Objective To understand the status quo of hierarchical diagnosis and treatment effect of medical alliance in Beijing and explore the influencing factors.Methods The convenience sampling method was used to select 26 vertical medical alliances,and the weighted TOPSIS method was used in combination with the index system established in the previous study to evaluate the effect of hierarchical diagnosis and treatment.The factors influencing the effect of hierarchical diagnosis and treatment in medical alliances were summarized through interviews with insiders,and the rank sum test was used to explore the factors influencing the effect of hierarchical diagnosis and treatment in medical alliances.Results The medical alliance B,A2 and A3 ranked high,and the implementation effect was relatively good in the four dimensions of"primary care first consultation,dual-way referral,acute and slow treatment,and vertical linkage";The C2,A8 and F2 medical alliances ranked low,and the implementation effect in the dimensions of"dual-way referral","acute and slow treatment"and"vertical linkage"was significantly lower than that of other medical alliances.The analysis results showed that the differences in the support intensity and core hospital level of different medical alliances were statistically significant(P＜0.05),which affected the hierarchical diagnosis and treatment effect of medical alliances.Conclusion While strengthening the information construction and improving the initiative of grassroots and the signing rate of family doctors,it is necessary to improve the support of core hospitals to promote the sinking of resources.Core hospitals should optimize resource allocation according to local conditions and promote hierarchical diagnosis and treatment.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective:To evaluate the benefits and risks of advanced age patients with acute anterior circulation large vessel occlusive stroke (ALVOS) accepted mechanical thrombectomy (MT), and explore the related influencing factors for prognoses in these patients.Methods:Six hundred and eighty patients with acute anterior circulation ALVOS accepted MT in 3 comprehensive stroke centers from January 2014 to December 2020 were sequentially collected. (1) Patients were divided into advanced age group (≥80 years old) and non-advanced age group (<80 years old) according to age, and the differences between the two groups were compared in successful postoperative vascular recanalization rate, incidence of perioperative complications, and good prognosis rate (modified Rankin scale [mRS] scores≤2) and mortality 90 d after onset. (2) Patients were divided into good prognosis group (mRS scores≤2) and poor prognosis group (mRS scores>2) according to the prognoses 90 d after onset; univariate analysis and multivariate Logistic regression analysis were used to investigate the independent factors for prognoses of the patients after MT. (3) According to the prognoses 90 d after onset, the advanced age patients were divided into good prognosis subgroup (mRS scores≤2) and poor prognosis subgroup (mRS scores>2). Univariate analysis and multivariate Logistic regression analysis were used to investigate the independent factors for prognoses of the elderly patients after MT.Results:(1) In these 680 patients, 92 patients (13.5%) were into the advanced age group and 588 patients (86.5%) were in the non-advanced age group; patients in the advanced age group had significantly lower successful recanalization rate (67.4% vs. 77.9%), significantly lower good prognosis rate 90 d after onset (20.7% vs. 50.2%), and statically higher mortality 90 d after onset (40.2% vs. 21.1%) as compared with the non-advanced age group ( P<0.05); however, there was no significant difference between the two groups in the incidences of symptomatic intracranial hemorrhage (sICH, 15.6% vs. 10.6%) and malignant cerebral edema (MCE, 12.2% vs. 17.6%, P>0.05). The baseline data of the advanced age and non-advanced age patients were further matched with propensity score matching analysis (1:1) and statistically analyzed: the 91 elderly patients had significantly lower good prognosis rate 90 d after onset (20.9% vs. 36.3%) and MCE incidence (12.4% vs. 33.3%) than the 91 non-elderly patients ( P<0.05); there was no significant differences in successful vascular recanalization rate (67.0% vs. 71.4%), sICH incidence (15.7% vs. 17.6%) or mortality 90 d after onset (39.6% vs. 37.4%) between the two groups ( P>0.05). (2) Among the 680 patients, 314 (46.2%) had good prognosis and 366 (53.8%) had poor prognosis. As compared with the good prognosis group, the poor prognosis group had significantly higher proportion of patients at advanced age, significantly lower proportion of male patients, significantly higher proportion of patients with hypertension, diabetes or atrial fibrillation, significantly lower baseline Alberta Stroke early CT (ASPECT) scores, significantly higher baseline National Institutes of Health Stroke Scale (NIHSS) scores, statistically higher proportion of patients with cardiogenic embolism, significantly lower incidence of tandem lesions, significantly shorter time from onset to sheathing, statistically higher proportion of internal carotid artery occlusion, significantly lower proportion of patients with grading 2 collateral circulation, and significantly lower proportion of successful vascular recanalization ( P<0.05). Advanced age ( OR=3.144, 95%CI: 1.675-5.900, P<0.001) was an independent factor for prognoses 90 d after MT, in addition to baseline ASPECT scores, baseline NIHSS scores, diabetes mellitus, successful recanalization, and collateral circulation grading. (3) In the advanced age group, there were 19 patients (20.7%) with good prognosis and 73 patients (79.3%) with poor prognosis. As compared with the good prognosis subgroup, the poor prognosis subgroup had significantly lower proportion of male patients, significantly lower proportion of patients with grading 2 collateral circulation or complete recanalization, and significantly higher baseline NIHSS scores ( P<0.05). Baseline NIHSS score ( OR=1.482, 95%CI: 1.187-1.850, P=0.001) was an independent factor for prognoses 90 d after MT in advanced age patients. Conclusion:Although advanced age is an independent risk factor for prognoses of patients with acute anterior circulation ALVOS accepted MT, there are still some advanced age patients benefiting from MT without increased complications, especially for those with low baseline NIHSS scores.