Malaria control and elimination in Kenya: economy-wide benefits and regional disparities

Abstract

Background: Malaria remains a public health problem in Kenya despite several concerted control efforts. Empirical evidence regarding malaria effects in Kenya suggests that the disease imposes substantial economic costs, jeopardizing the achievement of sustainable development goals. The Kenya Malaria Strategy (2019-2023), which is currently being implemented, is one of several sequential malaria control and elimination strategies. The strategy targets reducing malaria incidences and deaths by 75% of the 2016 levels by 2023 through spending around Kenyan Shillings 61.9 billion over 5 years. This paper assesses the economy-wide implications of implementing this strategy.

Methods: An economy-wide simulation model is calibrated to a comprehensive 2019 database for Kenya, considering different epidemiological zones. Two scenarios are simulated with the model. The first scenario (GOVT) simulates the annual costs of implementing the Kenya Malaria Strategy by increasing government expenditure on malaria control and elimination programmes. The second scenario (LABOR) reduces malaria incidences by 75% in all epidemiological malaria zones without accounting for the changes in government expenditure, which translates into rising the household labour endowment (benefits of the strategy).

Results: Implementing the Kenya Malaria Strategy (2019-2023) enhances gross domestic product at the end of the strategy implementation period due to more available labour. In the short term, government health expenditure (direct malaria costs) increases significantly, which is critical in controlling and eliminating malaria. Expanding the health sector raises the demand for production factors, such as labour and capital. The prices for these factors rise, boosting producer and consumer prices of non-health-related products. Consequently, household welfare decreases during the strategy implementation period. In the long run, household labour endowment increases due to reduced malaria incidences and deaths (indirect malaria costs). However, the size of the effects varies across malaria epidemiological and agroecological zones depending on malaria prevalence and factor ownership.

Conclusions: This paper provides policymakers with an ex-ante assessment of the implications of malaria control and elimination on household welfare across various malaria epidemiological zones. These insights assist in developing and implementing related policy measures that reduce the undesirable effects in the short run. Besides, the paper supports an economically beneficial long-term malaria control and elimination effect.

Keywords: CGE analysis; Economic performance; Household welfare; Human health; Kenya; Malaria; Public health policy.

Fig. 1

Malaria epidemiological map for Kenya. Source: Author's compilation based on [6]

Fig. 2

Types of economic costs of malaria. Source: Authors' compilation based on [4, 7, 9]

Fig. 3

Kenyan malaria epidemiological and agroecological zones included in the 2019 SAM. Source: Author's compilation based on [6, 21]

Fig. 4

Effects on factor prices (% change compared to the reference scenario). Source: Author's calculations based on simulation results

Fig. 5

Effects on quantities of sectoral domestic production (% change compared to the reference scenario). Source: Author's calculations based on simulation results

Fig. 6

Effects on household welfare (EV as a share of household expenditure in the base situation). Source: Author's calculations based on simulation results

Fig. 7

Effects on household welfare across malaria epidemiological and agroecological zones. Source: Author's calculations based on simulation results

Fig. 8

Sensitivity analysis of different instruments for financing the implemented policy on household welfare. Source: Author's calculations based on simulation results

Fig. 9

Changes in labour supplies in the LABOR scenario (% change compared to the reference scenario). Source: Author's calculations based on simulation results

Fig. 10

Effects on labour price across malaria epidemiological and agroecological zones, Source: Author's calculations based on simulation results

Fig. 11

Effects on domestic producer prices (% change compared to the reference scenario). Source: Author's calculations based on simulation results

Fig. 12

Effects on aggregate economy-wide indicators (% change compared to the reference scenario). Source: Author's calculations based on simulation results