# Endnotes

1 More fully: an HAI is a localized or systematic condition resulting from adverse reaction to the presence of infectious agent or its toxins acquired from health care settings that was not incubating or symptomatic at the time of admission to the healthcare facility (Horan et al, 2008).

2 Systematic reviews of antimicrobial drug resistance in Sub-Saharan Africa show that antimicrobial resistance varies widely for different first- and second-line drugs and across geographies from 0% to over 80% (Leopold et al, 2014; Tadesse et al, 2017; Kariuki et al, 2021). Murray et al (2022) report a systematic analysis of the global burden of bacterial antimicrobial resistance in 2019 and produce modeled estimates for resistance of several important pathogens to frontline drugs. The data extracted from the nine countries in the study are presented in Annex 1 section 2. In conclusion, the evidence presented above and in Annex 2 indicates significant variation in AMR rates across different bacteria/viruses, across different first line drugs and across different countries and sub-regions. However, given that the analysis is not conducted by different types of HAI, an average rate is needed for the present study. Rates of AMR are seen below 50%, well above 50%, and also around 50%. Hence, in the base case analysis, an average rate of 50% AMR is used.

3 This gave the following total number of inpatient admissions per country in 2022: Angola 1,423,520; DRC 3,680,000; Ethiopia 2,976,000; Kenya 3,188,916; Madagascar 1,184,000; Mozambique 3,607,172; Sudan 1,874,960; Tanzania 2,035,200; and Uganda 3,304,800.

4 Due to lack of published data available on the total costs of treating HAIs, it is necessary to construct the cost based on the cost per hospital bed day, the additional length of stay due to HAI, and the costs of drugs, procedures and laboratory tests related to the HAI. AMR will also lead to longer length of stay, and additional costs of drugs, procedures and laboratory tests.

5 The highest quality cost studies for different levels of the health systems were from IHME (2014) for Uganda which was extrapolated using PPP to other countries with GDP per capita below US$1,100 (DRC, Ethiopia, Madagascar, Mozambique, Sudan and Tanzania); and IHME (2015) for Kenya, which was extrapolated using PPP to Angola.

6 Gidey et al (2023) for Ethiopia, reflecting health center level - converted to each country using PPP.

7 Aerts et al (2022) for Mozambique, reflecting district hospital level - converted to each country using PPP.

8 Bocoum et al (2019) for Mali, reflecting referral, tertiary, or teaching hospitals - converted to each country using PPP.

9 Based on Ghana data, as follows. Fenny et al (2020) found in a teaching hospital additional average length of stay (ALOS) of 4.6 days for patients with SSI. Otieku et al (2023) found ALOS was 3 days longer between the HAI (non-AMR) group and the control group.

10 Otieku et al (2023) found patients in the AMR cohort stayed approximately 5 more days compared with HAI patients and 8 more days compared with uninfected cohorts.

11 As follows:

|**Variable**|**Angola**|**Cong**<br>**o,**<br>**Dem.**<br>**Rep. **|**Ethiopia**|**Kenya**|**Madag.**|**Mozam.**|**Sudan**|**Tanz.**|**Uganda**|
|---|---|---|---|---|---|---|---|---|---|
|% women employed|37.0%|60.3%|57.6%|62.9%|83.6%|78.1%|28.3%|76.2%|37.4%|
|% men employed|50.8%|66.8%|79.2%|73.0%|89.2%|80.1%|70.1%|85.0%|57.1%|
|Dailyvalue-added in agriculture|$8.9|$2.4|$4.6|$10.2|$2.1|$2.6|$22.0|$4.9|$4.3|
|Dailyvalue-added in industry|$284|$40|$23|$53|$9|$18|$50|$57|$60|

12 An exploratory literature review with a focus on the nine countries of the Hutton et al (2023) study found seven studies. Two studies presented case fatality of only HAI patients, while five compared HAI and non-HAI patients. Differences in case fatality ranged from 4.2% to 14.6%, with two studies from Ethiopia with a difference of 6.9%.

13 US$8 million VSL value from the USA (Banzhaf, 2022) converted to each country based on GDP per capita differential with the USA and using an income elasticity of 1.5. In sensitivity analysis, an income elasticity of 1.2 was used.

14 Experts in UNICEF’s country offices were surveyed for information regarding the average costs per facility of improving from an absence of WASH and waste services to meeting the JMP’s monitoring definitions for basic services. These per facility costs were applied to the current coverage estimate of WHO and UNICEF Joint Monitoring Programme in 2019 to estimate total costs per country, based on which an average was estimated across the 47 countries.

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