Objective: The aim of this study was to examine the most effective delivery format of cognitive behavioral therapy for insomnia (CBT-I) on insomnia in cancer patients. Methods: We searched five databases up to February 2021 for randomized clinical trials that compared CBT-I with inactive or active controls for insomnia in cancer patients. Outcomes were insomnia severity, sleep efficiency, sleep onset latency (SOL), wake after sleep onset (WASO), and total sleep time (TST). Pairwise meta-analyses and frequentist network meta-analyses with the random-effects model were applied for data analyses. Results: Sixteen unique trials including 1523 participants met inclusion criteria. Compared with inactive control, CBT-I could significantly reduce insomnia severity (mean differences [MD] = -4.98 points, 95% confidence interval [CI]: -5.82 to -4.14), SOL (MD = -12.29 min, 95%CI: -16.48 to -8.09), and WASO (MD = -16.58 min, 95%CI: -22.00 to -11.15), while increasing sleep efficiency (MD = 7.62%, 95%CI: 5.82% to 9.41%) at postintervention. Compared with active control, CBT-I could significantly reduce insomnia severity (MD = -2.75 points, 95%CI: -4.28 to -1.21), SOL (MD = -13.56 min, 95%CI: -18.93 to -8.18), and WASO (MD = -6.99 min, 95%CI: -11.65 to -2.32) at postintervention. These effects diminished in short-term follow-up and almost disappeared in long-term follow-up. Most of the results were rated as "moderate" to "low" certainty of evidence. Network meta-analysis showed that group CBT-I had an increase in sleep efficiency of 10.61%, an increase in TST of 21.98 min, a reduction in SOL of 14.65 min, and a reduction in WASO of 24.30 min, compared with inactive control at postintervention, with effects sustained at short-term follow-up. Conclusions: CBT-I is effective for the management of insomnia in cancer patients postintervention, with diminished effects in short-term follow-up. Group CBT-I is the preferred choice based on postintervention and short-term effects. The low quality of evidence and limited sample size demonstrate the need for robust evidence from high-quality, large-scale trials providing long-term follow-up data.

Taiwan's National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan's Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.

In this policy brief, we estimate and document the national magnitude of charges associated with hospitalizations due to ambulatory care sensitive conditions (ACSCs) in rural hospitals. The brief also reports this type of charge by hospital size. This research will inform policy makers about the magnitude of rural preventable hospitalizations and the associated potential savings in hospital resource utilization.