A Privacy-Preserving Distributed Medical Data Integration Security System for Accuracy Assessment of Cancer Screening: Development Study of Novel Data Integration System.

BACKGROUND: Big data useful for epidemiological research can be obtained by integrating data corresponding to individuals between databases managed by different institutions. Privacy information must be protected while performing efficient, high-level data matching. OBJECTIVE: Privacy-preserving distributed data integration (PDDI) enables data matching between multiple databases without moving privacy information; however, its actual implementation requires matching security, accuracy, and performance. Moreover, identifying the optimal data item in the absence of a unique matching key is necessary. We aimed to conduct a basic matching experiment using a model to assess the accuracy of cancer screening. METHODS: To experiment with actual data, we created a data set mimicking the cancer screening and registration data in Japan and conducted a matching experiment using a PDDI system between geographically distant institutions. Errors similar to those found empirically in data sets recorded in Japanese were artificially introduced into the data set. The matching-key error rate of the data common to both data sets was set sufficiently higher than expected in the actual database: 85.0% and 59.0% for the data simulating colorectal and breast cancers, respectively. Various combinations of name, gender, date of birth, and address were used for the matching key. To evaluate the matching accuracy, the matching sensitivity and specificity were calculated based on the number of cancer-screening data points, and the effect of matching accuracy on the sensitivity and specificity of cancer screening was estimated based on the obtained values. To evaluate the performance, we measured central processing unit use, memory use, and network traffic. RESULTS: For combinations with a specificity ≥99% and high sensitivity, the date of birth and first name were used in the data simulating colorectal cancer, and the matching sensitivity and specificity were 55.00% and 99.85%, respectively. In the data simulating breast cancer, the date of birth and family name were used, and the matching sensitivity and specificity were 88.71% and 99.98%, respectively. Assuming the sensitivity and specificity of cancer screening at 90%, the apparent values decreased to 74.90% and 89.93%, respectively. A trial calculation was performed using a combination with the same data set and 100% specificity. When the matching sensitivity was 82.26%, the apparent screening sensitivity was maintained at 90%, and the screening specificity decreased to 89.89%. For 214 data points, the execution time was 82 minutes and 26 seconds without parallelization and 11 minutes and 38 seconds with parallelization; 19.33% of the calculation time was for the data-holding institutions. Memory use was 3.4 GB for the PDDI server and 2.7 GB for the data-holding institutions. CONCLUSIONS: We demonstrated the rudimentary feasibility of introducing a PDDI system for cancer-screening accuracy assessment. We plan to conduct matching experiments based on actual data and compare them with the existing methods.

Availability of Financial and Medical Resources for Screening Providers and Its Impact on Cancer Screening Uptake and Intervention Programs.

Interventions for residents and medical/financial resources available to screening providers can improve cancer screening rates. Yet the mechanisms by which the interactions of these factors affect the screening rates remain unknown. This study employed structural equation modeling to analyze the mechanisms underlying these factors. Data for Japanese municipalities' medical/financial status, their implementation of screening interventions, and the number of municipality-based cancer screening appointments from April 2016 to March 2017 were obtained from an open database. Five cancer screenings were included: gastric, lung, colorectal, breast, and cervical cancer screening; all are nationally recommended for population screening in Japan. We defined two latent variables, namely, intervention for residents and medical/financial resources, and then analyzed the relationships between these variables and screening rates using structural equation modeling. Models were constructed for gastric, lung, and breast cancer screening, and similar relationships were observed. With these cancer types, medical/financial resources affected the intervention for residents, directly affecting screening rates. One limitation of this study is that it only included screening by municipalities, which may cause selection bias. In conclusion, financial pressures and lack of medical resources may cause a reduction in screening intervention programs, leading to stagnant screening rates. Ensuring consistent implementation of interventions for residents may improve local and regional cancer screening rates.

Impact of individual background on the unmet needs of cancer survivors and caregivers - a mixed-methods analysis.

BACKGROUND: Cancer survivors and their caregivers may have various unmet needs that are medically difficult to solve. Previous studies have suggested the relations between individuals' backgrounds and their unmet needs. We conducted a large-scale analysis to clarify the influence of individuals' backgrounds, primarily cancer type, on specific types of unmet needs. METHODS: Using a mixed-methods approach, we analyzed records of first-time callers to a cancer-focused telephone consultation service that was provided by the Kanagawa Cancer Clinical Research Information Organization from October 2006 to May 2014. The qualitative approach concerned extracting unmet needs mentioned in each consultation and classifying them into themes of specific needs, while the quantitative approach comprised multi-variated analysis of the relationships between the frequency by which the needs in each theme arose and the associated callers' backgrounds. RESULTS: A total of 1938 consultation cases were analyzed. In the qualitative analysis, the needs were classified into 16 themes. The mean number of unmet needs for each caller was 1.58 (standard deviation = 0.86). In the multi-variated analysis, caregivers for colorectal cancer survivors had a lower frequency of "emotional/mental health" needs (OR: 0.31, 95%CI: 0.11-0.88, p = 0.028) than did caregivers for breast-cancer survivors. Nevertheless, this was the only significant difference in needs frequency among callers (including survivors and their caregivers) with specific cancer types. Meanwhile, there significant difference in the frequency of occurrence of each unmet need theme was found among items concerning other background elements. Among survivors, sex was related to the frequency of needs among "physical" and "resources" themes, and "emotions/mental health"; their age group with "employment"; treatment course with "physical" and "resources" themes and "cure"; residence with "physical" themes; presence of symptom with "physical," "education/information," "resources," "emotions/mental health," and "cure" themes. CONCLUSIONS: This large-scale study suggests that cancer type is not a significant factor for specific unmet needs and that individuals' backgrounds and presence of symptoms play a more important role. Through this study, it was found that instruments to predict people's needs and a system to provide individualized cancer care across cancer types should be developed in the future.