Public Transportation Environment and Medical Choice for Chronic Disease: A Case Study of Gaoyou, China

Community medical service construction: identifying factors that influence medical choice for patients with non-communicable chronic diseases in the Southwest China

BACKGROUND

Community medical institutions play a vital role in China's healthcare system. While the number of these institutions has increased in recent years, their construction contents remain insufficient. The potential of community medical institutions in preventing, screening, diagnosing, and treating non-communicable chronic diseases (NCDs) has not been fully utilized. This study aims to assess the status of construction contents in community medical institutions in Southwest China and examine how these contents influence the medical choices of NCD patients.

METHODS

Descriptive statistics were used to evaluate the construction content of community medical institutions. Multiple-sets of multinomial logistic regression were employed to analyze the associations and marginal impacts between construction content and medical choices. Shapley value analysis was applied to determine the contribution and ranking of these impacts.

RESULTS

Descriptive statistics revealed satisfactory construction contents in community medical institutions. Notably, factors such as service attitude, nursing services, expert consultations, charging standards, medical equipment, medical examinations, privacy protection, and referrals significantly influenced medical choices. Among these, service attitude, charging standards, and privacy protection had the most significant marginal improvement effects on NCD patients' choices, with improvements of 12.7%, 10.2%, and 5.9%, respectively. The combined contribution of privacy protection, medical examinations, service attitude, charging standards, and nursing services to medical choices exceeded 80%.

CONCLUSION

Optimizing the service contents of community institutions can encourage NCD patients to seek medical care at grassroots hospitals. This study addresses crucial gaps in existing literature and offers practical insights for implementing new medical reform policies, particularly in underdeveloped regions of Southwest China focusing on hierarchical diagnosis and treatment.

Choices of medical institutions and associated factors in older patients with multimorbidity in stabilization period in China: A study based on logistic regression and decision tree model

Background

As China's population ages, its disease spectrum is changing, and the coexistence of multiple chronic diseases has become the norm with respect to the health status of its elderly population. However, the health institution choices of older patients with multimorbidity in stabilization period remains underresearched. This study investigate the factors influencing the choices of older patients with multimorbidity to provide references for the rational allocation of healthcare resources.

Methods

A multistage, stratified, whole-group random-sampling method was used to select eligible older patients from September to December of 2022 who attended the Community Health Service Center of Guangdong Province. We adopted a self-designed questionnaire to collect patients' general, disease-related, social-support information, their intention to choose a healthcare provider. A binary logistic regression and decision tree model based on the Chi-squared automatic interaction detector algorithm were implemented to analyze the associated factors involved.

Results

A total of 998 patients in stabilization period were included in the study, of which 593 (59.42%) chose hospital and 405 (40.58%) chose primary care. Our binary logistic regression results revealed that age, sex, individual average annual income, educational level, self-reported health status, activities of daily living, alcohol consumption, family doctor contracting, and family supervision of medication or exercise were the principal factors influencing the choice of medical institutions for older patients with multimorbidity (p < 0.05). The decision-tree model reflected three levels and 11 nodes, and we screened a total of four influencing factors: activities of daily living, age, a family doctor contract, and patient sex. The data showed that the logistic regression model possessed an accuracy of 72.9% and that the decision tree model exhibited an accuracy of 68.7%. Prediction using the binary logistic regression was thus statistically superior to the categorical decision-tree model based on the Chi-squared automatic interaction detector algorithm (Z = 3.238, p = 0.001).

Conclusion

More than half of older patients with multimorbidity in stabilization period chose hospitals for healthcare. Efforts should be made to improve the quality of healthcare services and increase the medical contracting rate and recognition of family doctors so as to attract older patients with multimorbidity to primary medical institutions.

Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment

Medical facilities are an important part of urban public facilities and a vital pillar for the survival of citizens at critical times. During the rapid spread of coronavirus disease (COVID-19), Wuhan was forced into lockdown with a severe shortage of medical resources and high public tension. Adequate allocation of medical facilities is significant to stabilize citizens’ emotions and ensure their living standards. This paper combines text sentiment analysis techniques with geographic information system (GIS) technology and uses a coordination degree model to evaluate the dynamic demand for medical facilities in Wuhan based on social media data and medical facility data. This study divided the epidemic into three phases: latent, outbreak and stable, from which the following findings arise: Public sentiment changed from negative to positive. Over half of the subdistricts in three phases were in a dysfunctional state, with a circular distribution of coordination levels decreasing from the city center to the outer. Thus, when facing major public health emergencies, Wuhan revealed problems of uneven distribution of medical facilities and unreasonable distribution of grades. This study aims to provide a basis and suggestions for the city to respond to major public health emergencies and optimize the allocation of urban medical facilities.

Patients' Willingness of First Visit in Primary Medical Institutions and Policy Implications: A National Cross-Sectional Survey in China

Background

The Chinese hierarchical treatment system expects patients to first visit primary medical institutions (PMIs), and patients' willingness determined their utilization of primary health care. The aim of this study was to explore the factors associated with patients' willingness to make their first visit to PMIs.

Methods

We employed multistage stratified sampling and convenience sampling to administer questionnaires to 1,507 patients in Beijing, Qinghai, and Fujian. Patients' willingness of first visit in PMIs was analyzed using Chi-square test and binary logistic regression.

Results

Of the 1,507 participants in the survey, 55.1% were willing to make their first visit in PMIs. Fewer patients in Beijing (17.6%) are willing to make their first visit in PMIs than those in Qinghai (71.9%) and Fujian provinces (72.0%). Binary logistic regression analysis revealed that higher recognition of the community first visit policy and higher satisfaction with the medical technology of PMIs are associated with patients' willingness of first visit in PMIs.

Conclusions

Due to differences in local economic conditions, medical resources, and policy formulation, there are differences among provinces in patients' willingness of first visit in PMIs. To increase patients' rate of visits in PMIs, it is important to improve service capacity and quality of PMIs and change residents' attitudes for PMIs.