Background: The complement system is made up of an abundance of unique plasma proteins that play an important role in innate immunity and inflammation, aiding in the fight against pathogenic microbes and viral diseases. The purpose of this study was to evaluate the serum complement C4 concentration in COVID-19 patients in Khartoum and compare them to healthy controls.Methods: A total of 100 samples were collected, 50 samples from COVID-19 patients who presented as cases and 50 samples from people who were evidently healthy. Overall, 33 (66%) the patient populations in the case group were not in the hospital’s intensive care unit (ICU), compared to 17 (34%) who were. The concentrations of C4 in each serum sample were calculated in milligrams per deciliter. SPSS version (20) was used to analyze the data.Results: The means level of complement C4 (mg/dL) were 37.44±18.618, 23.90±10.229 in the case group and in the control group, respectively. There was a statistically significant difference in complement C4 level between case and control . In addition, the mean complement C4 level in the ICU and non-ICU case groups was 25.00±17.85 and 43.85±15.712 mg/dL, respectively. There was a statistically significant variance in complement C4 level between ICU and non-ICU . Furthermore, the cases were divided into four age groups: 20-40, 40-60, 60-80, and over 80 years old. The one-way ANOVA test showed no statistically significant differences between age categories in complement C4 level (P = 0.735)Conclusions: The case group had a higher mean level of complement C4 than the control group, which could be understood by the stimulation of the complement cascade during the COVID-19 illness. Furthermore, the complement C4 level in severe COVID-19 patients was lower than in non-severe COVID-19. At the end of December 2019, a new coronavirus was determined to be the root of a cluster of pneumonia cases with an unknown cause in Wuhan, Huanan Fresh fish Supplier Market, the city in China’s Hubei Province that served as the initial site where cases of coronavirus disease 2019 (COVID-19) were identified. The novel coronavirus spread rapidly, causing an epidemic in China, and a pandemic with rising incidence in many other nations all over the globe. Symptomless infection, mild upper airway disease, severe and highly contagious pneumonia, respiratory distress, and sometimes even fatality are all on the clinical phenotype of SARS-CoV-2 infection.In the last twenty years, endemic cases of the Middle East respiratory syndrome (MERS)-CoV in 2012 and severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 both occurred, with case mortalities ranging from 14–15% to 35%. The COVID-19 virus can cause clinical and pathological lung lesions that resemble those seen in other types of acute respiratory distress syndrome (ARDS) because it enters lung tissue after sticking to viral Spike proteins (S) and ACE2 receptors.Initially, a link to a seafood restaurant in Wuhan that sold live animals and was frequented or worked at by the majority of the previous pneumonia patient populations was noticed. But as the pandemic disease spread, person-to-person transmission took over as the main way it was spread. Large droplets released during sneezing and coughing by symptomatic patients help spread the COVID-19 infectious disease, but transmission can also occur from people who are symptomless before developing symptoms. The main complement molecules C3 and C4 are cleaved by the proteolytic process when the complement system is activated, resulting in the cleavage products C3a, C3b, C4a, and C4b, which can cause the recruitment of inflammatory cells and the activation of neutrophils. Patients with mild COVID-19 have shown higher levels of C3 and C4 complement, whereas those with severe COVID-19 had lower levels, which might be clarified by higher consumption from the formation of immune complexes.In order to eliminate invasive pathogens, complement pathways, a crucial part of the innate immune system, are activated by coronaviruses.In light of this, the goal of this study was to compare the serum complement C4 levels of COVID-19 patients in Khartoum to those of healthy controls. Additionally, we compared the COVID-19 patients’ levels of C4 based on the severity of their illnesses.The Ethics Committee of Sudan University of science and technology approved this research . In addition, written approval was obtained from the Jabra hospital of Emergency and Trauma manager . Respondents provided written informed consent after being guaranteed that only investigators would have access to the data and that it would only be used for investigation. Respondents could fill the questionnaire privately. All procedures in this study were carried out in conformity with the rules and regulations specified in the Helsinki Declaration that were applicable.During the period from January to April 2022 a total of 100 subjects were involved in this study, of which 50 were cases confirmed to have COVID-19 infection and another 50 apparently healthy control were the control group. Cases were matched in sociodemographic characters. Cases and control were randomly selected from patients attending Jabra hospital of Emergency and Trauma. Gender was informed based on the self-administered questionnaire filling.The study included patients diagnosed with COVID-19 and apparently healthy controls. Patients who had received any care before being admitted other than antipyretics, had any recorded coinfections, or had immune dysfunction or malignant neoplasms were excluded.C4 ELISA kit was obtained from Abcam (United Kingdom). All the protocols were done according to the manufacturer instruction. After that all reagents were brought to 37°C for warming, then 800 μL from R1 (Diluent) were added to 20 μL from the sample or calibrator and 200 μL from R2 (Antibody); finally, the absorbance of calibrator and sample had been read. The concentration of C4 was obtained by calculating the differences between calibrator and sample reading.A volume of 3 mL of blood was collected in plain containers from both patients and controls at Jabra Hospital of Emergency and Trauma. Then the samples were centrifuged, serum separated and stored at -80°C until analysis.The Statistical Package for Social Science (SPSS) Version 20 was used for data analysis. The data was presented as mean and standard deviation, frequency; Pearson’s correlation, T – test results, and one way Anova have been used as specific tests with a significant threshold value of 0.05 .±18.044 years, 33 (66%) men, and 17 (34%) women. Additionally, there were 50 individuals who appeared to be in good health; their mean age was 59.68±19.464 years. Of these patients, 33 (66%) were men and 17 (34%) were women . Zinellu and Mangoni (2021), showed there was a noticeably higher level of complement C4 in the case than in the control, which supports this finding . In the current study, patients with severe COVID-19 disease had lower complement C4 levels than patients with less severe COVID-19 disease, and this difference was significant . This finding concurs with that of Ghazavi and colleagues in Iran, who discovered that complement C4 levels were lower in COVID-19 patients with severe disease (P=0.014) and higher in those without severe disease . Al-Hakeim and his colleagues also discovered in Iraq that patients who spent longer than two weeks in the hospital had lower complement C4 levels than those who were discharged earlier . This result supported the findings of Al-Hakeim and his colleagues in Iraq, who found no correlation between the mean complement C4 levels across age groups and sex (P=0.681). Males and females in the case group had average complement C4 levels of 40.15±20.095 and 32.18±14.466 respectively. There was no discernible gender difference in the complement C4 level among the cases group. Prior clinical evidence has revealed that, while males and females share a comparable illness rate, males are more likely to be hospitalized, experience a more severe illness progression, and die at a greater rate than females.Immunoassays are frequently used in clinical practice to measure complement C4 and determine and track complement activation. A decrease in serum C4 levels brought on by increased immune system C4 consumption. Complement C4 testing in COVID-19 patients may be able to shed light on how clinical risk is balanced between normal and abnormal complement activation.A study with a large number of patients in the asymptomatic and ICU groups using COVID-19 is recommended to generate clearer and more significant results. Due to funding limitations, this research was limited to 50 COVID-19 patients. As a result, it is recommended that similar studies be conducted on a greater number of COVID-19 cases. Also, research on the other complement components would be valuable in order to acquire a better understanding of the complements pathway’s involvement. There is considerable debate over how soon complement overuse must be monitored and which components of complement consuming must be evaluated.Comparing the case group to the control, there was a higher mean level of complement C4, which can be attributed to the activation of the complement system during COVID-19 infection. Furthermore, the complement C4 level was lower in patients with severe COVID-19 particularly in comparison to those with non-severe COVID-19, which could be because the immune system of patients with severe infection consumes more complement C4.YA performed main experiments, YA, NM, MY, BG collected’ samples and information. MY, designed the experiments and wrote the manuscript. All authors read and approved the final manuscript. Please give more details about the roles of complement C in the progression of infectious diseases so the manuscript's main idea can be clearly defined. In addition, the authors have cited research that shows the profile of complement c in COVID-19 (Ref.8). So, what is this study's new information or added value? Please define it clearly. In this study, the authors did not provide details of medication taken or consumed by the subjects. Please add the details since using different classes of antipyretics may affect the complement c levels.Regarding mild and severe cases, the authors defined them based on ICU and non-ICU cases. However, ICU patients often have comorbidities, causing poor prognosis of the disease. This study focuses on complement C and the severity level of COVID-19, so it needs specific data regarding the severity of COVID-19, for example, Ct values or any other COVID-19 markers. Please describe when the blood sampling was conducted in the method section. In addition, the incubation period of infection may affect the levels of complement c, and it can be analyzed from the day patients suffered from the symptoms and the day they were admitted to the hospital. Please add the data about this. Or, could the author provide the references that the incubation period does not affect the levels of complement c.Since the profiles of serum complement levels between control and case, and mild case and severe case had different results, which is affected by the immune response, please add the data regarding blood neutrophils so the analysis can be carried out well. This manuscript provides supporting data regarding the profiles of complement C in healthy subjects and COVID-19 patients with different severity of illnesses. Some information needs to be clarified or confirmed to improve the quality of the manuscript. Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Pharmaceutical SciencesI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. This manuscript includes a case-control study that attempted to correlate complement C4 levels with COVID-19 infection pathogenesis and outcome. The complement C4 level in the case group was lower than in the controls. The manuscript material could be viewed as additional support for the previous research highlighting the role of complement in the pathophysiology of COVID-19.The introduction is excessively brief and lacks critical information about the correlation between complement and COVID-19 pathogenesis, with only one paragraph detailing this crucial part. As a result, authors should cite some recent papers to validate their findings.Section of the methodology: 1. How and at what power was the sample size estimated? 2. The optimal Cases to Control ratio should be 1:4, but the authors used 1:1 why? 3. Why were patients with immunological dysfunction or malignant neoplasm excluded from the study? Inclusion and exclusion criteria section? It should be clearer or deleted.Results: This section is well-written, and the statistical analysis and interpretation are precise.In the discussion part, ''Prior clinical research has demonstrated that, whereas males and females share a comparable sickness rate, males are more likely to be hospitalized, experience a more severe illness progression, and die at a higher rate than females'' - is this comment a criticism or an interpretation?Conclusions: Well written and accurately reflects the author's findings. Despite the fact that the manuscript is nicely written, and adds to the current literature, I have some comments and questions that should be addressed by the author:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:MicrobiologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thanks you for your time to review our manuscript In the introduction part 1. Regarding the introduction part we will make the required modifications which will appear in the second version of the manuscript. In the methodology part 2. Regarding the sample size I would like to inform you that we used a total coverage sampling technique therefore we involved all COVID-19 patients in specific time manner and we use 1:1 case to control ratio because this is the least ratio that can be applied for sampling the case control study population and it's acceptable in many previous investigations. 3. patients with immune dysfunction or malignant neoplasm were excluded be uses such disorders will affect the immune Status of cases and controls and consequently will affect on the complement level including C4 and according this will  lead to bias in the results therefore we excluded them from the cases and controls. 5. in the discussion part Prior clinical research has demonstrated that, whereas males and females share a comparable sickness rate, males are more likely to be hospitalised, experience a more severe illness progression, and die at a higher rate than females. This section is interpretative part. I think this article is an important addition to the literature, because the complement is a vital component of innate immunity where it forms the first line of defense against potentially harmful microbes, but its role in COVID-19 is still not clear. In this article, the results showed that case group had a higher level of complement C4 than the control group, which means that the complement plays an important role in the severity of disease, and the C4 complement should be considered as a diagnostic marker. I hope the authors intend to evaluate level of anti-pro inflammatory cytokine, such as IL-37 and IL-38, soon in severe and non-severe COVID-19 cases.Are the authors discussing the role of complement in pathogenesis of COVID-19 patients or in immune system? Clear this point.Indicate the aim of this paper more clearly and the conclusion also needs improvement. I have carefully read this manuscript and below are my comments:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Clinical immunology, COVID-19I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Dear Dr. Adnan Mezher Maysam Thank you for your time to review our mansucript.  We carefully read your comments and this is our reply please track in a manner that comment followed by the authors response  Comment 1: I hope the authors intend to evaluate level of anti-pro inflammatory cytokine, such as IL-37 and IL-38, soon in severe and non-severe COVID-19 cases. Authors response: Thank you for your comment and we will consider it in our near future work to understand the role of these anti - pro inflammatory cytokine in the COVID-19 infection progression  Comment 2  Are the authors discussing the role of complement in pathogenesis of COVID-19 patients or in immune system? Clear this point Authors response: Actually this manuscript is discussing the role of complement C4 in the progression of COVID 19 infection and it was based on the evidence that complements are more likely to be consumed among severly ill COVID-19 patient due to development of complication like DIC  so the manuscript is more related to pathogenesis of COVID-19 based on immunological activity.  Comment 3 Indicate the aim of this paper more clearly and the conclusion also needs improvement. Authors response: Thank you for your comment we will carefully consider it in the revised version of the manuscript  Again thank you for your time and efforts


Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management. This study shows that climate change will alter the sea surface temperature - precipitation relationships and our ability to predict seasonal precipitation by 2100. It also structures socio-economic systems through influence on agricultural production4 and water resources5. Over the past several decades as a result of improvements in earth system models (ESMs) and the availability of high-quality climate observations, seasonal prediction of precipitation6 has become an increasingly important tool managers use to improve water and food security10 and support ecological restoration11. The predictability of precipitation at seasonal timescales is typically associated with large-scale variability of sea surface temperature (SST)13, a principal forcing driver of global atmospheric circulation. A number of studies, for example, have identified that the patterns of SST anomalies occurring over the Indian Ocean influence seasonal precipitation variability in Australia15, Africa19, and parts of Asia22. Similarly, precipitation anomalies in Amazonia are found to be associated with Atlantic and Pacific SST anomalies in the tropics25. The patterns of SST anomalies and SST-derived indices in the Pacific are also used for predicting winter precipitation over southern regions of the United States29.Precipitation is a critical component of the hydrologic cycle and plays a significant role in shaping the biodiversity of terrestrial ecosystems31. For instance, recent studies have shown that the variability of El Niño-Southern Oscillation (ENSO)-driven precipitation is likely to be enhanced over the central-eastern Pacific owing to surface warming, even though change in the strength of ENSO-related SST variability remains uncertain34. In addition, wintertime ENSO teleconnections to Pacific North America are found to change more strongly and consistently in models where the ENSO amplitude increases with future climate warming36. Precipitation-related teleconnections of ENSO also exhibit considerable changes by the end of the 21st century in other regions, including Australia, central Africa, and the west coast of South America36. While past work has evaluated how climate modes and related teleconnections may evolve with future climate, little is known about the consequences of these changes for seasonal precipitation predictability.Multiple lines of evidence indicate that modes of climate variability and associated teleconnections may change by the end of the 21st century38; however, it is typically estimated by the predictive skill of a predictive model . Here, seasonal precipitation predictability at each location during each of the four seasons  is assessed by the predictive skill of the best linear predictive model using as predictors the leading modes of global SSTs in the 3-month preceding season. We quantify changes in seasonal precipitation predictability as the difference between future (2049–2099) and historical (1964–2014) prediction skill using simulations from ESMs that contributed to the 6th Phase of the Coupled Model Intercomparison Project . We provide evidence that ESMs exhibit consensus regarding future changes in the spatial patterns of seasonal precipitation predictability. These changes are likely to have significant effects on sustainable water resources management, especially in regions where limited water availability requires apportionment between agriculture and natural ecosystems .In this study, we examine future changes of seasonal precipitation predictability over global land in response to climate change during the 21st century. Predictability, being an intrinsic property of a system and the theoretical upper limit to prediction, cannot itself be evaluated39 (NSE) as a skill metric (see “Methods” section). Second, we select the CMIP6 models40 that best capture the spatial patterns of the predictive skill in observations during the historical period and then use these models to project future changes of seasonal precipitation predictability for shared socio-economic pathways SSP2-4.5 (middle-of-the-road development) and SSP3-7.0 . SSP2-4.5 represents an intermediate scenario of future emissions and is closest to the current CO2 emission track43. In contrast, SSP3-7.0 represents medium-to-high scenario of emissions and has a higher signal-to-noise ratio than moderate emission scenarios, making it easier to discern the climate change signal from internal variability44.In our analysis, we first analyze the spatial patterns of seasonal precipitation predictability in observations and identify the main sources of predictability during the historical period. In particular, we use empirical orthogonal function (EOF) analysis to extract the principal spatial variability patterns (EOFs) and principal component time series (PCs) from global seasonal SST anomalies and apply multiple linear regression of one-season lagged precipitation anomaly series onto these PCs to develop predictive models of seasonal precipitation. We examine a combination of PCs as predictors and identify the best predictive model using the Nash-Sutcliffe Efficiency45. This low precipitation threshold was applied to avoid developing predictive models in areas with well-defined dry seasons and in desert areas where measurement uncertainties are higher. We compare in Fig. n-leading PCs (n-leading-PCs” models) in all grid cells. For this set of models, we observe an overfitting effect46 when more than 2 PCs are used, as revealed by a reduced NSE computed from an out-of-sample 5-fold cross validation, except during boreal summer ( JJA) where the best performance is achieved with 3 PCs. The second class of predictive models used as predictors only the subset of any of the 4 PCs of SSTs that provided the highest level of model performance for each grid cell  was computed at each 1°×1° grid cell of the land surface, excluding arid areas whose long-term mean total seasonal precipitation was <50 mm in each three month seasonOur analysis of the observations reveals strong seasonal variations in the degree of precipitation predictability, providing a useful benchmark for evaluating CMIP6 models. Boreal winter (DJF) precipitation is most predictable with about 58% of land surface area showing NSE > 0 and a larger fraction of land area showing predictability at or above any other NSE value, compared to precipitation in other seasons Fig. . In cont24. Relatively high values of seasonal precipitation predictability are also found in the northwestern and southern parts of the United States during boreal winter where precipitation is known to have teleconnections with Pacific SSTs28. In contrast, northern Australia has the highest predictability during boreal autumn  pattern, which has an ENSO character but is stronger at high northern latitudes and weaker in the tropics 53 patterns, respectively  contributes as a predictor of seasonal precipitation in over 42% of the global land with predictability above climatology  and plays an even more dominant role in places with higher levels of predictability, including northeastern Amazonia, southern United States, southern Africa, and the Maritime Continent. This pattern of influence is consistent with past analysessee Fig. .To evaluate future changes in seasonal precipitation predictability, we first identify from 26 ESMs  during boreal winter and boreal spring. The proportion of models that show an increase (By the end of the century for the medium-to-high emission SSP3-7.0) scenario, the selected CMIP6 models project several consistent continental-scale changes in seasonal precipitation predictability Fig. . In part.0 scenar59.We hypothesize that changing SST variability and total seasonal precipitation variability over land contribute to the alteration in seasonal precipitation predictability during the latter half of the 21st century. To investigate this hypothesis, we examined changes in SST and precipitation variability between the future (SSP3-7.0 scenario) and historical periods. For SST, depending on the season, an average of 7 of 10 selected climate models (70%) show an increase in both the total variance explained by the first PC  is projected to decline, interannual variability is projected to increase, and seasonal predictability is expected to decline, increasing the risk of fire ignition in tropical forest ecosystems, the likelihood that fires will grow to larger sizes70 or climate network analysis that can depict causality72.In contrast, the increase of predictability in central and northern Africa and across the Maritime Continent could be attributed to the increase of ENSO SST variability and changes in the tropical SST-precipitation relationship, and may strengthen resilience with respect to peat and ecosystem management. Further evaluation of the mechanisms responsible for the projected changes in precipitation predictability identified here will require use of idealized model experiments70 to decrease under global warming. PPP was defined based on the multiscale dynamics of precipitation alone and not considering changes in the sources of predictability  or atmospheric teleconnections under warming. SST persistence or ocean memory, a major source of predictability in the climate system, is projected to increase at week-to-week73 but decrease at year-to-year74 time scales. These opposite trends in SST persistence are likely to have different effects on precipitation variability at different time scales and on the lead times at which SST predicts precipitation. While these findings have implications for changes in persistence-based predictions of sea surface thermal conditions at corresponding timescales, the effect of changes in ocean memory on the relationships between SST and (lagged) precipitation at a seasonal timescale remains unknown and is an important topic for future study. Further examination of the changes in the persistence of SST at seasonal time scale may be helpful for understanding the mechanisms responsible for future changes in seasonal precipitation predictability.We note that seasonal precipitation predictability, as defined here based on the predictive skill of the best linear model that uses as predictors previous-season SSTs, cannot be directly compared to the “potential precipitation predictability” (PPP) concept, which was reported in a recent study76. In this study, we examine projected future changes of seasonal precipitation predictability from previous-season SSTs caused by anthropogenic forcing of the climate system. We show that climate warming may increase the interannual variability of SSTs and precipitation leading to changes in seasonal precipitation predictability in both the tropics and extra-tropics. Precipitation predictability is projected to increase in the tropics throughout most of the year except in northern South America. In the extra-tropics, precipitation predictability is likely to increase in central Asia during wintertime.Climate warming is expected to modify primary climate modes of variability across scales and also their teleconnections with precipitation over landChanges in seasonal precipitation amounts and their interannual variability under global warming pose acute challenges for regional water resources management. These challenges will be further amplified by a decrease in our ability to perform seasonal forecasts with several months lead time, especially in “hotspots” where precipitation is critical for agriculture and ecosystems or areas where base-state precipitation is very high or low. Collectively, the results from our work identify several high-risk regions and highlight the importance of understanding the causal SST-seasonal precipitation relationships in state-of-the art climate models for improving seasonal prediction of precipitation, a necessary condition for sustainable management of water resources during a period of rapid climate change.Seasonal average sea surface temperature (SST) at 1° × 1° spatial resolution was derived from the COBE-SST2 datasets. Seasonal total precipitation over land was derived from two data sets: the Global Precipitation Climatology Project  and Global Precipitation Climatology Center . The GPCP integrates analyses from satellite and gauge measurements but is only available since 1979. The GPCC is based on gauge analysis only and available since 1948. We used GPCP as the main precipitation dataset and combined GPCP (bilinearly interpolated to 1° × 1° resolution) and GPCC to extract observations of seasonal total precipitation (at 1° × 1° resolution) over land from 1964 to 2014, resulting in 50 years for each season. Specifically, at each location and season, using the overlapping period 1979–2014, the differences in the mean and variability of the two datasets were evaluated. A linear adjustment was then applied to GPCC data for the period 1964–1979 to correct for the potential differences of the two datasets before combining with GPCP.Seasonal anomalies of precipitation and SST referenced to the climatological mean of each season were constructed and detrended prior to analyses. Detrending was performed by subtracting a centered moving average from the original anomaly time series, where the centered moving average was calculated over a time window of length 78. For the SSP3-7.0 experiment, all simulations were prescribed with the same future emissions and land use changes that follow the regional rivalry SSP3 pathway with a global mean forcing of 7.0 Wm−2 by 2100 relative to the pre-industrial period80. Similarly, for SSP2-4.5, all the simulations followed the SSP2 pathway (middle of the road development) with a global mean forcing of 4.5 Wm−2 by 2100 relative to the pre-industrial period. All model outputs and observations were resampled to a common 1° × 1° spatial resolution by using bilinear interpolation. Similar to the observations, seasonal anomalies of each field referenced to the climatological mean of each season were constructed and detrended using the moving average method prior to all analyses. Some climate models do not reasonably capture the observed patterns of seasonal precipitation predictability  isolate dominant spatial modes of variability (EOFs) and (ii) reduce the precipitation predictors to only a smaller number of principal component (PC) time series. The spatial patterns of EOFs and the percent variance explained as extracted from the observations of SST are presented in Supplementary Fig. We performed an empirical orthogonal function (EOF) analysis46 and ensure a rigorous assessment. The regression model was then evaluated using the remaining out-of-sample data. Overall, the best-2-PCs predictor model exhibited the highest predictability score compared to other combinations of PCs. These best 2 predictors  were identified as the two primary sources of predictability at each grid cell and Fig. To examine seasonal precipitation predictability, we constructed the best multiple linear regression model between seasonal precipitation and the leading modes of global SSTs in the previous season. Specifically, the predictands mentclass2pt{minimFor each CMIP6 model, the above analysis was repeated independently for each ensemble member to get the model average (and uncertainty if multiple ensemble members were available), and the best-2-predictor model was applied independently across experiments for the historical and future periods.The Nash-Sutcliffe Efficiency (NSE) was used to evaluate the predictive skill of each fitted model and served as a measure of predictability (predictability score). For a particular season and at a specific grid point on the land surface, the NSE is defined as:To estimate the 95% confidence level that NSE is not significant shown in Fig. For a climate model M = 10). Note that, despite differences in the number of ensemble members The above equation implies that the climatology prediction is used if the best 2-PC linear model is worse than climatology. The multi-model ensemble (MME) mean  of the change of predictability is calculated as:To select climate models for assessing future changes in seasonal precipitation predictability, we examined the ability of each model in the historical period to reproduce the seasonal precipitation predictability in the observations. In particular, for a model We ranked models according to their The differences in seasonal total precipitation and EOFs between the historical and SSP3-7.0 (or SSP2-4.5) runs defined the future changes in seasonal total precipitation and EOFs, respectively.oS–5oN, 170oW–120oW) for each season. In Supplementary Fig. Detrended time series of seasonal SST anomalies were averaged over the region of Niño3.4 (5Supplementary Information


This study examines the relationship between the early identification of hearing loss and language outcomes for deaf/hard of hearing (D/HH) children, with bilateral or unilateral hearing loss and with or without additional disabilities. It was hypothesized that hearing loss identified by 3 months of age would be associated with better language outcomes. Using a prospective, longitudinal design, 86 families completed developmental instruments at two time points: at an average age of 14.8 months and an average age of 32.1 months. Multiple regression examined how hearing loss identified by 3 months of age contributed to later language outcomes while controlling for developmental level at the first time point. Hearing loss identified by 3 months of age was positively associated with better language outcomes for D/HH children at 32 months of age; however, D/HH children still exhibited language delays, compared to normative scores for same-aged hearing peers for reported measures. Language outcomes of children with unilateral hearing loss were not better than those of children with mild-to-moderate bilateral hearing loss. Children with additional disabilities and more severe bilateral hearing loss had lower language scores than those without. Before The Joint Committee on Infant Hearing (JCIH) published a 2019 position statement emphasizing early hearing screening (by 1 month of age), evaluation (by 3 months of age), and intervention (by 6 months of age) for all children . SeveralThe purpose of this paper is to examine the relationship between timing of hearing loss identification and language development for children with permanent hearing loss in Wisconsin who were recruited as part of a larger prospective, longitudinal study of developmental outcomes for D/HH children. Identity-first and person-first language are used throughout this paper to reflect terms preferred by both deaf individuals and individuals with hearing loss. To expand on previous research, this study included children with either unilateral or bilateral hearing loss, as well as participants with and without additional disabilities. Other co-variates were examined, such as degree of hearing loss and maternal level of education.2.2.1.In 2008, a partnership between the Wisconsin EHDI program and researchers at the University of Wisconsin-Madison and the University of Colorado-Boulder created the Assessment of Early Intervention Outcomes (AEIOu) study. UW-Madison was the Institutional Review Board (IRB) of record. Recruitment began when children were around 12 months of age. Initially, all D/HH children who were enrolled before 12 months of age in the Wisconsin Part C early intervention program, called the Birth to 3 Program, were eligible to participate in the study. After 2013, inclusion criteria were expanded to include all children reported to the WI EHDI program with a diagnosis of hearing loss regardless of enrollment in the Birth to 3 Program. Exclusion criteria included temporary hearing loss . A study team member contacted eligible families by phone around the time the child was 12 months of age and provided information about the AEIOu study. If the family agreed to participate, they were mailed both the study consent and developmental questionnaires. Because each participant was to be assessed at 2 time points , several strategies were used to reduce attrition and improve timely return of the completed forms. First, 3 weeks after parents were sent the forms, a phone and/or email reminder was sent. If the evaluation forms were not returned, up to 3 contact attempts were made, with the last contact attempt approximately 1 month before the participant was close to aging out of the target age ranges.2.2.The AEIOu study protocol was designed collaboratively with the CDC-funded National Early Childhood Assessment Project (NECAP) at the University of Colorado-Boulder , which aAt both Phase 1 and Phase 2, families were asked to complete demographic and developmental questionnaires. Demographic information included the child’s sex, age, hearing device used, communication method at home, and co-occurring conditions. Maternal demographic information collected included race, ethnicity, presence of parental hearing loss, and parent rating on the effect of co-occurring conditions on the child’s speech and/or language development.Results from two norm-referenced developmental instruments were analyzed for this paper, the MacArthur-Bates Communicative Development Inventories  and the Developmental forms were scored at the University of Colorado-Boulder by trained undergraduate students, checked for accuracy by a second scorer, and errors were corrected by consensus.Quotient scores were calculated using the following equation:A quotient of 100 indicates a child’s developmental age is exactly commensurate with their chronological age. Quotients below 80 are considered to indicate a clinically significant delay, relative to other children of the same age. Additionally, the manual for the CDI indicates that scores between 70–80 are suggestive of borderline delay .2.3.Data collection occurred over ten years (2009–2019) with a total of 118 children completing Phase 1 and 86 of those children completing Phase 2. This study focuses on the 86 children for whom both Phase 1 and Phase 2 data were available. Although there was some attrition due to losing contact with families or families choosing not to complete Phase 2, children completing Phase 2 did not differ from the group completing Phase 1 on important study group characteristics, such as communication method used at home, hearing status of parents, maternal level of education, types of childhood comorbidities, and degree of hearing loss.Demographic information for the study cohort of 86 children, as reported at Phase 2, is shown in Self-identified race in this study cohort was 90% White, and no families reported Hispanic ethnicity. Mothers reported a relatively high level of maternal education . The pre2.4.p > 0.05) for children in these two groups. The only variables that were significantly different between the two groups were age of diagnosis, age of receiving hearing amplification, and age of receiving early intervention, as shown in To examine whether meeting the EHDI recommendation for identification of hearing loss by 3 months of age influenced language development, children were considered to have “met” the 3 months diagnostic recommendation if their hearing loss was identified before 3.5 months of age. Sixty-two children 72%) met this criterion. This categorical variable (“met” or “not met”) was used as one of the predictors for the analyses. % met thiOutcome variables used in the regression model were the expressive vocabulary quotient from the MacArthur Bates Communicative Development Inventories  (Mac_LQ)To examine the impact of co-occurring conditions , 2 groups were created for analysis using parent-reported impact of co-occurring conditions on speech/language development. The first group was composed of children with no additional co-occurring conditions or who have a condition that parents reported as having no impact on speech/language development. The second group was composed of children with additional conditions that parents reported had a mild-to-significant impact on speech/language development.The transition regression model  was usedAs a follow up to the previous analyses, we applied hierarchical regression to determine if the age of intervention and the age of amplification could contribute significantly to the variation in developmental outcomes when children’s age of hearing diagnosis and other covariates were considered. Hierarchical regression is commonly used to evaluate the contributions of a variable above and beyond previously entered variables . The ord3.The subscale means and SDs are shown in f2 ≥ 0.02) [p values were less than or close to 0.05 after controlling for the other covariates. This model also demonstrates that the developmental quotient at Phase 1 was highly correlated with the same quotient at Phase 2 (p < 0.001) for most developmental quotients. After controlling for the developmental score at Phase 1, as well as the other covariates in the analysis model, hearing loss identification by 3 months of age was still significantly related to better outcomes at Phase 2. There were no interactions between identification by the age of 3 months and any of the other covariates, indicating identification of hearing loss by the age of 3 months was independently related to outcomes measured.As seen in  ≥ 0.02)  for assoThe identification of hearing loss must come before intervention for hearing loss can be initiated. Utilizing a hierarchical regression method, neither the age of intervention nor the age of amplification had a significant relationship with developmental quotients at Phase 2 when the identification of hearing loss by age 3 months and covariates were controlled (entered in the model first). The following analyses focused only on the models using hearing loss identified by age 3 months.Of additional interest is that laterality of hearing loss  did not have a separate relationship with these developmental quotients. However, having a moderately severe-to-profound bilateral hearing loss was associated with lower developmental quotients for the four outcomes shown on Predictive mean values at Phase 2 were computed to visualize the longitudinal trajectory when hearing loss was identified before or after 3 months using the four developmental quotients in 4.Analyses reported here consistently demonstrate that hearing loss identified by 3 months of age is associated with better developmental outcomes, at least up to an average of 32 months of age, as measured by the MacArthur vocabulary quotient (Mac_LQ) and three subscales of the CDI  shown in Children with additional disabilities and also those with moderately severe-to-profound bilateral hearing loss, predictably had more significant developmental delays than those who had lesser degrees of hearing loss independent of age of identification of hearing loss . There wAlmost all children were enrolled in intervention at some point in the study, since this was initially one of the criteria for study participation. The hierarchical regression indicated that the general age of enrollment in intervention services  did not have a significant unique contribution to the longitudinal language outcomes in this cohort when the age of early hearing loss identification was controlled. This finding may relate to the heterogeneity of interventions that participants received, which were not exclusive to language. For example, fewer than half of the children in this cohort received speech-language pathology services at either Phase 1 (45%) or Phase 2 (49%). The timing of audiological and speech and language intervention ,23, fittp < 0.05) associations with the CDI language quotient (Rec_LQ), CDI social skills quotient (Soc_Q), and CDI general development quotient (Gen_Q) but not for the MacArthur vocabulary quotient (Mac_LQ) . Further research is needed with culturally and linguistically diverse groups to examine associations between early identification of hearing loss and language outcomes.


The occurrence of obstructive and central sleep apnea syndromes, ventilator pump failure and reduced hypercapnic ventilatory drive in myotonic dystrophy type 1 (DM1) is well established, and there are indications for an impairment of the hypoxic ventilator drive, too. Yet, it is still unknown, to which extent the respiratory rhythm is affected by DM1, thus if a central bradypnea, cluster breathing or ataxic (“Biot's”) breathing can occur. Additionally, the causes of the impairment of the central respiratory drive in DM1 are not known.We present the case of a tracheotomized female patient with DM1 with central bradypnea and ataxic breathing. A 57-year-old woman with DM1 was admitted to our Neurointensive Care Unit (NICU) due to refractory tracheobronchial retention of secretions resulting from aspiration of saliva. Due to a combination of chronic hypercapnic respiratory failure, severe central bradypnea with a minimal breathing frequency of 3 per minute and ataxic breathing a pressure-controlled home ventilation was initiated.In our patient central bradypnea and ataxic breathing possibly were respiratory sequale of DM1, that may have been caused by pontine white matter lesions affecting the pontine respiratory nuclei. From a clinical viewpoint, polygraphy is a suitable tool to objectify disorders of the respiratory rhythm in DM1 even in tracheotomized patients. Clinical studies combining respiratory diagnostics as polygraphy, transcutaneous capnometry and blood gas analysis with brain magnetic resonance imaging (MRI) are required to better understand disorders of respiratory regulation in DM1, and to identify their anatomical correlates. CTG=cytosine-thymine-guanineDM1=myotonic dystrophy type 1FEES=fiberoptic endoscopic evaluation ofswallowingMI-E =mechanical insufflator-exsufflatorMRI=magnetic resonance imagingNICU=neurological intensive carePEG=percutaneous endoscopic gastrostomyMyotonic dystrophy type 1 (DM1) is an autosomal-dominant genetic disorder due to an expansion of a cytosine-thymine-guanine (CTG) repeat in the dystrophia myotonica protein kinase gene of chromosome 19q13.3 . NeuropaThe clinical phenotype is variable, generally associated with the length of the number of CTG-repeats and progresses from generation to generation . Cases wWe present a case of a tracheotomized female patient with genetically confirmed DM1 with central bradypnea as well as alveolar hypoventilation. Furthermore, we analyze and discuss polygraphic data and imaging data with regard to respiratory regulation. This clinical case report is conducted in accordance with the case report guidelines that were established by Gagnier et al. .A 57-year-old obese  and tetraparetic woman was referred from a regional hospital due to insufficient cough and refractory tracheobronchial retention of secretions. She was under treatment in our Neurointensive Care Unit (NICU) between May 2020 and September 2020, where we confirmed the genetic diagnosis of DM 1 with a CTG-repeat-expansion of > 500 to 800 copies. Before admittance she lived in a nursing home and was using a wheeled walking frame and a wheelchair. Her father also lived with DM1. Her two children had died within 24 hours after birth. The participant provided an informed consent to publish health data including polygraphic and imaging data.The diagnostic standard in patients admitted to our NICU with insufficient cough, tracheobronchial retention of secretions and imminent respiratory failure comprises blood gas analysis and fiberoptic endoscopic evaluation of swallowing (FEES). The x-ray of the chest revealed a reduced depth of inspiration but did not reveal any evidence for an emminent pneumonia, emphysema, atelectasis or restrictive components. In the previous clinic a cerebral magnetic resonance imaging (MRI)  was performed to identify brain stem lesions. This was conducted because an acquired central hypoventilation syndrome was suspected due to hypercapnic respiratory failure without tachypnea or dyspnea. The imaging of the central nervous system was supplemented by MRI of the upper cervical spine. Polygraphy was used to analyze the respiratory rhythm as apnea or desaturations are present.The electroencephalography revealed a background activity of 8 Hz without findings of epilepsy related potentials and no focal findings but an underlying theta activity that was present for more than 50% of the revulsion and was attributed to the mild hypercapnic state of the patient. After initiation of mechanical ventilation the vigilance was elevated, due to lack of consequences the EEG was not repeated.Electromyographic and electroneurographic investigation were in consistency with the diagnosis of a DM1.In the neurological examination the patient presented with anisocoria (left > right), but regular reaction of the pupil, and dysarthria. There was no sign of meningism, there were no additional pathologies seen in the investigation of the cranial nerves. A motor weakness was seen pronounced in the upper vs. the lower limb (grade 3 vs grade 4). Muscle reflexes presented symmetrical, weak. She demonstrated with a poor performance in all motor tasks . There was no sign of sensory deficit. She was able to manage short distances by usage of a 4-wheeled walker, otherwise dependent from a wheel-chair.Hypercapnic ventilatory failure with respiratory acidosis and metabolic compensation was present on admission: The blood gas analysis on admission showed a pH of 7,36, a pCO2 of 50 mmHg, a pO2 of 63 mmhg and a base excess of 1,7 mmol/l. The Saturation was measured at 98% as a first measurement on admission under nasal supplementation of 2 liters of oxygen per minute. One day later we found a pH of 7,40, a pCO2 of 56 mmHg, a pO2 of 68 mmhg and a base excess of 7,7 mmol/l.The patient was pretreated in another hospital with trimethoprim/sulfamethoxazole (1:5) and did not show signs of acute infection on admission to our hospital. The laboratory testing did reveal a slightly elevated leucocyte count (11.5 /nl) with a procalcitonin of 0.12 ng/ml and CRP of 11.7 mg/dl. Antibiotic treatment was changed to piperacilline /tazobactame for consecutive 8 days with and adequate normalization of laboratory markers indicative for infection. The microbiological screening did not help to identify any bacteria from catheters or secretions at this stage, however after tracheostomy pseudomonas aeruginosa was identified from the endotracheal secretion.Severe dysphagia with aspiration of saliva was identified by FEES. An MRI from the referring hospital already had shown subcortical and pontine signal alterations . PolygraWe found no competing factors leading to bradypnea, the thyroid and the regulating hormones were checked normal, there was an euglycaemia, no opioids were given in timely context of the polygraphy, no other drugs with potential impact on the breathing pattern were applied. Drugs applied prior to admittance to our hospital comprised trimethoprim/sulfamethoxazole (1:5) due to a pre-diagnosed pneumonia, apixaban due to the history of a pulmonary embolism, foliac acid due to a deficit, dorzolamide/timolol due to glaucoma, she received no sedatives. In order to exclude autoimmune or paraneoplasic involvement we performed serologic screening which came back negative for all screened antibodies, the rheumatological workup and additional laboratory testings was also negative . A lumbar puncture was not performed as the clinical findings did correspond to the patients’findings.The patient was electively tracheotomized one day after admission to our NICU due to salivary aspiration, tracheobronchial retention of secretions and threatening aspiration pneumonia. In addition, the patient was –according to the German guidelines for home ventilation  concerniDuring the stay in the NICU, the patient received neurological-neurosurgical early rehabilitation by an interdisciplinary team of doctors, nurses, respiratory therapists, speech- and language therapists, occupational therapists and other disciplines. One of the therapeutic strategies was to mobilize the patient in a positioning wheelchair.The invasive ventilation resulted in a stable clinical situation with adequate communication and without daytime sleepiness. At the beginning, the patient was mobilizable for a maximum of 30 minutes per day. At discharge, she was independently mobile with an assistant on a high walker and with her power wheelchair. Moreover, the patient was still fed by PEG, but she was able to eat once a day under supervision. She had also been provided with assisted communication but preferred to speak with a speaking valve.DM1 is a highly complex disorder, not only affecting the respiratory muscles, but also the regulation of breathing. As stated above the occurrence of neuromuscular respiratory failure, obstructive and central sleep apnea syndromes and an impairment of the hypercapnic respiratory drive in DM1 patients is already well established. Only one study has examined the hypoxic and the hypercapnic ventilator response in seven patients with DM1 and found the hypoxic ventilator response more consistently reduced than the hypercapnic ventilator response .Our case with severe central bradypnea and ataxic breathing in DM1 is to the best of our knowledge the first such case to be found in the scientific literature. Polygraphy was used as it comprises measurements not included in a standard NICU monitoring and is able to generate an output of data validated for the diagnosis of breathing irregularities. Despite the extremely low breathing frequency observed during monitoring and polygraphy hypercapnic coma did not result. Preserved residual breathing rhythmicity, intermittent higher breathing frequencies and possibly elevated tidal volumes might have contributed to sustain decarboxylation up to a degree sufficient for survival .Nonetheless, our case indicates that impairment of respiratory regulation in DM1 comprises not only reduced ventilatory responses to O2 and CO2 and central apnea, but also a wider spectrum of disorders of the respiratory rhythm. The causes of the impairment of central respiratory regulation in DM1 are as far as we know still unknown. The occurrence of white matter lesions at various cerebral locations has been well established in DM1 . While vNeuroanatomic and physiological studies have over the last decades well established the role of medullary and pontine regions in the control of breathing mainly in animal models, but also in humans. The Nucleus Kölliker-Fuse and the parabrachial-Komplex (KF-PB) in the dorsolataeral pons are the main pontine regions of interest, and they interact with the medullary centers of respiratory rhythmogenesis . There iTherefore, regarding our patient a direct link with DM1 causing pontine white matter lesions, which again causes impairment of the respiratory rhythm can be hypothesized. Alternatively, the pontine MRI-signal alteration is not caused by DM1. However, the impairment of the respiratory rhythm in our patient is either caused by DM1 directly or by her pontine lesions.A follow-up screening was not conducted. In addition, our case report does not include a statement by the patient about her experience and perspective. This perspective is important as additional needs can be identified and addressed. An interview study showed that patients with DM1 were satisfied with the support concerning their physical symptoms but they wished to receive more psychological an emotional support for them and their partners . We wereDM1 can cause ventilator pump failure, obstructive and central sleep apnea, acquired central hypoventilation and possibly central bradypnea and ataxic breathing. From a clinical viewpoint, polygraphy is a suitable tool to objectify disorders of the respiratory rhythm in DM1 even in tracheotomized patients. Mechanical ventilation during sleep may be the best therapeutic option to reverse daytime sleepiness. DM1 might also result in unexpected central neurological deficits caused by white matter lesions.Clinical studies combining respiratory diagnostics as polygraphy, transcutaneous capnometry and blood gas analysis with brain MRI are required to better understand the complexity of disorders of the respiratory regulation and to identify their anatomical correlates in DM1.We hereby confirm that the present study conforms to the ethical standards and guidelines of the journal.All authors have participated in the article preparation and agreed the contents of the submission.The authors have no conflict of interest to report.


What are the opportunities for accountable care organizations (ACOs) and other risk-bearing organizations operating in a specific geographic area to achieve savings by steering patients to efficient medical oncology practices in a region?This cohort study of 2 739 798 patients found that opportunities for steering varied across hospital referral regions (HRRs); the top quartile HRR in number of practices had 10 or more practices, but the bottom quartile had 3 or fewer oncology practices. Overall, total spending in the highest spending quartile of practices in a market was 19% higher than in the lowest quartile, and practice-level spending tendencies were persistent over time.These results suggest that there may be opportunities for ACOs and other risk-bearing organizations  to select or drive referrals to lower-spending oncology practices in many local markets. This cohort study examines opportunities for accountable care organizations and other risk-bearing organizations to achieve savings by steering patients to efficient medical oncology practices operating in specific geographic areas. As the US accelerates adoption of alternative payment through global payment models such as Accountable Care Organizations (ACOs) or Medicare Advantage (MA), high spending for cancer care is a potential target for savings.To quantify the extent to which ACOs and other risk-bearing organizations operating in a specific geographic area  could achieve savings by steering patients to efficient medical oncology practices.This observational study included serial cross-sections of Medicare beneficiaries with cancer from 2010 to 2018. Data were analyzed from August 2021 to March 2023.Total spending and spending by category in the 1-year period following an index visit for a patient with newly diagnosed (incident) or poor-prognosis cancer.The incident cohort included 1 309 825 patients with a mean age of 74.0 years; the most common cancer types were breast (21.4%), lung (16.7%), and colorectal cancer (10.0%). The poor prognosis cohort included 1 429 973 ; the most common cancer types were lung (26.6%), lymphoma (11.7%), and leukemia (7.3%). Options for steering varied across markets; the top quartile market had 10 or more oncology practices, but the bottom quartile had 3 or fewer oncology practices. Total spending (including Medicare Part D) in the incident cohort increased from a mean of $57 314 in 2009 to 2010 to $66 028 in 2016 to 2017. Within markets, total spending for practices in the highest spending quartile was 19% higher than in the lowest quartile. Hospital spending was the single largest component of spending in both time periods  followed by Part B (infused) chemotherapy ($8022 and $11 699). Correlations in practice-level spending between the first-year (2009) and second-year (2010) spending were high , but these attenuated over time.These results suggest there may be opportunities for ACOs and other risk-bearing organizations to select or drive referrals to lower-spending oncology practices in many local markets. In 2016, the Centers for Medicare & Medicaid Services (CMS) initiated the Oncology Care Model,13 These studies suggest there may be opportunities for savings under global payment due to improved care management, including potential savings related to selectively steering patients to low-spending oncology practices whether through selective contracting or other mechanisms. Organizations accepting risk for the costs of care such as an ACO or MA plan operate in specific geographic areas; however, the relevant unit to identify high-performing groups is within these geographic markets rather than across them.Several prior analyses have found substantial variability in spending on care for patients with cancer across geographic regions, with differences primarily in the use of chemotherapy and hospitals.14 Opportunities to save from steering patients to specific practices are stronger if there is greater variation in spending, it persists over time, and is consistent across categories of spending where variation is more likely to reflect variation in practice patterns as opposed to selection. We first sought to quantify the extent to which risk-bearing organizations could achieve savings by selecting efficient clinical practices in that market. In addition, we sought to examine the extent to which oncology practices’ relative spending was consistent over time. As a secondary outcome, we examined variation in subcategories of cancer spending in 2 different ways to gauge potential savings. First, we estimated variation in spending in dollars, which we expected to be higher when mean spending was higher and when practice patterns varied. Second, we calculated coefficients of variation to identify categories of spending in which spending varied more relative to its mean. The latter may suggest priority areas to focus on for oncologists and practice managers seeking to improve the value of cancer care in the context of local health care practice patterns.We used data on the treatment of patients with cancer for beneficiaries enrolled in fee-for-service Medicare to examine the spending patterns of oncology practices operating in regional health care markets where patients could conceivably obtain specialty care from any clinician in the market.International Classification of Diseases, Ninth Revision (ICD-9) or ICD-10 diagnosis code for cancer , which was necessary to identify oral chemotherapy. Cases were identified based on having at least 1 inpatient visit with a primary diagnosis or 2 face-to-face outpatient visits (≥30 days apart) with an From these cases, we identified 2 study cohorts. The incident cohort included all patients with a new diagnosis of cancer during a study year (2009-2017) who were continuously enrolled in Medicare Part A and Part B with no Medicare Advantage enrollment in the 2-year period before their first cancer claim and had no cancer-related claims over that period. We used a 2-year look back because some patients with previously treated cancers  were missed with a 1-year lookback whereas few additional cases were identified with a 3-year look back. Nonetheless, this might include a small percentage of patients with recurrent or progressive cancers for which they did not receive any cancer-related care in the 2 prior years. Of note, to avoid excluding the youngest Medicare-eligible patients, we included newly enrolled in Medicare with only a 1-year lookback. We excluded this latter group of patients in sensitivity analyses and found minimal differences.15 and updated by our team16 that included patients with poor-prognosis primary diagnoses, metastatic or ill-defined malignant diseases, and hematologic malignant diseases designated as relapsed or not in remission. As with the incident cohort, we identified a prospective cohort from the time of their first relevant code and then followed patients for the 1-year period after diagnosis or until death (within 1 year). For the poor-prognosis cohort, we required 1 year of continuous enrollment before the first qualifying claim.The poor-prognosis cohort was identified based on an algorithm developed by Obermeyer et al17 Oncology practices could range from small single-specialty oncology practices to larger multispecialty practices, some of which included radiation oncologists and/or surgeons who also care for patients with cancer. We divided the small number of practices spanning multiple hospital referral regions (HRRs) into practice/HRR subunits and assigned patients accordingly. Approximately 52.1% of the incident cohort (ranging from 71.5% for breast cancer to 9.6% for prostate cancer) and 66.3% of the poor-prognosis cohort (ranging from 88.3% for breast cancer to 65.0% for lung cancer) received care from an oncologist in the year following diagnosis.Oncology practices were identified as all tax identification numbers (TINs) that included at least 1 office visit claim with a CMS specialty code of medical oncology, hematology/oncology, hematology, or gynecologic oncology. Patient care episodes were attributed to an oncology practice TIN based on the plurality of evaluation and management visits with a cancer diagnosis code to an oncology practice as defined herein during the 1-year period of care following the qualifying first claim.We restricted these analyses to patients assigned to oncology practices with at least 20 assigned cancer cases (including both the incident and poor-prognosis cohorts) in at least 1 of the study years.7 who designed the categories to be mutually exclusive and cancer relevant. Though much of this spending is not directly with the oncology practice, our approach assumes that oncologists generally have the most direct control over medical decisions and consequent spending in the year after a new cancer diagnosis.The main outcome of interest was total spending in the 1-year period following the index diagnosis date. We chose 1 year because that is the spending period used for ACO spending calculations. Because prices in Medicare are set administratively, total spending can be thought of as a summary measure of the quantity and mix of services delivered. We summed Medicare payments, coinsurance amounts, and payments from other primary payers to assess total medical spending on all services covered by Medicare Parts A, B, and D. We assigned every claim to 1 of 13 service subcategories following the definitions of Keating et al,19We used the HRRs defined in the Dartmouth Atlas of Health Care for our geographic regions/markets in this study. Patients were assigned to an HRR based on their zip code of residence. The HRRs were defined by the Dartmouth Atlas of Health Care to reflect regional health care markets based on referral patterns for tertiary care.20)]), and comorbid conditions from the Chronic Conditions Warehouse (CCW) as of the index year. We identified patients with low incomes and limited financial resources based on enrollment in Medicaid or the Part D Low-Income Subsidy, and classified patients into 1 of 3 mutually exclusive groups: enrolled in full Medicaid for at least 1 month, enrolled in partial Medicaid for at least 1 month (without full Medicaid), or enrolled in the Part D Low-Income Subsidy for at least 1 month (without any Medicaid coverage). We also included hierarchical condition category risk scores calculated from the claims over the 12 months preceding the index cancer diagnosis.22 To examine variation in patients’ characteristics across practices that may contribute to spending differences, we also estimated similar models with each control variable as the dependent variable to estimate the extent of variation in patient characteristics across TINs.For each calendar year for each cohort, we estimated linear regression models that included random effects for each practice and fixed effects for HRR. Thus, the model estimates practice variation within a market while also negating the effects of variation in prices across geographic areas. We included control variables for cancer type as categorized by the Oncology Care Model as well as patient sociodemographic characteristics , Hispanic, White (non-Hispanic), or other  of the within-market variance (in dollars) estimated by the models. We then calculate the standardized coefficient of variation (ratio of the SD to the mean) for each category to describe how variation differed across categories after accounting for differences in mean spending. Finally, we characterized variation in relative terms  by dividing the mean of the top quartile of practices ranked by their adjusted spending estimated from the random effects model by the mean of the bottom quartile. We compared these results for the beginning and end of the study period, defined as the first 2 years (2009-2010) and last 2 years of the study period (2016-2017).23 Data for this study were analyzed from August 2021 to March 2023. This study used SAS Enterprise Guide statistical software  for analyses. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for reporting cohort studies  that postprocess the estimates obtained from individual models.The study cohorts included 1 309 825 patients in the incident cohort and 1 429 973 patients in the poor-prognosis cohort. The mean ages in the incident and poor-prognosis cohorts were 74.0 and 72.7 years, respectively, and approximately 80% of patients were White . The mosThe total number of oncology practices with at least 20 cases in any year decreased from 2394 in 2009 to 2036 in 2017. The mean number of cases per practice increased from 87 in 2009 to 137 in 2017. The mean number of practices per HRR decreased from 8.0 to 6.8 across the study period, but the median number of practices per HRR remained stable at 5. Opportunities for steering patients to the most efficient practices varied across HRRs; the top quartile of HRRs by number of practices had 10 or more oncology practices, but the bottom quartile had 3 or fewer.Total spending per patient increased from a mean of $57 314 in the first 2 years (2009-2010 cohorts) to $66 028 in the last 2 years (2016-2017 cohorts) . HospitaSpending on nonchemotherapy Part B medications exhibited the most relative within-region variation in the initial 2 years with 3-fold more spending on Part B medications in the most expensive quartile of practices compared with the least . OverallSpending for the poor-prognosis cohort followed a similar pattern . OverallAmong areas of spending accounting for at least 5% of spending, acute hospital care and infused chemotherapy demonstrated the combinations of the largest absolute variation in spending alongside a high coefficient of variation, suggesting the potentially largest areas for savings. In addition, radiation therapy, Part D spending, and postacute facility costs also exhibited high variation relative to its spending  and second-year (2010) spending were high  . When exOne method for risk-bearing organizations to succeed is to select efficient practices for their networks or for preferentially directing referrals for patients with cancer. Using national Medicare data, we documented several notable findings that have implications for how ACOs or other risk-bearing organizations might behave in local health care markets. First, acute hospital care and chemotherapy use were the largest drivers of variation within local geographic regions. Moreover, spending in these 2 areas was largely uncorrelated, suggesting that they are not substitutes (though less so in the poor-prognosis cohort). Second, for many areas of the country there is minimal opportunity for risk-bearing organizations to choose among oncology practices. Fully a quarter of HRRs had 3 or fewer practices that met our entry criteria, implying even fewer options in smaller areas. Finally, spending patterns of practices were reasonably correlated across years, suggesting that the spending behavior we identified is at least somewhat enduring, which is necessary if selective referrals is to work as a strategy.24 and hospitalizations for patients undergoing chemotherapy.7 In contrast, average spending on chemotherapy increased substantially.7 The findings related to the proportion of spending attributed to hospital care were consistent with a prior analysis that examined sources of variation in spending across (rather than within) regions using data ending in 2009.13 Since the time of that analysis, however, numerous anticancer therapies have entered the market with very high launch prices, while the prices of many existing anti-cancer therapies have also increased.27 Thus, decisions about chemotherapy use have become an increasingly important driver of variation. Our findings also are consistent with a more recent analysis that examined interregional spending for patients with 5 types of cancer who were undergoing chemotherapy, which found that acute hospital care spending and chemotherapy were the largest contributors to variation across regions.10 Nevertheless, the Oncology Care Model evaluation found no effect on use of hospital-based care in its first 3 years, despite this being an area of focus for participating practices.7 It also is true that not all spending in oncology treatment is controlled by oncologists (particularly for patients not receiving chemotherapy). For instance, Baumgardner et al11 showed that although most spending for conditions such as multiple myeloma, lung, and breast cancer is related to oncology care, 20% to 40% might not be. Thus, the spending patterns we observed may not be fully under the control of oncology practices.Acute hospital services and provision of chemotherapy were the highest cost areas of spending and also exhibited reasonably high variation. Nonchemotherapy Part B drugs also exhibited high variation. Importantly, total hospital spending was stable to decreased from 2009 to 2017, consistent with prior reports demonstrating decreased spending on surgical care28 If the introduction of future alternative payment models from CMS and commercial payers can successfully lower spending, the focus on shifting referral patterns may be less important over time. Finally, HRRs can sometimes be large, and lower-spending practices might not be accessible to all patients in an HRR. The advent of remote monitoring and telehealth strategies over the past several years is 1 potential strategy that could mitigate issues related to travel time.One important tool for risk-bearing organizations is the ability to identify and select lower-spending practices for inclusion in their networks or for targeting referrals. For such a strategy to be effective, there must be variation among practices that does not simply reflect substitution of one type of spending for another, the variation must reflect variation in practice patterns as opposed to nonrandom sorting of patients, and the variation must persist over time. In addition, there must be capacity at lower spending practices to take on more patients. Our findings suggest that such a strategy could be viable in many areas of the country , with a potential for achieving 10% to 15% overall savings on average. There are other challenges to pursuing such a strategy, however. Network inclusion often is dictated by ownership or other similar affiliations that effectively limit choices for organizations such as hospital-led ACOs. In addition, physicians directing referrals might prefer to keep care within their own system, to enable easier communication with oncologists or information exchange about a patient’s care, such as through a shared electronic health record. Another challenge for hospital-led ACOs is that expensive oncology practices may be a source of profit for hospital systems that is more beneficial to the system than the downstream rewards they might receive from savings.Our results have limitations. First, though we attempted to identify an incident cohort of patients with newly-diagnosed cancer, some patients might have had recurrent cancers. Second, though we studied a large and comprehensive sample of patients with cancer, we lacked clinical data that could be used for more accurate staging. Thus, there may have been clinical heterogeneity among those at different practices. Indeed, our analyses suggest that there was variation in patient characteristics across practices, suggesting that differential sorting of patients to specific types of practices contributed to the variation we observed. Third, this study included patients in the Medicare program who were enrolled in Part D and may not generalize to other patient populations. Fourth, for risk-bearing organizations to be able to steer patients to low-spending oncology practices, there must be excess capacity in these practices. Our analyses of caseload at the oncologist level, however, suggests that there is capacity. Finally, our analyses focused on spending but did not consider quality, which would be another important consideration for identifying high-value practices.In this national cohort study of cancer care for patients in traditional Medicare, our results suggest that there are opportunities for ACOs and other risk-bearing organizations  to select or drive referrals to lower-spending oncology practices in local markets.