The Impacts of Long-Term Care Insurance on Health Care Utilization and Expenditure: Evidence From China

Long-term care insurance (LTCI) is one of the important institutional responses to the growing care needs of the ageing population. Although previous studies have evaluated the impacts of LTCI on health care utilization and expenditure in developed countries, whether such impacts exist in developing countries is unknown. The Chinese government has initiated policy experimentation on LTCI to cope with the growing and unmet need for aged care. Employing a quasi-experiment design, this study aims to examine the policy treatment effect of LTCI on health care utilization and out-of-pocket health expenditure in China. The Propensity Score Matching with Difference-in-difference approach was used to analyse the data obtained from four waves of China Health and Retirement Longitudinal Study (CHARLS). Our findings indicated that, in the aspect of health care utilization, the introduction of LTCI significantly reduced the number of outpatient visits by 0.322 times (p<0.05), the number of hospitalizations by 0.158 times (p<0.01), and the length of inpatient stay during last year by 1.441 days (p<0.01). In the aspect of out-of-pocket health expenditure, we found that LTCI significantly reduced the inpatient out-of-pocket health expenditure during last year by 533.47 yuan (p<0.01), but it did not exhibit an impact on the outpatient out-of-pocket health expenditure during last year. LTCI also had a significantly negative impact on the total out-of-pocket health expenditure by 512.56 yuan. These results are stable in the robustness tests. Considering the evident policy treatment effect of LTCI on health care utilization and out-of-pocket health expenditure, the expansion of LTCI could help reduce the needs for health care services and contain the increases in out-of-pocket health care expenditure in China.

Approaches to Evaluations of Virtual Care in Primary Care

A literature search informed this Environmental Scan and identified 11 evaluations of virtual care in primary care health settings and 7 publications alluding to methods, standards, and guidelines (referred to as evaluation guidance documents in this report) being used in various countries to evaluate virtual care in primary care health settings. The majority of included literature was from Australia, the US, and the UK, with 2 evaluation guidance documents published by the Heart and Stroke Foundation of Canada. Evaluation guidance documents recommended using measurements that assess the effectiveness and quality of clinical care including safety outcomes, time and travel, financial and operational impact, participation, health care utilization, technology experience including feasibility, user satisfaction, and barriers and facilitators or measures of health equity. Evaluation guidance documents specified that the following key decisions and considerations should be integrated into the planning of a virtual care evaluation: refining the scope of virtual care services; selecting an appropriate meaningful comparator; and identifying opportune timing and duration for the evaluation to ensure the evaluation is reflective of real-world practice, allows for adequate measurement of outcomes, and is comprehensive, timely, feasible, non-complex, and non–resource-intensive. Evaluation guidance documents highlighted that evaluations should be systematic, performed regularly, and reflect the stage of virtual care implementation to encompass the specific considerations associated with each stage. Additionally, evaluations should assess individual virtual care sessions and the virtual care program as a whole. Regarding economic components of virtual care evaluations, the evaluation guidance documents noted that costs or savings are not limited to monetary or financial measures but can also be represented with time. Cost analyses such as cost-benefit and cost-utility estimates should be performed with a specific emphasis on selecting an appropriate perspective (e.g., patient or provider), as that influences the benefits, effects, and how the outcome is interpreted. Two identified evaluations assessed economic outcomes through cost analyses in the perspective of the patient and provider. Evidence suggests that, in some circumstances, virtual care may be more cost-effective and reduces the cost per episode and patient expenses (e.g., travel and parking costs) compared to in-person care. However, virtual care may increase the number of individuals treated, which would increase overall health care spending. Four identified evaluations assessed health care utilization. The evidence suggests that virtual care reduces the duration of appointments and may be more time-efficient compared to in-person care. However, it is unclear if virtual care reduces the use of medical resources and the need for follow-up appointments, hospital admissions, and emergency department visits compared to in-person care. Five identified evaluations assessed participation outcomes. Evidence was variable, with some evidence reporting that virtual care reduced attendance (e.g., reduced attendance rates) and other evidence noting improved attendance (e.g., increased completion rate and decreased cancellations and no-show rates) compared to in-person care. Three identified evaluations assessed clinical outcomes in various health contexts. Some evidence suggested that virtual care improves clinical outcomes (e.g., in primary care with integrated mental health services, symptom severity decreased) or has a similar effect on clinical outcomes compared to in-person care (e.g., use of virtual care in depression elicited similar results with in-person care). Three identified evaluations assessed the appropriateness of prescribing. Some studies suggested that virtual care improves appropriateness by increasing guideline-based or guideline-concordant antibiotic management, or elicits no difference with in-person care.

Health care utilization following “digi-physical” assessment compared to physical assessment for infectious symptoms in primary care

Background The use of chat-based digital visits (eVisits) to assess infectious symptoms in primary care is rapidly increasing. The “digi-physical” model of care uses eVisits as the first line of assessment while assuming a certain proportion of patients will inevitably need to be further assessed through urgent physical examination within 48 h. It is unclear to what extent this approach can mitigate physical visits compared to assessing patients directly using office visits. Methods This pre-COVID-19-pandemic observational study followed up “digi-physical” eVisit patients (n = 1188) compared to office visit patients (n = 599) with respiratory or urinary symptoms. Index visits occurred between March 30th 2016 and March 29th 2019. The primary outcome was subsequent physical visits to physicians within two weeks using registry data from Skåne county, Sweden (Region Skånes Vårddatabas, RSVD). Results No significant differences in subsequent physical visits within two weeks (excluding the first 48 h) were noted following “digi-physical” care compared to office visits (179 (18.0%) vs. 102 (17.6%), P = .854). As part of the “digital-physical” concept, a significantly larger proportion of eVisit patients had a physical visit within 48 h compared to corresponding office visit patients (191 (16.1%) vs. 19 (3.2%), P < .001), with 150 (78.5%) of these eVisit patients recommended some form of follow-up by the eVisit physician. Conclusions Most eVisit patients (68.9%) with respiratory and urinary symptoms have no subsequent physical visits. Beyond an unavoidable portion of patients requiring urgent physical examination within 48 h, “digi-physical” management of respiratory and urinary symptoms results in comparable subsequent health care utilization compared to office visits. eVisit providers may need to optimize use of resources to minimize the proportion of patients being assessed both digitally and physically within 48 h as part of the “digi-physical” concept. Trial registration Clinicaltrials.gov identifier: NCT03474887.

Profiles and predictors of healthcare utilization: using a cluster-analytic approach to identify typical users across conventional, allied and complementary medicine, and self-care

Introduction The identification of typologies of health care users and their specific characteristics can be performed using cluster analysis. This statistical approach aggregates similar users based on their common health-related behavior. This study aims to examine health care utilization patterns using cluster analysis; and the associations of health care user types with sociodemographic, health-related and health-system related factors. Methods Cross-sectional data from the 2012 National Health Interview Survey were used. Health care utilization was measured by consultations with a variety of medical, allied and complementary health practitioners or the use of several interventions (exercise, diet, supplementation etc.) within the past 12 months (used vs. not used). A model-based clustering approach based on finite normal mixture modelling, and several indices of cluster fit were determined. Health care utilization within the cluster was analyzed descriptively, and independent predictors of belonging to the respective clusters were analyzed using logistic regression models including sociodemographic, health- and health insurance-related factors. Results Nine distinct health care user types were identified, ranging from nearly non-use of health care modalities to over-utilization of medical, allied and complementary health care. Several sociodemographic and health-related characteristics were predictive of belonging to the respective health care user types, including age, gender, health status, education, income, ethnicity, and health care coverage. Conclusions Cluster analysis can be used to identify typical health care utilization patterns based on empirical data; and those typologies are related to a variety of sociodemographic and health-related characteristics. These findings on individual differences regarding health care access and utilization can inform future health care research and policy regarding how to improve accessibility of different medical approaches.