The Economics of Malaria Prevention
Summary and Keywords
Malaria is a potentially life-threatening disease transmitted through the bites of female anopheline mosquitos infected with protozoan parasites. Malaria remains one of the major causes of mortality by infectious disease: in 2015, there were an estimated 212 million cases and 429,000 deaths globally, according to the 2016 World Malaria Report. Children under 5 years in sub-Saharan Africa bear the greatest burden of the disease worldwide.
However, most of these cases could be prevented or treated. Several methods are highly effective in preventing malaria: in particular, sleeping under an insecticide-treated mosquito net (ITN), indoor residual spraying (IRS), and taking intermittent preventive treatment for pregnant women (IPTp). Regarding treatment, artesiminin-based combination therapy (ACT) is recommended as first-line treatment in many countries.
Compared with other actions, malaria prevention behaviors have some specific features. In particular, they produce public health externalities. For example, bed net usage creates positive externalities since bed nets not only directly protect the user, but also reduce transmission probabilities through reduction in the number of disease hosts, and in the case of ITNs, reduction of the vector itself. In contrast, ACT uptake creates both positive externalities when individuals with malaria are treated, and negative externalities in the case of overtreatment that speeds up the spread of long-run parasite resistance. Moreover, ITNs, IPTp, and ACTs are experience goods (meaning individuals only ascertain their benefits upon usage), which implies that current preventive actions are linked to past preventive behaviors.
Malaria prevention and eradication produce unambiguous benefits across various domains: economic conditions, educational outcomes, survival, fertility, and health. However, despite the high private returns to prevention, the adoption of antimalarial products and behaviors remains relatively low in malaria-affected areas.
A variety of explanations have been proposed for low adoption rates, including financial constraints, high prices, and absence of information. While recent studies highlight that all of these factors play a role, the main barrier to adoption is probably financial constraints. This finding has implications regarding the appropriate pricing policy for these health products. In addition, there is a shortage of causally identified research on the effect of cultural and psychological barriers to the adoption of preventive behaviors. The literature which does exist is from a few randomized control trials of few individuals in very specific geographic and cultural contexts, and may not be generalizable. As a result, there are still ample opportunities for research on applying the insights of behavioral economics to malaria-preventive behavior in particular. Moreover, little research has been done on the supply side, such as whether free or heavily subsidized distribution of prevention technologies is fiscally sustainable; finding effective methods to solve logistical problems which lead to shortages and ineffective alternative treatments to fill the gap; or training sufficient healthcare workers to ensure smooth and effective delivery. Given these gaps in the literature, there are still multiple fruitful avenues for research which may have a first-order effect on reducing the prevalence of malaria in the developing world.
Despite the adoption of the Millennium Development Goals at the United Nations in 2000 which emphasized malaria control, and subsequent progress since, malaria is still endemic in ninety-one countries. There were an estimated 212 million cases of the disease and 429,000 deaths globally in 2015. Sub-saharan Africa bears the brunt of the worldwide malaria burden, with 92 percent of total malaria deaths (World Malaria Report, 2016). Malaria is especially dangerous for children under 5—accounting for 70 percent of total deaths—and pregnant women.
Yet most of these consequences could have been avoided using available malaria-preventive technologies: insecticide-treated net (ITN) and long-lasting insecticidal net (LLIN) usage, indoor residual spraying (IRS), intermittent preventive treatment uptake during pregnancy (IPTp), use of mosquito repellants, cleaning of drains, and treatment of standing water with larvicidal chemicals. These interventions work by reducing the number of mosquitoes or preventing bites, except for IPTp, which reduces maternal malaria episodes. Among these technologies, LLIN usage, IRS, and IPTp are recommended by the World Health Organization (WHO) as the primary defense against malaria. Despite efforts to develop malaria vaccines, most of them are still at early stages of development: only one has been approved, and is not yet ready for large scale use. The distinction between malaria prevention and treatment is blurry, because treatment of confirmed cases decreases the number of human hosts of the disease, slowing malaria transmission. Therefore effective treatment plays an important role in prevention. The WHO recommends artemisinin-based combination therapy (ACT) as the main treatment, and recommends performing a rapid diagnosis test (RDT) before administering ACT to avoid the spread of resistant parasites.
Usage of malaria-preventive technologies is far from universal in malaria-affected areas. For example, 53 percent of individuals at risk of contracting malaria in sub-Saharan Africa slept under an ITN in 2015, 3.1 percent had their houses protected by IRS, 31 percent of pregnant women had three or more doses of IPTp (in the twenty countries for which data is available), and 14 percent of those with evidence of malaria infection received ACT (World Malaria Report, 2016). These low adoption rates may seem surprising given the high private returns of these preventive actions. Therefore, understanding why adoption is low is critically important.
Compared with other health products, malaria prevention technologies have some specific characteristics. First, individual preventive behavior produces public health externalities. Because of the mode of malaria transmission, bed net usage creates positive externalities both through a reduction in the number of disease hosts, and—in the case of ITNs or LLINs—reduction of the vector itself. In contrast, ACT uptake creates both positive externalities (when individuals with malaria are treated) and negative externalities (in the case of overtreatment that speeds up the spread of parasite resistance) (Arrow et al., 2004). Second, ITNs, LLINs, IPTp, and ACTs are experience goods, in that individuals ascertain their benefits only with use. This implies that current present preventive actions may be positively associated with past actions.
Most of the literature on the economics of malaria studies the effects of past malaria eradication campaigns on economic outcomes, in order to quantify the burden of the disease to society beyond mortality and direct healthcare costs. In contrast, research on the determinants of malaria prevention is more limited.
High returns to malaria prevention can be emphasized by summarizing its positive effect on economic, educational, survival, and health outcomes. However, some factors act as barriers to preventive actions, including financial constraints, prices, access to information, and psychological factors. In terms of financial constraints and prices, there is a question as to whether or not bed nets and ACTs should be provided to households free of charge. There are trade-offs between treatment and prevention, and between various types of preventive actions, and these also need to be examined.
Section 2 presents background information on malaria transmission, symptoms, and prevention. Section 3 highlights the large positive impact of malaria prevention on economic and demographic outcomes. Section 4 focuses on the determinants of prevention. Section 5 discusses policy implications and presents future research avenues.
Background on Malaria
Transmission and Symptoms
Malaria is transmitted to a human host through the bites of a female anopheline mosquito infected with protozoan parasites. There are four types of human malaria parasites: Plasmodium falciparum, P. vivax, P. malariae, and P. ovale. P. falciparum is the deadliest and is the main cause of infection in sub-Saharan Africa. A mosquito picks up the parasite when it bites an infected person. Approximately one week later, the mosquito will transmit malaria to another person during a blood meal.
In humans, malaria parasites first establish themselves in the liver where they grow and multiply, and then in the red blood cells.1 Generally, malaria does not directly spread from one person to another—transmission requires a mosquito which functions as a disease vector. However, person-to-person transmission occurs when the infection is passed from a pregnant woman to the fetus, or from a woman to her newborn baby during delivery (congenital malaria).
For all malaria species, after an incubation period an infection will cause flu-like symptoms in healthy adults, including high fever, headaches, and vomiting, among others. The most severe cases of malaria are caused by P. faciparum. The worst consequence of P. falciparum infection is cerebral malaria which is often fatal. Over their life course, with repeated contact with the parasites, people may develop partial immunity, allowing asymptomatic malaria infections.
Two population groups are particularly vulnerable to the disease: children under 5 years, because they have not yet developed immunity, and pregnant women, because they temporarily lose their immunity. Malaria in pregnant women is associated with infant mortality and low birth weight, among other problems. Note that children under 5 in Africa bear the greatest burden of the disease worldwide.
Prevention, Diagnosis, and Treatment
Fighting malaria involves interventions for prevention, diagnosis, and treatment. Malaria can be prevented by vector control and chemoprevention. Historically, the main prevention methods were the spraying of dichlorodiphenyl trichloroethylene (DDT) and mass drug administration (Nájera et al., 2011; Sadasivaiah et al., 2007). The insecticidal properties of DDT were discovered in 1939, and the use of DDT for disease vector control began in the 1940s. The first global antimalarial campaign, the Global Malaria Eradication Program (GMEP), was launched by the WHO in 1955 and was mainly conducted with DDT-based IRS (i.e. DDT was sprayed on walls in houses). The main antimalarial drug for the second half of the 20th century was chloroquine. This drug was introduced in the 1950s. Due to parasite resistance, the drug became ineffective in treating P. falciparum in the 1990s. The program permanently eliminated infection risk in Europe and North America, and in some parts of Asia, Latin America, and the Middle East, but efforts to combat malaria generally failed in Africa, with some exceptions like the successful eradication campaign in the Kigezi region in Uganda (Barofsky et al., 2015). The global antimalarial campaign stopped in 1969 after acknowledging that eradication was not achievable in all areas.
The most commonly used vector control method is sleeping under a bed net treated with insecticide. These bed nets prevent bites by repelling and killing mosquitoes. Older ITNs require periodic retreatments with insecticide. In contrast, newer long-lasting treated nets (LLINs) remain effective for three to five years without retreatment. Sleeping under an ITN is considered highly cost-effective (Lengeler, 2004). The WHO recommends that all people at risk of malaria sleep under an LLIN (WHO, 2014), with priority given to children under 5 and pregnant women.2 IRS is the second most important intervention. DDT is no longer recommended but is still used in some African countries. Supplementary vector control interventions include the treatment of standing water with larvicidal chemicals and the use of mosquito repellants.
Chemoprevention refers to the uptake of intermittent preventive treatment for pregnant women (IPTp) and for infants (IPTi) and to seasonal malaria chemoprevention (SMC) for children ages 3 to 5. IPTp is the administration of a full therapeutic course of sulfadoxine-pyrimethamine (SP) to pregnant women during pregnancy. The WHO recommends that three doses of IPTp be given to pregnant women during antenatal care visits in areas with moderate or high malaria transmission in Africa. The doses should be given one month apart, from the second semester onward.3 While IPTp is widely used in sub-Saharan Africa, the use of IPTi and SMC is limited worldwide.
The WHO recommends confirmation of diagnosis before treatment—by using rapid diagnostic tests (RDT) for instance—and the uptake of ACT for confirmed cases. The combination of artesimin with a partner drug should decrease the risks of drug resistance compared to a monotherapy. Note that in rural areas in Africa where access to formal, reliable diagnosis is limited, individuals who are ill often do not get tested and can choose to buy ACT over the counter in retail drug shops (Cohen et al., 2015). Overtreatment (i.e., individuals without malaria taking ACT) is common and contributes to parasite resistance in the long run.
Is Malaria Prevention Worth It? The Large Benefits of Preventive Measures
A substantial literature in public health shows that preventive measures are very effective at reducing malaria mortality and morbidity. In addition, recent economic literature highlights the positive, lasting impact of malaria prevention on economic and demographic outcomes. These studies cover a variety of countries (in Asia, the Americas, or sub-Saharan Africa) and employ both historical and contemporaneous data. To quantify the causal impact of malaria prevention or eradication on outcomes, a number of articles use a difference-in-differences methodology that compares outcomes for individuals born at different dates before and after various anti-malaria campaigns, both in areas with large reductions in prevalence and in areas with small reductions (Bleakley, 2010; Cutler et al., 2010; Lucas, 2010, among others). This section presents this economic literature on the benefits of prevention. Given their high returns, the low adoption rates of preventive measures may seem surprising.
Education and Economic Conditions
Most of the economic research on the impact of malaria has concentrated on education and economic outcomes. In the growth literature, macroeconomic studies reach somewhat contradictory conclusions: in particular, Gallup and Sachs (2001) find that P. falciparum malaria is associated with poverty and low growth using cross-country data, whereas Acemoglu and Johnson (2007) show that the international epidemiological transition that started in the 1940s had no large impact on the increase of GDP per capita. This is surprising, since microeconomic studies unambiguously find positive effects of malaria prevention on educational and economic outcomes. One possible explanation is that while improvements in health have positive labor productivity effects, they also produce negative demographic ones. For example, malaria disproportionately affects children under 5, and therefore reducing malaria incidence will save a larger number of children relative to working age adults, leading to higher dependency ratios and lower income per capita mechanically. Using a dynamic macro-simulation model to estimate the effects of curing specific diseases, Ashraf et al. (2008) find that while in the very long run curing malaria leads to higher income per capita, in the short- and medium-run curing malaria actually reduces income, with the “break-even” point being about fiftyyears after a successful eradication campaign. They hypothesize that this perhaps reconciles the contradictory literature.
On the microeconomics side, there are several possible routes through which malaria may have an impact on human capital accumulation and economic outcomes (Cutler et al., 2010). First, malaria can lead to impairment of conditions in utero for the fetus—such as nutrition—which affects future child development. Second, malaria has been shown to affect cognitive abilities in young children, both during and after malaria. Finally, infected children miss periods of school, hindering the accumulation of educational human capital. This may occur even when they are not directly affected: children living in malarious households may become caregivers themselves and miss school.
A large number of recent articles highlight positive correlations between malaria prevention and education, cognition, literacy, income, wealth, and employment outcomes. For example, Barofsky et al. (2015) focus on the effect of the 1959–1960 eradication campaign in southwestern Uganda on (male and female) education and (male) wage employment; Barreca (2010) on the impact of exposure to malaria on education and poverty later in life in the United States; Bleakley (2010) on the influence of eradication campaigns in the United States, Brazil, Colombia, and Mexico on income in adulthood; Cutler et al. (2010) on the role of the national malaria eradication program in India in the 1950s on economic status (for adult males); Hong (2011) on the impact of migration to high-risk malaria counties on real estate wealth, using US historical data; Lucas (2010) on the effect of eradication campaigns in Paraguay and Sri Lanka in the 20th century on education and literacy; and Venkataramani (2012) on the effect of the eradication campaign on cognition in Mexico. Even if the benefits of malaria eradication on human capital and economic conditions vary across countries, they are always economically significant.4
Mortality and Fertility
These works all show the positive impact of malaria prevention on economic outcomes. Moreover, malaria prevention also affects demographic outcomes, such as mortality and fertility. From a theoretical perspective, malaria prevention campaigns should indirectly decrease child mortality (beyond the direct effect on malaria) for two reasons (Lucas, 2013): first, an improvement in the health of pregnant women should have a positive impact on the fetus, thereby increasing birth weights and child survival; second, a decrease in malaria cases in households should expand the household’s budget constraint both through increased labor earnings and reduced demand for treatment, making more resources available for child nutrition and health.
In contrast, the theoretical effect of antimalarial campaigns on fertility is unclear. On the one hand, a decrease in malaria cases should improve maternal health, leading to less miscarriages, higher fecundity, and increased sexual activity, all of which should result in an increase in fertility. On the other hand, reduction in malaria deaths among children may reduce the demand for replacement children, resolve uncertainty surrounding the number of surviving children and thereby lower the need for precautionary childbearing, and move parents along the quantity-quality frontier, all of which lead to lower desired fertility. As a result, the effect of malaria campaigns on fertility is an empirical question.
Empirical findings suggest that preventive campaigns are associated with a decrease in mortality. For instance, focusing on an eradication campaign that occurred in one district in southwestern Uganda in 1959 and 1960, Barofsky et al. (2015) provide suggestive evidence that under-5 mortality decreases in the treated district around the time of the campaign.
Malaria prevention is also associated with an increase in fertility, at least in the short run. In particular, focusing on a campaign launched in Sri Lanka in 1945, Lucas (2013) shows that malaria eradication increases fertility for women of childbearing age.
Finally, recent economic research provides direct evidence on the influence of malaria prevention on health outcomes over the life course. First, Hong (2007) highlights the long-run effect of exposure to malaria in childhood on height and infections, using 19th-century data on Union Army Veterans. Moreover, Apouey et al. (2017) show that the scale-up of the distribution of ITNs decreases the probability of moderate or severe anemia, in the short run, for children under 5 in sub-Saharan Africa. In the United States, Hong (2013) finds that exposure to antimalarial campaigns is associated with a decline in work disability in old age. Finally, using macro data for 192 countries for the modern period (2004), Hong also demonstrates that individuals born in countries with a higher prevalence prior to eradication have a greater probability of being disabled5 in old age.
Malaria prevention and eradication have thus a large positive influence on multiple outcomes. Despite these benefits and the high returns of prevention, the adoption of preventive measures is rather low, particularly in sub-Saharan Africa. The remainder of the chapter focuses on the barriers to preventive actions.
Understanding Malaria Prevention
The roles of financial constraints, price, information, and psychological factors in malaria prevention are considered. Some authors have also highlighted the influence of these factors, but in the more general context of health behavior in developing countries (Banerjee & Duflo, 2011; Duflo, 2010; Dupas, 2011, 2014a, 2014b; Dupas & Miguel, 2017).
Financial Constraints and Price
An initial explanation of the low level of preventive behavior lies in household financial constraints and high prices of health products. In what follows, the focus is on bed nets and ACT only. Indeed, IRS is generally provided free of charge in malaria-affected regions. IRS is an expensive method for malaria vector control, and is unaffordable for most households. Moreover, IRS is considered a public good because the main benefit of spraying is observed at the community level through fewer mosquitoes, with minimal benefits to individuals living in the house that is sprayed (Hanson, 2004). Public provision of IRS at no cost is thus uncontroversial. In addition, to the best of our knowledge, no economic study has analyzed the effects of financial constraints and prices on IPTp uptake—IPTp is often delivered for free in antenatal care clinics.
The price of bed nets and ACT are relatively high compared to income in sub-Saharan Africa. For example, the price of a LLIN is $2.50 (in 2017).6 Given that PPP-adjusted income per capita in the region is approximately $750 per year (or about $2 a day) (Lakner & Milanovic, 2016) and that spending is prioritized to core needs such as food, these technologies represent a significant investment by individuals in spite of being the most cost-effective methods of prevention. Similarly, the cost of one adult dose of ACT is between $2 and $2.50 (in 2004).7
Because bed nets and ACT are highly effective against malaria and because they both create net positive externalities, there is a widespread agreement among policymakers and researchers alike that bed nets and ACT should be distributed at a subsidized price in malaria-affected areas. There is less common ground when it comes to the question of whether they should be provided for free (free distribution) or at a positive price (cost sharing). Unlike bed net usage which creates positive externalities only, ACT uptake is associated with both positive and negative externalities. For both bed nets and ACT, there is a theoretical tradeoff between free distribution and cost sharing at a point in time: while free distribution increases access, it may also waste resources on individuals who do not truly need the health products. In contrast, cost sharing may increase targeting and effective usage. Cost sharing may also be more fiscally sustainable than free distribution in the long run. For ACT, there is an additional trade-off between access to ACT at one point in time and effectiveness later (Cohen et al., 2015). For this reason, bed nets and ACTs may require different pricing strategies. This debate over free distribution of bed nets and ACTs is closely related to a more general debate over the effective use of scarce public resources and aid in developing countries.
Financial Constraints, Price, and Bed Net Uptake and Usage
Cost sharing of bed nets may be associated with effective targeting and usage for three reasons (Cohen & Dupas, 2010). First, cost sharing may select individuals who need bed nets more and who are going to use them more intensively (selection effect). Second, for psychological reasons, individuals who pay a positive price may then use bed nets more because of a sunk cost effect. Third, individuals may use bed nets more if they believe that cost sharing signals better quality.
However, if those who need bed nets the most are financially constrained, cost sharing will screen the neediest out. Moreover, cost sharing will decrease demand for bed nets due to the nonzero price, and one could argue that this will reduce positive health externalities associated with bed net usage if individuals intensively use the free bed nets. Therefore, the global effect of free distribution and cost sharing on bed net usage and malaria prevalence is unclear from a theoretical perspective, and the optimal delivery strategy of ITNs depends on the price elasticity of demand for bed nets, the effect of price on usage, and the importance of externalities (Cohen & Dupas, 2010).
Empirical findings suggest that financial constraints and price have a major effect on the demand for bed nets, and that price does not decrease usage. These findings imply that free distribution may be an appropriate strategy to deliver bed nets. First, studies indicate that demand for bed nets is very sensitive to price. Dupas (2009) conducts a field pricing experiment in eight rural markets in a district in rural Western Kenya. She finds that even small decreases in price increase LLIN take-up, consistent with a high price elasticity: a price increase from $0 to $1 decreases take-up by thirty-five percentage points. Moreover, Cohen and Dupas (2010) exploit data from a field experiment in Kenya, in which the prices at which twenty clinics could sell LLINs to pregnant women were randomized. They find even greater price elasticity: a price increase from $0 to $0.60 decreases take-up by sixty percentage points. These results imply that price is a major determinant of take-up. While these studies find a large price-elasticity of demand for bed nets, a small number of articles find lower price-elasticities in absolute values. However, these findings do not come from randomized experiments, and therefore the estimated effect may not be as well identified. In particular, Gingrich et al. (2011) focus on the effect of the Tanzanian National Voucher Scheme that started in 2004. This programme provides vouchers (worth US$2.29) to pregnant women attending antenatal clinics to purchase an ITN in retail shops. To get an ITN, a woman must pay a top-up payment that equals the retail price (which is fixed by the shopkeeper) minus the voucher amount. Payment thus depends on the retail price and ITN characteristics such as size. Using nationally representative survey data, the authors find a smaller effect of price (i.e. top-up amount) on demand for ITNs: specifically, the price elasticity equals -0.12. The main limitation of the study is that the top-up amount may be endogenous. Overall, the literature underscores a high responsiveness of demand to price. This is consistent with evidence for other health products: Kremer and Miguel (2007) show that the introduction of a small fee for a deworming drug leads to a dramatic drop in take-up in Kenya, Ashraf et al. (2010) find a high price-elasticity of demand for water disinfectant in Zambia, and Meredith et al. (2013) highlight that price is the main determinant of the purchase of three health products in four less-developed countries—specifically, rubber shoes that decrease the risk of hookworm infections for children in Kenya, and hand soap and multivitamins that can be used by children and adults in Guatemala, India, and Uganda.
This high elasticity of the demand for bed nets suggests that households are budget constrained. Other studies provide additional evidence on these financial constraints. Employing data on two districts in western Kenya, Guyatt et al. (2002) find that homesteads are unable to finance ITNs, although they are willing to pay for them. Moreover, using experimental data from Orissa (India), Tarozzi et al. (2014) show that 52 percent of households who are offered micro-loan contracts (to buy ITNs and retreatment on credit) decide to purchase at least one ITN. These two examples demonstrate that poverty and credit constraints represent a major barrier to prevention, and that relaxing the financial constraint dramatically increases the adoption rate.
Empirical studies also show that bed net price does not have any negative influence on usage. For instance, Cohen and Dupas (2010) show that women in Kenya who receive a free bed net are not less likely to use it than those who pay a positive amount. These results are consistent with Dupas (2009), who concludes that higher prices are uncorrelated with usage intensity by households, using different data from Kenya. Selection effects are thus negligible for bed nets. Moreover, using data from a field experiment in western Uganda, Hoffmann (2009) finds that children under 5 are more likely to use ITNs in households who receive ITNs free of charge compared with households who purchase them. On a related matter, Hoffmann et al. (2009) show that only a small fraction of households who receive ITNs for free are willing to sell them. These results imply that free distribution of bed nets does not increase wastage on individuals who do not need the nets.
Regarding the long-run effect of prices, another question that has motivated research is whether free distribution (or subsidies) of bed nets in the short run dampens adoption in the long run. This question is important because bed nets require repeat purchase, and subsidies may not be sustainable over time. Dupas (2014c) conducts a randomized pricing experiment in six villages in Kenya and finds that individuals who benefit from the highest subsidy level are significantly more likely to purchase a LLIN one year later.
These studies thus demonstrate that when financial constraints are important, free distribution dramatically increases take-up without increasing wastage, and that free distribution of bed nets at one point in time improves future adoption. Moreover, given the large private and social returns of bed net usage, free distribution is more effective at saving child lives than cost sharing (Cohen & Dupas, 2010; Dupas, 2014a). This conclusion in the economics literature is consistent with the widespread belief among policymakers and international organizations that without free provision, the demand for malaria preventive technologies would be too small to achieve significant infection reductions (Hanson, 2004; Sachs, 2005). In particular, the WHO recommends that LLINs be distributed for free or highly subsidized in areas of high transmission.8
Financial Constraints, Price, and ACT Uptake
Financial constraints could also be a critical factor to access to ACT. From a policy standpoint, providing ACT at a highly subsidized price may not be socially desirable due to the negative effects of overtreatment. As a result, cost sharing may be a useful tool to reduce inappropriate use of ACT.
We are only aware of one economics article that studies the impact of prices on the demand for ACT. Specifically, Cohen et al. (2015) investigate the effect of subsidies on ACTs and RDTs usage, for malaria-positive and malaria-negative individuals. Their article uses experimental data from Western Kenya generated by varying prices of ACTs and RDTs sold in four retail sector drug shops in 2009. The price elasticity of demand for ACT is large: a high subsidy level (92 percent) more than doubles ACT access (from 19 percent to 41 percent). However, half of the increase goes to individuals who do not suffer from malaria, especially teenagers and adults. The authors study two measures to limit overtreatment: an increase in prices and access to RDTs. Note that RDTs were newly introduced in the area and so people did not know whether the tests were reliable. Findings show that a small increase in the price of ACT is helpful to limit overtreatment in teenagers and adults. A high subsidy on RDTs (at least 85 percent) doubles the probability of being tested, but more than half of individuals who are tested negative still decide to take ACT. Thus affordable RDTs do not suppress ACT overtreatment. However, the article only speaks about the effect of RDTs on treatment decisions in the short run, but not about the potential for RDTs to change decisions in the long run when the reliability of RDTs becomes well known.
This evidence shows that price increases dampen demand for both bed nets and ACTs. From a policy standpoint, pricing strategies for bed nets and ACTs should be different: while free distribution may be appropriate in the case of bed nets whose usage only creates positive externalities, some degree of cost sharing is desirable for ACTs to prevent parasite resistance in the long run.
In addition to financial constraints, lack of information is commonly assumed to be a barrier to preventive actions. Information can be transmitted in several ways. First, individuals may learn the benefits of health technologies simply by using them (learning-by-doing). Second, people may learn by observing the behavior of people around them (social learning). Finally, they may get information from public health campaigns (e.g., through the media). The literature presented below show that these different sources of information have a significant effect on malaria-preventive behaviors.
In her study on the long run impact of subsidies on LLIN adoption, Dupas (2014c) provides evidence on learning-by-doing and social learning. The global effect of subsidies for bed nets in the short run on adoption in the long run is a priori unclear: individuals who experience the health benefits of bed nets may be willing to pay more for them in the future (learning-by-doing effect), but individuals who receive a bed net for free (or at a highly subsidized price) may be less willing to buy a bed net later when the subsidy is lower (anchoring effect). Dupas finds that the learning effect is positive and large, whereas the anchoring effect is null. This explains why individuals who benefit from the highest subsidy level are significantly more likely to purchase a bed net one year later. Regarding social learning, she observes that information about bed nets diffuses through spatial networks.
Another author who explores the learning-by-doing channel is Adhvaryu (2014). However, rather than estimating the positive learning-by-doing of effective treatment, he analyzes the converse—the negative learning effects of misdiagnosis. Since antimalarial treatments are often prescribed to many individuals who do not actually have malaria, this creates a noisy signal on the effectiveness of antimalarial treatments since those who believe they have malaria and are not cured by ACT may doubt its efficacy. He shows that groups which experienced higher rates of diagnostic accuracy are more likely to adopt preventative technologies.
Apouey and Picone (2014) provide evidence on social learning in preventative behavior against malaria (ITN usage and IPTp uptake). Using data on twenty-nine sub-Saharan countries between 1999 and 2012 from two sources—the Demographic and Health Survey (DHS) program and the Multiple Indicator Cluster Surveys (MICS)—they compute social multipliers associated with female education and household wealth. The social multiplier associated with a factor (education or wealth) depends on the correlation between the factor and a preventive behavior in individual-level data, and on the correlation between the factor and the preventive behavior in regional-level data. In the absence of interactions, the correlation at the regional level should equal the correlation at the individual level, whereas in the presence of interactions, the correlation at the regional level should be greater than the correlation at the individual level. Findings are consistent with the existence of social interactions associated with education and wealth. Moreover, the multipliers associated with IPTp uptake seem to be larger than those associated with bed net usage. This may be due to the fact that knowledge about neighbors’ preventive treatment uptake is more widespread than knowledge about bed net usage, because observing a neighbor going to a health facility to get a treatment is easier than observing a neighbor’s child’s bed net usage.
Informational campaigns on malaria causes, symptoms, and prevention also have a significant impact. For instance, Rhee et al. (2005) analyze data from a control trial in Mali in 2003, during which some households received information on the symptoms, transmission, and prevention of malaria, as well as on the benefits of ITN use—including information on nets impregnation with insecticide. Findings indicate that 49 percent of households who receive the educational component impregnate their ITNs vs. only 33 percent of households who do not receive the educational component. Moreover, Pylypchuk and Norton (2015) focus on Zambia, where an antimalarial program started in 2005 which included information campaigns, mainly through the radio. Using data from the 2010 National Malaria Indicator Survey (NMIS), the authors construct a maternal knowledge score that aggregates knowledge on the infection’s causes, symptoms, and prevention. They find that maternal knowledge has a positive influence on child ITN usage: A one standard deviation increase in the knowledge index increases the probability that the child sleeps under a bed net by 0.432 percentage points, conditional on net ownership.
Finally, part of this literature focuses on the influence of malaria prevalence in one’s area of residence on individual preventive actions. In our opinion, this literature is closely related to research on the role of information, because individuals learn about malaria risk in their area by observing their neighbor’s health status, interacting with health professionals, and being exposed to malaria awareness campaigns. Findings show that malaria prevalence is positively associated with bed net ownership and usage (Berthélemy et al., 2013; Picone et al., 2017; Seban et al., 2013), but evidence is uncertain regarding the size of the prevalence elasticity.
Overall, the literature shows that information is a relevant explanation for preventative behavior. From a policy perspective, these results imply that enabling learning and raising awareness through informational campaigns will strengthen preventive behaviors.
Psychological Factors: Commitment and Framing
While standard economic analysis focuses on the role of financial constraints, price, and information in prevention, a very limited number of studies focus on the influence of psychological factors. They contribute to the behavioral economics field by testing whether psychological cues could be used to increase prevention. To our knowledge, there are only two such articles—one on the role of financial commitment and another one on verbal commitment and framing. The results imply that while financial commitment shows promise of strengthening preventive actions, verbal commitment and framing do not have any significant impact. It should be noted that exploring the insights of behavioral economics in the context of malaria prevention remains an important margin for improving the literature.
For five districts of the state of Orissa in India, Tarozzi et al. (2009) explore the role of a financial commitment mechanism on preventive behavior. In this experiment, clients choose between two alternatives: purchasing a treated net (first contract), or purchasing—at a higher cost—a bundle that includes the treated net and two retreatments after six and twelve months (second contract). Individuals who buy the treated net without retreatment need to pay cash to get retreated six or twelve months later. A client who chooses the second contract thus commits to retreat the net in the future. The difference in prices between contracts is small. Using the sample of individuals who buy at least one net, results show that the share of nets that is retreated is much greater for nets purchased with the second contract than for nets purchased with the first contract. Moreover, contract choice seems to be random, since there is no evidence that it depends on household characteristics. This suggests that the retreatment gap between the contracts is mainly due to the commitment mechanism.
While this study emphasizes the beneficial role of financial commitment, verbal commitment and framing seem to be much less effective. There is a large literature in psychology and marketing on the role of framing in convincing individuals to invest in products. In particular, verbal commitment matters: asking individuals whether they will do something generally increases the chances that they actually do it. Dupas (2009) explores the role of two framing techniques on the demand for LLINs: first, varying the message of the perceived benefits of nets, and second, having people verbally commit to buy a net. Regarding the perceived benefits, both the potential health gains (i.e., the effect of malaria on morbidity and mortality) and the financial gains (i.e., the positive impact of not being infected on household finances) are examined. Using natural experimental data from Western Kenya, she shows that neither framing nor verbal commitment have any significant effect on LLIN purchase and use.
Are there Trade-offs Between Prevention and Treatment, or Between Different Preventive Actions?
The adoption of a specific preventative behavior may also depend on treatment behavior and alternative prevention strategies. In other words, there may be a trade-off between prevention and treatment, or between various preventive actions. Berthélemy et al. (2015) explore the trade-off between prevention and treatment in a theoretical model. They find that a relatively low price of treatment has a negative impact on prevention. However, to the best of our knowledge, there is no empirical study on this topic.
A limited number of papers explore the relationship between two preventive methods, specifically bed net usage and IRS. In particular, there is a concern that IRS—generally a public intervention—may crowd out individually desirable bed net purchase and usage. If campaigns rely on a mix of IRS and bed nets, then substitution between these two preventive methods will slow down malaria control, and could even lead to a resurgence of the disease. However, rather than being substitutes, IRS and bed net purchase or usage could also be complements. In particular, complementarity will emerge if individuals interpret IRS interventions, which are particularly visible, as signals of the importance of taking preventive actions against malaria. In that case, IRS campaigns should lead to an increase in bed net ownership and usage (Armand et al., 2017).
This empirical question on substitution and complementarity between preventive actions has not received a clear answer so far. On the one hand, Chase et al. (2009) find a substitution effect in a rural area of southern Mozambique, which is problematic from a policy standpoint since receiving IRS decreases the willingness to pay for a net. On the other hand, more recent research reaches an opposite conclusion. For example, Armand et al. (2017) find that IRS increases net ownership and usage, using data from a randomized control trial in the most malarious region in Eritrea, and Picone et al. (2017) show that IRS increases net usage, using data from nine sub-Saharan countries. These recent results are consistent with IRS campaigns causing an increase in malaria awareness that translates into intensified ITN usage. Armand et al. (2017) provide evidence for an increase in knowledge and concern about malaria directly linked to IRS campaigns. This line of research focuses on the short-run effects of IRS on bed net ownership and usage, but it is entirely possible that individuals change their response to IRS programs in the long run.
Malaria prevalence remains high in a number of countries, in sub-Saharan Africa in particular. The findings presented herein may guide decision-making for these countries. They indicate that financial constraints and price are a key determinant of take-up for preventive behaviors and treatment against malaria. Free distribution dramatically increases bednet take-up without increasing wastage in the short run, while still allowing individuals to learn the benefits of bednets by using them (Cohen & Dupas, 2010; Dupas, 2014c). However, when free distribution or heavy subsidization is impossible, micro-consumer loans could represent a sustainable solution to increase prevention (Tarozzi et al., 2014). From a policy perspective, this implies that improving financial access to savings accounts and credit will improve health behavior. In addition to learning-by-doing, social learning and informational campaigns also have a positive impact on preventative behavior.
However, all the results presented above may not generalize to all malaria-affected countries where malaria is endemic for several reasons. First, malaria strains vary across countries and have heterogeneous health effects. Indeed, P. falciparum—the most prevalent strain in sub-Saharan Africa—is associated with morbidity, while P. vivax—the most prevalent strain in the rest of the world—does not often cause death. Second, some studies focus on the determinants of ITN ownership and usage (Tarozzi et al., 2014) but LLINs—which are more expensive—are increasingly used to fight malaria in Africa. Third, some of the results discussed above use data from randomized controlled trials (RCTs), which generally cover small samples of individuals in specific regions. These findings may not generalize to regions or countries with different institutional structures. Consequently, there is a need to test the robustness of these findings for all countries of interest, or to supplement these RCTs with representative population-based studies.
To promote innovative public policies against malaria, additional analyses on malaria prevention are needed. First, evidence on the role of social learning in malaria prevention is scarce (Apouey & Picone, 2014; Dupas, 2014c). Conducting randomized experiments to understand the mechanisms behind the diffusion of social knowledge would represent an attractive avenue for future research. Second, many of the insights learned from behavioral economics could be applied to encourage malaria prevention. There are very few studies (in the early 21st century) on the role of psychological factors in decisions regarding malaria prevention. Third, since psychological factors may interact with local cultures and customs differently across ethnolinguistic groups, the external validity of the small research that does exist is suspect. As a result, studying these factors across various different cultural groups will be important to understand which psychological tools are the most effective to increase the adoption of these technologies.
Finally, economic research on the determinants of malaria prevention has mainly focused on demand-side factors. Future research should also pay attention to the supply side. First, the literature has not studied whether free distribution and heavy subsidies are fiscally sustainable in the long run. Moreover, there is evidence of logistic problems in the delivery of health products, and periodic shortages of IPTp and ACT have been reported (Berthélemy & Thuillez, 2015; Hanson et al., 2004). Similarly, due to supply shortages and the high price of malaria prevention technologies relative to local incomes, cheaper ineffective or counterfeit drugs have become widespread. A similar issue is the insufficient supply of knowledgeable healthcare professionals. Many countries effectively have a “dual system” of healthcare delivery—some health practitioners are of knowledgeable and credentialed, yet inaccessible due to high pecuniary or other costs (such as travel time), leading to unqualified and ineffective providers to fill the gap. Not only do both of these ineffective substitutes not deliver the necessary treatment to users, they also decrease long-run adoption of health technologies by lowering confidence in the efficacy of treatment. Therefore, understanding the determinants of appropriate supply of these preventive products would be an important avenue for future research.
Alonso, S., Munguambe, K., & Sicuri, E. (2017). Market for Artemether-Lumefantrine to Treat Childhood Malaria in a District of Southern Mozambique. Health Economics, 26(12), e345–e360.Find this resource:
Ashraf, N., Berry, J., & Shapiro, J. M. (2010). Can Higher Prices Stimulate Product Use? Evidence from a Field Experiment in Zambia. American Economic Review, 100(5), 2383–2413.Find this resource:
Cohen, J. L., & Dickens, W. T. (2012). Adoption of Over-the-Counter Malaria Diagnostics in Africa: The Role of Subsidies, Beliefs, Externalities, and Competition. In Ramanan Laxminaryan & Molly K. Macauley (Eds.), The Value of Information: Methodological Frontiers and New Applications in Environment and Health (pp. 173–192). New York: Springer.Find this resource:
Dunn, C. E., Le Mare, A., & Makungu, C. (2011). Malaria Risk Behaviours, Socio-Cultural Practices and Rural Livelihoods in Southern Tanzania: Implications for Bednet Usage. Social Science & Medicine, 72(3), 408–417.Find this resource:
Easterly, W. (2006). The White Man’s Burden: Why the West’s Efforts to Aid the Rest Have Done So Much Ill and So Little Good. New York: Penguin Press.Find this resource:
Fitzpatrick, A. (2017). Shopping While Female: Who Pays Higher Prices and Why? American Economic Review, Papers and Proceedings, 107(5), 146–149.Find this resource:
Juma, E., & Zurovac, D. (2011). Changes in Health Workers’ Malaria Diagnosis and Treatment Practices in Kenya. Malaria Journal, 10(1), 1.Find this resource:
Laxminarayan, R., Parry, I. W. H., Smith, D. L., & Klein, E. Y. (2010). Should New Antimalarial Drugs Be Subsidized? Journal of Health Economics, 29(3), 445–456.Find this resource:
Nevill, C. G., Some, E. S., Mung’ala, V. O., Muterni, W., New, L., Marsh, K., Lengeler, C., & Snow, R. W. (1996). Insecticide-Treated Bednets Reduce Mortality and Severe Morbidity from Malaria among Children on the Kenyan Coast. Tropical Medicine & International Health, 1(2), 139–146.Find this resource:
Stratton, L., O’Neill, M. S., Kruk, M. E., & Bell, M. L. (2008). The Persistent Problem of Malaria: Addressing the Fundamental Causes of a Global Killer. Social Science & Medicine, 67(5), 854–862.Find this resource:
Yacob, L., Dunning, R., & Yan, G. (2010). Indoor Residual Spray and Insecticide-Treated Bednets for Malaria Control: Theoretical Synergisms and Antagonisms. Journal of the Royal Society Interface, 8(59).Find this resource:
Wilde, J., Apouey, B. H., Coleman, J., & Picone, G. (2017). The Effect of Antimalarial Campaigns on Child Mortality and Fertility in Sub-Saharan Africa. USF Working Paper 06-16.Find this resource:
Acemoglu, D., & Johnson, S. (2007). Disease and Development: The Effect of Life Expectancy on Economic Growth. Journal of Political Economy, 115(6), 925–985.Find this resource:
Adhvaryu, A. (2014). Learning, Misallocation, and Technology Adoption: Evidence from New Malaria Therapy in Tanzania. Review of Economic Studies, 81(4), 1331–1365.Find this resource:
Apouey, B., & Picone, G. (2014). Social Interactions and Malaria Preventive Behaviors in Sub-Saharan Africa. Health Economics, 23(9), 994–1012.Find this resource:
Apouey, B. H., Picone, G., Wilde, J., Coleman, J., & Kibler, R. (2017). Paludisme et anémie des enfants en Afrique Subsaharienne : Effet de la distribution de moustiquaires. Revue Economique, 68(2), 163–197.Find this resource:
Armand, A., Carneiro, P., Locatelli, A., Mihreteab, S., & Keating, J. (2017). Do Public Health Interventions Crowd Out Private Health Investments? Malaria Control Policies in Eritrea. Labour Economics, 45, 107–115.Find this resource:
Arrow, K. J., Panosian, C., & Gelband, H. (2004). Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance. Washington, DC: National Academies Press.Find this resource:
Ashraf, Q. H., Lester, A., & Weil, D. N. (2008). When Does Improving Health Raise GDP? NBER Macroeconomics Annual, 23(1), 157–204.Find this resource:
Banerjee, A. V., & Duflo, E. (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. New York: Public Affairs.Find this resource:
Barreca, A. I. (2010). The Long-Term Economic Impact of In Utero and Postnatal Exposure to Malaria. Journal of Human Resources, 45(4), 865–892.Find this resource:
Barofsky, J., Anekwe, T. D., & Chase, C. (2015). Malaria Eradication and Economic Outcomes in Sub-Saharan Africa: Evidence from Uganda. Journal of Health Economics, 44, 118–136.Find this resource:
Berthélemy, J.-C., Gaudart, J., & Thuilliez, J. (2015). Prevention or Treatment? The Case of Malaria. Economics Letters, 131, 16–19.Find this resource:
Berthélemy, J.-C., & Thuilliez, J. (2014). The Economics of Malaria in Africa. In C. Monga & J. Yifu Lin (Eds.), The Oxford Handbook of Africa and Economics. New York: Oxford University Press.Find this resource:
Berthélemy, J.-C., Thuilliez, J., Doumbo, O., & Gaudart, J. (2013). Malaria and Protective Behaviors: Is There a Malaria Trap? Malaria Journal, 12, 200.Find this resource:
Bleakley, H. (2010). Malaria Eradication in the Americas: A Retrospective Analysis of Childhood Exposure. American Economic Journal: Applied Economics, 2(2), 1–45.Find this resource:
Chase, C., Sicuri, E., Sacoor, C., Nhalungo, D., Nhacolo, A., Alonso, P. L., & Menéndez, C. (2009). Determinants of Household Demand for Bed Nets in a Rural Area of Southern Mozambique. Malaria Journal, 8, 132.Find this resource:
Cohen, J., & Dupas, P. (2010). Free Distribution or Cost-Sharing? Evidence from a Randomized Malaria Prevention Experiment. Quarterly Journal of Economics, 125(1), 1–45.Find this resource:
Cohen, J., Dupas, P., & Shaner, S. (2015). Price Subsidies, Diagnostic Tests, and Targeting of Malaria Treatment: Evidence from a Randomized Controlled Trial. American Economic Review, 105(2), 609–645.Find this resource:
Cutler, D., Fung, W., Kremer, M., Singhal, M., & Vogl, T. (2010). Early-Life Malaria Exposure and Adult Outcomes: Evidence from Malaria Eradication in India. American Economic Journal: Applied Economics, 2(2), 72–94.Find this resource:
Duflo, E. (2010). Le développement humain. Vol. 1, Lutter contre la pauvreté. Paris: Seuil.Find this resource:
Dupas, P. (2009). What Matters (And What Does Not) in Households’ Decision to Invest on Malaria Prevention? American Economics Review, Papers and Proceedings, 99(2), 224–230.Find this resource:
Dupas, P. (2011). Health Behavior in Developing Countries. Annual Review of Economics, 3, 425–449.Find this resource:
Dupas, P. (2014a). Getting Essential Health Products to their End Users: Subsidize, But How Much? Science, 345(6202), 1279–1281.Find this resource:
Dupas, P. (2014b). Global Health Systems: Pricing and User Fees. In Anthony J. Culyer (Ed.), Encyclopedia of Health Economics (vol. 2). San Diego, CA: Elsevier.Find this resource:
Dupas, P. (2014c). Short-Run Subsidies and Long-Run Adoption of New Health Products: Evidence from a Field Experiment. Econometrica, 82(1), 197–228.Find this resource:
Dupas, P., & Miguel, E. (2017). Impacts and Determinants of Health Levels in Low-Income Countries. In A. Banerjee & E. Duflo (Eds.), Handbook of Field Experiments (Vol. 2, pp. 3–94). San Diego, CA: Elsevier.Find this resource:
Gallup, J. L., & Sachs, J. D. (2001). The Economic Burden of Malaria. American Journal of Tropical Medicine and Hygiene, 64(Suppl. 1), 85–96.Find this resource:
Gingrich, C. D., Hanson, K. G., Marchant, T. J., Mulligan, J.-A., & Mponda, H. (2011). Household Demand for Insecticide-Treated Bednets in Tanzania and Policy Options for Increasing Uptake. Health Policy and Planning, 26(2), 133–141.Find this resource:
Guyatt, H. L., Ochola, S. A., & Snow, R. W. (2002). Too Poor to Pay: Charging for Insecticide-Treated Bednets in Highland Kenya. Tropical Medicine and International Health, 7(10), 846–850.Find this resource:
Hanson, K. (2004). Public and Private Roles in Malaria Control: The Contributions of Economic Analysis. American Journal of Tropical Medicine and Hygiene, 71(2), 168–173.Find this resource:
Hanson, K., Goodman, C., Lines, J., Meek, S., Bradley, D., & Mills, A. (2004). The Economics of Malaria Control Interventions. Geneva: World Health Organization.Find this resource:
Hoffmann, V. (2009). Intrahousehold Allocation of Free and Purchased Mosquito Nets. American Economic Review, Papers and Proceedings, 99(2), 236–241.Find this resource:
Hoffmann, V., Barrett, C. B., & Just, D. R. (2009). Do Free Goods Stick to Poor Households? Experimental Evidence on Insecticide Treated Bednets. World Development, 37(3), 607–617.Find this resource:
Hong, S. C. (2007). The Burden of Early Exposure to Malaria in the United States, 1850–1860: Malnutrition and Immune Disorders. Journal of Economic History, 67(4), 1001–1035.Find this resource:
Hong, S. C. (2011). Malaria and Economic Productivity: A Longitudinal Analysis of the American Case. Journal of Economic History, 71(3), 654–671.Find this resource:
Hong, S. C. (2013). Malaria: An Early Indicator of Later Disease and Work Level. Journal of Health Economics, 32, 612–632.Find this resource:
Kremer, M., & Miguel, E. (2007). The Illusion of Sustainability. Quarterly Journal of Economics, 122(3), 1007–1065.Find this resource:
Lakner, C., & Milanovic, B. (2016). Global Income Distribution. From the Fall of the Berlin Wall to the Great Recession. World Bank Economic Review, 30(2), 203–232.Find this resource:
Lengeler, C. (2004). Insecticide-Treated Bed Nets and Curtains for Preventing Malaria. Cochrane Database of Systematic Reviews, 2, 1–55.Find this resource:
Lucas, A. M. (2010). Malaria Eradication and Educational Attainment: Evidence from Paraguay and Sri Lanka. American Economic Journal: Applied Economics, 2(2), 46–71.Find this resource:
Lucas, A. M. (2013). The Impact of Malaria Eradication on Fertility. Economic Development and Cultural Changes, 61(3), 607–631.Find this resource:
Meredith, J., Robinson, J., Walker, S., & Wydick, B. (2013). Keeping the Doctor Away: Experimental Evidence on Investment in Preventative Health Products. Journal of Development Economics, 105, 196–210.Find this resource:
Nájera, J. A., González-Silva, M., & Alonso, P. L. (2011). Some Lessons for the Future from the Global Malaria Eradication Programme (1955–1969). PLoS Medicine, 8(1), e1000412.Find this resource:
Picone, G., Kibler, R., & Apouey, B. H. (2017). Malaria Prevalence, Indoor Residual Spraying, and Insecticide-Treated Net Usage in Sub-Saharan Africa. Journal of African Development, 19, 19–32.Find this resource:
Pylypchuk, Y., & Norton, S. W. (2015). Preventing Malaria among Children in Zambia: The Role of Mother’s Knowledge. Health Economics, 24, 1389–1402.Find this resource:
Rhee, M., Sissoko, M., Perry, S., McFarland, W., Parsonnet, J., & Doumbo, O. (2005). Use of Insecticide-Treated Nets (ITNs) Following a Malaria Education Intervention in Piron, Mali: A Control Trial with Systematic Allocation of Households. Malaria Journal, 4, 35.Find this resource:
Sachs, J. (2005). Achieving the Millenium Development Goals - The Case of Malaria. New England Journal of Medicine, 352(2), 115–117.Find this resource:
Sadasivaiah, S., Tozan, Y., & Breman, J. G. (2007). Dichlorodiphenyltrichloroethane (DDT) for Indoor Residual Spraying in Africa: How Can It Be Used for Malaria Control? American Journal of Tropical Medicine and Hygiene, 77(6), 249–263.Find this resource:
Seban, J., Thuilliez, J., & Herbreteau, V. (2013). Possession of Bed Nets in Haut-Katanga (DRC): Prevalence-Elastic Behaviour or Performance of Health Care System Delivery? Health & Place, 24, 275–285.Find this resource:
Tarozzi, A., Mahajan, A., Yoong, J., & Blackburn, B. (2009). Commitment Mechanisms and Compliance with Health-Protecting Behavior: Preliminary Evidence from Orissa, India. American Economic Review: Papers & Proceedings, 99(2), 231–235.Find this resource:
Tarozzi, A., Mahajan, A., Blackburn, B., Kopf, D., Krishnan, L., & Yoong, J. (2014). Micro-Loans, Insecticide-Treated Bednets, and Malaria: Evidence from a Randomized Controlled Trial in Orissa, India. American Economic Review, 104(7), 1909–1941.Find this resource:
Venkataramani, A. S. (2012). Early Life Exposure to Malaria and Cognition in Adulthood: Evidence from Mexico. Journal of Health Economics, 31(5), 767–780.Find this resource:
World Health Organization (2014). Global Malaria Programme. Achieving Universal Coverage with Long-Lasting Insecticidal Nets in Malaria Control. Recommendation.
World Health Organization (2016). World Malaria Report. Retrieved from http://apps.who.int/iris/bitstream/10665/252038/1/9789241511711-eng.pdf?ua=1.
(1.) See the ecology of malaria on the Centers for Disease Control and Prevention website.
(5.) According to Hong (2013), disability is caused by the following diseases: sense-organ diseases, diabetes, musculoskeletal conditions, genitourinary disease, endocrine diseases, and skin diseases.