Our definition of an agricultural household for this study is the same as that used in our previous study ( Willis & Hamon, 2018) in which we used a definition provided by an agricultural census conducted in Ethiopia in 2010 for identifying the characteristics of an agricultural household:

In a recent study ( Willis & Hamon, 2018), we estimated that there are approximately 54 million agricultural households in malarious regions of sub-Saharan Africa farming less than 10 hectares. This study will focus on these households. Therefore, throughout this paper, the term “agricultural households” refers to agricultural households farming less than 10 hectares. The 35 countries in sub-Saharan Africa that are included in this analysis are: Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Republic of Congo, Democratic Republic of Congo, Equatorial Guinea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Côte d’Ivoire, Kenya, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Sudan, Tanzania, Togo, Uganda, Zambia and Zimbabwe.

Our analysis has two components.

First, we developed a model to analyze the impact of eliminating malaria by 2040 on the incomes of agricultural households in malarious regions of sub-Saharan Africa. Our analysis estimated malaria’s impact on the daily income of individuals in agricultural households from 2018 through 2040 by using a Malaria Elimination Path and a Status Quo Path. The Malaria Elimination Path corresponds to the average daily incomes of individuals in agricultural households if elimination were achieved by 2040. The Status Quo Path refers to the average daily incomes of individuals in agricultural households if the malaria burden were to remain at its current levels through 2040. Using our model, we examined ten scenarios for the long-term impact of suppressing malaria from 2018 through 2040 on daily per capita incomes.

Second, we identified research topics that, if addressed by the research community, could facilitate more accurate estimates of the potential long-term impact of eliminating malaria on agricultural households’ incomes.

In this section, we provide a more detailed description of our methodology for modeling the potential impact of eliminating malaria by 2040 on daily per capita incomes of individuals in agricultural households.

Three steps were involved in developing and applying our model. First, we developed estimates of the number of agricultural households in each of our target countries and the average income per capita for these households. Next, we identified ten sets of parameter values for estimating malaria’s impact on the income of agricultural households. Finally, we used a model to link the agricultural household data for each country with the ten sets of parameter values in order to estimate the impact the elimination of malaria by 2040 would have on incomes and poverty levels. Our estimates of malaria’s impact on the incomes of agricultural households are the product of comparing the incomes of these households if the malaria burden were to remain at its current level through 2040 with incomes if malaria elimination were achieved by 2040.

Average daily income for individuals in agricultural households. The first step in developing our model was estimating the average per capita income for agricultural households in each of our target countries. Our estimates of the number of agricultural households in each of the 35 countries included in our analysis came from a recently published dataset ( Willis, 2018).

We were unable to identify comprehensive estimates of agricultural household income for all 35 countries. As a result, we developed estimates of daily per capita income using the World Bank’s PovcalNet data set, which includes data on the median of monthly household per capita income in 2011 Purchasing Power Parity ( World Bank n.d.). These data are available for each of our target countries except Equatorial Guinea.

A World Bank report estimated that Equatorial Guinea’s poverty rate in 2006 was 76.8 percent ( Bassett et al., 2017). We assumed that this poverty rate reflects the poverty rate experienced by agricultural households in 2018. Using our model, we estimated that a median daily income of $1.35 would result in approximately 75 percent of individuals in agricultural households having daily incomes less than $1.90 (2011 PPP).

We assumed that these estimates provided in the PovcalNet data set for the median daily per capita income at the national level also reflect the daily per capita income of individuals in agricultural households. This is a conservative assumption given that poverty rates in rural areas are generally higher than in non-rural areas:

Table 1 summarizes the number of agricultural households and their median per capita daily income for each of our 35 countries.

Malaria’s short-term impact on agricultural households’ incomes. We defined malaria’s short-term impact on the income of agricultural households as the impact over one year if there were an unexpected decrease in the malaria burden during that year relative to previous years. For example, if an agricultural household expected to experience malaria infections in 2018 but in fact did not, then the difference between the household’s projected income with and without malaria infections would represent malaria’s short-term impact on income.

Malaria could have a short-term impact on household income in two ways. The first would be the number of work days that would be lost by adults due to malaria morbidity or the provision of care for children within the household. The second would be the cost of seeking medical care.

The best evidence available to estimate the short-term impact of malaria on agricultural households’ harvest values is a study conducted in Zambia in 2009, which found that households with access to a vector control intervention experienced an increase in harvest values of US$76 ( Fink & Masiye, 2015). This increase in harvest values corresponded to an increase in yields of approximately 15% ( Fink & Masiye, 2015). The authors attributed the higher harvest values to an increase in the number of people within agricultural households who could work as well as an increase in the number of hours those individuals could work ( Fink & Masiye, 2015).

Fink and Masiye described the households enrolled in their study as follows:

Although Fink and Masiye assume that the households included in their study are representative of the average agricultural household in Zambia, they may not be representative of the average agricultural household in other countries. This creates uncertainty as to how to use the results from Fink and Masiye’s study to inform the parameters in our model.

We therefore used a range of values in our model to address the uncertainty regarding malaria’s short-term impact on agricultural households in our target countries in sub-Saharan Africa. Fink and Masiye found that harvest values were approximately 15% higher due to access to vector control interventions. Most scenarios in our model used a more conservative approach as we assumed that malaria’s short-term impact on the income of agricultural households ranged from 3% to 21%.

Malaria’s long-term impact on agricultural households’ incomes. We defined malaria’s long-term impact on the income of agricultural households as the impact over more than one year if the malaria burden would have remained suppressed. Malaria may affect the long-term income of agricultural households in many ways. For example, malaria may affect household decisions regarding which crops to plant and the amount of resources to devote to purchasing agricultural inputs. However, we lack longitudinal studies that examine malaria’s impact on the incomes of agricultural households over long periods of time.

For the Status Quo Path, we assumed that the incomes of agricultural households will grow by 1% from 2018 through 2040. Our Elimination Path included ten scenarios for the annual growth in agricultural household income, with the growth rate ranging from 1.25% to 3.50%. Therefore, malaria’s impact on the annual growth in agricultural household income ranged from 0.25% (Scenario 1) to 2.50% (Scenario 10). Malaria’s long-term impact on agricultural households is the difference in household income from 2018 through 2040 between the Status Quo Path and the Elimination Path. Table 2 summarizes the parameter values used in our model for the Status Quo Path and for our ten Elimination Path scenarios.

We used Tanzania and Scenario 1 to provide a more detailed illustration of how our model was used to estimate malaria’s impact on poverty levels. In 2015, the International Poverty Line was increased from $1.25 per day to $1.90 per day (2011 PPP). Our analysis of each country estimates the number of individuals who have an income greater than $1.90 per day in 2018 as well as the number who achieve an income greater than $1.90 per day by 2040 for each scenario.

We estimated that there are approximately 22 million people living in agricultural households in Tanzania and that the median per capita income is $1.95 (2011 PPP) ( Table 1). Given that this is a median value, half of the individuals will have daily incomes greater than $1.95 and half will have incomes less than $1.95. To account for these differences in daily incomes among the individuals in Tanzania’s agricultural households, we assumed a discrete uniform distribution with the lowest value being 20% of $1.95 and the largest value being 80% higher than $1.95.

We estimated that in 2018 there were approximately 11.3 million individuals in Tanzania’s agricultural households with per capita incomes greater than $1.90 and approximately 10.5 million individuals with per capita incomes less than $1.90 ( Table 1). For our Status Quo Path, we assumed that the annual growth rate in per capita income was 1%. Based on this assumption, our model estimated that in 2040 approximately 2.6 million individuals who had incomes less than $1.90 in 2018 would escape poverty.

For our Elimination Path Scenario 1, we assumed that the median income of individuals in Tanzania’s agricultural households in 2018 was 3% higher (short-term impact) and that the annual growth rate of incomes through 2040 was 1.25% (0.25% higher than the Status Quo Path growth rate). These assumptions for Scenario 1 led to approximately 3.5 million individuals who had incomes less than $1.90 in 2018 escaping poverty ( Table 4). The parameter values for Scenario 1, therefore, lead to an additional 872,486 individuals (3.5 million versus 2.7 million) escaping poverty relative to the Status Quo Path ( Table 5).

Table 3, Table 4 and Table 5 display the results of our analysis of the impact of eliminating malaria on poverty among individuals in agricultural households. Table 3 summarizes the impact of eliminating malaria on the number and percentage of individuals in poverty for all of the 35 countries included in our analysis.

Summary of poverty among agricultural households in 2018. Approximately 54 million agricultural households currently exist in malarious regions of sub-Saharan Africa. Using an estimate of 6 individuals per household, this yields a total population in these households of approximately 324 million ( Table 1).

Using the dataset we developed with the median daily income of agricultural households in each country, we found that approximately 151 million individuals in agricultural households live in countries in which the median daily per capita income is less than $1.90 (2011 PPP). This population represents 47% of the total population of individuals in agricultural households. Approximately 154 million individuals, 48% of the total population, live in countries in which the median daily income of agricultural households is between $1.90 and $3.00 (2011 PPP). The remaining 5% of the population in agricultural households are in countries with a median daily per capita income greater than $4.00 (2011 PPP).

The total number of individuals in our study across all countries living in poverty in 2018 was approximately 163 million, which represented about 50% of the total population of all agricultural households. This percentage is consistent with estimates in other studies that approximately half of the rural population in sub-Saharan Africa lives in poverty ( World Bank & International Monetary Fund, 2013).

Status Quo Path. The next step in our analysis involved examining how poverty levels in agricultural households would change from 2018 through 2040 with our Status Quo Path. The Status Quo Path assumed that the malaria burden among agricultural households would remain at its 2018 level through 2040 and that the annual real growth (growth in excess of inflation) in incomes among agricultural households would be 1% during that same period.

Based on this assumed annual growth in incomes, we found that the number of individuals in poverty decreased from approximately 163 million in 2018 to 126 million in 2040, a decrease of approximately 40 million individuals ( Table 3). This represents a decrease from 50% of the total population living in poverty in 2018 to approximately 39% in 2040, a 24% decrease ( Table 3).

In 2018, 13 countries had poverty rates in excess of 50% for their agricultural households. Assuming the Status Quo Path, nine of these countries would continue to experience poverty rates greater than 50% in 2040. The poverty rate was in excess of 30% in 20 countries.

The Status Quo Path projects that only 7 countries (Angola, Botswana, Cameroon, Gabon, Gambia, Ghana and Zimbabwe) will have a poverty rate of less than 20% by 2040. The total 2018 population of these 7 countries represents 7.1% of the population of the 35 countries in our study ( Table 4). The Status Quo Path projects that Gabon will be the only country to eliminate poverty among its agricultural households by 2040 ( Table 4).

Elimination Path. We analyzed the impact of ten Elimination Path scenarios on poverty levels of agricultural households from 2018 to 2040. Each Elimination Path scenario assumed that malaria would be eliminated by 2040; the differences between the scenarios were the impact that malaria elimination would have on the incomes of agricultural households. Scenario 1 represents our most conservative estimate of the impact of suppressing malaria on the incomes of agricultural households while Scenario 10 represents our least conservative estimate ( Table 2).

Our analysis of the Elimination Path scenarios found that the number of individuals in poverty decreased from 2018 to 2040 by 53 million (Scenario 1) to 123 million (Scenario 10). These decreases in poverty represented a 33% and 76% reduction, respectively, in poverty rates as compared to 2018 ( Table 3). In contrast, only 40 million individuals escaped poverty by 2040 with the Status Quo Path, a 24% reduction in poverty rates.

While the Status Quo Path resulted in 9 countries with poverty rates greater than 50% in 2040, Scenarios 5 through 10 for the Elimination Path led to no countries having poverty rates greater than 50%. Six countries had poverty rates of more than 50% for Scenario 1 while the result was 3 countries for Scenario 2. Scenarios 3 and 4 each led to 2 countries having poverty rates of more than 50%.

The Status Quo Path led to twenty countries having poverty rates in excess of 30% in 2040. The number of countries with poverty rates of more than 30% for our Elimination Path scenarios varied from 18 for Scenario 1 to zero for Scenario 10.

Our analysis found that between 53 million and 123 million individuals in agricultural households would escape poverty by 2040 if malaria were eliminated by that year. This decrease in poverty represents a 33% to 76% decrease in the percentage of individuals in poverty relative to 2018 levels. In contrast, if the malaria burden were to remain at its current level in sub-Saharan Africa through 2040, we expect that only 40 million individuals in agricultural households would escape poverty by 2040, a decrease of only 24%. The impact, therefore, of eliminating malaria by 2040 is that approximately 13 million to 83 million individuals in agricultural households will escape poverty.

Our findings of malaria’s impact on the incomes of agricultural households should be interpreted as the difference between the incomes of these households if the malaria burden were to remain at its current level from 2018 through 2040 (the Status Quo Path) and incomes if malaria were suppressed over this same period of time (Elimination Path). Numerous factors could affect the incomes of agricultural households in sub-Saharan Africa over the next two decades, including macroeconomic risk, political risk and climate change. Progress towards eliminating malaria by 2040 in sub-Saharan Africa does not guarantee that incomes among agricultural households will increase and poverty rates will decline. For example, even if Ethiopia achieves significant progress towards eliminating malaria by 2040, the incomes of agricultural households in Ethiopia may not increase if climate change decreases crop yields. Therefore, it would not be appropriate to use the findings from this study to make claims that “if we eliminate malaria by 2040 we would also decrease poverty rates.” It would be more appropriate to use these findings to make more measured statements along the lines of the following “based on the best available evidence, suppressing malaria over the next two decades may facilitate a trend, assuming other conditions that affect agricultural productivity remain favorable, in which the growth rate of agricultural households’ incomes increase and poverty rates decline.”

Our estimates of the impact of eliminating malaria on poverty rates are conservative for two reasons.

First, our estimates of each country’s daily per capita income in 2018 likely overestimate the actual daily income of individuals in agricultural households. Our methodology for developing estimates of the daily per capita income of individuals in agricultural households assumed that the median per capita income for all individuals in a country reflected the per capita income for individuals in agricultural households. This assumption likely leads to an overestimation of the actual daily per capita income of individuals in agricultural households.

For example, our methodology led to an estimate of US$1.58 (2011 PPP) for the median per capita income of individuals in agricultural households in Zambia. In comparison, Fink and Masiye estimated a median per capita daily income for agricultural households in Zambia of US$0.26 based on median harvest values of US$463 per households and an average of six individuals per household ( Fink & Masiye, 2015).

Another study presented estimates of the mean annual per capita household income for Kenya, Ethiopia, Rwanda, Mozambique and Zambia based on surveys conducted in the 1990s and 2000s ( Jayne et al., 2003). Based on the annual per capita estimate of US$57.70 for agricultural households in Zambia in 2000, the daily per capita income of these households would be US$0.19 in 2018 if we assume growth in incomes of 1% per year. This estimate of US$0.19 for the daily per capita income of agricultural households in Zambia is consistent with the estimate of US$0.26 provided by Fink and Masiye but well below our estimate of $1.58 (2011 PPP). Using a similar approach for converting household income estimates in Jayne et al. to 2018 US dollars, we developed the following estimates for average daily per capita household income: Kenya (US$1.14), Ethiopia (US$0.25), Rwanda (US$0.28) and Mozambique (US$0.15). Our estimates for median daily per capita household income for the same four countries are five to ten times greater: Kenya (US$2.44), Ethiopia (US$2.79), Rwanda (US$1.76) and Mozambique (US$1.50).

As a result of using higher estimates of per capita income in 2018 for individuals in agricultural households, we are likely underestimating the number of these individuals who have incomes less than the poverty levels of $1.90 (2011 PPP). By underestimating the number of individuals in agricultural households who are in poverty in 2018, we are reducing the pool of individuals who can potentially escape poverty by 2040. We would, therefore, expect that our estimates of the number of individuals in agricultural households who escape poverty by 2040 for each scenario are conservative.

The second reason why we would expect our impact estimates to be conservative is the parameter values we used for estimating malaria’s impact on incomes in 2018. Fink and Masiye found that access to subsidized bed nets led to a 14.7% increase in the harvest value of agricultural households ( Fink & Masiye, 2015). Fink and Masiye did not attempt to quantify the cost of households seeking treatment for malaria infections experienced by household members. Therefore, we would expect that the actual cost of malaria to the household was greater than malaria’s impact on harvest values. Most of the parameter values we used in our Elimination Path scenarios for estimating malaria’s impact on household income in 2018 were below the 14.7% finding from Fink and Masiye. Our parameter values for malaria’s impact in 2018 ranged from 3% to 21%. If the Fink and Masiye study had accounted for additional means by which malaria affects the incomes of agricultural households in the short term (e.g., household expenditures on treatment for malaria), the total impact of malaria on incomes could have been greater than 21%. We can therefore assume that our parameter estimates for malaria’s impact on 2018 household income are likely conservative.

As with any study that attempts to estimate the impact of a disease on a large population over several decades, predicting with certainty how the population will response to an improvement in health is difficult.

For example, simply estimating the number of agricultural households annually in sub-Saharan Africa through 2040 is complex. We would expect that the population growth rate of rural areas of sub-Saharan Africa to gradually decrease from 2018 through 2040 due to the rapid urbanization that is projected for the region over that period. However, the suppression of malaria over that period and achieving malaria eradication in 2040 could make the quality of life in rural areas of Africa more attractive than if malaria remained at its current level. Increases in the expected quality of life in rural areas could, therefore, play a role in decreasing urbanization rates and increasing population growth in rural areas compared to malaria remaining at its current levels through 2040.

The objective in this study was to develop the most accurate projections possible of the potential long-term impact of eliminating malaria on agricultural households’ incomes in Africa given the data available. It is our hope that researchers will use the knowledge gaps identified in this study to inform future research questions in order to develop better projections of how the elimination of malaria could affect the incomes of agricultural households.

This study highlighted the need for research into how suppressing malaria over the next two decades would affect the incomes of agricultural households in sub-Saharan Africa. For our analysis, we assumed that the annual growth rate in incomes of agricultural households would be 0.25% to 2.50% higher for our Elimination Path scenarios relative to our Status Quo Path. In order to develop more precise estimates of the impact of the Elimination Path on income growth rates, we recommend that researchers focus on five channels through which malaria may affect agricultural households. The first channel is the impact of suppressing malaria on work days, caregiving days and gender equality among adults in agricultural households. The second channel is malaria’s impact on education levels attained by children in agricultural households. The long-term impact of suppressing malaria on agricultural households’ harvest values is the third channel. The fourth channel is the long-term impact of suppressing malaria on households’ decisions regarding the level of resources to devote to purchasing anti-malaria interventions to prevent and treat malaria cases. The final channel is the decisions of agricultural households concerning which crops to plant and how much to invest in agricultural inputs if households expect a decrease in risk of malaria infections.

We recommend the use of longitudinal data from agricultural households in sub-Saharan Africa to examine these five channels. There are two potential advantages of using longitudinal data to examine the long-term impact of suppressing malaria on agricultural households’ incomes. First, using longitudinal data to examine all five channels in a community would enable researchers to understand the interactions between these channels. For example, a household’s decision to increase the level of resources devoted to purchasing agricultural inputs may depend, in part, on the household’s decision to devote less resources to purchasing anti-malaria interventions to prevent malaria infections. Second, we would expect that there would be significant heterogeneities in the impact of suppressing malaria on agricultural households’ income across communities and over time. Using longitudinal data from a range of agro-ecological zones in sub-Saharan Africa would enable researchers to examine how the five channels contribute to heterogeneities in growth rates of household income from the suppression of malaria.

Future research of the impact of suppressing malaria on long-term growth rates in agricultural household income should proceed in two stages. First, we recommend an analysis of our level of knowledge about each of the five channels through which suppressing malaria may affect long-term growth rates in income among agricultural households. There is more than 100 years of evidence from studies around the world of malaria’s impact on the welfare of agricultural households. An analysis of the evidence related to the five channels we have identified will enable researchers to determine which channels should be prioritized for additional research using longitudinal data. The second stage of this research initiative should be to identify opportunities to collect data for these five channels using existing frameworks that are collecting longitudinal malaria data. Two examples of existing frameworks that are collecting longitudinal malaria data are the INDEPTH health and demographic surveillance systems and the International Centers of Excellence for Malaria Research progam.