You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Catastrophic health expenditure: a comparative analysis of empty-nest and non-empty-nest households with seniors in Shandong, China The aim of this study was to compare the catastrophic health expenditure (CHE) prevalence and its determinants between empty-nest and non-empty-nest elderly households. Shandong province of China. A total of 2761 elderly households are included in the analysis. CHE incidence among elderly households was 44.9%. The CHE incidence of empty-nest singles (59.3%, p=0.000, OR=3.19) and empty-nest couples (52.9%, p=0.000, OR=2.45) are both statistically higher than that of non-empty-nest elderly households (31.4%). An inverse association was observed between CHE incidence and income level in all elderly household types. Factors including 1 or more household elderly members with non-communicable chronic diseases in the past 6 months, 1 or more elderly household members being hospitalised in the past year and lower household income, are significant risk factors for CHE in all 3 household types (p<0.05). Health insurance status was found to be a significant determinant of CHE among empty-nest singles and non-empty-nest households (p<0.05). CHE incidence among elderly households is high in China. Empty-nest households are at higher risk for CHE than non-empty-nest households. Based on these findings, we suggest that special insurance be developed to broaden the coverage of health services and heighten the reimbursement rate for empty-nest elderly in the existing health insurance schemes. Financial and social protection interventions are also essential for identifie d at-risk subgroups among different types of elderly households.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["catastrophic", "Catastrophic", "non-communicable chronic diseases"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": [], "ClinicalAttribute": [], "Eukaryote": [], "Finding": ["hospitalised", "findings", "risk factors", "singles"], "Food": [], "HealthCareActivity": ["reimbursement rate", "Health insurance", "health services", "insurance", "health insurance"], "InjuryOrPoisoning": [], "IntellectualProduct": ["subgroups", "schemes"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["seniors", "elderly", "elderly members"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["study", "analysis"], "SpatialConcept": ["Shandong, China", "broaden", "China", "Shandong province of China"], "Virus": []}