Effect of provider and patient reminders, deployment of nurse practitioners, and financial incentives on cervical and breast cancer screening rates

<p>Objective : To evaluate the effect of the Provider and Patient Reminders in Ontario: Multi-Strategy Prevention Tools (P-PROMPT) reminder and recall system and pay-for-performance incentives on the delivery rates of cervical and breast cancer screening in primary care practices in Ontario, with or without deployment of nurse practitioners (NPs). </p> <p>Design : Before-and-after comparisons of the time-appropriate delivery rates of cervical and breast cancer screening using the automated and NP–augmented strategies of the P-PROMPT reminder and recall system. </p> <p>Setting : Southwestern Ontario. </p> <p>Participants : A total of 232 physicians from 24 primary care network or family health network groups across 110 different sites eligible for pay-for-performance incentives. </p> <p>Interventions : The P-PROMPT project combined pay-for-performance incentives with provider and patient reminders and deployment of NPs to enhance the delivery of preventive care services. </p> <p>Main outcome measures : The mean delivery rates at the practice level of time-appropriate mammograms and Papanicolaou tests completed within the previous 30 months. </p> <p>Results : Before-and-after comparisons of time-appropriate delivery rates (<30 months) of cancer screening showed the rates of Pap tests and mammograms for eligible women significantly increased over a 1-year period by 6.3% (P >< .001) and 5.3% (P < .001), respectively. The NP-augmented strategy achieved comparable rate increases to the automated strategy alone in the delivery rates of both services. </p> <p>Conclusion : The use of provider and patient reminders and pay-forperformance incentives resulted in increases in the uptake of Pap tests and mammograms among eligible primary care patients over a 1-year period in family practices in Ontario.</p>

O34: WHO SHOULD BE FOLLOWING UP IN PATIENTS WITH PROSTATE CANCER USING PSAS?

Introdution Prostate cancer patients are usually followed up using serial PSA testing. NICE provides guidelines for PSA follow up however whether this should be in primary care or secondary care is ambiguous. Consequently, there is a concern that PSAs are being missed. The primary aim of this study was to understand whether PSAs were being missed. Method A retrospective study was carried out at Whitehill General Practice. Patients were identified using codes for prostate cancer through Egton Medical Information Systems (EMIS) software. Data for patients followed up with PSAs between 2015-2019 was collected manually from documentation. Chi-squared statistical testing was used. Result 47 patients had prostate cancer follow up with repeat PSAs. 45% patients were followed up solely by primary care whilst 40% had primary care follow up directed by specialists. 7 patients were excluded from further analysis. 35% of patients had missed PSAs, of which 18% had no reason documented. Furthermore, 19% had missed PSAs with solely primary care follow up. The difference in missed PSAs between solely primary care follow up versus those directed regularly by specialists was not significant (chi-squared 0.0177, p&gt;0.05). Conclusion PSAs are being missed because of a lack of a recall system. An ‘out of hospital’ recall system through a local administrative hub would allow for follow up standardisation. Involving patients in their follow up care should also be considered. By placing the responsibility centrally rather than to individual clinicians provides multiple fail safes to reduce missed follow ups. Take-home message An ‘out of hospital’ recall system through a local administrative hub would allow for follow up standardisation. Involving patients in their follow up care needs to also be considered.

Protocol and Data Description: The Free-Living Activity Study for Health

Physical behavior can be assessed using a range of competing methods. The Free-Living Activity Study for Health (FLASH) is an ongoing study that facilitates the comparison of such methods. The purpose of this report is to describe the FLASH, with a particular emphasis on a subsample of participants who have consented to have their deidentified data released in a shared repository. Participants in the FLASH wear seven physical activity monitors for a 24-hr period and then complete a detailed recall using the Activities Completed Over Time in 24-hr online assessment tool. The participants can optionally agree to be video recorded for 30–60 min, which allows for direct observation as a criterion indicator of their behavior during that period. As of version 0.1.0, the repository includes data from 38 participants, and the sample size will grow as data are collected, processed, and released in future versions. The repository makes it possible to combine sensor data (e.g., from ActiGraph and SenseWear) with minute-by-minute contextual data (from the Activities Completed Over Time in 24-hr recall system), which enables the FLASH to generate benchmark data for a wide range of future research. The repository itself provides an example of how a powerful open-source tool (GitHub) can be used to share data and code in a way that encourages communication and collaboration among a variety of scientists (e.g., algorithm developers and end users). The FLASH data set will provide long-term benefits to researchers interested in advancing the science of physical behavior monitoring.

Evolutionary Game Analysis on Government Supervision and Dairy Enterprise in the Process of Product Recall in China

On the basis of stating recall and regulation mode, this paper analyzes long-term evolutionary trend between dairy enterprise and government supervision on bounded rationality with evolutionary game. The authors use Python matplotlib to simulate research results. Studies show that it is helpful to build a standard recall system of defect and dairy products. This system should reduce the costs of government supervision. In addition, in case of mandatory recall, it should strengthen punishment intensity of the government supervision branch on dairy enterprise, increase more losing costs of dairy enterprise, and decrease external environment benefits of dairy enterprise. In case of voluntary recall, the system should encourage various strategies and subsidy of the government supervision branch on dairy enterprise and amplify social influence of dairy enterprise. Especially, the paper puts forward detailed strategies for dairy enterprise.

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients (“Nutrient-Only”) or the nutrient and food descriptions (“Nutrient + Text”). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24.