The urban development of China is changing from incremental expansion to stock renewal mode. The study of urban functional areas has become one of the important fundamental works in current urban renewal and high-quality urban development. In recent years, big spatiotemporal data has been well applied in the urban function field. However, the study of spatial–temporal evolution characteristics and forecasting optimization for mixed-use urban functional areas has not been examined well. Thus, in this study, we proposed a new approach that applies a revised information entropy method to analyze the degrees of mixing for urban functional areas. We applied our approach in Jinan City, Shandong Province as the study area. We used Point-of-Interest, OpenStreetMap and other datasets to identify the mixed-use urban functional areas in Jinan. Then, the CA–Markov model simulated the urban layout in 2025. The results showed that: (1) the combination of road network and kernel density method has the highest accuracy of identifying urban functional areas. (2)The mixing degree model is constructed by using the improved information entropy, which makes up for the shortcoming of identifying the mixed functional areas simply by the frequency ratio of POI data. (3) The “residence and business” functional area has the highest proportion in the central area of Jinan from 2015 to 2020, and the total area of mixed-use unban functional areas continuously increased during this period. (4) The total area of the central area in Jinan has significantly increased in 2025. The optimization of urban functions should expand mixed-use functional areas and increase the proportion of infrastructure. Also, Jinan should improve the efficiency of space development. In order to propose suitable governance strategies, it is necessary to identify current urban functions, and simulate and analyze different spatial structures of urban functions4.A city is a complex organism composed of its structure and functions. The urban spatial structure is the skeleton of a city, and urban functional layout is the concrete expression of the urban spatial structure. The urban development of China is gradually shifting from incremental mode to stock-based mode5. Some scholars have examined well some parts of the urban functions, such as the identification of urban functional areas and evaluation of the current layout of urban functions. Some notable works are Chen et al.6 used Nanjing City as a study area to explore the similarity and differences between functional areas and administrative areas. They also analyzed the development history of urban functional areas and the trend of urban reorganization. Huang et al.7 provided an idea of sustainable development and five principles of urban zoning based on analyzing urban functional areas. Xing et al.8 examined the rationality of urban functional areas from the perspective of landscape patterns. Besides those works, some scholars also discussed the identification and prediction of mixed-use functional areas in western countries. For example, Song et al.9 reviewed a wide range of measures of urban land use mix and conducted a Monte Carlo simulation model to compare results from Hillsboro, Oregon, USA. They found that each measure provides a distinct perspective on mixed-use. Thus, the appropriate mixed-use measure should be determined by the number of land use dimensions of interest and the approximate scales at which land use mix influences the outcome of interest. Wandl and Hausleitner10 identified mixed-use functional areas in eight areas across Europe. In their work, they involved a number of different economic activities and the ratio of the working population to residential population within one area to describe mixed-use functions.The study of urban function is the basis of recognizing the complex urban system, exploring the change of urban land use, and realizing the sustainable development of the city. The research on identifying urban functional areas is conducive to a deeper understanding of urban spatial development. Also, the research on identifying urban functional areas plays an essential role in urban spatial planning and sustainable development decision-making11. However, the urban planning in China has been focusing on single-use functions for a long time and not paying enough attention to mixed-use functions. In fact, during the development and evolution of cities, a variety of mixed-use functions areas have been formed, such as high-rise buildings, which may contain residential, office, and commercial services. In the high-quality development of Chinese cities, the emphasis on mixed-use functions is the national demand in the future. Facing the urgent purpose of urban renewal in modern society, identifying mixed-use functional areas, excavating the modes of mixed-use urban functions, and evaluating the rationality of mixed-use functions have become vital scientific problems12. Those issues should be solved first, then decision-makers can make a better decision and then improves the sense of gain, security, and happiness of residents.However, due to historical reasons and the formation characteristics of urban functional areas, a single functional area per each land use category  is not suitable for the demand of current urban development. The reason is that single functional areas could cause extensive land use and low utilization of land use. From the urban renewal process in western countries, the construction of mixed-use functional areas has become the focus of urban renewal and development13. In existing studies of urban functions, the commonly used big data includes POI data, mobile phone signaling data, social media data, and GPS data etc. Because of the features of POI data, close to land use data, crowdsourcing data characteristics, easy to obtain, each record includes name, category, coordinates, classification code and so on, more than 70% of those studies use POI data18. POI data has become the core data for location-based services17.With the development and maturity of new technologies, identifying urban functional areas involving big spatiotemporal data has been a hotspot topic in recent years. Big spatiotemporal data contains traditional geospatial data . It also contains new types of geospatial data, such as cellular signaling data, social media data , and GPS track records, which can provide more insights into urban management and territorial space planning19. For example, Zhao et al.20 compared ten measurement models of mixing degrees. They found that models are suitable for measuring the overall mixing degree at mesoscale, such as district and block. Li et al.21 explored the residential planning mode of mixed-use land use. Hu et al.22 examined the recognition of urban functional areas and their mixing degree in Chongqing using POI data. Li et al.23 combined POI and taxi-OD data to evaluate the mixed urban functions quantitatively. Moreover, they revised the information entropy model to develop spatial entropy and temporal entropy. Li et al.24 typically applied random forest to determine the weights of POI, then they used the urban function calculation model to identify single dominant functional areas and information entropy to calculate the mixing degree of urban functions. Although some scholars investigated the identification of mixed-use functional areas and their mixing degree, the identification and analysis of mixed-use urban functions are not in-depth. The major reasons are different classification standards, unclear definitions of mixed-use function units, and different spatial scales.However, existing studies focused on using big spatiotemporal data to identify the boundaries of urban or main functional areas. Although some pioneers have already investigated the identification of mixed-use functional areas, these studies have not comprehensively examined identifying mixed-used functional areas25. Because it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions26.Furthermore, some scholars suggested that the study of the identification of urban functional areas should use multi-source data. Different levels and functional areas should use different datasets and identification units. These suggestions showed that the identification of urban function areas can improve accuracy and efficiency through new technologies and multi-source data and also need to create different identification processes and approaches for different levels and different functional areas. But, there is still no breakthrough in selecting appropriate functional identification units and approachesWe summarizde existing issues in the current urban functions field as follows: (1) there is a lack of comparative study on the approaches and identification units of different functional areas. (2) A personalized model for identifying urban mixed-use functions is lacking. (3) there is a lack of multi-level spatial analysis and optimization simulation analysis of urban function. Therefore, in this study, we adopted spatial grid and road network division units to identify Jinan functional areas using frequency density, category ratio and kernel density estimation method. Then, we evaluated the accuracy of our approach and found the most suitable identification approach for Jinan City. Also, we analyzed the spatial structure of Jinan's functional areas from 2015 to 2025 based on the CA–Markov model, and gave appropriate suggestions to optimize the layout of urban spatial structure. The approaches used in this study could provide a reference for the high-quality development of the city.2, the total area of the built-up region is 760.6km2, and the urbanization rate is 71.21%. According to the latest census data, the registered population of Jinan reached 8.16 million, which ranked 27th in the country.Jinan City is the capital of Shandong Province and is famous for its spring city. It is also the political, economic, cultural, technological, educational and financial center of Shandong Province. Jinan City is located between 36°01′N-37°32′N and 116°11′E-117°44′E. Until 2020, Jinan has 12 county-level administrative regions, including ten municipal districts and two counties. They are Shizhong district, Lixia district, Huaiyin district, Tianqiao district, Licheng district, Changqing district, Zhangqiu district, Jiyang district, Laiwu district, Gangcheng district, Pingyin county, and Shanghe county. The total area of the city is 10,244km27. Thus, in the first three quarters of 2022, the GDP of Jinan (864.201 billion Chinese yuan) ranked 20th in entire China. In this study, we choose the downtown area and central area of Jinan as the study area, the map of this study is shown in Fig. Jinan connects Beijing-Tianjin-Hebei region in the north, and Yangtze River Delta in the south. It is one of the important transportation hubs in East China. Because of springs, Jinan is a famous historical and cultural city and one of the first batch of excellent tourist cities in China. Jinan is also a national pilot city for the transformation of new and old kinetic energyOpenStreetMap (OSM) Data. OSM is one of road network data. It is easy to access and free for the public. OSM has high positioning accuracy and accurate topological relationship, the data includes longitude and latitude, road type, road name, maximum driving speed, and other attribute information. We downloaded the data from OSM website (https://www.openstreetmap.org/), and processed the data using ArcGIS software.Point-of-Interest (POI) Data. POI data is the point data of real geographical entities with spatial attribute information. Meanwhile, it has a number of advantages, such as large data size, wide coverage, easy to obtain, detailed information  and timely updates. Because of those advantages, POI data is a widely used dataset in urban studies. Currently, there are a set of available POI resources, such as Baidu, Gaode and Tencent in China, as well as Google and OSM in western countries. In order to provide an accurate and convincing result, we used Gaode POI data because it has better data integrity in our study area. In this study, we collected 2020 POI data from Gaode (https://maps.gaode.com/). Each POI record contains a series basic contents, for example, name, longitude and latitude, administrative region, and address.https://lbs.amap.com/api/webservice/download). We removed duplicate and missing records. Finally, 72,323 records in 2015 and 332,329 records in 2020 were obtained. Based on “Standards of Urban Land classification and Planning Construction Land” (GB50137-2011) and land use classification of Jinan City, we further reclassified the data into six new categories, including residential area, business area, industrial area, public service facilities, traffic facilities, green space and square  boundary map of administrative zones. The boundary map of administrative zones of Jinan City was obtained from the Geographical Information Monitoring Cloud Platform (http://www.dsac.cn/). (2) Remote sensing images. We collected the remote sensing images from Google Earth and Gaode in August 2015 and May 2020, the spatial resolution of those images is 30 m × 30 m. (3) Statistical yearbook data. We acquired those datasets from the Jinan Bureau of Statistics (http://jntj.jinan.gov.cn/col/col27523/index.html). (4) Map of urban planning in Jinan.These maps mainly come from the related data of Jinan city government's official website (http://gh.nrp.jinan.gov.cn/).Frequency Density and Ratio Index. Frequency density (FD) is the most commonly used index in the study of the identification of urban functions. This index gives the frequency per unit for the data in this class, where the unit is the unit of measurement of the data. In this study, FD is used to calculate the total records of POI data in each identification unit and then get the frequency density and ratio index per unit. For each functional area unit, we identify the functional areas based on frequency density and ratio index. The formulas are30:i indicates the type of POI, k is the total number of types of POI. ni represents the number of type i POI in a unit, whereas Ni is the total number of type i POIs. Fi is the frequency density of type i POIs in a unit, and Ci is the proportion of the frequency density of type i POIs in a unit. Wi represents the weight for each frequency density .Kernel density estimation (KDE) Method. Kernel density estimation is the most important and intuitive method to measure the aggregation degree of POI data30. It can not only clearly display the number of POI records in each unit, it also can compare the aggregation degree of POI records in different areas, and further visualize the results33. The calculation of kernel density estimation is shown in Eq. (f(x) is the estimated kernel density at x. ci is the ith spatial location of POIs within the bandwidth. h is the threshold of distance decay, i.e., bandwidth. n is the number of POIs that the distance between x is less than or equal to h. Φ is a spatial weight function. In this study, we use the quartic weight equation , n is the total types of urban functional area. Ai is the total area of each type, and A is the total land area.n in Eq. 39,40:5\dJ is the equilibrium degree, H is the actual information entropy, and Hmax is the maximum information entropy. When J = 0, the spatial structure is in the most unbalanced state; When J = 1, the spatial structure reaches the ideal equilibrium state. In the meantime, dominance degree (J) reflects the degree to which one or multiple types of functional areas dominate the region. It is opposite to the equilibrium degree. The equation is shown in Eq.  within a unit. A typical example is commercial  and residential  buildings. In order to address the shortcomings of FD, we apply revised information entropy to measure the degree of mixing for urban functions. We adopt ratio index into the conventional information entropy, which overcomes the shortcoming that determines the mixed-use urban functions only depending on FD and ratio index. The high value of revised information entropy stands high degree of mixing (more functions for one unit)41, the equation of revised information entropy is:M is the degree of mixing. n is the number of POI types in one unit, Ci is the ratio index of POI types in one unit.In this study, we compared the performance among three identification approaches: (1) spatial grid and FD, (2) OSM, KDE and FD, and (3) OSM and FD. We also did mutual verification experiments among those three approaches.The spatial grid and FD approach first divides the research area into grid units with fixed unit size. Based on our previous work, we empirically use 50 m × 50 m grid cells. Then POI data is involved in calculating FD and ratio index per grid. When two-category functional areas have larger Ci values (20% ≤ Ci ≤ 50%) in one grid unit, this grid unit is assigned to a mixed-use functional area. When only one functional area has larger Ci values (Ci ≥ 50%), this grid unit is assigned to a single functional area. When Ci equals 0, this grid unit is no data area. The rest situations are integrated areas.The OSM, KDE and FD approach divides the whole study area into units based on road network, and then KDE calculates the density based on POIs. We also involve FD and ratio index to group the identification units. According to the classifications of functional areas in Jinan, we use 200 m as the bandwidth of KDE. The standard of classification of FD and ratio index is the same as the spatial and FD approach.The OSM and FD approach can be seen as a combination of previous two approaches. That is, the identification unit is divided based on the road system, and the identification results are calculated by FD. For detailed information, please refer to spatial grid and FD approach and OSM, KDE and FD approach.We use confusion matrix and kappa coefficient to evaluate the performance of each approach. Typically, the range of kappa coefficient is between 0 and 1. The higher value of kappa coefficient, the result is more accurate. The kappa coefficient value is between 0 and 0.2, the results have the lowest accuracy. When the value of kappa coefficient is between 0.41 and 0.6, the results are acceptable. The value between 0.61 and 0.8 stands for higher accuracy, whereas the value is greater than 0.81, the results have the highest accuracy.The confusion matrix result depends on remote sensing images of Jinan in 2020 from Google Earth and Gaode. We randomly selected 40 identification units to calculate the confusion matrix and kappa coefficient. Table Comparing overall accuracy and kappa coefficient, the OSM, KDE and FD approach has the higher accuracy, which overall accuracy is around 80% and kappa coefficient is around 0.77. Thus, we choose this approach to identify the urban functional areas in this study. The confusion matrix is shown in Table 2  and 225.35km2 (34.62%). For mixed-use functional areas, residential/industrial (538.36km2) and residential/business (105.40km2) areas have the largest area in downtown and central area, respectively. Those results are consistent with the actual situation.In 2020, the downtown area of Jinan City has 13,113 units based on the road system division, and the total area is 3254.72 km43, they also found similar overall characteristics as ours. Fan et al.42 used kernel density analysis method and Gaode POIs data to study the functional zoning structure of Jinan city. Dou43 used multiple spatial analysis methods and models based on multi-source data such as POI data, remote sensing images, road network data, Baidu heat map and traditional statistical data. Although we used different methods and data sources, the findings are consistent. We found that the urban functional spatial structure of Jinan city has a significant agglomeration characteristic of residential and commercial function elements, and the industrial production function elements transfer to the periphery of the city.The spatial distribution of urban functional areas in Jinan City can be found in Fig. 44 also found that the urban functional areas of Jinan City have prominent features of mixed functional areas. In 2015, the mixing degree of urban functional areas showed a trend that diffuses from the center to the edge of the city. In 2020, the entire central area has a higher mixing degree. This finding indicates that the development of the central area in Jinan City is becoming more and more consummate.The mixing degree of urban functions in the downtown and central areas has been increasing from 2015 to 2020 Fig. . Yu et a45. From the analysis of the evolution characteristics of urban spatial structure and mixing degree, the values of degree of urban function combination in Jinan were unstable from 2015 to 2020 46. CA–Markov model is widely used in the prediction of land use land change, and is a relatively mature model. Because of the dispersibility and randomness of mixed-use functional areas, our predictive model was based on single functional areas. Therefore, for mixed-use functional areas, the function with the highest proportion was assigned to the closest single functional area, for example, 55% of residential/business functional area is a residential area, and this area will assign to the residential area. The spatial and temporal distribution patterns of urban functional areas in Jinan City is shown in Fig. In this study, based on identified results of the combination of road network and kernel density, we used CA–Markov model to simulate the urban functional areas of Jinan City in 2025. The entire predictive process is implemented in IDRISI software spatial grid and FD, (2) OSM, KDE and FD, and (3) OSM and FD. They all perform well, but the OSM, KDE and FD approach perform better than others.From 2015 to 2020, the total area of mixed-use functional areas was continuously increasing. Therefore, the distribution of functional areas in downtown and central areas is rationality.In 2025, residential and business areas will continually expand, but their related ancillary facilities, such as road traffic facilities and green space, cannot satisfy residents’ needs.In order to optimize the spatial layout of urban functions in the future, Jinan should reasonably expand the proportion of mixed-use functional areas and infrastructure land and improve the efficiency of urban space development and utilization.This study compared three identification approaches to identify urban functional areas in Jinan City based on POI data. Meanwhile, confusion matrix and kappa coefficient to verify the accuracy of each approach. This study also analyzed the urban spatial structure of Jinan City, summarized existing issues in the structure of urban functions in Jinan, and provided a number of suggestions to adjust the spatial structure of Jinan in the future. The primary conclusions of this study are as follows:Our future work will focus on a set of trends. First, we only involved POI data in this study. We will use more datasets in the future to compare their performance. Second, although the OSM, KDE and FD approach achieved the highest accuracy in this study. The limitations of OSM and POIs  may cause bias. Thus, we will apply multiple road network datasets to examine the results. Third, our simulation model only had two historical datasets. We will use more historical datasets and different simulation models to do a contrastive analysis. In addition, a gold standard/benchmark to validate functional areas and associated uses should be examined in the future.


The Dutch guideline for general practitioners (GPs) advises biannual surveillance of hepatitis B (HBV) patients and referral of every hepatitis C (HCV) patient. We aimed to study the prevalence, incidence, and the management of hepatitis B and C in primary care.This is a retrospective cohort study using the Rijnmond Primary Care database (RPCD), including health care data of medical records of GPs of approximately 200,000 patients in the area of Rotterdam, the Netherlands. Patient records were selected based on laboratory results, International Classification of Primary Care (ICPC) codes, and free-text words.In total, 977 patients were included: 717 HBV, 252 HCV, and 8 HBV/HCV coinfected patients. Between 2013 and 2019, the prevalence of HBV and HCV declined from 5.21 to 2.99/1,000 person-years (PYs) and 1.50 to 0.70/1,000 PYs, respectively. We observed that the majority of the patients had been referred to a medical specialist at least once (71% HBV and 89% HCV patients). However, among chronic patients, we observed that 36.2% of the HBV patients did not receive adequate surveillance by their GP  or a medical specialist. In addition, 44.4% of the HCV patients had no record about successful antiviral treatment.This study demonstrated a declining prevalence in viral hepatitis B and C in primary care in the Netherlands. However, a substantial part of the patients did not receive adequate surveillance or antiviral therapy. It is therefore crucial to involve GPs in case finding and in follow-up after treatment. Viral hepatitis B and C are associated with cirrhosis and liver cancer.General practitioners (GPs) play a key role in case finding and surveillance.In the last couple of years, the treatment options are significantly improved.Therefore, the WHO adopted a strategy aimed to eliminate viral hepatitis.This is a study using a large health care database of medical records of GPs.We observed that the prevalence and incidence of hepatitis B/C is declining.However, many patients did not received adequate surveillance or therapy.Therefore, GPs must be involved in case finding and follow-up after treatment.Chronic infection with viral hepatitis B or C is a global health threat, as it is associated with the development of liver cirrhosis and primary liver cancer (hepatocellular carcinoma) ,2. ArounDuring the last years, the treatment of viral hepatitis B and C has improved significantly. Suppression of the hepatitis B virus can be achieved with nucleos(t)ide analogs (NAs) ,6 and erGeneral practitioners (GPs) play a key role in case detection and management of viral hepatitis. Indication for HCV and HBV screening include migrants originating from high endemic countries, PWID and MSM, as well as patients with elevated liver enzymes. Patients with an active infection should be referred to a medical specialist to evaluate the presence of liver-related complications and initiate antiviral therapy when indicated. However, several studies demonstrated that many patients who are at risk for viral hepatitis infection are not screened accordingly, resulting in many undiagnosed ,18.This could be explained by the fact that most clinical practice guidelines in European countries lack information about the management of patients with viral hepatitis in primary care. In a semiquantitative study, a few GPs in Germany, Spain, and Italy have indicated to be involved in monitoring of serum liver enzymes and refer hepatitis B patients based on clinical indicators  ,20. HoweIn this study, we therefore aim to provide insight in the prevalence of viral hepatitis B and C in a multiethnic area in the Netherlands, as well as in the management of hepatitis B and C patients in primary care.This is a retrospective cohort study using the Rijnmond Primary Care database (RPCD). The RPCD is a region specific product of the Integrated Primary Care Information (IPCI) database, supervised by the department of General Practice of the Erasmus MC, University Medical Center, Rotterdam, the Netherlands. More information about the IPCI database has been prescribed in detail elsewhere . This isPatient records were selected based on laboratory results, ICPC codes and/or key words for hepatitis B and C. The ICPC classification is managed by the Dutch College of GPs and adopted by all Dutch GPs . The datAfter selecting the patients that met these inclusion criteria, the medical charts were reviewed. Cases were labeled as certain cases or uncertain cases . All cases were manually categorized as viral hepatitis B , hepatitis C (stratified as acute hepatitis C and chronic hepatitis C) and chronic HBV/HCV coinfection. Acute hepatitis B/C was defined as HBsAg positivity for hepatitis B and HCV RNA positivity (or anti-HCV in case of absent HCV RNA testing) for hepatitis C, typically with concomitant jaundice and/or elevation of serum liver enzymes, that occurred within 6 months after viral exposure. Chronic hepatitis B/C was defined as serum HBsAg/HCV RNA positivity (or anti-HCV in case of absent HCV RNA testing) of at least 6 months. HBV reactivation was defined as HBsAg positivity and elevated HBV DNA, in previously HBsAg negative patients but anti-HBc positive patients who underwent high-risk-immunosuppressive treatment. If the medical diagnosis was clearly formulated in a GP note or letter from a hepatitis specialist, this diagnosis was adopted as well. Date of first diagnosis was extracted from the RPCD, but manually altered during chart review if it was evident that the date of diagnosis was different.First, we studied the prevalence and incidence of viral hepatitis B and C in the study period. Follow-up ended when a patient transferred out of the GP practice, died, or when the end of the study period was reached, whichever occurred first. In addition, follow-up ended as well when a patient cleared the virus spontaneously or after antiviral treatment, i.e. HCV RNA or HBsAg negativity in previous HCV RNA or HBsAg positive patients.Next, the management of viral hepatitis B and C patients was studied. For chronic hepatitis B patients, management was categorized as surveillance by medical specialist , surveillance by GP (at least 2 ALT checks within 3 years), and no surveillance. The management was only determined in a subcohort of certain cases with a chronic infection. For hepatitis C, the number of referrals and antiviral treatment was studied. Prescriptions for antivirals were extracted using the ATC codes: J05AP , J05AF (nucleoside and nucleotide reverse-transcriptase inhibitors), and L03AB (interferons). Subsequent curation rate , with the corresponding date, was recorded.Descriptive data were described as numbers (with percentages), medians (IQR), and means (±SD). Analyses were performed in the overall included study population, as well as a subpopulation of certain cases. Incidence was calculated by dividing the number of incident cases by the midterm population at risk (person-years [PYs] at risk within the study population on July first) . PrevaleIn total, 1,381 patients were identified by the initial search. After reviewing the medical records, 977 patients were included: 717 HBV, 252 HCV, and 8 HBV/HCV coinfection . BaselinThe prevalence of viral hepatitis B and C are displayed in The incidence rates are displayed in Among the 588 certain hepatitis B cases, 406 patients chronic hepatitis B patients were studied to assess the management of viral hepatitis in primary care. Among those, 289 patients (71.2%) were referred to a medical specialist at least once . HoweverTo gain insight in the adherence of the updated GP guideline, we extracted a subcohort of patients, including certain cases of chronic hepatitis B patients who had at least 2 years of follow-up, including the period after March 2016 (when the GP guideline update was published), and who did not had HBV surveillance by medical specialist at that moment. In total, this subcohort included 226 patients of whom 148 patients (65.5%) received ALT surveillance at least once. Among these 148 patients, the mean number of ALT tests was 2.3 per patient (range 1–8) in 4 years. In addition, ALT levels were elevated  at least once in 34/148 patients . Consequently, after reviewing the medical records of those 34 patients with elevated ALT levels, 20 patients (58.8%) were referred to a medical specialist. Thus, no consequence was given to abnormal liver test in 14/34 patients . Among those 14 patients, ALT levels were repeatedly increased in 8 patients (57.1%). HBsAg was only tested among 11 patients (4.9%).Among the 153 certain hepatitis C cases who had at least 2 years of follow-up, 136 patients (88.9%) were referred to a specialized hepatitis treatment center . In totaP = 0.005).The Netherlands is a low-endemic country for viral hepatitis B and C ,27. HoweIn 2016, the WHO has implemented a global viral hepatitis elimination target . In thisIn the Netherlands, risk groups account for most cases of hepatitis B and C, including (first-generation) migrants, PWID, and men who have sex with men (MSM) . A possiIn addition, we demonstrated that the referral rate to a hepatitis specialist was 71–89%. However, our data showed that 36.2% of the hepatitis B patients was not under surveillance by a hepatitis specialist or GP. Furthermore, we observed that many patients received ALT performance at least once, but an annual ALT check was only performed in the minority of patients. This is in line with the study results of Hofman et al. . In thisA possible explanation for the suboptimal surveillance of hepatitis B patients by the GP might be the lack of an appointment scheduling system. Consequently, the responsibility for the biannual ALT check lies with the patient instead of the GP. Another explanation could be limited knowledge about the updated guideline or viral hepatitis in general. This could be the consequence of the small number of viral hepatitis patients in every GP practice due to the low prevalence of viral hepatitis in the Netherlands . This haMultiple interventions are needed to improve hepatitis B surveillance and retrieval of untreated HCV patients in primary care. First, an appointment scheduling system is warranted to invite hepatitis B patients biannually for their laboratory check. Second, IT changes, such as pop-up messages, could facilitate GPs to perform adequate HBV surveillance, referral of untreated HCV patients or screening among high-risk individuals. Hence, a registration system is crucial, including information about medical diagnosis, laboratory results, and background information such as country of birth. Third, GPs should be encouraged to participate in already available training courses for viral hepatitis. Fourth, a standard set of serum hepatitis markers and liver tests on laboratory forms could facilitate screening, which has been supported by the study of Helsper et al. . FinallyDespite that this is a large GP-based database, the following limitations need mentioning. Since migrants account for the majority of prevalent HBV and HCV cases, our results cannot be not directly translated to other (low-endemic) areas of the Netherlands, as our results are based on a multiethnic area in the Netherlands. Another limitation that should be acknowledged is the retrospective design and the fact that our results depend on the available data within the GP database, which is subjected to the input of individual GPs. Therefore, management and laboratory results of medical specialist are only available if the specialist sends communication to the GP. This could give an underestimation of the real number of patients that receive adequate HBV screening in the hospital or successfully treated hepatitis C patients. Since our data indicated that a few patients with liver cirrhosis or liver transplantation would not receive surveillance by a hepatitis specialist supports the suggestion that our data is limited by the retrospective design. Finally, due to privacy restrictions, if a patient changes GP within the network of affiliated GP practices of the RPCD, the patient enters the database with a new patient number. This could have resulted in duplicate cases. However, in the Netherlands, very few patients change of GP over time, when they do it is because of moving to a different region. Thus, the impact of duplicate cases to our findings is limited. In addition, for our calculations of the incidence and prevalence, we took the medical history into account. Therefore, a change of GP will not influence the incidence and prevalence rates.In conclusion, we observe that the prevalence of viral hepatitis B and C is declining in a multiethnic area of the Netherlands. This implies that the Netherlands seems on track to achieve the WHO elimination target. However, many patients with hepatitis B and C might not receive adequate surveillance or antiviral therapy. It is therefore crucial to involve primary care in the road to complete elimination.cmac070_suppl_Supplementary_TablesClick here for additional data file.cmac070_suppl_Supplementary_ChecklistClick here for additional data file.


Human Respiratory Syncytial Virus (HRSV), Human Parainfluenza Virus (HPIV), and Human Adenovirus (HAdV) epidemics differ in geographical location, time, and virus type. Regions prone to infections can be identified using geographic information systems (GIS) and available methods for detecting spatial and time clusters. We sought to find statistically significant spatial and time clusters of HRSV, HPIV, and HAdV cases in different parts of Kenya.To analyse retrospective data, we used a geographical information system (GIS) and the spatial scan statistic. The information was gathered from surveillance sites and aggregated at the county level in order to identify purely spatial and Spatio-temporal clusters. To detect the presence of spatial autocorrelation, the local Moran's I test was used. To detect the spatial clusters of HRSV, HPIV, and HAdV cases, we performed the purely spatial scan statistic. Furthermore, space-time clusters were identified using space-time scan statistics. Both spatial and space-time analyses were based on the discrete Poisson model with a pre-specified statistical significance levelof p<0.05.The findings showed that HRSV, HPIV, and HAdV cases had significant autocorrelation within the study areas. Furthermore, in the Western region of the country, the three respiratory viruses had local clusters with significant positive autocorrelation (p<0.05). Statistically, the Western region had significant spatial clusters of HRSV, HPIV, and HAdV occurrence. Furthermore, the space-time analysis revealed that the HPIV primary cluster persisted in the Western region from 2007 to 2013. However, primary clusters of HRSV and HAdV were observed in the Coastal region in 2009-11 and 2008-09, respectively.Human respiratory syncytial virus (HRSV), human parainfluenza virus (HPIV), and human adenovirus (HAdV) hotspots (clusters) occurred in Kenya's Western and Coastal regions from 2007 to 2013. The Western region appeared to be more prone to the occurrence of allthree respiratory viruses throughout the study period. Strategic mitigation should focus on these locations to prevent future clusters of HRSV, HPIV, and HAdV infections that could lead to epidemics. In 2015, HRSV incidence accounted for approximately 33.1 million of the total number of acute lower respiratory tract infections (ALRTIs) in children under the age of five years worldwide.2 While HRSV is the most common cause of ALRTIs in children under the age of five, HPIV and HAdV also contribute to the burden of acute respiratory infections.4 These tree respiratory viruses spread efficiently among humans through direct transmission such as self-inoculation, and indirect transmission such as fomites as well as droplets or aerosols.5 In 2018, the global burden of HPIV–ALRTIs was estimated to be 29.5 million cases in children under the age of five.6 Furthermore, outbreaks caused by HAdV have raised public health concern, as emerging and re-emerging types have been linked to severe pneumonia in healthy children and adults8 Outbreaks of these respiratory viruses occur in various locations around the world, and the timing of epidemics varies depending on population and location.9 The variation in occurrence rates has been linked to a number of factors, including demographic, socioeconomic, and climate variables12.Acute respiratory tract infections caused by the human respiratory syncytial virus (HRSV), human parainfluenza virus (HPIV), and human adenovirus (HAdV) account for a significant portion of the global public health burden of disease13Several African countries have reported data indicating a significant proportion of HRSV, HPIV, and HAdV infections. Human respiratory syncytial virus (HRSV) alone had a 14.6% prevalence in people with (acute respiratory tract infections) ARTIs across the continent, indicating that the ecosystem of this continent contributes to the epidemiology of these respiratory virus infections.16 For the period 2000-2015, the proportion of ARTIs in children under the age of five years ranged from 4% to 35% of HRSV, 0.3% to 75.8% of HPIV, and 1% to 26% HAdV in Sub-Saharan Africa.17Despite the lack of pooled prevalence data for HPIV and HAdVs from the continent, both viruses are typically recorded through syndromic surveillance systems such as influenza-like illness (ILI) and severe acute respiratory illness (SARI) or pneumonia.18 However, HRSV peaks have also been recorded south of the equator during a dry season.19 Similar to HAdV serotypes, varying seasonal peaks were observed with HPIV types (1-4), and both respiratory viruses occur throughout the year with less defined seasonality in the northern and southern hemispheres.21Human respiratory syncytial virus (HRSV) epidemics have been reported in temperate regions during the coldest months of winter, whereas in tropical areas epidemics are mostly associated with rainy seasons.22 Techniques such as scan statistics, which combines exploratory and confirmatory capabilities, enable an unambiguous statistical assessment of spatial and spatiotemporal patterns across defined regions.24Kenya, like other countries, has reported a substantial burden of disease from HRSV, HPIV, and HAdV infections. However, the identification of geographical areas prone to HRSV, HPIV, and HAdV infections, or “hotspots,” in the country is lacking. These regions can be identified using geographic information systems (GIS) and the available methods for detecting spatial and space-time clusters “hotspots”.The objective of this study was to identify statistically significant spatial and time clusters of HRSV, HPIV, and HAdV infections in Kenya at the county and regional levels. The hypothesis was that HRSV, HPIV, and HAdV infections occur in clusters with both spatial and spatio-temporal characteristics.25 Kenya's borders are 3,457 kilometers long, and it shares borders with South Sudan, Ethiopia, Somalia, Tanzania, Uganda, and the Indian Ocean.26 The equator cuts the country in half, with roughly half of Kenya located in the northern hemisphere.The research was carried out in Kenya, which is located in Eastern Africa at a latitude of -0.0236° S and a longitude of 37.9062° E.27 In 2010, the country was reorganized into 47 counties and 290 sub-counties. From 2013 onwards, this geographic boundary hierarchy was incorporated into national administration. Counties were established in accordance with the previous provinces  is divided into six counties covering an area of 79,686.1 km2. The region experiences cooler temperatures ranging from 14°C to 28°C, as well as moderate rainfall ranging from 1016 mm to 2540 mm per year.33 Nairobi is Kenya's capital city and the county seat, with a land area of 694.9 km2. Nairobi's climate is more similar to that of the country's central region. The average temperature is around 19°C, and the average annual rainfall is 958 mm.34 Four counties account for 7,400.4 km2 of the area in the Western region. It has a climate with equatorial tropical features as well as some temperate savanna areas. This region has a mean annual rainfall of 2087mm, with the heaviest rain falling in April, and temperatures ranging from 14°C to 36°C.29 The Nyanza region borders the Western region; it has a land area of 12,477.1 km,2 six counties, and a similar climate as the Western region.Kenya's central region is made up of five counties totaling 11,449.1 kmHuman respiratory syncytial virus (HRSV), parainfluenza (HPIV), and adenoviruses (HAdV) cases were identified in this study using Kenya's influenza-like illness (ILI) syndromic surveillance system. The Kenya Medical Research Institute (KEMRI) and the US Army Medical Research Directorate - Kenya (USAMRD-K) implemented the ILI surveillance system. The case datasets were collected between 2007 and 2013. Human respiratory syncytial virus (HRSV), human parainfluenza virus (HPIV), and human adenovirus (HAdV) infections were confirmed in the laboratory using assays such as polymerase chain reaction (PCR), viral culture, and immunofluorescence. The spatial coordinates of the participants’ village/estate of origin was collected from the geocoded location via Earth Pro (7.3 Google LLC), recording latitude and longitude in degrees for each HRSV, HPIV, and HAdV case. In addition, the date of illness onset was recorded in months and years.36 The 2009 National Population Census also recorded population counts by district. Nonetheless, population projections for each district in their respective counties have been published from 2010 to 2020.37 The population data used for this study in 2009 were the population counts from the 2009 census, not population projections; the projections were used for the inter-census years of 2007, 2008, 2010, 2011, 2012, and 2013. From the geocoded location, spatial coordinates including latitude and longitude centroid files of counties were created using the vector layer of Kenya administration boundaries.38The Kenya National Bureau of Statistics (KNBS) provided population data based on the 1999 and 2009 national population censuses. In the 1999 census, each district was enumerated and its population count was recorded. As a result, the following ten-year population projection from 2000 to 2010 was published as population counts per district.22In the first step of the analysis, the dataset was visualised during exploratory analysis to identify obvious errors and to describe the spatial distribution of HRSV, HPIV, and HAdV cases per county. The colour symbol categories were used to indicate the pattern of a large to a small number of cases per county. A higher number of cases was represented by a darker colour, while a lower number of cases was represented by a lighter colour.39. However, from KDE it is not possible to determine the significant positive spatial autocorrelation.The second step constituted smoothing thenvisualisation. Kernel density estimation (KDE) was used for describing the spatial distribution of HRSV, HPIV, and HAdV cases in terms of the hotspots in the study area. In qGIS (V3.10.3-A Coruña), the Heatmap plugin generates a density heatmap raster from the input point of a vector layer. The density of the HRSV, HPIV, and HAdV case points is calculated based on the number of points in a location, which is the mean number of events per unit area. This allowed identifying hotspots with the use of a 10 km radius of the kernel (kernel bandwidth)22 The indicators, known as “Local Indicators of Spatial Association (LISA)” were calculated as:In the third step, Local Moran's I test was applied to detect significant positive spatial autocorrelation from the aggregated data of HRSV, HPIV, and HAdV cases in their counties.i and Zj are the observed values in a standardised form, and Wij is a spatial weights matrix in row-standardised form. The enabled qGIS hotspots analysis plugin allowed for the execution of a local Moran's I test with a queen's cases contiguity matrix and 999 random permutations at a 5% level of significance.40Where Z44 LISA, on the other hand, does not provide spatial cluster or hotspot characteristics.LISA reveals spatial clusters with values in quadrants defined as hot spots (high-high), cold spots (low-low), spatial outliers (high-low/low-high), and no significant local spatial autocorrelation.The fourth step of the analysis consisted Kulldorff's scan statistic. This was performed to account for different aspects of spatial pattern analysis. It was based on the discrete Poisson model to identify purely spatial clusters HRSV, HPIV, and HAdV cases by counties. Besides purely spatial analysis, to determine the presence of space-time clusters of HRSV, HPIV, and HAdV cases per year over the study period, space-time scan statistic was applied. It used a Space-Time Poisson model which considered the study period of 2007-2013.4To complete the fourth step of the analysis, different datasets were prepared to fit the format used in SaTScan software (SaTScan V9.6.1). A case file containing annual HRSV, HPIV, and HAdV cases per county for a study period of 2007 to 2013 was made. A coordinate file with geographic coordinates of the centroid of each county, and a population file including the projected total population per county for each year from 2007 to 2013 were also made. These datasets were prepared in order to determine whether the incidence of HRSV, HPIV, and HAdV cases was clustering in space and time retrospectively. SaTScan uses a random process to search for and identify a significantly increased risk of HRSV, HPIV, and HAdV cases exceeding the expected number within the specified spatial window.The analysis was based on aggregated data per county with sites that participated in the surveillance program in Kenya from 2007 to 2013. Previously, the centroid coordinate of each county was extracted from the geocoded location of Kenya administrative boundaries vector layer with the qGIS geometry tool. By the centroids algorithm, a new point layer representing the centroids of Kenya counties was generated. The features were joint with annual HRSV, HPIV, and HAdV cases for each year to the centroid of the counties. This was completed with qGIS and later converted to SaTScan format for further cluster analysis.45 A cluster was pre-specified to be statistically significant at the 0.05 level. Sensitivity analysis was performed using different maximum scanning window sizes to test for the robustness of the clusters. Thereafter, nvisualisation of clusters was performed in qGIS to obtain the map of the clusters' locations in study regions.To identify clusters, a cylindrical window was used with a circular geographic formbasedon each county centroid. The window moved in space and time across the study region. The spatial dimension was represented by the circular base of the cylinder with a varying radius from 0 up to the maximum value specified. The height of the cylinder constituted the temporal dimension with a time precision of 1 year. The maximum value of up to 50% of the total population at risk in space and time was considered. It was assumed that for each cylinder, HRSV, HPIV, and HAdV cases were Poisson-distributed. To assess the space-time clusters, it was assumed thatcases were randomly distributed in space and time. From the incidence of cases counted inside and outside the cylinder, the likelihood ratio was calculated for each cluster. A primary cluster was identified as the cylinder with the maximum likelihood ratio. The 999 Monte Carlo randomisations were used to obtain likelihood ratio statistics and p-values of the test. This allowed us to identify significant space-time clusters.Ethical approvals for this study were obtained from Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (SERU) with reference number KEMRI/SERU/CVR/003/3802 and the Walter Reed Army Institute of Research (WRAIR) with reference number WRAIR#1267G.From the surveillance period of 2007-2013, HRSV was reported in 13 counties (number of cases [n]=539), and HPIV and HAdV were described in 14 (n=922) and 11 (n=581) counties respectively .The geographical distribution of HRSV, HPIV, and HAdV cases varied by county . HRSV caThe hotspot regions for HRSV, HPIV, and HAdVs cases were identified using a kernel density estimate . HRSV hoFurthermore, the coastal regions were identified as a hotspot for HPIV and HAdV cases. Nonetheless, HPIV hotspots were discovered in Kenya's central (Nairobi) and western regions from 2007 to 2013. Similarly, HAdV hotspots were observed in the central (Nairobi), and western regions.The results of spatial autocorrelation showed significant local hotspots (clusters) for HRSV, HPIV, and HAdVs . The 3 rHuman respiratory syncytial virus (HRSV) had a positive hotspot or cluster (high-high/HH) with a p-value (p=0.004) covering counties including Kericho, Kisumu, and other adjacent counties. Besides, the positive hotspot (high-high/HH)was noted with a p-value (p=0.02) in the adjacent area of HomaBay county. However, the observed HRSV cases (High-Low/HL or Low-High/LH) with p-value (p=0.002) were an outlier cluster in the adjacent area of the Bomet county.Human parainfluenza virus (HPIV) positive hotspot (high-high/HH) were also observed covering the Busia, Kisumu, HomaBay, and adjacent counties with a p-value range from (p=0.002) to (p=0.04). The outlier clusters (High-Low/HL or Low-High/LH) of HPIV cases were recorded in the adjacent region with p-value varies from (p=0.002) to (p=0.03).Human adenovirus (HAdV) positive hotspots (high-high/HH) with a p-value range from (p=0.002) to (p=0.03) were located in the same area of western regions and had outlier clusters (High-Low/HL or Low-High/LH) with (p=0.03) in the neighboring areas.The results of the purely spatial scan statistic revealed statistically significant (P< 0.05) primary and secondary spatial clusters of HRSV, HPIV, and HAdVs occurrence .HRSV cases were concentrated in a 55-kilometer radius in the western region, encompassing three major counties. Kericho, Bomet, and Kisumu counties were included, as well as an overlapping secondary cluster with an 80-kilometer radius that included Nyamira and Kisii counties. Other secondary clusters were less likely and had a radius of less than a kilometre.Human parainfluenza virus (HPIV) primary cluster was discovered over an 80-kilometer radius in the western region. This cluster included Kericho, Kisumu, Nyamira, and Kisii counties. Secondary clusters occurred in various regions, including the coastal area (Mombasa County) and the north eastern region (Isiolo County), with a radius of less than a Km. These secondary clusters were significantly smaller size compared to the primary cluster observed in western region.Human adenovirus (HAdV) cases were concentrated in an 80-kilometer radius that included Kericho, Kisumu, and Kisii counties. In Isiolo County, a secondary cluster with a radius of less than one kilometre was discovered.The results of spatiotemporal cluster analysis indicated different characteristics in the spatial and temporal distribution of HRSV, HPIV, and HAdV cases in Kenya .From 2009 to 2011, the HRSV primary cluster had cases concentrated in a 93-kilometer radius at the coastal region, and it covered the main counties of Mombasa and Kilifi. A secondary cluster with an 80-kilometer radius covering Bomet, Nyamira, Kisii, Kericho, and Kisumu County was discovered in the western region.This secondary cluster occurred from 2010 to 2011. HPIV primary cluster occurred from 2008 to 2010 with cases identified in Kericho, Kisumu, Nyamira, and Kisii County. This cluster had an 80-kilometer radius, while two secondary clusters in Mombasa and Isiolo counties had radiuses of less than a kilometre.The primary cluster of HAdVs occurred within a 93-kilometer radius of the coastal region, including Kilifi and Mombasa counties, with cases identified from 2008 to 2009. From 2007 to 2009, the western region experienced a large secondary cluster of HAdV. This secondary cluster had an 80-kilometer radius and covered Kericho, Kisumu, and Kisii counties. Another secondary cluster of HAdV with a radius of less than a kilometre was recorded in Isiolo County between 2010 and 2012.46 The three respiratory viruses were found at every surveillance site that participated in the influenza-like illness (ILI) surveillance programme. The surveillance sites represented Kenya's geographical regions, including Western-Nyanza, Rift Valley, Central, Eastern, and Coastal regions.The analysis of spatial and temporal patterns of HRSV, HPIV, and HAdV occurrence in this study provided characteristics of the influenza-like illness (ILI) distribution in Kenya from 2007 to 2013. Previous study using ILI surveillance data from the same time period reported influenza viruses causing respiratory infections. Besides that, it indicated the occurrence of other respiratory viruses in Kenya.Human respiratory syncytial virus (HRSV) occurred in 13 counties, with a high number of cases in Kisii County. The Western region was identified as the major hotspot for HRSV using Kernel density estimates (KDE), which was confirmed by local spatial autocorrelation.47Following the statistical scan, the primary cluster in this region was purely spatial. Human respiratory syncytial virus (HRSV) clustered in the Coastal region from 2009 to 2011, according to the change in time. Not surprisingly, an HRSV hotspot was also observed in the coastal region by KDE. According to a study conducted by Nyiro et al. 2018, HRSV has been identified as one of the most common pathogens causing acute respiratory tract infections in outpatients in Kenya's coastal region.49 Rainfall and warm temperatures, which characterize the climate of the Western and Coastal regions, were the climatic conditions most commonly associated with HRSV cases.50Besides the purely spatial cluster in the Western region, HRSV had primary and secondary clusters over space and time for both Western and Coastal regions respectively. The HRSV clusters could be attributed to regional population characteristics, social and climate factors. Several factors, including climate parameters, have been linked to HRSV infections in previous studies.50 Furthermore, no significant climate parameters were associated with HPIV infections, as observed in other studies published elsewhere.51 HPIV clusters could thus be attributed to factors other than climatic parameters.Human parainfluenza virus (HPIV) was found in 14 counties, with Kisii County having the highest number of cases. Kernel Density Estimate (KDE)indicated a major HPIV hotspot in the Western region. Other HPIV hotspots, however, were identified in the coastal and central regions. The local spatial autocorrelation indicated that the Western HPIV hotspot was statistically significant. This was confirmed with the purely spatial scan statistics. The space-time analysis revealed the occurrence of the HPIV cluster in the Western region from 2008 to 2010. Unlike HRSV infections, HPIV infections had an erratic distribution with no clear seasonality in Kenya.50 However, other factors such as population demographics, health, and socioeconomic determinants have been linked to HAdV infections in the literature54 A purely temporal analysis has previously been performed based on the same period of 2007 to 201350; this study expands and augments that analysis.The occurrence of HAdV was documented in 11 counties, with Nairobi County having the highest number of cases. Despite the fact that the number of HAdV cases in Nairobi was high, the major hotspot was identified in the Western region, where several counties had a larger number of HAdVs. Other hotspots were found in the coastal and central regions. The major HAdV hotspot had a significant spatial autocorrelation with the Western region. This agreed with the HAdV primary cluster observed in the same region by the purely spatial scan statistics. However, space-time analysis indicated the HAdV primary cluster occurred in the Coastal region from 2008 to 2009. Human adenovirus (HAdV) cluster in the Western region, on the other hand, was secondary and occurred from 2007 to 2009. In Kenya, there were no seasonality patterns or climate parameters associated with HAdV infections. Only warm temperatures were suggested to have a positive influence on these infections.55 Also, not all county had an ILI surveillance site. Another limitation of this study could be the use of retrospective data that cannot be extrapolated to the present. The study outputs also could not be extrapolated beyond the regions without surveillance sites. Although these data-related limitations may have an impact on the study's findings, scan statistics are a well-established method for detecting disease clusters in the spatial and temporal model.58 They are used to analyse retrospective and prospective routine data from disease surveillance programs, and the outputs can provide insights into identifying space-time clusters, which inform intervention planning and local and national public health policies.A major shortfall in this study may be due to the use of annual county population projections which are based on the inter-census data; these may not accurately count the actual population per county.Following the results of the study, HRSV, HPIV, and HAdV hotspots (clusters) occurred in Kenya's Western and Coastal regions between 2007 and 2013. Throughout the study period, the Western region appeared to be more prone to the occurrence of all three respiratory viruses. Furthermore, epidemiological studies should investigate the factors that influence the occurrence of clusters in these areas. Besides that, strategic mitigation should target those areas in order to prevent future clusters of HRSV, HPIV, and HAdV infections which could lead to epidemics.


Pseudomonas aeruginosa, as one of the bacterial species with ARGs, have not yet been completely demonstrated. In this study, we explored the TA systems from the public genomic sequencing data and genome sequences. A small scale of genomic sequencing data in 281 isolates was selected from the NCBI SRA database, reassembling the genomes of these isolates led to the findings of abundant TA homologs. Furthermore, remapping these identified TA modules on 5,437 genome/draft genomes uncovers a great diversity of TA modules in P. aeruginosa. Moreover, manual inspection revealed several TA systems that were not yet reported in P. aeruginosa including the hok-sok, cptA-cptB, cbeA-cbtA, tomB-hha, and ryeA-sdsR. Additional annotation revealed that a large number of MGEs were closely distributed with TA. Also, 16% of ARGs are located relatively close to TA. Our work confirmed a wealth of TA genes in the unexplored P. aeruginosa pan-genomes, expanded the knowledge on P. aeruginosa, and provided methodological tips on large-scale data mining for future studies. The co-occurrence of MGE, ARG, and TA may indicate a potential interaction in their dissemination.The toxin-antitoxin (TA) system is a widely distributed group of genetic modules that play important roles in the life of prokaryotes, with mobile genetic elements (MGEs) contributing to the dissemination of antibiotic resistance gene (ARG). The diversity and richness of TA systems in  Toxin-antitoxin (TA) system is a group of genetic modules widely occurring in prokaryotes. A typical TA system consists of a toxin component, providing toxicity to kill the host cells, and an antitoxin, conferring immunity to the toxin protein/RNA . They weDue to the bacterial inhibitory activity of toxin components, the TA systems have been proposed as an alternative antibacterial strategy to traditional antibiotics. Though the antibacterial activity of the TA was activated under limited conditions, the lethality of most of the toxins is confirmed in the absence of corresponding antitoxins . BesidesBased on the regulatory mechanism, the TA systems could be classified into eight classes. The type I–VI TA systems have been well documented as six main classes of TA systems . The typin silico prediction, which accounts for over 95% of the whole TADB2. It is hard to conclude that type II is the most abundant class in nature since the bias is caused by available reference sequences and the limited species of which TA systems have been systematically characterized. The diversity, richness, and preference of TA genes depend on complicated factors. A distinct distribution pattern of TA was observed among the Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria  collected were 47, 105, 7, 1, 1, and 1 of type I, type II, type III, type IV, type V, and type VI, respectively . The typbacteria , which ibacteria . The typbacteria . Though bacteria .Pseudomonas aeruginosa is a pathogenic bacteria widely distributed in nature. Being an opportunistic pathogen, it could cause a variety of acute infections   database (fections . The intst drugs . Especias (MGEs) . The TA ntenance , biofilmntenance , drug rentenance , virulenntenance , and lonntenance . The divisolates . The TA nd PasTI . Anothernd PasTI . All TA database . These rP. aeruginosa as the research object, aiming to search for TA homologs from the unexplored parts of P. aeruginosa pan-genome through in silico analysis. Genomic sequencing data of the selected isolates from P. aeruginosa were downloaded from the NCBI SRA database  on the TA-carrying sequences. The diversity, abundance, and prevalence of TA families will be characterized, and new findings of TA modules on P. aeruginosa will be discussed. Some methods were raised in this study to automate TA characterization, and the difficulties and unresolved problems in the analysis will be discussed.In this study, we selected database , reassemdatabase . Orphan P. aeruginosa). Finally, the identified TA modules were used to update the reference data set and to identify TA systems on the 5,437 genomes to characterize TA distribution in the population. Key parameters of the software used and a brief explanation are listed in The analysis workflow of this study is described in 1To select qualified genomic sequencing data of P. aeruginosa from the NCBI SRA database , data taP. aeruginosa were downloaded from the NCBI Genome database, including assemblies of complete circular replicons and draft genomes (scaffold and contigs). Among all the strains, 52 included complete plasmid sequences and they are also included in this study. For assemblies at scaffold/contig level with no corresponding plasmid information, all sequences were gone through plasmid/phage prediction with viralVerify . Given a query contig Q and a subject sequence S (reference matched by query), equal to or more than one hit (H) was observed, and the AIMH was calculated as below:Where I is the sequence identity of an alignment.The L indicates the maximal value of non-redundant merged coverage between the query and the subject. The value of L can be calculated as follows:The cov(Q) is calculated by dividing the sum of the accumulated alignment region (deduplicated regions without overlap) size on the query by the total query size. The cov(S) is calculated in the same way.E-value.The AIMH is designed for calling the best hit in batch data without further human interpretation and overcoming the difficulties of comparing local hits with only bit-score and in silico prediction. A summary of reference loci with experimental proof was recorded in The TA database TADB2  was used2 were used to predict linear helix structures and protein 3D models. These results were manually inspected, serving as additional hints for evaluating the TA result. For orphan toxins, this information serves as additional features to further confirm possible novel antitoxins. Redundant information (inner-contig and inter-sample duplication) was filtered using customize Shell/Python scripts.Extra information was collected for further evaluation of some putative TA systems. The multiple sequence alignment (MSA) was conducted using MUSCLE  or the S3 against the NR database, and the standalone rps-blast was implemented in BLAST+ against the CDD database  was introduced to evaluate the relative distance of two species based on a taxonomy tree. The pKD is calculated by comparing lineages of two species. A qualified lineage for calculating pKD should consist of seven levels: kingdom (L1), phylum (L2), class (L3), order (L4), family (L5), genus (L6), and species (L7). Given a query species A and reference species B, the pKD was calculated as below:r from −1 to −7. The edges between the L7 node of A (or B) and the lowest common ancestor (LCA) were counted, and the raw distance from each species to LCA was measured by subtracting r(A) or r(B) from r(LCA) followed by summing up both the values. The sum was then divided by the maximum distance two species could reach, which is 12, to map the raw distance in the range of . All lineage information of reference species was extracted from the NCBI taxonomy database (NTD)  . The pytTo reduce the bias introduced by poor lineage annotations in NTD, a correction method was developed to pre-process the lineage information before pKD calculation. Unqualified lineages contain missing ranks and additional ranks which do not belong to any of the seven levels of a qualified lineage as defined above ; such annotations would lead to inaccuracy. Regarding the lineages with missing ranks, the lineages will be dropped or corrected based on different scenarios: (1) if only one missing node is present and it is at L7, this lineage will be considered as a complete lineage; (2) else, check the nodes between the highest missing node (HMN) and the observed LCA (oLCA); if the rank of HMN is higher than oLCA, this lineage will be seen as a complete lineage; (3) if the HMN rank is lower than oLCA, and a node exists in between oLCA and HMN, the lineage will seem like a complete lineage; (4) if the HMN rank is lower than oLCA and no nodes exist in between oLCA and HMN, this lineage will be dropped since it cannot exclude the possibility that the real LCA is the same as HMN instead of oLCA.P. aeruginosa, or to confirm possible contamination from other species, the metagenome composition was checked. The MetaPhlAn3  were aligned to each other to see if they overlap; matches of at least 50 bp with over 40% identity were observed, and these sequences were considered to be circular.The circularity of putative plasmid or phage sequences was evaluated with reference to a previous study . In brierpoB, gapA, tufA, and gryB was calculated to represent the average copy level of all sequences in each SRA data. If the ANC of the sequences with TA was twice higher as the highest ANC among the five genes, these sequences were considered as high coverage.The copy number was also used as hints for plasmid and phage identification. In extreme cases, circular plasmids and phages have much higher copies than chromosomes. The average nucleotide coverage (ANC) of these sequences was calculated with the assistance of hisat2  to deterCombined with all the information mentioned, contamination was manually determined. Sequences with short size (<500), low coverage, and non-integrative, were considered as possible contamination and removed from this study. This information is added to P. aeruginosa PAO1 (assembly accession number GCF_000006765.1) and P. aeruginosa PA14 (GCF_000014625.1) were downloaded from the NCBI Genome database. The final identified TA modules were aligned against the chromosomal genome sequence of the model strain, P. aeruginosa PAO1 and the P. aeruginosa PA14 to find out homologous regions in these strains. Detailed information on the identified homologs in both strains is summarized in The genome sequence of cis-mobilizable elements (CIMEs), and 662 type IV secretion system (T4SS) type ICEs, respectively. Several steps for automating valid MGE region calling based on blast alignments: (1) hits with less than 5% sequence coverage over reference were filtered; (2) for global alignment (coverage on reference sequence ≥ 90%), the longest high scoring pair (HSP) with the highest identity to was chosen; (3) for local alignment (multiple shorter HSPs refer to the same reference MGE), the overlap or containment hits were merged first, then all the non-overlap HSPs had gone through distance check; that is, using a dynamic cutoff (20% of the reference size) to a pair of HSPs were distant or adjacent, to determine distant HSP groups. Finally, given an HSP group, if the accumulated size of all non-overlap HSP members were less than 120% of the reference size, the HSP group was considered an MGE region.The MGE homologs were identified with reference to the ICEberg database . This daFor ARG detection, nucleotide sequences of ARG genes from the CARD database  were col2 = 84.535, P = 2.2e-16). The identity score range between TA-chromosome and TA-bacteriophage was roughly the same. As active MGEs, the bacteriophage and plasmid should have higher mutation rates, let alone the genes on them. This is further confirmed by checking the distribution of TA count by identity grouping, as most of the TA from the putative plasmid subset belongs to a low similarity group (AIMH lower than 50%)  were identified from HTS data of all strains, among which 94% were identified on chromosome sequences, while small fractions of TA systems were identified in the putative bacteriophage  or plasmids . Interestingly, the TA hits found on chromosomes and putative bacteriophages showed higher homology to the reference compared to those on plasmids, , as supphan 50%) .A summary barplot was created to visualize the overall TA system counts grouped by TA classification and identity . RegardiIn general, the abundance of type II TA homologs is expected since it is the most abundant TA system uncovered from nature so far. This may be a bias introduced from the unbalanced reference database. In view of the TA families, most type II gene hits uncovered at this stage were of reported TA systems, as documented in two studies . A descrhok homologs of type I, the dtas1_hok and the dtas2_hok, were identified from two distinct chromosomal contigs of the sample SRR10338934. The dtas2 was omitted for further analysis of the low homology (34%) toward reference and missing a convincing toxin domain. The dtas1_hok nucleotide sequence was 73% identical to the reference hok T6324, combined with the result of domain scan, open reading frame (ORF) prediction, and MSA, we manually confirmed the complete hok homolog with the similar size of T6324 (155/161 bp)  . A mok-lediction .sok-like antitoxin. The type I TA system is featured by antisense RNA antitoxin which binds the toxin mRNA transcript to disrupt the toxin translation (mok and hok were required for the full-size hok mRNA to form proper hyper-structure involved with expression regulation (hok (sok target (sokT) of flm locus, a divergent form of hok, was also observed (sok homolog since the sok locus was reported to overlap with mok locus (sok was determined on the flank sequence of dtas1_hok containing these signatures through the database searching function of Rfam (sok of Enterobacter sp. WP_7_1 (URS0000E30588_1907563) with an identity of 78%.An attempt to find the “missing” antitoxin led to the finding of a nslation . The Shigulation . Scanninion . The genetic organization of dtas4_ralD-ldrD loci was similar to the ralD-ldrD reported in Escherichia coli K-12  from E. coli str. K-12 substr. MG1655. It is reported that the toxin cptA is featured by a transmembrane domain which may spatially limit the cptA activity only near the membrane  and Hha  is highly similar to the NP_414994.1. Two cysteine residues, C18 and C124 were observed in the TomB sequence of JUPS01002551.1. Though they conserve residues of the TomB family, these are not essential for the antitoxin activity of YomB toward Hha . The fouP. aeruginosa, e.g., the hok-sok. This finding drew our attention since some TA systems were reported to carry a narrow antibacterial activity spectrum (P. aeruginosa with the aims to (a) provide primitive hints to confirm the antibacterial spectrum of specific TA systems; (b) find out whether TA components were from different species, or, more conserved, to measure their homology.During the progression of this study, we noticed that a number of TA systems have not yet been reported in spectrum , though P. aeruginosa and reference species was observed: regardless of the type II TA systems, all references were of remote species. A dot plot was created to visualize the pKD of TA homologs; all pKD values of TA systems/orphans were either 8 or 12  , and one is in a single copy (LDR-D)  found in E. coli K12), the toxin dtas_ldrD does not. Considering the toxin sequence only shared a low identity with known references, we further inspect the possible impacts of the SNPs on its function.The dtas4_ (LDR-D) . The ldrtrans-membrane domain ; also there was no sign of a region similar to the promoters reported in ralD-ldrD loci . We also noted that another assembly GCA_001181985.1 has a genome size of 11.85 Mb although no qualified type VII/VIII gene hits were detected. Interestingly, these three assemblies have roughly the same genome size but from distinct geographical sources, , and it is not like they are just simply contaminated. Though results from MetaPhlAn3 confirmed that the contigs carrying these TA modules were classified as P. aeruginosa, it cannot be concluded that these type VII and type VIII homologs were detected on P. aeruginosa, or another unknown species close to Pseudomonas.Besides the TA modules mentioned above, we also identified several homologs belonging to type VII and type VIII TA, which are two systems established in recent years. These homologs have high similarity to the reference sequences and solid genetic evidence to prove they are qualified TA systems. Based on the analysis of the conserve/essential residues and protein structure, it could be assumed that the protein product or sRNA product of the TomB, Hha, and sdsR homologs may be functional. However, a question was raised about the type VII and type VIII homologs regarding their location in the actual species, in silico homology scan and iteration , TraG-RHH, and TraG-arbB which are 1,0651, 6,405, 1,565, and 1,004 count in total, individually. All TraG hits refer to a group of reference sequences which are all in silico predicted toxins recorded in the TADB2 database. Thus it is uncertain whether their products are toxic. Another interesting finding is a group of abiEii-X modules. This group includes the abiEii-abiEi homolog with a total count of 358 in P. aeruginosa population, which is a well-established type IV TA system  and abiEii-higA (771). Also, two type IV-antitoxin with type II toxin combination, the relE-abiEi, and GNAT-cbtA were observed, but their count were as small as 14 and 1, individually. It is unsure how to categorize these modules, such as the combo of TA was labeled as “others” in A group of TA modules, with all the toxin components, are TraG, were also present in great numbers in the 5,437 genome or draft genomes. The top 4 abundant Trag-X TA modules are TraG-A system ; thus thabiEii-X modules as mentioned above, the type IV homologs, cbeA-cbtA, cptA-cbtA, and four putative orphans were uncovered in the 5,000 = strain population. But they are limited in several strains  and all these strains were draft genomes (contig level) . A similar situation could be observed in other systems, including the type I, type VII, and VIII modules. We manually inspected all sequences and found that all candidate orphans were located on small contigs (≤1000 bp). Considering their small number in population and uncertainty to conclude their source , the identity of these homologs requires further evaluation.The few non-type II systems uncovered in the 5,437 genomes were also interesting findings. Besides the g level) . Though In this study, we uncovered ample genomic sequences not only limited to chromosomal sequences but also those belonging to putative plasmids and putative viruses. This is due to the advantage of the HTS, from which the generated data has the feasible ability to contain all sorts of genomic elements. But the power of HTS on data mining has not been fully unleashed. The isoforms of genes of interest were not explored in this study and it is usually overlooked in other studies, but it is proved to be capable to uncover isoforms and used in diversity research . AnotherP. aeruginosa data is difficult to achieve, let alone the increasing size of available sequencing data, and the reference sequences uncovered by advanced research. It is expected that novel findings could be found in future studies based on data on a larger scale.It is noted that other types of genetic elements involving antibacterial activity have not been explored; these include but are not limited to the genes coding lytic enzyme/proteins, anti-biofilm compounds, and abortive infection system. Considering the discovery of abundant TA systems in this study, we assume that abundant homologs or new systems belonging to these gene types could be found. Besides, more than 100 strains were involved in this study. The findings were based on data on a limited scale, an exhaustive exploration of all available A large number of TA and ARG are located in or close to MGEs, as depicted in in silico identification of genomic public sequencing data of P. aeruginosa. A wealth of divergent TA homologs was identified on the chromosomal, putative bacteriophage/plasmid regions, and other MGEs. The new type II TA combinations were an extension of our knowledge of the type II systems in P. aeruginosa, while most of the non-type II TA systems have not yet been reported in P. aeruginosa. Besides, a large number of TA homologs are located close to the MGEs and ARGs on the genomes, which indicates an interaction among these three genomic elements. These new findings expanded our knowledge of P. aeruginosa and conclude the great prospect of TA mining from the unexplored bacteria pan-genome. Our methods provide prime knowledge and experience in TA findings based on large-scale data mining. The additional experimental proof is required to confirm the production capability of the identified TA systems and the bioactivity of the corresponding product.In this study, we uncovered a great number of TA systems through The original contributions presented in this study are included in the article/ZD was the major contributor who made substantial contributions to the conception, data collection, and manuscript writing of this project. TW, SX, LaZ, and WT provided the crucial technical support to this study. EH, LiZ, and MC provide support in data visualization for this study. GY as the supervisor of this study, provide support in all aspects throughout the progression of this study, and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.


Inflammatory bowel disease (IBD) may be associated with depression which is considered an important cause of dementia and Parkinson’s disease (PD). In the present study, the effects of depression on the development of dementia and/or PD in patients with IBD were evaluated.A nationwide population-based cohort study was conducted using claims data from the Health Insurance Review and Assessment Service in Korea. The incidence of dementia and PD were analyzed based on the presence of depression in patients with IBD.p < 0.001) and PD  significantly more than IBD patients without depression. Compared with IBD patients without depression, the risk of developing dementia was significantly higher in IBD patients with depression . Compared with IBD patients without depression, the risk of developing PD was significantly higher in IBD patients with depression . The cumulative incidence of dementia and PD in IBD patients with depression was significantly higher than in IBD patients without depression and showed a steady increase after a diagnosis of depression.During a mean follow-up of 8 years, IBD patients with depression experienced dementia (6.7 vs. 2.0%; The risk of dementia and/or PD increased after a diagnosis of depression in patients with IBD. Dementia and Parkinson’s disease (PD) are the most common neurodegenerative disorders characterized by chronic progressive disease that may lead to death , 2. The Inflammatory bowel disease (IBD), such as Crohn’s disease (CD) and ulcerative colitis (UC), are immune-related, and chronic inflammatory diseases may play a role in development of neurodegenerative disorders through chronic systemic inflammation and disruption of intestinal and blood-brain barriers , 10. FurSimilarly, the risk of developing depression increases in patients with IBD , and depThe present population-based cohort study was performed using data from the Health Insurance Review and Assessment Service (HIRA), which is the nationwide mandatory health care system that covers approximately 97% of healthcare service providers and citizens in South Korea . The HIRFrom January 2010 to December 2017, all patients with IBD assigned both the ICD-10  and V codes  were included in the present study. The study participants were divided into the following two groups: the incident group, defined as patients with IBD who were newly diagnosed with depression between January 2010 and December 2017, and the prevalent group, defined as patients previously diagnosed with depression and coded with IBD between January 2010 and December 2017. In this study, the incident group was analyzed separately from the prevalent group to determine whether newly developed depression in IBD patients increased the risk of dementia and/or Parkinson’s disease.Depression was detected using ICD-10 codes (F32–34) as defined in previous studies , 14. A wDemographic data of the study population , comorbidities, and medication use for IBD were collected. Information regarding comorbidities, including hypertension  treated with anti-hypertensive agents or systolic/diastolic blood pressure ≥ 140/90 mmHg), diabetes mellitus , dyslipidemia [ICD-10 code (E78) treated with anti-hyperlipidemic agents or fasting total cholesterol ≥ 240 mg/dl], myocardial infarction (ICD-10 codes (I21–22) with hospitalization) or stroke (ICD-10 codes (I63–64) with hospitalization and brain imaging using computed tomography or magnetic resonance imaging) were defined using ICD-10 codes as defined in previous studies , 20. In The primary outcome was newly diagnosed dementia and/or PD after the onset of depression in patients with IBD. Alzheimer’s dementia was diagnosed with ICD-10 codes (F00 and G30) and vascular dementia with F01 code, as defined in a previous study , 22. Patt-tests were used for continuous variables and chi-square tests were used for categorical variables. Continuous variables were presented as means ± standard deviation (SD) and categorical variables as number and percentage. Cox proportional hazards regression models were used to assess the risk of newly developed dementia and/or PD associated with depression in patients with IBD. Competing risk analysis was performed using the Fine-Gray Competing risk model. Results were expressed as adjusted hazard ratio (aHR) and 95% confidence interval (CI). The potential modification effect based on age, sex, residence, comorbidities, and therapeutic drug use for IBD was evaluated through stratified analysis and interaction testing using a forest plot. The cumulative incidence for each group was plotted with Kaplan-Meier curves and compared using the log-rank test. All statistical tests were two-tailed and the statistical significance level was set at a p < 0.05. Statistical analyses were performed using R programming version 3.4.3  and SAS Version 9.2  for Windows.To compare characteristics between two groups, p < 0.001), female (p < 0.001), and have a rural residence (p < 0.001). The prevalence of comorbidities including hypertension, DM, dyslipidemia, and stroke was significantly higher in patients with dementia than patients without dementia (p < 0.001 for each variable). However, therapeutic drug use for IBD, including 5-ASA and immunomodulators, was significantly more frequent in patients without dementia than in patients with dementia (p < 0.05 for each drug).From January 2010 to December 2017, a total of 17,391 IBD patients were enrolled in the present study; 421 individuals (2.42%) developed dementia and 64 (0.37%) developed PD during a median follow-up of 8 years. The baseline characteristics of the study population based on the occurrence of dementia and PD are shown in p < 0.001) and have higher prevalence of comorbidities including hypertension and dyslipidemia (p < 0.001 for each variable). However, IBD patients without PD used immunomodulators more frequently than patients with PD (p = 0.023).Regarding the development of PD in IBD patients with or without depression, there were 64 subjects with PD and 17,327 without PD . IBD patp < 0.001). The incidence and risk of dementia in the IBD with depression were significantly higher than in the IBD without depression after adjusting for age, sex, residence, comorbidities  and therapeutic drug use for IBD . When the depression cohort was divided into the incident and prevalent groups, the incidence and risk of dementia were also higher in the IBD with depression than in the IBD without depression . Further sensitivity analysis was conducted for the competing risk of stroke and myocardial infarction and the increased risk dementia in IBD patients with depression remain robust .During the follow-up period, 104 IBD patients (6.7%) with depression and 317 IBD patients (2.0%) without depression developed dementia (p = 0.033 in Model 3). Similar results were also found between the incident and prevalent groups but without statistical significance in the incident group . In UC patients with depression, the incidence and risk of dementia were also higher than in UC patients without depression . The incidence and risk of dementia showed similar results in both the incident and prevalent groups . The incidence and risk of PD in the IBD with depression were significantly higher than in the IBD without depression after adjusting for age, sex, place of residence, comorbidities, and therapeutic drug use for IBD . Similar results were also found between the incident and prevalent groups, however, statistical significance was not observed in the incident group . Further sensitivity analysis for the competing risk of stroke and myocardial infarction also confirmed the robustness of main results .During the follow-up period, 17 IBD patients (1.1%) with depression and 47 IBD patients (0.3%) without depression developed PD (p = 0.470 in Model 3). Further analyses based on incident and prevalent depression showed similar results that incidence and risk of PD was also higher in the CD patients with depression than in CD patients without depression, however, statistical significance was not observed . The incidence and risk of PD were higher in UC patients with depression than in UC patients without depression  which was similar to the incident and prevalent groups. However, statistical significance was not observed in the incident group , and therapeutic drug use for IBD  were evaluated. The effects of depression in IBD patients on developing dementia and/or PD were not significantly different based on subgroups indicating depression independently increased the incidence of dementia and/or PD regardless of the subgroup . HoweverIn this nationwide, population-based cohort study including 17,391 patients with IBD in the HIRA database, a significantly increased risk of developing dementia and/or PD in IBD patients with depression was found compared with IBD patients without depression. IBD patients with depression were 2.0- and 2.5-fold more likely to develop dementia and PD, respectively, compared with IBD patients without depression, even after adjusting for age, sex, residence, comorbidities, and therapeutic drug use for IBD.The effects of depression on developing dementia and/or PD were consistent across IBD subtypes and the cumulative incidence showed a steady increase for 8 years after diagnosis of IBD with depression. The effects of depression on the risk of developing dementia and/or PD in IBD patients were independent of age, sex, and therapeutic drugs for IBD, except the association of depression in IBD patients with the development of Alzheimer’s dementia was greater in patients with DM.To the best of our knowledge, this is the first study in which the effects of depression on the development of dementia and/or PD in patients with IBD were analyzed. Furthermore, this was the largest population-based cohort study to date in which claims data were used to explain the risk of developing dementia and/or PD in IBD patients with depression. In previous studies, IBD patients were shown to have a high risk of depression, and results of the present study demonstrated the presence of depression in IBD patients can be a risk for neurodegenerative disorders including dementia and PD . ConsequFirmicutes and reduced abundance of Proteobacteria, have been identified in previous studies (Several plausible mechanisms may contribute to increased risk of developing dementia and/or PD in IBD patients with depression. First, psychological stress, such as depression, can induce microglial activation, peripheral cell mediated immune reactions, and oxidative and nitrosative stress . IBD can studies , 31. In  studies .The results of this study showed depression independently increased the risk of dementia and/or PD regardless of sex and age. In a recent meta-analysis , the risThe present study had several limitations due to its retrospective design. First, the risk of developing dementia and/or PD based on IBD disease severity could not be determined because the disease severity was not available from the HIRA database. To overcome this limitation, analysis based on the use of therapeutic drugs for IBD, including 5-ASA, steroids, and immunomodulators, as indicators of the disease severity was performed and difference in the incidence of dementia and PD was not found. Second, multivariable Cox regression models were used for minimizing confounding effects on developing dementia and/or PD, however, some potential confounding factors, such as lifestyle or personal health behaviors, could not be included. Smoking and alcohol consumption may be associated with developing neurodegenerative disorders as well as IBD, thus, further investigations are required to evaluate the risk of developing dementia and/or PD based on these factors , 38. ThiIn conclusion, the risk of developing dementia and/or PD increased after a diagnosis of depression in patients with IBD. Therefore, if depression is accompanied by IBD, physicians should consider the risk of developing dementia and/or PD may increase.The data used and/or analyzed in this article will be shared upon reasonable request to the corresponding author.The studies involving human participants were reviewed and approved by Seoul National University College of Medicine-Seoul National University Hospital Institutional Review Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.KC: conceptualization, data curation, and writing—original draft preparation. KH: methodology, formal analysis, and resources. KC, HJL, and JK: validation. HJL, JPI, and S-JK: writing—review and editing. KC, KH, HJL, and JPI: visualization. HJL: supervision and project administration. All authors: approval of the final manuscript.