appendix se dev as an intervention agst malaria

8/13/2019 Appendix SE Dev as an Intervention Agst Malaria

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Supplementary appendix

This appendix formed part of the original submission and has been peer reviewed.We post it as supplied by the authors.

Supplement to: Tusting LS, Willey B, Lucas H, et al. Socioeconomic development asan intervention against malaria: a systematic review and meta-analysis. Lancet2013;published online June 19. http://dx.doi.org/10.1016/S0140-6736(13)60851-X.



1

All references citations are to the appendix-specific reference list, pp 12 –14.

Supplementary panel 1: Constructing the human development index for income and education1

The human development index (HDI) for income and education was calculated using the Build Your Own Index

tool available from the UN Development Program website.

2

The index is the geometric mean of normalisedindices of income (2011 gross national income [GNI] per capita in purchasing power parity terms [constant

international 2005 $]) and education (expected years of schooling as of 2011 [of children]; mean years of

schooling as of 2011 [of adults]). Sub-indices (dimension indices) for each of the three components were created

by setting minimum and maximum values for each component. Maximum values were set to the observed value

of the indicators from the included countries between 1980 and 2010. Minimum values were set to those

deemed to be subsistence values or ‘natural’ zeros; 0 years for bo th education variables and US$163 for per-

capita GNI. The minimum value for education is set at 0 years since societies can subsist without formal

education, while a basic income is necessary for survival; US$163 is the lowest value recorded from any

country (Zimbabwe 2008, corresponding to less than 45 cents per day). Dimension indices were then calculated

as follows:

For education, the geometric mean of the two subcomponents was taken, and the above equation applied tocreate the dimension index. The HDI was then calculated as the geometric mean of the two dimension indices:

1



2

Supplementary table 1: Cumulative probability of malaria death and human development index (HDI) for

income and education, by country

HDI 2011

(income and

education)

Cumulative probability

of malaria death (per

1000 population) for all

ages (adults andchildren), 2010

Cumulative probability

of malaria death (per

1000 population) in

children aged 0 –

5 years,2010

Angola 0·484 79·8 11·1

Benin 0·370 138·3 26·2

Botswana 0·696 18·5 0·6

Burkina Faso 0·255 184·1 40·4

Burundi 0·257 141·6 41·1

Cameroon 0·474 99·6 19·7

Central African Republic 0·300 165·0 37·4

Chad 0·275 87·7 23·3

Comoros 0·354 57·1 9·2

Congo 0·506 105·0 14·6Côte d'Ivoire 0·339 141·7 26·5

Djibouti 0·364 26·6 1·4

Democratic Republic of the Congo 0·229 145·8 27·1

Equatorial Guinea 0·563 110·8 14·5

Eritrea 0·255 55·3 8·3

Ethiopia 0·278 50·6 9·1

Gabon 0·674 111·9 7·2

The Gambia 0·349 93·3 26·6

Ghana 0·477 96·7 14·8

Guinea 0·276 121·7 40·8

Guinea Bissau 0·315 142·7 52·6Kenya 0·475 63·4 8·9

Liberia 0·247 158·2 33·3

Madagascar 0·387 110·7 2·9

Malawi 0·344 66·9 15·9

Mali 0·306 169·7 46·8

Mauritania 0·391 44·2 7·1

Mozambique 0·264 176·1 34·5

Namibia 0·604 27·3 1·2

Niger 0·217 119·1 36·0

Nigeria 0·438 155·0 47·9

Rwanda 0·376 108·9 12·3

Senegal 0·395 95·0 9·6

Sierra Leone 0·295 152·1 41·7

South Africa 0·678 1·4 0·001

São Tomé and Príncipe 0·432 105·0 8·2

Sudan 0·322 34·9 7·3

Swaziland 0·561 31·3 1·5

Tanzania 0·410 81·1 15·9

Togo 0·375 119·9 25·2

Uganda 0·406 111·0 17·1

Zambia 0·417 113·6 17·1

Zimbabwe 0·328 60·9 1·8



3

Supplementary panel 2: Search strategy in Medline and Embase

Socio-economic status

1. malaria (MeSH term (Medical Subject Headings))2. socioeconomic factors (MeSH term)3. risk factors (MeSH term)

4. socio economic (key word)5. socioeconomic (key word)6. socio-economic (key word)7. wealth (key word)8. income (key word)9. case-control studies (MeSH term)10. survey (key word) or Data Collection (MeSH term)11. poverty (key word)12. 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 1113. 1 and 1214. limit 13 to (English language and humans and year="1980 -Current")

Forestry

1. disease vectors (MeSH term) or arthropod vectors (MeSH term)2. malaria (key word) or malaria (MeSH term)3. forest (key word) or trees (MeSH term)4. deforestation (key word)5. trees (key word)6. plants (MeSH term) or vegetation (key word)7. deforested (key word)8. 1 or 29. 3 or 4 or 5 or 6 or 710. 8 and 911. limit 10 to (English language and humans and year="1980 -Current")

Urbanisation

1. disease vectors (MeSH term) or arthropod vectors (MeSH term)2. malaria (key word) or malaria (MeSH term)3. urban (key word)4. cities (key word) or cities (MeSH term)5. town (key word)6. urbanization (MeSH term) or urbanisation (key word)7. 3 or 4 or 5 or 68. 1 or 29. 7 and 810. limit 9 to (English language and humans and year="1980 -Current")

Agriculture

1. disease vectors (MeSH term) or arthropod vectors (MeSH term)

2. malaria (key word) or malaria (MeSH term)3. agriculture (MeSH term) or agricultural irrigation (MeSH term) or aquaculture (MeSH term) or fisheries(MeSH term) or gardening (MeSH term) or hydroponics (MeSH term) or organic agriculture (MeSH term) orweed control (MeSH term)4. irrigation (key word)5. agriculture (key word)6. farm (key word)7. rice (key word)8. paddy (key word)9. paddies (key word)10. cereals (MeSH term) or crops, agricultural (MeSH term) or vegetables (MeSH term)11. insecticide (MeSH term) or insecticide resistance (MeSH term)12. cotton (key word)

13. 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 1214. 1 or 2



4

15. 13 and 1416. limit 15 to (English language and humans and year="1980 -Current")

Water development schemes

1. disease vectors (MeSH term) or arthropod vectors (MeSH term)2. malaria (key word) or malaria (MeSH term)

3. water (key word)4. dam (key word)5. rivers (MeSH term)6. lake (key word) or lakes (key word)7. exp Sanitary Engineering (MeSH term)8. 1 or 29. 3 or 4 or 5 or 6 or 710. 8 and 911. limit 10 to (English language and humans and yr="1980 -Current")



5

Supplementar y table 2: The effect of development on the ecology of malaria transmission and possible

interventions

Type of

development

Location Effect on malaria Possible interventions Source

Deforestation Sub-SaharanAfrica eg, Kenya

Increase Major vector A gambiae sl breeds in open sunlit pools.

Prevention of deforestation; malaria prophylaxisand health care for mobile workers in forests;

reforestation of water-logged ground with forestcash crops e.g. eucalyptus to provide shade andeliminate surface water. Large-scalereforestation could be financed by developedcountries to offset their carbon budgets.

3,4

India Increase A minimus replaced by An

fluviatilis, a more efficientvector.

5

South America eg,Brazil, Peru

Increase Major vector A darlingi breeds insunlit pools.

6,7,8,9,10

Thailand Decrease(thenincrease ifcrop plantationsare created)

Major vector A dirus breeds inforests; however, rubber plantations are associated withincrease in malaria.

Malaria control in rubber plantations, buildinghuman settlements further away from plantations.

3,11

Urbanisation Sub-Saharan

Africa eg, SudanDecrease Fewer vector breeding sites

(water pollution, betterdrainage); lower human exposure

to vectors (better housing, greater population density); better accessto health care.

Town planning to improve drainage and wastedisposal; removal or treatment of standing water(larval source management), good house

construction (concrete walls, closed eaves,window and door screening, iron roofs).

12,13,14,15,16,17

Agriculture Sub-Saharan

Africa eg, Ghana,Cote d'Ivoire,Uganda

Increase Urban agriculture creates vector breeding sites.

Larval source management; town planning tolimit the extent of urban farming, crops grownwithout water-logging.

18,19

Sub-Saharan

Africa eg, TheGambia

Decrease ‘Paddys paradox’ is whereirrigation for rice increasesvector breeding, but infectiondeclines or stays the same inrural areas.

It is hypothesised that rice cultivation increaseswealth and this leads to less malaria due to betterhousing, better nutrition and improved access tomalaria interventions.

20

Worldwide: areasof unstabletransmission, eg,Ethiopia,Madagascar

Increase Irrigation increase vector breeding which increases malariadue to low immunity in local population.

Careful planning of irrigation schemes, drainage,intermittent wet/dry irrigation, environmentalmanagement (as demonstrated in SichuanProvince, China and in the Blue Nile Riverregion).

21,22,23,24,25,26

Worldwide Increase Agricultural pesticides select forresistance to DDT (eg, Thailand)and pyrethroids (eg, West Africa)in vectors.

Greater awareness of cross-resistance in bothhealth and agricultural sectors, especially with pyrethroids which are the only availableinsecticide for LLINs.

27,28, 2930, 31

Water

development

projects

Worldwide: areasof unstabletransmission

Increase More vector breeding sites. Construction of dams at high altitudes or farfrom human settlements; larval sourcemanagement (as demonstrated in Indonesia,Singapore).

32,33



6

Supplementary table 3A: Risk of bias assessment for studies included in quantitative analysis (case-control studies)

Reference

Selection Comparability Exposure

Overall quality

assessment score (ofa maximum of 8)

Is the case

definition

adequate?

Representativeness

of the cases

Selection of

controls

Definition of

controls

Comparability of cases and

controls on the basis of the

design or analysis

Ascertainment of

exposure

Same method of

ascertainment

for cases and

controls

Al-Taiar etal, 2009

Yes, withindependentvalidation

Consecutive orobviouslyrepresentative seriesof cases

Communitycontrols

No history ofdisease

Study does not control forother factors

Interview not blinded tocase/control status

Yes 5

Koram et al,1995



Communitycontrols


Study controls forinsecticide use, place ofresidence, travel history,ownership of housing plot,house type, crowding,mother's knowledge ofmalaria, medicine use


Yes 7

Ong'Echa etal, 2006



Hospitalcontrols


Study controls formosquito control measures,house type, wasting, parents'education


Yes 6

Yamamoto etal, 2010



Communitycontrols

No descriptionof source

Study does not control forother factors


Yes 4



7

Supplementary table 3B: Risk of bias assessment for studies included in quantitative analysis (cross-sectional studies)

Reference

Selection Comparability Exposure

Overall quality

assessment score (of

a maximum of 5)Representativeness of the

sample

Ascertainment of

exposure

Comparability of groups on the basis of

the design or analysisAssessment of outcome

Baragatti et al, 2009 Somewhat represenative of theaverage chld int he community

Structuredinterview

Study controls for age, land tenure, building density, equipment, education, bednet use, season

Independent blindassessment

5

Clarke et al, 2001 Truly representative of the

average child in the community

Structured

interview

Study does not control for other factors Independent blind

assessment

3

Custodio et al, 2009 Truly representative of theaverage child in the community

Structuredinterview

Study does not control for other factors Independent blindassessment

3

Gahutu et al, 2011 Truly representative of theaverage child in the community

Structuredinterview


3

Ghebreyseus et al,2000

Selected group of children (fromvillages near dams)

Structuredinterview


2

Krefis et al, 2010 Somewhat representative of theaverage child in the community

Structuredinterview

Study controls for age, sex, ethnicity,number of children in family, mother' age,

place of residence


5

Pullan et al, 2011 Truly representative of theaverage child in the community

Structuredinterview


3

Ronald et al, 2006 Truly representative of theaverage child in the community

Structuredinterview

Study controls for age, travel to ruralareas


4

Slutsker et al, 1996 Somewhat representative of theaverage child in the community

Structuredinterview


3

Villamor et al, 2003 Somewhat representative of theaverage child in the community

No description Study does not control for other factors Independent blindassessment

2

Winskill et al, 2011 Truly representative of theaverage child in the community

Structuredinterview


3



8

Supplementary f igur e: Funnel plots to assess publication bias

Plots show study size as a function of effect size for studies included in the meta-analysis. (A) Studies reporting unadjustedresults. (B) Studies reporting adjusted results.

A

B

0

. 1

. 2

. 3

. 4

1 2 3 4Odds ratio

Asset ownership Household wealth

Parents' occupation SES index

Lower CI Upper CI

Pooled

0

. 1

. 2

. 3

. 4

1 2 3 4 5Odds ratio

Asset ownership Parents' occupation

SES index Lower CI

Upper CI Pooled



9

Supplementary panel 3: How cost-effective are development interventions for malaria control?

The systematic review and meta-analysis strongly suggest that interventions which promote pro-poor socio-economicdevelopment are also likely to complement malaria control. Given the substantial resources devoted to specificmalaria interventions, such as long-lasting insecticidal nets (LLINs), it is worth reflecting on whether developmentinterventions may offer cost-effective complementary measures against malaria, and how these compare with

malaria-specific interventions.

Malaria-specific interventions

The key interventions currently recommended by the World Health Organization (WHO) for the control of malariaare the use of LLINs and/or indoor residual spraying (IRS) for vector control, prompt access to diagnosis andtreatment of clinical malaria, and intermittent preventative treatment (IPT) for pregnant women and infants inmoderate-to-high transmission areas in sub-Saharan Africa.34 All are demonstrated to be highly cost-effective, whenmeasured against commonly cited cost-effectiveness thresholds.

35 A recent review reported that the cost-effectiveness

of LLINs ranged between $8-110 per disability adjusted life year (DALY) averted, IRS between $135-$150 perDALY averted and IPT $1-$44 per DALY averted.36 The cost-effectiveness of artemisinin combination therapies(ACT), the recommended treatment for uncomplicated falciparum malaria, has not been as extensively evaluated, butthe drug is reported to be cost-effective, in some cases cost-saving, compared to other malaria drugs measured interms of cost per case averted.37-39 The same applies for artesunate, the recommended first line treatment for severe

malaria.36

The diverse contexts of the economic analyses make it difficult to make a relative comparison of the cost-effectiveness ratios reported; for example drugs are generally compared with alternative drug treatment(s) andwithout consideration of resistance, while economic evaluations of malaria-specific interventions have beenconducted both alongside intervention trials and within implementation programmes.

A few economic models have evaluated the combination of both treatment and prevention interventions.40-43

Themost recent of these found high coverage with ACTs (95% coverage) to be the most cost effective strategy for controlof malaria in most countries in sub-Saharan Africa, at a cost of $10-12 per DALY averted in the region, while acombined approach with LLINs, IRS, case management with ACT and intermittent preventive treatment in

pregnancy (IPTp) (95% coverage) was estimated to cost five to six times more, yet increasing the number of DALYsaverted with an additional 70%, resulting in an average cost-effectiveness of $30-40 per DALY averted.43 The funds

needed to scale up and sustain malaria control measures in countries most heavily affected by malaria have beenestimated to $1∙5-3 per capita per year

44-46 – an investment that is likely to pay off due to cost savings on the

individual and societal level of a reduced malaria burden.44

Other interventionsIn contrast to these interventions, there are a range of other interventions not usually considered for malaria control.For example, we know that surface water is critical to malaria: no water means no aquatic habitats for malariamosquitoes and no malaria. Furthermore, different malaria vectors are adapted for breeding in different types of water

bodies, so, for example, changing a small water body from one that is shaded to one that is sunlit may lead to changesin vector species and thus alterations in malaria transmission, depending on the local vector ecology. Surface watermay be increased or reduced by many aspects of development, such as deforestation, urbanisation, agriculturaldevelopment and water development projects, evidence of which is given in Table 2, Web Extra material. Thereforedeforestation, agricultural development and water development projects can increase malaria in much of the world,

although not everywhere, highlighting a potential conflict between health and development. In contrast, urbanisationin sub-Saharan Africa generally lowers transmission, through its impact on mosquito breeding, improved quality ofhousing and better access to malaria prophylaxis and treatment.

Since most malaria transmission occurs indoors at night, simple measures to reduce house entry by malaria vectorscan reduce malaria transmission.

47 This was convincingly demonstrated in the beginning of the 20

th century in India,

48

Italy,49

South Africa and the United States, but house screening in a systematic way as a means to control malaria waslargely abandoned with the advent of chemical control methods and malaria drug treatment, which is less complex tointroduce and administer. However, in the last few years several studies have again looked to the relation betweenhouse design and indoor mosquito densities. For instance, a randomised controlled trial of screened homes in TheGambia reduced the risk of anaemia in children, an important cause of mortality, by 50%.50 At a cost of around $11

per person, full screening is similar to the cost of LLINs or IRS. There are enormous opportunities for incorporatingscreening in houses. In Uganda for example, it is common to find houses with unscreened air bricks where the doors

and windows are close fitting, suggesting that simply screening the air bricks may reduce the entry of malaria vectors.A ceiling is another simple improvement in traditional houses that have proven effective in keeping mosquitoes out.



10

In The Gambia, the introduction of an untreated screened ceiling, under the level of the eaves, reduced transmission by 50% using untreated screening and cost $8∙69 assuming the netting was donated or $21 ∙17 if not.50 In Kenya, building ceilings from papyrus reeds (a traditional building material) and ensuring that occupants slept under a LLINreduced mosquito densities by 78-86% in houses. Ceiling construction was relatively inexpensive, at about $1 per

person protected.51

The scale of new building in tropical cities is unprecedented and even in rural areas, homes are continually being built or improved. In The Gambia around 5% of the rural housing stock is rebuilt each year,52 and there is a generalrecognition that traditional mud-walled houses with thatched roofs are progressively being replaced by cement-wallhomes with metal roofs. Since vector numbers are 44% higher in mud-walled homes than concrete homes, 16 generalimprovements in housing are likely to lead less malaria. A study in Sri Lanka also found levels of malaria 2∙5 timeshigher in residents of poorly constructed houses (where houses with incomplete and/or mud walls and roofs made ofcoconut palm thatch were classified as being of poor construction type and houses with completely plastered brickwalls and tiled or corrugated iron roofs as being of good construction type) and calculated that an investment madeinto improving the construction of the houses that were of poor construction would be $850 per house.53

Conclusion

Clearly, malaria-specific interventions have been very successful and are highly cost-effective in many cases.However, this overview illustrates that there are a number of more development-specific interventions that may

positively affect malaria control, may compare favourably in economic terms, and are likely to have substantialimpacts on malaria; although obviously a great deal more evidence of their impact on malaria would be required toestablish cost-effectiveness in a manner directly comparable with the more traditional malaria-specific interventions.

Nonetheless, it is worth noting that development interventions are not primarily targeted at malaria, so the health benefits they provide in this respect are essentially additions to core focus, and the cost at present is not borne byheath agencies. What they do illustrate is the potential benefit from those in health and development workingtogether. The likely synergistic benefits of this are illustrated in the case-study of Khartoum, provided in Appendix 3.



11

Supplementary panel 4: Malaria control today in Khartoum, Sudan

Malaria control today in Khartoum demonstrates that the responsibility for malaria control can be successfullydelegated beyond the Ministry of Health, as part of development and broader improvements to infrastructure. Malariawas the major cause of outpatient attendances, admissions and deaths in Khartoum in the 1980s and 1990s and thisled to the launch of the Khartoum Malaria Free Initiative (MFI) in 2002 by the State and Federal Ministry of Health,54 which targets a total population of 2,073,300 in urban areas, 3,201,021 in peri-urban areas and 640,672 in ruralareas.55 Since the implementation of the program, total malaria deaths (confirmed and unconfirmed) have declined byalmost 75% from 1,070 in 1999 to 274 in 200454 and parasite prevalence has declined from 0∙78% to 0∙04% (1995-2008).

55

Integral to the success and sustainability of the program has been strong political support for the control program at both State and Federal level

56 together with close coordination of the Ministries of Health, Education, Public Works

& Agriculture. This delegation of responsibilities has also helped maintain the total annual cost, which is coveredlargely by the government, at the relatively low level of US$600,000, 55 or around US$0∙10 per person protected peryear. The robust structure of the program is particularly important given that funding is so difficult to maintain, newagricultural schemes and new construction sites continually create more breeding sites

55 and the health system has

been weakened by two decades of conflict.56

While the MFI has three main components (diagnosis & treatment, prevention and epidemic surveillance), its

mainstay is the control of the population of the primary mosquito vector Anopheles arabiensis, which largely breedsin irrigation canals, pools created from broken water pipes, water basins and storage tanks.56

To achieve this, theremoval of water basins and storage tanks is enforceable by law and the Ministry of Health collaborates with thePublic Works Department (PWD) to repair broken water pipes. The MFI is responsible for surveillance, reporting andtransportation while the PWD provides engineers and equipment. By 2004, just under 4km of water pipes had beenreplaced and over 6km repaired.

56 Similarly, the regular drying of irrigated fields, which reduces vector breeding, is

compulsory in both Government and private irrigation schemes. This initiative is supported by the Farmers Union andthe Ministry of Agriculture. In 2011, 98∙2% irrigation schemes were dried for at least 24 hours.55 Leakages fromirrigation canals are also repaired and vegetation around canals is cleared in conjunction with the Ministries ofIrrigation and Agriculture.

56 In addition, the MFI itself employs 14 trained medical entomologists, 60 public health

officers, 180 sanitary overseers, 360 assistant sanitary overseers and 1170 spraying men55 who are responsible forroutine larviciding and environmental management to reduce mosquito breeding.

Another factor contributing to the sustainability of the MFI is strong community support, generated through the

distribution of information leaflets, regular radio broadcasts and television coverage, health education in schools incollaboration with the Ministry of Education, the organisation of an annual ‘Khartoum State Malaria Day’, publicmeetings and the establishment of malaria control committees and societies.

56 405 schools and 287,000 pupils are

involved in mosquito larval control activities.55

Indoor residual spraying (IRS) and long-lasting insecticidal net(LLIN) distributions are not conducted in Khartoum, however LLINs are exempt from import tax in order toencourage private sector sales.56 The MFI also seeks to strengthen case management through the improvement ofmicroscopy, staff training and provision of antimalarial drugs through the ‘revolving drugs fund’.



12

Supplementary panel 5: Malaria and development in Sri Lanka

In Sri Lanka, P vivax and P falciparum are both prevalent, transmission is low and seasonal and the primary vector A

culicifacies breeds primarily in river and stream bed pools.57,58 Sri Lanka has Dry, Intermediate and Wet Zones andtransmission is traditionally lower in the Wet Zone because its fast-flowing rivers are less conducive to vector

breeding.

Malaria posed an economic problem to the Portuguese, Dutch and British colonial administrations and malaria vectorlarval control was initiated in the 1920s

59 in the interest of industrial development. This control program aimed to

prevent epidemics in the plantations and to make the Dry Zone inhabitable for agricultural development,57,60

though itis debatable whether malaria was the cause or the consequence of Dry Zone underdevelopment. 57 The strengtheningof the Sri Lankan economy through the replacement of subsistence agriculture with specialist coffee, tea, rubber andcinchona exportation may have increased malaria, since the island’s wealth was concentrated within a small minorityand the nutritional status of the local population declined.61 Indeed a huge epidemic occurred in 1934-1935, with a20-fold increase in mortality and 47,000 deaths, triggered by drought which created river bed pools, ideal breedinghabitats for A culicifacies. The rubber plantations were worst affected.

61

In 1945 the Sri Lankan Malaria Control Programme was initiated using DDT IRS with the aim of general economicdevelopment and improving ‘quality of life’ and this was associated with a remarkable increase in estimated lifeexpectancy at birth from 43 to 52 years (1946-1947) and a 100-fold reduction in malaria morbidity within a decade. 61 This control program was subsumed into the Global Malaria Eradication Programme in 1958 and incidence wasfurther reduced. In 1963, only six autochthonous cases were reported. As DDT IRS was abandoned malaria soonresurged; small P vivax outbreaks in 1967 became epidemic in 1968.57,62 Although DDT IRS was rapidlyreintroduced, this failed due to insect resistance. Malathion was substituted as the residual insecticide but with limitedsuccess, due to the need for frequent reapplication, high cost and low acceptability. A USAID-assisted program ofcase-detection and treatment was initiated in 1977.

62 Malaria slowly declined and this may have been partly

attributable to socio-economic development; between the 1940s and 1970s infant mortality declined alongside greaterrice production, the introduction of free education and improved health care.

61

After a period of relatively low incidence in the 1970s and 1980s, ecological changes linked to major irrigation,hydroelectric and resettlement schemes in the densely populated upper Mahaweli River region produced severemalaria outbreaks in previously non-endemic areas

63 and total nationally reported malaria cases surged from 38,566

in 1982 to 687,599 in 1987. Dam construction had slowed the water flow, producing river bed pools and allowingmalaria vectors to breed

64, 65 and the resettlement of over 5000 families from non-malarious areas to new irrigated

ricelands increased the pool of susceptibles, aiding malaria transmission.63 The use of organophosphate andcarbamate pesticides in agriculture also selected for resistance in A nigerrimus.

66 This also contributed to the increase

in malaria during the 1980s.67

Since then malaria has significantly declined57 and in 2008 the island entered the pre-elimination phase of malariacontrol, following a dramatic reduction in the total number of nationally reported cases from 41,411 in 2002 to 198 in2007.

68 This is attributed mainly to focused IRS, high LLIN coverage in malarious areas and larval control.



13

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