This document discusses the cost-benefit analysis of malaria interventions in Ghana, highlighting that malaria is the leading cause of death and disability in the country, with significant healthcare and economic burdens. The analysis includes three primary interventions: increasing long-lasting insecticide-treated nets (LLINs) coverage, implementing seasonal malaria chemoprevention, and enhancing testing and treatment of suspected malaria cases. It presents the substantial benefits of these interventions, emphasizing their cost-effectiveness and urging policymakers to prioritize their implementation.

1. EminentPanelConference,Accra,August7th -9th,2020
Cost Benefit Analysis of Selected Malaria
Interventions in Ghana
Edward Nketiah-Amponsah,
Timothy Awine, Sheetal Silal, Brad Wong

2. • Malaria continues to be the number 1 cause of death and disability in Ghana.
• According to the Global Burden of Disease
- ~19,000 deaths per year
- 5.9 million cases per year
- Pregnant women and children under 5 most at risk
• Large burden on health system
- In 2018, 34% of all outpatient attendance were for suspected cases of malaria
• Large burden on the economy
- Annual economic burden of malaria on the macro-economy is estimated to
be 1-2% of GDP (UNICEF, 2007)
Why concerned?

3. • Incidence rate of malaria has fallen
dramatically from about 1/3rd of all people
in 2010 to 1/5th in 2018 (GBD, 2017).
• Main goal of NSP 2014-2020 is to reduce
malaria burden by 75%.
• This paper examines policies informed by
NSP with time period 2018 to 2030.
2010: 1/3rd of
all people
contract
malaria
2017: 1/5th of
all people
contract
malaria
National Malaria Control Program has achieved much success
but more work to be done
Figure 1

4. Epi-Mathematical model
Our paper draws upon a recently completed malaria
transmission model for Ghana

5. The Model and Map of Ecological Zones
• The model is an expanded Susceptible
Infected Recovered Susceptible (SIRS) in
design.
• Replicated for the three ecological zones of
Ghana (Guinea savannah, Transitional forest
and Coastal savannah).
• The vector compartments are driven by zone
specific average monthly rainfall and
temperature.
Figure 2 Map of Ghana showing zones

6. Valuation of health benefits
Based on recently completed guidelines by the
Harvard working group

7. Valuation of lives saved
• Each year of life saved is valued 1.2x - 1.5x GDP per capita
Valuation of cases of malaria avoided
• Individuals who don’t seek treatment -> GHS 61
• Individuals who do seek treatment at health facility
-> GHS 91
• Severe cases of malaria -> GHS 496
All valuations
account for
i) cost of illness
ii) productivity loss
and
iii) intrinsic value of
life
Note: Based on surveys of cost of malaria in Ghana (Tawiah et al. 2016; Sicuri et al. 2014) and adjusted for
inflation and insurance
Lives saved and cases avoided

8. Interventions focus on prevention and diagnosis of malaria
1. Distribute and sustain 90% universal coverage of LLIN (BCR= 44):
Targeting to achieve a 90% coverage in Ghana with 3 million bed nets distributed.
2. Seasonal Malaria Chemoprevention to 90 per cent of children in the Guinea
Savannah zone (BCR = 14):
Treatment with SP +AQ for four rounds(four months) through the rainy season (July to Oct)
for children 3-59 months old.
3. (Near) universal testing and treatment of suspected cases presenting at heath
facilities (BCR= 134):
Increasing the probability of being tested (RDT or by microscopy ) and treated for clinical
malaria for suspected cases presenting at health facilities across Ghana.

9. 1st analyzed Intervention
Distribute and sustain 90% universal coverage
through LLINs

10. Overview of Intervention
• Continuous distribution of long-lasting insecticide treated bed nets to ensure
90% universal coverage (maintaining usage at baseline).
•Baseline: 56% household coverage of LLIN
•Intervention: 90% household coverage of LLIN
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Burden
of
malaria
(millions)
Baseline Intervention
Burden of malaria, LLIN intervention vs baseline
-
5,000
10,000
15,000
20,000
25,000
30,000
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Deaths
from
malaria
Baseline Intervention
Deaths from malaria, LLIN intervention vs baselin
Figure 4 Figure 5

11. Target population
• Scale up of LLIN coverage from baseline levels of 56% to 90 % via mass distribution.
• Procurement cost per LLIN = GHS 13.7
• Distribution cost per LLIN = GHS 10.8
• Total Cost per bed net distributed= GHS 25
• Total cots of GHS 45 million in 2018, rising to GHS 73 million in 2030
• Total Intervention costs GHS 442m (2018-2030)
To distribute and sustain 90% coverage of LLIN
costs GHS 442 million

12. • 13.7% decrease in the burden of
malaria.
• 989,000 incident cases avoided per
year.
• 736,000 treated cases avoided per year.
• 3,107 avoided deaths per year.
Intervention delivers benefits worth GHS 19,359
million
Intervention
Discount
Rate
Benefit
(GHS m)
Cost
(GHS m) BCR
Mass
Distribution
of LLIN
5% 24,450 533 46
8% 19,359 442 44
14% 12,653 318 40
• Morbidity avoided benefits of GHS 230 million in 2030
• Mortality avoided benefits of GHS 4,803 million in 2030.

13. 2nd analyzed Intervention
Seasonal Malaria Chemoprevention in Northern
Ghana, 90% coverage

14. • In 2012, WHO recommended SMC, hitherto known as Intermittent Preventive
Treatment in children (IPTc) and subsequently released an implementation guideline in
2013.
• Most childhood malarial disease and deaths occur during the rainy season.
- Giving effective antimalarial treatment at monthly intervals during this period has been shown
to be 75% protective against uncomplicated and severe malaria in children under 5 years of
age.
• The intervention involves scaling up SMC from ~22% to 90% of children U5 years
(roughly 400,000 more children per year) in the Guinea Savannah region of Northern
Ghana, a high malaria endemic zone.
Overview of the Intervention

15. Increasing coverage of SMC would cost GHS 167
million over 10 years
• The total cost per fully dosed child is GHS43.
• Total cost of increased SMC is estimated at GHS 17 million in the first year, rising to GHS 29
million by 2030.
• Total cost of intervention is estimated at GHS 167 million

16. • The intervention reduces burden of malaria in overall population by 6%.
• This translates to an average of 82,027 of all malaria cases avoided per year.
• On average 251 malaria attributable deaths are averted per year.
• Intervention delivers a benefit of in GHS 116 million in the initial year, rising to GHS
496 m in 2020
Intervention delivers a benefit of GHS 2,303
million
-
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Deaths
from
malaria
Baseline Intervention
-
0.2
0.4
0.6
0.8
1.0
1.2
1.4
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Burden
of
malaria,
millions
Baseline Intervention

17. Intervention
Discount
Rate
Benefit
(GHS m)
Cost
(GHS m) BCR
SMC
5% 2,839 202 14
8% 2,303 167 14
14% 1,586 120 13
SMC intervention is cost-
effective
BCR of SMC (Targeting 90% of the U5s)

18. 3rd analyzed Intervention
Increased testing and treating of suspected cases
who present in health facilities

19. • To reduce mis-diagnoses and/or over-diagnoses due to a physician’s judgement without
recourse to clinical test, the WHO, in 2012 recommended a treatment protocol for malarial
called T3-test, treat and Track in malaria endemic countries.
• Consequently, every suspected malaria case (fever) should be tested and every confirmed
case treated with a quality-assured antimalarial medicine.
• While access to microscopic testing of malaria is limited, RDT ensures a prompt
parasitological confirmation of malaria
• This intervention targets ~100% testing and treatment of suspected cases who present at
health facilities up from current levels of 90%.
Overview of the Intervention (Test and Treat)

20. Intervention costs GHS 87 million
Target population
• ~100% of people presenting at health
facilities in Ghana.
Costs = GHS 87 million
• Marginal cost per person tested for
malaria via RDT (fully loaded societal
costs, includes treatment, patient costs
etc = GHS 259.
• We conjecture a remoteness premium of
10x to account for the extra cost of
reaching remaining 10%.
• Tawiah et al. (2016b), RDT in a non-
remote setting (GHS 24.9) per suspected
patient (in 2018 values).
• Given that the average cost of RDT is
GHS24.9, the remoteness cost per
additional suspected patient is
approximately GH249.
Figure 10
0
2
4
6
8
10
12
14
16
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
Total Cost of intervention (2018 GHS, millions)

21. Benefit of GHS11,595 million is largely due to
Avoided mortality
Benefits = GHS 11,595
million
• 24,770 overall deaths
avoided
• 7,431 child deaths
avoided
• 17,339 adult deaths
avoided
• Main pathway of
benefits is reduction in
cases progressing to
severe malaria (9%
drop)
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
GHS
million
Total Benefit from Avoided Mortality and Morbidity (GHS million)
Avoided mortality Avoided morbidity
Figure 11

22. ~100% Testing and
treatment is highly cost-
effective even after
factoring in a large
remoteness cost
Intervention
Discount
Rate
Benefit
(GHS m)
Cost
(GHS m) BCR
Test & Treat
5% 14,448 104 139
8% 11,595 87 134
14% 7,799 63 124
BCR of Testing and Treatment (~100% of
suspected patients)

23. • Policy makers should strongly consider scaling up the coverage and usage of
LLIN in the country given the high BCR, and the robust strength of evidence
in support of this intervention.
• The High BCR of ‘testing and treatment of suspected cases’ suggests that
NMCP should consider scaling up this intervention in all health facilities.
- Extending testing and treatment of malaria to 100% of the population who present at
health facilities is highly cost effective.
• Although scale up of SMC has the lowest BCR of the three interventions, a
BCR of GHS14 is large enough to warrant attention by policy makers.
Policy recommendations

24. Thank you

25. Model structure
• Sn, Snn and Snnp represent the
susceptible human compartments
respectively for younger children under 6
years of age, adults and pregnant women.
• L1, L2 and L3 are stages of latent
infection.
• Ic, Ia and Is and Ism represent
symptomatic infection, asymptomatic
infection, severe infection and sub-
microscopic infection respectively.
Figure 3

26. Model structure (ii)
• Pregnant women at antenatal clinic (ANC) without infection, IANCN or IANCP once infected.
• Tr1, Tr2 and Tr3 represent treatment compartments for confirmed uncomplicated malaria (Ic),
severe malaria (Is) and monthly SP prophylaxis for pregnant women at ANC.
• Treatment failure is captured in Trf1, Trf2 and Trf3 respectively for the three treatment options.
• Vector population: Lv represents larva population and Sm susceptible mosquitoes. Exposed
mosquitoes are captured in Em compartment. Whereas infectious mosquitoes are in Im.
compartment.

27. Key features of the model
• The models account for the transmission diversity of malaria morbidity across the country
split into three zones.
• Incorporate multiple infections of the population.
• Allows for differential infection between children below 6 years, preg. women & the rest of
the population.
• Coupled with the vector population dynamics which depend on zone specific monthly
average temperature and rainfall.

28. Sources of data and model calibration
• Data from 2008 to 2017 obtained from the District Health Information Management System
(DHIMS) platform as well rainfall and temperature data from the Meteorological agency
were used.
• DHIMS data includes routine health facility confirmed malaria cases and deaths in all
districts across Ghana.
• Approximate Bayesian Computation techniques were used for model calibration.
• All predictions were adjusted for population growth using projected mid-year population
estimates from the DHIMS.

29. Model outputs for malaria incidence-baseline
Note: Assuming all conditions at baseline (2018) remained constant over time and without pop growth
Guinea savannah zone Transitional forest zone
Figure 12 Figure 13

30. Model outputs-baseline
Note: Assuming all conditions at baseline (2018) remained constant over time
Coastal savannah zone
Figure 14