After the European colonization of the Americas, there was a dramatic population collapse of the Indigenous inhabitants caused in part by the introduction of new pathogens. Although there is much speculation on the etiology of the Colonial epidemics, direct evidence for the presence of specific viruses during the Colonial era is lacking. To uncover the diversity of viral pathogens during this period, we designed an enrichment assay targeting ancient DNA (aDNA) from viruses of clinical importance and applied it to DNA extracts from individuals found in a Colonial hospital and a Colonial chapel (16th–18th century) where records suggest that victims of epidemics were buried during important outbreaks in Mexico City. This allowed us to reconstruct three ancient human parvovirus B19 genomes and one ancient human hepatitis B virus genome from distinct individuals. The viral genomes are similar to African strains, consistent with the inferred morphological and genetic African ancestry of the hosts as well as with the isotopic analysis of the human remains, suggesting an origin on the African continent. This study provides direct molecular evidence of ancient viruses being transported to the Americas during the transatlantic slave trade and their subsequent introduction to New Spain. Altogether, our observations enrich the discussion about the etiology of infectious diseases during the Colonial period in Mexico. th century, contributed to the spread of new viruses amongst Indigenous people. This led to massive outbreaks of disease, and millions of deaths that caused an important Native population to collapse. The exact viruses that caused these outbreaks are unknown, but smallpox, measles, and mumps are all suspected.The arrival of European colonists to the Americas, beginning in the 15During these times, traders and colonists forcibly enslaved and displaced millions of people mainly from the West Coast of Africa to the Americas. The cruel, unsanitary, and overcrowded conditions on ships transporting these people across the Atlantic contributed to the spread of infectious diseases onboard. Once on land, infectious diseases spread quickly, partly due to the poor conditions that enslaved and ndigenous people were made to endure. Native people were also immunologically naïve to the newly introduced pathogens, making them susceptible to severe or fatal outcomes. The new field of paleovirology may help scientists identify the viruses that were circulating in the first years of colonization and trace how viruses arrived in the Americas.th century. These remains were found in mass graves that were used to bury epidemic victims at a colonial hospital and chapel in what is now Mexico City. The experiments identified two viruses, human parvovirus B19 and a human hepatitis B virus. These viral genomes were recovered from human remains of first-generation African people in Mexico, as well as an individual who was an Indigenous person.Using next-generation DNA sequencing and other cutting-edge techniques, Guzmán-Solís et al. extracted and enriched viral DNA from skeletal remains dating back to the 16Although the genetic material of these ancient viruses resembled pathogens that originated in Africa, the study did not determine if the victims died from these viruses or another cause. On the other hand, the results indicate that viruses frequently found in modern Africa were circulating in the Americas during the slave trade period of Mexico. Finally, the results provide evidence that colonists who forcibly brought African people to the Americas participated in the introduction of viruses to Mexico. This constant influx of viruses from the old world, led to dramatic declines in the populations of Indigenous people in the Americas. Cocoliztli (‘pest’ in Nahuatl), followed by Africans and to a lesser extent European people   , the fir’ (HSJN) .The study of ancient viral genomes has revealed important insights into the evolution of specific viral families , as wellHere we describe the recovery and characterization of viral pathogens that circulated in New Spain during the Colonial period, using ancient DNA (aDNA) techniques . For thiWe sampled the skeletal remains from two archeological sites, a Colonial Hospital and a Colonial chapel in Mexico City . For theHepadnaviridae, Herpesviridae, Parvoviridae, and Poxviridae viral families , or (3) RNA viruses with a DNA intermediate . Given the size constraints of the probe kit, only a couple of genes were selected from some viral families , while three more libraries had an ~50–200-fold increase of Parvoviridae-like hits (B19V)  , compares (B19V) .Tannerella forsythia) were reconstructed and described in more detail by Independently, a metagenomic analysis using Kraken2  and Pavipostmortem preservation of dsDNA compared to ssDNA (We verified the authenticity of the reads mapped to HBV (BWA) or B19V (BWA/blastn) in the enriched libraries  by querying the reads against the non-redundant (nr) NCBI database using megaBLAST . This stto ssDNA . In addito ssDNA  at 30.8×The reconstructed ancient HBV genome shows a 6 nucleotide (nt) insertion in the core gene, which is characteristic of the genotype A . Further2 = 0.1351; correlation coefficient = 0.3676)    . In the  0.3676) . The com 0.3676)  occurred 0.3676) , we anal 0.8498) . In the  0.6194) , in agre 0.6194) . Further 0.6194) , where n 0.6194) .–5 (95% HPD: 8.66 × 10–6–1.21 × 10–5) s/s/y under a strict clock and a constant population prior, and a substitution rate of 2.62 × 10–5 (95% HPD: 1.50 × 10–5–3.98 × 10–5) s/s/y under a relaxed log normal clock and a constant population prior. The divergence times from the most recent common ancestor of genotypes 1, 2, and 3 under a strict clock were 7.19 (95% HPD: 6.98–7.46), 2.11 (95% HPD: 1.83–2.51), and 3.64 (95% HPD: 3.04–4.33) ka, respectively. The inferred substitution rates and divergence times from the most recent common ancestor for genotypes 1 and 2 were similar to previous estimations  or parietal bone (HSJNC81) to provide insights on the places of birth (adult enamel) and where the last years of life were spent . The 87Sr/86Sr ratios measured on the enamel of the individual HSJNC81 (0.71098) and HSJN240 (0.71109) are similar to average 87Sr/86Sr ratios found in soils and rocks from West Africa  and HSJN240 (0.70755) show lower values similar to those observed in the Trans Mexican Volcanic Belt where the Mexico City Valley is located , in previous studies  as well ,  and a po, in Brazi,, India  were born in West Africa and spent their last years of life in New Spain  nor if the cause of their deaths can be attributed to such infections, the identification of ancient B19V and HBV in contexts associated with Colonial epidemics in Mexico City is still relevant in light of their paleopathological marks and the clinical information available for the closest sequences in the phylogenetic analyses. Notably, individual HSJNC81 displayed cribra orbitalia in the eye sockets and porotic hyperostosis on the cranial vault . The recCocoliztli epidemic treated at the HSJN describe the presence of enlarged hard liver and jaundice    or hemore virus) .Furthermore, it is important to acknowledge that both viruses have also been previously identified in aDNA datasets not necessarily associated with disease or epidemic contexts . AdditioIn the case of HSJN194, we cannot establish if he acquired HBV vertically or horizontally, nor if this individual presented an acute or chronic infection. Finally, although our data does not allow us to associate these viruses to a specific epidemic outbreak, the identification of HBV and B19V in Post-Contact remains opens up new opportunities for investigating their presence in similar contexts and expand our knowledge on their evolution and potential link to disease in Colonial Mexico. This type of research is particularly relevant when considering previous hypotheses favoring the synergistic action of different types of pathogens in these devastating Colonial epidemics .It is important to emphasize that our findings should be interpreted with careful consideration of the historical and social context of the transatlantic slave trade. This cruel episode in history involved the forced displacement of millions of individuals to the Americas  were obtained from 21 individuals from the skeletal collection associated with the HSJN and were selected based on morphological features indicating a possible African origin . Five adBone samples were transported to a dedicated ancient DNA clean-room laboratory at the International Laboratory for Human Genome Research , where DNA extraction and NGS library construction was performed under the guidelines on contamination control for aDNA studies . Teeth wPooled libraries were sequenced on an Illumina NextSeq550 at the ‘Laboratorio Nacional de Genómica para la Biodiversidad’ , with a mid-output 2 × 75 format (paired-end). The reads obtained (R1 and R2) were merged (>11 bp overlap) and trimmed with AdapterRemoval 1.5.4 . Overlapcount of class/total count of sample)* count of smallest sample) and compared to all the samples from the same archeological site with MEGAN 6.8.0  were taxonomically classified with Kraken2  using a Twenty-nine viruses were included in the design of biotinylated probes , includiThe HBV majority consensus genome (>50% conservation per site) was constructed using an alignment of modern references (A–H genotype) and a well-covered (>5× coverage) ancient genotype , structural , and immune host regulation , and were obtained from all the available VARV genomes including three ancient genomes , 6 from human cytomegalovirus , and 7 from Epstein–Bar virus . GenBank IDs are shown in For the Herpesviridae were defined as 40 bp or 60 bp upstream the start codon, and downstream the stop codon, respectively, in order to ensure a uniform coverage of the entire coding region in case of a positive sample.Selected genes from VARV and Poxviridae, Hepadnaviridae, Parvoviridae, Herpesviridae, Retroviridae, Papillomaviridae, Polyomaviridae, Circoviridae). To avoid a simultaneous false-positive DNA enrichment, low-complexity regions and human-like (hg38) sequences were removed (in silico). The customized kit was produced by Arbor Biosciences .The resulting design comprised 19,147 ssRNA 80 nt probes targeting, with a 20 nt interspaced distance, the whole or partial informative regions of eight viral families (6 non-human reads (Capture-enrichment was performed on 30–90 ng (depending the availability) of the indexed libraries to pull-down viral aDNA using 60°C during 48 hr for hybridization, based on the manufacturer’s protocol (version 4). Libraries were amplified with 18–20 cycles (Phusion U Hot Start DNA Polymerase by Thermo Fischer Scientific) using primers for the adaptors of each post-capture library. PCR products were purified with SPRISelect Magnetic Beads (Beckman Coulter) and quantified with a Bioanalyzer 2100 (Agilent). Amplified libraries were then pooled in different concentrations and deep sequenced on an Illumina NextSeq550 (2 × 75 middle output) yielding >1 × 10an reads . In ordean reads  to evaluThis comprises 38 HBV genomes from modern A–J human genotypes, 2 well-covered ancient HBV genomes, and a wholly monkey genome. Genotype A: HE974381, HE974383, AY934764, GQ331046. Genotype B: B602818, AB033555, AB073835, AB287316, AB241117. Genotype C: AB111946, X75656, AB048704, AF241411, AP011102, AP011106, AP011108, AB644287. Genotype D: FJ899792, GQ477453, JN688710, HE974373, FJ904430, AB033559. Genotype E: HE974384. Genotype F: AY090458, AB116654, AY090455, DQ899144, HE974369, AB116549, AF223962, AB166850. Genotype G: AP007264. Genotype H: AB516395. Genotype J: AB486012. Ancient: LT992443, LT992459. Outgroup (Woolly Monkey): AF046996.This comprises 593 whole genomes downloaded from the NCBI database in August 2020 that included the union of curated datasets used in four previous studies , from wh56 genomes assigned to genotype A, based on our ML analysis plus the ancient genome from the present study (HSJN194).This comprises 13 B19V sequences of genotypes 1–3: KT268312, AY504945, FJ591158, EF216869, AY064476, DQ333427, AB550331, AY582124, DQ408305, FJ265736, AJ249437, NC_004295, NC_0008831, plus one outgroup (Bovine Parvovirus): NC_001540.human parvovirus b19[organism] not rna[title] not clone[title] not clonal[title] not patent[title] not recombinant[title] not recombination[title] and 3000:6000[sequence length],” which considers only whole genomes (3–6 kb), resulting in a total of 109 B19V genomes from genotypes 1–3. This dataset included the 10 best-covered ancient genomes from genotypes 1 and 2 (All B19V genomes retrieved from the NCBI database were downloaded (August 2020) using the next search command “ 1 and 2  as well Non-human reads were simultaneously mapped to HBV_DS1 with BWA  with seedling disabled (-l 1050) . The refThe reconstruction of the B19V ancient genome was done as previously reported from archeological skeletal remains , but witDeamination patterns for HBV and B19V were determined with mapDamage 2.0  and damaHBV_DS2 and B19V_DS2 were aligned independently in Aliview  and curaTo test the temporal structure of our ML trees, a root-tip-dated analysis was performed on Tempest 1.5.3  for both–3–1 × 10–7 s/s/y  mass spectrometer with a 1 V energy accelerator. Radiocarbon dates were estimated based on InCal13  for the individuals in this study with complete skeletons (HSJN194 and HSJN240). From these individuals, phalanx bones (left hand) were cleaned, dried, and powdered to be digested in a HCl 0.5 M solution followed by a NaOH 0.01 M and HCl 0.2 M treatment. Collagen was then filtered (>30 kDa) and graphitization was performed on an AGEIII (Ion Plus).  InCal13  calibrat InCal13 .2O2 and 1–1.5 N HNO3. In between, rinses were performed with deionized water (Milli-Q). Ultrasonic bath (USB) was used to accelerate these processes. After obtaining the desired grain size, samples were treated with 30% H2O2, 1 N NH4Cl, and alternated with water washes. To get rid of any secondary contaminant or any postmortem external agent that could alter the Sr isotopic values, tooth samples were treated with a three-step leaching technique: the first leachate is obtained with 0.1 N acetic acid for 30 min (USB). The solution is decanted and dried under infrared light (Lix 1). The residue was leached for 15 min in 1 N acetic acid (USB) and subsequently stored overnight for 12 hr in the same acid. The solution was decanted and dried to obtain the second leachate (Lix 2). The residue (Res) is dissolved in 8 N HNO3 as well as Enamel Lix 1 and Enamel Lix 2 in closed Teflon beakers on a hot plate at 90°C. A total of three aliquots from each molar were obtained from this leaching process. After sample digestion, Sr from teeth and bone samples was extracted with Sr-Spec (EICHROM) ion exchange column chemistry. Detailed analytical procedures are described in 86Sr/88Sr = 0.1194. The mean value for the NBS 987 Sr standard was 87Sr/86Sr = 0.710254 ± 0.000012  and the analytical blank yielded 0.23 ng Sr. 87Sr/86Sr ratios were performed on the tooth enamel (crowns) of individuals HSJNC81 and HSJN240. Similar analyses were done for HSJNC81 and HSJN240 using the parietal and phalanx bone, respectively. In the case of the two individuals analyzed in this study, bone 87Sr/86Sr values 0.70672 (HSJNC81) and 0.70755 (HSJN240) (Table S6) are comparable to those obtained from soil samples from the eastern TMVB rim in Veracruz with a mean 87Sr/86Sr of 0.70703 (n = 6)  . For Wes n = 20, . Data arlsqproject to project the ancient individuals into the PC space defined by the modern individuals.Human-mapped reads  obtained from the pre-capture sequence data of viral-positive samples were used to infer the genetic ancestry of the hosts using PCA. The genomic alignments (to hg19) of the four ancient individuals  was intersected with the genotype data of 400 present-day individuals from eight populations  in the 1000 Genomes Project . Pseudo A total of 58,670 SNPs intersected between the 1000 Genomes Project reference panel and the COYC4 ancient genome (see previous section for details). The program ADMIXTURE  was run NGS reads were mapped to the human mitochondrial genome reference (rCRS) with BWA , the alignment file was then used to generate a consensus mitochondrial genome with the program Schmutzi  The assi In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This work, which characterizes the genetic diversity and spread of ancient viruses circulating in the Americas during the Colonial era, a period marked by the emergence of epidemics in the continent, the cruel slave trade, and the contact between Indigenous and non-Indigenous peoples, will be of interest to microbiologists, anthropologists, and anyone interested in the intersection of history and infectious disease. With appropriate research ethics in place, the authors demonstrate that respectful destructive analyses of ancient DNA from teeth collected from individuals dated to Colonial period Mexico can uncover the source and impact of infectious diseases introduced to the Americas with European colonization.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by George Perry as the Senior and Reviewing Editor. The following individuals involved in review of your submission have agreed to reveal their identity: C. Eduardo Amorim (Reviewer #1); Charlotte J Houldcroft (Reviewer #2).Thank you for submitting your article "Ancient viral genomes reveal introduction of HBV and B19V into Mexico during the transatlantic slave trade" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential Revisions (for the authors):eLife. I agree with their assessment. Some excellent reviewer discussions are appended below for your consideration as you revise. There are several points on which expanded detail is required, and other suggestions concerning the balance between the results and discussion. In our consultation session we focused primarily on developing consensus around how to address the question that was consistently raised by the reviewers – what about the bacterial pathogen data? Ultimately, our consensus is that (i) we encourage you to expand on these results as much as possible in the present study. However, (ii) if you prefer to not present the full bacterial results in this study (perhaps due to a forthcoming study with that focus), then at the very least you still need to present an overview of the metagenomic content of the shotgun libraries and address this directly. In addition, considerably further detail into mapping results across all stages of the analysis needs to be provided in order for the reviewers (and future readers) to more fully assess the strength of your results.The reviewers individually and collectively offered praise of your study and its potential for Reviewer #1:In my opinion, the major strength of this work is its novelty regarding its questions, results, and ethical imperative of preventing misinterpretation of the results and the stigmatization of the studied subjects/communities. The latter aspect, although only briefly highlighted in the manuscript, is of utmost importance for human population genetics studies in general and for ancient DNA studies in particular, since these studies are reaching the general public beyond academia and research results are often misinterpreted and misused.Although it is widely known that colonial epidemics had a strong impact on Indigenous populations from the Americas, there is still much speculation on the etiology of these epidemics and direct molecular evidence for the presence of specific viruses is lacking. In this context, Guzmán-Solís et al.'s work pioneer into addressing these questions using state-of-the-art methods and techniques in the genomic sciences. Their results represent the initial steps into characterizing ancient viruses and their spread into the continent during the Colonial era. This manuscript offers a concrete example of how human migrations shaped pathogen dispersal, challenges views about the etiology of colonial epidemics, and adds new evidence for the presence of African viral strains in the Americas. In addition, the phylogenetic contextualization of these ancient viruses sheds new light on the genetic diversity of HBV and B19V in different time transects and the history of the spread of their associated diseases around the world. Authors' claims and conclusions are fully justified by their data and the study is technically sound. I envision Guzmán-Solís et al.'s findings being of interest for scholars from diverse fields which include, human genetics, virology, epidemiology, history of medicine, archaeogenetic, phylogenetics, anthropology, among others.It is my belief the inclusion of more detailed methods description and explicit research questions would potentially improve this manuscript. For instance, details about how the hits in the metagenomics analysis were normalized are still lacking. In that regard, I am confused with the claim that the "viral abundances were normalized based on the smallest sample size" (line 534). What does "sample size" refer to? Are these sample sizes from the study ? Would that be instead the number of hits? In addition, aspects such as the viral temporal dynamics could be further discussed and the rationale behind this analysis could be more explicit. The manuscript currently does not fully address what it means for the temporal structure to be detected (or not) in their datasets. In addition, I do not see why the root-to-tip distances should depend on sampling times and what is the contribution of this analysis to the understanding of the general questions raised in the paper. It is my belief that other analyses such as population clustering/structure inference would be more appropriate to infer temporal structure. This is not a criticism about the methods employed, but a suggestion for explicitly explaining the rationale behind these analyses and for further discussing the corresponding results. In my detailed review, authors can find additional questions about how double-stranded libraries are expected to perform in the sequencing of single-stranded DNA viruses, as well as whether the overrepresentation of Africans in the human population structure analysis could be biasing their results - however, I believe these potential issues do not invalidate the authors' conclusions in any way. Finally, I miss mapping statistics and contamination estimates for the human ancient genomes.1. Line 140: regarding the sentence "…contained at least one normalized hit to viral DNA…", could you please specify in the text how this normalization is done  and/or what is the purpose of this normalization? This relates to the Methods section, lines 534-536. This part, I believe, would benefit from being more detailed. For instance, I am confused with the claim that the "viral abundances were normalized based on the smallest sample size" (line 534). What does "sample size" refer to? Are these sample sizes from your study ? Would it be instead the number of hits of the sample with the least hits? In addition, how does the number of hits (line 140) translate to viral abundances (line 534)?2. Line 167: Why only HBV and B19V, if you searched for other viruses as well? Were there cases in which the top hit was not HBV or B19V? If so, why removing these? If there weren't any other cases, then the sentence (line 167) is, in my opinion, somewhat misleading in that it seems a choice was made to focus on HBV and B19V.3. "DNA extraction and NGS library construction" section: I wonder how your choice for dsDNA libraries could be affecting the detection of B19V, whose genetic material is mostly ssDNA. The cited reference  explicitly says that their method allows for preparing libraries from any type of dsDNA and, to my knowledge, they do not mention if their method is adequate for ssDNA. Apparently, this has not been an issue for your experiment, since you were able to detect B19V DNA. Could you explain why?4. I really appreciate the effort put into designing a capture assay for clinically relevant viruses in human remains and the level of detailing about it in the manuscript. This is a very relevant tool for the community, and I would personally be thrilled to use it to study ancient pathogens in my own research.5. Virus temporal dynamics/structure : I suggest explaining in the main text what authors mean with "temporal dynamics" or "temporal structure." My understanding is that the root-to-tip distance in phylogenetic trees reflects the number of substitutions in a lineage. Why would this be related to sampling times? I believe you would expect a strong dependency between root-to-tip distances and sampling times if, within each lineage, substitution rates accrued with time and did not depend on other factors. However, at least across mammal lineages, we know that this is not the case because life-history traits, mutation rate modifiers, and effective population sizes are thought to drive variation in the root-to-tip distances across lineages. I wonder if this also applies to viruses. On a related note, even considering this relationship between root-to-tip distances and sampling time to be perfect, I am not sure how it informs about temporal dynamics or temporal structure. Unless this is something well established in the field, I would suggest explaining the rationale behind this analysis. My understanding is that other types of analysis would better characterize the temporal structure. For instance, wouldn't an ADMIXTURE or STRUCTURE analysis be a better choice to characterize temporal structure? Or alternatively, using phylogenetic trees, wouldn't it be better to use the pattern of lineage clustering as an indication for temporal structure ? Additionally, I strongly suggest the authors further discuss their results in their manuscript. For instance, what does it mean that the HBV does not have a temporal structure while B19V has one? What does it mean that genotype A has a stronger temporal signal than all HBV genotypes together? What does this analysis say about the HBV and B19V lineages found in these human remains in relation to others from the Americas and Africa?6. I appreciate the paragraph starting at the end of page 15 that explains the introduction of these pathogens is a consequence of the slave trade and not the African peoples themselves. All aDNA studies would benefit from this type of observation that prevents both the misinterpretation of the research results and the stigmatization of the human subjects/communities involved.Reviewer #2:In this paper, Guzman-Solis and colleagues present a study of the host genetic origins, and selected viral pathogens, of a group of individuals buried in two cemeteries soon after the arrival of European colonisers in Mexico. The paper provides an important insight into the health of people who are often little thought of in conversations about disease brought by European colonisers to the Americas: enslaved and forcibly transported Africans and their descendants. They identify three individuals with parvovirus B19 infections and one person infected with hepatitis B virus. These viruses have been notoriously difficult to accurately date using molecular clock analysis of modern, circulating strains, and thus direct ancient DNA evidence is a very important way of identifying these viral pathogens  and dating important events in their evolution and spread.This paper also presents an example of how to ask questions about infectious disease in the past, and shows the difficulty of trying to identify historical or ancient pathogens even where palaeopathological and written records exist. The authors include much discussion of cocoliztli  but this paper is not really an answer to which pathogen(s) caused cocoliztli or whether the individuals studied died of cocoliztli. Ancient DNA studies of infectious diseases are always affected by our current limitations of studying only pathogens with a DNA genome . Whereas many hypotheses about cocoliztli suggest it was caused by an RNA virus, which would not preserve well in human remains over hundreds of years. The work of the authors points to a view that Europeans - and forcibly transported Africans - brought a package of diseases with them to the Americas. If cocoliztli is in fact a polymicrobial infection or a name for a series of independent epidemics, this will shape how ancient DNA studies of this period of time are conducted in future.This work selected for individuals who were thought to be of African ancestry (by birth or recent genetic relationships) at one of the sites studied. Therefore it is not surprising that the authors find hepatitis B virus and parvovirus B19 strains which are today associated with African individuals, including in the admixed individual. While it is possible that the forced migration of African individuals to the Americas introduced these viruses to a naïve population, it is also possible that there were (and perhaps still are) local genotypes which have yet to be detected. Patchy modern sampling of these viruses in the Americas today makes the authors' jobs harder. With such a small sample size, partially biased by design towards Africans, this study can't shed much light on whether HBV and B19V were already in the Americas before the arrival of European colonisers or not.eLife. It's an important contribution to the growing field of ancient viral DNA, and focuses on an exciting period of time and of the world that has been somewhat neglected. I thought the paper had an excellent methods section in particular, very detailed.I think this should be published in The authors don't mention whether bacterial pathogens were also found – I respect that the authors may want to publish such findings elsewhere (if applicable) but I would like the authors to be honest about this – especially as paratyphi has been found associated with remains from C16 Mexico. This is also important to discuss as the authors hint at polymicrobial causes cocoliztli .I think the title needs to be slightly changed, as at present it seems to suggest that enslaved Africans brought HBV and B19V to the Americas, which isn't the conclusion of the paper. I thought the discussion of cocoliztli needed focusing, because this paper and its methods aren't really able to answer that question . However this paper can and very neatly does contribute new knowledge on the milieu of viruses present at the time.A comment for future experimental design not something to be changed for this study: I thought the design of the viral enrichment panel was sub-optimal. It is missing the rash-causing virus varicella-zoster (chicken pox and shingles) which is highly infectious, associated with more severe disease if primary infection occurs in adulthood, and also thought to be one of the package of diseases carried to the New World by Europeans; KSHV was missing, which would have been common among enslaved Africans and their direct descendants, and can cause endemic disease even in relatively healthy people; adenoviruses would have been another sensible choice given they persist for weeks or months in the host and are spread by respiratory infection. Whereas the choice of cycloviruses perplexed me. I also thought the choice of targets in eg CMV was a bit odd. UL54 (DNA pol) is highly conserved which is good for detection, but wouldn't give you much geographic/phylogenetic signature, and I have only seen functionally significant UL54 mutations after extensive drug treatment. I respect that the composition of the viral enrichment panel will reflect the local interests and experience of the team(s) involved, but if the authors do redesign the panel in future, they should think more broadly among the human DNA viruses and about the sub-genomic regions chosen.Reviewer #3:Guzmán-Solís, Blanco-Melo, and colleagues present a study of ancient viruses recovered from remains dating to the Colonial period in Mexico. Dental samples from 26 individuals in two different sites with burial contexts suggesting epidemic disease were processed for the recovery of ancient DNA. Following preliminary shotgun results, 12 samples were enriched with a custom bait set designed to capture molecules originating from clinically important viral taxa to examine questions of infectious disease introductions by colonial powers. From these 12 samples, the authors recovered sufficient molecules to reconstruct the genomes of three human parvovirus B19 viruses and one hepatitis B virus.The weaknesses of this paper are those common to all ancient DNA papers, namely the small sample size, low average coverage, genomes that cannot be fully reconstructed, and the lack of metadata to contextualize epidemiological factors such as acute or chronic infections and cause of death. However, the achievements of this paper far outweigh the weaknesses of the field in general and advance our scientific understanding of colonial impacts on the health and well-being of the Indigenous people of Mexico as well as people of African origin in Mexico during the colonial period. The identification of B19V and HBV within this timeframe does not negate or erase previous scholarship studying the description of colonial epidemics but rather augments those narratives.All three B19V genomes and the reconstructed HBV genome fall within diversity associated with the African continent suggesting African origins for these infections. African ancestry of the individuals themselves was confirmed via mtDNA for three of the individuals, as well as autosomal data compared to the 1000 Genomes reference panel, and further supported by isotopic analysis of tooth enamel. The fourth individual carried an mtDNA haplogroup associated with Native American populations and autosomal PCA analysis suggested admixed ancestry of Native American and African origin which demonstrates that viruses were transmitted between individuals of different ancestries during this colonial period.Though the sample size is small, this study demonstrates the effects of European colonization in Mexico through the introduction of humans, both European and enslaved Africans, and pathogenic species carried with these individuals. The interpretation of the data is both biologically sound and conscientiously contextualized for those who had no agency.This paper was a pleasure to read. I have a few suggestions to improve clarity and transparency, particularly in the supplementary tables, but have no suggestions for additional experimentation or analyses.– I understand that the emphasis of this paper was to specifically address the presence of viruses in the samples but found it curious that bacteria were not addressed at all. The metagenomic content, even just relative proportions of different Kingdoms, was not conveyed at all. Were no bacterial species identified from the shotgun data? Is there a separate publication expected to cover the bacterial taxa recovered?– Lines 159-160 and Table 1B. The legend of Table 1B does explain that this is calculated from hits that were normalized by MEGAN. I think that should be reported in lines 159-160 and would also be curious to know if the fold-enrichment values remain approximately constant when not normalized. If reported as ((target capture reads)/) / ((target shotgun reads)/)?– Lines 207. While Krause-Kyora did perform evolutionary temporal analysis of HBV, their work was preceded by Kahila Bar-Gal et al., 2012 and Patterson Ross et al. 2018. These studies should be cited here as well.– Line 239. For consistency, swap "shotgun" for "de novo".– Lines 468-470. While not as old as the samples reported in this study and by Barquera et al., and also not from archaeological contexts, the genomes reported in both Schrick et al., 2017 and Duggan, Klunk et al., 2020 are ancient viral genomes from the Americas. The authors may wish to rephrase this passage.– Lines 520-524. Missing from this discussion and any of the supplementary tables is reference to how deeply the libraries were shotgun sequenced. I understand the sensitivity in releasing human genomic data, particularly from contexts such as these, and have no objection to only the viral reads appearing on Dryad, I do believe that the scope of all data generated needs to be represented somewhere in the paper. There is also no indication anywhere of the number of reads that were used to generate the mitochondrial genomes or the autosomal data for the PCA plots which takes away some of the reader's ability to evaluate the findings. It also makes it difficult to interpret some of the viral data, such as Figure 1c.– Figure 2c. Adding a label as part of the plot to indicate HBV as the reference would be beneficial.– Throughout. The nomenclature and meaning of DS1 and DS2 is challenging to follow as both HBV and B19V seem to have DS1s and DS2s but Table 1c lists only DS1 without specifying if this a singular dataset, separate datasets for HBV and B19V applied to separate samples in the same column, or two datasets merged together. Reviewer #1:1. Line 140: regarding the sentence "…contained at least one normalized hit to viral DNA…", could you please specify in the text how this normalization is done  and/or what is the purpose of this normalization? This relates to the Methods section, lines 534-536. This part, I believe, would benefit from being more detailed. For instance, I am confused with the claim that the "viral abundances were normalized based on the smallest sample size" (line 534). What does "sample size" refer to? Are these sample sizes from your study ? Would it be instead the number of hits of the sample with the least hits? In addition, how does the number of hits (line 140) translate to viral abundances (line 534)?We appreciate the reviewer’s concerns. To avoid confusion, we now provide a more detailed description about our motivation to normalize the hits in the main text (lines 130-133); and the way MEGAN does it at methods (lines 555-559). Just to clarify, throughout the manuscript we refer to the number of hits when talking about a specific virus or viral family, while we use viral abundances in a more general way, when referring to a group of viruses.Lines 130-133:“… revealed seventeen samples containing at least one normalized hit to viral DNA  (Methods)”Lines 555-559:“The RMA files were used for the normalization of the viral abundances based on the library with the smallest number of reads * count of smallest sample) and compared to all the samples from the same archeological site with MEGAN 6.8.0”2. Line 167: Why only HBV and B19V, if you searched for other viruses as well? Were there cases in which the top hit was not HBV or B19V? If so, why removing these? If there weren't any other cases, then the sentence (line 167) is, in my opinion, somewhat misleading in that it seems a choice was made to focus on HBV and B19V.We thank the reviewer for pointing out this source of confusion. Upon enrichment, only four libraries showed an increase in virus-like hits corresponding to the Hepadnaviridae and Parvoviridae families, therefore we focused our attention on these viral families (lines 154-158). Additionally, to double check that the reads were actually from HBV or B19V, we took the reads mapped to HBV or B19V and then queried these against the nr database. If the top hit was to HBV or B19V, then we retained the reads, otherwise we excluded it before assembling the genome. We explain this now with more detail in lines 170-176 and methods (lines 663-665 and 675-677).Lines 154-158:“Only one post-capture library had a ~100-fold increase of Hepadnaviridae-like hits (HBV), while three more libraries had a ~50-200-fold increase of Parvoviridae-like hits (B19V) , compared to their corresponding pre-capture libraries (Methods).”Lines 170-176:“We verified the authenticity of the reads mapped to HBV (BWA) or B19V (BWA/blastn) in the enriched libraries (Methods) by querying the reads against the non-redundant (nr) NCBI database using megaBLAST . This step was performed to avoid including in the genome assembly reads that were mapped by BWA or blastn as HBV or B19V, but with a similar identity to a different taxon in the nr database (and absent in DS1. Methods). Therefore, we only retained reads for which the top hit was to either B19V or HBV (Supplementary file 1C).”Lines 663-665:“All the HBV mapped reads were analyzed through megaBLAST  using the whole NCBI nr database to verify they were assigned uniquely to HBV ).”Lines 675-677:“The resulting reads were analyzed with megaBLAST  using the whole NCBI nr database to verify the top hit was to B19V ).”3. "DNA extraction and NGS library construction" section: I wonder how your choice for dsDNA libraries could be affecting the detection of B19V, whose genetic material is mostly ssDNA. The cited reference  explicitly says that their method allows for preparing libraries from any type of dsDNA and, to my knowledge, they do not mention if their method is adequate for ssDNA. Apparently, this has not been an issue for your experiment, since you were able to detect B19V DNA. Could you explain why?We acknowledge the reviewer's concerns as it makes us realize that we weren’t clear in our attempt to discuss this in the first version of the manuscript. We now add a more thorough explanation in lines 304-318.Lines 304-318:“The recovery of aDNA from B19V, which has a ssDNA genome , in previous studies  as well as in our samples, is noteworthy considering the NGS libraries were constructed using dsDNA as a template. Therefore, we would not expect to recover the ssDNA from B19V with this library building method. However, it is known that dsDNA intermediates form during the B19V replication cycle , and that throughout the viral infection the replicating genomes are present in both the ssDNA and dsDNA forms. The sequences we retrieved must therefore correspond to the cell-free dsDNA replication intermediates. This is coherent with the peculiar coverage pattern on the B19V genome, where the dsDNA hairpins at its terminal sites and are highly covered, reflecting a better stability of these regions over time . Similarly, the partially circular dsDNA genome from HBV was poorly covered at the ssDNA region , which also goes through a dsDNA phase during replication, a similar coverage is reported in three previous ancient HBV genomes ”4. I really appreciate the effort put into designing a capture assay for clinically relevant viruses in human remains and the level of detailing about it in the manuscript. This is a very relevant tool for the community, and I would personally be thrilled to use it to study ancient pathogens in my own research.We thank the reviewer for the interest. We have included all the details to reproduce the probe set in the manuscript. However we can also share the sequences for each probe if it would be useful for others.5. Virus temporal dynamics/structure : I suggest explaining in the main text what authors mean with "temporal dynamics" or "temporal structure." My understanding is that the root-to-tip distance in phylogenetic trees reflects the number of substitutions in a lineage. Why would this be related to sampling times? I believe you would expect a strong dependency between root-to-tip distances and sampling times if, within each lineage, substitution rates accrued with time and did not depend on other factors. However, at least across mammal lineages, we know that this is not the case because life-history traits, mutation rate modifiers, and effective population sizes are thought to drive variation in the root-to-tip distances across lineages. I wonder if this also applies to viruses. On a related note, even considering this relationship between root-to-tip distances and sampling time to be perfect, I am not sure how it informs about temporal dynamics or temporal structure. Unless this is something well established in the field, I would suggest explaining the rationale behind this analysis. My understanding is that other types of analysis would better characterize the temporal structure. For instance, wouldn't an ADMIXTURE or STRUCTURE analysis be a better choice to characterize temporal structure? Or alternatively, using phylogenetic trees, wouldn't it be better to use the pattern of lineage clustering as an indication for temporal structure ? Additionally, I strongly suggest the authors further discuss their results in their manuscript. For instance, what does it mean that the HBV does not have a temporal structure while B19V has one? What does it mean that genotype A has a stronger temporal signal than all HBV genotypes together? What does this analysis say about the HBV and B19V lineages found in these human remains in relation to others from the Americas and Africa?We thank the reviewer for the constructive suggestions. We realized our analyses required further explanation to avoid confusion and a better discussion. We have modified the manuscript to explain the rationale of the analysis in lines 215-222:Lines 215-222:“In order to use our reconstructed viral genomes as molecular fossils to recalibrate each virus phylogeny and perform evolutionary inferences; we first needed to estimate if the phylogenetic relationships among B19V or HBV genomes had a temporal structure  . In the context of viruses, temporal structure is canonically tested with a root-to-tip distance anxd date-randomization analyses .”We do not believe ADMIXTURE or STRUCTURE approaches assess in a quantitative way (as we do with the used tests) the presence or absence of temporal structure.6. I appreciate the paragraph starting at the end of page 15 that explains the introduction of these pathogens is a consequence of the slave trade and not the African peoples themselves. All aDNA studies would benefit from this type of observation that prevents both the misinterpretation of the research results and the stigmatization of the human subjects/communities involved.We thank the reviewer for the comment. This is a very delicate and painful topic and we did our best to try to address it with the most sensibility to avoid misinterpretation and further damage.Reviewer #2:I think this should be published in eLife. It's an important contribution to the growing field of ancient viral DNA, and focuses on an exciting period of time and of the world that has been somewhat neglected. I thought the paper had an excellent methods section in particular, very detailed.We appreciate the encouraging comments by the reviewer.The authors don't mention whether bacterial pathogens were also found – I respect that the authors may want to publish such findings elsewhere (if applicable) but I would like the authors to be honest about this – especially as paratyphi has been found associated with remains from C16 Mexico. This is also important to discuss as the authors hint at polymicrobial causes cocoliztli .As assumed by the reviewer, the bacterial pathogens in the collections analyzed here were described in a different publication that we now refer to . In that paper, we reported the identification of oral pathogens in several ancient individuals, including two individuals (HSJN194 and HSJN204) from this study. We did not identify other pathogenic bacteria in the individuals from this study. Nonetheless, in the spirit of transparency we now include more comprehensive metagenomic profiles for the individuals in this study as figure supplement . We also include a brief description of the taxonomic results in lines 161-168 and the relevant information in the Methods section (line 560-566).Lines 161-168.“Independently, a metagenomic analysis using Kraken2  and Pavian  was performed on the non-human (unmapped) reads as part of a different study . Our samples presented bacterial constituents of human oral and soil microbiota at different proportions between the samples . Although no lethal bacterial pathogen was retrieved, some ancient dental pathogens (Tannerella forsythia) were reconstructed and described in more detail by Bravo-Lopez et al., 2020 ”Lines 560-566.https://github.com/smdabdoub/kraken-biom) and then visualized with Pavian . Detailed description of the results can be found in Bravo-Lopez et al., 2020.”“Independently, unmapped reads (non-human) were taxonomically classified with Kraken2  using a reference database composed of NCBI RefSeq bacterial, archaea and viral genomes . The Kraken2 output was transformed to a BIOM-format table using Kraken-biom . However this paper can and very neatly does contribute new knowledge on the milieu of viruses present at the time.Following the suggestion by another reviewer we now propose an alternative title:“Ancient viral genomes reveal introduction of human pathogenic viruses into Mexico during the transatlantic slave trade”Additionally, we acknowledge that our study is really inconclusive regarding the causative agent of Cocoliztli, and that the emphasis on this can be misleading. We have therefore removed a paragraph in the introduction detailing the symptoms and current hypotheses of the causative agents. Furthermore we have made substantial changes in this regard in the discussion, also removing emphasis on Cocoliztli. With these changes, we believe the introduction and discussion are more focused and do not hint to the study being in any way able to conclude anything about Cocoliztli, but highlight the overall relevance of learning about the viruses circulating in Colonial Mexico.A comment for future experimental design not something to be changed for this study: I thought the design of the viral enrichment panel was sub-optimal. It is missing the rash-causing virus varicella-zoster (chicken pox and shingles) which is highly infectious, associated with more severe disease if primary infection occurs in adulthood, and also thought to be one of the package of diseases carried to the New World by Europeans; KSHV was missing, which would have been common among enslaved Africans and their direct descendants, and can cause endemic disease even in relatively healthy people; adenoviruses would have been another sensible choice given they persist for weeks or months in the host and are spread by respiratory infection. Whereas the choice of cycloviruses perplexed me. I also thought the choice of targets in eg CMV was a bit odd. UL54 (DNA pol) is highly conserved which is good for detection, but wouldn't give you much geographic/phylogenetic signature, and I have only seen functionally significant UL54 mutations after extensive drug treatment. I respect that the composition of the viral enrichment panel will reflect the local interests and experience of the team(s) involved, but if the authors do redesign the panel in future, they should think more broadly among the human DNA viruses and about the sub-genomic regions chosen.The choices made during the design of the probes had the objective to maximize the number of viral families (and subfamilies) susceptible to be captured by the probes, while having a strict length constraint given the limits of the kit size that we were able to afford at the time. Given the large genome size of the Herpesviridae family, we choose a few genes of only one representative virus from each of the three Herpesvirus subfamilies , based on their prevalence in the modern population. Nevertheless, we appreciate and acknowledge the reviewer’s concerns and will take them into account for future more comprehensive probe designs. We have clearly stated the size constraints of the probe kit in methods (line 149-150).Line 149-150:“.”Reviewer #3:This paper was a pleasure to read. I have a few suggestions to improve clarity and transparency, particularly in the supplementary tables, but have no suggestions for additional experimentation or analyses.– I understand that the emphasis of this paper was to specifically address the presence of viruses in the samples but found it curious that bacteria were not addressed at all. The metagenomic content, even just relative proportions of different Kingdoms, was not conveyed at all. Were no bacterial species identified from the shotgun data? Is there a separate publication expected to cover the bacterial taxa recovered?As stated in a previous response to reviewer 2, we screened for bacterial aDNA as part of a different study. We now include the reference to said study and a broader metagenomic profile for the individuals analyzed here . See response to reviewer 2.– Lines 159-160 and Table 1B. The legend of Table 1B does explain that this is calculated from hits that were normalized by MEGAN. I think that should be reported in lines 159-160 and would also be curious to know if the fold-enrichment values remain approximately constant when not normalized. If reported as ((target capture reads)/) / ((target shotgun reads)/)?The fold-enrichment reported at Supplementary File 1B was calculated as  as now mentioned at figure legend (Line 1394) and lines 154-158.The normalization between shotgun and capture was performed because each sequencing round had a different number of reads so we would expect a higher number of reads in the library sequenced deeper. The reported fold enrichment in Supplementary File 1B is based on the library with fewer reads. We now indicate the way MEGAN normalizes data and our motivation to do this step at lines 130-133 and 555-559.Lines 154-158:“Only one post-capture library had a ~100-fold increase of Hepadnaviridae-like hits (HBV), while three more libraries had a ~50-200-fold increase of Parvoviridae-like hits (B19V) , compared to their corresponding pre-capture libraries (Methods)”Lines 130-133:“… revealed seventeen samples containing at least one normalized hit to viral DNA  (Methods)”Lines 555-559:“The RMA files were used for the normalization of the viral abundances based on the library with the smallest number of reads * count of smallest sample) and compared to all the samples from the same archeological site with MEGAN 6.8.0”– Lines 207. While Krause-Kyora did perform evolutionary temporal analysis of HBV, their work was preceded by Kahila Bar-Gal et al., 2012 and Patterson Ross et al., 2018. These studies should be cited here as well.We thank the reviewer for pointing out this omission. The citation for Patterson Ross et al., 2018 has now been included in the manuscript at line 222. KahilaBar-Gal et al., 2012 performed their molecular-clock analysis directly in beast without corroborating the presence of temporal structure in their dataset, so we did not include the citation.Lines 222:“Similarly to previous studies…”– Line 239. For consistency, swap "shotgun" for "de novo".Modified at line 254.– Lines 468-470. While not as old as the samples reported in this study and by Barquera et al., and also not from archaeological contexts, the genomes reported in both Schrick et al., 2017 and Duggan, Klunk et al., 2020 are ancient viral genomes from the Americas. The authors may wish to rephrase this passage.We appreciate the suggestion and have now included the suggested citations and modified the text accordingly.Lines 489-492:“The isolation and characterization of these ancient HBV and B19V genomes represent an important contribution to the ancient viral genomes reported in the Americas .”– Lines 520-524. Missing from this discussion and any of the supplementary tables is reference to how deeply the libraries were shotgun sequenced. I understand the sensitivity in releasing human genomic data, particularly from contexts such as these, and have no objection to only the viral reads appearing on Dryad, I do believe that the scope of all data generated needs to be represented somewhere in the paper.Thank you for the suggestion, the shotgun statistics and human analyses  for the individuals reported in this study are now included at Supplementary File 2A.There is also no indication anywhere of the number of reads that were used to generate the mitochondrial genomes or the autosomal data for the PCA plots which takes away some of the reader's ability to evaluate the findings. It also makes it difficult to interpret some of the viral data, such as Figure 1c.We thank the reviewer for pointing out this omission. The human mapping statistics including the number of SNPs used for the PCA and mitochondrial analyses is now included at Supplementary File 2A.– Figure 2c. Adding a label as part of the plot to indicate HBV as the reference would be beneficial.We appreciate this suggestion and have modified Figure 2c accordingly.– Throughout. The nomenclature and meaning of DS1 and DS2 is challenging to follow as both HBV and B19V seem to have DS1s and DS2s but Table 1c lists only DS1 without specifying if this a singular dataset, separate datasets for HBV and B19V applied to separate samples in the same column, or two datasets merged together.We thank the reviewer for this comment. We have now modified the nomenclature of the distinct datasets as follow:HBV_DS1HBV_DS2B19V_DS1B19V_DS2


This prognostic study compares the performance of the Universal Vital Assessment with that of other common illness severity tests for predicting the risk of in-hospital death among febrile hospitalized patients in northern Tanzania. How does the Universal Vital Assessment (UVA) score perform compared with other common illness severity scores in predicting in-hospital death for febrile hospitalized patients in a lower-income setting?In this prognostic study of 597 adults hospitalized with febrile illness in northern Tanzania, the UVA score and National Early Warning Score outperformed other illness severity scores at predicting risk of in-hospital death. Scores of 2 or higher on the UVA were associated with significant increases in risk of death.The findings of this study indicate that, as a clinical illness severity measure using readily available clinical data appropriate to resource-limited settings, the UVA shows promise for predicting risk of in-hospital mortality among febrile inpatients in lower-income settings. Severity scores are used to improve triage of hospitalized patients in high-income settings, but the scores may not translate well to low- and middle-income settings such as sub-Saharan Africa.To assess the performance of the Universal Vital Assessment (UVA) score, derived in 2017, compared with other illness severity scores for predicting in-hospital mortality among adults with febrile illness in northern Tanzania.This prognostic study used clinical data collected for the duration of hospitalization among patients with febrile illness admitted to Kilimanjaro Christian Medical Centre or Mawenzi Regional Referral Hospital in Moshi, Tanzania, from September 2016 through May 2019. All adult and pediatric patients with a history of fever within 72 hours or a tympanic temperature of 38.0 °C or higher at screening were eligible for enrollment. Of 3761 eligible participants, 1132 (30.1%) were enrolled in the parent study; of those, 597 adults 18 years or older were included in this analysis. Data were analyzed from December 2019 to September 2021.Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS) assessment, and UVA.The main outcome was in-hospital mortality during the same hospitalization as the participant’s enrollment. Crude risk ratios and 95% CIs for in-hospital death were calculated using log-binomial risk regression for proposed score cutoffs for each of the illness severity scores. The area under the receiver operating characteristic curve (AUROC) for estimating the risk of in-hospital death was calculated for each score.P < .05 for all comparisons) except for NEWS (P = .08).Among 597 participants, the median age was 43 years ; 300 participants (50.3%) were female, 198 (33.2%) were HIV-infected, and in-hospital death occurred in 55 (9.2%). By higher risk score strata for each score, compared with lower risk strata, risk ratios for in-hospital death were 3.7  for a MEWS of 5 or higher; 2.7  for a NEWS of 5 or 6; 9.6  for a NEWS of 7 or higher; 4.8  for a qSOFA score of 1; 15.4  for a qSOFA score of 2 or higher; 2.5  for a SIRS score of 2 or higher; 9.1  for a UVA score of 2 to 4; and 30.6  for a UVA score of 5 or higher. The AUROCs, using all ordinal values, were 0.85  for the UVA score, 0.81  for the NEWS, 0.75  for the MEWS, 0.73  for the qSOFA score, and 0.63  for the SIRS score. The AUROC for the UVA score was significantly greater than that for all other scores (This prognostic study found that the NEWS and the UVA score performed favorably compared with other illness severity scores in predicting in-hospital mortality among a hospitalized cohort of adults with febrile illness in northern Tanzania. Given its reliance on readily available clinical data, the UVA score may have utility in the triage and prognostication of patients admitted to the hospital with febrile illness in low- to middle-income settings such as sub-Saharan Africa. In sub-Saharan Africa and other LMIC settings, the etiology of febrile illness differs from that in temperate high-income countries.6 Furthermore, severe febrile illness outcomes likely differ owing to patterns of comorbidities and the availability of intensive care.11 It is unsurprising that studies of existing illness severity scores derived in high-income countries have shown mixed results for predictive performance for adverse outcomes in LMICs.16A major component of research on the management of acute febrile illness focuses on the appropriate identification and triage of severely ill patients once they reach the health care system. In high-income countries, several illness severity scores have been developed for patients with severe illness, including patients with febrile illness.17 Derived from pooled data from studies of hospitalized patients in sub-Saharan Africa, the score uses easily measured clinical data, including temperature, heart and respiratory rates, systolic blood pressure, oxygen saturation, Glasgow Coma Scale scores, and HIV infection status.17 Initial internal validation showed a greater area under the receiver operating characteristic curve (AUROC) than that for the Modified Early Warning Score (MEWS)18 and the quick Sequential Organ Failure Assessment (qSOFA) score8 in the pooled cohort. To our knowledge, to date, 3 prospective external assessments have compared UVA scores with other predictive scores in sub-Saharan Africa: 1 showed only moderate performance across scores for predicting mortality at a single center in Rwanda19; another showed poor performance in a single-center, primarily surgical intensive care cohort in Malawi20; and a study in Gabon showed favorable performance for the UVA but was markedly limited by sample size and statistical power.21In response to the limitations of existing predictive outcome models to identify hospital-admitted patients at high risk of death in resource-limited settings, the Universal Vital Assessment (UVA) score was derived and initially proposed in 2017 to predict inpatient mortality in sub-Saharan Africa.22 the MEWS, the qSOFA score, and the Systemic Inflammatory Response Syndrome (SIRS) score.9We assessed the predictive performance of the UVA score for in-hospital mortality within a prospective cohort of patients admitted with febrile illness to 1 of 2 hospitals in northern Tanzania. We also compared the performance of the UVA with that of other common predictive tools—the National Early Warning Score (NEWS),STARD) 2015 reporting guideline  and the administrative center of the Kilimanjaro Region (population >1.6 million) in northern Tanzania. In 2016-2017, the estimated prevalence of HIV among adults 15 to 64 years in the Kilimanjaro Region was 2.6%.17 Within 24 hours of admission, all adult and pediatric patients admitted to the medical ward were screened for those with a history of fever within 72 hours or a tympanic temperature of 38.0 °C or higher. Enrolled participants had a standardized clinical history and physical examination performed by trained study staff that included measurement of vital signs, mid–upper arm circumference,27 percutaneous oximetry, and assessment of mental status by both the Glasgow Coma Scale and alert-voice-pain-unconscious scores. Blood testing was performed, including HIV antibody testing, blood cultures, complete blood cell counts, blood smear for parasites, and lactate testing. Testing for HIV antibodies was performed using SD BIOLINE HIV 1/2 3.0 (Abbott Laboratories), and positive tests were confirmed by Uni-Gold HIV (Trinity Biotech). Blood culture bottles were BACT/ALERT FA PLUS bottles loaded into the BACT/ALERT 3D microbial detection system (bioMérieux), and 2 sets of culture bottles were collected when feasible. Testing for CD4+ T lymphocytes was performed for HIV-infected participants using the FACSCalibur platform (BD Biosciences). Lactate testing was performed using the StatStrip Lactate System . Participants were followed up during their hospitalization for ascertainment of in-hospital outcome.We conducted a prospective cohort study of patients with febrile illness admitted to Kilimanjaro Christian Medical Centre or Mawenzi Regional Referral Hospital in Moshi, Tanzania, from September 2016 to May 2019. Both adult and pediatric patients were included in the study. For the data analysis of the present prognostic study, participants were included if 18 years or older, consistent with the initial derivation and validation of the UVA score.Micrococcus species, coagulase-negative Staphylococcus species, and Streptococcus species other than S pneumoniae, were not considered to be potential pathogens. Hypoxemia was defined as peripheral oxygen saturation of less than 92% on room air or the use of supplemental oxygen. A lactate level cutoff of 36 mg/dL or more  was chosen in the analysis given its greater likelihood of representing participants with shock than a cutoff of 18 mg/dL or more; both thresholds were assessed in the Third International Consensus Definitions for Sepsis and Septic Shock definition.8The primary outcome for this study was in-hospital death, defined as participant death at Kilimanjaro Christian Medical Centre or Mawenzi Regional Referral Hospital during the same hospitalization as their enrollment. Blood cultures were considered positive if there was growth from either set of cultures collected. Single cultures positive for normal skin flora, including 22 The full calculation scheme for each severity score is depicted in eTable 1 in the 22 the MEWS and NEWS are primarily used to assess the risk of inpatient clinical deterioration,22 and the qSOFA and SIRS scores have been used to assist in both classifying and triaging patients with sepsis.9Scores for predictive models were determined based on the original definitions proposed for each model, using clinical data collected by the study team at enrollment.28 as having septic shock or severe respiratory distress without shock. Septic shock was defined by the Integrated Management of Adolescent and Adult Illness guidelines as systolic blood pressure less than 90 mm Hg and either a heart rate greater than 100/min, a respiratory rate greater than 24/min, or a temperature greater than 38 °C or less than 36 °C. The Integrated Management of Adolescent and Adult Illness guidelines defined severe respiratory distress without shock as a respiratory rate greater than 30/min or peripheral oxygen saturation less than 90% and a systolic blood pressure of 90 mm Hg or greater. Presence of radiographic pneumonia or clinical heart failure were excluded from the latter definition because these data were not available.Participants were also classified based on the World Health Organization’s Integrated Management of Adolescent and Adult Illness guidelines29 With an expected mortality of 10% based on a prior febrile cohort at our 2 study hospitals3 and assuming at least moderate correlation between UVA and qSOFA scores (r ≥ 0.4) for the 597 participants included in the study, we estimated that our analysis had a minimum of 80% power to detect a difference of 0.1 between AUROC values for UVA and qSOFA scores.We performed an assessment of statistical power for this study based on our ability to find a difference between the UVA and qSOFA scores as the primary comparators of interest. Based on published literature, we expected AUROC values for predicting in-hospital mortality of 0.85 for the UVA and 0.75 for the qSOFA.2 test of independence was used to compare proportions for binomial variables where all observed counts of cells in a 2 × 2 contingency table were 5 or greater; the Fisher exact test was used for any with fewer than 5. Crude risk ratios and 95% CIs for in-hospital death were calculated using log-binomial risk regression for each proposed score cutoff. Performance characteristics for predicting in-hospital death were calculated for each score, which included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and the AUROC. The AUROCs were compared using a method suggested by DeLong et al30 as implemented in Stata, version 16 (StataCorp LLC). Statistical significance was defined as 2-sided P < .05 for each of these comparisons. All statistical analyses were performed using Stata, version 16. Data were analyzed from December 2019 to September 2021.Descriptive statistics were used, including medians and IQRs for nonnormally distributed variables, as assessed by the Shapiro-Wilk test. No normally distributed continuous variables were included. Categorical variables were presented as frequencies. To assess for differences between those who died in the hospital and those who survived, the rank sum test was used for nonnormally distributed continuous variables. The χP = .08), and 300 participants (50.3%) were female (P = .13).Of 3761 eligible participants, 1132 (30.1%) were enrolled in the parent study, 618 of whom (54.6%) were adults 18 years or older and eligible for the present analysis . Of the + T lymphocyte count among all participants with an HIV infection was 121 cells/mm3 . Among participants who reported no HIV infection but tested positive for HIV, the median CD4+ T lymphocyte count was 138 cells/mm3 . Of the 161 participants who reported HIV infection at the time of enrollment, 120 (74.5%) reported taking any antiretroviral regimen and 95 (59.0%) reported taking trimethoprim-sulfamethoxazole.Baseline characteristics from the full cohort and the HIV-infected subgroup by outcome are compared in The median scores for each prognostic test and the risk of death based on proposed cutoff scores by prognostic test for the total cohort and the HIV-infected cohort are summarized in The sensitivity of the respective scores and their cutoffs for predicting death ranged from 56.4% to 96.4%, whereas specificity had a range of 25.3% to 87.5% . PositivP < .001 for each) but not NEWS (P = .08). Scores for the participants with an HIV infection are presented in eTables 3 and 4 in the The AUROC values by severity score cutoff are also shown in In this prognostic study of illness severity scores among hospitalized adult patients with febrile illness, the UVA score and NEWS showed the best performance for estimating the risk of in-hospital death among the study cohort, with AUROC values of 0.85 and 0.81, respectively. The AUROC values decreased for the MEWS, qSOFA score, and SIRS score, respectively. Most of the cutoffs for each prognostic score were statistically associated with an increased risk of in-hospital death with increasing score. However, the UVA score showed the greatest discriminatory power, with statistically significant and meaningful increases in estimated risk of death for scores of 2 to 4 and 5 or higher; NEWS values of 5 to 6 compared with those of 4 or lower were not associated with a significant increase in mortality.21 In the UVA derivation study, among participants with suspected infection, the AUROC for the UVA was the greatest  compared with that of the MEWS  and the qSOFA .17 The prognostic score performance data also are consistent with the findings from a prospective validation study from Gabon, in which the UVA score had an AUROC of 0.90  compared with that of the qSOFA score  and the MEWS  among 187 patients with suspected infection,21 although that study had limited statistical power to detect differences between the scores. The results of the current study differ from the findings of a study of 647 patients with suspected infection in Rwanda, in which the AUROC of the UVA score  did not significantly differ from that of an adapted MEWS  or the qSOFA score .19 However, the Rwandan study and this study differed substantially in terms of inclusion criteria, enrollment , and time points for data collection. In contrast, because we enrolled participants within 24 hours of hospital admission, our analysis assessed the performance of risk scores at a similar and important time period for febrile inpatients. This study’s data also differ from those of a Malawi study,20 wherein the AUROC for the UVA score was 0.54 . However, the Malawi study was notably different than the current study in that the cohort was smaller and primarily included participants requiring surgical intensive care, with all severity scores performing poorly in predicting outcomes.20 Overall, the current analysis provides additional support for use of the UVA score in patients in sub-Saharan Africa who are seen in the hospital with febrile illness, a common cause of hospital admission in LMIC settings.31These data are consistent with the findings of derivation and validation studies of the UVA score’s predictive performance compared with that of other illness severity scores in LMICs.8 The shortcomings of SIRS are reflected in this study’s findings of comparatively high sensitivity but poor specificity and poor positive predictive value for in-hospital death. For the qSOFA, despite this study’s finding of meaningful increases in risk of death with scores higher than 0, the limited performance based on the AUROC is consistent with the findings of other studies in LMICs.32Other notable findings in this study include the limited performance of SIRS and qSOFA scores for estimating mortality risk in the study’s cohort of persons with fever. Both the SIRS criteria and the qSOFA were intended for the diagnosis and triage of patients with sepsis rather than prediction of in-hospital death. However, it is notable that SIRS criteria have since been abandoned in the most recent sepsis definition, in part owing to concerns about its poor specificity.21 but the NEWS has performed better in LMICs despite fewer publications assessing its potential utility in such settings.34 The performance of these tests is notable in that both scores were originally derived to predict inpatient clinical deterioration as opposed to predicting outcomes. However, both scores rely on frequent vital sign monitoring and are better suited to institutions with electronic health records that can provide real-time feedback on decompensation risk to prompt provider interventions, neither of which are available in many LMIC settings.34The NEWS and MEWS had moderate predictive performance in this study’s cohort. The MEWS has shown limited performance in prior studies in LMICs to date,1 The use of a unified time point for clinical variables in this study’s cohort allowed for an assessment of the utility of the scores for early triage and prognostication on hospitalization. Furthermore, very few clinical data points were missing for score calculation. This not only allowed for improved assessment of the clinical scores but also allowed for a strong prospective validation of score performance for the large subset of participants with HIV infection (eTables 3 and 4 in the This study has several strengths. To our knowledge, it provides the first prospective external validation of the UVA score in a cohort of this size in which variables were assessed at a single time point early within a participant’s hospitalization.This study also has limitations. First is the limited generalizability of the data. The study was conducted at only 2 medical centers in northern Tanzania. Thus, demographic and epidemiological differences would likely be associated with different score performance elsewhere. Second, the strength of UVA and other scores is contingent on how they are used by clinicians, including whether a single cutoff score or the entire scoring system is used for risk stratification. For that reason, we presented data on each score for both proposed cutoffs and by all ordinal values. Further exploration of appropriate risk strata for each score is needed. Another limitation of the study is that relatively few eligible patients chose to participate in the study, which inherently risks introducing bias into this analysis. In addition, this study was conducted only with participants with febrile illness as opposed to a general population admitted to a medical ward. Further prospective validation of UVA scores is needed in populations with other common noninfectious conditions in LMICs.In this prognostic study of the performance of the UVA and other illness severity scores in a cohort of hospitalized patients with febrile illness in northern Tanzania, the UVA score showed favorable performance characteristics compared with other illness severity scores. Given the simplicity of calculation of the UVA score based on its reliance on readily available clinical parameters at a single time point, the UVA may be appropriate for triage and prognostication for hospitalized patients with febrile illness in LMIC settings such as sub-Saharan Africa.


Aim: Exposure to repeated gait perturbations improves the balance of older adults (OAs) and decreases their risks of falling, but little is known about the underpinning mechanical adjustments. We aimed to quantify the changing temporo-spatial and kinetic characteristics of balance recovery following repeated backward slips to better understand the mechanical adjustments responsible for improved balance.Methods: We exposed 17 young adults (YAs) (25.2 ± 3.7 years) and 17 OAs (62.4 ± 6.6 years) to 10 backward slips simulated on an instrumented treadmill by unilateral backward belt accelerations. We measured the balance of the participants (margin of stability: MoS), balance recovery (nsteps: number of steps necessary to return to a steady gait for at least three consecutive steps), temporo-spatial (step length), and kinetics  for 15 steps following each slip. The results were compared with baseline.Results: Participants in both groups improved their MoS and nsteps with repeated exposure to the slips, but no significant effect of age was detected. During the perturbed step, the GRF vector was directed more posteriorly during mid-stance and more anteriorly during push-off than baseline, which resulted in a longer step. These adjustments were maintained from the first (Slip01) to the last (Slip10) slip, and by Slip10 were correlated with better balance (MoS) on the second recovery step. During the first recovery step following Slip01, participants developed lower plantarflexor and larger knee extensor moments whilst taking a shorter step, these adjustments were correlated with poorer balance and were not maintained with repeated slips. Joint moments and step length of the first recovery step returned to normal levels by Slip10.Conclusion: Young adults and OAs improved their balance with repeated slips. The adjustments that were positively correlated with balance  were maintained whilst those that were not (changes in joint moments) were discarded. All the responses observed in Slip10 were observed in Slip01. The observed balance improvements were achieved by refining the initial strategy rather than by developing a new one. The underlying mechanics were correlated with step length of the first recovery steps, which was associated with balance and should be monitored in fall prevention interventions. We used the aforementioned protocol to expose young and older participants to 10 repeated backward slips, and for each slip, we measured their balance on 15 recovery steps (margin of stability) and recovery of balance  and 17 older  adults volunteered to take part in this study. All participants were able to walk unassisted for at least 15 min, and were free from any lower limb injury in the last 6 months, surgery in the last 2 years, and balance, neurological or musculoskeletal disorders.−1, we first recorded baseline data , and then triggered the perturbation at random and unexpected times by an acceleration (5 m·s−2) of the belt, followed by a return to normal speed. Perturbations were randomly assigned either to the right or the left side using MinimPy (http://sourceforge.net/projects/minimpy) and applied consistently to that limb. Belt accelerations were designed to start at 20% stance phase with the belt speed returning to 1.2 m·s−1 at 70% stance. Mechanical latency and quicker stance phase during the perturbation than in normal gait meant that the perturbation actually occurred slightly later than these timings (−1 . These accelerations produced a forward loss of balance during the second half of stance, from which the participants had to adjust to avoid falling.Participants were exposed to 10 backward slip-like perturbations while walking on an SBI-treadmill , and the protocol of the perturbation has been described in detail previously  and contralateral (Pre1) steps prior to the perturbed step (Pert) and up to the 15th recovery step (Rec15). Zero-dimensional data (margin of stability and step length) were measured at heel strike and one-dimensional data  over 100% of stance phase .Since the primary aim of this study was to determine the mechanisms by which the participants achieved better recovery and a very large data set was developed during the experiment, it was necessary to include in the main manuscript only the results that helped achieving this aim. Therefore, we only report kinetics and temporo-spatial results (1) if they were significantly different from normal and (2) if they were correlated to balance recovery. Results not meeting these criteria are reported in the This study was carried out with the approval of the Liverpool John Moores University and National Health Service (NHS) ethics committees (18/NW/0700). Written consent was obtained in accordance with the declaration of Helsinki.Participants' balance was quantified from the margin of stability (MoS)  before being exported to Matlab  for calculation of the MoS.steps as the first step of at least three consecutive steps within one standard deviation of normal MoS, which was determined as the average of five gait cycles recorded during steady gait on the treadmill after the familiarisation period. When participants did not reach stable gait by the last recorded step (Rec15), we set nsteps to 16 (nsteps was set at 16 for 14 participants (7 YAs and 7 OAs) during Slip01, and for 4 participants (2 YAs and 2 OAs) during Slip10).To quantify how long it took the participants to recover their balance following each slip, we quantified nWe used a 6DoF marker set with 68 retroreflective markers tracked by 12 motion capture cameras  to measure three-dimensional whole-body kinetic and kinematic data while the participants were walking on the treadmill. Force data were recorded using Vicon at a sample rate of 1,200 Hz.θ, + = anterior) as the inverse tangent of the ratio between the anterior-posterior and vertical GRF vectors.We evaluated changes in trunk angle  and step length (anterior-posterior distance between the centres of mass of each foot at heel strike of the leading foot) for each step of each slip trial. These parameters were chosen, as they could be easily targeted in a fall prevention intervention. To allow comparisons between the participants, step length was computed in percentage of body height (% BH). To understand how participants adapted their gait pattern between the first and last slips, we measured the internal joint moments at the hips, knees, and the ground reaction force angle to the vertical (GRFθ and step length were computed.Kinetics (joint moments) and kinematics (trunk angle) data were computed in Visual3D, using inverse dynamics for the joint moments, and filtered using a low-pass fourth-order Butterworth filter with a cut-off frequency of 8 Hz. The same filter was used on the anterior-posterior and vertical GRF vectors and temporo-spatial (location of the feet's COM) data that were then exported to Matlab where the GRFAll the variables were tested for normality by Shapiro–Wilk's test.post hoc tests were performed and alpha adjusted to the number of tests . For the MoS, step length, and joint moments during Pre, we performed non-parametric tests , and parametric mixed-design ANOVAs for the trunk angle.To test whether the participants changed their gait pattern in anticipation of the slip, we compared the MoS, kinetic and temporo-spatial variables during normal with Pre2 and Pre1 of Slip01 and Slip10. When main effects of Age  or Conditions  were detected, Bonferroni *Conditions. Because we repeated the analysis 10 times, α was adjusted to 0.005.To test whether participants' balance was different from normal following each slip, we compared the MoS of each recovery step (Rec1–Rec15) with Normal using mixed-design ANOVAs: Age , Conditions , Agesteps between each slip trial using Mann-Whitney , Friedman (Conditions: Slip01 to Slip10), and Wilcoxon signed rank tests for post-hoc comparisons with Bonferroni adjustments (α = 0.0011).To test for differences in the number of recovery steps required to return to normal balance with repeated slips, we compared npost-hoc tests were performed and alpha adjusted to the number of tests (α = 0.007). When a main effect of Condition was detected, Bonferroni post-hoc tests were performed  and alpha adjusted to the number of tests performed (α = 0.0056).To test for differences in the biomechanics of recovery following the first (Slip01) and last (Slip10) slips, we evaluated the changes in the reactive kinetic and temporo-spatial variables measured during the perturbation and the first (Rec1) and second (Rec2) recovery steps of Slip01 and Slip10. The following conditions were included in the analysis: Normal, Slip01_Pert (perturbed step of Slip01), Slip01_Rec1 (first recovery step of Slip01), Slip01_Rec2 (second recovery step of Slip01), Slip10_Pert, Slip10_Rec1, and Slip10_Rec2. Although the perturbed step cannot be considered as a recovery step per se, we included it in the present analysis to then evaluate whether and how changes in the biomechanics during the perturbation affected the balance and balance recovery in the following steps. When a main effect of Age was detected, Bonferroni steps) on that trial. Specifically, we ran a correlation analysis to understand (1) whether and how participants' balance (MoS) prior to the slip was related to participants' balance following the slip, (2) whether and how participants' balance (MoS or nsteps) in the first slip trial was related to the balance in the last slip trial, and (3) whether and how the kinetic, kinematic, and temporo-spatial adjustments made when recovering from Slip01 and Slip10 affected the MoS of the next recovery steps and nsteps on that slip. For (1), we ran a correlation analysis between the MoS during Slip01_Pre2 and Slip01_Rec1, Slip01_Pre1 and Slip01_Rec1, Slip10_Pre2 and Slip10_Rec1, and between Slip10_Pre1 and Slip10_Rec1. For (2), we ran a correlation analysis to understand whether the MoS of Slip01_Rec1 and Slip01_Rec2 was correlated with the MoS of Slip10_Rec1 and Slip10_Rec2, respectively, and whether nsteps of Slip01 was correlated with nsteps of Slip10. For (3), when a kinetic, kinematic, or spatio-temporal variable was significantly different from Normal levels, we ran a correlation analysis to evaluate whether this variable was related to the MoS of the next steps or to nsteps of that slip trial. To use one-dimensional variables  in the correlation analysis, we used the average from the region of interest . Because nsteps was not normally distributed, the correlations between kinetics or temporo-spatial parameters and nsteps should be treated with caution. Participants whose nsteps was set to 16 were excluded from the correlation analysis including nsteps.Finally, when we found a significant effect of condition on the MoS in the Pre steps, kinetics, kinematics and temporo-spatial variables, we used bivariate parametric and non-parametric correlations to understand whether and how these variables affected the balance (MoS) on the following recovery steps, and the balance recovery , and we performed statistical parametric mapping in Matlab for one-dimensional data .Statistical analysis was performed using SPSS 26  for zero-dimensional data . Irrespective of age, there was no difference in the MoS between the first slip (Pre2 or Pre1) and Normal, but the MoS was larger in the Pre2 and Pre1 of the last slip than both Normal and the equivalent steps of the first slip , or between Slip10_Pre2 and Slip10_Pre1 (p = 0.567), suggesting that although the participants might have adjusted their balance in anticipation of a potentially upcoming perturbation, they did not anticipate its timing.The margin of stability at heel strike of the two steps prior to the first and last slips (Pre2 and Pre1) was not significantly different between the age groups (p > 0.05). Significant main effects of Age and Conditions existed for both step length and hip moments during the pre-slip steps, but these changes were not correlated with balance .We found no main effect of either Age or Conditions on the knee and ankle moments or trunk angle during the steps preceding the perturbations (steps (p > 0.005 for MoS and p = 0.052 for nsteps). We found a significant effect of Conditions on the MoS from Slip01 to Slip06 (p < 0.005) and on nsteps (p < 0.001). A post hoc analysis showed that following Slip01 and Slip02, the MoS was significantly lower than Normal  until the sixth and fifth recovery steps, respectively . From Slip03 to Slip06, only Rec1 had a significantly lower MoS compared with Normal  , and between the MoS of Slip01_Rec2 and the MoS of Slip10_Rec2  indicate that the participants who recovered well during the first slip trial tended to also recover well during the last trial.We found no significant effect of Age on the MoS or n= 0.001) . Signifisteps decreased with the number of slips  until Slip03, from which nsteps was not significantly larger than in Slip10 . From Slip03 to Slip10, nsteps was constantly lower than nsteps in Slip01 (p ≤ 0.001)  .Trunk angle data have not met the criteria for being reported in the main manuscript and are reported in the During the perturbation, the participants' sagittal GRFθ was directed more posteriorly during mid stance than in Normal condition for Slip01  were those who better recovered their balance during Rec2 of Slip10  was correlated with ankle and knee moments during that step, with the participants who developed the larger ankle plantarflexor moment during push-off being those who took the longer step  balance recovery following repeated slip-like perturbations simulated by treadmill belt accelerations can be improved with repeated exposure in young and older adults, which supports our first hypothesis, and more importantly, (b) the older adults demonstrated improvements that were not different to those of younger adults. Following the first slip, participants utilised biomechanical responses that were associated with both better and worse recovery. However, the recovery strategy was optimised with repeated exposures to preferentially retain only the responses associated with better recovery or which resulted in a rapid return to normal balance following the slip. Generally, this optimal recovery strategy requires changes in the orientation of the GRF vector , length During Slip01, the direction of the GRF vector was adjusted and the step length increased during the perturbed step; these characteristics were associated with a more optimal strategy. However, participants also developed low ankle plantarflexor moments during the slip and first recovery step, high knee extensor moments during Rec1, and took a small step during Rec1, which were all associated with poor balance recovery. The recovery strategy did not differ between the age groups; therefore, we accept the hypothesis that OAs developed a similar recovery strategy as YAs on the first slip. However, independent of age, this gross, generalised reaction to the first slip was then fine-tuned to retain only the beneficial characteristics by the 10th slip, in which the participants demonstrated a more optimal recovery strategy. Specifically, by Slip10, participants' GRF vector was still directed more posteriorly during mid stance and anteriorly during push-off, they took a longer step during the slip, generated larger plantarflexor moments compared with Slip01 during both Pert and Rec1, and had returned their knee moments and step length of Rec1 back to normal levels. We, therefore, accept the hypothesis that step length is altered with repeated backward slip-like perturbations, but reject the hypothesis that the joint moments would be redistributed to rely more on the hip and knee joints and less on the ankle joint.This improved balance and shift towards an optimal strategy by Slip10 for both YAs and OAs validate our hypothesis on the recovery strategy developed by young and older adults, and show that task-specific perturbation training by exposure to multiple mechanical perturbations can be used as an intervention to improve balance recovery from backward slip-like perturbations, as already demonstrated elsewhere , they might be correlated with other markers of balance. These adjustments may have been maintained because they had a positive impact on the vertical state of the COM rather than its horizontal (anterior posterior) state, as evaluated in this study (MoS), or on the regulation of the whole-body angular momentum. Therefore, the optimal recovery strategy described here only reflects the optimal strategy used to recover balance as measured by the anterior-posterior MoS, and other factors might affect dynamic stability.Increased step length, as it can compensate for larger COM displacement, independently of the direction and type of perturbation, has often been associated with better balance for postural perturbations . One possibility for this lack of difference between the age groups could be that the perturbation triggered in this study did not present a mechanical demand high enough to discriminate the two groups. Indeed, although the MoS was lower than in normal condition it remained, on average, positive. Despite this lack of significant effect of age, trends were apparent on both the balance and the recovery strategy developed by the participants. We have previously shown that the MoS of YAs was lower than normal up to the fourth recovery step on the first exposure to a backward slip-like perturbation  improved with repeated exposures to backward slip-like perturbations. This is consistent with findings that ageing does not affect the capacity to learn new motor tasks per se  suggest an improvement with repeated slips within one session, which is consistent with previous studies on different kinds of gait perturbations  successfully induced a lasting improvement in balance ; therefore, because step length and stability . However, participants' MoS variability ranged from 0.4 to 3.1 cm, which is not larger than the variability reported by McCrum et al. (n = 14 following the first slip). Careful considerations should be made concerning the number of steps used to determine the MoS variability in future studies, particularly when transferring from treadmill to overground tasks. Lastly, we found that participants did not make anticipatory adjustments in their MoS prior to Slip01, which is consistent with results reported on predictive changes in balance in unexpected perturbations  lab-induced forward slips were found to be 2.3 times less likely to fall within a year than those exposed to a single slip.Some limitations exist in this study that should be taken into account. First, we used a fixed walking speed in this study  ethics committee (18/NW/0700). The patients/participants provided their written informed consent to participate in this study.HD, TO'B, and CM: contributed to the conception and design of the research, interpretation of the results, edited and revised the manuscript, and agreed to manuscript submission for publication. HD: data acquisition and data analysis and drafted the manuscript. All authors contributed to the article and approved the submitted version.This study was co-funded by Minerva Research Labs Ltd.  and Liverpool John Moores University . The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.