The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions—the United States, China, and Africa—differ from one another and from those at the global level.Based on a comprehensive list of human-infective RNA viruses, we collated published data on first discovery of each species in each region. We used a Poisson boosted regression tree (BRT) model to examine the relationship between virus discovery and 33 predictors representing climate, socio-economics, land use, and biodiversity across each region separately. The discovery probability in three regions in 2010–2019 was mapped using the fitted models and historical predictors.The numbers of human-infective virus species discovered in the United States, China, and Africa up to 2019 were 95, 80, and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was better predicted by land use and socio-economic variables than climatic variables and biodiversity, although the relative importance of these predictors varied by region. Map of virus discovery probability in 2010–2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region  were discovered in high-risk areas as predicted by our model.The drivers of spatiotemporal variation in virus discovery rates vary in different regions of the world. Within regions virus discovery is driven mainly by land-use and socio-economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. Potential new discovery hotspots in 2010–2019 are identified. Results from the study could guide active surveillance for new human-infective viruses in local high-risk areas.https://darwintrust.bio.ed.ac.uk/). MEJW has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 874735 (VEO) (https://www.veo-europe.eu/).FFZ is funded by the Darwin Trust of Edinburgh ( RNA viruses are the primary cause for emerging infectious diseases with epidemic potential, given that they have a high rate of evolution and high capacity to adapt to new hosts . In receSocio-economic, environmental, and ecological factors related to both virus natural history and research effort have been found to affect the discovery of emerging RNA viruses . HoweverThere have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. In this study, we applied a similar methodology from our previous study of global patterns of discovery of human-infective RNA viruses  to invesHuman torovirus abolished and a new species—Heartland banyangvirus—added by International Committee on Taxonomy of Viruses (ICTV) in 2018  model to examine the relationship between discovery of RNA virus and 33 predictors for each 1° resolution of grid cell across each region separately, following codes from our previous study  and one All BRT models were fitted in R v. 3.6.3, using packages dismo and gbm. BRT models require the user to balance three parameters including tree complexity, learning rate, and bag fraction. Tree complexity reflects the order of interaction in a tree; learning rate shrinks the contribution of each tree to the growing model; bag fraction specifies the proportion of data drawn from the full training data at each step. We set these parameters as recommended from By fitting 1000 replicate BRT models, the relative contribution plots and partial dependence plots with 95% quantiles were plotted. We defined variables with a relative contribution greater than the mean (3.03%) as influential predictors in all three regions . The parTwo statistics were calculated to evaluate the model’s predictive performance: (a) the deviance of the bootstrap model , (b) intExploratory subgroup analyses distinguishing viruses firstly discovered in regions and those that had been discovered elsewhere in the world were performed. We used the same BRT modelling approach as we described above, and relative contribution of each predictor was calculated for each subgroup. We were unable to perform subgroup analysis for China because only nine human-infective RNA viruses have been firstly discovered in it, and the BRT model cannot be fitted to a sample as small as 9.R software, version 3.6.3  was used for all statistical analyses. All maps were visualised by using ArcGIS Desktop 10.5.1 .The numbers of human-infective virus species discovered in the United States, China, and Africa up to October 2019 were 95, 80, and 107, respectively . Most fiThe discovery curves for the United States and Africa have seen a broadly similar pattern, with China lagging behind these two regions . The medIn the United States, six variables including three predictors related to land use , two socio-economic variables , and one climatic variable  were identified as important predictors for discriminating between locations with and without virus discovery . The parIn China, twelve variables including four socio-economic variables , five predictors involving land use , and three climatic variables  were identified as important predictors for discriminating between locations with and without virus discovery . GDP, urIn Africa, ten variables including two socio-economic variables , seven predictors related to land use , and one biodiversity variable  were identified as important predictors for discriminating between locations with and without virus discovery . All impOur BRT models reduced Moran’s I value below 0.15 in all three regions , suggestIn comparison with the whole world, human-infective RNA virus discovery was more associated with land use and socio-economic variables than climatic variables and biodiversity in all three regions . The comWe mapped human-infective RNA virus discovery probability in 2010–2019 for the three regions, based on the fitted BRT models and values of all 33 predictors in 2015 . OutsideBased on our subgroup analysis distinguishing viruses firstly discovered in regions and those that had been discovered elsewhere in the world, discoveries of human-infective RNA viruses first discovered from either United States or Africa were better predicted by climatic and biodiversity variables, while discoveries of viruses that had been discovered from elsewhere in the world were better predicted by socio-economic variables .To our knowledge, this analysis represents the first investigation of human-infective RNA virus discovery in three large regions of the world which have experienced distinct socio-economic, ecological and environmental changes over the last 100 years. In total, 95 human-infective RNA virus species had been found in the United States; 80 in China; 107 in Africa. The discovery maps of human-infective RNA virus in the three regions indicated areas with historically high discovery counts: eastern and western United States, eastern China, and central and southern Africa. BRT modelling suggested that the relative contribution of 33 predictors to human-infective RNA virus discovery varied across three regions, though climatic and biodiversity variables were consistently less important in all three regions than at a global scale. We mapped the probability of human-infective RNA virus discovery in 2010–2019 which would continue to be high in historical hotspots but, in addition, we identified several new hotspots in central-eastern and southwestern United States, eastern and western China, and northern Africa. These results offer a tool for public health practitioners and policymakers to better understand local patterns of virus discovery and to invest efficiently in surveillance systems at the local level.In recent decades, factors that drive pathogen discovery have been comprehensively studied, e.g., . In geneIn all three regions, GDP and/or GDP growth were identified as important predictors for virus discovery. This is consistent with our previous analysis that GDP and GDP growth play a major role in discovering viruses . In genePredictors other than GDP and university count are likely to be linked to virus natural history. In all three regions, the area of urban land and further urbanization made great contribution to virus discovery. This reinforced previous studies that urbanization was linked to the detection of new human pathogens through the denser urban population, increased human-wildlife contact rate, spill-over of human infection from enzootic cycle, and the contamination of the urban environment with microbial agents . In the Climate had less influence on human-infective RNA virus discovery in all three regions in comparison to other predictors, in contrast to virus discovery at a global scale . The undIn addition, a relatively smaller proportion of strictly zoonotic viruses in three regions  than that in the world (58.7%)  made bioOur discovery probability maps for 2010–2019 in three regions captured most historical hotspots, though several small new areas in central-eastern and southwestern United States, eastern and western China, as well as northern Africa would also make greater contribution to virus discovery . Our modOur subgroup analyses distinguishing viruses firstly discovered in regions and those that had been discovered elsewhere in the world suggested in both the United States and Africa, discoveries of viruses firstly discovered in regions were more likely to be associated with climatic and biodiversity variables while discoveries of viruses had been discovered elsewhere in the world were more likely to be associated with socio-economic variables. This is plausible, again because after a novel virus was discovered elsewhere in the world, it is usually areas with a higher socio-economic level that first capture the virus in the local region.This study had limitations. First, one common problem for data collected from literature review is the time lag between virus discovery and publication, in which case the virus data are likely to be matched to covariates in later decades. Second, we acknowledge that it is possible we have not identified the earliest report for some well-known viruses such as yellow fever virus, measles virus, especially in the post-vaccination era. Third, we were unable to identify robust and comprehensive data for all three regions on virus discovery effort , although we interpret GDP and university count as being an indirect measure of resources available for this activity. Previous studies have tried to use the bibliographic data to correct for the discovery effort . However, this strategy worked less well for our data as the frequency of published paper from virus-related scientific journals has only a weak link to publications on novel human-infective RNA virus .The study adds to our previous study  in severIn conclusion, a heterogeneous pattern of virus discovery-driver relationships was identified across three regions and the globe. Within regions virus discovery is driven more by land use and socio-economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. We mapped with good accuracy that in 2010–2019 three regions where human-infective RNA viruses had previously been discovered would continue to be the discovery hotspots, but in addition, several new areas in each region would make great contribution to virus discovery. Results from the study could guide active surveillance for new human-infective viruses in high-risk areas. This study will be of interest to readers in the field of virus discovery. This study attempts to identify predictors of human-infective RNA virus discovery and predict high risk areas in a recent period in the United States, China, and Africa using an ecological modeling framework. The study has potential to inform future discovery efforts for human-infective viruses. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and George Perry as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Benn Sartorius (Reviewer #1).Thank you for submitting your article "Predictors of human-infective RNA virus discovery in the United States, China and Africa, an ecological study" 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:1) It seems unlikely that virus discovery effort is homogenous over the large geographic areas considered in this analysis and we would like to see additional work to address remaining discovery bias by extending the analysis to include adjustment for more granular measures of discovery effort. While we don't want to be too prescriptive about how this should be done, we would suggest that bibliographic data potentially provides a valuable and readily-available source of information about how research effort related to virus discovery varies with space and time. It should be possible to use such data to derive sub-national indicators that are both more granular than national economic indicators, and that might be expected to be more directly related to virus discovery effort. We suggest that governance indicators (transparency) and laboratory infrastructure/technology indicators are also considered as predictors.2) Clarification is needed about how space and time is currently dealt with in the boosted regression tree model.3) Currently it seems that virus discovery data were matched to covariate data using the nearest decade. Since there might be important changes in virus discovery effort and other covariates at finer timescales, we feel it is important to at least perform a sensitivity analysis to check for this variation and consider how it might impact on the results.4) We would like to see a consideration of lag periods between the covariates/predictors and virus discovery. i.e. changes in predictor in t-1 year may be more predictive of virus discovery at year t.5) Clarification is needed regarding handling of how potential collinearity between the predictors.6) We would like the authors address the likely differences in underlying drivers/predictors for vector-borne (V) vs non-vector-borne (N) viruses and strictly zoonotic (Z) vs human transmissible (T) viruses. This could be done, for example, by using separate models.7) It would be useful to provide an appraisal of the impact related past studies have had on improving surveillance and control.Reviewer #1 (Recommendations for the authors):This study attempts to identify predictors of human-infective RNA virus discovery and predict high risk areas in a recent period in the United States, China and Africa using a ecological modelling framework. The study is relevant in the current context and identification of areas at threat of emerging viral pathogens.According to findings from their previous study published in 2020, the main predictors for virus discovery at the global scale were GDP-related i.e. and they concluded that this may largely have driven by research effort rather than the underlying biology. In the current study, they have attempted to focus on more restricted and homogenous regions where they suspect research effort is less heterogeneous to an attempt to identify predictors more associated with virus biology. I have some comments and concerns below that would need to be addressed in my opinion prior to a final decision being taken.While I do understand the rationale for the restricted analysis, I am still concerned that inherent "discovery" bias in the data and how these vary by region and within country/region and across time may still drive the observed associations rather than real distal predictors of virus discovery.Furthermore the relative lack of accurate geolocating of data in China relative to the other two regions may also misalign the covariate values attached to these data points and potential skew association particular of the more influential covariates vary substantial across space.Was the model applied at location-year level i.e. spatial and temporal? This was not entirely clear to me under Boosted regression tree model description.If I understood correctly virus discovery data were matched to covariate data using the nearest decade? While some covariates are likely to change more slowly, some may be far more dynamic  and vary within a decade. Where any sensitivity analyses conducted to check for this variation within decade and how it might impact on the associative coefficients/influence?Did the authors consider or test for various lag periods between the covariates/predictors and virus discovery? i.e. changes in predictor in t-1 year may be more predictive of virus discovery at year t.Did you consider collinearity between the 33 predictors as many of these would likely be highly correlated?What other predictors were considered in the ecological framework i.e. governance indicators (transparency) and laboratory infrastructure/technology indicators? These would seem to be important re: potential bias in reporting of newly discovered viruses for the former  and likelihood of detection for the latter .Did you consider separate models (#4) for vector-borne (V) vs non-vector-borne (N) viruses and strictly zoonotic (Z) vs human transmissible (T) given the likely difference  in underlying drivers/predictors across these envelopes?Reviewer #2 (Recommendations for the authors):As this type of work has been ongoing for some time, It would be useful to provide an appraisal of the impact these past studies have had on improving surveillance and control.eLife. Your revised article has been evaluated by George Perry (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Predictors of human-infective RNA virus discovery in the United States, China and Africa, an ecological study" for further consideration by The authors have gone some way to addressing the essential revisions from the first round of reviewers and have conducted some of the additional analyses requested. However, this additional analysis does not appear to have been included in the revised manuscript files. While additional analysis is reported in the document with the responses to reviewer comments, it is important that the changes are also included in the manuscript.Considering the requested essential revisions in turn:Essential revision 1 asked for additional work to address discovery bias by extending the analysis to include adjustments for more granular measures of discovery effort. The response to this was to clarify aspects of the analysis (e.g. the granularity of GDP indicators) and to add some text to the discussion (lines 408-412) referring to approaches to this problem taken in other papers. There was also a short exploration  showing the relationship between "published human-infective RNA virus count and the total number of papers from the journals which published all human-infective RNA viruses in Web of Science", and accompanying text that argued that attempts to adjust for discovery effort used by other authors would not be applicable here. I was unable to follow this argument, and given that no additional analysis addressing discovery bias appears in the main manuscript it seems that the essential revision was not undertaken. While the changes the authors have made to the manuscript are welcome, given that (following consultation) round 1 reviewers highlighted this as the key point to address it is important that the authors reconsider how they can change the manuscript to address this point. This may just amount to additional analysis in the supplementary material demonstrating that approaches used by other authors have little applicability here as claimed in the rebuttal letter, but if so the reasoning needs to be expressed more clearly and the evidence for any such assertions clearly laid out.Essential revision 2 appears to have been addressed adequately.Essential revision 3 asked for sensitivity analysis to consider matching covariates at finer timescales. This additional analysis appears to have been done but has not yet (as far as I could tell) been included in the revised manuscript.Essential revision 4 asked for additional analysis that included consideration of lag periods between the covariates/predictors and virus discovery. Again, this appears to have been done and is shown in the rebuttal letter, but I could not locate the new analysis in the revised manuscript files.Essential revision 5 appears to have been addressed adequately.Essential revision 6 again appears to have been addressed with additional work but this does not appear to have been included in the revised manuscript.Essential revision 7 appears to have been addressed adequately  do not correspond to areas of highlighted changes in the manuscript with changes marked). Essential revisions:1) It seems unlikely that virus discovery effort is homogenous over the large geographic areas considered in this analysis and we would like to see additional work to address remaining discovery bias by extending the analysis to include adjustment for more granular measures of discovery effort. While we don't want to be too prescriptive about how this should be done, we would suggest that bibliographic data potentially provides a valuable and readily-available source of information about how research effort related to virus discovery varies with space and time. It should be possible to use such data to derive sub-national indicators that are both more granular than national economic indicators, and that might be expected to be more directly related to virus discovery effort. We suggest that governance indicators (transparency) and laboratory infrastructure/technology indicators are also considered as predictors.Thanks for your useful suggestions. We agree the virus discovery effort is non-homogenous over the three regions included in this analysis and we have tried a couple of ways to adjust for it. The first direct method is that we restricted the study area to three specific regions where the research effort is less variable than the whole globe. Second, we included GDP, GDP growth, and university count to adjust for the discovery effort in each region as these variables partially explain the infrastructure and technology that are available for virus research. One thing we want to clarify is that the economic indicators  we used were all time-varying and at the grid cell level  (see Appendix figure 6–Appendix figure 8)—they were also granular data.We considered using bibliographic data to adjust for discovery effort at first, but we rejected this strategy after a couple of tests. Jones et al[1] estimated the discovery effort for emerging infectious diseases (EID) by calculating the number of papers published by each country (denoted by the address for every author) in the Journal of Infectious Diseases (JID) since 1973. The hypothesis is that countries publishing more papers in JID are likely to discover more EID events. We tested if this method worked for our analysis by plotting the relationship between published human-infective RNA virus count and total number of papers from all journals which published on human-infective RNA viruses in Web of Science (as of 21 Feb 2018). Both the total number of papers  and totaWe were unable to include government indicators  and laboratory infrastructure/technology indicators in our model because the data is scarce. Please see our responses to comment (7) from # Reviewer 1.Refs:1] Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D., Gittleman, J. L., and Daszak, P. (2008). Global trends in emerging infectious diseases. Nature, 451(7181), 990-993.2] Allen, T., Murray, K. A., Zambrana-Torrelio, C., Morse, S. S., Rondinini, C., Di Marco, M.,... Daszak, P. (2017). Global hotspots and correlates of emerging zoonotic diseases. Nat Commun, 8(1), 1124.2) Clarification is needed about how space and time is currently dealt with in the boosted regression tree model.Please see our responses to comment (3) from # Reviewer 1.3) Currently it seems that virus discovery data were matched to covariate data using the nearest decade. Since there might be important changes in virus discovery effort and other covariates at finer timescales, we feel it is important to at least perform a sensitivity analysis to check for this variation and consider how it might impact on the results.We have performed additional sensitivity analyses by considering yearly changes of covariates. Please see our responses to comment (4) from # Reviewer 1.4) We would like to see a consideration of lag periods between the covariates/predictors and virus discovery. i.e. changes in predictor in t-1 year may be more predictive of virus discovery at year t.Lag effects have been considered in the revised version. Please see our responses to comment (5) from # Reviewer 1.5) Clarification is needed regarding handling of how potential collinearity between the predictors.Please see our responses to comment (6) from # Reviewer 1.6) We would like the authors address the likely differences in underlying drivers/predictors for vector-borne (V) vs non-vector-borne (N) viruses and strictly zoonotic (Z) vs human transmissible (T) viruses. This could be done, for example, by using separate models.We additionally fitted 8 separate models for the United State and Africa. Please see our responses to comment (8) from # Reviewer 1.(7) It would be useful to provide an appraisal of the impact related past studies have had on improving surveillance and control.See details in our responses to #Reviewer 2.Reviewer #1 (Recommendations for the authors):This study attempts to identify predictors of human-infective RNA virus discovery and predict high risk areas in a recent period in the United States, China and Africa using a ecological modelling framework. The study is relevant in the current context and identification of areas at threat of emerging viral pathogens.According to findings from their previous study published in 2020, the main predictors for virus discovery at the global scale were GDP-related i.e. and they concluded that this may largely have driven by research effort rather than the underlying biology. In the current study, they have attempted to focus on more restricted and homogenous regions where they suspect research effort is less heterogeneous to an attempt to identify predictors more associated with virus biology. I have some comments and concerns below that would need to be addressed in my opinion prior to a final decision being taken.1) While I do understand the rationale for the restricted analysis, I am still concerned that inherent "discovery" bias in the data and how these vary by region and within country/region and across time may still drive the observed associations rather than real distal predictors of virus discovery.Thanks for the comments. We agree the virus discovery effort is non-homogenous over the three regions included in this study and we have tried a couple of ways to adjust for it. As you noted, the first direct method is that we restricted the study area to three specific regions where the research effort is less variable than the whole globe. Second, we included GDP, GDP growth, and university count to adjust for the discovery effort in each region as these variables partially explain the infrastructure and technology that are available for virus research. One thing we want to clarify is that the economic indicators  we used were all time-varying and at the grid cell level (see Appendix figure 6–Appendix figure 8)—they partially reflect the variation in discovery bias within country/region and across time.Discovery bias is a common problem for data collected from published literature. We acknowledged identifying robust and comprehensive data on discovery effort is difficult, and we listed it as a limitation for our study (lines 396-400). Previous studies have tried to use the bibliographic data to correct for the discovery effort, but we rejected this strategy after a couple of tests (see our responses to editors’ comment 1 for details).2) Furthermore the relative lack of accurate geolocating of data in China relative to the other two regions may also misalign the covariate values attached to these data points and potential skew association particular of the more influential covariates vary substantial across space.Although the majority discovery locations in China involved polygon data at province level, the average number of grid cells per virus in China (243/80=3.03) were similar with the other two regions  (Appendix table 2). More than half discovery locations in China were at the city/county level, but the average number of grid cells each city/county covered is no more than 2 (70/47<2) (Appendix table 2). By using the bootstrap resampling strategy that we described in the main manuscript can effectively avoid any potential skew associations. We re-wrote this in lines 122-124 in the revised manuscript.3) Was the model applied at location-year level i.e. spatial and temporal? This was not entirely clear to me under Boosted regression tree model description.Before fitting the boosted regression tree (BRT) model, we matched the virus data with all predictors by geographical coordinates and time (using the nearest decade for time-varying predictors), making sure each virus discovery event has been linked to the “correct” predictors, i.e., with the same time and location. We further explained this in line 159. However, this is different with the traditional spatiotemporal model that modelling a spatial and temporal outcome that exists at a certain time t and location x. Virus discovery is a rare event, so that if we see it as a spatial and temporal outcome there will be many zeros in the study period for a certain location. Therefore, we care more about the spatial property of the virus discovery data in this analysis, and the BRT model we were using can well correct for spatial autocorrection within the data.4) If I understood correctly virus discovery data were matched to covariate data using the nearest decade? While some covariates are likely to change more slowly, some may be far more dynamic  and vary within a decade. Where any sensitivity analyses conducted to check for this variation within decade and how it might impact on the associative coefficients/influence?Yes, virus discovery data were matched to TIME-VARYING covariates using the nearest decade. We agree some covariates vary within a decade, but gridded covariates back to 1901 are often not available at finer timescales. In our model, covariates, particularly those are associated with the human-animal-environmental interactions including mammal species richness and livestock headcount were static. Data for climatic variables, population, GDP, and land use are time-varying and have been matched with RNA virus discovery count by decade. In the revised version, we re-collected yearly data for climatic variables and land use from the same sources used in the analysis. Yearly population data at grid level before 1970 and GDP data before 1980 are not available, and we extrapolated them back to 1901 using the yearly growth rate at country level (Source: Our World in Data). For population, the WorldPop provides yearly gridded data for 2000-2020[1], and we used the growth rate by grid to extrapolate values after 2000.Sensitivity analyses were performed by matching virus discovery data and time-varying covariate data by YEAR. Though there are several changes of relative contribution, the leading predictors broadly keep consistent with our model based on data matched by decade .Data sources1] https://www.arcgis.com/home/item.html?id=56eb0f050c61434782f008a08331d23a5) Did the authors consider or test for various lag periods between the covariates/predictors and virus discovery? i.e. changes in predictor in t-1 year may be more predictive of virus discovery at year t.Thanks for the useful suggestion. We have tested for 1~5-year lag between the predictors and virus discovery, and the relative contribution of most predictors stay consistent with the model based on data matched by 0-year lag  Did you consider collinearity between the 33 predictors as many of these would likely be highly correlated?We do not consider collinearity between the 33 predictors as the BRT model we used can automatically capture complex relationships and interactions between variables (lines 146-148) and provides robust estimations even if collinearity exist. In spite of this, we took the United States as the example and validated our results by removing predictors with high correlation (correlation coefficient > 0.7 in 7) What other predictors were considered in the ecological framework i.e. governance indicators (transparency) and laboratory infrastructure/technology indicators? These would seem to be important re: potential bias in reporting of newly discovered viruses for the former  and likelihood of detection for the “atter .We agree that government indicators  could partially correct for the potential bias in reporting and the laboratory infrastructure/technology indicators for the potential bias in detecting novel viruses. However, we did not include them in our model as we cannot obtain the appropriate data. Some agencies such as the World Bank[1] and Our World in Data[2] provide data on national government indicators, but it seems none of them provide data at the sub-national level. There are some sub-national data on the quality of governance for Europe[3], but there is not much for regions of interest in our study. Data on laboratory infrastructure/technology indicators are also scarce. There is some data on biosafety level 4 (BSL4) labs around the world[4], but of the 42 labs where foundation dates are available, approximately half have been established in the recent decade, making less contribution to virus discoveries in earlier years. In our study, we included the university count in the model, and should partially adjust for the university labs (Quite a few viruses in our study have been discovered in university labs). We acknowledged identifying robust and comprehensive data on virus discovery effort is difficult, and we re-wrote this as a limitation for our study (lines 396-400).Data links1] https://info.worldbank.org/governance/wgi/2] https://ourworldindata.org/grapher/government-transparency-index3] https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.367.6086&rep=rep1&type=pdf4] Mapping+BSL4+Labs+Globally+EMBARGOED+until+27+May+2021+1800+CET.pdf8) Did you consider separate models (#4) for vector-borne (V) vs non-vector-borne (N) viruses and strictly zoonotic (Z) vs human transmissible (T) given the likely difference  in underlying drivers/predictors across these envelopes?Thanks for your suggestion. In our global analysis , we have fitted separate models for these four categories of viruses and have detected the differences in underlying predictors across them, i.e., vector-borne viruses and strictly zoonotic viruses are more associated with climate and biodiversity whereas non-vector-borne viruses and human transmissible viruses are more associated with GDP and urbanization. In this study, we additionally fitted 8 separate models for the United States and Africa  and found that the pattern of differences detected from our global analysis holds true in Africa . In the Unites States, we did not see the similar pattern (Author response images 8-10) and this is likely due to the smaller proportions of vector-borne and strictly zoonotic viruses in this region (lines 213-216).Reviewer #2 (Recommendations for the authors):As this type of work has been ongoing for some time, It would be useful to provide an appraisal of the impact these past studies have had on improving surveillance and control.Thanks for your suggestions and we have provided an appraisal in lines 311-330 of the impact these past studies have had on improving surveillance and control.The authors have gone some way to addressing the essential revisions from the first round of reviewers and have conducted some of the additional analyses requested. However, this additional analysis does not appear to have been included in either of the revised manuscript files (neither the clean version nor the version with changes marked which doesn't include figures or appendices). While additional analysis is reported in the document with the responses to reviewer comments, it is important that the changes are also included in the manuscript.Considering the requested essential revisions in turn:Essential revision 1 asked for additional work to address discovery bias by extending the analysis to include adjustments for more granular measures of discovery effort. The response to this was to clarify aspects of the analysis (e.g. the granularity of GDP indicators) and to add some text to the discussion (lines 408-412) referring to approaches to this problem taken in other papers. There was also a short exploration  showing the relationship between "published human-infective RNA virus count and the total number of papers from the journals which published all human-infective RNA viruses in Web of Science", and accompanying text that argued that attempts to adjust for discovery effort used by other authors would not be applicable here. I was unable to follow this argument, and given that no additional analysis addressing discovery bias appears in the main manuscript it seems that the essential revision was not undertaken. While the changes the authors have made to the manuscript are welcome, given that (following consultation) round 1 reviewers highlighted this as the key point to address it is important that the authors reconsider how they can change the manuscript to address this point. This may just amount to additional analysis in the supplementary material demonstrating that approaches used by other authors have little applicability here as claimed in the rebuttal letter, but if so the reasoning needs to be expressed more clearly and the evidence for any such assertions clearly laid out.Thanks for your suggestions. We have now added in lines 139-145 (Methods) and lines 419-421 (Discussion) in the manuscript that we test approaches used by other authors for adjusting for discovery effort but found they were not appliable for our data. Details for how we test these approaches are now put in Appendix 2. These approaches used by other authors hypothesized that there is a linear relationship between the frequency of published papers and published novel pathogens in scientific journals and so authors’ addresses in these scientific journals could be used to adjust for spatial discovery effort. However, according to our test, virus-related journals publishing more papers are not necessarily publishing more novel human RNA viruses.Essential revision 2 appears to have been addressed adequately.Thanks for the comment.Essential revision 3 asked for sensitivity analysis to consider matching covariates at finer timescales. This additional analysis appears to have been done but has not yet (as far as I could tell) been included in the revised manuscript.Thanks for the comment. We only mentioned in the text we did sensitivity analysis by matching covariates and virus discovery data by year in lines 172-173 and reported the results in 273-277 in our last revised manuscript. Now we have additionally presented the result in Appendix 3—figure 7.Essential revision 4 asked for additional analysis that included consideration of lag periods between the covariates/predictors and virus discovery. Again, this appears to have been done and is shown in the rebuttal letter, but I could not locate the new analysis in the revised manuscript files.Thanks for the comment. We only mentioned in the text we did sensitivity analysis by matching virus discovery at year t and predictors at t-1 to t-5 year in lines 173-174 and reported the results in 273-277 in our last revised manuscript. Now we have additionally presented the result in Appendix 3—figure 8.Essential revision 5 appears to have been addressed adequately.Thanks for the comment.Essential revision 6 again appears to have been addressed with additional work but this does not appear to have been included in the revised manuscript.We did not include subgroup analyses by transmission modes in our last revised manuscript because (i) we could not do the analysis for China given the small sample size in some subgroups and (ii) Again due to the small sample size for vector-borne viruses and strictly zoonotic viruses in this United States, it gives inconsistent results with Africa. We do not want to address these inconclusive findings in the main manuscript.Essential revision 7 appears to have been addressed adequately  do not correspond to areas of highlighted changes in the manuscript with changes marked).Thanks for the comment. Sorry we gave the wrong line numbers—we were asked to move the funding information into the abstract after we submitted the revised version, and this has made all line numbers changed. We forgot to correct these in the response letter when we re-submitted.


We introduce a modified Hotelling model setup that incorporates the original model and considers logistics’ related costs incurred by the firms. Both the linear and quadratic logistics related costs cases are studied. We find that the relative magnitudes of firms’ logistics costs to customers’ transportation and/or search costs have significant effects on firms’ decisions in terms of location choices. In the linear case, we show, under a large set of cost structures, that substantial differentiation exists and is stable. In the quadratic case, maximum differentiation is the only equilibrium for all cost structures in the model but firms’ profits are higher than in the original case. We also consider the case where customers transportation costs are quadratic while firms logistics costs are linear. Two of the most important works on location theory are the linear city model by Hotelling  and the There is a vast literature that discusses extensions of the Hotelling’s original model. Brenner  providesIn Hotelling’s original model, consumers pay transportation costs and the competing firms charge a mill price. In reality, in most “shopping experiences”, both consumers and suppliers may incur some logistics-related costs. Indeed, consumers might bear only search costs while firms would incur the transportation costs; a consumer might visit a “shopping” center, select the product, and have the supplying firm deliver it to her home. This is also the case with online buying. This trend is likely to continue because of the digitalization of the economy and the “smartness” of urban development.On the other hand, consumers might bear both search and transportation costs, while suppliers incur only some support costs. This is the case, for example, if firms are responsible for support services such as assembling and commissioning at customers’ locations but are not responsible for delivery. Another example is support services for remote installation and configuration of IT products.From the examples above, it is evident that in many “shopping experiences”, firms do incur logistics-related costs that can be either for support-only costs, for transportation only costs, or for both support and transportation services costs. From the customers’ perspective, the costs could be either transportation costs , search costs (as in online buying) or both transportation and search costs.Our main contribution is to model all of these logistical arrangements, from both the firms and consumers’ perspectives, in the same modified Hotelling framework. The original Hotelling model would be a special case of this modified setup. Our aim is to revisit the equilibrium stability issue of Hotelling’s original model with linear transportation costs, after introducing linear logistics related costs incurred by firms. We find that the logistics costs influence the firms’ decisions related to their locations choices. The Hotelling model with quadratic transportation costs for firms is also revisited using quadratic logistics costs.Another contribution is the emphasis on the nature and the magnitude of consumers’ transportation costs (and obviously the nature and magnitude firms logistics costs), which is an overlooked element in Hotelling model literature. On the consumers’ side, we consider the costs paid, by consumers, above the product price, in a more disaggregated way. Indeed, such costs could be only search costs, when buying online for example. They are likely to be lesser in magnitude in such a case. For transportation costs  as in the original Hotelling model, it is reasonable to assume they are higher.On the suppliers’ side, we introduce the logistics-related costs incurred by firms. Such costs may be lower in magnitude if there are no deliveries (only support costs) or higher if deliveries are involved. The relative magnitudes of firms’ logistics costs compared to customers’ transportation and/or search costs have important effects on firms’ decisions in terms of locations choices.The remaining of this article is as follows. Section III is a description of the model with linear customers transportation costs linear logistics costs. Section IV considers both transportation and logistics quadratic costs and section V considers the mixed case where transportation costs are quadratic and logistics costs are linear. Section VI concludes.We consider two firms A and B competing to supply a product to a population of consumers, which is uniformly distributed along the line  with a density equal to 1. A and B are located within the line  at points a and b respectively, such that:Our model differs from Hotelling’s original model in that both firms incur delivery and/or support costs in addition to production and other costs. In Hotelling’s original model, only the customers pay the transportation costs. In our model, these costs, can be interpreted as search costs or search and transportation costs depending on their levels relative to the costs paid by suppliers.We consider identical firms and as in Hotelling’s original model and we are interested in symmetric equilibria. Our main aim is to see how the logistics-related costs incurred by suppliers and introduced here influence firms’ decisions and how the results compare to the ones in Hotelling’s original model. In particular, we are interested in whether there is a symmetric equilibrium in Hotelling’s original model framework, to which we have added the linear delivery and/or support costs.A (PB) to maximize its profit ПA (ПB).We consider a two-stage game. In the first stage, both firms choose their locations (a and b). In the second stage, the two firms set their prices. The model is solved by backward induction. In the second stage, firm A (B) sets its price PProduction costs are equal to zero and the customer reservation price for the product is very high, which ensures that all consumers will be buy regardless of the product price.The logistics-related cost function for firm A is piecewise linear and is defined as follows:A (PB) to maximize its profit ПA (ПB). To derive the demand for each firm, we define the indifferent consumer by its location xI as the consumer that would be indifferent in buying from firm A or B.In the second stage, firm A (B) sets its price Ptc is the unit consumer transportation and/or search cot, then we have:If The firms’ profits are:Let Solving the FOCs (Appendix.A1 in S1 Appendix), we obtain,It is interesting to notice that the slope of the best reaction function of, say firm A is:It is increasing from ½, which is the case in Hotelling’s original model, and converges asymptotically to one. This would indicate that as α increases, competition/differentiation becomes even stronger. Alternatively, this also indicates that when customers’ transportation and/or search costs are lower than logistics costs, which is the case with online shopping for example, competition becomes fiercer as the price dispersion discovery process is less costly.Solving the system of Eqs  and 2),,2), we gNotice that when α = 0, the results from Eqs –5) are  are 5) aIn the first stage, the two companies choose their positions a and b to maximize their profits. The FOCs for the positions (Appendix.A.2 in S1 Appendix) yield:In the following, we will define the conditions under which the profit function is concave (proposition.1), then we will narrow down the range to the values of α for which the symmetric optimal solution is an interior point (proposition.2) and finally we check that the profit functions are positive (proposition.3).Proposition.1:0, where α0 ≃0.03049.The profit function is concave for α>α0, the profit function is increasing in the position a. The two firms would locate at the middle of the line.For α≤αProof. (Appendix.A.3 in S1 Appendix)We introduced some realistic features of the “shopping experience” to Hotelling’s original model, while preserving the linear costs that were responsible for the non-stability of the equilibrium. In contrast to Hotelling’s original model, the profit function is concave for a wide range of ratios of firms’ logistics costs and consumers’ transportation and/or search costs. This range includes both smaller and larger values, accounting for cases where integrated firms, for example, are responsible for the transportation while customers pay only smaller search costs, and cases where customers pay the higher transportation costs while firms pay smaller support costs.α in] 0, α0], extending the (non-stable) equilibrium results in the original Hotelling model. This range of cost ratios is rather closer to the case where consumers payi transportation costs while firms bear some support costs.On the other hand, the Principle of Minimum Differentiation applies for values of Proposition.2. defines the conditions for interior solutions.Proposition.2:1 ≃0.237≥ α0.The interior symmetric location in stage one of the game is an interior solution in the semi-open interval] 0,1/2] for α≥ αα1, 1/2] given by Eq Contrary to Hotelling’s original model with a linear transportation cost, Proposition.1 shows that an equilibrium solution exists in this case. It is required to verify that the optimal solution is an interior one.Indeed, the symmetric equilibrium is such that (Appendix.4 in S1 Appendix):1 ≃0.237. Notice that the interior solutions satisfy the concavity condition α ≥ α0.We have interior solutions for all α≥ α1 > α≥ α0, the profit function is concave and is increasing on the segment  but the optimal solution is larger than 1/2. As such, the firms would locate at the middle of the line.For αα1, 1/2] given by Eq .In this section, Hotelling’s original model is extended by considering the logistics-related costs that suppliers might incur. We focus on symmetric equilibria to preserve the main features of Hotelling’s original model for comparison purposes. Logistics-related costs are captured through the ratio of delivery and/or support costs to the customer’s search and/or transportation cost. We find that the results in Hotelling’s original model with linear transportation costs can be extended to cases where the supplying firms incur lower delivery and/or support costs (firms locating at ½). Most importantly, for medium and higher delivery costs, symmetric equilibria are found and firms tend to locate, asymptotically, at (1/8 and 7/8) of the market line. Looking at the distance to the location of selling firms as consumers’ perception of the quality offered by firms, the modified model shows that the customization of firms’ offers translates into an increasing differentiation of those offers.a≤b≤1. We also consider a similar two-stage game, where a firm decides on its location a (b) in the first stage, then sets the price PAq (PBq) to maximize its profits ПAq (ПBq).In the quadratic model, we consider the same setup in the previous sections, namely two firms A and B competing to supply a uniform distribution of consumers along the line  and located at points a and b such that 0≤We keep the same assumptions regarding production costs and customer’s reservation price. However, in the present case, the costs are quadratic with respect to distance. The logistics-related cost function for firm A is now defined as:Aq (respectively PBq) to maximize its quadratic profit ПAq (respectively ПBq). Here again, we solve for the location of the independent consumer xIq, who is indifferent between buying from firm A or B, which will help derive the demand for each firm. The location of consumer xIq is such that:In the second stage, and similar to the linear model, firm A (respectively Firm B) sets its price PWith quadratic costs, the profits of the firms are:Aq and PBq (Appendix.B.1 in S1 Appendix), we get:Given With quadratic costs, the slope of the best reaction function of, say firm A, becomes:a = 0, and converging asymptotically to 1, to reflect more aggressive competition.It is interesting to notice that the same trends subsist as in the linear costs case, with the reaction (1/2) simulating the original Hotelling model at Solving the system of Eqs  and 9),,9), we gAq and PBq, are different from those derived from the equilibrium with linear costs, the location of the independent consumer is similar to the one in the linear case. Also, with quadratic transportation costs, the case with α = 0 would bring about the original Hotelling model.Notice that while the firms’ prices, PAs in the linear costs case, the two firms choose their positions, a and b, in the first stage, to maximize their profits. The FOCs for the positions (Appendix.B.2 in S1 Appendix) give:Proposition.4:In the Hotelling model with quadratic logistics costs, firms A and B locate, respectively, at a = 0 and b = 1. In particular, we have the following results:ΠAq reaches its maximum at a = 0;The profit function ΠBq reaches its maximum at b = 1;The profit function The profit functions of firms A and B are positive at a = 0 and b = 1, respectively;The profits in this model are higher than in Hotelling’s original model without logistics costs.α<4The equilibrium is Nash perfect in the prices and locations subgames if 0 ≤Proof.a, (Appendix B.3.ii in S1 Appendix). As such, the first derivative is decreasing in a. The first derivative of firm A’s profit is negative at a = 0 (Appendix B.3.i in S1 Appendix). As such, it is negative for all 0≤a. Therefore, firm A’s profit function is maximal at a = 0 for interior solutions.The second derivative of the profit function of firm A is negative for all 0≤b≤1. Therefore, firm B’s profit function is maximal at b = 1 for interior solutions.Similarly, the second derivative of the profit function for firm B is negative in the unit interval (Appendix B.3.ii in S1 Appendix). As such, the first derivative is decreasing in b. The first derivative of firm B’s profit is positive at b = 1 (Appendix 3.i in S1 Appendix). As such, it is positive for all 0≤The resulting prices are:xIq = +1/2, and the profits are positive and given as:The independent consumer is located, as in the linear costs case, at the middle In Hotelling’s original model with quadratic transportation costs and no logistics costs, the profit of say firm A is:In the Hotelling model with quadratic logistics costs, a profit “premium” of α<4.The interior solution in the quadratic case  is Nash equilibrium in the prices and locations subgames if 0<In this section, Hotelling’s original model with quadratic costs is extended by considering quadratic logistics related costs that suppliers might incur. Logistics-related costs are captured through the ratio of delivery and/or support cost to customer search and/or transportation costs. We find that the results of Hotelling’s original model with quadratic transportation costs (maximum differentiation principle) hold in all cases where producing firms incur logistics-related costs. However, the profits achieved in the modified Hotelling model are higher than in the original model.In this case, we consider that firms have linear logistics costs and that customers have quadratic transportation costs.Am (respectively PBm) to maximize its mixed profit ПAm (respectively ПBm). Here again we solve for the location of the independent consumer xIm, indifferent between buying from firm A or B, which will help deriving the demand for each firm. The location of consumer xIm is (similar to the quadratic model) such that:In the second stage, and similarly to the linear model, firm A (respectively Firm B) sets its price PWith mixed cost structures, the profits of the firms are:Am and PBm (Appendix.C.1 in S1 Appendix), we get:Given Therefore, we get:Similar to the case with linear costs, the two companies chose their positions, a and b, in the first stage, to maximize their profits.Proposition.5.The interior solutions are defined as:α goes to infinity. Hence, it is an interior solution.The position of firm A is positive and increasing for The interior solutions defined above are Nash equilibria in prices and locations,Proof, Appendix. C in S1 Appendix.We have provided an extension of the original Hotelling model for both the linear and quadratic costs cases. The only additional assumption introduced concerns the logistics-related costs incurred by firms. Indeed, most of the “shopping experiences”, in reality, involve some logistics costs to the suppliers. We focused on symmetric equilibria to preserve the main features of Hotelling’s original model for comparison purposes. The magnitude of customers and firms’ costs are captured through the ratio of the firms logistics costs to the customer search and/or transportation cost. We have considered three cases. In the first case, the customers have linear transportation costs while the firms have also linear logistics costs. In the second case, customers (firms) have quadratic transportation (logistics) costs. In the third case, customers (firms) have quadratic (linear) transportation (logistics) costs.Our main result is that, as opposed to the principle of minimum differentiation and the non-stable equilibrium of Hotelling’s original model with linear transportation costs, substantial differentiation exists and is stable. Hotelling’s original model is a particular case of ours and we find that the principle of minimum differentiation holds when the firms’ logistics costs are lower compared to consumers’ transportation costs.tc) to be reduced to a lower value (tc-βt) following firms customization decision (t).Looking at the distance to the location of selling firms’ as consumers’ perception of the quality offered by firms, the modified model shows that customization of firms’ offers translates into an increasing differentiation of such offers. A limitation of the model, when used to assess customization decision, is on the non-incorporation of customers reactions to such customization efforts. However, the same set up can be used to capture customers’ reactions to customization investments by allowing, for example, the customers disutility  position tends to 1/8 (7/8) when S1 Appendix(DOCX)Click here for additional data file.


Length of hospital stay (LOHS) is the main cost-determining factor of hospitalization for stroke patients. However, previous analyses involving LOHS did not consider confounding or indirect factors, or the effects of other factors on LOHS and inpatient costs.To investigate the direct and indirect effects of LOHS on the hospitalization costs of inpatients with ischemic and hemorrhagic stroke.This was a population-based, retrospective, and observational study that analyzed data acquired from the Nationwide Inpatient Sample between 2015 and 2020 relating to ischemic and hemorrhagic stroke in Ningxia, China.Hospitalizations were identified by the International Classification of Diseases 10th Revision (ICD-10). Inpatient costs were described by the median M . We used a quantile regression model to estimate the linear relationships between a group of independent variables X and the quantile of the explained variable hospitalization cost (Y). A structural equation model (SEM) was then used to investigate the direct and indirect effects of LOHS on inpatient costs.25, P75) of inpatient costs was $1020 (742–1545) for ischemic stroke and 2813 (1576–6191) for hemorrhagic stroke. The total effect of LOHS on inpatient costs was 0.795 in patients with ischemic stroke. The effect of yearof discharge (X4) and CCI (X8) on inpatient costs was dominated by an indirect effect through the LOHS. The indirect effect was −0.071  and 0.034 , respectively. The total effect of LOHS on inpatient costs in patients with hemorrhagic stroke was 0.754. The influence of CCI on inpatient costs was dominated by an indirect effect through LOHS; the indirect effect value was −0.028 . The payment type, surgery, method of discharge, and hospital level also exerted an impact on inpatient costs by direct and indirect effects through the LOHS.The study included 129,444 patients with ischemic stroke and 15,525 patients with hemorrhagic stroke. The median LOHS was 10 (8–13) days for ischemic stroke and 15 (10–22) days for hemorrhagic stroke. The median M  was identified as the main factor influencing hospitalization costs. However, other social factors were shown to indirectly influence hospitalization costs through the LOHS. Taking effective measures to further reduce hospitalization costs remains an effective way to control hospitalization costs for stroke patients. In addition to the direct effect on hospitalization costs, do other social factors indirectly affect hospitalization costs through the length of hospital stay (LOHS)?via direct and indirect effects acting on the LOHS, including year of discharge, Charlson Comorbidity Index (CCI), payment type, surgery, discharge method, and hospital level.This was a retrospective study of hospitalizations involving 129,444 cases of ischemic stroke and 15,525 cases of hemorrhagic stroke. We found that LOHS was the main factor that influenced hospitalization costs. Other social factors also had an impact on inpatient costs Length of hospital stay (LOHS) not only exerts direct effects on the cost of hospitalization; it also exerts indirect effects on other social factors, thus suggesting that taking effective measures to further reduce hospitalization costs remains an effective way to control hospitalization costs for stroke patients.The Global Burden of Disease study in 2016 estimated that the overall lifetime risk of stroke in the Chinese population was 39.9%, thus ranking first in the world . The staOver the past 30 years, China's economy has achieved unprecedented growth; medical health provision and public health expenditures have also grown rapidly. The observed growth in medical expenses has exceeded that of economic growth. It is also evident that the out-of-pocket medical expenses of patients are gradually decreasing. Since 2007, China has implemented comprehensive medical reforms with medical insurance coverage increasing from 45% in 2006 to more than 95% in 2017 . Excludivia both factors. However, most researchers still use traditional linear regression models to analyze the influence exerted by inpatient costs  is the main cost-determining factor for hospitalization in stroke patients –14. Howent costs , 15–17; We designed a population-based and retrospective observational study that utilized data from the Nationwide Inpatient Sample (NIS). The medical records manager concealed the names, ID numbers, phone numbers, and addresses of inpatients before we extracted the data; each patient was identified by a unique medical records number. The ethics review committee of The People's Hospital of Ningxia Hui Autonomous Region approved this study .th in China's 31 provinces. We collected all discharged patient variables from the first page of medical records in Grade 2 medical and health institutions  and Grade 3 medical and health institutions . The first pages of the inpatient medical record, including medical record number, demographic characteristics, primary, primary and secondary diagnoses, procedures, length of stay (LOHS), and method of payment were collated; in total, 235 variables were acquired.We extracted the first page of each inpatient medical record from 2015 to 2020 in Ningxia, located in the northwest of China; the medical resources and comprehensive medical quality of this hospital were ranked 14According to the International Classification of Diseases 10th Revision (ICD-10), patients with a primary diagnosis of hemorrhagic stroke (coded as I60-I62) and ischemic stroke (coded as I63) were selected. In order to avoid the impact of extreme values in inpatient costs, we excluded 808 observations  with LOHS < 1 day or ≥ 180 days, 897 observations (accounted for 0.46%) with inpatient costs < 100 Renminbi (RMB) or > 300,000 RMB and 1,628 observations (accounted for 0.84%) aged < 20 years or > 90 years. In addition, we excluded 20,417 observations (accounted for 10.58%) that were lacking important data such as age, sex, discharge mode, and method of payment.Stroke patients often experience complications that are defined as a clinical condition that exists at the time of the onset event that is likely to influence inpatient costs. In the present study, we used the Charlson Comorbidity Index (CCI) as a measure to evaluate the severity of comorbidity in stroke patients .In order to eliminate the interference of price factors on the inpatient costs of stroke patients in different years, before performing hospitalization cost analysis, we discounted the hospitalization cost by the Consumer Price Index (CPI), taking 2020 as the benchmark year. As inpatient costs followed a skewed distribution, we described these data as a median M . Usually, the influence of X on Y (Y is quantitative) uses linear ordinary least squares (OLS) regression to determine the significance of the regression coefficient and the influence of the direction. However, OLS regression is unable to determine this relationship. OLS regression requires the dependent variable Y to be normally distributed. OLS regression is also sensitive to outliers and problems related to a heterogenous variance. In comparison with OLS regression, quantile regression can investigate the influence of X on Y and provide strong robustness to outliers, dependent variable normality, or heteroscedastic problems.F[y(t)] where P indicates the probability, and F[y(t)] indicates the cumulative (probability) distribution function of y. In this study, we used the web version of the Data Science Algorithm platform tool SPSSAU (https://spssau.com/en/index.html) for quantile regression. The inpatient costs were taken logarithmically and then included in the quantile regression model as a dependent variable for analysis. Payment mode, age, patterns of admission, year of admission, length of stay, CCI, surgery, pattern of discharge, and hospital level were set as the independent variable X; the quantile point of the explained variable hospitalization cost (Y) was divided into 10 sections, ranging from 0.1 to 0.9, with an interval of 0.1. Estimates of the change point and other regression parameters are reported for quantiles .The quantile regression model is a modeling method that is used to estimate the linear relationship between a group of regressors X and the quantile of the explained variable Y, thus emphasizing changes in the conditional quantiles , 19. QuaThe Structural Equation Model (SEM) can handle multiple dependent variables, analyze the relationship between single variables and scientifically reflect indirect effects between variables . SEM can25, P75) of inpatient costs for ischemic stroke and hemorrhagic stroke was highest in 2016 at $1,294  and $3,366 , respectively. These values were lowest in 2020 at $936  and $2,365  respectively (p < 0.001), the Scatter Chart was showed in In this study, we analyzed 144,969 stroke samples in Yinchuan between 2015 and 2020, including 15,525 hemorrhagic stroke patients  and 129,444 ischemic stroke patients . The median LOHS was 10 days (interquartile range: 8–13 days) for ischemic stroke and 15 days (interquartile range: 10–22 days) for hemorrhagic stroke. The trends for discharge cases and LOHS are shown in ectively . Pearsonp = 0.280) and CCI (p = 0.393). The median hospitalization cost of hemorrhagic stroke patients differed statistically according age group, hospital level, LOHS, payment type, with or without surgery, patterns of admission, and the method of discharge (p < 0.001). All of these influencing factors showed significant differences in terms of the median hospital costs of patients with ischemic stroke (p < 0.001) (p > 0.05) at different quantile points and the payment method was below P10  with regards to inpatient costs. However, LOHS , with or without surgery , CCI , and hospital level  had significant positive effects on inpatient costs. Years of discharge  and patterns of admission  had significant negative effects on inpatient costs. Patterns of discharge had negative effects on inpatient costs when below P30  and had a positive impact on inpatient costs when above P50 . For patients with hemorrhagic stroke, hospital level , patterns of discharge , payment method , LOHS , with or without surgery  and CCI  had a significant positive impact on inpatient costs. Patterns of admission had a significant negative effect on inpatient costs . Age group had positive effects on inpatient costs when below P30  and had a negative impact on inpatient costs when above P90 , but there was no statistical difference in P50  and P70  with regards to inpatient costs  . With rent costs .p < 0.01), with or without surgery , and CCI  had significant positive effects on LOHS. Year of discharge had negative effects on LOHS . Age group had no effect when below P50  and had a negative impact on LOHS when above P70 . Patterns of discharge had negative effects on LOHS when below P90  and had a positive impact when at P90 . Patterns of admission had positive effects on LOHS when below P30  and had a positive impact when above P70 . For patients with hemorrhagic stroke, hospital level , patterns of admission , with or without surgery  and CCI  had a significant positive impact on LOHS. Age group , patterns of discharge , payment method  had a negative impact on LOHS.Of the factors influencing LOHS, when considering patients with ischemic stroke, hospital level . Of all variables, gender (X2) and patterns of admission (X3) only had direct effects on LOHS (p < 0.001) but did not exert effects on the inpatient costs. Correspondingly, the payment type (X1), year of discharge (X4), surgery (X5), method of discharge from hospital (X6), hospital level (X7), and CCI (X8) exerted an impact on inpatient costs via two mechanisms: direct effects and indirect effects exerted by LOHS; the path coefficient is shown in p < 0.001) with inpatient costs but had a negative correlation with the LOHS ; the gross effect was −0.014. Similar results were evident for the method by which patients were discharged from the hospital (X6). The indirect effect of year of discharge (X4), and CCI (X8), was greater than the direct effect.According to our structural equation modeling results , the totThe total effect value of LOHS on inpatient costs in patients with hemorrhagic stroke was 0.754. Of all variables, payment type (X1), gender (X2), and patterns of admission (X3) only had a direct effect on LOH but did not exert an effect on inpatient costs. Year of discharge (X4), surgery (X5), methods by which patients were discharged from hospital (X6), hospital level (X7), and CCI (X8) exerted effects on both inpatient costs and LOHS. Detailed assignments of X1-X8 are shown in Over recent years, Ningxia, as with other parts of China, has implemented a series of medical reform policies to control medical costs and to ensure that appropriate medical services are provided to all residents. The cost incurred by stroke inpatients has been declining since 2016; this is due to a series of medical reform policies implemented in the region, such as payment systems for disease diagnosis-related groups and single diseases –23. MediIn this study, we analyzed the factors influencing the hospitalization cost of stroke patients by quantile regression. The quantile regression model showed that LOHS was the largest contributor to inpatient costs; these findings were similar to those of previous studies. In general, for a particular patient, and assuming that his/her condition is certain, the total amount of treatment he/she receives increases as the number of days in hospital increases. An increase in the number of days in hospital was accompanied by a series of basic costs such as more beds, drugs, and nursing care, as well as greater consumption of health resources, thus resulting in higher inpatient costs. When controlling for other variables, some variables such as hospital level (X7) and CCI (X8) showed consistent trends in their effects on LOHS and inpatient costs, thus suggesting a synergistic effect between LOHS and inpatient costs. The inpatient cost and LOHS in tertiary hospitals are higher than those in secondary hospitals because tertiary hospitals have better medical equipment, services, and higher treatment standards when compared to secondary hospitals. Therefore, the tertiary hospitals also admit patients with more complex and serious conditions, thus resulting in higher inpatient costs and inpatient days for patients attending tertiary hospitals. However, in terms of the coefficient of determination, tertiary hospitals had a greater effect on inpatient costs. However, in terms of the coefficient of determination, tertiary hospitals had a greater impact on inpatient costs and a relatively smaller impact on the number of days in the hospital. This also suggested that further enhancement of the hierarchical diagnosis and treatment system could reduce medical burden; for example, by increasing the grading of patients according to their own priorities and difficulty of treatment with different levels of medical institutions undertaking the treatment of different diseases, and by gradually realizing the medical process from general practice to specialization.via LOHS.Furthermore, we applied AMOS path analysis to construct an SEM to investigate the direct and indirect effects of total patient hospitalization costs. We showed that LOHS contributed the most to the direct effect of hospitalization costs. The CCI score had a greater direct impact on the number of days in the hospital than on the cost of hospitalization while the CCI score indirectly affected the cost of hospitalization via the number of days in the hospital. Comorbidities are a hallmark of stroke that both increase the incidence of stroke and worsen the outcome . Higher This study described the impact of patient demographic characteristics, payment methods, comorbidities, disease regression, and other indicators, on costs. In the future, we should add other influencing factors such as nursing hours, whether to transfer departments, and the value of the skilled labor provided by medical staff, and further validate the optimization model to propose more refined and targeted measures to effectively control and reduce the inpatient costs of stroke patients.This study applied a quantile regression model to analyze the factors that can influence stroke hospitalization cost and an SEM to investigate the interrelationship between these factors. The results of this study revealed the direct and indirect effects of LOHS on hospitalization costs and will help to provide effective operational measures to control and reduce hospitalization costs in the future.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.PL had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis and responsible for concept and design. MS and DP drafted the manuscript. PL critically revised the manuscript for important intellectual content. MS carried out the statistical analysis and DP supervised the research. All authors were involved in data acquisition, cleaning, analysis, contributed to the article, and approved the submitted version.This study was funded by the Ningxia Natural Science Foundation of China (Reference: 2020AAC03354) and the Ningxia Hui Autonomous Region Key Research and Development Plan Project (Reference: 2021BEG03099).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.


Pancreaticojejunal (PJ) anastomosis occasionally fails several months after pancreaticoduodenectomy (PD) with Child reconstruction and can ultimately result in a late-onset complete pancreaticocutaneous fistula (Lc-PF). Since the remnant pancreas is an isolated segment, surgical intervention is necessary to create internal drainage for the pancreatic juice; however, surgery at the previous PJ anastomosis site is technically challenging even for experienced surgeons. Here we describe a simple surgical procedure for Lc-PF, termed redo PJ anastomosis, which was developed at our facility. Between January 2008 and December 2020, six consecutive patients with Lc-PF after PD underwent a redo PJ anastomosis, and the short- and long-term clinical outcomes have been evaluated.The abdominal cavity is carefully dissected through a 10-cm midline skin incision, and the PJ anastomosis site is identified using a percutaneous drain through the fistula tract as a guide, along with the main pancreatic duct (MPD) stump on the pancreatic stump. Next, the pancreatic stump is deliberately immobilized from the dorsal plane to prevent injury to the underlying major vessels. After fixing a stent tube to both the MPD and the Roux-limb using two-sided purse-string sutures, the redo PJ anastomosis is completed using single-layer interrupted sutures. Full-thickness pancreatic sutures are deliberately avoided by passing the needle through only two-thirds of the anterior side of the pancreatic stump.The redo PJ anastomosis was performed without any intraoperative complications in all cases. The median intraoperative bleeding and operative time were 71 (range 10–137) mL and 123 (range 56–175) min, respectively. Even though a new mild pancreatic fistula developed postoperatively in all cases, it could be conservatively treated within 3 weeks, and no other postoperative complications were recorded. During the median follow-up period of 92 (range 12–112) months, no complications at the redo PJ anastomosis site were observed.This research shows that the redo PJ anastomosis for Lc-PF we developed is a safe, feasible, and technically no demanding procedure with acceptable short- and long-term clinical outcomes. This procedure has the potential to become the preferred treatment strategy for Lc-PF after PD. Late-onset complete pancreaticocutaneous fistula (Lc-PF) can occur several months after pancreaticoduodenectomy (PD) with Child reconstruction and is thought to be due to pancreatic juice pooling at the anastomosis site, resulting in a pseudocyst; subsequent pseudocyst enlargement causes failure of the PJ anastomosis. Eventually, an Lc-PF develops because the placement of a percutaneous drain into the pseudocyst allows the pancreatic juice to entirely discharge out through the fistula tract. Since the main pancreatic duct (MPD) of the remnant pancreas does not communicate with the gastrointestinal tract, creating internal drainage for the pancreatic juice is necessary for treating the fistula –6.Direct surgical management of such failure of the PJ anastomosis is considered technically demanding because intraabdominal dissection in the chronic phase after PD is unsafe due to the presence of tight adhesions, scar tissue, obscure anatomy, and risk of critical injury to adjacent major vessels or organs, even if performed by experienced pancreatic surgeons , 7–9. AlTo overcome these problems, we have developed a simple surgical procedure for Lc-PF after PD and have safely performed it on six consecutive patients. Here, we describe the procedure and retrospectively review its safety, feasibility, and short- and long-term clinical outcomes.Data in the pancreatic resection database of our institution were used after the approval of the institutional board .Between January 2007 and December 2020, 293 patients underwent PD with Child reconstruction at our institution; among them, 6 patients (2.0%) developed Lc-PF who all underwent the redo PJ anastomosis. Primary conditions requiring PD are listed in Table The PJ anastomosis at PD was performed in all 293 patients using the Kakita procedure , one of Lc-PF typically presented as upper abdominal pain with computed tomography (CT) revealing the failure of the PJ anastomosis with pseudocyst formation and MPD dilation of the remnant pancreas Fig. a. TherefAn approximately 10-cm midline skin incision was created just above the primary PJ anastomosis site, which was identified using CT or ultrasonography imaging. The upper abdominal cavity was carefully and minimally dissected using the drain placed through the fistula tract that acted as the guide Fig. a, b. AftThe previous Roux-limb could be easily identified because it is located opposite the pancreatic stump. As the wall is flexible and nonfragile, which is in contrast to that observed in the acute phase after PD, there is no need to newly create another Roux-limb. An 8-Fr pancreatic tube, trimmed to approximately 3 cm in length, was placed into both sides as a lost stent (Fig. A peritoneal drain was placed around the PJ anastomosis; however, a feeding jejunostomy for enteric nutrition was not needed, and neither of the postoperative somatostatin analogs were necessary.The redo PJ anastomosis was successfully performed in all six patients without any intraoperative complications, including injuries to the adjacent organs or vessels. Table The redo PJ anastomosis for Lc-PF after PD described here appears to be both safe and feasible with acceptable short- and long-term clinical outcomes.In Lc-PF, completion of pancreatectomy or internal drainage of the pancreatic juice into the gastrointestinal tract is required to treat the fistula because the remnant pancreas is an isolated segment and the entire pancreatic juice drains out only through the percutaneous fistula. However, completion of pancreatectomy is not recommended due to substantial postoperative metabolic abnormalities, including severe brittle diabetes –24. ThusRecently, a few small numbers of case studies have reported successful radiologic or endoscopic interventions for establishing internal drainage –14; howeThis redo PJ anastomosis has the following four technical advantages. First, the percutaneous drain through the fistula tract can be used as a guide identifying the previous PJ anastomosis site. This tube allows secure assessment and minimal range of intraabdominal dissection even through a small skin incision. Surrounding adhesions are intentionally left untouched to prevent or limit postoperative inflammation due to PF, bacterial infection, or other causes. Thus, the previous PJ anastomosis site could be easily accessed using this method in all our patients, and importantly, neither postoperative intraabdominal abscess formation nor extensive inflammation was observed despite new PF formation occurring in all patients. Second, the adjacent major vessels and organs remain covered and protected by chronic connective scar tissues, a finding in contrast to that observed in the acute phase after PD , 26. TheCurrently, the duct-to-mucosa PJ anastomosis is increasingly considered a standard procedure during PD ; howeverFistuloenterostomy has been reported as an effective management strategy for internal drainage in refractory and persistent PF after PD , 29, whiDespite these advantages, this study still has some limitations. First, this study comprised a small number of patients, which is due to the low incidence of Lc-PF. Further, since this was a single-center study, identifying eligible patients was also difficult. Thus, future studies must collect clinical data from multiple centers. Second, all six patients had no pancreatic cancer at PD Table . For patThe redo PJ anastomosis for Lc-PF after PD described here is both safe and feasible with acceptable short- and long-term clinical outcomes. Further, we believe that the redo PJ anastomosis is not as demanding as expected; thus, this procedure may become the preferred management option for Lc-PF after PD.