Evidence for the impact of malaria on agricultural household income in sub-Saharan Africa

Progress in suppressing malaria over the next two decades Background may have a significant impact on poverty among agricultural households in sub-Saharan Africa. A recent study found that if malaria were eradicated by 2040, poverty rates among such households would fall by 4 to 26 percentage points more from 2018 to 2040 than if the burden of malaria remained at its current level. The relatively wide range of these estimates is due to a lack of evidence regarding the long-term impact of suppressing malaria on the incomes of agricultural households. The objective of this study is to describe a research framework that would generate the necessary evidence for developing more precise estimates. : First, we developed a conceptual framework for understanding Methods the potential long-term impact of suppressing malaria on the incomes of agricultural households. Next, we established a research framework for examining each component of the conceptual framework. : Our proposed research framework enables a comprehensive Results examination of how malaria affects the decisions, productivity, harvest value and expenditures due to morbidity and mortality within an agricultural household. This contrasts with the 27 existing relevant studies that we have identified, of which 23 focused only on household productivity and expenditures, two focused on decisions, and two focused on harvest values. : By implementing the research framework presented in this Conclusion study, we will increase our knowledge of how suppressing malaria over the next two decades would affect the incomes of agricultural households in sub-Saharan Africa. Evidence generated from the framework will inform funding allocation decisions for malaria elimination initiatives.


Introduction
Ambitious goals have been established to eradicate malaria by 2040. The suppression of malaria over the next two decades would have a significant impact on the welfare of malarious communities. During the first Global Malaria Eradication Campaign from 1955 to 1969, bold claims were made about how eradication would affect the welfare of these communities (Packard, 2009). Many of these claims focused on how suppressing malaria would facilitate economic development by increasing the productivity of agricultural households. Packard notes that in 1958 the World Health Organization (WHO) initiated an effort to collect the necessary data to estimate the economic impact of eliminating malaria (Packard, 2009). However, by 1962 its Director-General had recognized the challenges to identifying and collecting the necessary data, ex-post, to assess the impact of malaria on economic and social development: "As you probably know, we have been interested in the problem of the relationship between malaria eradication and its tangible economic benefits. On several occasions we have attempted to produce solid proof of the relationship and express it either in £.s.d or Dollars. Again and again we have noticed that this is a much more difficult subject than we had anticipated and all our preliminary documents were considered unfit for publication" (cited in Packard, 2009).
A research framework is needed today to ensure that the current malaria eradication campaign can generate evidence regarding the long-term impact of suppressing malaria on the incomes of agricultural households. Developing credible evidence regarding how progress towards eradicating malaria would affect poverty rates among agricultural households for the next two decades could affect the amount of funding allocated to anti-malaria initiatives. A lack of continuous funding for these programs was one of the main reasons that previous anti-malaria initiatives failed to achieve elimination or prevent resurgence (Cohen et al. 2012).
A previous study found that eliminating malaria in sub-Saharan Africa by 2040 would lead to a reduction in poverty rates among agricultural households by 4 to 26 percentage points (Willis & Hamon, 2018). This implies that between 13 million and 83 million individuals in agricultural households would escape poverty by 2040 due to the eradication of malaria as compared to the counterfactual scenario in which the malaria burden remains at 2018 levels. One reason for the relatively wide range in these estimates is that the model that was used in the study relied on evidence that was primarily generated from studies conducted before the goal of eradicating malaria was established. A research framework tailored to generating evidence regarding the impact of eradicating malaria would facilitate more precise estimates of how the incomes of agricultural households would be affected.
The objective of this study is to present a research framework for examining the long-term impact of suppressing malaria on the incomes of agricultural households. This framework would help determine which longitudinal data should be collected from malarious communities.

Methods
Our methodology has two parts: building a conceptual framework for understanding the potential long-term impact of suppressing malaria on agricultural households and then developing a research framework for examining that relationship.
The first part of our methodology involved developing a conceptual framework for understanding the potential channels through which suppressing malaria may have a long-term impact on the income of an agricultural household.
Next, we established a research framework for examining this long-term impact comprising two components. The first is the research topics that inform how suppressing malaria will affect the incomes of agricultural households over the next two decades. We define a research topic as the difference between some aspect of a household's experience when there is malaria transmission within the community versus what it would have been if no malaria transmission had occurred over the same time period. The second component defines the characteristics of the data that need to be collected to examine each research topic.
We used several steps to develop our dataset (Willis, 2018). First, we identified studies that examined the impact of malaria on the welfare of agricultural households in sub-Saharan Africa. We included only sub-national studies that used primary or secondary data to analyze the impact of malaria on these households. Reviews of the literature of how malaria affects agricultural households were excluded. Figure 1 provides a concise summary of our conceptual framework for how the suppression of malaria over a sustained period could have a long-term impact on the income of an agricultural household. It illustrates a continual process in which an increase in household income both depends on the health of household members and affects their health.

Conceptual framework
The figure begins by showing how anti-malaria programs could improve the health of an agricultural household in two ways. First, they may have a direct impact on reducing malaria morbidity and mortality. Second, the suppression of malaria may increase the income of the household, which in turn may affect its nutrition security.
Next, the figure illustrates that the decisions of an agricultural household affecting its income depend on the health of its members, its income from the previous growing season and its expectations regarding productivity in the next growing season. The productivity of adults in the household during the next growing season will depend on the number of malaria cases experienced by members of the household.
The impact of suppressing malaria on the harvest value of the household will depend on the decisions the household makes and the productivity of its members. Therefore, the suppression of malaria can impact the income of an agricultural household by affecting harvest values and household expenditures related to malaria morbidity and mortality. Finally, to close the loop, the impact of suppressing malaria on nutrition security depends on changes in household income from the suppression of malaria.

Research framework
In the previous section, we described a conceptual framework for understanding the potential long-term impact of suppressing malaria on the incomes of agricultural households. In this section, we describe a research framework for examining each component of that conceptual framework.

Research topic: Decisions of an agricultural household
We focus on four types of decisions made by agricultural households. The first two are which crops to plant and how much land to cultivate. The third is how much investment should be made in agricultural inputs, defined as fertilizer and any technology that could increase crop yield. The fourth is the decision made regarding the level of resources to devote to purchasing anti-malaria interventions.
Our framework assumes that the choices made regarding these four key decisions depend on the resources available to the household and its expectations regarding the productivity of household members. The resources available depend on the value of the harvest during the previous growing season and on expenditures during that growing season.
Few studies have examined the impact of malaria on these four decisions within agricultural households. A study conducted in Paraguay in the early 1970s did examine the relationship between malaria and the decisions made by farmers regarding which crops to plant. It found that an expectation of loss of labor due to malaria led farmers to plant less labor-intensive crops, even though those crops would provide lower revenues than more labor-intensive crops (Conly, 1975). A study in southern Africa in the 1950s suggested that, after malaria was suppressed, farmers began farming lands that had previously been considered too malarious (Annecke, 1950).
Research topic: Productivity of individuals in an agricultural household Suppressing malaria may increase the income of an agricultural household by increasing productivity. Our research framework focuses on two ways that suppressing malaria could increase productivity. First, the suppression of malaria would reduce the number of work days that are lost by adults due to adult malaria cases or to the need for adults to provide care for other household members with malaria. Second, suppressing malaria may increase gender equality between men and women in the household by reducing the number of caregiving days provided by women to household members.
A wide range of methodologies have been used to estimate the number of work days lost and caregiving days provided for each case of malaria. As a result, it is difficult to determine the extent to which differences in estimates are due to methodological differences or other factors. Most estimates of work days lost by adults due to a malaria case range from 1 to 9 days, while work days thought to be lost due to caregiving range from 1 to 7 days. Table 1 summarizes the figures arrived at by 12 studies.
One of the first studies to examine the impact of malaria on the productivity of farmers analyzed the natural history of malaria among men aged 20 to 30 in Liberia (Miller, 1958). Using data from 1957, the study found that the clinical duration of a malaria case was 6.5 days and that the average number of work days lost per case was 3 (Miller, 1958). An analysis of the economic impact of malaria in Kenya arrived at estimates that two to four work days were lost by adults due to adult malaria cases and caregiving on the basis of focus group interviews conducted in 1992 (Leighton & Foster, 1993). In Nigeria, focus groups in rural areas estimated that one to three work days were lost (Leighton & Foster, 1993). Sauerborn et al., 1996a; Burkina Faso 1.4 days (mild illness in dry season); 1.3 days (mild illness in rainy season); 7 days (severe illness in dry season); 5.8 days (severe illness in rainy season) 0.9 days (mild illness in dry season); 0.4 days (mild illness in rainy season); 5.6 days (severe illness in dry season); 1.9 days (severe illness in rainy season) Three studies examined the impact of malaria on the productivity of agricultural households in Ghana (Akazili, 2000;Asante & Asenso-Okyere, 2003;Tawiah et al., 2016). A study in the Kassena-Nankana District of northern Ghana used a crosssectional survey of 423 households to estimate 5.3 work days lost for a case of severe malaria and 2.6 work days lost for a mild case (Akazili, 2000). Another study in Ghana, published in 2003, provides one of the few detailed analyses of differences in the number of lost work and caregiving days between men and women (Asante & Asenso-Okyere, 2003): "The economically active male patients who could not perform their normal activities lost 9.35 workdays during the period of illness, while 8.87 workdays were lost by the female patients… Adults constituted 96% of the caretakers. Almost 83% of the caretakers were females. More than five workdays on the average were sacrificed by caretakers to take care of the sick who were mostly children. Male caretakers lost between 3.20 and 5.87 workdays in the districts. The intensity of malaria transmission in Ethiopia is generally lower than in the equatorial regions of Africa. As a result, we would expect that adults in Ethiopia would have lower levels of acquired immunity and longer durations of symptomatic infections. These expectations are consistent with two studies of the impact of malaria on work days and caregiving days (Cropper et al., 2000;Deressa et al., 2007). Cropper et al. (2000) found that a malaria case resulted in 18 lost work days for an adult and the provision of 11 days of caregiving by a household member. These findings are based on a cross-sectional survey of 569 individuals in subsistence agricultural households conducted in the Tigray region of northern Ethiopia in January of 1997 (Cropper et al., 2000). Another study that used crosssectional data collected from 2,195 households in the Adami Tulu district from October to November of 2003 found that a malaria case resulted in 6.8 lost work days and 6.7 caregiving days (Deressa et al., 2007).
A study of agricultural households in a district in Burkina Faso assumed that a malaria case in an adult led to a loss of 1-5 work days and that cases among children resulted in one-third of a day to one and two-thirds days being lost to caregiving activities (Sauerborn et al., 1991). Those assumptions are consistent with a study using cross-sectional data collected from 3,065 patients from three villages in the Bobo-Dioulasso region of Burkina Faso, which found that a malaria case resulted in an average of 4 lost work days (Guiguemdé et al., 1997). A third study in Burkina Faso collected longitudinal data from 566 households at six time points in 1992, carrying out two surveys during the dry season and four in the rainy season (Sauerborn et al., 1996b). By collecting data from the same households at different time points, the study could examine seasonal differences in the impact of malaria. Household revenue was lower during the rainy season than during the dry season, but the opportunity cost of time during the rainy season was higher, as that this was the peak production period for the household. The lower revenues and higher opportunity costs during the rainy season led to a reduction in work days missed and caregiving days provided per case (Sauerborn et al., 1996b).
While studies of the impact of malaria on work days provide a range for days missed per case, the coping mechanisms used by households when work days are lost are less well understood. Two studies of farmers in Gezira, Sudan provide the most detailed evidence in this regard (Nur, 1993;Nur & Mahran, 1988). The first study concluded that the malaria burden was highest during the months in which labor was most needed for agricultural activities (Nur & Mahran, 1988). When men were not able to work due to malaria, women and children compensated for the loss of labor, which affected school attendance (Nur & Mahran, 1988). The same Sudanese community was studied again later (Nur, 1993), and it was confirmed that although the malaria burden was primarily on men, other members of the household felt an indirect impact: "Thus, although the brunt of the infection fell upon men, it was clearly the women and children who had to compensate for this loss by providing a greater number of hours in physical labor in the fields. The result was that agricultural output was maintained, but at considerable cost to other persons and their activities (schooling, household activities) within the family" (Nur, 1993).
A growing body of research has shown how gender inequality, also described as the gender gap, affects the productivity of agricultural households in sub-Saharan Africa. A report developed by the United Nations and the World Bank found that reducing the gender gap in agricultural productivity could enable as many as 119,000 people to escape poverty in Uganda (Women & Others, 2015). A separate study by the World Bank found that women farmers produce significantly less per hectare than men (O'Sullivan et al., 2014). For example, in Uganda the production of farm plots managed by women was 13% less per acre than the plots that men managed (O'Sullivan et al., 2014). This study suggests that the provision of community-based childcare centers could reduce the gender gap (O'Sullivan et al., 2014), positing that childcare duties are, in part, responsible for the gender gap. A study in Ghana found that 83% of caregiving activities for malaria cases in agricultural households were provided by women (Asante & Asenso-Okyere, 2003). Although the importance of addressing gender inequality to increase agricultural productivity has been recognized, only two studies have rigorously examined differences in malaria-related caregiving provided by women versus men within a household (Asante & Asenso-Okyere, 2003;Leighton & Foster, 1993).

Research topic: Incomes of agricultural households
Agricultural households face three primary types of expenditures related to malaria morbidity and mortality: for interventions to prevent malaria, for interventions to treat malaria cases, and related to a death due to malaria. These expenditures depend on the number of malaria cases a household expects to experience and the number of malaria cases and deaths the household actually experiences. Our framework assumes that the impact of suppressing malaria on the harvest value of a household depends on the impact of malaria suppression on its decisions and productivity.
Many of the studies listed above that examined the number of work days lost due to malaria also quantified the expenditures by households on interventions to prevent or treat malaria infections (Asenso-Okyere & Dzator, 1997;Asante & Asenso-Okyere, 2003;Akazili, 2000;Asante et al., 2005;Akazili et al., 2007;Alaba & Alaba, 2009;Ettling & Shepard, 1991;Ettling et al., 1994;Guiguemdé et al., 1997;Hailu et al., 2017;McElroy et al., 2009;Omotayo & Oyekale, 2013;Sauerborn et al., 1991;Shepard et al., 1991;Sauerborn et al., 1996a;Tawiah et al., 2016). An additional set of studies focused exclusively on such expenditures (Guiguemde et al., 1994;Sauerborn et al., 1996b). For example, a study conducted in agricultural households in Burkina Faso found that expenditures on malaria prevention and treatment interventions were, on average, about 4.5% of family income over a 6-month period (Guiguemde et al., 1994). While a significant number of studies have examined these expenditures over a short period of time, often less than 1 year, we are not aware of any studies that have analyzed how suppressing malaria transmission leads to a long-term change in household expenditures on anti-malaria interventions.
Studies of the cost of treatment for a malaria case often include expenditures for antimalarial drugs, transport and care for the patient, and the time spent seeking care. Expenditures by households to treat malaria cases often fluctuate throughout the year. Sauerborn et al. (1996b) found that expenditures during the rainy season were significantly less than during the dry season, due, in part, to differences in the opportunity costs of time. In some settings, the opportunity cost of the time for a patient to seek care may be higher than the cost of the treatment (Asenso-Okyere & Dzator, 1997).
Households are often unable to pay for treatment in cash and must use other coping mechanisms (Sauerborn et al., 1996a) to generate sufficient resources to cover treatment costs. A study of agricultural households in Burkina Faso identified multiple strategies: "Cash sources included agricultural and craft production and migrant remittances… The sale of assets was the second most common way to meet health care expenditures. For households who did not possess sufficient cash, the sale of assets in the form of livestock was a widespread response to crises of many kinds. Indeed, in this part of Africa, animals were perceived as 'ambulatory savings-banks'. The sale of cereals, on the other hand, was considered a last resort" (Sauerborn et al., 1996a).
Only two studies have examined the relationship between malaria and harvest values. The first examined the impact of malaria on the revenues of farmers from vegetable cultivation (Girardin et al., 2004). The revenues from cabbage were 53% lower for farmers who missed more than two days of work due to malaria than the revenues of farmers who missed less than two days of work (Girardin et al., 2004). The second study found that providing agricultural households with access to vector control interventions increased harvest values by 14.7%, likely due to the additional number of days worked (Fink & Masiye, 2015). Table 2 summarizes our research framework for examining the long-term impact of suppressing malaria on the income of agricultural households.

Data necessary to examine research topics
The two types of data that could be used to examine the research topics described in the previous section involve the collection of either cross-sectional data or longitudinal data. Crosssectional data are collected from different individuals in a study population over one or more surveys in order to examine some aspect of that population. Longitudinal data are data collected from the same study population over time in order to understand the effect of changing conditions in the community. Collecting cross-sectional data from a community is generally easier and requires fewer resources than collecting longitudinal data.
With the exception of two studies (Nur, 1993;Sauerborn et al., 1996b), which used longitudinal data collected within 1 year, each study that examined the impact of malaria on the decisions, productivity, expenditures and harvest values of agricultural households used cross-sectional data. Almost all of these studies, 25 of the 28, used less than 2 years of cross-sectional data.
The objective of our research framework is to provide a means of enriching our understanding of the cumulative impact of suppressing malaria over time on the income of agricultural households with regards to all of these topics. Given this objective, long-term longitudinal data are needed. For example, with data collected from the same agricultural household over time, a researcher could examine how the suppression of malaria transmission affected its decisions, productivity, and income.
In Table 3 we display the results of our analysis of the number of studies that have examined each research topic and the type of data that each study used. None of the studies we examined used longitudinal data. Furthermore, only two studies examined the impact of malaria on the decisions of agricultural households, and both of these studies were published before 1980.
Most of the studies that we identified that examined the productivity of individuals in agricultural households addressed the number of work days lost due to malaria cases directly or from caregiving. No studies examined the impact of malaria on gender inequality in agricultural households. For topics related directly to the income of an agricultural household, most studies examined household expenditures on interventions to prevent or treat malaria infections. Only one studied the impact of malaria on harvest values.
In Figure 2, we illustrate the number of years of cross-sectional data that were used in each of the 27 studies that examined one or more of the topics in our research framework. Each line in Figure 2 represents the number of years of data that were used in each study. For example, the line in the lower left section of the figure indicates that Annecke's findings that the suppression of malaria led to farming of areas that were previously considered too malarious were based on his observations from 1932 through 1935 (Annecke, 1950). The number on the vertical axis represents the number we assigned to each of the 28 studies.
Most of the studies, 23 of 27, used cross-sectional data collected over a period of less than 1 year. There were two studies that used cross-sectional data from a period of more than 2 years (Annecke, 1950;Audibert, 1986;Girardin et al., 2004) and two studies used longitudinal data (Nur, 1993;Sauerborn et al., 1996b). Table 4 provides additional information about each study included in Figure 2.

Discussion
We have developed a research framework for understanding how suppressing malaria may affect long-term growth in agricultural household income. This framework was informed by the literature regarding the impact of malaria on the welfare of those households.
As the global malaria community has shifted from controlling malaria to eradicating malaria, a concurrent shift is needed in the methodologies used to examine its impact on agricultural households. Malaria policy-makers and researchers should not assume that the methodologies that were used to examine the impact of malaria on agricultural households during the period in which the goal was to control malaria will be equally appropriate now that the goal has shifted to eradicating malaria by 2040.

Summary of research framework recommendations
Our research framework for examining the long-term impact of malaria on the incomes of agricultural households uses longitudinal data to examine the impact on household decisions, productivity, expenditures and harvest values. By using longitudinal data, researchers will be able to examine how the decisions, productivity, expenditures and harvest values of households change over time and their relationships with changes in the intensity of malaria transmission. Our framework focuses on the impact of malaria on four decisions within an agricultural household: which crops to plant, how much land to cultivate, how much to invest in agricultural inputs, and how much to invest in interventions to reduce the risk of malaria infection. The available research evidence suggests that these decisions will depend on the household's resources and expectations regarding the productivity of its members during the next growing season. Second, our research framework includes three means by which malaria can have a long-term effect on a household through its impact on the productivity of individuals: work days lost due to adult malaria cases, work days lost due to caregiving, and the impact on gender inequality. Finally, the framework defines the impact of malaria on the income of an agricultural household as being dependent on its expenditures related to malaria morbidity and mortality, and the value of the harvest. The harvest value will depend on the decisions made and productivity of individuals in the household. Existing systems for collecting longitudinal data from households in malarious communities should begin to collect the data necessary to address the topics described in this research framework.

Focus of this research framework on agricultural households
In many malarious regions, a significant number of households are engaged in agricultural activities. We focus on agricultural households because of the unique relationship between these households and malaria as compared to non-agricultural households. The malaria burden creates a unique challenge for these households given the labor-intensive nature of agriculture. Labor is particularly critical for these households during certain periods of the year, namely the planting and cultivation seasons. Given that rural agricultural households are often poorer than non-agricultural rural households, the additional burden that malaria imposes on agricultural households could be a significant challenge to their efforts to escape poverty.
Historically, improvements in agricultural productivity have played an important role in enabling developing countries to increase their economic growth and escape poverty (Awokuse & Xie, 2014;Datta, 2013;Gallup & Sachs, 2001;Mccarthy et al., 2000;Thirtle et al., 2003;Valdés & Foster, 2010). Four times the level of poverty reduction can be achieved by increasing growth in the agricultural sector than by increasing growth in other sectors of an economy (as outlined by the Gates Foundation).  ics in this study has been informed by studies conducted in communities in which elimination was never achieved. Therefore, there may be other channels through which suppressing malaria may have a significant long-term impact on the income of an agricultural household that were not identified in this

Potential weaknesses in this study
One of the challenges in developing the research framework presented in this study is that malaria elimination has never been achieved and sustained in an agricultural region in sub-Saharan Africa. As a consequence, the identification of the research top-study. If malaria elimination were to be achieved in southern Africa over the next decade, evidence generated from that region may result in modifications to this framework as applied to equatorial regions of Africa where there are higher levels of malaria transmission.

Future research
We recommend that researchers examine how the suppression of malaria transmission over the next two decades may affect the duration of symptoms from a case of malaria. The suppression of malaria may lead to a reduction in acquired immunity levels among individuals in agricultural households, which may affect the probability of a malaria inoculation leading to a symptomatic case of malaria as well as the duration of the symptoms. Researchers should also examine how suppressing malaria transmission affects the seasonality of the impact of malaria on agricultural households and how the coping mechanisms used by households change over time.
We also recommend an analysis of the factors that contribute to heterogeneities across communities and over time in the long-term impact of suppressing malaria on the incomes of agricultural households. Given that the cost of illness methodology has been used more than any other methodology to examine the economic impact of malaria on agricultural households, we recommend a critique of this methodology to determine if it should continue to be used. Future research should also develop models that can utilize the longitudinal data that would be collected with this framework in order to develop prospective estimates of the impact of suppressing malaria on the welfare of agricultural households

Data availability
The dataset for this research, detailing each of the studies used in this framework, has been deposited in CSV format with Harvard Dataverse. DOI: https://doi.org/10.7910/DVN/RQGNLX (Willis, 2018).
Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Grant information
This work was supported by the Bill and Melinda Gates Foundation (OPP1148615).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
agricultural production. If these are left aside for specific study design reasons, it should be explicit.
The payment related to malaria treatment and hospitalization has been shown to represent a large part of household income with the consequence of a fall into poverty. Except for two paragraphs (p 7), this factor is not carefully examined nor represented on figure 1 by the authors while a number of studies have highlighted to role with poverty (Castillo-Requilme 2008 , Onwujekwe 2000/2010 ).
Another major comment is related to assumption of malaria suppression by 2040. This seems to be a starting point for the authors, although malaria control with decrease in incidence and mortality since 2000 also have an effect on productivity and potentially household income. Why is this reality not taken into account ? Also malaria burden, access to treatment, diagnostic and disease impact has considerably changed over the last decades. Similarly one could question the evolution on agricultural techniques and productivity over that period. The authors should also provide their elimination assumption and data collection in that evolutive context.
The study comparator and time horizon are not well defined. Authors mention the long term effect of suppressing malaria by 2040, but are they comparing an elimination scenario to a status quo situation? Is the comparison period over 2018-2040 or also after 2040? If the comparison is only on years before 2040, then the difference would be only due to a reduction in malaria burden not the elimination as such. The potential benefits of elimination compared with improved control are not clearly delineated is this framework.
Regarding the literature review summarized in Table 1, a list of search terms and databases searched should be presented for the reader to understand how were the studies identified. Ideally a PICOS framework should be defined and PRISMA checklist should be used to clarify the review approach. The relevance of data collected several decades ago should be discussed, few studies after 2010 are available. Are they presenting significantly different results?

Minor comments:
In most sub-Sahara African countries, different interventions would be required to "suppress" malaria while the framework only mentions vector control measures. Figure 1: Most of the items are too broad, breaking them down to sub-components could help the readers better. The term 'nutrition security' sounds too ambitious within this framework, may be increased food availability/security, or please describe clearly in the text the pathway you are proposing. You might also want to consider the component 'reduced health expenditure', 'higher disposable income'. How does your framework help adjust the estimates for potential confounders? Is there any estimate on how much investment will be required for how long till eradication?
Eradicating malaria will certainly boost economic productivity and lower poverty levels but the question is to what extent, as the issue is generally embroiled with various others e.g. HIV, which means that malaria eradication will require investment in addressing the risk factors as well. The scenario is certainly complex and hence it is necessary to provide a broader picture of the problems to facilitate effective interventions.
The impact of malaria on decision making among farming households on cropping/area of cultivation is a remote one or not readily understood. This can be explained by citing relevant papers.
Malaria is prevalent not only among farming communities, so focusing on farming communities alone will not help. The comment 'Evidence generated from the framework will inform funding allocation decisions for malaria elimination initiatives' needs a better justification.
Overall, the topic is too broad and hard to make sense of the frameworks in the fast changing health and agricultural landscapes. Despite the limitations, the present study is a good contribution to the literature. It is recommended that authors explain the frameworks by taking into account the proximate/distal factors