Antibiotic resistance is a global health crisis that requires urgent action to stop its spread. To counteract the spread of antibiotic resistance, we must improve our understanding of the origin and spread of resistant bacteria in both community and healthcare settings. Unfortunately, little attention is being given to contain the spread of antibiotic resistance in community settings (i.e., locations outside of a hospital inpatient, acute care setting, or a hospital clinic setting), despite some studies have consistently reported a high prevalence of antibiotic resistance in the community settings. This study aimed to investigate the prevalence of antibiotic resistance in commensal Escherichia coli isolates from healthy humans in community settings in LMICs. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we synthesized studies conducted from 1989 to May 2020. A total of 9363 articles were obtained from the search and prevalence data were extracted from 33 articles and pooled together. This gave a pooled prevalence of antibiotic resistance (top ten antibiotics commonly prescribed in LMICs) in commensal E. coli isolates from human sources in community settings in LMICs of: ampicillin (72% of 13,531 isolates, 95% CI: 65–79), cefotaxime (27% of 6700 isolates, 95% CI: 12–44), chloramphenicol (45% of 7012 isolates, 95% CI: 35–53), ciprofloxacin (17% of 10,618 isolates, 95% CI: 11–25), co-trimoxazole (63% of 10,561 isolates, 95% CI: 52–73), nalidixic acid (30% of 9819 isolates, 95% CI: 21–40), oxytetracycline (78% of 1451 isolates, 95% CI: 65–88), streptomycin (58% of 3831 isolates, 95% CI: 44–72), tetracycline (67% of 11,847 isolates, 95% CI: 59–74), and trimethoprim (67% of 3265 isolates, 95% CI: 59–75). Here, we provided an appraisal of the evidence of the high prevalence of antibiotic resistance by commensal E. coli in community settings in LMICs. Our findings will have important ramifications for public health policy design to contain the spread of antibiotic resistance in community settings. Indeed, commensal E. coli is the main reservoir for spreading antibiotic resistance to other pathogenic enteric bacteria via mobile genetic elements.

Background Multimorbidity is a rising global phenomenon, placing strains on countries’ population health and finances. This systematic review provides insight into the costs of multimorbidity through addressing the following primary and secondary research questions: What evidence exists on the costs of multimorbidity? How do costs of specific disease combinations vary across countries? How do multimorbidity costs vary across disease combinations? What “cost ingredients” are most commonly included in these multimorbidity studies? Methods We conducted a systematic review (PROSPERO: CRD42020204871) of studies published from January 2010 to January 2022, which reported on costs associated with combinations of at least two specified conditions. Systematic string-based searches were conducted in MEDLINE, The Cochrane Library, SCOPUS, Global Health, Web of Science, and Business Source Complete. We explored the association between costs of multimorbidity and country Gross Domestic Product (GDP) per capita using a linear mixed model with random intercept. Annual mean direct medical costs per capita were pooled in fixed-effects meta-analyses for each of the frequently reported dyads. Costs are reported in 2021 International Dollars (I$). Results Fifty-nine studies were included in the review, the majority of which were from high-income countries, particularly the United States. (1) Reported annual costs of multimorbidity per person ranged from I$800 to I$150,000, depending on disease combination, country, cost ingredients, and other study characteristics. (2) Our results further demonstrated that increased country GDP per capita was associated with higher costs of multimorbidity. (3) Meta-analyses of 15 studies showed that on average, dyads which featured Hypertension were among the least expensive to manage, with the most expensive dyads being Respiratory and Mental Health condition (I$36,840), Diabetes and Heart/vascular condition (I$37,090), and Cancer and Mental Health condition in the first year after cancer diagnosis (I$85,820). (4) Most studies reported only direct medical costs, such as costs of hospitalization, outpatient care, emergency care, and drugs. Conclusions Multimorbidity imposes a large economic burden on both the health system and society, most notably for patients with cancer and mental health condition in the first year after cancer diagnosis. Whether the cost of a disease combination is more or less than the additive costs of the component diseases needs to be further explored. Multimorbidity costing studies typically consider only a limited number of disease combinations, and few have been conducted in low- and middle-income countries and Europe. Rigorous and standardized methods of data collection and costing for multimorbidity should be developed to provide more comprehensive and comparable evidence for the costs of multimorbidity.

acknowledgements All authors are members of the Health and Social Protection Action Research & Knowledge Sharing Network (SPARKS), an international interdisciplinary research network. SPARKS' multisectoral team characterises and evaluates the direct and indirect effects of social protection strategies on health, economic and wider outcomes. Website: https:// sparksnetwork. ki. se.