Attachment J.7-PMI_Uganda_ Analytical Study of the Cost Effectiveness of Interventions to Prevent, Detect and Treat Malaria in Uganda.pdf

July 14, 2020

PMI/Uganda: Analytical Study of the Cost Effectiveness of Interventions to Prevent, Detect and Treat Malaria in Uganda

Final Analytical Report

Summary:

Given the fixed budget for PMI Investment to Prevent, Detect and Treat Malaria in Uganda, PMI/Uganda seeks to identify the most cost-effective intervention(s) towards the outcomes of interest in the Uganda Ministry of

Health Malaria Reduction and Elimination Strategic Plan 2021-2025: 1) Reduce Malaria Morbidity 50% by 2025 and 2) Reduce Malaria Mortality 75% by 2025. PMI/Uganda engaged Mission Measurement (MM) to develop a return on investment (ROI) model for PMI to explore the most cost-effective mix of Malaria intervention component strategies. Informed by primary and secondary data collected and analyzed by MM, the USAID-

PMI: Malaria Intervention ROI Model includes estimated efficacy and expected return on investment for intervention component strategies identified by PMI/Uganda.

Data Collection & Analysis:

To inform the intervention efficacy model, Mission Measurement collected and analyzed primary and secondary data of malaria intervention efficacy. Primary data collection included key stakeholder interviews with experts in the field of malaria prevention and control (n=6). Key informant data were combined with existing empirical literature on malaria intervention efficacy to establish estimated intervention efficacy.1

The Model:

For the outcomes of interest (reduce malaria morbidity and reduce malaria mortality), the Final ROI Efficacy

Estimator Model yields two primary results:

1) Estimated Intervention efficacy

2) Estimated cost per beneficiary impacted

To obtain these data, users input component strategies for their Intervention (e.g. case management, nets, behavior change communication, etc.), estimated cost to implement the strategies and anticipated reach for the combined set of strategies. The model assumes the value entered in anticipated reach (can be individuals, households, etc.) will receive all component strategies indicated.

Once component strategies, associated costs and anticipated reach are entered, the model uses the intervention efficacy matrices and a calculator input to output an expected return on investment. For each outcome of interest, this output includes estimated intervention efficacy and cost per beneficiary impacted.

Informed by data collected and analyzed by MM for PMI/Uganda, Estimated Intervention Efficacy is presented as a range (<60%; 60-80%; 80-95%; >95%) based on the synergistic effect of component strategies identified.

For a list of individual intervention efficacy based on data collected and analyzed by MM see Appendix B.

Estimated cost per beneficiary impacted is presented as a range in relation to intervention efficacy (e.g. for the

1 Key Informants and full citation list included in Appendix A https://missionmeasurementcorp-my.sharepoint.com/:x:/g/personal/epearson_missionmeasurement_com/EZ_cXF_5vTxKhQtwXq25z-oB4oY90EHOH0PsnXodH0GnKQ?e=hWAD15 https://missionmeasurementcorp-my.sharepoint.com/:x:/g/personal/epearson_missionmeasurement_com/EZ_cXF_5vTxKhQtwXq25z-oB4oY90EHOH0PsnXodH0GnKQ?e=hWAD15

PMI/Uganda: Malaria Intervention Efficacy Final Analytical Report | Pg. 2 component strategies selected, the higher the cost, the lower the efficacy and the higher the efficacy, the lower the costs).

The model includes intervention efficacy matrices for each outcome of interest. The efficacy data presented in the tabs for each outcome of interest can be updated as PMI/Uganda tests the model and collects additional intervention efficacy data. Each matrix tab includes a key outlining values and their corresponding efficacy estimate and guidelines on which cells can/cannot be updated by the user. 2

Accessing and Downloading the Model:

The link to access the Model is accessible online here. The file can be downloaded and/or shared digitally.

Integrating PMI/Uganda Feedback:

Following a presentation with PMI/Uganda on June 29, 2020, the Final USAID-PMI: Malaria Intervention ROI

Model includes feedback received for users to input cost per intervention component strategy to be implemented. These costs are now summed in Part I of the model instead of input in aggregate in Part II. The model cannot account for varying degrees of intervention dosage for those included in anticipated reach. To obtain estimated efficacy and estimated cost per beneficiary impacted for different intervention dosages, it is advised that PMI/Uganda should run those specific components, costs and anticipated reach through the calculator independently.

Conclusion and Recommendations:

As PMI/Uganda seeks to identify the most cost-effective interventions, MM recommends the Malaria

Intervention ROI Model be used in conjunction with the core set of intervention strategies towards malaria prevention and reduction outlined by the Centers for Disease Control and Prevention and World Health

Organization (long-lasting insecticidal nets, rapid diagnostics tests & optimal treatment provided by case management and, intermittent preventive treatment in pregnancy). As advised by these agencies and the key informants who contributed as experts in this process, it is advised that intervention strategies be selected, tailored and implemented to meet the needs of the local community served. In addition to tailoring delivery for the local context, PMI/Uganda could utilize an 80/20 approach to investment, e.g., invest 80% of funds on the component strategies with the highest degrees of efficacy and allocate 20% of funds for innovative combinations towards reduced malaria morbidity and mortality.

Prepared by:

Mission Measurement

Jason Saul, Founder & CEO

Emalee Pearson, Associate Consultant

2The model’s interaction matrices include individual efficacy of each intervention towards the outcomes of interest. When individual components are selected in the model their collective efficacy is calculated to obtain Estimated Intervention Efficacy. The matrices include synergistic efficacy data for the intervention pairs identified as core component strategies (Case Management, Nets, IPTp, and IRS). The data and resources needed to complete the remaining synergistic intervention efficacy were beyond the scope of this project. The interaction matrices are designed to be continually updated by the user should such data become available.

https://missionmeasurementcorp-my.sharepoint.com/:x:/g/personal/epearson_missionmeasurement_com/EZ_cXF_5vTxKhQtwXq25z-oB4oY90EHOH0PsnXodH0GnKQ?e=I7AzJ5

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Appendix A: Key Informants and Citations

Thank you to the following key informants who provided their expertise on Malaria intervention efficacy.

Key Informant, Affiliation Interview Date

Dr. Kassahun Belay, United States Agency for International, Development June 12, 2020

Dr. Bayo Segun Fatunmbi, World Health Organization June 12, 2020

Dr. Robinah Lukwago, United Kingdom Department for International Development June 29, 2020

Dr. Godfrey Magumba, Malaria Consortium June 25, 2020

Dr. Flavia Mpanga, United Nations International Children’s Fund June 25, 2020

Dr. Jimmy Opigo, Ministry of Health Republic of Uganda June 13, 2020

Citations:

Aponte, John J., David Schellenberg, Andrea Egan, Alasdair Breckenridge, Ilona Carneiro, Julia Critchley, Ina

Danquah et al. "Efficacy and safety of intermittent preventive treatment with sulfadoxine-pyrimethamine for malaria in African infants: a pooled analysis of six randomised, placebo-controlled trials." The Lancet

374, no. 9700 (2009): 1533-1542.

Bekolo, Cavin Epie, and Thomas D’Arcy Williams. "Adding proactive and reactive case detection into the integrated community case management system to optimise diagnosis and treatment of malaria in a high transmission setting of Cameroon: an observational quality improvement study." BMJ open 9, no. 6

(2019): e026678.

Bigira, Victor, James Kapisi, Tamara D. Clark, Stephen Kinara, Florence Mwangwa, Mary K. Muhindo, Beth

Osterbauer et al. "Protective efficacy and safety of three antimalarial regimens for the prevention of malaria in young Ugandan children: a randomized controlled trial." PLoS Med 11, no. 8 (2014):

e1001689.

Centers for Disease Control and Prevention. "Features: 2020 World Malaria Day". Centers for Disease Control and Prevention, 2020.

Eisele, Thomas P., David A. Larsen, Philip A. Anglewicz, Joseph Keating, Josh Yukich, Adam Bennett, Paul

Hutchinson, and Richard W. Steketee. "Malaria prevention in pregnancy, birthweight, and neonatal mortality: a meta-analysis of 32 national cross-sectional datasets in Africa." The Lancet infectious diseases 12, no. 12 (2012): 942-949.

Johnson, Ari D., Oumar Thiero, Caroline Whidden, Belco Poudiougou, Djoumé Diakité, Fousséni Traoré, Salif

Samaké, Diakalia Koné, Ibrahim Cissé, and Kassoum Kayentao. "Proactive community case management and child survival in periurban Mali." BMJ global health 3, no. 2 (2018).

Katureebe, Agaba, Kate Zinszer, Emmanuel Arinaitwe, John Rek, Elijah Kakande, Katia Charland, Ruth Kigozi et

al. "Measures of malaria burden after long-lasting insecticidal net distribution and indoor residual spraying at three sites in Uganda: a prospective observational study." PLoS medicine 13, no. 11 (2016):

e1002167.

PMI/Uganda: Malaria Intervention Efficacy Final Analytical Report | Pg. 4

Pinchoff, Jessie, David A. Larsen, Silvia Renn, Derek Pollard, Christen Fornadel, Mark Maire, Chadwick Sikaala et

al. "Targeting indoor residual spraying for malaria using epidemiological data: a case study of the

Zambia experience." Malaria journal 15, no. 1 (2016): 11.

Sherrard-Smith, Ellie, Jamie T. Griffin, Peter Winskill, Vincent Corbel, Cédric Pennetier, Armel Djénontin, Sarah

Moore et al. "Systematic review of indoor residual spray efficacy and effectiveness against Plasmodium falciparum in Africa." Nature communications 9, no. 1 (2018): 1-13.

Ssempiira, Julius, John Kissa, Betty Nambuusi, Carol Kyozira, Damian Rutazaana, Eddie Mukooyo, Jimmy Opigo, Fredrick Makumbi, Simon Kasasa, and Penelope Vounatsou. "The effect of case management and vector-control interventions on space–time patterns of malaria incidence in Uganda." Malaria journal

17, no. 1 (2018): 162.

Thwing, Julie, Thomas P. Eisele, and Richard W. Steketee. "Protective efficacy of malaria case management and intermittent preventive treatment for preventing malaria mortality in children: a systematic review for the Lives Saved Tool." BMC Public Health 11 (2011).

Tukei, Betty Bawuba, Andy Beke, and Héctor Lamadrid-Figueroa. "Assessing the effect of indoor residual spraying (IRS) on malaria morbidity in Northern Uganda: a before and after study." Malaria journal 16,

no. 1 (2017): 4.

Tusting, Lucy S., Julie Thwing, David Sinclair, Ulrike Fillinger, John Gimnig, Kimberly E. Bonner, Christian

Bottomley, and Steven W. Lindsay. "Mosquito larval source management for controlling malaria."

Cochrane Database of Systematic Reviews 8 (2013).

Uganda Ministry of Health. "Uganda Malaria Programme Review Report: 2014-2010 Thematic Desk Report."

Uganda Ministry of Health, 2019.

Uganda Ministry of Health. "Uganda Malaria Reduction and Elimination Strategic Plan 2021-2025.” Uganda

Ministry of Health, 2020.

World Health Organization. "Global Technical Strategy for Malaria 2016-2030. World Health Organization, 2015.

World Health Organization. "Guidelines for the treatment of malaria." World Health Organization, 2015.

World Health Organization. "Larval Source Management, a Supplementary Measure for Malaria Vector Control, Operational Manual." World Health Organization, 2013

World Health Organization. "Malaria Eradication: benefits, future scenarios and feasibility." World Health

Organization, 2019.

World Health Organization. "Mass drug administration, mass screening and treatment and focal screening and treatment for malaria." Geneva, World Health Organization,2015.

World Health Organization. "Overview of malaria surveillance." World Health Organization, 2018.

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Appendix B: Individual Malaria Intervention Efficacy Rankings

For the outcomes of interest in the Uganda Ministry of Health Malaria Reduction and Elimination Strategic Plan

2021-2025 (1) Reduce Malaria Morbidity 50% by 2025 and 2) Reduce Malaria Mortality 75% by 2025), the following tables provide single intervention efficacy based on Mission Measurement analysis of existing literature and key informant interviews from experts in the field of malaria prevention, control and reduction

(see Appendix A for citations). To obtain estimated efficacy data for additional intervention packages, the

USAID-PMI: Malaria Intervention ROI Model can be used.

Table 1: Estimated Single Intervention Efficacy Towards Reduced Malaria Morbidity

>95% Effective

• Indoor Residual Spraying

• Focal Indoor Residual Spraying

80-95% Effective

• Integrated Community Case Management

• Intermittent Preventive Treatment of Malaria in Pregnancy

• Mass Screen and Treat

• Focal Screen and Treat

• Behavior Change Communication

60-80% Effective

• Case Management

• Private Sector Case Management

• Proactive Case Management

• Intermittent Preventive Treatment of Malaria in Infants

• Seasonal Malaria Chemoprevention

• Net Distribution

<60% Effective

• Routine Surveillance

• Larval Source Management

Table 2: Estimated Synergistic Intervention Efficacy Towards Reduced Malaria Morbidity

>95% Effective

• Indoor Residual Spraying + Intermittent Preventive Treatment of Malaria in Pregnancy

• Indoor Residual Spraying + Case Management

• Nets + Indoor Residual Spraying

80-95% Effective

• Case Management + Intermittent Preventive Treatment of Malaria in Pregnancy

• Intermittent Preventive Treatment of Malaria in Pregnancy + Nets

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Appendix B: Malaria Intervention Efficacy Rankings, Continued

Table 3: Estimated Single Intervention Efficacy Towards Reduced Malaria Mortality

>95% Effective

• Proactive Case Proactive Case Management

• Indoor Residual Spraying

• Focal Indoor Residual Spraying

• Intermittent Preventive Treatment of Malaria in Infants

• Behavior Change Communication

80-95% Effective

• Case Management

• Integrated Community Case Management

• Private Sector Case Management

• Seasonal Malaria Chemoprevention

60-80% Effective

• Intermittent Preventive Treatment of Malaria in Pregnancy

• Net Distribution

• Focal Screen and Treat

<60% Effective

• Mass Screen and Treat

• Routine Surveillance

• Larval Source Management

Table 4: Estimated Synergistic Intervention Efficacy Towards Reduced Malaria Mortality

>95% Effective

• Indoor Residual Spraying + Intermittent Preventive Treatment of Malaria in Pregnancy

• Case Management + Indoor Residual Spraying

• Nets + Indoor Residual Spraying

80-95% Effective

• Case Management + Intermittent Preventive Treatment of Malaria in Pregnancy

• Case Management + Nets

• Net Distribution + Intermittent Preventive Treatment of Malaria in Pregnancy