Eliminating malaria by 2040 among agricultural households in Africa: potential impact on health, labor productivity, education and gender equality

Status Quo Path versus Elimination Path

Our methodology enables us to compare our selected indicators for the SDGs for the Status Quo Path and Malaria Elimination Path. The Status Quo Path refers to the indicators that would occur if the annual number of malaria cases in each country in sub-Saharan Africa remained at their 2018 levels through 2040. The Malaria Elimination Path is the counterfactual case for the corresponding indicators from 2018 through 2040 if the annual number of malaria cases in each country decreased from the 2018 levels to nil in 2040.

We compare the selected SDG indicators from the Status Quo Path and Malaria Elimination Path for two time periods.

The first time period, from 2018 to 2030, corresponds to the target year of 2030 for achieving the SDGs as well as the final year of the ‘Global Technical Strategy for Malaria’ (World Health Organization, 2015). The ‘Global Technical Strategy for Malaria’ has established a goal of suppressing the incidence of malaria cases by 90 percent by 2030 (relative to 2015 levels) (World Health Organization, 2015).

The second time period, from 2018 to 2040, corresponds to the goal of eradicating malaria by 2040 (“Malaria No More | From Aspiration to Action” n.d.).

Three scenarios: most conservative, base case and least conservative

A range of parameter values could be used to develop and analyze our dataset (Willis, 2018) in order to estimate the impact of eliminating malaria on our indicators. Given this uncertainty, we use three different scenarios for developing and analyzing our dataset: the most conservative scenario, the base case scenario, and the least conservative scenario.

Our most conservative scenario represents our parameter estimates which are associated with a lower impact of suppressing malaria on the indicators. The least conservative scenario is our set of parameter values associated with the largest impact of suppressing malaria on the indicators. An intermediate set of parameter values are modeled in our base case scenario.

Definition of smallholder agricultural households

We will define agricultural households using the same standards that have been used in agricultural censuses of countries in sub-Saharan Africa. The following definition in Ethiopia’s agricultural census in 2010 is representative of the definitions used in most agricultural censuses conducted in Africa:

We defined smallholder agricultural households in sub-Saharan Africa as agricultural households that farm areas of 10 hectares or less.

Annual estimates of population of smallholder agricultural households in sub-Saharan Africa

Our analysis included only countries in sub-Saharan Africa in which more than half of the country experienced malaria transmission. We therefore excluded Namibia and South Africa from our analysis and focused on the following countries: 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.

We use several steps to develop our estimates of the number of smallholder farmers in each country in 2018 as these data are not available from existing datasets.

First, we developed estimates of the total number of farms in 2018 for each country using a study published in 2016 that includes agricultural census data from most countries in sub-Saharan Africa (Lowder et al., 2016). Using these agricultural census data, we estimated that there are approximately 67 million agricultural households in the 35 countries that we are targeting for this study. Agricultural census data were not available for any year for Equatorial Guinea and South Sudan. We estimated the total number of agricultural households Equatorial Guinea and South Sudan for 2018 using the proportion of smallholder agricultural households relative to the total population of neighboring countries.

The next step in developing our dataset (Willis, 2018) was estimating the number of smallholder agricultural households among the total number of agricultural households estimated in the first step. Data for the number of smallholder agricultural households in each country were not available for all of our targeted countries in sub-Saharan Africa. We therefore estimated that 75% of all agricultural households in a country were smallholder households. This estimate is consistent with smallholder household data that were available from Uganda where there were 1,251,179 smallholder households in 1991, out of a total number of 1,704,721 agricultural households in the entire country for that year (Lowder et al., 2016).

We would expect that these estimates for the number of smallholder agricultural households for the sub-Saharan African countries in our study to represent lower-bound estimates as some of the estimates rely on agricultural census data from the 1980s and 1990s (Lowder et al., 2016).

Annual estimates of total population in agricultural households per country from 2018 to 2040

Next, we developed annual estimates of the population of smallholder agricultural households in each of the 35 sub-Saharan African countries from 2018 through 2040 as these population estimates were not available in the literature.

We assumed that the number of agricultural households in 2018 remained the same through 2040. Although we would expect the overall population growth rate of sub-Saharan African countries to be around 2.16% through 2040 (World Bank, 2018), the population in rural areas of Africa is not expected to grow at the same rate (“2018 Revision of World Urbanization Prospects” 2018). By contrast, sub-Saharan Africa will experience high urban population growth through 2040:

We assumed that, on average, there are six individuals in each agricultural household. This estimate was based on the number of individuals per household in a recent study of malaria’s impact on harvest values in Zambia (Fink & Masiye, 2015). Estimates of the annual population of smallholder agricultural households for each country are calculated by multiplying six times the annual number of smallholder agricultural households.

Annual estimates of number of malaria cases among agricultural households per country from 2018 to 2040 - Status Quo Path

We use the term Status Quo Path to refer to the number of malaria cases in each sub-Saharan African country from 2018 through 2040 if the current anti-malaria programs being implemented in those countries remain unchanged during that period.

We use three different approaches (i.e., most conservative scenario, base case scenario, least conservative scenario) for estimating the Status Quo Path for the annual number of malaria cases among agricultural households per country from 2018 through 2040.

Most conservative scenario. For our most conservative scenario, we estimated the annual number of malaria cases among agricultural household per country by using the total number of malaria cases per country reported in the World Malaria Report for 2017 (WHO, 2017). We divided the total number of malaria cases in a country in 2016 by that country’s total population in 2016 in order to calculate the number of malaria cases per person in 2016. We assumed that that estimate of the number of malaria cases per person in 2016 is the same as the number of malaria cases per person in 2018. By multiplying the number of malaria cases per person in 2018 by the total population of smallholder agricultural households in 2018, we estimated the number of malaria cases among agricultural households in 2018.

For the Status Quo Path, we assumed that the number of malaria cases among these households in a country in 2018 remained unchanged for the subsequent years through 2040. This approach to estimating the number of malaria cases among smallholder households assumed that the malaria risk experienced by agricultural households is the same as the malaria risk of non-agricultural households.

There are two reasons why this approach to estimating the number of malaria cases was conservative and will likely underestimate the actual annual number of malaria cases in each country. First, agricultural households will be primarily in rural areas and the malaria risk of households in rural areas will likely be significantly greater than the risk of households in urban areas. Therefore, assuming malaria risk is the same among all households will underestimate the number of malaria cases experienced by households in rural areas. Second, a significant proportion of malaria cases that actually occur in a country are undiagnosed and therefore not included in official estimates of the number of malaria cases in a country (World Malaria Day: Which Countries Are the Hardest Hit? Get the Full Data, 2012; Roca-Feltrer et al., 2008).

Base case scenario. To account for the potential number of malaria cases that are unreported, our base case scenario assumes that approximately half of the actual number of malaria cases that occur among agricultural households are unreported. We therefore multiply the annual number of malaria cases estimated under the most conservative scenario by two in order to derive annual estimates for each country for this scenario. This approach of multiplying the annual number of malaria cases by two is justified based on a statement in 2016 by Dr. Richard Cibulskis of the WHO’s Global Malaria Programme that approximately 90 percent of malaria cases globally are not reported (World Malaria Day: Which Countries Are the Hardest Hit? Get the Full Data, 2012)

Least conservative scenario. For estimates of the annual number of malaria cases under our least conservative scenario, we assumed that only one third of the actual number of malaria cases that occur among agricultural households are reported. Accordingly, we multiply the annual number of malaria cases from the most conservative scenario by three in order to estimate the annual number of malaria cases for the least conservative scenario.

Annual estimates of number of malaria cases among agricultural households per country from 2018 to 2040 - Malaria Elimination Path

For the malaria elimination path, we used the same estimates for the number of malaria cases in 2018 for each scenario from the status quo path. However, we assumed the annual number of malaria cases for each scenario decreased to zero in 2040.

Annual estimates of number of lost work-days due to malaria cases among agricultural households per country from 2018 to 2040 - Status Quo Path and Malaria Elimination Path

In order to estimate the potential impact of eliminating malaria among agricultural households on poverty we use the number of lost work-days due to malaria cases as an indicator.

Studies of the number of lost work-days due to a malaria case among individuals in an agricultural household yield a wide range of values. Most studies estimated the number of lost work-days by adults in sub-Saharan Africa due to a malaria case a ranging from 2 to 6 days (Asenso-Okyere & Dzator, 1997; Attanayake et al., 2000; Gazin et al., 1988; Guiguemdé et al., 1997; Larochelle & Dalton, 2005; Leighton & Foster, 1993; Nur & Mahran, 1988; Organization & Espd, 2005; Sauerborn et al., 1991). We therefore used values within this range in our scenarios in order to examine the sensitivity of the model results to these values.

Most conservative scenario. For our analysis using our most conservative scenario, we assumed that the annual number of work-days lost due to a malaria case was two days.

Base case scenario. The base case scenario assumed that the annual number of work-days lost due to a malaria case was four days.

Least conservative scenario. We assumed that six work-days were lost per malaria case for our least conservative scenario.

Annual estimates of missed school days due to malaria cases among agricultural households per country from 2018 to 2040 - Status Quo Path and Malaria Elimination Path

Our indicator for the impact of malaria cases on the SDG of improving education was the number of school days missed due to malaria. We assumed that children experienced 90 percent of the annual number of malaria cases among agricultural households in each country and that the number of missed school days by children per malaria case varied from 4 to 8 days (Brooker et al., 2000; Bundy et al., 2000; Chima et al., 2003; Chuma et al., 2010; Konradsen et al., 1997; Leighton & Foster, 1993; Sachs & Malaney, 2002).

Most conservative scenario. For our most conservative scenario, we assumed that the number of school days missed per case of malaria was four days.

Base case scenario. We assumed that six school days were missed due to each malaria case for our base case scenario.

Least conservative scenario. Our least conservative scenario assumed that eight school days were missed per malaria case.

Annual estimates of missed school days by girls due to malaria cases and caregiving days by women for malaria cases

The two indicators we used to estimate malaria’s impact on gender equality are missed school days by girls due to malaria cases and the number of caregiving days by women for malaria cases among children in their household.

We estimated the number of malaria cases among girls by assuming that half of all malaria cases among children are experienced by girls. We used the same number of missed school days per malaria case for each scenario as described for the previous indicator.

Most studies of the number of caregiving days provided by women for malaria cases in the households estimated the number of days as ranging from 1 to 4 days (Asenso-Okyere & Dzator, 1997; Ettling et al., 1994; Leighton & Foster, 1993; Nur, 1993).

Most conservative scenario. Our most conservative scenario assumed that a woman spent 1.5 days providing care for each child’s case of malaria.

Base case scenario. We assumed that 2.5 caregiving days were provided for each child’s case of malaria by a woman in the household.

Least conservative scenario. Our least conservative scenario assumed that 3.5 days were spent by a woman providing care for each child’s case of malaria.

Population of smallholder agricultural households in sub-Saharan Africa

Our analysis of the agricultural census data for the countries included in our study found that there were approximately 53 million smallholder agricultural households in 2018 out of a total number of approximately 70 million agricultural households. Based on our estimate of 6 people per household, the total population of smallholder agricultural households in 2018 was approximately 316 million.

2018 to 2030: Impact of Malaria Elimination Path on SDGs

Table 4 summarizes the impact of suppressing malaria among smallholder agricultural households from 2018 to 2030. We analyze this time period due to the target year for achieving the SDGs being 2030.

Malaria cases among smallholder agricultural households. Under our base case scenario, there would be approximately 780 million malaria cases among smallholder agricultural households from 2018 through 2030 if malaria elimination was achieved in 2040. By contrast, approximately 1.1 billion malaria cases would occur over that 12-year time period if the status quo was maintained. As a result, approximately 300 million malaria cases would be prevented from 2018 through 2030 by pursuing the path to eradication by 2040.

A lower bound for the number of malaria cases prevented by pursuing eradication can be estimated with the most conservative scenario (195 million) while an upper bound is provided by the least conservative scenario (389 million).

Work-days among smallholder agricultural households. The base case scenario estimate for the number of additional work-days from 2018 to 2030 by suppressing malaria is approximately 1.2 billion days. The lower bound for this estimate is 390 million work-days from our most conservative scenario analysis and the upper bound estimate is 2.3 billion work-days.

School days among children in smallholder agricultural households. There would be approximately 1.4 billion additional school days by children of smallholder agricultural households if malaria was suppressed from 2018 to 2030. Our most conservative scenario analysis produces a lower bound estimate of 620 million additional school days while the upper bound estimate was approximately 2.5 billion school days.

School days by girls in smallholder agricultural households. If the analysis of lost school days is limited to only girls, the number of additional school days from 2018 to 2030 would be approximately 700 million school days.

Caregiving days by women in smallholder agricultural households. With our base case scenario analysis, there would be a reduction in the number of caregiving days by women for children with malaria of approximately 580 million days. The lower bound for this estimate is 230 million caregiving days while the upper bound is 1 billion days.

2018 to 2040: Impact of Malaria Elimination Path on SDGs

In Table 5, we present the results of our analysis of how eliminating malaria would affect our SDG indicators from 2018 through 2040.

Malaria cases among smallholder agricultural households. Approximately 950 million malaria cases would be prevented among smallholder agricultural households from 2018 through 2040 based on our base case scenario analysis. The lower bound of this estimate of the number malaria cases prevented is 630 million, while the upper bound estimate is approximately 1.3 billion.

Work-days among smallholder agricultural households. According to our base case analysis, there would be approximately 3.8 billion additional work-days among smallholder agricultural households if malaria was eliminated by 2040 relative to the status quo path. Our most conservative scenario analysis generates an estimate of 1.3 billion work-days prevented for the lower bound and our least conservative scenario estimates the number of work-days prevented as 7.5 billion.

School days among children in smallholder agricultural households. Approximately 4.5 billion school days would be gained from 2018 through 2040 by achieving malaria elimination in 2040. The lower bound estimate for this analysis is 2 billion school days while the upper bound estimate is 8 billion school days.

School days by girls in smallholder agricultural households. The number of additional school days of girls from 2018 through 2040 would be approximately 2.25 billion school days, with a lower bound estimate of 1 billion school days and an upper bound estimate of 4 billion school days.

Caregiving days by women in smallholder agricultural households. The number of caregiving days by women reduced from 2018 to 2040 would be 1.9 billion days according to our base case analysis. Our most conservative scenario generates a lower bound estimate of 750 million caregiving days reduced while the upper bound estimate is approximately 3.5 billion caregiving days decreased.

Limitations of this research

One limitation of this study is our methodology for estimating the number of malaria cases among smallholder agricultural households in sub-Saharan Africa. A more rigorous means of generating sub-national estimates of malaria cases among these households would facilitate better estimates of the impact of suppressing malaria on the SDGs.

Policy implications

The proportion of farmers in sub-Saharan Africa relative to its total population in 2018 is similar to the proportion of farmers in the United States in 1916. In 1916, the farm population of the United States was approximately 32 percent of the country’s total population (AP n.d.). Our analysis suggests that about 32% of sub-Saharan Africa’s total population are smallholder farmers as we estimated that there are 315 million smallholder farmers out of a total population in sub-Saharan Africa of approximately 1 billion (World Bank, 2018).

Malaria was endemic in the southern part of the United States until the 1930s but declined steadily throughout the 1940s (Sledge & Mohler, 2013). One factor that contributed to malaria being eliminated in the United States in 1949 was economic development and housing improvements in the US South (Humphreys, 1998)

Our study represents the first attempt to quantify the impact that eliminating malaria would have on smallholder farmers across sub-Saharan Africa in terms of work-days missed, school days missed and caregiving days provided.

The potential impact of eliminating malaria on the welfare of smallholder farmers will not be limited to these indicators. Fewer work-days missed could contribute to higher incomes for smallholder farmers, which could lead to investments in improved housing and, as a result, fewer cases of malaria. An increase in the incomes of smallholder farmers could also lead to increased investments by farmers in agricultural inputs to improve crop yields.

Achieving malaria eradication by 2040, and the resulting positive effect on the Sustainable Development Goals, will require that vector control interventions continue to play a key role in anti-malaria programs. Vector control has been shown to be the most effective intervention for reducing the malaria burden (Bhatt et al., 2015). Long-lasting insecticide-treated bed nets (LLINs) and indoor residual spraying (IRS) were responsible for 78% of the reduction in malaria cases between 2000 and 2015 (Bhatt et al., 2015). Most successful vector control interventions use insecticides that have been repurposed from modern agriculture. The impact of these interventions is being compromised by the development of resistance to the classes of insecticides currently used in vector control.

Investments by agricultural chemical companies in developing and delivering novel vector control tools will likely be essential to achieving malaria eradication by 2040. These investments will not only play a key role in improving the health of smallholder farmers in Africa but could significantly accelerate growth in the incomes of these farmers as well by increasing their productivity.

Future work

In order to better understand how investments by agricultural chemical companies in vector control tools will affect smallholder farmers over the next 20 years, additional research is needed in three areas. First, researchers need to examine how suppressing malaria risk among smallholder farmers in Africa affects their decisions of which crops to plant. Second, analyses are needed of the relationship between an increase in the income of smallholder farmers and any change in their malaria risk. There is some evidence that higher farmer incomes leads to reduced malaria risk (Ijumba & Lindsay, 2001) and increased investment in housing (Sachs, 2018), but additional research is needed. Finally, researchers should examine the relationship between higher incomes of smallholder farmers and farmers’ decisions of how much to invest in agricultural inputs.