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OBJECTIVES: In this study, we aim to evaluate the effect of urban and rural resident medical insurance scheme (URRMI) on the utilisation of medical services by urban and rural residents in the four pilot provinces. SETTING AND PARTICIPANTS: The sample used in this study is 13 305 individuals, including 2620 in the treatment group and 10 685 in the control group, from the 2011 and 2015 surveys of China Health and Retirement Longitudinal Study. OUTCOME MEASURES: Propensity score matching and difference-in-differences regression approach (PSM-DID) is used in the study. First, we match the baseline data by using kernel matching. Then, the average treatment effect of the four outcome variables are analysed by using the DID model. Finally, the robustness of the PSM-DID estimation is tested by simple model and radius matching. RESULTS: Kernel matching have improved the overall balance after matching. The URRMI policy has significantly reduced the need-but-not outpatient care and significantly increased outpatient care cost and inpatient care cost for rural residents, with DID value of −0.271, 0.090 and 0.256, respectively. After robustness test, the DID competing results of four outcome variables are consistent. CONCLUSIONS: URRMI has a limited effect on the utilisation of medical and health services by all residents, but the effect on rural residents is obvious. The government should establish a unified or income-matching payment standard to prevent, control the use of medical insurance funds and increase its efforts to implement URRMI integration in more regions to improve overall fundraising levels.

IntroductionSurvivors of stroke have complex needs from ongoing disabilities and have increased risk of cardiovascular diseases. The societal costs are therefore substantial. Person-level longitudinal data on the longer-term hospital utilizations of patients with stroke in Australia, and the factors that may influence usage in this setting, are rarely reported.
Objectives and ApproachWe used person-level linkages between the Australian Stroke Clinical Registry (AuSCR: 2009-2013) and hospital admission and Emergency Department (ED) data from four states to examine determinants of hospital utilisation following stroke. The index event was the first event recorded in AuSCR. The rate of hospital contacts/person/year was calculated from contacts 30-365 days post-discharge. Disability was determined from responses to EQ-5D-3L data collected at 90-180 days post-stroke. Comorbidities were identified using ICD-10 discharge diagnosis codes (5 year look back including the index event). Negative binomial regression was used adjusting for patient clustering by hospital and pre-stroke contacts and stratified by disability.
ResultsAmong 10,082 adults with acute stroke (55% male, median age 74 years, 81% ischaemic, 14% hemorrhagic, 5% undetermined, 44% with disability) from 39 hospitals, 57% had a hospital admission or ED contact in the first 30-365 days post-hospital discharge, with median contacts/person/year post-stroke of 1.09 (Q1, Q3: 0, 3.27) compared to a pre-contact rate of 0 (Q1, Q3: 0, 2.18). The strongest associations with subsequent hospital contacts were prior contacts (IRR:1.10, 95%CI:1.07, 1.13), not able to walk on admission (stroke severity) (IRR:1.19, 95%CI 1.07, 1.31) and having a higher comorbidity index score (IRR:1.18, 95%CI:1.14, 1.22). Within stratified cohorts younger age was associated with increased contacts in those with disability (
Conclusion/ImplicationsIn a large linked cohort of patients we have demonstrated the substantial ongoing burden that stroke imposes on hospital systems, particularly regarding survivors with other comorbidities and younger survivors with disability. Knowledge of disability and comorbidity burden may assist with targeting community and hospital interventions to reduce post-stroke hospital usage.

IntroductionRecent advances in data linkage infrastructure in Australia mean that data can be linked based on various identifiers across datasets. In a first for Australia, we tested the feasibility of linking data between a clinical quality disease registry with Australian and state government health data across multiple jurisdictions.
Objectives and ApproachTo determine whether high quality linked data for stroke can be obtained using a non-government managed registry (Australian Stroke Clinical Registry, AuSCR), national death registry data (Australian government), and hospital admission and emergency presentation data (state governments) to assess the accuracy of consistent variables across the different datasets. We used a cohort design with probabilistic data linkage to merge patient-level records. Descriptive statistics presented for matching concordance and Cohen's kappa for concordance across demographic variables. The sensitivity and specificity of in-hospital deaths collected in the AuSCR was assessed against national death registrations.
ResultsThere were 16,214 registrants in the study cohort. Their identifiers in the AuSCR from 2009-2013 were linked with death, emergency department and hospital discharge data from April 2004 to December 2016. In total, 99% of the AuSCR registrants were linked to one or more datasets; 98\% were linked with emergency presentation (80%) and/or admission (95%) data. Linkage to national death registrations identified 4,183 death; 1440 of these were identified as in-hospital deaths in both data sets demonstrating that in-hospital death classification in AuSCR had a 98.7% sensitivity and 99.6% specificity. Concordance between common demographic variables was excellent (kappa 0.84 for aboriginal status and kappa 0.99 for sex).
Conclusion/ImplicationsThe majority of AuSCR registrants were accurately linked to the Australian and state government datasets. Linkage quality was excellent and there was high concordance between common variables. The ability to reliably merge the datasets assures future comprehensive analyses of stroke care, ongoing health care resource utilisation and patient outcomes.

Disaster medicine education in medical curricula is scarce and frequently nonexistent. It is reasonable to initiate educational approaches for physicians in this field at the medical school level. An understanding of disaster medicine and the health care system during massive casualty incidents has been recommended as an integral part of the medical curriculum in the United States and Germany. The goal of the reformed curriculum was to develop a longitudinal integrated disaster and military medicine education program extending from the first year to the sixth year based on previously separated clinical and military medicine topics. Emergency medicine physicians, military emergency medical technicians, and Tactical Combat Casualty Care instructors formed an interprofessional faculty group and designed a learning curriculum. A total of 230 medical students participated in the revised disaster preparedness curriculum. Satisfaction survey response rates were high (201/230, 87.4%). Most of the free-text comments on the program were highly appreciative. The students considered the number of teaching hours for the whole program to be adequate. The students showed significant improvements in knowledge and judgment regarding disaster medicine after the program. We found that medical students were highly interested, were appreciative of, and actively participated in this longitudinal integrated disaster and military medicine education program, but gaps existed between the students' scores and the educators' expectations. The educators believed that the students needed more disaster preparedness knowledge and skills.

Abstract

Background
Hearing loss and lifestyle factors have been associated with cognitive impairment. We aimed to explore the joint association of combined healthy lifestyle factors and hearing loss with cognitive impairment, which has been scarcely studied.


Methods
This baseline study used data from the CHOICE-Cohort study (Chinese Hearing Solution for Improvement of Cognition in Elders). Hearing loss was assessed by the better-ear pure-tone average (BPTA). A composite healthy lifestyle score was built based on never smoking, never drinking, regular physical activity, and balanced diet. Cognitive impairment was diagnosed by the Mini-Mental State Examination (MMSE) score of less than 24.


Results
We included 17057 participants aged 60 years or older in China (mean age 69.8 [SD 6.2] years, 55.7% female). Among the participants, 48.3% (n=8234) had mild hearing loss, and 25.8% (n=4395) had moderate or greater hearing loss. The proportion of participants with healthy lifestyle scores of 0-1, 2, 3, and 4 was 14.9% (n=2539), 29.3% (n=5000), 37.4% (n=6386), and 18.4% (n=3132), respectively. 29.6% (n=5057) participants had cognitive impairment. When compared to those with normal hearing and healthy lifestyle (scores of 3-4), participants with hearing loss plus unhealthy lifestyle (scores of 0-2) exhibited approximately twofold increased risk of cognitive impairment (OR=1.92, 95% CI 1.70-2.18). Conversely, the risk was greatly attenuated by adherence to healthy lifestyle in individuals with hearing loss (OR=1.57, 95% CI 1.40-1.76).


Conclusions
Our findings demonstrated adherence to a broad range of healthy lifestyle factors was associated with a significantly lower risk of cognitive impairment among participants with hearing loss.

OBJECTIVE: To describe the challenges of obtaining state and nationally held data for linkage to a non-government national clinical registry. METHODS: We reviewed processes negotiated to achieve linkage between the Australian Stroke Clinical Registry (AuSCR), the National Death Index, and state held hospital data. Minutes from working group meetings, national workshop meetings, and documented communications with health department staff were reviewed and summarised. RESULTS: Time from first application to receipt of data was more than two years for most state data-sets. Several challenges were unique to linkages involving identifiable data from a non-government clinical registry. Concerns about consent, the re-identification of data, duality of data custodian roles and data ownership were raised. Requirements involved the development of data flow methods, separating roles and multiple governance and ethics approvals. Approval to link death data presented the fewest barriers. CONCLUSION: To our knowledge, this is the first time in Australia that person-level data from a clinical quality registry has been linked to hospital and mortality data across multiple Australian jurisdictions. Implications for Public Health: The administrative load of obtaining linked data makes projects such as this burdensome but not impossible. An improved national centralised strategy for data linkage in Australia is urgently needed.