Staphylococcaceae bacteria (48 strains of Staphylococcus aureus and 16 non-S. aureus species spanning 2 genera) to the bacteriophage ISP, which is currently under investigation for use in phage therapy. Using three methods–plaque assays, optical density (OD) assays, and quantitative (q)PCR–we find that the host phylogeny explains a large proportion of the variation in susceptibility to ISP across the host panel. These patterns were consistent in models of only S. aureus strains and models with a single representative from each Staphylococcaceae species, suggesting that these phylogenetic effects are conserved both within and among host species. We find positive correlations between susceptibility assessed using OD and qPCR and variable correlations between plaque assays and either OD or qPCR, suggesting that plaque assays alone may be inadequate to assess host range. Furthermore, we demonstrate that the phylogenetic relationships between bacterial hosts can generally be used to predict the susceptibility of bacterial strains to phage infection when the susceptibility of closely related hosts is known, although this approach produced large prediction errors in multiple strains where phylogeny was uninformative. Together, our results demonstrate the ability of bacterial host evolutionary relatedness to explain differences in susceptibility to phage infection, with implications for the development of ISP both as a phage therapy treatment and as an experimental system for the study of virus host shifts.Virus host shifts, where a virus transmits to and infects a novel host species, are a major source of emerging infectious disease. Genetic similarity between eukaryotic host species has been shown to be an important determinant of the outcome of virus host shifts, but it is unclear if this is the case for prokaryotes where anti-virus defences can be transmitted by horizontal gene transfer and evolve rapidly. Here, we measure the susceptibility of 64 strains of  Staphylococcus bacteria and a virus that infects them to investigate how the relationship between host species influences their susceptibility to infection. We find high variation in susceptibility to infection across the host panel and that most of that variation can be explained by the relationship between hosts. This effect is seen consistently using three methods for assessing susceptibility and can be seen both within and between species. Additionally, we find that the relationship between hosts allows us to predict the susceptibility of an unknown host with some accuracy, although there are instances where susceptibility cannot be predicted. Overall, this suggests that closely related hosts will show similar susceptibility to infection and that the relationship between bacterial hosts can be used to predict the susceptibility of an unknown host with limited success.Virus host shifts, where a virus jumps into a new host from another species, are a major source of emerging infectious diseases. Virus host shifts have been shown to be more likely between animals and plant species that are closely related. However, there is much to learn about how relatedness predicts susceptibility in bacterial hosts. Here, we used a panel of 64  Host shifts, where a pathogen jumps into a novel host species and establishes onward transmission, are a major source of emerging infectious diseases. While host shifts can occur with many types of pathogen, viruses are the most prolific , 2. AccoThe evolutionary relationships between hosts are a key factor in determining the success of a pathogen following transmission to a novel host species, and several studies have investigated the ability of host evolutionary relatedness to explain variation in infection traits. These studies have shown that virulence tends to increase –18, and Staphylococcus [While most studies investigate the role of eukaryotic host phylogenies, few have examined the influence of the host phylogeny on susceptibility to infection in a prokaryotic system. Host phylogenetic effects follow the conventional wisdom that more closely related species share more similar phenotypes, and so present similar environments to invading pathogens. In bacteria, there are several mechanisms by which bacteriophage  resistance may be acquired in a phylogenetically intractable way, including horizontal gene transfer (HGT) , recombilococcus , meaninglococcus , 43. TogStaphylococcus aureus (MRSA) [Pseudomonas aeruginosa [Recent years have seen a resurgence in interest in the use of phage to treat bacterial infections due to the continuing emergence of antimicrobial resistance –47. Phags (MRSA) –50 and Pruginosa –55 to brruginosa , 53–59, ruginosa –62. Addiruginosa , 57, 59.S. aureus hosts to several staphylococcal phages when measured using plaque assays [An ability to explain and predict variation in phage host range and susceptibility would be beneficial in the design of future therapies, allowing for the more efficient design of broad range phage cocktails with high efficacy against multiple pathogens. Recently, the evolutionary relationships between bacterial hosts were shown to explain some of the variation in susceptibility of a panel of e assays , suggestStaphylococcaceae isolates (encompassing 2 genera and 17 Staphylococcus species) to investigate how patterns of bacterial susceptibility are influenced by the evolutionary relationships between hosts. ISP, a double stranded DNA virus in the family Myoviridae, is closely related to Staphylococcus phage G1 [S. aureus, infecting 86% isolates tested in a previous study [S. haemolyticus isolates, suggesting that this host range may be species-specific. Staphylococci are a well-established model for bacteria-phage interactions, with investigations into staphylococcal phages occurring since the 1910s. Lytic staphylococcal phages have demonstrated broad host ranges [Here, we use a broad host range bacteriophage  and a panel of 64 phage G1  and has phage G1 . ISP wasphage G1  and has us study . Howevert ranges , 68, 69,t ranges , and vart ranges –73. Sevet ranges –79, and t ranges –82, restt ranges –85, and t ranges . DespiteStaphylococcaceae. Given that different methods of quantifying host range can give different estimations, we assessed the susceptibility of our bacterial host panel to ISP using three methods: plaque assays, optical density assays, and quantitative PCR. We observed considerable variation in susceptibility to ISP across the host panel and showed that a high proportion of the observed variation in susceptibility was attributable to the relationship between host species.In this study we demonstrate that ISP is a broad host range bacteriophage, able to infect both within and among species of Staphylococcaceae, representing a broad phylogenetic and geographic range of hosts. These strains spanned 2 genera and 17 species that were estimated to have last shared a common ancestor ~122mya [S. aureus were represented in this panel, including major complexes CC1 and CC8 (This study made use of 64 strains of  ~122mya . Multi-L and CC8 .Staphylococcaceae sample was streaked on a LB-agar Miller (Formedium) plate (1.5% agar) and incubated for 24 hours at 37°C. A single colony was isolated and used to inoculate 5mL of LB broth, which was incubated at 37°C, 180rpm for 24 hours. All isolates were stored in 25% glycerol at -80°C. When required, isolates were grown up by inoculating 5mL of high-salt LB Miller broth (Formedium) in a sterile 30mL glass universal with a scraping of the frozen culture and incubating at 37°C, 180rpm overnight. To ensure that any observed differences in susceptibility to ISP were not a function of host availability , calibration curves using optical density (OD) and colony forming units (CFU)/mL were generated for each strain. Overnight cultures were then normalised by dilution in LB Miller broth to give similar host densities prior to their use in susceptibility assays.Each S. aureus strain 13S44S9 . ISP was propagated on the  studies ; hereaft0 to 10−8. 100μL of overnight culture of 13S44S9 and 50μL of diluted phage were added to 5mL of LB-agar (0.5% agar), gently mixed, and poured over a 20mL LB-agar Miller plate. Plates were left to dry before being inverted and incubated at 37°C for 24 hours. Plaques were counted and plaque forming units (PFU)/mL determined based on the dilution with the highest number of discernible plaques. Each plaque assay was repeated at least 3 times to account for plating error.The number of infectious phage present was quantified using plaque assays. ISP was 10-fold serially diluted in 1X M9 buffer (Merck Life Sciences) from 108 PFU/mL, determined by plaquing on 13S44S9) were prepared in 1X M9 buffer, ranging from 100 to 10−8. Bacterial isolates were diluted in LB Miller broth, plated out to a final density of 2x1010 bacteria/mL, and left to dry before 5μL of each ISP dilution was spotted on each LB-agar Miller plate. The plates were then inverted and incubated at 37°C. Plaques were counted after 24 hours and converted to PFU/μL, based on the dilution with the highest number of discernible plaques. Six biological replicates of the plaque assay were performed for each bacteria-phage pairing. As the range of PFU/mL across technical replicates in preliminary plaque assays was small (1.4–1.7x108 PFU/mL), only a single technical replicate per biological replicate was performed. Of the 64 bacterial hosts tested, 6 biological replicates were obtained for 42 strains, 5 replicates for 12 strains, and 4 replicates for 10 strains.10-fold serial dilutions of ISP . A low MOI was chosen to ensure any large changes in OD were due to multiple rounds of phage infection within the sample, requiring the production of viable phage progeny. For control plates, 10μL of 1X M9 buffer was added in place of ISP to maintain a final concentration of 5x104 CFU/mL. Samples with and without phage were kept on separate plates to minimise the chance of phage contaminating uninfected samples. Within-plate position was kept constant between infected and control plates for each biological replicate but randomised between replicates to minimise the effect of within-plate position effects. The effects of between-plate variation were estimated by comparing OD readings between biological replicates of the same infection conditions (standard deviation of 0.14) and were found to account for a small amount of variation compared with the effect of infection (standard deviation of 0.67). Three LB Miller broth and three 1X M9 buffer controls were added to each plate to check for contamination. The plates were sealed with an adhesive PCR plate seal (Thermo Scientific) and incubated for 24 hours at 37°C, 180rpm. Following the incubation, OD was read at 600nm on a MultiSkan Sky Microplate Spectrophotometer (Thermo Fisher). To standardise for any differences in bacterial growth rate, susceptibility was calculated as a proportion change in OD due to infection, as follows:In each well of a 96-well plate, 180μL of LB Miller broth was added alongside 10μL of each Several collected data points reported a proportion change in OD considerably less than zero. As this was likely the result of anomalous OD readings, outlier analysis was performed. Datapoints more than 1.5 times the interquartile range outside the upper and lower quartile of the data were considered minor outliers, and those more than 3 times the interquartile range outside the upper and lower quartiles were considered major outliers. All analyses included here were robust to the removal of both major and minor outliers  Tables, Staphylococcaceae strains were diluted 1:20 in LB Miller broth and extracted to provide an initial viral load at infection timepoint zero. To extract the viral DNA, 100μL of 10% w/v Chelex 100 (Merck Life Sciences), 2ul of 20ng/μL Proteinase K (Merck Life Sciences), and ~10 1mm zirconia beads (Thistle Scientific) were added to each sample before mechanically lysing the bacteria using an Omni Bead Ruptor 24 (Camlab). Samples were centrifuged briefly, and heat treated for 10 minutes at 95°C to inactivate the Proteinase K before being centrifuged for a further 5 minutes at 18 x g to sediment out the Chelex. For each sample, 50μL of the supernatant containing bacterial and viral DNA was transferred and stored at -20°C.Infections used to measure susceptibility using quantitative (q)PCR were set up identically to those in the OD assays described above. Again, a low MOI of 0.05 was used to ensure that any large changes in viral load were due to multiple rounds of phage infection, requiring the production of viable phage progeny. Following the 24 hour incubation, 100μL of each sample was transferred to a sterile 1.5mL screw cap microcentrifuge tube and heat treated at 90°C for 10-minutes to inactivate the bacteria. Additionally, 12 samples of the aliquot of ISP used to infect the qPCR was performed on each of the samples using an Applied Biosystems StepOnePlus system with a Sensifast Hi-Rox Sybr kit (Bioline). Cycle conditions were as follows: initial denaturation at 95°C for 120 seconds, then 40 cycles of 95°C for 5 seconds, and 60°C for 30 seconds. ISP was measured using the following primers: forward, 5’- CCTGTACCGGCTTGACTCTC -3’; reverse, 5’- AGCTACAACCGAGCAGTTAGA -3’, which were confirmed to have a near 100% efficiency when tested in the presence of each bacterial isolate. Pilot experiments showed that normalisation to either staphylococcal genomic DNA or an exogenous DNA spike made little difference to the between sample variation in ISP viral load and, therefore, no normalisation was used.t values from technical replicate pairs (Ct:24) were normalised to an initial dose of ISP (Ct:0) and converted to fold change in viral load using the 2–ΔCt method, where ΔCt = Ct:24 –Ct:0. Prior to analysis, these values were converted to a log10 fold change in viral load over 24 hours. Of the 64 bacterial strains tested, 6 biological replicates were obtained for 58 strains, 5 replicates for 5 strains, and 4 replicates for 1 strain.For each sample, two technical replicates of the qPCR reaction were performed. Amplification of the correct product was confirmed by melt curve analysis: samples that had failed to amplify the product, showed evidence of melt curve contaminants, or departed from the melt curve peak of positive samples by ±1.5°C were excluded. Correction for plate effects between technical replicates was performed using a linear model as previously described , 92. MeaStaphylococcaceae isolates, a core genome phylogeny was constructed using BEAST v1.10 [To infer the evolutionary relationships between ST v1.10 . Brieflye novo assemblies were annotated using Prokka (v2.8.2) [Staphylococcaceae isolates. Phylogenetic trees were constructed using BEAST v1.10 [st/2nd and 3rd codon positions, to reflect differences in selective constraint [D(v2.8.2)  and orth(v2.8.2)  using a ST v1.10 . SequencST v1.10 . Separatnstraint . Two indnstraint .S. aureus and among-species phylogenies were constructed by dropping the relevant tips from the 64-strain tree using the R package ape [S. aureus samples or species isolates with 13S44S9 as a representative S. aureus strain  also includes a strain-specific random effect that is independent of the host phylogeny (μhms:). This explicitly estimates the non-phylogenetic component of between-strain variance and allows for the calculation of phylogenetic heritability (described below). μhms: was removed from model (2) as model (1) failed to separate the phylogenetic and strain-specific effects. An additional version of model (1) was run with evolutionary distance from the propagation host added as a fixed effect (d:hmβ). This was done to examine if the evolutionary distance from the propagation host explained any observed differences in susceptibility between strains, but was found to be non-significant and thus excluded from further models.In these models, Susceptibilities measured by OD and qPCR were treated as normally distributed. However, to account for the zero-inflated nature of the plaque assay data, plaque assay susceptibility was divided into two separate variables–equivalent to a hurdle modelling approach–with a binary variable indicating whether the bacteria was permissive (1) or non-permissive (0) to infection, modelled using probit link function , and , including both within-strain genetic variance and measurement error. Because each biological replicate consists of a measurement from a single method, the covariances of Ve cannot be estimated and were set to 0. Additionally, the residual variance for the binary variable cannot be estimated and was fixed at 1.Within each of these models, the random effects and residuals were assumed to follow a multivariate normal distribution with a mean of 0 and covariance structure Models were run for 13 million MCMC generations, sampled every 5,000 iterations with a burn-in of 3 million generations. Parameter expanded priors were placed on the covariance matrices, resulting in multivariate F distributions with marginal variances being scaled by 1000. Inverse-gamma priors were placed on the residual variances, with a shape and scale equal to 0.002. To ensure the model outputs were robust to changes in prior distribution, models were also fitted with inverse-Wishart priors, which gave quantitatively similar results.Staphylococcaceae to ISP, we calculated several metrics. First, repeatability, used in the study of quantitative traits and defined as how ‘repeatable’ a measurement is within a group compared with measurements made across groups, was calculated from model (2) as Vp/Vp+Ve, where Vp represents the phylogenetic  variation, and Ve the within strain variation [h2) from model (1). Phylogenetic heritability is defined in two ways in the literature: either as the variance in average phenotype across strains that can be explained by phylogeny or as the variance in phenotype that can be explained by phylogeny [Vs) such that total variance explained by phylogeny, defined as the “phylogenetic heritability of total variance” in this paper). Inter-strain correlations in viral load between each method were calculated from model (2) Vp matrix as β) of each relationship as To estimate an effect of phylogeny on the susceptibility of ariation –103. To hylogeny , 105. Thhttps://doi.org/10.6084/m9.figshare.7756526.v1). These plots take the trait values for each terminal node  and the inferred trait values of ancestral nodes from model (2) and plot them as colour gradients across the phylogeny, with changes in trait values along branches assumed to be smooth, fixed rate transitions as expected when following a Brownian model of trait evolution.To visualise the outputs of our phylogenetic GLMMs as patterns of bacterial susceptibility across the host phylogeny, we plotted ancestral state reconstructions using methods described elsewhere . The Staphylococcaceae phylogeny is characterised by high susceptibility to ISP across a large number of isolates, with smaller clades showing reduced susceptibility to infection . Some clades show intermediate levels of susceptibility  while some contain both permissive and non-permissive strains .The phylogeny of ese taxa , with thStaphylococcaceae hosts to ISP can be further seen in ancestral state reconstructions  . It is likely that the observed difference in ability to estimate heritability between the whole phylogeny model compared to the reduced phylogeny models is down to reduced statistical power, causing the latter models to struggle to separate the phylogenetic and strain-specific effects.To determine if the observed phylogenetic signal is consistent across evolutionary scales, we reduced the phylogeny to one containing only the  samples  and one 13S44S9) . SimilarS. aureus strain, 13S44S9). No effect of distance from the propagation host on the susceptibility of Staphylococcaceae to ISP was found , suggesting that the observed phylogenetic signal was not being driven by adaptation of ISP to the propagation host.Finally, to ensure that ISP had not adapted to its propagation host prior to the experiment, we looked for an effect of distance from the propagation host , and a strong positive correlation was observed between the binary measure of plaque assay and OD  and the binary measure of plaque assay and qPCR . However, no evidence of a correlation was observed between the continuous plaque assay data and either OD or qPCR, with correlation coefficients approximately zero and credible intervals spanning -1 to 1  . A stron -1 to 1 . Similar. aureus  and amon. aureus  phylogenStaphylococcaceae strains and species to be predicted based on their evolutionary relationships to staphylococci with known susceptibilities. To investigate the ability of the bacterial host phylogeny to predict susceptibility, leave-one-out cross-validation was used, whereby multiple versions of model (2) were fitted, each with the data from a single bacterial strain removed, and the model challenged to predict the susceptibility of the “unknown” host given only its evolutionary relationships to other staphylococci strains and their measured susceptibilities , as in the phylogenetic model most of the variation in PFU/μL was partitioned into the model residuals, causing trait values predicted from the phylogenetic component to cluster around the across-strain mean . However, when predicting viral loads from qPCR data .For the binary component of plaque assay, the phylogenetic model was able to correctly predict a strain’s permissiveness to ISP infection in 73% of cases , whereasain mean . When asain mean , the errPCR data , the phyS. haemolyticus and S. hominis, OD prediction for S. xylosus and S. pseudoxylosus). However, when hosts had susceptibility near the across-strain mean and had close relatives that differed from this mean, the phylogenetic model produced larger errors in prediction than the null model . Lastly, where hosts differed from the across-strain mean but had close relatives that conformed to the mean, both phylogenetic and null models were poor predictors of susceptibility . Together, these results suggest that the host phylogeny may allow for the limited prediction of host susceptibility, but may mislead predictions in host strains that differ strongly from the susceptibilities of their close relatives.In the OD and qPCR assays most strains were both similar in susceptibility to their close relatives and showed susceptibility close to the across-strain mean Figs , and 4D,Staphylococcaceae bacteria and determined what proportion of the variation in phage susceptibility is explained by the relationships between bacterial hosts. ISP was capable of infecting 97% of the host strains investigated here when measured by OD and qPCR assays, but only 64% of these strains appeared permissive when tested by plaque assay. We found that variation in susceptibility across our Staphylococcaceae panel–measured using OD, qPCR, and the binary component of plaque assays–was largely explained by the host phylogeny, and these effects were seen at both within-species, and among-species phylogenetic scales. No effects of evolutionary distance from the propagation host were seen, with the patterns instead being driven by the existence of host clades sharing similar levels of susceptibility to ISP. Strong positive correlations were seen between the susceptibility measures from the binary plaque assay, OD, and qPCR assays, but not between these methods and the PFU/μL measures taken from plaque assays. Together, these results suggest that ISP has a broad ability to infect bacterial hosts within the Staphylococcaceae family, and that a large proportion of the variation in susceptibility to ISP across Staphylococcaceae hosts can be explained by the evolutionary relationship between bacterial hosts.Closely related host species present similar environments to novel viruses , 108, antaphylococcaceae family. This expands upon the findings of a previous study which demonstrated that ISP was able to infect many strains of S. aureus [Recent studies investigating the host range of phages have employed host panels of varying phylogenetic scales, including analyses across diverse panels of bacterial species , 109–111. aureus  and exte. aureus .S. aureus and among Staphylococcaceae species. This among-strain and among-species variation is likely due to differences in the molecular pathways influencing host range. For example, previous studies on phages have demonstrated that the highly conserved wall teichoic acid (WTA) serves as the primary receptor for staphylococcal phages [S. aureus and found that a combination of type I and type III restriction modification systems prevent the uptake of DNA originating from other species [S. aureus lineages [S. aureus bacteriophages, including: the TarJ, TagH, and TarP proteins which are involved in the production and transport of WTA to the bacterial cell surface; HsdS which determines the sequence specificity of the SauI restriction modification system; and several prophage associated genes, suggesting that superinfection immunity is playing a role in the susceptibility of S. aureus to bacteriophage [Both permissiveness and susceptibility of bacterial hosts to phage are key considerations when designing phage therapies. In our results, variation in permissiveness and susceptibility to ISP was apparent, both within l phages , contrib species  and betwlineages , respectlineages . A Genomriophage . Given tStaphylococcaceae, then their influence on phage susceptibility may be phylogenetically conserved and this variation may have been captured in the core genome phylogeny. In any case, our results indicate that more closely related Staphylococcaceae strains and species are more likely to share similar susceptibilities to phage infection, consistent with patterns seen in previous studies of animal hosts and viruses [Staphylococcus [Staphylococcaceae to phage infection, as well as the evolutionary scale at which the relationship between host species is unable to explain variation to susceptibility.Bacterial evolution is known to involve several processes that could disrupt the phylogenetic heritability of susceptibility to phage. Notably, cell surface receptors can be easily altered by point mutations and components of phage resistance may be frequently gained and lost through horizontal gene transfer, which can lead to high levels of variation in the phage defence systems present in bacterial genomes , 116–120 viruses –21, 26. lococcus , we havelococcus , superinlococcus , and reclococcus  in the rStaphylococcaceae to a bacteriophage: plaque assays, optical density assays, and qPCR. Susceptibility measured by binary plaque assay, OD, and qPCR were strongly positively correlated. However, no correlation was seen between the continuous component of plaque assay and either other method. While OD and qPCR suggest a broad host range for ISP, plaque assays underestimated the susceptibility of the host panel, with only 63% of strains appearing permissive to infection. This discrepancy may in part be due to differences in the environmental conditions imposed by each assay on the bacteria and phage. Bacteria in the plaque assay are sessile and aerobic, whereas bacteria are planktonic and anaerobic in the OD and qPCR assays. For facultative anaerobes, such as Staphylococcus, anoxia has been shown to lead to the differential expression of over 200 genes influencing a multitude of biological processes [Staphylococcaceae species under varying oxygen concentrations is understudied, it is likely that differences in oxygen availability would influence bacterial physiology in a way that may affect phage infection [In this study, we used three methods to assess the susceptibility of rocesses –126. Whinfection , 127. FuS. simiae is isolated within the phylogeny, with few close relatives, and had a poorly estimated susceptibility due to the distance between it and its nearest neighbour. Several additions may improve the accuracy of predictions of susceptibility using these models. Firstly, improving the depth of our phylogeny–i.e., adding more strains and species that reduce the number of isolated strains on long evolutionary branches–would increase the number of observations that the model can use to predict susceptibility. Secondly, furthering our understanding of the mechanisms by which susceptibility can change in a non-phylogenetically tractable way  may improve our ability to predict susceptibility when phylogeny is not informative. Predictive models that incorporate the evolutionary relationships between hosts (core genome phylogeny) alongside additional information about the presence of anti-phage defence systems and prophages may be better able to predict susceptibility in instances where phylogeny alone is not informative. Alternative metrics, such as genome composition bias, have been shown to outperform phylogeny in the prediction of infection traits [how susceptible a host will be to infection, would allow us to better prioritise resources to the prevention and control of emerging infectious diseases in humans and wildlife.Being able to predict and prevent emerging infectious diseases is a major goal of scientific research, and one of the first steps of that process is being able to predict whether a host will be susceptible to a novel pathogen . While on traits , and mayn traits –130. UndTogether, our results demonstrate that the bacterial host phylogeny is an important determinant of phage susceptibility and replication across novel hosts, and that the relationship between host species may be a useful addition to models aiming to predict virus host range both in the context of emerging infectious disease and phage therapy. Further work is required to understand the specific interactions underlying variation in ISP susceptibility across bacterial hosts; the relationship between host damage, virus replication, and virus persistence in this system; and how the patterns of phage susceptibility across bacterial phylogenies may vary under different infection conditions and contexts.S1 Text(DOCX)Click here for additional data file.S1 TableS. aureus strains missing a ST are coagulase negative and therefore do not have an MLST scheme and the S. aureus strains with STs but no CCs are not similar enough to other strains to be assigned a CC.Clonal complex and sequence type, where available, were determined using PubMLST . S. aure(DOCX)Click here for additional data file.S2 TableEstimates of repeatability are taken from model (2) and estimates of phylogenetic heritability (the proportion of phylogenetic and non-phylogenetic strain variation explained by the host phylogeny) and phylogenetic heritability of the total variance  are taken from model (1). PA = plaque assay, CI = credible interval.(DOCX)Click here for additional data file.S3 TableEstimates of repeatability are taken from model (2) and estimates of phylogenetic heritability (the proportion of phylogenetic and non-phylogenetic strain variation explained by the host phylogeny) and phylogenetic heritability of the total variance  are taken from model (1). PA = plaque assay, CI = credible interval.(DOCX)Click here for additional data file.S4 TableNumbers show the mean estimates for the correlation strength  and slope  between pairs of methods, with 95% credible intervals (CIs) indicated in brackets. The slopes were calculated with columns as x and rows as y. Estimates with CIs that do not span zero are highlighted in bold. PA = plaque assay, *value on a probit scale.(DOCX)Click here for additional data file.S5 TableNumbers show the mean estimates for the correlation strength  and slope  between pairs of methods, with 95% credible intervals (CIs) indicated in brackets. The slopes were calculated with columns as x and rows as y. Estimates with CIs that do not span zero are highlighted in bold. PA = plaque assay, *value on a probit scale.(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 TableEstimates of repeatability are taken from model (2) and estimates of phylogenetic heritability (the proportion of phylogenetic and non-phylogenetic strain variation explained by the host phylogeny) and phylogenetic heritability of the total variance  are taken from model (1). PA = plaque assay, CI = credible interval.(DOCX)Click here for additional data file.S9 TableEstimates of repeatability are taken from model (2) and estimates of phylogenetic heritability (the proportion of phylogenetic and non-phylogenetic strain variation explained by the host phylogeny) and phylogenetic heritability of the total variance  are taken from model (1). PA = plaque assay, CI = credible interval.(DOCX)Click here for additional data file.S10 TableNumbers show the mean estimates for the correlation strength  and slope  between pairs of methods, with 95% credible intervals (CIs) indicated in brackets. The slopes were calculated with columns as x and rows as y. Estimates with CIs that do not span zero are highlighted in bold. PA = plaque assay, *value on a probit scale.(DOCX)Click here for additional data file.S11 TableNumbers show the mean estimates for the correlation strength  and slope  between pairs of methods, with 95% credible intervals (CIs) indicated in brackets. The slopes were calculated with columns as x and rows as y. Estimates with CIs that do not span zero are highlighted in bold. PA = plaque assay, *value on a probit scale.(DOCX)Click here for additional data file.S1 FigMDS visualization of tree distances when λ  =  0. As the projection often requires that multiple trees are plotted at the same co-ordinates, contour lines are used to indicate the density of points.(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.


Patients with diabetes and comorbid substance use disorders (SUD) experience poor diabetes management, increased medical complications and mortality. However, research has documented that patients engaged in substance abuse treatment have better management of their comorbid conditions. The current study examines diabetes management among patients with type 2 diabetes, with and without comorbid SUD, receiving care at Florida-based Federally Qualified Health Centers (FQHC) of Health Choice Network (HCN).A retrospective analysis was conducted using deidentified electronic health records of 37,452 patients with type 2 diabetes who received care at a HCN site in Florida between 2016 and 2019. A longitudinal logistic regression analysis examined the impact of SUD diagnosis on achievement of diabetes management [HbA1c < 7.0% (53 mmol/mol)] over time. A secondary analysis evaluated, within those with an SUD diagnosis, the likelihood of HbA1c control between those with and without SUD treatment.The longitudinal assessment of the relationship between SUD status and HbA1c control revealed that those with SUD  were less likely to control HbA1c over time . Among those with SUD, patients engaged in SUD treatment were more likely to control HbA1c .Findings highlight that untreated SUD could adversely affect diabetes control and sheds light on the opportunity to enhance care delivery for patients with diabetes and co-occurring SUD. The number of individuals with diabetes continues to rise in the US, with ~37 million Americans having either diagnosed or undiagnosed diabetes. An estimated 90–95% of these cases are type 2 diabetes . The incThe diabetes management gap is also notable in patients with diabetes and other comorbid conditions. Research shows that most people with type 2 diabetes have a comorbid condition , 12 thatWhile previous studies have documented adverse outcomes in patients with type 2 diabetes and SUD with the use of electronic health care records, these studies have been conducted in large healthcare systems and academic settings , 20, 36.A retrospective analysis was conducted using a limited dataset of patients with type 2 diabetes, 18–75 years old, who received care within the HCN network of FQHCs in Florida between January 1, 2016 and December 31, 2019. Data were housed and analyzed within a secure server at the University of Miami Clinical and Translational Sciences Institute. The study was approved by the University of Miami Institutional Review Board on July 22, 2022.The study assessed demographic information recorded in EHR, including patient age at the beginning of data collection, gender, and self-identified race and ethnicity. Patient diagnoses were determined using a standard clinical diagnostic approach. The diagnostic status for type 2 diabetes was defined using relevant International Classification of Disease Ninth Edition (ICD-9) and Tenth Edition (ICD-10) diagnosis codes. Type 2 diabetes diagnosis, based on these ICD-9 and/or ICD-10 codes, was determined over a baseline period of two years, including all patients with a diagnosis between January 1, 2016 and December 31, 2017. Specific SUD status  was defined by (1) ICD-9 and ICD-10 codes, (2) Current Procedural Terminology (CPT) codes related to SUD-specific treatment, and/or (3) key medication terms for SUD-specific medications. Similar to type 2 diabetes, SUD status was determined during the 2-year baseline (2016–2017), and only patients without any SUD diagnosis during the entire study period from 2016 to 2019 were counted in the non-SUD group. Patients with comorbid diabetes and SUD had diagnostic codes for both Type 2 diabetes and SUD as described above. A table of codes and key words are included in Patient engagement in diabetes treatment was characterized by pharmacological treatment, using medication key terms to identify the percentage of days covered by any type 2 diabetes medication prescription during the measurement period. A table of key words are included in Patient engagement in SUD treatment was characterized by both behavioral treatment and pharmacological treatment, for those with an SUD diagnosis at baseline. Behavioral treatment was identified by relevant CPT codes, while pharmacological treatment was identified by key medication terms. Both behavioral and pharmacological treatments were specific to the type of SUD diagnosis . Patients were classified as “engaged” in SUD treatment if their records indicated at least two visits  AND/OR if they had at least one SUD-specific prescription  during the measurement period . Participants who met the criteria for both behavioral and pharmacological were also categorized as “engaged” in SUD treatment. This flexible and overinclusive approach was to mimic what occurs in practice. While most patients are prescribed behavioral treatment, it is possible that some patients are only prescribed pharmacological treatment, such as the case in individuals with OUD, or are prescribed both. Engagement in the pharmacological treatment component for those with tobacco use disorder (TUD) was characterized by medication keywords for BOTH nicotine replacement AND tobacco anti-craving medications. Patients without at least two visits and without specific medication key terms were classified as “not engaged” in SUD treatment during the measurement period.As a clinically important indicator of diabetes management, the American Diabetes Association (ADA) threshold of HbA1c at 7.0% (53 mmol/mol) was used to assess diabetes management. Patients were considered to have “uncontrolled” HbA1c if they had any lab value ≥7.0% during the measurement period or zero HbA1c labs reported in the given year of observation. All numeric HbA1c lab values were categorized as “controlled” [HbA1c <7.0% (53 mmol/mol)] or “uncontrolled” [HbA1c ≥ 7.0% (53 mmol/mol)]. HbA1c results coded in the EHR as free text  or as numerical ranges above 7.0% (53 mmol/mol)  were coded classified as “uncontrolled”. Similarly, HbA1c results coded in free text as numerical ranges <7.0% (53 mmol/mol) were coded only categorically and classified as “controlled.”via unique patient IDs.The HCN EHR dataset contains several patient-level variables in distinct datafiles, including demographics, encounters, problem lists, procedures, medications, and laboratory data. Each datafile with the fields necessary for this analysis were merged Summary statistics were computed for each of the demographic and medical characteristics of the total sample and by SUD/non-SUD subgroups. Categorical variables were summarized using frequencies and percentages, while means and standard deviations were calculated for continuous variables.p < 0.05 were considered statistically significant. Both models included a random intercept for individuals to account for the repeated measures across patients and a fixed effect for clinic to account for variation among clinics. All analyses were conducted with SAS statistical software version 9.4  with a binary distribution and a logit link function was used to examine the impact of SUD diagnosis on achievement of diabetes management  over time, adjusting for age, race, ethnicity, gender, and engagement in treatment for diabetes and SUD. Next, we conducted a secondary GLMM to evaluate the impact of engagement in SUD treatment  among those with an SUD diagnosis at baseline on achievement of diabetes management over time, controlling for age, race, ethnicity, gender, and diabetes treatment  had a diagnosis of type 2 diabetes during 2016–2017 and available demographic information. Among these, 37,352 met criteria of binary SUD status of either having a baseline SUD diagnosis  vs. never having an SUD  over the study period and were included in the study. Longitudinal assessment of the relationship between baseline SUD status and HbA1c control reveals that those with SUD were less likely to control HbA1c over time as compared to those without an SUD . Patients who engaged in diabetes treatment were more likely to control HbA1c compared to those not engaged in treatment . Likelihood of control increased with age ; for every year increase in age, there was an increased odds (2.0%) of having HbA1c controlled. Finally, females as compared to males  and black individuals as compared to white individuals  were also more likely to have controlled HbA1c .Among the 6,878 individuals with type 2 diabetes and a baseline SUD, 770 (11.2%) were not engaged in any SUD treatment. Among 6,108 individuals categorized as “engaged” in SUD treatment, 62 (1.0%) met criteria for only pharmacological treatment, 5,001 (81.9%) met criteria for only behavioral treatment, and 1,045 (17.1%) met criteria for both behavioral and pharmacological. Longitudinal assessment of the relationship between engagement in SUD treatment and HbA1c control reveals that those engaged in treatment were more likely to manage HbA1c over time as compared to those not engaged in SUD treatment  . SimilarIt is important to note that the interaction of engagement and time was significant in the model assessing the relationship between engagement in SUD treatment and HbA1c control. As observed in the Least Squares means results in Results of this study demonstrate that patients with type 2 diabetes and any SUD were less likely to achieve glycemic control and that engagement in SUD treatment was associated with higher likelihood of diabetes control. These results add to other studies in the literature that documented worse diabetes management, medical complications, and mortality for patients with comorbid diabetes and SUD , 20, 38.Remarkably, the prevalence of substance use disorder was low in the patient population. Several studies have documented lack of adequate screening of SUD in primary care, causing inadequate identification , 41. SomNot surprisingly, patients with type 2 diabetes and SUD that were engaged in SUD treatment were more likely to achieve glycemic control than those not engaged . EngagemThis work has several strengths. First, to our knowledge this is the first examination of type 2 diabetes and SUD in FQHCs in a Medicaid non-expanded state and provides the chance of further understanding diabetes care quality for patients that are vulnerable to the inequities driven by social determinants of health. Second, results highlight the disparate likelihood of management experienced by patients with diabetes who also have comorbid SUD. Third, findings underscore the importance of SUD treatment and highlight that untreated SUD could affect glycemic control. This is relevant as it can inform treatment resource allocation for SUD within health care centers. Fourth, this study relied and capitalized on the use of EHR and point of care data drawn from FQHCs, offering population level understanding of those most challenged by adverse health outcomes. This low-cost research approach can critically inform systems-level strategies to improve patient outcomes and could lower overall healthcare costs.The current study has several limitations. First, the design of this study supported findings that are correlational in nature; therefore, causation cannot be concluded. Second, the eligibility timeframe was established by convenience and data availability. The University of Miami, through an agreement with the Miami Clinical and Translational Science Institute and HCN, started receiving data for research purposes in 2016, hence the start date of the eligibility period. Follow up data on HbA1C control were established through 2019, to avoid with confounding effects of COVID-19, starting in 2020. Third, it is possible that additional confounders could explain the associations observed. Diet and exercise, two important factors in glycemic control, are inconsistently documented in the records, and when so, only as unstructured data. This study relied only in a limited data set containing only structured data. Fourth, relying on EHR data is prone to inherent limitations, such as missing data and misclassification. Fifth, while the network of FQHCs represented in this study offers substance abuse treatment in both primary care delivery locations and satellite locations, it is possible that patients received substance abuse treatment outside of the FQHC network therefore precluding those data from being documented in the EHR. Finally, the results of this study might not be generalizable to populations not served in Florida or not served in FQHCs.In conclusion, this analysis demonstrated that patients with comorbid type 2 diabetes and SUD have worse glycemic control and that untreated SUD could adversely affect diabetes control, thereby shedding light on opportunities to enhance care delivery for patients with diabetes and co-occurring SUD. Understanding diabetes management in the context of comorbidities helps identify opportunities for improving diabetes management outcomes and reducing the risk of diabetic consequences and premature death . Revealivia data use agreement to limit the transfer of identifiable information, and are not available to the public.The data analyzed in this study is subject to the following licenses/restrictions: electronic health record data analyzed in this study were accessed The studies involving human participants were reviewed and approved by University of Miami Institutional Review Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.via University of Miami UHealth IT and provided guidance on datafile and variable management. VH, RS, RD, DP, KC-B, JB, KE, and DF reviewed and decided on operationalization of all study variables. DF provided methodological guidance and approach for the models created for data analyses. RD performed the analyses. RS and RD drafted the methods and results sections. KC-B directs research and DP serves as a data analyst for Health Choice Network, and as such, both provided guidance on understanding data structure and interpreting results. SG and CD conducted literature searches and background review and contributed to the referencing system. All authors provided revisions and approved the submission of the manuscript for publication.VH formulated the research question and drafted the first version of the manuscript. PT routed the data feed


Ethiopia has implemented maternity waiting homes over the last several decades; however, its utilization is low. This study aimed to assess the factors associated with knowledge of and attitude towards maternity waiting homes among pregnant women in rural Ethiopia. The baseline survey was conducted from September 15 to October 30, 2022, in rural Southern Ethiopia. Survey data were collected from 320 women in their second trimester of pregnancy. The data analysis was performed using SPSS version 25. The mean age of the participants was 27.79 (SD ± 6.242) years. Nearly two-thirds (57.5%) of the participants had no formal education and more than three-fourths (72.5%) were housewives. Only approximately one-fourth (23.75%) of the participants used maternity waiting homes. Furthermore, 33.75% had good knowledge, 28.75% had favorable attitudes, and around one-fourth (26.25%) had good male partner involvement. Age group 30 to 39 years , household income , having pregnancy intention , and history of obstetric complications  were significantly associated with good knowledge about maternity waiting homes. Similarly, age group 30 to 39 years , household income , having pregnancy intention , and history of obstetric complications  were significantly associated with favorable attitudes towards maternity waiting homes. Providing health education and promoting male partner participation through educating couples may improve women’s access to maternity waiting homes. Sub-Saharan Africa alone accounts for approximately three-fourths of maternal deaths, and the maternal mortality ratio in Ethiopia is estimated to be 401 per 100,000 live births2. The leading causes of maternal deaths are obstetric hemorrhage, hypertensive disorders in pregnancy, non-obstetric complications, pregnancy-related infections, and unsafe abortion4. Most maternal deaths owing to these causes are preventable by enabling timely access to obstetric care5. However, poor access to obstetric facilities due to long distances, lack of transportation, and unfavorable road conditions aggravated women’s inability to receive timely obstetric care7.Globally, the maternal mortality ratio was estimated to be 223 per 100,000 live births in 2020 and majority (95%) of maternal deaths have been reported to occur in resource-limited settings8. Although health facility delivery has been promoted in Ethiopia, home delivery is still common, primarily in geographically isolated areas9. The 2019 Mini Ethiopian Demographic and Health Survey showed that 48% of live births were delivered in a health facility, and access to health facilities is mentioned to be more difficult in rural areas than in urban areas because of distance, scarce transport, and a lack of appropriate facilities10. Maternity waiting home (MWH) has been recognized as a strategy to improve maternal health outcomes by bringing women in hard-to-reach areas closer to health facilities11. MWH is a shelter located near or in a health facility where women near their delivery date can stay and be transferred to obstetric facility shortly before childbirth or earlier if complications arise12. Ethiopia has implemented MWHs for the last several decades; however, its uptake is low14.Increasing health facility deliveries is vital for reducing maternal and neonatal mortality17. Use of MWHs also depends largely on male partners’ decisions19. Furthermore, a study from Northwest Ethiopia showed that antenatal care (ANC) visits, short distance to health facilities, women’s involvement in decision making, and MWH use were found to be associated with maternal knowledge, whereas higher education status, ANC visits, and short distance to the facility were associated with maternal attitudes towards MWHs20. However, further studies are required to have a comprehensive understanding about factors associated with women’s knowledge and attitudes regarding MWHs in rural Ethiopia.Lack of awareness about MWHs, women’s perceptions of the quality of care at MWHs, poor provider interactions with women staying at MWHs, poor physical aspects of MWHs, staff shortages, and household chores are some of the important barriers to staying at MWHsThe baseline data used in this analysis were collected from Ana Lemo and Gibe districts of Hadiya Zone of southern Ethiopia from September 15 to October 30, 2022. The two districts were purposefully selected based on the availability of functional MWHs. Based on the information we obtained from the Zonal health department, the total estimated population of the two districts was 265,000 in 2021. The two districts were divided into 52 clusters/kebeles . One cluster has an average of 5000 population. There was one primary hospital, 10 health centers, 10 MWHs and 42 health posts in the two districts including two health centers and MWHs added from an adjacent district (Misha district). The livelihood of the population mainly depends on agriculture.21 and had limited access to public transportation.The study participants were pregnant women in the beginning of second trimesters of pregnancy (14–16 weeks of gestation) who were permanent residents of the study area, gave birth within the last 5 years preceding the current pregnancy, were living with their male partners at the time of data collection, were living ≥ 2 h of walking distance from the nearest health facilityThe data source for this analysis was a baseline survey conducted prior to intervention roll-out in an ongoing cluster-randomized trial aimed to evaluate the effectiveness of health education provided to couples on improving knowledge, attitude, and uptake of MWHs: group health education, home visits and provision of take-home print materials . The intervention was provided at three contact points. The first contact point was the group health education at baseline whereas the second, and third contacts were home visits. Leaflets /print health messages were provided at each contact.22. To illustrate within intra-cluster correlation coefficient (ICC) and between ICC, the technique comprises the measurement of two design effects, with the product of the two being used to inflate the sample size for individual randomization. The within ICC was the correlation between any two pregnant women in the same cluster, while the between ICC was the correlation between any two pregnant women in different clusters. The first design effect (dc) attributable to cluster randomization was measured using a within ICC of 0.05 obtained from a community-based cluster randomized trial in Ethiopia23. The design effect (dc) was calculated as:The Hooper and Bourke method for cluster randomization studies of parallel arms with repeated cross-sections was used to calculate the sample sizer) attributable to repeated evaluations (baseline/endline) was calculated by using the within ICC and a cluster autocorrelation coefficient (π) of 0.8022.The second design effect (dr) was calculated as:The second design effect . It was calculated as:25. According to Hooper and Bourke, the number of clusters (K) for the sample was determined using the formula In addition, the following parameters were considered: 95% CI 80% power, 1:1 allocation ratio of intervention to control, 10% potential loss to follow up, and tabulated sample size https://clinicaltrials.gov/ct2/show/NCT05015023. All methods were performed in accordance with the relevant guidelines and regulations including the ethical guidelines of the Jimma University Ethical Review Committee and the declaration of Helsinki.Ethical approval letter was received from the IRB of Jimma University with reference number JUIRB-33/22, dated 09/02/2022. Consequently, a letter of permission was obtained from the Health Department of the Hadiya Zone, southern Ethiopia. The study participants were informed about the objective of the study, and written informed consent was obtained from each participant prior to the start of data collection. This study was registered in ClinicalTrials.gov with Identifier: NCT05015023. Link: 27. The questionnaires were prepared in English, translated into local language (Amharic) and pre-tested. The pre-test was performed on 10% of the calculated sample size in Misha Woreda, a district outside of our study sites. Next, Cronbach’s Alpha was calculated using SPSS version 25 to test internal consistency of the knowledge, attitude and male partner involvement questions and Cronbach’s Alpha value greater than 0.7 was considered reliable. The data were collected on study variables such as socio-demographic characteristics, knowledge, attitude, and health service utilizations including MWH use  × 100. Knowledge was measured using 7 “Yes” or “No” questions. “Yes” was denoted by “1” and “No” was represented by “0”. Then the sum average for a respondent above the median (Y ≥ 4) was considered good knowledge and below 4 was considered poor knowledge. Attitude was measured using 5 points Likert scales . Five questions were used to measure attitude. Average scores above median were considered favorable attitude whereas average scores below median were considered unfavorable attitude.27. The global framework has 5 domains . Each of the 5 domains has 3–5 questions, with a total of 21 questions. We carefully adopted the tool to our context and collected the data. In this analysis, we selected 9 indicators (“Yes” or “No” questions) to measure the level of male partner involvement in maternal health. “Yes” scores above median (Y ≥ 5) were considered good male partner involvement and “Yes” scores below median (Y ≤ 4) were considered poor male involvement.To assess male partner involvement, we adopted a tool from a global framework for assessing male involvement in maternal healthThe data analysis was conducted using SPSS Version 25. Descriptive statistics were performed and the results were presented using text, tables, and graphs. Bivariate and multivariate logistic regression analyses were conducted to identify the factors associated with pregnant women’s knowledge of and attitude toward MWHs. In logistic regressions, crude odds ratio (COR) and adjusted odds ratio (AOR) with the corresponding 95% confidence intervals were computed to show the strength of the association. Variables with p-value < 0.25 at bivariate analysis were fitted into the multivariate analysis. In the multivariate analysis, statistical significance was declared at a p-value of < 0.05. The Hosmer–Lemeshow test was used to check the goodness of fit for the logistic regression.Three hundred-twenty participants were included in the study. Nearly three-fourths (68.75%) of the participants were aged 20–29 years, and the mean age was 27.79 (SD 6.242) years. About half (53.75%) were from the ethnic group of Hadiya. One hundred eighty-four (57.5%) participants had no formal education, and more than three-fourths (72.5%) were housewives Table .Table 1STwo hundred thirty-two 72.5%) and 224 (70%) participants reported parity and gravidity of four or fewer, respectively. Two hundred and seventy-two (85%) participants reported planned pregnancies, 40 (12.5%) reported gestational age at birth less than or equal to 37 weeks, and 88 (27.5%) reported a history of obstetric complications. Most of the deliveries (83.75%) were normal vaginal deliveries, whereas instrumental assisted and cesarean section births were 12.5% and 3.75%, respectively. Stillbirth was reported by 36 (11.25%) respondents (Table .5% and 2The proportion of participants who received ANC (at least once) was 124 38.75%), and 76 (23.75%) used MWH. There were 84 (26.25%) institutional deliveries and 68 (21.25%) PNC visits within two weeks after delivery. Eighty-four (26.25%) participants were assessed to have good involvement of male partners in maternal health  of the participants demonstrated good knowledge of MWHs and 92 (28.75%) had favorable attitude towards MWHs.In the bivariate logistic regression, household income, pregnancy intention, and history of obstetric complications were associated with both knowledge and attitude towards MWHs. Educational status was inversely associated with knowledge and attitude whereas age was not associated with either knowledge or attitude towards MWHs. In the multivariate regression, age (30–39 years), household income, pregnancy intention (having wanted pregnancy), and history of obstetric complications were statistically associated with good knowledge of and favorable attitude towards MWHs.Consequently, women in the age group 30–36 years  were nearly 5 times more likely to have good knowledge about MWHs compared to women in the age group ≥ 40 years. Women with average household monthly income below 7985 ETB  were 6 times more likely to have good knowledge of MWHs compared to women with household income above 7985 ETB per month. Likewise, women having wanted pregnancy  and those having a history of obstetric complications  were nearly 3 times and 7 times more likely to have good knowledge of MWHs compared to women having unwanted pregnancy and those had no history of obstetric complications, respectively   were 4 times more likely to have a favorable attitude towards MWHs compared to women in the age group ≥ 40 years. Participants with household income below 7895 ETB  were 7 times more likely to have good attitude towards MWHs compared to those with household income above 7895 ETB. Similarly, women having wanted pregnancy  and those having a history of obstetric complications  were nearly 3 times and 6 times more likely to have good attitude towards MWHs compared to women having unwanted pregnancy and those had no history of obstetric complications, respectively  of the participants reported having awareness about MWHs30. We suggest one of the reasons for this difference may be the differences in health system related factors such as leadership, and access to health facilities including transportation; however, it requires further research. Furthermore, the current study showed that only 28.75% of the participants had favorable attitude towards MWHs. Possible reasons for this low-level attitude towards MWH may be poor ANC attendance, low institutional delivery practice, lack of awareness about MWH, and a low level of male partners’ participation in maternal health. Likewise, another study showed that less than half (48.8%) of women had favorable attitude towards using MWHs31, though this is still far from our finding. In contrast, a study showed that more than two-thirds (65.3%) of women had favorable attitude towards MWHs32. The possible reason for these differences may be sociodemographic or health system related factors.In this study, we found that around one-third (33.75%) of women had good knowledge about MWHs. Possible reasons that only one-third had good knowledge may be that most women who participated in this study had no formal education, low access to ANC, and poor access to health information due to long distances from health facilities. This finding is in line with studies from southern and central Ethiopia, which revealed that only 7% and 47.7% of women, respectively, had awareness about MWHs34. The reason for this may be the observed similarity in participants educational status  in study settings; however, this requires further investigation. In contrast, another study from the Sidama Zone, Ethiopia, showed that more than two-thirds (67.25%) of women used MWHs30. One reason for this significant variance may be the differences in the level of awareness of MWHs. For example, in our study, only one-third of the women had good knowledge of MWHs, whereas in the above-mentioned study from the Sidama Zone30, more than three-fourths reported that they were aware of MWHs. Furthermore, a systematic review revealed that a lack of knowledge about MWHs was one of the reasons for the poor utilization of MWHs35. This finding implies that a lack of awareness may affect women’s access to MWHs. Therefore, improving maternal knowledge and attitude regarding MWH may improve access to MWHs among pregnant women in rural Ethiopia.The study revealed that the MWH utilization was 23.75%. The reasons for this low utilization of MWHs may be the low levels of good knowledge and favorable attitudes toward MWHs among the participants. Poor involvement of male partners in maternal health might also have affected women’s access to MWHs, as males are the main decision makers. In support of this, other studies from Ethiopia found that the MWH uptake was low31 whereas it contrasts with a study from Somaliland36. Further studies are needed to justify why having no or lower educational status was found to be associated with the knowledge of MWHs. Besides, in the current study, a history of obstetric complications and pregnancy intention (wanted pregnancy) were associated with knowledge of and attitudes toward MWHs. One reason may be that women who experienced pregnancy-related complications had painful experiences and, therefore, developed better health-seeking behavior and preferred to stay near health facilities to seek immediate obstetric care for their current pregnancy. Moreover, women who want to get pregnant may also have a better attitude towards staying at MWH, as they need newborns. Similarly, other studies have shown that a history of complications in previous childbirth and pregnancy intention was associated with the intention to use MWHs28. Educating women to have a planned pregnancy and providing them with information about MWH during health care visits may improve maternal knowledge and attitude towards MWHs. This may in turn improve maternal and newborn health outcomes by improving women’s access to MWHs.The current study showed that educational status was inversely associated with knowledge and not associated with attitudes towards MWHs. This might imply that those who have some formal schooling may have less concerns to the MWH services or were not using MWHs. This is consistent with findings from a study in EthiopiaRecall bias may have affected our findings. Because the participants were asked about their experiences of last childbirth.The majority of participants had poor knowledge of MWHs, unfavorable attitudes toward MWHs, and poor male partner involvement. The utilization of MWHs is low. Age, household income, pregnancy intention, and history of obstetric complications were associated with knowledge and attitude towards MWHs. Providing health education may improve women’s awareness and attitude towards MWHs. Age group and level of household income should be considered in planning and implementing MWH related behavioral interventions. Promoting male partner participation through educating couples may improve women’s access to MWHs.Supplementary Information.


Manual sutures, staples, and tissue adhesives are indicated for pulmonary surgery. However, despite its well-recognized use, no suture material has yet been pointed out as completely safe in preventing air leakage, a major complication of pulmonary surgeries both in humans and dogs. The aim of this study was to investigate the efficacy of the most common suture techniques and sealing devices after pulmonary lobectomy in dogs considering ventilatory pressures: physiological and supraphysiological. This study concludes that manual/mechanical sutures and synthetic glue sealants are efficient in preventing air leakage after partial lobectomy in dogs.2O) and supraphysiological levels (above 14.7 mmHg) to evaluate the performance of the sealing methods. At physiological ventilatory pressure levels, there was no difference between groups. Sealing with surgical glue was superior to interlocking sutures and stapling devices at supraphysiological levels of ventilatory pressure.Pulmonary loborraphy can be performed using manual sutures and staples, although other methods, such as tissue adhesives, are also cited in the veterinary literature. Although the surgery is well tolerated in the canine species, failure in pulmonary aerostasis is still a reality since all the methods described so far eventually lead to air leakage after the use of the partial lobectomy technique in the lungs. Within this context, the aim of this research was to compare the effectiveness of different hermetic sealing methods after partial lobectomy of the right caudal lung lobe (RCLL) in dogs. 30 cadavers models were divided in 6 groups: G1—cobbler suture associated with simple continuous; G2—overlapping continuous suture associated with simple continuous suture; G3—Ford interlocking suture; G4—Stapling device; G5—Tissue glue (cyanoacrylate). After performing the sealing techniques, the lungs were submerged in water and inflated with oxygen at positive ventilatory pressures at physiological (up to 14.7 mmHg, which is equivalent to up to 20 cmH Pulmonary lobectomy is a surgical technique occasionally performed in dogs to resect pulmonary tumors, bullae, and damaged lung lobes ,2,3. Thi®; Ethicon, Johnson, and Johnson, São Paulo, SP, Brazil); G2—Loborrhaphy with overlapping continuous suture associated with simple continuous suture, with Polyglactin 910 suture thread; G3—Loborrhaphy with Ford interlocking suture pattern, with Polyglactin 910 suture thread; G4—Loborraphy with metal staples by TA linear stapler, two units 45 mm reload inserted in opposite directions into the lobe ; G5—Loborraphy with 1 mL of synthetic adhesive containing n-butyl cyanoacrylate (NBCA) and methacryloisofolane (MS) .Thirty dogs’ cadavers were obtained after euthanasia following the diagnosis of leishmaniasis at the Zoonosis Control Center. Criteria for inclusion were: mixed breed dogs; weight between 5 and 10 kg; variable body condition score; macroscopically normal respiratory tract during necroscopy. The respiratory tract was removed en block, including the larynx, trachea, bronchi, and lungs. The experiment was carried out within 12 h after macroscopic collection of the respiratory block. An appropriately sized endotracheal tube was inserted into the trachea and secured with a nylon seal. The tracheal tube was connected to a calibrated manometer  and oxygen gas for positive pressure. All lungs were initially pressurized slowly to 10 mmHg and then deflated to check for pre-existing atelectasis and/or previous air leakage before performing the experiment. The right caudal lung lobe (RCLL) was chosen, and the partial lobectomy was performed, followed by tissue sealing and aerostasis assessment. Different lung synthesis methods were organized into five experimental groups, each containing six experimental units: G1—Loborrhaphy with cobbler or Vachetta suture pattern, followed by simple continuous suture, with Polyglactin 910 suture thread  or supraphysiological if above 14.8 mmHg. The value of air pressure at which any leakage occurred was also recorded.After performing the sealing techniques, the lungs were placed into a transparent plastic container and submerged in water. The lungs were then inflated, and all models were filmed to allow for accurate monitoring and recording of any potential air leak. A leakage was considered only when air bubbles were visualized at the level of the suture line or on the surface of the cut. Any occurrence of air leakage was recorded at physiological pressure  were considered significant. The cadavers were randomized into five groups of six. The Kolmogorov–Smirnov test was used to assess the normality of the variables studied. The homogeneity of the groups was evaluated by comparing body weight, body score, height, and width of the RCLL. The comparison of the means between the groups of the parametric variable with normal distribution (body weight) was performed using ANOVA and Fisher’s ad hoc Tukey test. The comparison of the medians for the non-parametric variable (body score) and for those with non-normal distribution (height and width of the lobe) was performed using the Kruskall-Wallis ad hoc Dunns test. The pressure related to the air leaks also showed a non-normal distribution, and, therefore, the medians between the groups were compared using the same test. Furthermore, the groups were compared regarding the aerostatic variable, that is, whether or not there is air leakage under physiological ventilatory pressures considered up to 14.7 mmHg, using the Kurskall–Wallis ad hoc Dunns test. Fisher’s exact test was also used to evaluate the frequency of distribution in relation to leakage under physiological pressure, according to the sealing methods.For statistical analysis, the software GraphPadPrism v. 6.02 was used. Differences with a p = 0.1901) and lung lobe width (p = 0.0637). However, they differed in the median for the body condition score (p = 0.0202) and lung lobe height (p = 0.0347). Regarding the body condition score of the animals used in this study, there was a statistical difference only between the medians of groups G1 (cobbler suture followed by simple continuous suture) and G3 (Ford interlocking suture). Lung lobe height differed only between groups G4  and G5 (synthetic glue)  .The pressure in mmHg needed to cause lung lobe air leakage varied according to the type of sealing techniques view in .p = 0.3263).Although some experimental units demonstrated a rupture in physiological pressures in groups G1, G3, and G4, there was no difference between the groups (p = 0.0164 (p = 0.2873) in the sealing method’s efficacy against physiological pressure (up to 14.7 mmHg). However, while the G2 (overlapping continuous suture) and G5 (synthetic glue) did not have any air leakage at physiological air pressure, aerostasis failure was observed in 1/6 in s G1 (cobbler suture + continuous simple suture 2/6 in G3 (Ford interlocking suture), and 2/6 in G4 (stapling device).The pressure needed to cause air leakage in the lung lobe sutured with Ford interlocking suture (G3) and TA stapler (G4) was lower than the synthetic glue (G5), with = 0.0164  . According to veterinary guidelines, pressure levels on lung tissue from 15 to 25 cmH2O can be considered physiological [2O are considered more accurate for small animals under normal respiratory mechanics conditions [2O after a lung biopsy in anesthetized dogs [2O was considered not safe and the sealing device inadequate [2O to be considered safe. All the methods used for sealing that were investigated in this study were effective against established physiological ventilatory pressure.The physiological ventilatory pressure considered in this study was 14.8 mmHg  was the Ford interlocking suture pattern A, characIn the group of lung lobes in which the mechanical suture was performed with metal staples using the TA linear surgical stapler, the results showed a good response in terms of aerostasis, with no difference compared to the other groups. However, two out of six cases failed due to air leakage laterally to the staple line at physiological ventilatory pressures of 10 mmHg and 14 mmHg. The effective aerostasis with mechanical stapling sealing devices observed in the present experimental study was also reported by other authors . The saf2O (14.8 mmHg) [2O [2O. This is the likely explanation for what may have caused air leakage in the two models in the staples group  . When pemHg) [2O . At highmHg) [2O , even ates group B. Althou® surgical sealant (G5) did not result in aerostatic failure in any of the six lungs when challenged by physiological ventilatory pressure. Air leakage occurred only at very high ventilatory pressure, with a minimum of 20 mmHg and a maximum of 80 mmHg  [Similar results were achieved in an experimental study using rabbit lungs and sealant that contains NBCA, one of the monomers present in Glubran-2® . In such® [2O) . Similar® [2O) . Another® [2O) . The aut® sealant is one of the commercial names of NBCA, an important synthetic biodegradable surgical glue, which is modified using the addition of MS monomer [® in surgeries involving lobectomy in dog lungs. In a study with six healthy cats after performing partial pulmonary lobectomy, NBCA has effective hemostatic and aerostatic properties [®) was superior to that observed in G3 (Ford interlocking suture) and G4 (staples). In addition, if the surgical time variable had been evaluated in the different groups studied, sealing with surgical glue would probably stand out in reducing surgical time due to its application method and chemical property, reaching maximum mechanical force on the tissue within 1 to 2 min [The Glubran-2 monomer ,31,32. I monomer ,34. The  monomer ,34,35,36operties . The resoperties . From raoperties . The seaoperties ,32,36. Ito 2 min ,31,32.In the present study, the canine lung lobes of the three groups that received the different manual sutures made with the same type of surgical material were not able to achieve aerostasis at pressure levels of 40 mmHg. Similarly, as demonstrated in the study carried out with sheep lungs after partial lobectomy, all lobes that received manual sutures failed aerostasis after applying this pressure . HoweverIn our study, synthetic cyanoacrylate adhesive was significantly superior to the manual Ford interlocking suture and to mechanical suturing by staples. The excellent seal maintained by the synthetic glue can be explained by the low viscosity of the sealant when in the liquid phase, filling the small gaps and creating better sealing between the cutting surfaces of the lung than other sealing methods . The gluLeishmania sp. is known to cause histological changes, including mild to severe peribronchial inflammatory infiltration, fibroblast proliferation in lung tissues, edema, and congestion in the alveolar wall, but histological examination of the lungs in this study was not performed [Leishmania sp. or some subclinical respiratory disease, could interfere with lung compliance and adequate sealing, resulting in rupture at lower pressures, and could justify some results of this study [The main limitation of this study is the experimental model. The lungs were evaluated outside the thoracic cavity, a condition that prevents adequate lung distension and retraction during the respiratory cycle . The reserformed . Microscis study ,13,38.® at supraphysiological ventilatory pressures (greater than 14.7 mmHg). Glubran-2® was superior to the Ford interlocking suture and the TA 45 mm linear surgical stapler, but no difference was found between Glubran-2 and cobbler suture associated with simple continuous and overlapping continuous with simple continuous suture.The result of this study proved the safety and efficacy of all investigated sealing methods after partial lobectomy in the middle third of the right caudal lung lobe of dogs. There was no difference in physiological ventilatory pressures. However, greater efficacy was achieved by the surgical tissue glue Glubran-2


The AHWT is the southern source of the Congo Basin, the western source of the Zambezi Basin, and the sole water source of the endorheic Okavango Basin and Okavango Delta, a UNESCO World Heritage Site. On average, approximately 133 km3 (92.36%) of the gross annual precipitation volume for the headwater Cuito and Cubango catchments of the Okavango River is lost before reaching the Okavango Delta. Estimates of the annual flooding of the Okavango Delta during a 35-year period (1985–2019) were correlated to precipitation in the headwater catchments. Correlation coefficients are stronger for the entire rainfall season (0.76) and early rainfall season (0.62) for the combined Cuito–Cubango catchment in comparison to late rainfall season (0.50), which suggests that the antecedent conditions (first and second flood pulse) during the early rainfall season allows for greater Okavango Delta flood inundation. The correlation coefficients between the Cubango (0.72) and Cuito (0.78) Rivers and annual flood inundation are not significantly different (P > 0.05); however, these rivers have fundamental hydrological differences that influence the functioning of the Okavango Delta. The Cubango River, described as a flushing system, has much steeper gradient, more compact and shallow soils and flows faster with significant rapids, whereas the peatland rich, absorbent, seepage-driven baseflow of the Cuito River sustains the Okavango Delta during the dry season. The dynamics of seasonal precipitation, hydrology and climate change in the AHWT have important repercussions on water budgets, food security and biodiversity throughout southern Africa, requiring continued collaboration between countries to ensure that future development is sustainable.Angola is a source of many major rivers in southern Africa and is referred to as the “water tower” of the region. The lack of a defined area delineating the Angolan Highlands water tower (AHWT) limits the conservation of this important freshwater source. This study hydrologically defines the boundary of the AHWT as areas > 1274 m above mean sea level within the Central Bié Plateau of Angola. Using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, this study provides a 41-year precipitation budget of the AHWT and surrounding basins. Between 1981 and 2021, the average annual precipitation over the AHWT was 1112 mm and the gross annual average precipitation volume was approximately 423 kmThe online version contains supplementary material available at 10.1007/s10661-023-11448-7. RS derived inundation maps from each study were produced using different products at different resolutions: National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) 1000 m: McCarthy et al.  using web plot digitizer , Climate Change Initiative (CCI), land cover classification  river catchments according to the WWF HydroSHEDS Basins level 4 product  from 1981 to 2021 occurred in the Democratic Republic of Congo (DRC). The Congo River basin is the largest ; however, only 24,995 km2 (2.79%) lies within the AHWT area. High precipitation over the Congo Basin means that the AHWT delineation only applies to the other catchments where lower reaches have reduced precipitation relative to the AHWT. More than half of the area within the Cuito (59.65%) and Cubango (53.23%) catchments lies within the AHWT area, with 65.57% and 64.14% of the precipitation estimated to occur within the AHWT area respectively. The highest  proportion of precipitation within the AHWT area occurred within Cuvo and Cunene catchments, respectively.The AHWT, as defined above, occupies an approximate area of 380,382 kmThe CHIRPS data illustrates the strong seasonality in precipitation in the headwater catchments of the Okavango Delta Fig. . The mea2), 2010  and 2011  and lowest occurred during 2019 (3470 km2), 1996 (4775 km2) and 2003 (5232 km2). By comparison, highest total precipitation for the combined Cuito–Cubango catchment occurred during 2011 (1065 mm), 1989 (956 mm) and 2010 (954 mm) and lowest occurred during 2019 (550 mm), 1996 (641 mm) and 2015 (650 mm). On average, precipitation in the Cuito catchment was 842 mm per year between 1985 and 2019, by comparison the Cubango catchment was 788 mm per year. See supplementary material Fig. Flood inundation estimates of the Okavango Delta spanning the period from 1985 to 2019 were derived from five separate studies  from each rainfall year were used to correlate precipitation in the Cuito, Cubango and combined Cuito–Cubango catchments with RS derived flood inundation estimates of the Okavango Delta Table . Correla2, 88 km2, 140 km2 and 0.8 km2 of area classified as Croplands, Herbaceous cover, Mosaic croplands and urban areas respectively. By comparison, the Cubango catchment contains 2100 km2, 1166 km2, 1663 km2 and 174 km2 of area classified as Croplands, Herbaceous cover, Mosaic croplands and Urban areas respectively  and 2019 (3470 km2). The year 2019 was documented as the driest year  on record since 1981, having serious implications on food security and health in the southern provinces of Angola  and 53 km3 (36.81%) of precipitation falls over the Cubango and Cuito catchments each year respectively. Over the AHWT, the proportion of the Cuito is slightly higher, with 58 km3 (62.37%) and 35 km3 (37.63%) of precipitation in the Cubango and Cuito catchment respectively. The gross annual average precipitation volume of 144 km3 per year over the two headwater catchments provides approximately 11 km3 to the Okavango Delta  of the precipitation is lost to groundwater recharge, evaporation, transpiration or water abstraction.The Okavango River basin is a transboundary basin with a network of river systems that transverse through Angola, Namibia and Botswana  rises between Cangoa and Sachiambe at 1430 masl in the western part of Moxico Province  was established in 1994 to increase cooperation between Angola, Namibia and Botswana with the recognition that future development in the Cubango–Okavango (includes Cuito) River Basin (CORB) is inevitable but must be coordinated to balance the conflicting demands of humans and the ecosystem , livestock sector (25.25%) and use in settlements (15.15%) make up 96.90% of the average annual water usage in CORB. The estimated total annual water usage from rivers was 90.1 million m3, with Angola (53.09%) and Namibia (42.48%) disproportionally higher than Botswana (4.43%). Table The CORB water audit presents the domestic water use for each country in 1000 s of mk sector 5.25% andCroplands, Herbaceous cover, Mosaic croplands and Urban areas. The lower correlation coefficients for precipitation in the Cubango catchment in comparison to the Cuito are likely due to the different hydrological characteristics of each river catchment and in part due to the major river abstractions on the Cubango River, as large quantities of rainwater never reach the Okavango Delta.The river network and its water are an important resource for each country. Described as the best-known feature of the basin, Botswana’s Okavango Delta remains one of the most important areas for biodiversity in the world and makes up most of the country’s tourism industry which generates an estimated 13% of the GDP  provided by high rainfall in the early rainfall season allow for greater Okavango flood inundation. The driest year on record (2019: 550 mm) between 1985 and 2019 coincided with the lowest annual Okavango Delta flood inundation total (3470 km2). Steeper gradients, more compact soils, faster flowing waters, rapids and water abstractions along the Cubango River are likely responsible for lower correlation coefficients with Okavango Delta flooding in comparison to the Cuito River. The absorbent, seepage-driven baseflow of the Cuito River provides baseflow to the Okavango Delta during the dry season, facilitated by precipitation during the early rainfall season in the Angolan Highlands. The rivers of the AHWT are largely unregulated and the paucity of literature, because of historical conflict, concerning the hydrological significance of these rivers provides important future research avenues that will have strong implications on water budgets, food security and biodiversity throughout southern Africa. Shared transboundary rivers originating in the AHWT will require continued collaborative efforts to ensure future development is sustainable.The AHWT is a water source region for major rivers of southern Africa. This study is the first to delineate the boundary of the AHWT that is hydrologically defined as areas within the Bié Plateau that are > 1274 masl. The gross annual average precipitation volume of the AHWT is 423 kmSupplementary file1 (DOCX 22 KB)Below is the link to the electronic supplementary material.