Attributable societal cost of antimicrobial resistance in Ghana: A microsimulation study focusing on socio-demographic groups

Attributable societal cost of antimicrobial resistance in Ghana: A microsimulation study focusing on socio-demographic groups | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Attributable societal cost of antimicrobial resistance in Ghana: A microsimulation study focusing on socio-demographic groups Evans Otieku, Ama Pokuaa Fenny, Robert Ofori Amoah, Monica Jejeti, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6574614/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Investment case for antimicrobial resistance (AMR) is needed to stimulate the willpower of national governments to invest in AMR mitigation. Objective: We performed a microsimulation analysis predicting the potential societal cost savings for reducing the prevalence of AMR in Ghana. Methods: This study combined bacterial resistance epidemiology and cost data from Ghana to perform a microsimulation analysis focusing on socio-demographic groups, predicting the potential societal cost savings should Ghana mitigate AMR. Case definition was enterobacterial 3GC resistant infections, methicillin-resistant staphylococcus aureus (MRSA), and multi-drug-resistant mycobacterial tuberculosis. Costs were calculated under a business-as-usual scenario considering a 2% annual population growth rate, 5% discount rate for future costs, age-specific resistant risk profile, and a seven-year time horizon from 2024 to 2030. We reported the cost in purchasing power parity equivalent in international United States dollars, adjusting for mortality, age groups, gender, and wealth quintile. Results: Using 0.124 and 0.109 resistant probability risk between females and males, we predicted almost 78,000 annual AMR infections and about 6,300 attributable deaths. MRSA and 3GC resistant infections made up 20.2% and 79.2% of the predicted annual infections, corresponding to an estimated mean societal cost of about USD 435 million. In decreasing order of magnitude, the estimated mean annual cost of productivity loss due to AMR-attributable mortality accounted for 40.6% of the mean annual societal cost, followed by the cost to healthcare providers (24.1%), direct medical cost to patients and caregivers (22.4%), productivity loss for surviving patients and caregivers (10.4%), and direct non-medical costs to patients and caregivers (2.6%). Resistant infections in under-five children and persons above 60 years contribute 48.2% and 26.9% of the estimated annual societal cost, respectively. Except for the number of resistant infections, the estimated mean annual costs between wealth quintile groups were significantly different (p=0.03) due to differences in productivity costs between wealth quintile groups. Conclusion. The study shows that AMR-attributable societal cost implications are enormous, requiring a concerted effort by society to mitigate the development and spread of AMR organisms. Health Economics & Outcomes Research AMR Societal Cost Public Health Microsimulation Health Policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Strengths and limitations of the study This study used practical and context reliable data with input from multidisciplinary experts to project the societal cost of antimicrobial resistance in Ghana. It makes significant contribution to knowledge and literature as it is the first to provide empirical evidence on societal cost of AMR in sub-Saharan Africa using a proven microsimulation technique. Given that human behaviour is largely unpredictable, a key limitation is that the odds of hospital admission and remission due to AMR infections in the population may become worse or better in the future compared to present circumstances. Another limitation is case definition represents only a fraction of resistant infections and may lead to an underestimation. INTRODUCTION There is a global consensus that the mounting public health threat attributable to antimicrobial resistance (AMR) requires investment case studies to persuasively mobilize the willpower of policymakers and the wider global health funding institutions to invest in National Action Plans (NAPs) to mitigate AMR. The health and economic threat posed by AMR increases considerably when viewed with the one health lens [1-4]. Estimates by the Institute of Health Metric and Evaluation indicate that AMR contributed to an estimated 4.95 million deaths worldwide in 2019 [5]. Between regions, the Western Sub-Saharan Africa recorded the highest case fatality rate [5]. Again, the World Bank predicted that AMR may cause a 3.8% decline in global gross domestic product (GDP) by 2050 if no concrete steps are taken to mitigate the spread of AMR [6]. The projected decline in GDP is expected to be more (4.4%) for lower middle-income countries like Ghana. Data from grey literature shows that as of December 2023, only 25% of the 164 countries that have developed National Action Plans (NAP) have allocated a budget for their implementation[1]. Progress in these countries has been sluggish, primarily due to a lack of commitment and prioritization by national governments. For instance, a study in WHO African countries highlight major gaps in NAP implementation [7]. Likewise, the end-term assessment of Ghana’s NAP shows that most NAP interventions and activities budgeted for between 2017 and 2022 were not funded and even those funded were primarily through external support by donor agencies, indicating the level of underfunding by national government [8,9]. Deductively, national governments need to be persuaded more with investment case studies like this current one to galvanise domestic resource mobilization for AMR interventions. Our earlier study in Ghana shows that if AMR is prevented, patients and healthcare providers could avoid 5 extra hospital days corresponding to an estimated mean patient cost savings of USD1300, and provider cost savings of USD 929 per case (10,11). As important as the existing evidence may be, the recommendation by the Global Leaders Group on AMR is for research to forecast the societal cost of AMR over longer time horizon to incentivize policy makers to commit resources to tackling AMR [12]. Resistant infections are harder to treat and imposes increase costs to society, thereby hindering universal health coverage, more broadly. Therefore, to stimulate policy discussion and investment in AMR mitigation within the context of pursuing Sustainable Development Goal 3 by 2030, we aimed to predict the societal economic cost attributable to AMR in Ghana over a seven-year time horizon spanning 2024 to 2030. Furthermore, we argue that exposure to AMR infections, use of health care and costs may vary with age, gender and wealth. For instance, children and elderly may be more susceptible; women when giving birth, caring for sick children and elderly may be more exposed; poor people may be more exposed, richer people may be more insisting on antibiotics, possibly because the value of a lost working day is higher than the cost of antibiotics. Therefore, we aim to take to consider the impact of AMR on different socio-demographic groups to understand the broader economic benefits to society if AMR is prevented. [1] Unpublished annual review of AMR surveillance data in Ghana by the National Technical Working Group. METHODS Design We performed a microsimulation modelling using real world national data on AMR epidemiology and costs to quantify the societal cost attributable to AMR in Ghana. The analysis focused on socio-demographic groups, that is, men and women of all ages and wealth quintile defined as the poorest segment of the population (quintile 1) to the richest (quintile 5). Setting. Ghana is a lower middle-income country in West Africa with a population of about 34 million people in 2023 and a ten-year mean annual population growth rate of about 2.1% [13]. The prevalence of community and hospital acquired AMR infections in Ghana vary depending on the microorganism. For instance, among the leading causative pathogens of AMR, available ongoing surveillance data shows that an average of 66% resistant bacterial infections in Ghana are hospital-acquired, including Acinetobacter spp . 95%, Enterococcus Faecalis 75%, K. Pneumoniae 60%, E. Coli 34%, S. aureus 37% [14]. Thus, a major mechanism of resistance of concern in Ghana is resistance to 3GC as has been highlighted by Donkor and colleagues [15]. Model overview The microsimulation involved input data from four modules, comprising, population demographic module, infection epidemiology module, healthcare resource use and expenditure module, and labour market module for Ghana (Figure 1). First, we designed a demographic module to replicate population characteristics in ways that synthesize individual attributes annually depending on gender, age, and wealth quintile. With a pooled 2.1% annual population growth, we considered birth, death and net migration for precision simulation and assumed constant birth rate, age and gender-specific mortality rates. Next, we used bacterial infection epidemiology module to forecast the annual AMR infection episode. We considered different rates of both hospital and community-acquired resistant bacterial infections caused by gram-negative pathogens (n=19) and gram-positive organisms (n=5) in all socio-demographic groups. For community-acquired infections, we focused on severe cases leading to hospital admissions of which the onset of bacteraemia is clinically confirmed on admission or within 48 hours post admission, whereas for nosocomial infections, onset of bacteraemia is confirmed 48-hours post admission. In both scenarios, case definition was enterobacterial 3GC resistant infections, methicillin-resistant staphylococcus aureus (MRSA), and multi-drug-resistant mycobacterial tuberculosis. Data was sourced from primary and secondary sources, including expert opinion. Consistent with WHO priority list of resistant pathogens [17], most dominant resistant organisms included in the model were Klebsiella spp., pseudomonas aeruginosa, Citrobacter spp. Enterobacter spp., S. aureus, mycobacterium tuberculosis , and E. coli . For each organism, the rate of community and hospital-acquired resistant infections were considered to understand their potential impact on population health. Beside incidence or rate of resistant infection, the risk of sequelae and fatality were simulated and updated to depict individual health status at annual transition point considering every infection episode to be independent. For AMR-attributable mortality, data was sourced from the Institute of Health Metrics and Evaluation (IHME) [18]. We quantified the overall impact of AMR on disability-adjusted life years (DALY) by multiplying disability weights 0.125 and 0.655 for moderate and severe infections depending on infection syndrome. The applied disability weights were derived from the OECD Choice modelling [16]. Next, we used weighted average length of stay (LOS) and cost data to develop healthcare resource consumption and costs module to simulate the potential cost implications of AMR for Ghana. Doing so, we combined previously published data on LOS and costs attributable to AMR in two tertiary hospitals [10,11] with additional data from 12 secondary hospitals across the three geographic belts in Ghana. For patients and carers, cost included direct medical and non-medical costs for admission and post-discharge hospital care attributable to AMR infections. For providers, the mean cost per hospital bed day is multiplied by the mean length of stay due to AMR infections. By considering costs for patients, carers, and providers, the analysis focused on quantifying AMR cost from societal perspective. In our estimation, the cost to the government is embedded in both patient and provider cost because, the government pays the salaries of health professionals in public and most mission hospitals where patients have equal access to care. The government also support the provision of public hospital infrastructure like the construction of microbiology laboratory and procurement of essential capital-intensive equipment. Patients who possess valid national health insurance also benefit from government financial support through cost-sharing mechanisms that involves government fiscal allocation to the National Health Insurance Authority to augment premium contributions by patients that enables subsidised cost of healthcare. Finally, we design a labour market module considering 30-day morbidity for surviving patients and discounted productive years lost due to mortality. Concerning surviving patients, the analysis valued the cost of presenteeism and absenteeism from work as we have shown elsewhere [10]. For non-surviving patients, we quantified the discounted cost of lost productivity due to AMR-attributable mortality (premature deaths), computed as the number of working years lost up to the compulsory legal retirement age 60 years multiplied by the discounted average annual gross salary obtained from the national labour statistics report for 2023 [19] for formal sector workers and a different source for informal sector workers.[2] All wage data were age and sex specific (Table 1). Following the work of Krijkamp and colleagues [20], we performed the simulation using R-programming application. Model transparency, validation and reporting followed the approach used by the Organization for Economic Cooperation and Development for estimating the cost of AMR in Europe [16]. Additionally, reporting quality was checked using the Consolidated Health Economic Evaluation Reporting Standard Checklist.[3] Table 1. Summary model data sources Data Ghana Source Annual population growth rate x World Bank [15] Percentage of the population with access to general medical care for community-acquired AMR infections x Harmonized health assessment report [21] Infection Prevention and Control compliance in hospitals X Harmonized health assessment report [21] Percentage of the population with access to ICU care due to community-acquired infections x Pooled national estimate, 2023 Hospital-acquired AMR prevalence rate (by age and gender, and wealth quintile) x Pooled national estimate, 2023 Extra days of hospital admission due to AMR (by age, gender and wealth quintile) Pooled national estimate, 2023 Number of outpatient care visits due to AMR (by age and gender, and wealth quintile) x Pooled national estimate, 2023 Mean per capita cost of inpatient care per day (by age and gender, and wealth quintile) x Pooled national estimate, 2023 Mean per capita cost of ICU care per day (by age and gender, and wealth quintile) x Pooled national estimate, 2023 Share of the workforce in the informal sector (by age and gender, and wealth quintile) x Labor Statistics Report [19] Average monthly wage for formal sector workers (by age and gender, and wealth quintile) x Labor Statistics Report [19] Average monthly wage for informal sector workers (by age and gender, and wealth quintile) X Baah-Boateng and Vanek, 2020 Sensitivity analysis As we observed significant uncertainty in mortality data from the Institute of Health Metrics and Evaluation, we performed a deterministic sensitivity analysis using the 95% uncertainty intervals to measure changes in the simulated societal cost attributable to premature mortality. We also considered using the extreme parameter values in the one-way deterministic sensitivity analysis to measure the potential maximum and minimum variations in the simulated mean endpoint costs. Likewise, direct medical cost parameters were varied for precision consideration. [2] Baah-Boateng, William, and Vanek, Joann. "Informal Workers in Ghana: A Statistical Snapshot." WIEGO Statistical Brief No. 21 , WIEGO, 2020. https://www.wiego.org/research-library-publications/informal-workers-ghana-statistical-snapshot/ [3] Husereau D, Drummond M, Augustovski F, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Explanation and Elaboration: A Report of the ISPOR CHEERS II Good Practices Task Force. Value Health 2022;25. doi:10.1016/j.jval.2021.10.008 RESULTS Predicted episode of AMR resistant infections The simulation predicted 77,760 resistant bacterial infections annually based on age-specific resistant risk profile, of which about 60% occur in children 60 years old in the business-as-usual scenario (Figure 2). Excluding mycobacterium tuberculosis, up to 66% of the predicted resistant infections are hospital-acquired driven mostly by MRSA, Streptococcus pneumoniae, E. coli, and K. pneumoniae. Subgroup analysis shows that resistant infections are relatively higher in males below 5 years than in females. However, this dynamic changes beyond 5 years old, with a relatively higher number of resistant infections in females than males (Table S1). As expected, the predicted mean difference in the annual number of resistant bacterial infections was relatively higher in the lowest wealth quintile group compared to the highest wealth quintile and the difference was statistically significant (p=0.009). Predicted mortality attributable to AMR infections The simulation shows that under a business-as-usual scenario, up to 6,269 people may die annually from AMR infections. Overall, the analysis by infection syndrome shows that bloodstream infections, considered the most severe form of infections, may contribute to an estimated 41.1% of the mortalities, followed by 24.4% for lower respiratory tract infections (LRIs), 16.7% for peritoneal/abdominal infections, etc. (Table 2). Between causative organism types, we projected gram-negative bacterial infections to contribute to about 59.1% of the predicted annual mortalities. Between age groups, 37.3% of the predicted mortalities may occur in children <5yrs alone compared to 16.5% in the legally mandated working-age population 15 to 60 years old. Between wealth quintile groups, 61.3% of the predicted mortalities may occur in the first two poorest quintiles compared to 29.1% deaths in the fourth and fifth quintiles. Table 2. Predicted annual mortalities due to AMR infections Infection syndrome Gram negative resistant bacterial infections N (%) Gram positive resistant bacterial infections N (%) Total N (%) Bloodstream 1981 (53.5) 597 (23.3) 2,578 (41.1) Lower respiratory infections & thorax - 1532* (59.8) 1532 (24.4) Peritoneal & abdominal infection 835 (22.5) 209 (8.1) 1044 (16.7) Tuberculosis 407 # (11.0) - 407 (6.5) Meningitis & central nervous system infection 339 (9.1) - 339 (5.4) Bacterial skin infection - 199 (7.8) 199 (3.2) Urinary tract infection & pyelonephritis 93 β (2.5) 2 (0.05) 95 (1.5) Endocarditis & cardiac infection 21 (0.6) 17 (0.7) 38 (0.6) Diarrhoea 19 (0.5) 5 (0.2) 24 (0.4) Bones & joints infections 11 (0.3) 2 (0.05) 13 (0.2) Predicted annual mortalities 3,706 (100.0) 2563 (100.0) 6,269 (100.0) *Predominantly caused by Streptococcus pneumoniae , # Highly controversial mycobacterium organism in terms of classification under gram negative and gram positive bacterial [22], β Mostly caused by E. coli infection. Predicted societal costs attributable to bacterial resistance infections From a societal perspective, the modelling result shows that AMR infections may cost Ghana an estimated average of $435 million annually between 2024 and 2030. Table 3 shows the projected disaggregated costs, including direct medical costs, measured as the systemic cost of healthcare due to AMR borne by patients and their caregivers, amounts to 22.4% of the estimated annual societal costs while direct non-medical costs average $11.3 million per annum, equivalent to 2.6 of the projected total cost. Indirect costs of productivity loss to surviving patients and those who succumb to resistant bacterial infections will amount to USD 221.9 million equivalent to 51% of the estimated mean yearly societal costs. The cost to healthcare providers contributes to 24%. With an overall cost change factor of about 1.8, the estimated endpoint costs will almost double between 2024 and 2030. Table 3. Summary of predicted annual societal costs attributable to AMR infections in 2024 and 2030 (2024 PPP adjusted in international US$) Cost components Predicted average cost over study duration (in million USD) Predicted average cost in 2024 (in million USD) Predicted average cost in 2030 (in Million USD) % of total cost Patient out-of-pocket expenditures Direct medical cost 97.5 67.9 123.5 22.4 Direct non-medical cost 11.3 7.8 14.2 2.6 Provider costs 104.4 73.2 132.9 24.0 Sub-total Direct costs 213.2 148.9 270.7 Indirect costs (productivity loss) Indirect cost for surviving patients 45.2 31.7 57.5 10.4 Indirect cost due to mortality 176.6 123.3 224.1 40.6 Sub-total Indirect costs 221.9 155.0 281.6 Total 435.1 303.9 552.3 100.0 Predicted cost by gender and wealth quintile About 48% of the costs will be due to infections in under-five children. Those between 40 and 49 years old are least susceptible to the estimated costs due to a comparatively lower rate of bacterial-resistant infections in that age group (Figure S1). Again, the simulation shows that between male and female genders, the projected annual societal cost is about seven million dollars higher for under-five males than female (108 million for <5-year males versus 101million for <5-year females), but that changes from age five upwards, making infections in females account for 51.8% of the yearly cost (Figures S2 & S3). Between wealth quintile groups, we observed that though the population in the fourth and fifth quintiles may have a relatively lower rate of infections, they contribute to 41.3% of the estimated annual societal costs due to higher value of a day of lost work (Figure 3). Sensitivity results Results from the multiway sensitivity analysis shows the estimated number of AMR attributable mortality could range between 1,567 and 9,113 if all lower and upper uncertainty values replaced mean mortality estimate, respectively. In decreasing order of magnitude, the one-way sensitivity results show the estimated mean mortality is more sensitive to bloodstream infections (27%), lower respiratory infections (22%), mycobacterium tuberculosis (19%), etc (Figure 4). By replacing the 95% uncertainty values with the extreme range values for disease specific mortality in a one-way sensitivity analysis, we observed the estimated mean mortality at baseline may reduce by 72% or increase by 109% (Figure S4). Additionally, we observed that the estimated annual societal cost is about 31% sensitive to probability of mortality, followed by length of stay (19.4%), probability of resistant infection (15%), etc (Figure 5). DISCUSSION This study used country level data to project the societal cost attributable to AMR in a business-as-usual scenario using a microsimulation analysis and found that AMR could have profound health and cost implications for Ghana if bacterial resistant infection rates persist at current level. Overall, more than 6,200 patients may succumb to AMR and the estimated total average cost of resistance infections to society was about $ 435 million annually, of which direct medical and non-medical cost to households account for 25% while the cost to healthcare providers amount to 24%. In our estimation, the cost of productivity loss for surviving patients and premature deaths for the working age population contribute to 51% of the projected cost impact of AMR. More so, we observed during model development that six out of the 24 pathogens included in the simulation account for 77% and 61.8% of the estimated mortalities and costs, respectively. These organisms were K. pneumoniae, S. aureus, Acinetobacter spp. E. coli, Enterococcus spp. and Mycobacterium tuberculosis. Sensitivity results suggest the probability of mortality, length of stay and the probability of resistant infections were among the dominant drivers of the estimated costs. For instance, assuming AMR-attributable mortality is averted, Ghana could reduce the projected annual cost by 40% and if hospital-acquired infections are prevented the cost savings will be about 60%. In a worst-case scenario considering upper values of the cost parameter uncertainties, we project the estimated base annual cost to increase by 17.3%. The simulation considered several assumptions driving AMR spread in Ghana. Regarding community-acquired infections and the role of society in general, we reiterate that growing income level in Ghana means the middle to upper-wealth group population is expanding. This change in wealth may affect the projected costs in various ways. This change may affect the projected costs in various ways. On one hand, the expansion may drive inappropriate antibiotic use as wealthy people are more likely to press providers for expensive and reserved antibiotics when ill [ 23 , 24 ]. On the other hand, the lower wealth quintile groups, generally classified as the poor people in society, are more likely to disregard general infection prevention and control practices based on limited access to improved water and sanitation facilities [ 25 ] and are more likely to use leftover antibiotics or practice self-medication more generally [ 26 ]. Also, they are least likely to seek hospital care early and cannot afford the cost of treatment for AMR infections. The potential implications of this mixed population health behaviours may increase AMR spread when considering the behavioural factors influencing the natural causes of antibacterial resistance [ 27 – 29 ]. The implication of the result is multifaceted as we have shown elsewhere that beyond the estimated societal costs lies the impact of AMR on healthy life expectancy and disability-adjusted life years. In that report, we projected it will cost an average amount of USD 8.82 per capita to implement 11 interventions with demonstrated complementarity efficiency to dramatically reduce both nosocomial and community-acquired AMR infections (see Supplementary Table 2). Some of the interventions include scaling up antimicrobial stewardship programs in hospitals and communities, improved food safety, mass media campaign and prescriber education, among others. Because nosocomial infections account for about 66% of resistant bacterial infection outbreak in Ghana, we argue that investment in IPC for example, should be a top priority by way of investment as it will require 11.3 million annually to achieve 70% IPC compliance coverage nationwide. While the simulated AMR-attributable mortalities among the working-age group are minimal compared to children and the elderly, the potential economic and psychological impact from a societal perspective may be more significant than the individual impact, as this select group is mostly the breadwinners in society. This could be a reason why everyone should be concerned about preventing bacterial infections, considering that up to 74% of the projected direct medical costs may be covered by families of AMR patients and society at large, including philanthropic gestures, government social welfare contributions to poor patients, and donations by religious groups [ 10 ]. Concerning the implications for hospitals, bring to question the financial incentive for IPC compliance and mitigation of hospital-acquired infections. In our estimation, hospitals, can save about $ 104 million annually if hospital IPC measures are effectively implemented within the NAP framework [ 31 ]. For instance, available evidence suggests only 39% hand hygiene compliance by physicians before and after patient contact as documented in Ghana’s AMR Policy formulation consideration [ 29 ] and 28% of health facilities comply with IPC guidelines as reported in the 2023 harmonized health facility assessment report [ 21 ]. The strength of this study involves the use of practical and context reliable data with input from multidisciplinary experts in the field of microbiology, pharmacy, health economic evaluation and decision modelling, health promotion, health policy and systems research, development economics and development studies. Moreover, this study makes significant contribution to literature in LMIC. While microsimulation is not a new field in science, only a few studies in Europe have utilized similar approach to quantify the cost of AMR [ 16 , 31 ]. Thus far, our study is the only study doing same in sub-Saharan Africa and clearly indicate the perspective of the analysis, which builds on our previous work looking at the patient and provider costs of AMR [ 10 , 11 ]. This study faced some limitations. Except for one study published in 2015 [ 32 ], it was difficult obtaining AMR epidemiology data disaggregated into community and hospital-acquired infections. Therefore, we relied on expert opinion based on ongoing national AMR surveillance data to quantify the proportion of community-acquired AMR infections in Ghana. By focusing on AMR infections in humans, we may underestimate the economic impact of AMR on animals, plants and the environment. However, the one health implications of AMR suggest some resistant bacterial in humans originate from the environment, especially in the case of community-acquired infections [ 1 ]. Given that human behaviour is largely unpredictable, the odds of hospital admission and remission due to AMR infections in the population may become worse or better in the future compared to present circumstances. Additionally, source data for projected AMR-attributable mortality has wide uncertainty intervals that may contribute to underestimation or overestimation of the societal cost of AMR. For instance, the controversial nature of mycobacterium tuberculosis has implication for misclassification and wider uncertainty estimate when considering deaths due to TB resistant infection [ 18 , 22 , 33 ]. Though we recognized the impact of these uncertainties on the endpoint estimate, we followed the example of Larsson et al [ 30 ] and dwell on mean input values for this simulation. Another limitation is that our pathogen pool is only a fraction of resistant infections and may lead to an underestimation. Besides hospital-acquired infections, we included community-acquired resistant infections that patients would be admitted and treated in hospitals using probability estimate from ongoing national AMR surveillance. This means that we may underestimated the cost for excluding community infections that are not reported in hospitals. CONCLUSION This study shows that AMR-attributable societal cost implications are enamours, requiring a concerted effort by society to mitigate the development and spread of AMR organisms. Therefore, prioritization of investment in the implementation of national action plans by government and all interested stakeholders is the solution to avert the projected societal costs attributable to AMR. Declarations Aknowledgement . The authors are grateful for the support received from clinical microbiologists in volunteering reliable AMR surveillance data for this study. We also appreciate the interest, feedback and support received from the scientific community during international and local conference presentations of our abstract findings. Contributors. Conceptualization:EO,UE; Data curation and investigation: EO, MJ, AKL, ROA; Methodology: EO, APF, UE, MJ; Formal analysis and writing original draft: EO; Project Administration: EO, APF, UE, AKL; Supervision: APF, AKL, UE, GKH; Validation: MJ, ROA, GKH, AKL, APF, UE; Visualization: EO; Writing reviews and editing: GKH, AKL, APF, UE. Funding. Thisstudyis an extension of a PhD research projection that received training supplement funding from the Graduate School of Health at Aarhus University to cover subsistence allowance. Grant/Award number: N/A Competing interests : None Ethics approval statement. For the supplementary data collection,the study received ethics approval from the Ethics Review Committee of the Faculty of Health Sciences at Ghana Christian University College in Accra with registration number GH-FOHS-ERC-S20249. Data availability statement. All data used for this study are cited, included in the paper or attached as supplementary file.Microsoft excel version of the model output is available upon request to the corresponding author due to institutional guidelines. References Morel CM, Alm RA, Årdal C, Bandera A, Bruno GM et al; GAP-ON€ network. A one health framework to estimate the cost of antimicrobial resistance. Antimicrob Resist Infect Control . 2020 Nov 26;9(1):187. https://doi.org/10.1186/s13756-020-00822-6 The Brooklyn Antibiotic Resistance Task Force. The cost of antibiotic resistance: effect of resistance among Staphylococcus aureus, Klebsiella pneumonia, Acinetobacter Bauman pseudomonas aeruginosa on length of hospital stay. 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Attributable Patient Cost of Antimicrobial Resistance: A Prospective Parallel Cohort Study in Two Public Teaching Hospitals in Ghana. Pharmacoecon Open . 2023; 7(2):257-271. https://doi.org/10.1007/s41669-022-00385-9 Otieku E, Kurtzhals JAL, Fenny AP, Ofori AO, Labi AK, Enemark U. Healthcare provider cost of antimicrobial resistance in two teaching hospitals in Ghana. Health Policy Plan . 2024; 39(2):178-187. https://doi.org/10.1093/heapol/czad114 Global Leaders Group (GLG) on AMR. Towards specific commitments and action in the response to antimicrobial resistance, 2024. Available: https://www.amrleaders.org/resources World Bank Group. Population growth (annual %) – Ghana. 2024. Available: https://data.worldbank.org/indicator/SP.POP.GROW?locations=GH Ghana AMR Surveillance Team. Global Antimicrobial Surveillance System survey in Ghana, 2024. Accra, Ghana Donkor, E.S.; Muhsen, K.; Johnson, S.A.M.; Kotey, F.C.N.; Dayie, N.T.K.D.; Tetteh-Quarcoo, P.B.; Tette, E.M.A.; Osei, M.-M.; Egyir, B.; Nii-Trebi, N.I.; et al. Multicenter Surveillance of Antimicrobial Resistance among Gram-Negative Bacteria Isolated from Bloodstream Infections in Ghana. Antibiotics, 2023; 12, 255. Organization for Economic Co-operation and Development (OECD). Embracing a one health framework to fight antimicrobial resistance, 2023. Available: https://www.oecd.org/en/publications/embracing-a-one-health-framework-to-fight-antimicrobial-resistance_ce44c755-en.html WHO. WHO bacterial priority pathogens list, 2024: Bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance, 2024. Available: https://www.who.int/publications/i/item/9789240093461 Institute of Health Metrics and Evaluation (IHME). The burden of antimicrobial resistance (AMR) in Ghana AMR represents a global challenge, 2023. Available: https://www.healthdata.org/sites/default/files/2023-09/Ghana.pdf Ghana Statistical Service. Third quarter labour statistics report. 2023. Available: https://statsghana.gov.gh/searchread.php?searchfound=NzkyMjcwNDM5NzAuMjUz/search/pn13pn3q80 Krijkamp EM, Alarid-Escudero F, Enns EA, Jalal HJ, Hunink MGM, Pechlivanoglou P. Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial. Med Decis Making . 2018; 38(3):400-422. https://doi.org/10.1177/0272989X18754513 Ghana Health Service. Ghana harmonized health facility assessment report, 2023. Ghana Health Service, Accra, Ghana Fu LM, Fu-Liu CS. Is mycobacterium tuberculosis a closer relative to gram-positive or gram-negative bacterial pathogens? Tuberculosis (Edinb) . 2002; 82(2-3):85-90. https://doi.org/10.1054/tube.2002.0328 Radyowijati A, Haak H. Improving antibiotic use in low-income countries: an overview of evidence on determinants. Soc Sci Med . 2003; 57(4):733-44. https://doi.org/10.1016/s0277-9536(02)00422-7 Mallah, N., Orsini, N., Figueiras, A. et al. Income level and antibiotic misuse: a systematic review and dose–response meta-analysis. Eur J Health Econ 23 , 1015–1035 (2022). https://doi.org/10.1007/s10198-021-01416-8 Shears P. Poverty and infection in the developing world: healthcare-related infections and infection control in the tropics. J Hosp Infect . 2007; 67(3):217-24. https://doi.org/10.1016/j.jhin.2007.08.016 Darby, E. M., Trampari, E., Siasat, P., Gaya, M. S., Alav, I., Webber, M. A., & Blair, J. M. A. (2022). Molecular mechanisms of antibiotic resistance revisited. Nature Reviews Microbiology , 2022; 21: 280-295 Vikesland, P., Garner, E., Gupta, S., Kang, S., Maile-Moskowitz, A., & Zhu, N. Differential drivers of antimicrobial resistance across the world. Accounts of Chemical Research , 2019; 52(4), 916-924. Roberts, M.G., Burgess, S., Toombs-Ruane, L.J., Benschop, J., Marshall, J.C., & French, N.P. Combining mutation and horizontal gene transfer in a within-host model of antibiotic resistance. Mathematical Biosciences , 2021; 339, 108656. Government of Ghana. Policy on antimicrobial use and resistance. First Edition, 2017. Ministry of Health, Ghana, Accra; 2017 Otieku E, Hedidor G, Lerouge A, Morel C, Twum-Barimah AT, Buabeng KO, Labi A-K, Sasu B, Salifu A, Yevutsey SK, Wekem MA, Azaglo GS, Kisseh R, Kudjawu J, Opintan JA, Investment case for tackling antimicrobial resistance in Ghana. 2025, WHO Ghana Country Office, Accra, Ghana. Government of Ghana. National action plan for antimicrobial resistance in Ghana. 2017. Ministry of Health, Ghana, Accra; 2017 Larsson S, Prioux M, Fasth T, Ternhag A, Struwe J, Dohnhammar U, Brouwers L. A microsimulation model projecting the health care costs for resistance to antibacterial drugs in Sweden. Eur J Public Health . 2019; 29(3):392-396. https://doi.org/10.1093/eurpub/cky209 Opintan JA, Newman MJ, Arhin RE, Donkor ES, Gyansa-Lutterodt M, Mills-Pappoe W. Laboratory-based nationwide surveillance of antimicrobial resistance in Ghana. Infect Drug Resist . 2015; 8:379-89. https://doi.org/10.2147/IDR.S88725 Noguchi Memorial Institute for Medical Research NMIMR. Tuberculosis, 2024. NMIMR, University of Ghana, Legon. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6574614","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450818971,"identity":"0b720387-97c3-493b-b76e-830573f36f66","order_by":0,"name":"Evans 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Ghana","correspondingAuthor":false,"prefix":"","firstName":"Ama","middleName":"Pokuaa","lastName":"Fenny","suffix":""},{"id":450818973,"identity":"ab1e4607-feb1-4af7-bb17-ec72464339b1","order_by":2,"name":"Robert Ofori Amoah","email":"","orcid":"","institution":"Ghana Christian University College","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"Ofori","lastName":"Amoah","suffix":""},{"id":450818974,"identity":"1fb6048d-f4d2-42b6-af3f-4a1d0cb92e8f","order_by":3,"name":"Monica Jejeti","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Monica","middleName":"","lastName":"Jejeti","suffix":""},{"id":450818975,"identity":"f0ec7b98-cea7-4627-b9cb-3829e68208ba","order_by":4,"name":"Appiah-Koran Labi","email":"","orcid":"","institution":"Korle Bu Teaching 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00:20:00","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6574614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6574614/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81965582,"identity":"4e467f7a-2f21-4786-b2fe-bd12681f51e4","added_by":"auto","created_at":"2025-05-05 11:29:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210783,"visible":true,"origin":"","legend":"\u003cp\u003eA microsimulation modelling predicting the societal cost of AMR infections in Ghana\u003c/p\u003e\n\u003cp\u003eSource: Adapted and modified from OECD microsimulation model [16].\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6574614/v1/9cdd90b8c7411c1ea5cf2092.png"},{"id":81966709,"identity":"2d907ff8-48d1-4222-ba76-5fd6425ff6df","added_by":"auto","created_at":"2025-05-05 11:37:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33877,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted annual episode of AMR infections by wealth quintile and gender, 2030\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003eQ1 – poorest quintile, Q5 – wealthiest quintile\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6574614/v1/147f07dcf1a325303ab5e3a2.png"},{"id":81965558,"identity":"ccf94973-eb1f-4284-8b51-d93dbd33ef5b","added_by":"auto","created_at":"2025-05-05 11:29:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25437,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated societal cost by gender and wealth quintile\u003c/p\u003e\n\u003cp\u003eNote. Q1 – poorest quintile, Q5 – richest quintile\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6574614/v1/6cfc1689c23f4233ed55c806.png"},{"id":81965565,"identity":"260ed038-4499-4033-903c-7360e3daff00","added_by":"auto","created_at":"2025-05-05 11:29:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47053,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity result for simulated mortality estimate\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6574614/v1/9d009898d85817141aeb0262.png"},{"id":81965564,"identity":"1bf3d38f-b26a-4851-89c0-9bd393254c28","added_by":"auto","created_at":"2025-05-05 11:29:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32709,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity result for simulated societal cost estimate\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6574614/v1/c8dea1f6b5c0286a3cd7d053.png"},{"id":81969034,"identity":"4bd3f31b-e85d-489f-866a-5caac8c92db3","added_by":"auto","created_at":"2025-05-05 12:01:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1138073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6574614/v1/a2052e87-8b8c-48bd-a657-8e2264f7a6bc.pdf"},{"id":81967954,"identity":"19767b85-dca9-4a01-80e1-cbea226c2e6d","added_by":"auto","created_at":"2025-05-05 11:45:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6574614/v1/64a51b2d0e7ac5b1bb85a72a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAttributable societal cost of antimicrobial resistance in Ghana: A microsimulation study focusing on socio-demographic groups\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Strengths and limitations of the study","content":"\u003cul\u003e\n \u003cli\u003eThis study used practical and context reliable data with input from multidisciplinary experts to project the societal cost of antimicrobial resistance in Ghana.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIt makes significant contribution to knowledge and literature as it is the first to provide empirical evidence on societal cost of AMR in sub-Saharan Africa using a proven microsimulation technique.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGiven that human behaviour is largely unpredictable, a key limitation is that the odds of hospital admission and remission due to AMR infections in the population may become worse or better in the future compared to present circumstances.\u003c/li\u003e\n \u003cli\u003eAnother limitation is case definition represents only a fraction of resistant infections and may lead to an underestimation.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eThere is a global consensus that the mounting public health threat attributable to antimicrobial resistance (AMR) requires investment case studies to persuasively mobilize the willpower of policymakers and the wider global health funding institutions to invest in National Action Plans (NAPs) to mitigate AMR. The health and economic threat posed by AMR increases considerably when viewed with the one health lens [1-4]. Estimates by the Institute of Health Metric and Evaluation indicate that AMR contributed to an estimated 4.95 million deaths worldwide in 2019 [5]. Between regions, the Western Sub-Saharan Africa recorded the highest case fatality rate [5]. Again, the World Bank predicted that AMR may cause a 3.8% decline in global gross domestic product (GDP) by 2050 if no concrete steps are taken to mitigate the spread of AMR [6]. The projected decline in GDP is expected to be more (4.4%) for lower middle-income countries like Ghana.\u003c/p\u003e\n\u003cp\u003eData from grey literature shows that as of December 2023, only 25% of the 164 countries that have developed National Action Plans (NAP) have allocated a budget for their implementation[1]. Progress in these countries has been sluggish, primarily due to a lack of commitment and prioritization by national governments. For instance, a study in WHO African countries highlight major gaps in NAP implementation [7]. Likewise, the end-term assessment of Ghana’s NAP shows that most NAP interventions and activities budgeted for between 2017 and 2022 were not funded and even those funded were primarily through external support by donor agencies, indicating the level of underfunding by national government [8,9]. Deductively, national governments need to be persuaded more with investment case studies like this current one to galvanise domestic resource mobilization for AMR interventions.\u003c/p\u003e\n\u003cp\u003eOur earlier study in Ghana shows that if AMR is prevented, patients and healthcare providers could avoid 5 extra hospital days corresponding to an estimated mean patient cost savings of USD1300, and provider cost savings of USD 929 per case (10,11). As important as the existing evidence may be, the recommendation by the Global Leaders Group on AMR is for research to forecast the societal cost of AMR over longer time horizon to incentivize policy makers to commit resources to tackling AMR [12]. Resistant infections are harder to treat and imposes increase costs to society, thereby hindering universal health coverage, more broadly. Therefore, to stimulate policy discussion and investment in AMR mitigation within the context of pursuing Sustainable Development Goal 3 by 2030, we aimed to predict the societal economic cost attributable to AMR in Ghana over a seven-year time horizon spanning 2024 to 2030.\u003c/p\u003e\n\u003cp\u003eFurthermore, we argue that exposure to AMR infections, use of health care and costs may vary with age, gender and wealth. For instance, children and elderly may be more susceptible; women when giving birth, caring for sick children and elderly may be more exposed; poor people may be more exposed, richer people may be more insisting on antibiotics, possibly because the value of a lost working day is higher than the cost of antibiotics. Therefore, we aim to take to consider the impact of AMR on different socio-demographic groups to understand the broader economic benefits to society if AMR is prevented. \u003c/p\u003e\n\u003cp\u003e[1] Unpublished annual review of AMR surveillance data in Ghana by the National Technical Working Group. \u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a microsimulation modelling using real world national data on AMR epidemiology and costs to quantify the societal cost attributable to AMR in Ghana. The analysis focused on socio-demographic groups, that is, men and women of all ages and wealth quintile defined as the poorest segment of the population (quintile 1) to the richest (quintile 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGhana is a lower middle-income country in West Africa with a population of about 34 million people in 2023 and a ten-year mean annual population growth rate of about 2.1% [13]. The prevalence of community and hospital acquired AMR infections in Ghana vary depending on the microorganism. For instance, among the leading causative pathogens of AMR, available ongoing surveillance data shows that an average of 66% resistant bacterial infections in Ghana are hospital-acquired, including \u003cem\u003eAcinetobacter spp\u003c/em\u003e. 95%, \u003cem\u003eEnterococcus Faecalis\u003c/em\u003e 75%, \u003cem\u003eK. Pneumoniae\u003c/em\u003e 60%, \u003cem\u003eE. Coli\u003c/em\u003e 34%, \u003cem\u003eS. aureus\u003c/em\u003e 37% [14]. Thus, a major mechanism of resistance of concern in Ghana is resistance to 3GC as has been highlighted by Donkor and colleagues [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microsimulation involved input data from four modules, comprising, population demographic module, infection epidemiology module, healthcare resource use and expenditure module, and labour market module for Ghana (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, we designed a demographic module to replicate population characteristics in ways that synthesize individual attributes annually depending on gender, age, and wealth quintile. With a pooled 2.1% annual population growth, we considered birth, death and net migration for precision simulation and assumed constant birth rate, age and gender-specific mortality rates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we used bacterial infection epidemiology module to forecast the annual AMR infection episode. We considered different rates of both hospital and community-acquired resistant bacterial infections caused by gram-negative pathogens (n=19) and gram-positive organisms (n=5) in all socio-demographic groups. For community-acquired infections, we focused on severe cases leading to hospital admissions of which the onset of bacteraemia is clinically confirmed on admission or within 48 hours post admission, whereas for nosocomial infections, onset of bacteraemia is confirmed 48-hours post admission. In both scenarios, case definition was enterobacterial 3GC resistant infections, methicillin-resistant staphylococcus aureus (MRSA), and multi-drug-resistant mycobacterial tuberculosis. Data was sourced from primary and secondary sources, including expert opinion. Consistent with WHO priority list of resistant pathogens [17], most dominant resistant organisms included in the model were \u003cem\u003eKlebsiella spp., pseudomonas aeruginosa, Citrobacter spp. Enterobacter spp., S. aureus,\u003c/em\u003e \u003cem\u003emycobacterium tuberculosis\u003c/em\u003e, and \u003cem\u003eE. coli\u003c/em\u003e. For each organism, the rate of community and hospital-acquired resistant infections were considered to understand their potential impact on population health. Beside incidence or rate of resistant infection, the risk of sequelae and fatality were simulated and updated to depict individual health status at annual transition point considering every infection episode to be independent. For AMR-attributable mortality, data was sourced from the Institute of Health Metrics and Evaluation (IHME) [18]. We quantified the overall impact of AMR on disability-adjusted life years (DALY) by multiplying disability weights 0.125 and 0.655 for moderate and severe infections depending on infection syndrome. The applied disability weights were derived from the OECD Choice modelling [16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we used weighted average length of stay (LOS) and cost data to develop healthcare resource consumption and costs module to simulate the potential cost implications of AMR for Ghana. Doing so, we combined previously published data on LOS and costs attributable to AMR in two tertiary hospitals [10,11] with additional data from 12 secondary hospitals across the three geographic belts in Ghana. For patients and carers, cost included direct medical and non-medical costs for admission and post-discharge hospital care attributable to AMR infections. For providers, the mean cost per hospital bed day is multiplied by the mean length of stay due to AMR infections. By considering costs for patients, carers, and providers, the analysis focused on quantifying AMR cost from societal perspective. In our estimation, the cost to the government is embedded in both patient and provider cost because, the government pays the salaries of health professionals in public and most mission hospitals where patients have equal access to care. The government also support the provision of public hospital infrastructure like the construction of microbiology laboratory and procurement of essential capital-intensive equipment. Patients who possess valid national health insurance also benefit from government financial support through cost-sharing mechanisms that involves government fiscal allocation to the National Health Insurance Authority to augment premium contributions by patients that enables subsidised cost of healthcare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we design a labour market module considering 30-day morbidity for surviving patients and discounted productive years lost due to mortality. Concerning surviving patients, the analysis valued the cost of presenteeism and absenteeism from work as we have shown elsewhere [10]. For non-surviving patients, we quantified the discounted cost of lost productivity due to AMR-attributable mortality (premature deaths), computed as the number of working years lost up to the compulsory legal retirement age 60 years multiplied by the discounted average annual gross salary obtained from the national labour statistics report for 2023 [19] for formal sector workers and a different source for informal sector workers.[2] All wage data were age and sex specific (Table 1). Following the work of Krijkamp and colleagues [20], we performed the simulation using R-programming application. \u0026nbsp;Model transparency, validation and reporting followed the approach used by the Organization for Economic Cooperation and Development for estimating the cost of AMR in Europe [16]. Additionally, reporting quality was checked using the Consolidated Health Economic Evaluation Reporting Standard Checklist.[3] \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Summary model data sources\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eData\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eAnnual population growth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eWorld Bank [15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003ePercentage of the population with access to general medical care for community-acquired AMR infections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eHarmonized health assessment report [21]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eInfection Prevention and Control compliance in hospitals\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eHarmonized health assessment report [21]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003ePercentage of the population with access to ICU care due to community-acquired infections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003ePooled national estimate, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eHospital-acquired AMR prevalence rate (by age and gender, and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003ePooled national estimate, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eExtra days of hospital admission due to AMR (by age, gender and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003ePooled national estimate, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eNumber of outpatient care visits due to AMR (by age and gender, and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003ePooled national estimate, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eMean per capita cost of inpatient care per day (by age and gender, and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003ePooled national estimate, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eMean per capita cost of ICU care per day (by age and gender, and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003ePooled national estimate, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eShare of the workforce in the informal sector (by age and gender, and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eLabor Statistics Report [19]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eAverage monthly wage for formal sector workers (by age and gender, and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eLabor Statistics Report [19]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 330px;\"\u003e\n \u003cp\u003eAverage monthly wage for informal sector workers (by age and gender, and wealth quintile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eBaah-Boateng and Vanek, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs we observed significant uncertainty in mortality data from the Institute of Health Metrics and Evaluation, we performed a deterministic sensitivity analysis using the 95% uncertainty intervals to measure changes in the simulated societal cost attributable to premature mortality. We also considered using the extreme parameter values in the one-way deterministic sensitivity analysis to measure the potential maximum and minimum variations in the simulated mean endpoint costs. Likewise, direct medical cost parameters were varied for precision consideration.\u003c/p\u003e\n\u003cp\u003e[2] Baah-Boateng, William, and Vanek, Joann. \u0026quot;Informal Workers in Ghana: A Statistical Snapshot.\u0026quot; \u003cem\u003eWIEGO Statistical Brief No. 21\u003c/em\u003e, WIEGO, 2020. https://www.wiego.org/research-library-publications/informal-workers-ghana-statistical-snapshot/\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[3] Husereau D, Drummond M, Augustovski F, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Explanation and Elaboration: A Report of the ISPOR CHEERS II Good Practices Task Force. Value Health 2022;25. doi:10.1016/j.jval.2021.10.008\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePredicted episode of AMR resistant infections\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe simulation predicted 77,760 resistant bacterial infections annually based on age-specific resistant risk profile, of which about 60% occur in children \u0026lt; 5 years and 13.1% in the older population \u0026gt;60 years old in the business-as-usual scenario (Figure 2). Excluding mycobacterium tuberculosis, up to 66% of the predicted resistant infections are hospital-acquired driven mostly by MRSA, Streptococcus pneumoniae, E. coli, and K. pneumoniae. Subgroup analysis shows that resistant infections are relatively higher in males below 5 years than in females. However, this dynamic changes beyond 5 years old, with a relatively higher number of resistant infections in females than males (Table S1). As expected, the predicted mean difference in the annual number of resistant bacterial infections was relatively higher in the lowest wealth quintile group compared to the highest wealth quintile and the difference was statistically significant (p=0.009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicted mortality attributable to AMR infections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe simulation shows that under a business-as-usual scenario, up to 6,269 people may die annually from AMR infections. Overall, the analysis by infection syndrome shows that bloodstream infections, considered the most severe form of infections, may contribute to an estimated 41.1% of the mortalities, followed by 24.4% for lower respiratory tract infections (LRIs), 16.7% for peritoneal/abdominal infections, etc. (Table 2). \u0026nbsp;Between causative organism types, we projected gram-negative bacterial infections to contribute to about 59.1% of the predicted annual mortalities. Between age groups, 37.3% of the predicted mortalities may occur in children \u0026lt;5yrs alone compared to 16.5% in the legally mandated working-age population 15 to 60 years old. Between wealth quintile groups, 61.3% of the predicted mortalities may occur in the first two poorest quintiles compared to 29.1% deaths in the fourth and fifth quintiles.\u003c/p\u003e\n\u003cp\u003eTable 2. Predicted annual mortalities due to AMR infections\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInfection syndrome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGram negative resistant bacterial infections\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGram positive resistant bacterial infections\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eBloodstream \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1981 \u0026nbsp; (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e597 \u0026nbsp; \u0026nbsp;(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2,578 \u0026nbsp; \u0026nbsp;(41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eLower respiratory infections \u0026amp; thorax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1532* \u0026nbsp; \u0026nbsp;(59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1532 \u0026nbsp; \u0026nbsp;(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePeritoneal \u0026amp; abdominal infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e835 \u0026nbsp; (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e209 \u0026nbsp; \u0026nbsp; \u0026nbsp;(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1044 \u0026nbsp; \u0026nbsp;(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eTuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e407\u003csup\u003e# \u0026nbsp;\u003c/sup\u003e (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e407 \u0026nbsp; \u0026nbsp; \u0026nbsp;(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eMeningitis \u0026amp; central nervous system infection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e339 \u0026nbsp; \u0026nbsp; (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e339 \u0026nbsp; \u0026nbsp; \u0026nbsp;(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eBacterial skin infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e199 \u0026nbsp; \u0026nbsp; \u0026nbsp;(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e199 \u0026nbsp; \u0026nbsp; \u0026nbsp;(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eUrinary tract infection \u0026amp; pyelonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e93\u003csup\u003e\u0026beta; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sup\u003e(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 \u0026nbsp; \u0026nbsp;(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e95 \u0026nbsp; \u0026nbsp; \u0026nbsp;(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eEndocarditis \u0026amp; cardiac infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e21 \u0026nbsp; \u0026nbsp; (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e17 \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e38 \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eDiarrhoea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e19 \u0026nbsp; \u0026nbsp; (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5 \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24 \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eBones \u0026amp; joints infections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e11 \u0026nbsp; \u0026nbsp; (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 \u0026nbsp; \u0026nbsp;(0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13 \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003ePredicted annual mortalities\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3,706 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2563 \u0026nbsp;(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6,269 \u0026nbsp;(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Predominantly caused by \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, \u003csup\u003e#\u003c/sup\u003eHighly controversial mycobacterium organism in terms of classification under gram negative and gram positive bacterial [22], \u003csup\u003e\u0026beta;\u003c/sup\u003eMostly caused by \u003cem\u003eE. coli\u003c/em\u003e infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicted societal costs attributable to bacterial resistance infections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom a societal perspective, the modelling result shows that AMR infections may cost Ghana an estimated average of $435 million annually between 2024 and 2030. Table 3 shows the projected disaggregated costs, including direct medical costs, measured as the systemic cost of healthcare due to AMR borne by patients and their caregivers, amounts to 22.4% of the estimated annual societal costs while direct non-medical costs average $11.3 million per annum, equivalent to 2.6 of the projected total cost.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndirect costs of productivity loss to surviving patients and those who succumb to resistant bacterial infections will amount to USD 221.9 million equivalent to 51% of the estimated mean yearly societal costs. The cost to healthcare providers contributes to 24%. With an overall cost change factor of about 1.8, the estimated endpoint costs will almost double between 2024 and 2030.\u003c/p\u003e\n\u003cp\u003eTable 3. Summary of predicted annual societal costs attributable to AMR infections in 2024 and 2030 (2024 PPP adjusted in international US$)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCost components\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted average cost over study duration (in million USD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted average cost in 2024 (in million USD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted average cost in 2030 (in Million USD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e% of total cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003ePatient out-of-pocket expenditures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Direct medical cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e123.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Direct non-medical cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eProvider costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e104.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e132.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSub-total Direct costs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e213.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e148.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e270.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eIndirect costs (productivity loss)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Indirect cost for surviving patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e57.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Indirect cost due to mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e176.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e123.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e224.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSub-total Indirect costs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e221.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e155.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e281.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e435.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e303.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e552.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePredicted cost by gender and wealth quintile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout 48% of the costs will be due to infections in under-five children. Those between 40 and 49 years old are least susceptible to the estimated costs due to a comparatively lower rate of bacterial-resistant infections in that age group (Figure S1). Again, the simulation shows that between male and female genders, the projected annual societal cost is about seven million dollars higher for under-five males than female (108 million for \u0026lt;5-year males versus 101million for \u0026lt;5-year females), but that changes from age five upwards, making infections in females account for 51.8% of the yearly cost (Figures S2 \u0026amp; S3). Between wealth quintile groups, we observed that though the population in the fourth and fifth quintiles may have a relatively lower rate of infections, they contribute to 41.3% of the estimated annual societal costs due to higher value of a day of lost work (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults from the multiway sensitivity analysis shows the estimated number of AMR attributable mortality could range between 1,567 and 9,113 if all lower and upper uncertainty values replaced mean mortality estimate, respectively. In decreasing order of magnitude, the one-way sensitivity results show the estimated mean mortality is more sensitive to bloodstream infections (27%), lower respiratory infections (22%), mycobacterium tuberculosis (19%), etc (Figure 4). By replacing the 95% uncertainty values with the extreme range values for disease specific mortality in a one-way sensitivity analysis, we observed the estimated mean mortality at baseline may reduce by 72% or increase by 109% (Figure S4). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we observed that the estimated annual societal cost is about 31% sensitive to probability of mortality, followed by length of stay (19.4%), probability of resistant infection (15%), etc (Figure 5).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study used country level data to project the societal cost attributable to AMR in a business-as-usual scenario using a microsimulation analysis and found that AMR could have profound health and cost implications for Ghana if bacterial resistant infection rates persist at current level. Overall, more than 6,200 patients may succumb to AMR and the estimated total average cost of resistance infections to society was about \u003cspan\u003e$\u003c/span\u003e435\u0026nbsp;million annually, of which direct medical and non-medical cost to households account for 25% while the cost to healthcare providers amount to 24%. In our estimation, the cost of productivity loss for surviving patients and premature deaths for the working age population contribute to 51% of the projected cost impact of AMR.\u003c/p\u003e \u003cp\u003eMore so, we observed during model development that six out of the 24 pathogens included in the simulation account for 77% and 61.8% of the estimated mortalities and costs, respectively. These organisms were K. pneumoniae, S. aureus, Acinetobacter spp. E. coli, Enterococcus spp. and Mycobacterium tuberculosis. Sensitivity results suggest the probability of mortality, length of stay and the probability of resistant infections were among the dominant drivers of the estimated costs. For instance, assuming AMR-attributable mortality is averted, Ghana could reduce the projected annual cost by 40% and if hospital-acquired infections are prevented the cost savings will be about 60%. In a worst-case scenario considering upper values of the cost parameter uncertainties, we project the estimated base annual cost to increase by 17.3%.\u003c/p\u003e \u003cp\u003eThe simulation considered several assumptions driving AMR spread in Ghana. Regarding community-acquired infections and the role of society in general, we reiterate that growing income level in Ghana means the middle to upper-wealth group population is expanding. This change in wealth may affect the projected costs in various ways. This change may affect the projected costs in various ways. On one hand, the expansion may drive inappropriate antibiotic use as wealthy people are more likely to press providers for expensive and reserved antibiotics when ill [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. On the other hand, the lower wealth quintile groups, generally classified as the poor people in society, are more likely to disregard general infection prevention and control practices based on limited access to improved water and sanitation facilities [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and are more likely to use leftover antibiotics or practice self-medication more generally [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Also, they are least likely to seek hospital care early and cannot afford the cost of treatment for AMR infections. The potential implications of this mixed population health behaviours may increase AMR spread when considering the behavioural factors influencing the natural causes of antibacterial resistance [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe implication of the result is multifaceted as we have shown elsewhere that beyond the estimated societal costs lies the impact of AMR on healthy life expectancy and disability-adjusted life years. In that report, we projected it will cost an average amount of USD 8.82 per capita to implement 11 interventions with demonstrated complementarity efficiency to dramatically reduce both nosocomial and community-acquired AMR infections (see Supplementary Table\u0026nbsp;2). Some of the interventions include scaling up antimicrobial stewardship programs in hospitals and communities, improved food safety, mass media campaign and prescriber education, among others. Because nosocomial infections account for about 66% of resistant bacterial infection outbreak in Ghana, we argue that investment in IPC for example, should be a top priority by way of investment as it will require 11.3\u0026nbsp;million annually to achieve 70% IPC compliance coverage nationwide. While the simulated AMR-attributable mortalities among the working-age group are minimal compared to children and the elderly, the potential economic and psychological impact from a societal perspective may be more significant than the individual impact, as this select group is mostly the breadwinners in society. This could be a reason why everyone should be concerned about preventing bacterial infections, considering that up to 74% of the projected direct medical costs may be covered by families of AMR patients and society at large, including philanthropic gestures, government social welfare contributions to poor patients, and donations by religious groups [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConcerning the implications for hospitals, bring to question the financial incentive for IPC compliance and mitigation of hospital-acquired infections. In our estimation, hospitals, can save about \u003cspan\u003e$\u003c/span\u003e104\u0026nbsp;million annually if hospital IPC measures are effectively implemented within the NAP framework [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For instance, available evidence suggests only 39% hand hygiene compliance by physicians before and after patient contact as documented in Ghana\u0026rsquo;s AMR Policy formulation consideration [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and 28% of health facilities comply with IPC guidelines as reported in the 2023 harmonized health facility assessment report [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strength of this study involves the use of practical and context reliable data with input from multidisciplinary experts in the field of microbiology, pharmacy, health economic evaluation and decision modelling, health promotion, health policy and systems research, development economics and development studies. Moreover, this study makes significant contribution to literature in LMIC. While microsimulation is not a new field in science, only a few studies in Europe have utilized similar approach to quantify the cost of AMR [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus far, our study is the only study doing same in sub-Saharan Africa and clearly indicate the perspective of the analysis, which builds on our previous work looking at the patient and provider costs of AMR [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study faced some limitations. Except for one study published in 2015 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], it was difficult obtaining AMR epidemiology data disaggregated into community and hospital-acquired infections. Therefore, we relied on expert opinion based on ongoing national AMR surveillance data to quantify the proportion of community-acquired AMR infections in Ghana. By focusing on AMR infections in humans, we may underestimate the economic impact of AMR on animals, plants and the environment. However, the one health implications of AMR suggest some resistant bacterial in humans originate from the environment, especially in the case of community-acquired infections [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Given that human behaviour is largely unpredictable, the odds of hospital admission and remission due to AMR infections in the population may become worse or better in the future compared to present circumstances. Additionally, source data for projected AMR-attributable mortality has wide uncertainty intervals that may contribute to underestimation or overestimation of the societal cost of AMR. For instance, the controversial nature of mycobacterium tuberculosis has implication for misclassification and wider uncertainty estimate when considering deaths due to TB resistant infection [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Though we recognized the impact of these uncertainties on the endpoint estimate, we followed the example of Larsson \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and dwell on mean input values for this simulation. Another limitation is that our pathogen pool is only a fraction of resistant infections and may lead to an underestimation. Besides hospital-acquired infections, we included community-acquired resistant infections that patients would be admitted and treated in hospitals using probability estimate from ongoing national AMR surveillance. This means that we may underestimated the cost for excluding community infections that are not reported in hospitals.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study shows that AMR-attributable societal cost implications are enamours, requiring a concerted effort by society to mitigate the development and spread of AMR organisms. Therefore, prioritization of investment in the implementation of national action plans by government and all interested stakeholders is the solution to avert the projected societal costs attributable to AMR.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAknowledgement\u003c/strong\u003e. The authors are grateful for the support received from clinical microbiologists in volunteering reliable AMR surveillance data for this study. We also appreciate the interest, feedback and support received from the scientific community during international and local conference presentations of our abstract findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors.\u0026nbsp;\u003c/strong\u003eConceptualization:EO,UE; Data curation and investigation: EO, MJ, AKL, ROA; Methodology: EO, APF, UE, MJ; Formal analysis and writing original draft: EO; Project Administration: EO, APF, UE, AKL; Supervision: APF, AKL, UE, GKH; Validation: MJ, ROA, GKH, AKL, APF, UE; Visualization: EO; Writing reviews and editing: GKH, AKL, APF, UE.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThisstudyis an extension of a PhD research projection that received training supplement funding from the Graduate School of Health at Aarhus University to cover subsistence allowance. Grant/Award number: N/A \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: None \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement.\u0026nbsp;\u003c/strong\u003eFor the supplementary data collection,the study received ethics approval from the Ethics Review Committee of the Faculty of Health Sciences at Ghana Christian University College in Accra with registration number GH-FOHS-ERC-S20249.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement.\u0026nbsp;\u003c/strong\u003eAll data used for this study are cited, included in the paper or attached as supplementary file.Microsoft excel version of the model output is available upon request to the corresponding author due to institutional guidelines.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorel CM, Alm RA, \u0026Aring;rdal C, Bandera A, Bruno GM et al; GAP-ON\u0026euro; network. 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Available: https://www.healthdata.org/sites/default/files/2023-09/Ghana.pdf\u003c/li\u003e\n\u003cli\u003eGhana Statistical Service. Third quarter labour statistics report. 2023. Available: https://statsghana.gov.gh/searchread.php?searchfound=NzkyMjcwNDM5NzAuMjUz/search/pn13pn3q80 \u003c/li\u003e\n\u003cli\u003eKrijkamp EM, Alarid-Escudero F, Enns EA, Jalal HJ, Hunink MGM, Pechlivanoglou P. Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial. \u003cem\u003eMed Decis Making\u003c/em\u003e. 2018; 38(3):400-422. https://doi.org/10.1177/0272989X18754513 \u003c/li\u003e\n\u003cli\u003eGhana Health Service. Ghana harmonized health facility assessment report, 2023. Ghana Health Service, Accra, Ghana\u003c/li\u003e\n\u003cli\u003eFu LM, Fu-Liu CS. 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Poverty and infection in the developing world: healthcare-related infections and infection control in the tropics. \u003cem\u003eJ Hosp Infect\u003c/em\u003e. 2007; 67(3):217-24. https://doi.org/10.1016/j.jhin.2007.08.016 \u003c/li\u003e\n\u003cli\u003eDarby, E. M., Trampari, E., Siasat, P., Gaya, M. S., Alav, I., Webber, M. A., \u0026amp; Blair, J. M. A. (2022). Molecular mechanisms of antibiotic resistance revisited. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e, 2022; 21: 280-295\u003c/li\u003e\n\u003cli\u003eVikesland, P., Garner, E., Gupta, S., Kang, S., Maile-Moskowitz, A., \u0026amp; Zhu, N. Differential drivers of antimicrobial resistance across the world. \u003cem\u003eAccounts of Chemical Research\u003c/em\u003e, 2019; 52(4), 916-924.\u003c/li\u003e\n\u003cli\u003eRoberts, M.G., Burgess, S., Toombs-Ruane, L.J., Benschop, J., Marshall, J.C., \u0026amp; French, N.P. Combining mutation and horizontal gene transfer in a within-host model of antibiotic resistance. \u003cem\u003eMathematical Biosciences\u003c/em\u003e, 2021; 339, 108656.\u003c/li\u003e\n\u003cli\u003eGovernment of Ghana. Policy on antimicrobial use and resistance. First Edition, 2017. Ministry of Health, Ghana, Accra; 2017 \u003c/li\u003e\n\u003cli\u003eOtieku E, Hedidor G, Lerouge A, Morel C, Twum-Barimah AT, Buabeng KO, Labi A-K, Sasu B, Salifu A, Yevutsey SK, Wekem MA, Azaglo GS, Kisseh R, Kudjawu J, Opintan JA, Investment case for tackling antimicrobial resistance in Ghana. 2025, WHO Ghana Country Office, Accra, Ghana. \u003c/li\u003e\n\u003cli\u003eGovernment of Ghana. National action plan for antimicrobial resistance in Ghana. 2017. Ministry of Health, Ghana, Accra; 2017\u003c/li\u003e\n\u003cli\u003eLarsson S, Prioux M, Fasth T, Ternhag A, Struwe J, Dohnhammar U, Brouwers L. A microsimulation model projecting the health care costs for resistance to antibacterial drugs in Sweden. \u003cem\u003eEur J Public Health\u003c/em\u003e. 2019; 29(3):392-396. https://doi.org/10.1093/eurpub/cky209 \u003c/li\u003e\n\u003cli\u003eOpintan JA, Newman MJ, Arhin RE, Donkor ES, Gyansa-Lutterodt M, Mills-Pappoe W. Laboratory-based nationwide surveillance of antimicrobial resistance in Ghana. \u003cem\u003eInfect Drug Resist\u003c/em\u003e. 2015; 8:379-89. https://doi.org/10.2147/IDR.S88725 \u003c/li\u003e\n\u003cli\u003eNoguchi Memorial Institute for Medical Research NMIMR. Tuberculosis, 2024. NMIMR, University of Ghana, Legon. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AMR Societal Cost, Public Health, Microsimulation, Health Policy ","lastPublishedDoi":"10.21203/rs.3.rs-6574614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6574614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eInvestment case for antimicrobial resistance (AMR) is needed to stimulate the willpower of national governments to invest in AMR mitigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eWe performed a microsimulation analysis predicting the potential societal cost savings for reducing the prevalence of AMR in Ghana.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study combined bacterial resistance epidemiology and cost data from Ghana to perform a microsimulation analysis focusing on socio-demographic groups, predicting the potential societal cost savings should Ghana mitigate AMR. Case definition was enterobacterial 3GC resistant infections, methicillin-resistant staphylococcus aureus (MRSA), and multi-drug-resistant mycobacterial tuberculosis. Costs were calculated under a business-as-usual scenario considering a 2% annual population growth rate, 5% discount rate for future costs, age-specific resistant risk profile, and a seven-year time horizon from 2024 to 2030. We reported the cost in purchasing power parity equivalent in international United States dollars, adjusting for mortality, age groups, gender, and wealth quintile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eUsing 0.124 and 0.109 resistant probability risk between females and males, we predicted almost 78,000 annual AMR infections and about 6,300 attributable deaths. MRSA and 3GC resistant infections made up 20.2% and 79.2% of the predicted annual infections, corresponding to an estimated mean societal cost of about USD 435 million. In decreasing order of magnitude, the estimated mean annual cost of productivity loss due to AMR-attributable mortality accounted for 40.6% of the mean annual societal cost, followed by the cost to healthcare providers (24.1%), direct medical cost to patients and caregivers (22.4%), productivity loss for surviving patients and caregivers (10.4%), and direct non-medical costs to patients and caregivers (2.6%). Resistant infections in under-five children and persons above 60 years contribute 48.2% and 26.9% of the estimated annual societal cost, respectively. Except for the number of resistant infections, the estimated mean annual costs between wealth quintile groups were significantly different (p=0.03) due to differences in productivity costs between wealth quintile groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion. \u003c/strong\u003eThe study shows that AMR-attributable societal cost implications are enormous, requiring a concerted effort by society to mitigate the development and spread of AMR organisms.\u003c/p\u003e","manuscriptTitle":"Attributable societal cost of antimicrobial resistance in Ghana: A microsimulation study focusing on socio-demographic groups","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 11:29:45","doi":"10.21203/rs.3.rs-6574614/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd73f1fa-025c-4650-a7ce-f0a3a29d291b","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47965341,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2025-05-05T11:29:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 11:29:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6574614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6574614","identity":"rs-6574614","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}