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The ESP Coordinating Center (ESP CC) is responding to a request from the VHA Performance Workgroup for an evidence brief on the accuracy of patient self-report for cervical and breast cancer screening. Findings from this evidence brief will help the VHA Performance Workgroup decide whether to continue the current practice of accepting patient self-reported data on cervical and/or breast cancer screening or require medical record documentation of prior screening, which is currently the standard for colorectal cancer screening.

Data Accuracy. --- Breast Neoplasms.

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Health surveys --- Health status indicators. --- Public health --- Interviews as Topic. --- Health Surveys. --- Data Accuracy.

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This study analyzes the quality of perinatal mortality and retrospective contraceptive prevalence rates calculated from various instruments used in the Demographic and Health Surveys. Perinatal mortality: In this report we compared methods for estimating perinatal mortality in The DHS Program. None of the methods appear to adequately capture perinatal mortality by the standard that we selected. However, we found that the pregnancy history and the birth history supplemented by special questions performed better than the birth history supplemented by the reproductive calendar. Contraceptive prevalence tabulated from the reproductive calendar: We assessed the consistency of contraceptive use reporting in the calendar by comparing retrospective contraceptive prevalence rates tabulated from the calendar with independently estimated current status contraceptive prevalence rates from a prior survey. We compared estimates from the two data sources for the same point in time among women in the same age groups. We found evidence of substantial underreporting of retrospective contraceptive use in the majority of calendars analyzed relative to current status estimates. Results suggest that both stillbirths and contraceptive use are underestimated in data collected using the reproductive calendar. We recommend experiments in future DHS surveys: random assignment of some households to receive a birth history plus calendar and others a pregnancy history, or a forward pregnancy history versus a backward pregnancy history to assess the impact on reporting of stillbirths; and experiments with shorter calendars and potentially alternative methods of electronic data collection to assess the impact of these changes on reporting of contraceptive use and discontinuation.

Contraception --- Perinatal death --- Demographic surveys --- Health surveys --- Contraception --- Contraceptive Devices --- Perinatal Mortality --- Health Surveys --- Data Accuracy --- Statistics as Topic

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Discover actionable steps to maintain healthy data pipelines to promote data observability within your teams with this essential guide to elevating data engineering practices Key Features Learn how to monitor your data pipelines in a scalable way Apply real-life use cases and projects to gain hands-on experience in implementing data observability Instil trust in your pipelines among data producers and consumers alike Purchase of the print or Kindle book includes a free PDF eBook Book Description In the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You'll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you'll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you'll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again. What you will learn Implement a data observability approach to enhance the quality of data pipelines Collect and analyze key metrics through coding examples Apply monkey patching in a Python module Manage the costs and risks associated with your data pipeline Understand the main techniques for collecting observability metrics Implement monitoring techniques for analytics pipelines in production Build and maintain a statistics engine continuously Who this book is for This book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. Organizations seeking to adopt data observability practices and managers responsible for data quality and processes will find this book especially useful to increase the confidence of data consumers and raise awareness among producers regarding their data pipelines.

Data mining. --- Database management. --- Digital libraries. --- Semantic Web. --- Data Management --- Database Management Systems --- Data Curation --- Data Accuracy --- Data Mining --- Data Analysis.