in 14 African countries– Similar initiative started in EMRO region– IVM adopted as WHO Global Strategic Framework– Greater support for IVM within Roll Back Malaria

Integrated Vector Management

Vector Control Methods & Settings

1999– Exclusive emphasis on insecticide-treated nets– Larval control not used in Africa

Integrated Vector Management

Vector Control Methods & Settings

1999– Exclusive emphasis on insecticide-treated nets– Larval control not used in Africa

2004– More balanced interest in all effective vector control methods– Larval control:

Strong international interest8 field studies underway in Africa (5 EHP, 3 NIAID)Appropriate in arid zones, urban areas

Integrated Vector Management

Lessons Learned

IVM is now the accepted technical framework for vector control programs for malaria and other vector-borne diseases.

Integrated Vector Management

Lessons Learned

National malaria programs want to make greater use of all proven vector control methodsProfessional training and experience in entomology and vector control are critical

Integrated Vector Management

Future Directions

Completing larval control field studiesDeveloping new models for organizing vector control – decentralized, community-basedBuilding human resource capacity in medical entomology and vector control

Integrated Vector Management

Improving VBD Surveillance

Promote standard methods for disease surveillanceClinical and lab diagnosisCase definitionReportingData managementData access and analysis

Background

Improving VBD Surveillance

Develop and promote methods for integrated analysis of epidemiological, entomological, environmental, and demographic data to better understand disease distribution

Background

Disaggregating the historical data base

Improving VBD Surveillance

FIG 1: ANNUAL TREND IN OPD CASES BY ZOBA - Hospitals and Health Centres

0

20000

40000

60000

80000

100000

1996 1997 1998 1999 2000 2001year

case

s

ANSEBA DEBUB GASH BARKA MAAKEL NRS SRS

Background

Disaggregating the historical data base

Improving VBD Surveillance

Fig.8: DEBUB - ADI QUALA

0

200

400

600

800

1000

Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02Month

Num

ber o

f mal

aria

cas

es

OPD IPD

Background

Distribution of An. gambiae s.l. in Eritrea

Improving VBD Surveillance

Background

Improving VBD Surveillance

Rainfall

010203040506070

Stream

Swamp

Rain po

olSpri

ng

DamPon

dsRive

rbed

Breeding habitat

No.

of l

arva

e/ 1

00 d

ips

Density

Larval habitats

Background

Improving VBD Surveillance

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logistic regression model of probability of village with malaria infection0 - 0.10.1 - 0.20.2 - 0.30.3 - 0.40.4 - 0.50.5 - 0.60.6 - 0.70.7 - 0.80.8 - 0.90.9 - 1No Data

% 1-15 year olds with malaria infection# 0# 0 - 0.06# 0.06 - 0.12# 0.12 - 0.19# 0.19 - 0.29

ZobaAnsebaDebubGash-BarkaMaekelNorthern Red SeaSouthern Red Sea

0.0008 0 0.0008 0.0016 Miles

N

EW

S

Key Roles for EHP

Support in Nepal, South Asia & EritreaMethods development for malaria stratification and epidemic forecasting

Surveillance

VBD Surveillance in Nepal & South Asia

1998– Substantial

data gaps for 3 VBDs

– Minimal collaboration among countries

Surveillance

VBD Surveillance in Nepal & South Asia

2004– Malaria: regional agreement on drug resistance

monitoring, recent Indo-Nepal joint study– JE: regional agreement on surveillance methods

institutionalized as part of WHO guidelines– Kala-azar: Improved diagnosis via new reference

labs in Nepal; Indo-Nepal collaboration on diagnostic methods, case reporting, and IEC

Surveillance

Malaria Surveillance in Eritrea

1999– Trends

only by year and zone

– clinical diagnosis

– Limited access to data

Surveillance

Malaria Surveillance in Eritrea

2004– Case data disaggregated to facility and period– Sentinel sites providing confirmed diagnoses– Malaria stratification complete, detailed maps in use– Zonal staff have access to case & rainfall data– Epidemic forecasts available for 2004-05 season

Surveillance

Eritrea: Malaria Incidence 1997

Insufficient data

19970 - 2525 - 5050 - 7575 - 100100 - 125125 - 150150 - 175175 - 200Over 200

100 0 100 200 Miles

N

EW

S

Surveillance

Eritrea: Malaria Incidence 1998

Insufficient data

19980 - 2525 - 5050 - 7575 - 100100 - 125125 - 150150 - 175175 - 200Over 200

100 0 100 200 Miles

N

EW

S

Surveillance

Eritrea: Malaria Incidence 2002

Insufficient data

20020 - 2525 - 5050 - 7575 - 100100 - 125125 - 150150 - 175175 - 200Over 200

100 0 100 200 Miles

N

EW

S

Surveillance

Lesson Learned

In areas prone to epidemics of malaria and other vector-borne diseases, understanding the local distribution of the disease and its relation to environmental and demographic factors will help public health officials improve the prevention and control activities they direct.

Surveillance