Jump to content
Italian Sausage

Mosquitoes (nets, spray, GM bugs, etc)

Recommended Posts

http://allafrica.com/stories/printable/200703210653.html

 

Resistant Mosquito Developed to Combat Malaria

March 21, 2007

 

By Gboyega Akinsanmi

Lagos

 

A team of transgenic mosquito researchers at the Malaria Research Institute of the Johns Hopkins University, Maryland, USA, have developed resistant mosquitoes to combat the malaria parasite.

 

This development was revealed in an article published in the journalproceedings of the National Academy of Sciences, stating that resistantmosquitoes capable of combating malaria parasite have been developed.

 

Marcelo Jacobs-Lorena, one of the researchers said his team createdgenetically engineered mosquitoes that were more hardy than so-calledwild-type mosquitoes that carry the parasite around.

 

He said the investigators had put the blood sucking insects to test andassured that the resistant mosquitos are stronger than those which normally tyransmit malaria parasites.

 

"What we have shown in this work that will be published is that the mosquito that carries the gene has an advantage if it bites an infected individual," he said.

 

Jacobs-Lorena and other researchers report having an equal number of genetically engineered mosquitoes and wild-type insects feast on a malaria-infected mouse.

 

After nine generations, the percent of altered mosquitoes increased from 50 percent to 70 percent.

 

If and when scientists are able to breed transgenic mosquitoes in numbers that might make a difference in the spread of malaria, Jacobs-Lorena says researchers have to make sure such insects do not cause an unforeseen harm.

 

"We have to be absolutely sure that any genetically modified mosquito does not cause any harm in the environment or cause unpredictable harm to people that they bite. But I think we are on the way," Jacobs-Lorena said.

 

This, according to the researchers, could lead to a cure for the disease as they try and stop the spread of malaria.

 

Despite the obstacles, Jacobs-Lorena is optimistic that genetically altered mosquitoes will one day take their place alongside other strategies to fight malaria.

 

The mosquitoes did a great job to to manage to survive when they fed on mice that were infected with malaria.

 

They had a far higher survival rate cmopared to normal mosquitoes who were not resistant to malaria.

 

The study found that the resistant mosquitoes made up 70 percent of the mosquito population after nine generations went through the process of feeding on infected mice. This means that resistant mosquitoes could potentially replace the nonresistant ones.

Share this post


Link to post
Share on other sites

Here's something really basic: (be sure to check out the card below this)

 

Copyright 2006 Nationwide News Pty Limited

All Rights Reserved

Sunday Telegraph (Australia)

 

December 17, 2006 Sunday

BodySoul Edition

 

SECTION: FEATURES; Health Issues Body and Soul; Pg. 34

 

LENGTH: 965 words

 

HEADLINE: JUNGLE FEVER

 

MATP

 

BYLINE: with Dr Cindy Pan

 

BODY:

 

 

Malaria kills two million people in the world every year.

 

 

 

We've all been bitten by pesky mosquitoes from time to time, but imagine if your next mosquito bite had the potential to kill you. Every year 300 to 500 million people worldwide contract malaria and around two million people die of it. Most of these deaths occur in young children in developing countries. In Africa, for example, a child dies from malaria every 30 seconds.

 

Fortunately, in Australia we don't have endemic malaria, although the mosquito that transmits malaria, known as the anopheles mosquito, is present in many parts of the country, particularly in northern Australia.

 

When travelling to or returning from malarial countries it is vital to take appropriate precautions to try to prevent malaria, as well as identify, diagnose and treat it promptly if it does occur.

 

 

 

WHERE DOES MALARIA OCCUR?

 

Typically, malaria is found in tropical and subtropical regions, since the anopheles mosquito thrives in hot climates. High-risk areas include Africa, the Indian subcontinent, South-East Asia, the Middle East, Papua New Guinea, Vanuatu and the Solomon Islands, as well as large parts of Central and South America. It is less of a problem in highly urbanised areas, but can still occur on the outskirts.

 

 

 

HOW IS IT TRANSMITTED?

 

The malarial parasite is transmitted via the bite of an infected female anopheles mosquito. The mosquito herself becomes infected as a result of having previously taken a bite from a malaria-infected person.

 

When an infected mosquito bites you, she injects malaria parasites into your bloodstream. The parasites then travel to your liver, where they grow and multiply for anything from eight days to several months. During this period, known as the "incubation period", you have no symptoms.

 

After the malaria parasites leave your liver cells they enter your red blood cells, where they continue to grow and multiply, eventually causing the red blood cells to rupture. This releases the parasites to attack and enter other red blood cells. The toxins released when the red cells burst cause the typical malaria symptoms.

 

 

 

WHAT ARE THE TYPICAL SYMPTOMS OF MALARIA?

 

Symptoms of malaria include fevers, chills, shivering and excessive sweating. There may also be headaches, nausea, diarrhoea and tiredness.

 

There are actually four different types of malaria parasites: plasmodium vivax, plasmodium ovale, plasmodium malariae and plasmodium falciparum. Falciparum malaria is the most serious form of the disease, since without prompt, effective treatment it can be fatal. Symptoms and complications of falciparum malaria include jaundice, clotting problems, rupture of the spleen, kidney failure, liver failure, difficulty breathing, coma and death.

 

 

 

HOW CAN MALARIA BE PREVENTED?

 

Prevention involves taking stringent measures to avoid being bitten, as well as using anti-malarial medication appropriate for the area you are visiting. In some cases your doctor may also advise taking emergency stand-by treatment with you, as well as instructing you in the use of self-diagnosis kits.

 

Pregnant women are particularly at risk, since studies have shown that they are twice as attractive to mosquitoes as non-pregnant women. There are several hypotheses as to why this is. One is that pregnant women's increased skin circulation releases odours that are attractive to mossies; another is that it is because pregnant women get out of bed to go to the toilet more often.

 

Malaria in pregnancy increases the risk of miscarriage and premature labour, as well as potentially affecting foetal development. There is also the risk of transmission of the infection to the unborn baby.

 

Since prevention and treatment options for pregnant women are limited, they are usually advised to avoid travelling to malarial areas.

 

 

 

CAN MALARIAL MOSSIES HITCHHIKE?

 

Yes! Mossies have been known to get on board planes in countries where there is malaria and bite people on board, as well as on arrival in the country of destination. These unexpected cases of malaria in countries far away from known malarial areas have been noted to occur near airports receiving planes from malaria-affected countries. Fortunately it is a relatively rare occurrence, with around one case a year reported in Europe and only one recorded case in Australia.

 

 

 

( Q ) What can be done for gastroesophageal reflux other than taking medication?

 

 

 

( A ) There are various approaches to managing reflux, from dietary and lifestyle modification to having surgery.

 

Most people with mild to moderate gastroesophageal reflux will find their condition improves markedly with lifestyle changes alone. These may include such measures as raising the bed head by 10 to 15 centimetres, avoiding eating for at least three or four hours before going to bed, eating smaller meals and avoiding aggravating foods such as spicy or fatty foods, chocolate and peppermint, as well as coffee, tea, cola and alcohol.

 

Stopping smoking, losing excess weight and wearing looser clothing, in particular avoiding tight belts and waistbands, are all helpful measures too.

 

If the condition persists in spite of these measures, medication is usually tried next. There are various types of medication, and in most cases the problem can be managed successfully with a combination of medication and lifestyle changes.

 

Fortunately very few people require surgery to relieve their gastroesophageal reflux, and these days the surgery can be performed endoscopically (via a small incision using a flexible fibre-optic microscope).

 

 

 

If you have a question for Dr Cindy Pan, write to her at GPO Box 4245, Sydney NSW 2001 or email her at panc@newsltd.com.au Due to the large volume of questions, Dr Pan cannot respond personally to each question. Dr Pan's advice is not a substitute for consultation or advice from your GP.

 

LOAD-DATE: December 16, 2006

 

I haven't had a chance to look all the way through this, but this looks like an awesome card.

 

Copyright 2007 The Center for Strategic and International Studies and the Massachusetts Institute of Technology

All Rights Reserved

Review of Economic & Statistics

 

February 2007

 

SECTION: Pg. 165

 

LENGTH: 9454 words

 

HEADLINE: FIGHTING AGAINST MALARIA: PREVENT WARS WHILE WAITING FOR THE "MIRACULOUS" VACCINE

 

BYLINE: Jose G. Montalvo; Marta Reynal-Querol*

 

BODY:

 

 

ABSTRACT

 

The World Health Organization estimates that 300 million clinical cases of malaria occur annually and observed that during the 80s and part of the 90s its incidence increased. In this paper, we explore the influence of refugees from civil wars on the incidence of malaria in the refugee-receiving countries. Using civil wars as an instrumental variable, we show that for each 1,000 refugees there are between 2,000 and 2,700 cases of malaria in the refugee-receiving country. On average 13% of the cases of malaria reported by the WHO are caused by forced migration as a consequence of civil wars.

 

I. Introduction

 

WITH the number of clinical cases of malaria on the rise, reaching some 300 million a year, there is increasing concern over the economic and public health burden of this disease. Over ninety countries suffer from the incidence of malaria and some 36% of the world's population live in areas of risk of transmission. Malaria causes around two million deaths worldwide; a large proportion of these deaths are among children in sub-Saharan Africa. 1

 

* Universitat Pompeu Fabra and Instituto Valenciano de Investigaciones Economicas (IVIE), and Universitat Pompeu Fabra, respectively.

 

We thank J. Caballe, M. Reiter, A. Cabrales, participants in the European Economic Association Meetings (Venice 2002), and the Vancouver Conference (2004) "Deadly connections: the war-disease nexus" for many useful comments. We are also very grateful for the valuable comments of two anonymous referees and the editor (Daron Acemoglu). Financial support from the IVIE and the Ministerio de Ciencia y Tecnologia (SEC2004-03619 for Montalvo and SEC2003-04429 for Reynal-Querol) is kindly acknowledged. Jose G. Montalvo thanks the hospitality of the Poverty Group of the Research Department (DECRG) of the World Bank, where most of the revision of this paper was done.

 

There are two predominant views with respect to the incidence of malaria. The first one, represented by J. Sachs, and also expressed in some reports from the World Health Organization, is that malaria is basically determined by the ecological conditions of the tropics. 2 The second view is that economic, social, and political institutions have a very important influence on the incidence of malaria. 3 It is not clear, therefore, to what extent malaria has an important effect on a country's income or the correlation between the incidence of malaria and income reflects the reverse causality of income on malaria. The current paper reexamines this particular issue and finds evidence of a large increase in malaria prevalence in response to social disruption and migration due to civil wars.

 

During the last decades, many civil conflicts have taken place in areas where malaria is a major public health concern. The forced migration caused by those conflicts has led to a significant increase in the transmission of malaria in areas that for a long time have been considered of low risk. In fact, 29% of the world's population "live in areas where malaria was once transmitted at low level or not at all but where significant transmission has been reestablished." 4

 

Recently Ghobarah, Huth, and Russett (2001) have found that the burden of death and disability incurred in 1999 from the indirect effect of civil wars in the period 1990-1997 is equal to the direct effect of wars during 1999. In this paper, we also study the health consequences of civil wars beyond the direct causalities. These effects span beyond the war period and the country that suffered the conflict. We analyze the effect of forced migration and, in particular, refugees from civil wars, on the incidence of malaria in the refugee-receiving countries. As far as we know this is the first attempt to measure this relationship from a macro perspective and using panel data. 5 We find that refugees coming from a country with a high incidence of malaria have an important impact on the incidence of malaria in the refugee-receiving country. Our estimation suggests that for each 1,000 refugees from a malaria-endemic country involved in a civil war, there are between 2,000 and 2,700 new cases of malaria in the refugee-receiving country.

 

The paper is organized as follows: In section II we analyze the nexus between malaria and forced migration, with special emphasis on the impact of civil wars. Section III describes the basic econometric specification and the sources of data. In section IV, we present the results of the estimation and discuss several robustness tests. In particular, we report the sensibility of the results in considering only African countries, to instrumental variables estimation and also to changes in the frequency of the data (from yearly to five-year averages). Section V contains a discussion of the relative importance of refugees from civil wars in the explanation of the total cases of malaria. Finally, in section VI we present the conclusions.

 

II. Malaria and Forced Migration

 

In general, malaria transmission depends on the dynamics of the relationship between men. vector, parasite, and environment. Malaria transmission is not widespread in densely populated urban areas. 6 The outbreak of a civil war or an important social conflict very often generates the movement of people fleeing from its consequences. If there is risk of malaria transmission in the country, even if it is small, and the vector is present, then forced migration is a likely cause for a serious public health concern. There are many reasons for the increase in malaria incidence as a consequence of forced migration. First of all, most of the population that flees from urban areas is generally not immune to malaria. Secondly, malaria incidence is high in rural areas where the vector can live longer in a favorable environment. Also, the anarchic situation caused by this social unrest and the military importance on paved roads force people to walk through unfamiliar rural areas, dumps, and forests to avoid areas of military activity, actually helping facilitate the incidence of malaria. In fact, population movement (due to political conflicts or civil wars) is potentially the most important factor in the transmission of malaria (conditional on the dynamics between vector, parasite, and environment). 7

 

The contact of a nonimmune individual with an immune rural population in a high-risk area also increases the risk of transmission. The importance of contact with immune individuals is critical because repeated infection among individuals of rural endemic areas generates an immune response in the host, who controls the infection. This fact implies that among the rural population, the prevalence of malaria could be very high, but with only a small number of reported cases. Even without reinfection, the persistence of the malaria parasites could last from two years (Plasmodium falciparum) to four years (Plasmodium vivax) or even up to as many as fifty years (Plasmodium malariae). However, the risk of life-threatening malaria is exclusively borne by nonimmune populations. 8 Paradoxically, it is in low-endemicity areas where the risk of severe infection is highest among the adult population, because they may grow up without developing immunity. Moreover, migrants in general would not carry nets, tents, or other protective devices and, therefore, they are even more exposed to the vector. War also generates the collapse of healthcare infrastructure. In addition, private shows and pharmacies close down during wars, further restricting the access to antimalarial drugs. The displaced population often relocates near water sources, which is dangerous since water is also the breeding site for mosquitoes. In addition, in rural areas livestock may attract mosquitoes that may also feed on people.

 

Apart from these factors, it is also the case that the population that lives in rural areas with a high risk of malaria has different degrees of immunity with respect to their time exposure to malaria. 9 The contact of a population that moves from an area of high transmission to an area of low transmission also raises the likelihood of a large increase in malaria incidence. Finally, the area of origin and the area of destination may be quite different in terms of the prevalence of drug-resistant malaria. This implies that even if other people in the area of destination take antimalarial drugs, their efficiency may be affected by the drug-resistant malaria of migrants. Notice also that even if an effective antimalarial drug was available, there would be serious complications over its distribution in areas suffering from civil wars or a high degree of social conflict.

 

For all these reasons, forced migration is very likely to be the source of an important increase in the incidence of malaria. Not only that, many civil wars take place in countries with a high incidence of malaria. It is well-known 10 that malaria was the primary cause of mortality among Cambodian refugees that arrived to eastern Thailand in 1979. The same was true for adult Mozambican refugees in Malawi and Ethiopian refugees in eastern Sudan. The annual incidence of malaria among the refugees fleeing Myanmar and going to western Thailand was 1,037 cases per thousand. 11 The five-year civil war in Tajikistan led to the reemergence of malaria in an area that had been malaria free for many years. Malaria is still a major problem among forced migrants in the Democratic Republic of the Congo, Ethiopia, and Guinea.

 

We argue then that civil wars and social conflict are a basic source of the observed increase in the incidence of malaria, either directly (that is, nonimmune refugees come in contact with infected individuals when they flee through rural and rainforest areas to reach a foreign country) or indirectly (that is, civil wars make it very difficult or even impossible to keep active control measures against malaria). Notice that if this is the case, the problem of creating more effective drugs against malaria is not only the economic cost for developing countries of making the drugs available to the population, but also the fact that frequent civil wars in developing countries will make administration of the drugs very difficult. In fact antimalarial drugs could also become a "weapon" for some of the factions involved in a civil war. Therefore, as in the case of control efforts, the effectiveness of the new drugs 12 will depend not only on socioeconomic development and the incentives for vaccine research but also on political stability.

 

Figure 1 presents a general view of the relationship between the official data on cases of malaria and civil wars. With respect to the total cases of malaria, it should be borne in mind that the number of reporting countries varies over time. In particular, two countries have a determinant influence on the number of cases: China and India. China started to report officially to the World Health Organization (WHO) in 1977. Initially it reported close to four million cases, but from 1977 onward it reported an exponentially decreasing number of cases. India is also an important case in terms of its effect on the total number of cases. For this reason, in figure 1 we also depict the relationship between the number of civil wars and the cases of malaria in the world, without counting India and China. Still, after eliminating the influence of India and China, there exists the problem of the African region. The countries in this area are known to have irregular reports, in many cases due to the difficulties caused by sociopolitical conflicts. For this reason, we have performed an interpolation procedure 13 to attribute for the missing data of these countries. The interpolation is performed using the latest available data before the missing period and the first available figure once reporting resumes. In this way, if the incidence reporting was stopped because of a civil war and the number of malaria cases rose during the war period, then the initial figure of the next reporting period would incorporate most of the increase in malaria.

 

Figure 2 represents the total cases of malaria obtained using this interpolation procedure and the number of refugees worldwide. The high correlation of these variables is one of the motives for this research on refugees and the incidence of malaria.

 

Obviously the increase in the incidence of malaria cannot only be the result of "tropical destiny," since this is invariant over time. There must be a combined effect of ecological and nonecological factors that explain this tendency. Among them we argue that the interaction between civil wars and tropical location is one of the basic factors.

 

III. Econometric Specification and Data Sources

 

In this section, we discuss the basic determinants of malaria incidence and data sources. For the purpose of finding the determinants of malaria, we use the basic arguments proposed by Najera et al. (1992), who distinguished different patterns of reported malaria cases. "Group B," which generates most of the cases, includes "countries characterized by either recent efforts to increase the exploitation of natural resources (through agricultural colonization of forest or jungle areas) or by civil war and sociopolitical conflict (including illegal drug trade) and large movements of refugees or other mass migrations" (Najera et al., 1992).

 

Our basic regression has the following form:

 

MALjt = j + Xjt + Zjt + ujt, (1)

 

where MAL is the number of new cases of malaria in the refugee-receiving country, X contains a measure of the refugees in country j, and Z includes the variables of the refugee-receiving country that may have an effect on the number of cases of malaria. The determinants of malaria incidence included in the regressions follow the factors cited by Najera et al. (1992), Sachs and Malaney (2002), and Bloland and Williams (2003). There are basically two groups of factors: ecological conditions and social conditions. The ecological conditions include the African savannah, the plains and valleys outside of Africa, the highlands, seashore, and coastal areas. All these geographical conditions are country specific but time invariant and, therefore, are included in the "country-specific effect" of our regression. The individual effect, j, represents also the difference in the reporting practices among countries, if they are stable over time. For instance, it is well documented that in many African countries the cases of malaria are usually counted as clinically diagnosed cases instead of laboratory confirmed ones. However, the availability of a panel data of countries helps to disentangle these effects, if reporting practices do not change too much over time. 14

 

The social conditions that affect malaria incidence include the agricultural colonization of forest, the construction of refuse tips and irrigation systems, the migrant agriculture labor force, the worsening of the health system, and the displacement of population. We proxy these social factors with data on the extension of land irrigation, the percentage of rural population, the number of physicians per thousand population, and the incidence of civil wars and natural disasters. These variables are grouped in Z. We include the displaced populations, in different versions, in the X variable. Table 1 presents the summary statistics for the main variables in the specification, which are described below.

 

TABLE 1.--SUMMARY STATISTICS

Variable Mean

Malaria 173,339

Tropical (dummy) 0.76

Refugees 47,937

Civil wars (dummy) 0.14

Drought (dummy) 0.09

Physicians per 1,000 inhabitants 0.55

Proportion rural pop. 0.60

MCID 0.59

 

 

MCID is the proportion of each country's area where there is risk of malaria transmission.

 

A. Malaria Incidence

 

Data on the number of diagnosed malaria cases come from WHO. From 1982 to 1997, the data was reported in the Weekly Epidemiological Record. From 1962 to 1981, the data was published in the World Health Statistics Annual (1983). The values represent the number of malaria cases reported by countries and the WHO regional offices during the period 1962-1997. While this is the most reliable information on malaria incidence, the WHO points out that for Africa, the figures refer only to clinically diagnosed cases (except for the North African countries, Cape Verde, Djibouti, Mauritius, Réunion, Somalia, and South Africa). The figures from the other continents represented are mostly laboratory-confirmed cases.

 

There are 162 countries that have reported cases of malaria between 1962 and 1997. In 27 of those countries, the cases of malaria were imported by tourists that traveled to tropical countries. Because of the purpose of our study we are not going to consider these cases, which correspond basically to the OECD countries. Therefore our final sample includes 135 countries.

 

B. Geographical Variables

 

The dummy variable for tropical country comes from the Global Development Network Growth Database (GDNG). The original source of this reference is the Global Demography Project, 15 which considers that a country is tropical if the absolute value of the latitude of the quadrilateral 16 that contains the largest number of people in the country is less than or equal to 23.5 degrees (between the Tropic of Cancer and the Tropic of Capricorn). In our sample we have 103 tropical countries.

 

C. Refugees

 

There are two basic sources of information for the data on refugees: the United Nations High Commissioner for Refugees (UNHCR) and the U.S. Committee for Refugees (USCR). The data on refugees that we use comes from the UNHCR. This data is publicly available only from 1993 until 1999. Thanks to Susanne Schmeidl, we had access to the internal data of the UNHCR dating from 1951 to 1999. 17 Following the UNHCR definition, refugees are persons recognized as refugees under the 1951 United Nations Convention relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa, persons recognized as refugees in accordance with the UNHCR Statute, persons granted humanitarian or comparable status, and those granted temporary protection. This data set is organized by country of origin and country of asylum and provides information on the number of refugees that arrive to the asylum country at time t coming from different origin countries.

 

Internally displaced persons (IDPs) are those who are displaced within their country. The data on IDPs collected by the UNHCR are very scarce and only provide information on IDPs where the UNHCR provides assistance to them. We also have information on IDPs from the USCR, which is the only systematic database for internal displacement that exists. However, it covers very few years. Because of these shortcomings, the use of this variable is very problematic and, consequently, we decided to work only with refugees and not with internally displaced people.

 

D. Civil Wars

 

The data on civil wars come from Doyle and Sambanis (2000), which involves as part of the definition an intensity indicator. This definition is nearly identical to the definition of Singer and Small (1982, 1994).

 

E. Natural Disasters

 

Data on natural disasters come from the EM-DAT: The OFDA/CRED International Disaster Database. 18 Since 1988 the WHO-collaborating Centre for Research on the Epidemiology of Disasters (CRED) has been maintaining an emergency events database, EM-DAT. EM-DAT was created with the initial support of the WHO and the Belgian government.

 

EM-DAT contains essential core data on the occurrence and effects of over 12,500 mass disasters in the world from 1900 to the present day. The disaster data are subdivided into three types: natural, technological, and conflicts. The database is compiled from various sources, including UN agencies, nongovernmental organizations, insurance companies, research institutes, and press agencies. OFDA/CRED offers information on the occurrence; the number of people injured, killed, or made homeless; and the total number affected.

 

There are many different types of natural disasters included in the database: drought, earthquake, extreme temperature, flood, landslide, volcano, tidal wave, wildfire, and windstorm. From all these natural disasters, we are interested in only the ones that imply mass movements of people. One situation that causes mass migration with very high probability is drought, and its main consequence, famine. Droughts usually have a lengthy duration and cannot be handled easily without moving to other areas.

 

F. Health Data

 

We also control for the extension of the health system in each country. The health data comes mainly from the World Development Indicators of the World Bank. We consider the number of hospital beds per 1,000 population and the number of physicians per 1,000 population. 19 These two variables are highly correlated. Data on hospital beds are available from 1970, and data on physicians are available from 1965. Before 1985, the information on hospital beds and physicians was basically collected every five years (1965, 1970, 1975, 1980, and 1985). Only for some countries are there any yearly data. Since information on hospital beds is more scarce than information on physicians and they have a high correlation, we decided to use the number of physicians per thousand inhabitants as the explanatory variables. Since the number of hospital beds and the number of physicians move smoothly, we have interpolated the data on the number of physicians in order to avoid a large reduction in the sample size. 20

 

G. Other Variables

 

Data on the hectares of irrigated land (IRRIG) and the proportion of rural population (RURAL) comes from the World Development Indicators. We also use in our estimation the proportion of each country's area where there is risk of malaria transmission (MCID). The last variable comes from the Center for International Development (CID) at Harvard University. It represents the percentage of land area in each country affected by Anopheles species calculated in equal-area cylindrical projection. From some comments in Gallup and Sachs (2001), we believe that the original information of the CID data on the land area affected by Anopheles species come from four digitalized maps: for 1946 the map in Pampana and Russell (1955); for 1966 the source is WHO (1967); for 1982 the source is WHO (1984); and for 1994 the source is WHO (1997). We construct the variable MCID by merging these data. For years before 1967 we use the data for 1946; after 1966 and before 1982 we use the data corresponding to 1967; after 1981 and before 1994 we use the information for 1982; and, finally, after 1993 we use the data for 1994.

 

IV. Empirical Results

 

Taking into account the previous considerations, the econometric specification

 

MALjt = j + REFjt + 1RURALjt + 2PHYSjt + 3IRRIGjt + 4DRjt + 5CWjt + 6MCIDjt + ujt, (2)

 

[sEE ORIGINAL SOURCE]

 

where MAL represents the new cases of malaria in the refugee-receiving country j at time t, REFijt are the refugees of country i to country j 21 at time t, RURAL is the proportion of rural population in the refugee-receiving country, PHYS is the number of physicians per thousand inhabitants in the refugee-receiving country, and IRRIG is the irrigated-land area, also in the refugee-receiving country. Since the data on internally displaced population are very scarce, we include a dummy for drought (DR), another for civil war (CW), and the percentage of population that lives with the risk of malaria transmission (MCID). All three variables refer to the refugee-receiving country and try to capture the determinants of the likelihood and the intensity of movement of population inside the refugee-receiving country. Rapid urbanization, and therefore the reduction of the proportion of rural population, of marginal areas within cities is usually done in an uncontrolled way, which leads to poor-quality housing, lack of proper drainage, and inadequate vector-borne disease control. These conditions lead to an exponential growth of mosquito vectors and increase exposure to them. Therefore, we expect RURAL to have a negative effect on malaria incidence. A high proportion of physicians (PHYS) per thousand inhabitants should also have a negative effect on malaria, given that it represents a good health system and the possibility of improved prevention. The proportion of land irrigated (IRRIG) should have a positive effect for two reasons. First, the increase of water surfaces favors the proliferation of mosquito larvae. Second, this variable is also a proxy for agricultural colonization of new areas. Droughts (DR) and civil wars (CW) in the refugee-receiving country will also favor the displacement of people and, therefore, should increase the incidence of malaria 22 through the slackening of preventative measures and the other mechanisms discussed in the previous section. MCID should obviously have a positive effect on the incidence of malaria.

 

Table 2 presents the results of these basic regressions using all the observations (tropical and nontropical destination countries). The sample covers the period from 1962 until 1997. The estimates are obtained by using the fixed-effects estimator for unbalanced panel data. 23 In the first column we can observe that the total number of refugees does not have an effect on the malaria cases in the refugee-receiving country, while the proportion of rural population and physicians per inhabitant have, as expected, a negative effect. The area of irrigated land however, does have a positive and significant effect, while the dummies of drought and civil war in the refugee-receiving country have no significant effect on malaria incidence. Finally, the variable MCID has a positive and significant effect on malaria.

 

TABLE 2.--FIXED-EFFECTS PANEL DATA ESTIMATION

Destination All Countries Tropical Countries

Origin (O) All TR TR + CW All TR TR + CW

REF 0.016 -0.078 -0.070 0.865 -0.060 0.10

(0.36) (-1.63) (-1.49) (5.90) (-0.22) (0.51)

REFO 1.14 1.38 1.30 1.41

(7.15) (8.35) (4.06) (5.71)

RURAL -1.62 -1.45 -1.43 -1.75 -1.68 -1.65

(-8.65) (-7.77) (-7.70) (-7.58) (-7.26) (-7.17)

PHYS -32.1 -29.9 -29.6 -26.5 -24.9 -24.8

(-5.25) (-4.94) (-4.90) (-3.15) (-2.97) (-2.97)

IRRIG 0.038 0.037 0.037 -0.008 -0.008 -0.007

(3.94) (3.86) (3.83) (-0.15) (-0.14) (-0.13)

DR 3.69 -8.96 2.99 2.33 1.66 3.46

(0.10) (0.25) (0.08) (0.52) (0.37) (0.78)

CW -5.15 -6.36 -6.13 -5.55 -5.54 -5.28

(-1.42) (0.76) (0.71) (1.14) (1.14) (1.10)

MCID 1.14 1.10 1.09 -1.05 0.18 0.20

(2.18) (2.12) (2.12) (0.15) (0.03) (0.03)

R2 0.12 0.15 0.17 0.12 0.14 0.16

Countries 104 104 104 72 72 72

N obs. 2,722 2,722 2,722 1,919 1,919 1,919

 

 

REF refers to all the refugees. REFO refers to refugees by origin: refugees could be from a tropical country (TR) or a tropical country suffering a civil war (TR + CW). RURAL is the proportion of rural population. IRRIG refers to hectares of irrigated land. PHYS is the proportion of physicians. DR is a dummy variable for a drought in the refugee-receiving country. CW is a dummy variable for a civil war in the refugee-receiving country. MCID is the proportion of each country's area where there is risk of malaria transmission.

 

Table 2, columns (2) to (3) present the results of aggregating the refugees by specific characteristics of the country of origin (REFO). The new variable REFO computes as refugees coming from a tropical country (O = TR) or from a tropical country with a civil war (O = TR + CW). 24 Therefore

 

[sEE ORIGINAL SOURCE] (3)

 

where Oi is a dummy that takes value 1 if refugees come from a country i that has the specific characteristic considered in each column (tropical, or tropical and civil war). In the second column the variable REFO refers to refugees going to country j from a tropical country. In this case the coefficient is significantly different from 0 and higher than 1. The rest of the variables have the expected sign and, with the exception of DR and CW, they are significantly different from 0. The results are even stronger if we constrain the variable REFO to reflect only refugees coming from tropical countries where there is a civil war (column [3]).

 

Columns (4) to (6) of table 2 present the same regressions but using the sample of tropical destination countries. In this case all the refugees, independently from their origin, have a significant effect on the incidence of malaria. In column (6) though, the coefficient increases dramatically if the origin of the refugees is a tropical country with a civil war. In this case 1,000 refugees generate 1,406 cases of malaria in the refugee-receiving country. Another interesting and expected result is the loss of statistical significance of MCID. This implies that the percentage of population that lives with the risk of malaria transmission is irrelevant if we work only with tropical destination countries.

 

Table 2 shows a very strong and consistent story. The estimated coefficients of the variables have the predicted sign, and the size of the coefficient on refugees increases monotonically in the right direction. In fact, the only situation in which refugees are shown not to have any impact on the incidence of malaria is when there is no vector to transmit the illness: that is to say, refugees do not come, or do not go, to a tropical country.

 

A. Robustness Check I: Africa versus the Rest of the World

 

Are these results brought about by specific countries or areas? The results of the estimations show that the degree of impact of civil war refugees on the incidence of malaria in the refugee-receiving country depends on the tropical nature of the origin country and the destination one. However, as we expressed before, there are problems of irregular data collection on the incidence of malaria in African countries. The problem of irregular reporting is not important, as the estimation of incomplete panel data does not present any particular econometric difficulty. The most important difference with respect to reporting cases of malaria between African countries and other countries is the fact that in Africa, cases are counted on a clinically diagnosed basis 25 while in other countries they consider confirmed cases of malaria (through blood analysis). China is an exception, as not all cases are confirmed by laboratory diagnosis. Therefore the reporting procedure varies across countries. We assume that the method of determining a patient with malaria and the intensity of "counting" cases of malaria in each country is stable over time. However, if that were not the case, the ratio of physicians per inhabitant would compensate for it because the clinically diagnosed cases should be recognized by a specialist. From our estimation it seems that the preventative effect of physicians is larger than the increase in the intensity of counting, if there is any such effect.

 

Nevertheless, in order to perform robustness checks, in columns (1) to (3) of table 3 we include the results of the estimation of the tropical countries, but without all African countries. The regressions distinguish, as previously, between total refugees, those refugees coming from a tropical country, or those refugees from a tropical country that is suffering a civil war. Column (1) confirms that total refugees do not have any explanatory power on the incidence of malaria. Column (2) (O = TR) shows that refugees coming from a tropical country have a significantly positive effect on the incidence of malaria in the refugee-receiving country, even if we eliminate Africa. The results in column (3) confirm the findings of previous columns: refugees coming from a tropical country with a civil war have a larger effect on malaria than the refugees coming only from tropical countries. Just as we were expecting, the size of the coefficient is much smaller than in the case of the samples that include the African countries. However, notice that the high transmission rates in sub-Saharan Africa reflect the enormous efficiency of Africa's main vector, the Anopheles gambiae, due mostly to its tendency toward biting human beings. 26 Finally, columns (4) to (6) of table 3 report the results of the same estimation using only African countries. As in previous regressions, the refugees coming from a tropical country involved in a civil war have a positive and significant effect on the cases of malaria. Since our sample includes African countries, this coefficient is much larger than the coefficient obtained in column (3), as expected.

 

TABLE 3.--FIXED-EFFECTS PANEL DATA ESTIMATION

Destination Tropical without Africa Only Africa

Origin (O) All O = TR O = TR + CW All O = TR O = TR + CW

REF 0.00 -0.02 -0.01 1.13 2.22 0.11

(0.02) (0.68) (0.54) (4.61) (1.47) (0.28)

REFO 0.24 0.21 -1.05 1.35

(2.21) (1.91) (0.70) (3.29)

RURAL -0.05 -0.05 -0.05 -2.16 -2.19 -2.12

(1.19) (1.16) (1.16) (5.87) (5.94) (5.77)

PHYS 0.49 0.64 0.61 -49.27 -39.86 -46.26

(0.41) (0.53) (0.51) (1.95) (1.53) (1.84)

IRRIG 0.06 0.05 0.05 0.26 0.27 0.33

(8.63) (7.77) (7.98) (0.70) (0.74) (0.89)

DR -0.65 -0.58 -0.56 2.21 2.26 4.42

(0.75) (0.67) (0.65) (0.30) (0.31) (0.60)

CW 2.72 2.64 2.62 -1.30 -1.18 -1.21

(3.75) (3.63) (3.61) (1.04) (1.27) (1.32)

MCID 3.43 3.38 -0.49 -7.23 -7.06 -6.99

(1.06) (1.04) (0.54) (0.41) (0.40) (-0.40)

R2 0.12 0.13 0.13 0.07 0.08 0.09

Countries 35 35 35 44 44 44

N obs. 1,091 1,091 1,091 1,023 1,023 1,023

 

 

REF refers to all the refugees. REFO refers to refugees by origin: refugees could be from a tropical country (TR) or a tropical country suffering a civil war (TR + CW). RURAL is the proportion of rural population. PHYS is the proportion of physicians. IRRIG refers to hertares at irrigated land. DR is a dummy variable for a drought in the refugee-receiving country. CW is a dummy variable for a civil war in the refugee-receiving country. MCID is the proportion of each country's area where there is risk of malaria transmission.

 

B. Robustness Check II: Instrumental Variables Estimation

 

In the previous section, we considered refugees as an exogenous variable. However, there may be reasons to argue that the number of refugees may be endogenous to the incidence of malaria. Therefore, we should find an instrument for the number of refugees in order to obtain a consistent estimator for the regressions.

 

We consider two possible instruments. The first one is a civil war in the refugees' country of origin. The identifying assumption in this case would be that civil wars generate refugees and do not have a direct effect on malaria in other countries. We believe that this is a plausible hypothesis. However, civil wars in the refugees' country of origin may be correlated with some unobservable factors that affect the refugee-receiving country and are not included in the regression. 27 For this reason, we consider a second instrument: the predicted number of refugees. We constructed a model to explain bilateral refugees using some particular geographic characteristics (such as distance between countries and sizes). The identifying assumption in this case is that geographical characteristics are not correlated to the residual of the main regression. 28 So then, we use the predicted number of refugees as an instrument for the actual number of refugees. 29 Therefore, there may be other factors that affect the incidence of malaria in the refugee-receiving country but, since our instrument is constructed using geographical characteristics, there is no reason to expect that they will be correlated with the same instrument. The econometric specification for the (log) number of refugees is the following:

 

ln REFij = 1 + 2 ln Dij + 3 ln Pi + 4 ln Ai + 5Li + 6Bij + 7Bij ln Dij + 8Bij ln Pi + 9Bij ln Ai + 10BijLi + ij, (4)

 

where REFij is the number of refugees from country i (origin) to country j (destination), Dij is the distance between i and j, Pi is the population of the country of origin, Ai is the area, Li is a dummy for landlocked country, and Bij is a dummy for common-border countries. As in Frankel and Romer (1999), we also include the interaction of all the variables with the variable borders. Distance is measured as the great-circle distance between countries' principal cities. Rand McNally (1993) is used as the source for the size of the country, common borders, and landlocked countries. The data on population come from the World Development Indicators (World Bank, 2000).

 

The results of this regression are presented in table 4 and coincide with what anyone would have expected. The distance between two countries is negatively related with the number of refugees, while sharing a common border has a large and positive effect on the number of refugees. The result of being landlocked by border is also statistically significant and has a positive effect: having a common border increases the number of refugees in landlocked countries. Finally, the size of population in the origin country has a positive effect, if it has a common border with the refugee-receiving country. The R2 of the regression is 0.27. The correlation between log of the predicted and actual refugees is 0.52.

 

TABLE 4.--PREDICTING REFUGEES BY GEOGRAPHY

Variables

Ln Distance -0.20

(-13.2)

Ln Population (country i) 0.01

(1.28)

Ln Area (country i) 0.00

(0.35)

Landlocked (country i) 0.01

(0.44)

Border 5.33

(7.05)

Border X Ln Distance -0.66

(-6.65)

Border X Ln Population 0.13

(2.37)

Border X Ln Area 0.03

(0.52)

Border X Landlocked 2.17

(13.57)

Constant 1.62

(10.3)

R2 0.27

F 527

N 12,998

 

 

After estimating that regression, we calculate the predicted number of refugees going to country j by adding up the predicted refugees going to a particular country and coming from all the other countries. Since the regression is in logs, the number of predicted refugees to country j is

 

[sEE ORIGINAL SOURCE] (5)

 

where W contains all the explanatory variables (In Dij, In Pi, In Ai, Li, Bij) and the cross products with B.

 

In table 5 we present the results of the estimation of the panel using these two instruments: civil wars (CW) and predicted refugees (PREF), in the case of tropical destination countries. As in table 2, we consider all the refugees and refugees from tropical countries. The standard deviation of the regressions is calculated as in any instrumental variables estimation. The fact that we are using generated instruments does not affect the standard error of the IV regression, since under the condition that E(u|X) = 0, the asymptotic standard errors and the test statistics are still asymptotically valid. 30 The estimation in table 5 shows that the effect of refugees on the incidence of malaria in the refugee-receiving countries is positive and significantly different from zero. In fact it is higher than in the noninstrumented case. The use of civil wars, column (1), or predicted refugees, column (2), does not make much of a difference. Columns (3) and (4) show the estimation using as an explanatory variable the refugees from a tropical country. As in the first two columns, the estimated coefficient for refugees is larger than in the noninstrumented panel data estimation, and the choice of instrument has a minor effect on the estimation. In addition, as shown also in table 2, the estimated coefficient for refugees from a tropical origin is higher than the one corresponding to refugees of any country.

 

TABLE 5.--INSTRUMENTAL VARIABLES ESTIMATION

Destination Tropical Destination Countries

Origin All Countries Tropical Countries

Instrument CWI PREF CWI PREF

REF 1.97 2.03 2.66 2.77

(2.80) (2.84) (2.80) (2.84)

RURAL -1.49 -1.51 -1.36 -1.36

(5.28) (5.18) (4.32) (4.20)

PHYS -2.19 -2.19 -1.91 -1.88

(2.44) (2.34) (2.04) (1.93)

IRRIG -0.04 -0.06 -0.04 -0.06

(0.76) (0.99) (0.72) (0.94)

DR 1.09 1.01 2.07 3.80

(0.24) (0.21) (0.04) (0.93)

CW -7.27 -7.35 -7.13 -7.32

(1.44) (1.35) (1.41) (1.35)

MCID -2.76 -3.02 -1.13 -2.43

(0.39) (0.41) (0.02) (0.03)

F (first stage) 24.21 23.49 22.09 21.27

Countries 72 68 72 68

N obs. 1,919 1,823 1,919 1,823

 

 

REF refers to all the refugees. RURAL is the proportion of rural population. PHYS is the proportion of physicians. IRRIG refers to hectares of irrigated land. DR is a dummy variable for a drought in the refugee-receiving country. CW is a dummy variable for a civil war in the refugee-receiving country. MCID is the proportion of each country's area where there is risk of malaria transmission. Column CWI contains the results of the estimation using as an instrumental variable the existence of a civil war in any origin country. PREF also uses the predicted number of refugees. F is the F-statistic of the first-stage regression.

 

C. Robustness Check III: Changing the Frequency

 

One possible problem with the fixed-effect panel data estimation presented in the previous sections is the existence of serial correlation in the data. We could try to estimate the model including some hypothesis about the form of that autocorrelation. However, the fact that there is frequently missing data complicates that simple experiment. For these reasons (possibility of autocorrelation and frequent missing data), we have run the previous regression at a higher level of time aggregation. Table 6 presents the same regressions as table 2 but using five-year averages instead of yearly data. The estimates are remarkably similar. Perhaps the only exception is the estimated coefficient for refugees from tropical countries suffering a civil war, which is clearly higher than in table 2. It is also interesting to note that the variable MCID, which was significantly different from zero in table 2, turns out to be statistically insignificant when using five-year averages.

 

TABLE 6.--FIXED-EFFECT PANEL DATA REGRESSIONS: FIVE-YEAR AVERAGES

Destination All Countries Tropical Countries

Origin (O) All TR TR + CW All TR TR + CW

REF 0.05 -0.07 -0.10 1.02 -0.12 -1.17

(0.47) (0.63) (0.94) (3.22) (2.51) (-1.90)

REFO 1.09 1.83 1.34 2.38

(3.28) (4.58) (2.51) (4.10)

RURAL -2.15 -1.97 -1.83 -2.20 -2.15 -2.00

(5.16) (3.28) (4.53) (4.42) (4.33) (4.08)

PHYS -4.92 -4.67 -4.53 -4.12 -4.01 -3.73

(3.38) (3.24) (3.17) (2.02) (1.97) (-1.87)

IRRIG 0.02 0.02 0.01 -0.04 -0.03 -0.02

(0.84) (0.81) (0.75) (0.29) (0.24) (0.22)

DR 2.09 1.26 1.32 3.71 3.64 4.30

(1.30) (0.79) (0.83) (1.92) (1.88) (2.26)

CW -1.32 -1.27 -0.77 3.38 1.17 3.11

(0.14) (0.13) (0.08) (0.03) (0.09) (0.25)

MCID 1.21 1.20 1.18 -4.24 -2.54 -1.75

(0.96) (0.97) (0.95) (0.26) (0.15) (0.11)

R2 0.14 0.19 0.21 0.11 0.12 0.18

Countries 104 104 104 72 72 72

N obs. 630 630 630 451 451 451

 

 

REF refers to all the refugees. REFO refers to refugees by origin: refugees could be from a tropical country (TR) or a tropical country suffering a civil war (TR + CW). RURAL is the proportion of rural population. PHYS is the proportion of physicians. IRRIG refers to hectares of irrigated land. DR is a dummy variable for a drought in the refugee-receiving country. CW is a dummy variable for a civil war in the refugee-receiving country. MCID is the proportion of each country's area where there is risk of malaria transmission.

 

Are the results of the instrumental variable regressions affected then by the change in frequency of the data? Table 7 presents the IV regressions of table 5, but using the five-year-average data instead. The results follow the pattern previously discussed for the case of yearly data. The IV estimator for the coefficient on refugees increases with respect to the one obtained in table 6, but less than in the case of yearly data. For this reason, the estimates of that coefficient using yearly data or five-year averages are closer in the IV estimation than in the standard fixed-effect estimation, in particular when we restrict our attention to the refugees that come from tropical countries.

 

TABLE 7.--INSTRUMENTAL VARIABLES: FIVE-YEAR AVERAGES

Destination Tropical Countries

Origin All Countries Tropical Countries

Instrument CWI PREF CWI PREF

REF 2.34 2.36 2.70 2.71

(1.98) (2.24) (1.94) (2.30)

RURAL -1.82 -1.86 -1.75 -1.78

(3.01) (3.10) (2.78) (2.78)

PHYS -3.39 -2.41 -3.20 -3.21

(1.55) (1.51) (1.45) (1.41)

IRRIG -0.07 -0.10 -0.05 -0.09

(0.54) (0.71) (0.44) (0.61)

DR 2.43 2.46 2.34 2.37

(1.07) (1.05) (1.02) (1.00)

CW 1.17 2.61 2.82 4.23

(0.09) (0.19) (0.22) (0.31)

MCID -5.64 -6.05 -2.16 -2.51

(0.33) (0.35) (0.13) (0.15)

Countries 72 68 72 68

N 451 451 451 451

 

 

REF refers to all the refugees. RURAL is the proportion of rural population. PHYS is the proportion of physicians. IRRIG refers to hectares of irrigated land. DR is a dummy variable for a drought in the refugee-receiving country. CW is a dummy variable for a civil war in the refugee-receiving country. MCID is the proportion of each country's area where there is risk of malaria transmission. Column CWI contains the results of the estimation using as an instrumental variable the existence of a civil war in any origin country. PREF also uses the predicted number of refugees.

 

V. Geography versus Dislocation

 

The relationship between disease and development has recently attracted a lot of attention. 31 However, the negative effect of malaria on growth has been recognized for a long time. Initially, the studies on the economic impact of malaria were concerned with the loss of labor input (Ross, 1911). However, malaria has an important effect even if there is no human loss. Frequent malaria attacks increase school absenteeism 32 and lost work time. In addition, they reduce productivity by affecting work intensity, reducing the scope for specialization and the intensity of workers' mobility. The productivity effect, however, is not only reduced to the agricultural sector. The areas with high incidence of malaria have difficulties promoting tourism and foreign direct investment, suffering also an infrastructure deficit since the cost of construction increases with the likelihood of malaria and the need to invest in protection measures.

 

Using the estimates of the previous section, we can calculate the proportion of malaria cases that can be attributed to geography and poverty versus the dislocation caused by civil wars. We can estimate this ratio by dividing the cases of malaria attributed to the refugees caused by civil wars (the average yearly number of refugees from civil wars multiplied by the corresponding parameter estimate) over the fitted values of the regression. 33 Figure 3 presents the evolution of this ratio during the sample period. The average ratio is 13.24%, although it oscillates depending on the beginning or the end of civil wars in tropical areas. It is also interesting to notice that the mean in the period previous to the beginning of the 1980s is smaller than the average for the period after 1980. Figure 3 also shows that the proportion of malaria cases caused by forced migration has decreased drastically in the last few years of the sample.

 

Another way to give an idea of the potential impact of refugees from civil wars on the distribution of malaria is to estimate the proportion of the variance of malaria cases accounted for by those refugees. This also serves to demonstrate the potential scope of international interventions targeted at avoiding civil conflicts. The upper-bound estimate of the variance accounted for by the forced migration caused by civil wars is the adjusted R2 from the linear regression of malaria cases on the refugees from tropical countries in a civil war. For comparison, we calculate a lower bound as the increase in the adjusted R2 when the refugees from tropical countries in a civil war are added to a regression that contains the country dummies and the MCID variable (proportion of area of the country at risk of malaria transmission). The upper-bound estimate reaches 9.2%, while the lower bound is 4.7%.

 

VI. Conclusions

 

The burden of malaria transmission in the world, especially in underdeveloped countries, is very large in terms of diagnosed cases and deaths. It is estimated that it affects 300 million people and kills two million people every year. Many researchers have found that malaria has a very negative effect on development through its effects on productivity (such as repeated worker absences on the workplace and reduction of geographical job flexibility). But it is also the case that economic underdevelopment increases malaria incidence.

 

Several authors have argued that malaria is basically a result of geographical destiny. However, there are efficient vectors in many places outside of the tropics and malaria is not transmitted in those areas. There are also perfectly efficient vectors capable of surviving cold winters. For these reasons, even entomologists think that, in the end, human behavior and economic factors are the most important causes of malaria incidence. Negative socioeconomic conditions can favor the spread of malaria and make the control tasks very difficult. Therefore, there are technical factors and social conditions, especially the ones that generate mass migration, that explain the incidence of malaria. Moreover, technical factors are also affected by social conditions.

 

In fact, we could talk about two alternative views of malaria: for some researchers malaria is basically a social disease with socioeconomic causes, while for others malaria is primarily a clinical problem that requires medical research. As the search for a vaccine could last for a long time and the effectiveness of other control measures depends on social conditions, it is reasonable to think about policies that may prevent the basic cause of mass migration: civil wars and social conflicts.

 

It is true that drug resistance in the Plasmodium parasite and insecticide resistance in the vectors have hindered the attempts to combat the disease. However, we have shown that the size of the refugee population coming from tropical countries with civil wars make an important contribution to the number of cases of malaria in the refugee-receiving countries. Our instrumental variables estimates show that 1,000 refugees generate between 2,000 and 2,770 new cases of malaria in the refugee-receiving country. Therefore, the prevention of civil wars, especially in tropical countries, and the control of its causes are very important for the development on the control of malaria. However, more effective control methods will not mean the end of malaria if civil conflicts make their application impossible. An example of a simple device made in the twentieth century that was crucial in stopping malaria transmission in Europe and North America is the window screen. Obviously, homeless refugees fleeing from civil wars and walking through forests and dumping sites are not likely to have any protection whatsoever against repeated biting by Anopheles mosquitoes.

 

Our estimates point out that approximately 13.2% of the yearly cases of malaria during the period 1962-1997 can be attributed to dislocation, by contrast with geography or poverty. Therefore, any effort to reduce the spread of civil wars and control their causes can help to moderate, at least partially, the extension of malaria transmission and its impact on economic development.

 

Received for publication October 31, 2002. Revision accepted for publication January 12, 2006.

 

FOOTNOTES

 

1

 

For instance, in the Kilombero Valley (Tanzania) half of all deaths are children younger than one year. See Schellenberg et al. (2001). Sachs and Malaney (2002) report that 2,000 children die of malaria each day.

 

2

 

Paul Reiter (quoted by Budiansky, 2002), a medical entomologist at the U.S. Centers for Disease Control, notes that "we associate malaria with the tropics only because we've forgotten--because we've relegated malaria to the tropics." In fact, many areas of North America and Europe have important populations of efficient malaria vectors.

 

3

 

In the first edition of Bruce-Chwatt's reference book on malaria (1978), emphasis is placed on epidemiological causes. It is noticeable the change in the general vision of the problem from the first to the second edition (1985), where the author emphasizes the effect of adverse social and economic conditions, due to internal difficulties. In the economic literature, the current debate between Sachs (2003), McArthur and Sachs (2001), and Acemoglu, Johnson, and Robinson (2001) is a vivid example of this controversy.

 

4

 

Bioland and Williams (2003).

 

5

 

Other contributions have considered only a particular, and normally very small, geographical area and a short time period.

 

6

 

In some tropical cities, the existence of large slums facilitates the transmission of malaria.

 

7

 

See, for instance, Curtin (1989, 1998) and Marques (1987).

 

8

 

Najera, Liese, and Hammer (1992).

 

9

 

Immunity to malaria is reduced over time in the absence of exposure.

 

10

 

Glass et al. (1980).

 

11

 

This estimate is smaller than our estimates for the total effect of malaria. The reader should also notice that it refers to an Asian country. The basic vector in Africa (Anopheles gambiae) is much more efficient in the transmission of malaria than the vectors in Asia (for instance the Anopheles stephensi or the culicifacies).

 

12

 

The recent completion of the DNA map of the Plasmodium parasite (Gardner et al., 2002) and the Anopheles gambiae (Holt et al., 2002) open some new hopes for the future of antimalarial drugs and even vaccines. However, the prediction of Najera et al. (1992) is valid for the future: "Even if vaccines, new drugs, or new insecticides are developed, in view of the time required for their final testing in the field, it is difficult to expect a significant impact on malaria for a long time."

 

13

 

We use the ipolate function of STATA in order to apply a standard procedure instead of using our own criterion.

 

14

 

From this section on we use the original data, without the interpolation we considered in the previous section for aggregation purposes, jointly with methods of estimation apropriate for incomplete panel data.

 

15

 

Tobler et al. (1995).

 

16

 

The total number of polygons, generated by the grid used by the project, that cover the world is 19,032. The population of the countries was assigned to five minute-by-five minute quadrilaterals.

 

17

 

The data from 1951 to 1992 are not public and come from the work of Schmeidl and Jenkins (2001). We are indebted to them for providing us this data, which is not publicly available. Schmeidl and Jenkins (2001) also describe the difference between the data compiled by the UNHCR and the USCR. They argue that the data from the UNHCR are of a higher quality than the ones coming from the USCR.

 

18

 

EM-DAT: The OFDA/CRED International Disasater Database, http://www.cred.be/emdat, Université Catholique de Louvain, Brussels, Belgium.

 

19

 

We also considered using the access that rural population has to the health system, but this information is available only for a few countries and only from 1983 to 1993.

 

20

 

From 3,214 observations to only 789 observations. Montalvo and Reynal-Querol (2002) show that using the interpolated series produces very similar results to the ones obtained using the noninterpolated variable.

 

21

 

The results for the proportion of population infected with respect to total population and refugees per capita are qualitatively the same as the ones that appear in the tables. See Montalvo and Reynal-Querol (2002).

 

22

 

If the data on internally displaced people had a larger temporal and spatial coverage than they have, we could have used them instead of the natural disaster and civil war dummies.

 

23

 

We do not use the interpolated data for refugees and malaria incidence. We only used the interpolation to construct the aggregate figures we presented in the previous section. To facilitate the reading of the tables, the coefficients of the dummy variables and RURAL, PHYS, and MCID have been divided by 10,000.

 

24

 

The previous version of this paper (Montalvo & Reynal-Querol, 2002) also considers separately the refugees from civil wars.

 

25

 

Except for the North African countries, Cape Verde, Mauritius, Réunion, Somalia, and South Africa, which report laboratory-confirmed cases.

 

26

 

Garrett-Jones and Shidrawi (1969).

 

27

 

However, notice that from the first regression we include as an explanatory variable the dummy for civil war in the refugee-receiving country. Therefore, if the civil war in the country of origin of the refugees' spreads to the refugee-receiving country and this is the only link between both, then the estimator using the civil war instrument will be consistent.

 

28

 

We obviously do not use any geographic characteristic related with latitude or longitude that would be correlated with the residual.

 

29

 

See Frankel and Romer (1999) for an application of this strategy to the estimation of the effect of trade on growth.

 

30

 

Frankel and Romer (1999) correct the usual variance-covariance matrix of the IV coefficients claiming that the instruments depend on the parameters of an estimated regression. This argument is not correct for the case of generated instruments, although it would be correct for generated regressors (see, for instance, Wooldridge, 2002).

 

31

 

For a historical perspective, see Acemoglu, Johnson, and Robinson (2003).

 

32

 

Bleakley (2003) uses individual-level data to analyze the effect of malaria erradication on school attendance in the South of the United States between 1900 and 1950. Miguel and Kremer (2004) show evidence of the effect of hookworm and other infectious diseases on schooling using randomized experiments.

 

33

 

This procedure is just an approximation since there may be compensations.

 

BIBLIOGRAPHY:

 

 

REFERENCES

 

Acemoglu, D., S. Johnson, and J. A. Robinson, "The Colonial Origins of Comparative Development: An Empirical Investigation," American Economic Review 91:5 (2001), 1369-1401.

 

______ "Disease and Development in Historical Perspective," Journal of the European Economic Association 1:2-3 (2003), 397-405.

 

Bleakley, Hoyt, "Disease and Development: Evidence from the American South," Journal of the European Economic Association 1:2-3 (2003), 376-386.

 

Bloland, P., and H. Williams, Malaria Control during Mass Population Movements and Natural Disasters (Washington, DC: National Academy Press, 2003).

 

Bruce-Chwatt, L., Essential Malariology (New York: John Wiley and Sons, 1985).

 

Budiansky, S., "Creatures of Our Own Making," Science 298 (2002), 80-86.

 

Curtin, P., Death by Migration: Europe's Encounter with the Tropical World in the Nineteenth Century (New York: Cambridge University Press, 1989).

 

______ Disease and Empire: The Health of European Troops in the Conquest of Africa (New York: Cambridge University Press, 1998).

 

Doyle, Michael W., and Nicholas Sambanis, "International Peacebuilding: A Theoretical and Quantitative Analysis," American Political Science Review 94:4 (December 2000).

 

EM-DAT: The OFDA/CRED International Disaster Database, http://www.cred.be/emdat, Université Catholique de Louvain, Brussels, Belgium.

 

Frankel, J. and D. Romer, "Does Trade Cause Growth?" American Economic Review 89:3 (1999), 379-399.

 

Gallup, J., and J. Sachs, "The Economic Burden of Malaria," supplement to The American Journal of Tropical Medicine and Hygiene 64:1-2 (2001), 85-96.

 

Gardner, M. J., et al., "Genome Sequence of the Human Malaria Parasite Plasmodium falciparum," Nature 419 (Oct. 3, 2002), 498-511.

 

Garrett-Jones, C., and J. R. Shidrawi, "Malaria Vectorial Capacity of a Population of Anopheles Gambiae: An Exercise in Epidemiological Entomology," Bulletin of the World Health Organization 40 (1969), 531-545.

 

Ghobarah, H., P. Huth, and B. Russett, "Civil Wars Kill and Maim People--Long after the Shooting Stops," Yale University mimeograph (2001).

 

Glass, R., W. Cates, P. Nieburg, C. Davis, R. Russbach, H. Nothdurft, S. Peel, and R. Turnbull, "Rapid Assessment of Health Status and Preventive-Medicine Needs of Newly Arrived Kampuchean Refugees to Sa Kaeo (Thailand)," Lancet 8173 (1980), 868-872.

 

Global Development Network Growth Database, GDNG, World Bank.

 

Holt, R. et al., "The Genome Sequence of the Malaria Mosquito Anopheles gambiae," Science 298 (2002), 129-148.

 

Marques, Cruz, "Human Migration and the Spread of Malaria in Brazil," Parasitology Today 3:6 (1987), 166-170.

 

McArthur, J., and J. Sachs, "Comment on Acemoglu, Johnson and Robinson," NBER working paper 8114 (2001).

 

Miguel, E., and M. Kremer, "Worms: Education and Health Externalities in Kenya," Econometrica 72:1 (2004), 159-217.

 

Montalvo, J. G., and M. Reynal-Querol, "Fighting against malaria: Prevent Wars while Waiting for the 'Miraculous' Vaccine," IVIE working paper WP-EC 2002-31 (2002).

 

Najera, J., B. Liese, and J. Hammer, "Malaria: New Patterns and Perspectives," World Bank technical paper number 183 (1992).

 

Pampana, E., and P. Russell, Malaria (Geneva: World Health Organization, 1995).

 

Rand McNally, Quick Reference World Atlas (Chicago: Rand McNally, 1993).

 

Ross, R., The Prevention of Malaria (New York: E. P. Dutton, 1911).

 

Sachs, J., "Institutions Don't Rule: Direct Effect of Geography on Per Capita Income," NBER working paper 9490 (2003).

 

Sachs, J., and J. Gallup, Geographical Dataset, Center for International Development (CID) (2001).

 

Sachs, J., and Malaney, "The Economic and Social Burden of Malaria," Nature 415 (2002), 680-685.

 

Schellenberg, D., C. Menendez, E. Kahigwa, J. Aponte, J. Vidal, M. Tanner, H. Mshinda, and P. Alonso, "Intermittent Treatment for Malaria and Anaemia Control at Time of Routine Vaccinations in Tanzanian Infants: A Randomized, Placebo Controlled Trial," The Lancet 357 (2001), 1471-1477.

 

Schmeidl, S., and J. Jenkins, "Global Refugee and Displaced Dataset," Mershon Center for International Security, Ohio State University (2001).

 

Singer, J. D., and M. Small, Resort to Arms: International and Civil War, 1816-1980 (Beverly Hills, CA: Stage, 1982).

 

______ Correlates of War Project: International and Civil War Data, 1816-1992 (data file; April 1994; ICPSR 9905); Ann Arbor, University of Michigan.

 

Tobler, W., U. Deichmann, J. Gottsegen, and K. Maloy, The Global Demography Project, Technical Report TR-95-6 (1995).

 

WHO, "Malaria Eradication in 1966." WHO Chronicle 21 (September 9, 1967), 373-388.

 

WHO, "The World Malaria Situation, 1982: Malaria Action Programme," WHO World Health Statistics Quarterly 37:2 (1984).

 

WHO, "World Malaria Situation in 1994, Part I," WHO Weekly Epidemiological Record 36 (1997), 269-274.

 

WHO, "Malaria 1982-1997," Weekly Epidemiological Record (August 13, 1999), 265-270.

 

Wooldridge, J., Econometric Analysis of Cross Section and Panel Data (Cambridge, MA: MIT Press, 2002).

 

World Bank, World Development Indicators (2000).

 

World Health Statistics Annual (Geneva: World Health Organization. 1983), 791-795.

 

GRAPHIC: Figure 1, CASES OF MALARIA AND CIVIL WARS, Source: WHO (1999); Doyle and Sambanis (2000).

Figure 2, REFUGEES AND CASES OF MALARIA, Sources: UNHCR; WHO (1999).

Figure 3, PROPORTION OF MALARIA CASES EXPLAINED BY REFUGEES FROM CIVIL WARS OVER TOTAL CASES (YEARLY ESTIMATES)

 

LOAD-DATE: March 8, 2007

Share this post


Link to post
Share on other sites
PLAN: The United States Federal Government should air drop 1 million cans of bug spray over various parts of SSA. Funding and Enforcement guarenteed.

 

Seriously, how about PLAN: The United States Federal Government should provide 1 million cans of bug spray to SSA. Funding and Enforcement guarenteed.

Share this post


Link to post
Share on other sites
Seriously, how about PLAN: The United States Federal Government should provide 1 million cans of bug spray to SSA. Funding and Enforcement guarenteed.

 

you could also use like bed nets and things like that, and bug spray/ education to not go to areas were mosquitos would be if possible

Share this post


Link to post
Share on other sites

Genetically-Modified Mosquitoes still seems like the best malaria option... John Hopkins has made a functioning one

:

 

Copyright 2007 ABC, Inc.

All Rights Reserved

ABC News Now

 

SHOW: INSIDE THE NEWSROOM #3 12:10 PM EST

 

March 21, 2007 Wednesday

 

LENGTH: 307 words

 

HEADLINE: MALARIA CURE;

SCIENTISTS ON THE VERGE OF MALARIA CURE

 

ANCHORS: BIANNA GOLODRYGA

 

REPORTERS: GLORIA RIVIERA (LONDON, ENGLAND)

 

BODY:

 

 

CONTENT: JOHNS HOPKINS UNIVERSITY, MALARIA, BILL GATES, MELINDA GATES, GM MOSQUITOES, AFRICA

 

GRAPHICS: MALARIA CURE?

 

BIANNA GOLODRYGA (ABC NEWS)

 

(Off-camera) Overseas now, malaria kills an estimated one million people every year. The majority of them children in Africa, but just as scientists believe they were on the verge of a medical breakthrough, ethical dilemmas came in and slowed their progress. ABC's Gloria Riviera has more from London.

 

GLORIA RIVIERA (ABC NEWS)

 

(Voiceover) It's called the forgotten epidemic. The disease spread by mosquitoes that kills one African child every 30 seconds. Now scientists at Johns Hopkins University are producing genetically modified or GM mosquitoes that can block malaria.

 

RESEARCHER (MALE)

 

These types of experiments will, hopefully, ultimately lead one day to the release of genetically modified mosquitoes that are unable to transmit the malaria parasite.

 

GLORIA RIVIERA (ABC NEWS)

 

(Voiceover) The idea is to release GM mosquitoes into infected areas. If the survival of the fittest theory proves true, the GM mosquitoes will wipe out the weaker species. But is the world ready to welcome such science?

 

RESEARCHER (MALE)

 

We need to deal with all the social, ethical and legal issues associated with releasing a genetically modified mosquito into the environment. These social issues in getting people to want to accept genetically modified mosquitoes is gonna take a long time.

 

DOCTOR (MALE)

 

Was admitted yesterday in the morning...

 

GLORIA RIVIERA (ABC NEWS)

 

(Voiceover) Bill and Melinda Gates have made it their foundation's goal to wipe out malaria worldwide. They've not yet commented on the Johns Hopkins GM mosquito.

 

GLORIA RIVIERA (ABC NEWS)

 

(Off-camera) Researchers at Johns Hopkins University say the science will be ready long before the ethical issues will be settled. Gloria Riviera, ABC News, London.

 

LOAD-DATE: March 22, 2007

Share this post


Link to post
Share on other sites

mosquito nets aff

 

i was thinking of running this aff basically for the malaria advantage. but i don't know if this alone will be enough to pull. the evidnece on impacts is pretty sweet for malaria but i don't know if the nets plan will be large enough.

Share this post


Link to post
Share on other sites

 

http://www.ctv.ca/servlet/ArticleNews/story/CTVNews/20070424/malaria_day_070424/20070426?hub=Health

 

Simple bed nets key to preventing malaria

 

Updated Thu. Apr. 26 2007 9:13 AM ET

CTV.ca News Staff

In the next minute, while you are reading this article, two children somewhere in the world will die of malaria. By the end of the day, malaria will have claimed the lives of 3,000 children.

Most of malaria's victims live in sub-Saharan Africa. In fact, malaria kills more children under five years old in Africa each year than any other disease. Every April 25th since 1998, the World Health Organization, UNICEF, the United Nations Development Programme (UNDP) and the World Bank work to raise awareness about malaria through Africa Malaria Day. The day is an opportunity to bring together governments of countries affected by malaria along with aid organizations, to help coordinate a global response to the disease. This year, the slogan for Africa Malaria Day is, "Free Africa from Malaria NOW!", a slogan that recognizes the urgency of the disease that the Roll Back Malaria program says is holding back the development of an entire continent. Spread by mosquitoes, there is no vaccine to prevent malaria. But infection is easily preventable with something as simple as an insecticide-treated bed-net wrapped around a bed to prevent mosquito bites. But for the thousands of people at risk, the nets are far too expensive. The Canadian Red Cross has been trying to help through its Campaign Against Malaria. The Campaign has already delivered more than 2.5 million free mosquito nets to six countries in Africa over the last few years. This year, Canadian Red Cross plans to distribute almost as many nets to Burkina Faso alone - the CRC's biggest bed-net program ever. Another 600,000 bed nets will be distributed in the island nation of Madagascar. As well, UNICEF Canada is leading an initiative called Spread the Net. The program's goal is to raise $5 million over three years for UNICEF to purchase and distribute 500,000 insecticide-treated bednets to families in Rwanda and Liberia. The cost of purchasing a net, distributing it to families and training them to use it properly is only Cdn $7 -- making the nets one of the most cost-effective disease prevention tools available.

The long-lasting, insecticide-treated nets will be distributed free of charge by thousands of volunteers as part of an integrated child health strategy in Africa. Since malaria-infected mosquitoes feed between dusk and dawn, a properly used insecticidal net can cut the risk of infection by about 50 per cent, estimates the Roll Back Malaria program. The bed net program alone will therefore save tens of thousands of lives in Africa, most of them children. Last fall, the Canadian Red Cross completed its largest bed-net campaign to date, distributing nearly 875,000 nets through 900 distribution points across Sierra Leone. With the involvement of 4,000 trained Sierra Leone Red Cross volunteers, some 90 per cent of all Sierra Leonean children under five years of age were reached. While it was the nets that drew families from all over to the distribution centres, the campaign was also able to provide measles vaccinations, Vitamin A supplementation and de-worming treatments. It is expected the Sierra Leone program will save the lives of 5,000 children under the age of five in the first year alone. The Canadian Red Cross was recently able to expand its malaria program through a commitment from the federal government of $20 million. The funding builds upon the Canadian International Development Agency's previous contribution of $26 million to the Canadian Red Cross to support similar malaria programs in Africa.

Share this post


Link to post
Share on other sites
i was thinking of running this aff basically for the malaria advantage. but i don't know if this alone will be enough to pull. the evidnece on impacts is pretty sweet for malaria but i don't know if the nets plan will be large enough.

 

An anti-mosquito aff can incorporate more than just sleeping nets. A more comprehensive plan could include pesticides, larvicides, bug repellent (for personal use and/or saturated bed sheets), traps, introduction of mosquito-killing bacteria, fungi, fish, and insects, breeding habitat control, and surveillance programs. Watch out for PIC ground though. Most affs will have enough ev in their tub to run "exclude pesticides."

 

You can also solve for more than just malaria. Yellow fever, West Nile virus, and dengue fever are all spread by mosquito. The impacts aren't as big as malaria, but it can only help the 1AC. You can claim stability advantages off the meatball "Disease --> War" cards if you want. Finally, you can get ecology advantages by phasing out harsh pesticides and replacing them with more eco-friendly alternatives.

 

Mosquito cases will be solid next year. They're nothing to get all excited about, but they can hold their own.

i was also looking for a vaccine/treatment plan for malaria. but that probably could be the only advantage.
Sadly, there isn't a practical malaria vaccine yet. Treatment plans will work, but you'll link to too many generics and the solvency leaves much to be desired. I think prevention - like anti-mosquito campaigns - will look nicer on the solvency flow than treatment.

 

It would be VERY hard to make them actually use the misquito nets, no moderation, and no clear incentive.
Empirically, people use them if they get them. The problem is availability. They aren't stupid, after all.

Share this post


Link to post
Share on other sites
It would be VERY hard to make them actually use the misquito nets, no moderation, and no clear incentive.

yeah, because africans wants malaria..

Share this post


Link to post
Share on other sites

Here's the mechanism to do it

 

PEACE CORPS CAN HELP SOLVE MALARIA IN AFRICA

 

STATES NEWS SERVICE, 07 [PEACE CORPS VOLUNTEERS' WORK RAISES AWARENESS ABOUT MALARIA, access LN]

 

Peace Corps Volunteers are working to mitigate the devastating impact that malaria has on many communities in Africa and elsewhere around the world. Volunteers fill a needed niche in carrying out grassroots community-based education, and salient health education activities focused on malaria control. In Africa, health sector Volunteers work alongside their counterparts (health agents, youth groups, women's groups and others) to train local community members on the prevention of malaria. Here are a few examples:

 

In Guinea, Volunteer Anders Hyatt, has been collaborating with a non-governmental organization, his health center and community volunteers to demonstrate to community members the importance of buying and using insecticide-treated mosquito nets. Hyatt and his counterparts pass through remote villages on bicycles and perform short health awareness sessions, sell nets, and give demonstrations on how to attach a net to a bed. In the past year and a half, they have distributed over 1,500 nets.

 

In Burkina Faso, Volunteer Rebecca Egner worked closely with a drama troupe to address a range of health topics, including malaria, which the troupe itself decided was an important theme to address. They acted out a tale of two families; the first family wisely and quickly chooses to accompany their young child to the clinic and receive the correct treatment for malaria. Unfortunately, the second family, after choosing traditional treatments and spending lots of money in a last desperate attempt to save their child, find it is too late.

 

Peace Corps Volunteers in Malawi have encouraged communities to purchase bed nets at subsidized prices for pregnant women and mothers with children less than five years. As a result, 800 community members are now using bed nets for malaria prevention.

 

Peace Corps Volunteers in Mali have been working with their communities to build simple water drainage systems called soak pits and washing areas in order to eliminate standing water, reducing potential breeding ground for mosquitoes. As a result, a total of 380 such systems have been built.

Share this post


Link to post
Share on other sites
i like the idea of nets but i just think that its not practical (ie spending)

 

Those nets are like $5.

Share this post


Link to post
Share on other sites

i think a more practical way to stop malaria and mosquitos would be ddt. no one has proved that ddt has real harmful effects. it would be alot more absolute and effective than nets or cans of bug spray. take several planes and blanket subsaharan africa with chemicals. an ounce of prevention is worth a pound of cure.

Share this post


Link to post
Share on other sites
i think a more practical way to stop malaria and mosquitos would be ddt. no one has proved that ddt has real harmful effects. it would be alot more absolute and effective than nets or cans of bug spray. take several planes and blanket subsaharan africa with chemicals. an ounce of prevention is worth a pound of cure.

 

Other then the whole "causes cancer thing", prevention of development in children, and the destruction of the environment, you are pretty much right that there are no real "harmful effects" to using ddt.

Share this post


Link to post
Share on other sites

Uganda: No Money for DDT Spraying

s_trans.gifcursw10_2_ffffff_e8e8ff_e8e8ff.gifs_trans.gifcurse10_2_ffffff_e8e8ff_e8e8ff.gifs_trans.gif

The Monitor (Kampala)

April 24, 2007

Posted to the web April 23, 2007

Steven Kibuuka

Kampala

DEATHS from malaria are likely to continue after the government yesterday expressed doubt that the planned widescale spraying of DDT to fight the disease will start in July.

Speaking in an interview with Daily Monitor, Emmanuel Otaala, the minister of State for Primary Healthcare said the government has failed to raise close to $400 million (Shs750b) needed to kick-start the spraying.

adimage.php?filename=2k7_inset.gif&contenttype=gif adlog.php?bannerid=140&clientid=82&zoneid=0&source=en%2C_inset%2C_uganda%2C-nonstory%2Cen%2C_inset%2C_malaria%2C-nonstory%7Cen%2C_inset%2C_ros%2C-nonstory&block=0&capping=0&cb=d14c20a8ed8b0c71fb97a807260fe10e

 

"We don't have money to buy DDT right now," Dr Otaala said.

"We did not budget for it this financial year so we cannot use it this year."

The Ministry of Health had announced plans to begin using DDT to combat malaria in June 2007, in the pioneer districts of Apac in northern Uganda, Kanungu in the southwestern and all IDP camps.

An estimated 320 people die of malaria in Uganda daily. According to the World Health Organisation, there are between 1.5 million and 2.5 million deaths due to malaria in the world with 90 per cent of cases in sub-Saharan Africa.

Dr Emmanuel Otaala

Added to the deaths are the social-cultural costs which cannot be easily monetised. But such costs indicate that the diaseas is also the leading cost of poverty in Uganda.

Dr Otala, however, said the government has started requesting development partners to help out. "Already Usaid has agreed to help us," he said. "Usaid has promised to give us US$50 million but this can cover only 7-8 districts," Dr Otaala said.

However, the minister said the American agency would only release the funds after carrying out its own assessment of the effects of the chemical.

"The Usaid promise is not yet guaranteed because they are right now carrying out an impact assessment which involves finding the impact of DDT on the environment and that takes long," Dr Otaala said.

"The earliest they can finish this is June and then make a report which may also take a lot of time. Development partners who are willing to help us out should not put difficult conditions as usual because people's lives are at stake here," he said.

The National Environment Management Authority approved the use of the chemical last year as a means of controlling Malaria in the country.

Nema said that DDT would have no harm as feared by environmental activists.

DDT has not been used in Uganda since the 1970s when international environmental activism led to a ban on its production and use in most countries.

The World Health Organization also approved its use in Uganda as long as it's sprayed indoors.

Dr Otaala says that DDT would be used as a malaria prevention programme by the government, malaria has caused many deaths among Ugandans.

DDT will be sprayed on the inside walls of homes and buildings. The crystalline solid residue left behind serves to repel and kill mosquitoes, the vector responsible for the spread of malaria.

"DDT is one of the cheapest, most effective tools in the fight against malaria in many developing countries and we also want to use it here," Dr Otaala said.

There has been considerable controversy surrounding the harmful effects of DDT since 1962, when Rachel Carson, an American environmentalist, published Silent Spring, a comprehensive study detailing the damage that wide-scale DDT use had inflicted on the environment and wildlife in the United States.

Concerns regarding the impact of DDT are not unfounded. There is little doubt within the scientific community that the chemical can cause serious environmental harm; however, its precise impact on humans is a subject of debate.

Anarfi Asamoa-Baah, the Director General for Malaria at WHO said in a statement on September 15, 2006 that "DDT presents no health risks when used properly." however Jay Feldman, the executive director of Beyond Pesticides, an American company specializing in DDT issues, says this view is "short-sighted and doesn't recognise the long-term problems and hazards."

There was concern, that if Uganda began using DDT, its agricultural products could be banned in Europe. DDT use has been prohibited in Europe for over 20 years.

Dr Otaala believes that by weighing in favour of DDT use, WHO has completely laid these concerns to rest.

Currently the government is spraying lambdathylothrin (Icon) in malaria-infected regions.

Relevant LinksEast Africa

Uganda

Malaria

 

The National Coordinator of Roll Back Malaria Control Programme, John Bosco Rwakimari, said last week they are spraying Icon after all traditional ways they have been using in fighting malaria failed.

The ministry has been using case management, mosquito netting, and clearing bushes as methods of fighting malaria.

Additional reporting by Joseph Miti

 

an easy way to argue that is whatever possible environmental or human impact cannot measure up to the daily death toll of malaria, like this article said, like 320 deaths daily.

 

another article:

 

Global Health

WHO Backs Use of DDT Against Malaria

 

[/url] by Joanne Silberner

 

malaria75.jpg Olivier Martel/Corbis

 

Villagers wait for care at an antimalaria health center in the Bao Thang district of Vietnam. © 2006

 

 

 

 

 

 

 

 

All Things Considered, September 15, 2006 · The World Health Organization today announced a major policy change. It's actively backing the controversial pesticide DDT as a way to control malaria. Malaria kills about 1 million people a year, mainly children, and mainly in Africa, despite a decades-long effort to eradicate it.

The WHO previously approved DDT for dealing with malaria, but didn't actively support it. While DDT repels or kills mosquitoes that carry the malaria parasite, it doesn't get much good press. In 1962, environmentalist Rachel Carson wrote a book, Silent Spring, about how it persists in the environment and affects not just insects but the whole food chain.

As activist Malvina Reynolds once sang, "It kills the bugs in the apple tree, I eat the pie and it's killing me. DDT on my brain, on my brain."

In the early 1960s, several developing countries had nearly wiped out malaria. After they stopped using DDT, malaria came raging back and other control methods have had only modest success.

Which is why Arata Kochi, head of the WHO's antimalaria campaign, has made the move to bring back DDT. His major effort at a news conference Friday in Washington, D.C., was not so much to announce the change, but to deflect potential opposition from environmental groups.

"We are asking these environmental groups to join the fight to save the lives of babies in Africa," Kochi said. "This is our call to them."

A number of major environmental groups support the limited use of DDT, such as spraying only inside of houses and huts once or twice a year. That type of use is supported by the Sierra Club and Environmental Defense, which was originally founded by scientists concerned about DDT. The limited application is also part of President Bush's new malaria initiative.

But some environmental groups say spraying DDT will be harmful. Jay Feldman, executive director of a group called Beyond Pesticides, says using it is a war plan without an exit strategy.

"WHO holds a lot of clout in the world health community and the fact that they're now changing policy and advocating use of DDT will have dramatic impact," Feldman says. "They announced today that they expect 85 percent receptivity, that is knocking on people's doors and convincing them -- that's their language -- to use DDT."

Looking at the medical literature, he predicts harmful effects.

"This is a chemical that has been studied and evaluated," Feldman says, "and over the years has been found to cause cancer, endocrine disruption, adversely affect the immune system and is very problematic from the standpoint that it is persistent." DDT collects "in fatty tissue and in the environment," he adds, and can also be passed on in breast milk.

But supporters of the new policy discount those studies and point to others showing it's safe. Richard Tren, member of a group called Africa Fighting Malaria, says that while there may be lab studies showing DDT could potentially cause cancer, no large studies show an actual increase in cancer in people.

Some opponents say DDT will be diverted to more direct and more harmful agricultural use. Tren has watched indoor-spraying campaigns in Zambia.

"You're not seeing leakage into the environment," Tren says. "You're not seeing leakage into agriculture. What you are seeing are sharp dramatic reductions in malaria deaths and disease."

The field of malaria control has historically been dogged by problems with resistance. Each time scientists find a way to fight the parasite, the parasite finds a way to fight back. It has become resistant to most treatments, for example. And some mosquitoes have already adapted to tolerate DDT. The WHO's Kochi says resistance can be limited if DDT is used carefully, and only where it's likely to be effective.

Share this post


Link to post
Share on other sites
an easy way to argue that is whatever possible environmental or human impact cannot measure up to the daily death toll of malaria, like this article said, like 320 deaths daily.

 

another article:

 

Anarfi Asamoa-Baah, the Director General for Malaria at WHO said in a statement on September 15, 2006 that "DDT presents no health risks when used properly." however Jay Feldman, the executive director of Beyond Pesticides, an American company specializing in DDT issues, says this view is "short-sighted and doesn't recognise the long-term problems and hazards."

There was concern, that if Uganda began using DDT, its agricultural products could be banned in Europe. DDT use has been prohibited in Europe for over 20 years.

 

 

The WHO previously approved DDT for dealing with malaria, but didn't actively support it. While DDT repels or kills mosquitoes that carry the malaria parasite, it doesn't get much good press. In 1962, environmentalist Rachel Carson wrote a book, Silent Spring, about how it persists in the environment and affects not just insects but the whole food chain.

As activist Malvina Reynolds once sang, "It kills the bugs in the apple tree, I eat the pie and it's killing me. DDT on my brain, on my brain."

In the early 1960s, several developing countries had nearly wiped out malaria.

 

But some environmental groups say spraying DDT will be harmful. Jay Feldman, executive director of a group called Beyond Pesticides, says using it is a war plan without an exit strategy.

"WHO holds a lot of clout in the world health community and the fact that they're now changing policy and advocating use of DDT will have dramatic impact," Feldman says. "They announced today that they expect 85 percent receptivity, that is knocking on people's doors and convincing them -- that's their language -- to use DDT."

Looking at the medical literature, he predicts harmful effects.

"This is a chemical that has been studied and evaluated," Feldman says, "and over the years has been found to cause cancer, endocrine disruption, adversely affect the immune system and is very problematic from the standpoint that it is persistent." DDT collects "in fatty tissue and in the environment," he adds, and can also be passed on in breast milk.

 

Your own articles say straight-out using DDT has negative impacts.

  • Downvote 1

Share this post


Link to post
Share on other sites
Those nets are like $5.

Even so, there are more huge amounts of people in SSA. Even if it is only one million people, that would be five million dollors. Then you have to replace them if they break

Share this post


Link to post
Share on other sites

I assure you that the USFG will find funding for the nets, ie take away money from countries that don't need it or other uneffective malaria programs in SSA

Share this post


Link to post
Share on other sites

i acknowledge that there would be many negative impacts of ddt, but human life overpowers all other implacts. like one of the articles said, uganda (i think) used ddt to almost wipe out malaria there, but then it was outlawed, malaria came back, and killed lots of people. ddt solves for human death which beats anything the negative could run (but anyway, you're right, there would be too many possible disads and other stuff to make it a really solid case.)

Share this post


Link to post
Share on other sites
i acknowledge that there would be many negative impacts of ddt, but human life overpowers all other implacts. like one of the articles said, uganda (i think) used ddt to almost wipe out malaria there, but then it was outlawed, malaria came back, and killed lots of people. ddt solves for human death which beats anything the negative could run (but anyway, you're right, there would be too many possible disads and other stuff to make it a really solid case.)

 

Human life can be lost in a solvency turn or a disad, and in no-way can plan uniquely "overpower" all arguements, especially when your advantages are being turned. Human life will be lost by the side effects from DDT, and the negative team could just pic out a more effective way to solve malaria without the side effects. Therefore they give a alternate policy to saving lives, while also preventing the "side-effects" that the aff creates.

Share this post


Link to post
Share on other sites

×
×
  • Create New...