Validation of a Certified Nurse‐Midwifery Database for Use in Quality Monitoring and Outcomes Research
Abstract: Findings indicate that a CNM/CM birth database is a valid data source for quality monitoring and outcomes research and may contain more complete information for some variables than the oEHR.
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Cited by 2 publications
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Background Ministries of health, donors, and other decision‐makers are exploring how they can use mobile technologies to acquire accurate and timely statistics on births and deaths. These stakeholders have called for evidence‐based guidance on this topic. This review was carried out to support World Health Organization (WHO) recommendations on digital interventions for health system strengthening. Objectives Primary objective: To assess the effects of birth notification and death notification via a mobile device, compared to standard practice. Secondary objectives: To describe the range of strategies used to implement birth and death notification via mobile devices and identify factors influencing the implementation of birth and death notification via mobile devices. Search methods We searched CENTRAL, MEDLINE, Embase, the Global Health Library, and POPLINE (August 2, 2019). We searched two trial registries (August 2, 2019). We also searched Epistemonikos for related systematic reviews and potentially eligible primary studies (August 27, 2019). We conducted a grey literature search using mHealthevidence.org (August 15, 2017) and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies in Web of Science and Google Scholar (May 15, 2020). We searched for studies published after 2000 in any language. Selection criteria For the primary objective, we included individual and cluster‐randomised trials; cross‐over and stepped‐wedge study designs; controlled before‐after studies, provided they have at least two intervention sites and two control sites; and interrupted time series studies. For the secondary objectives , we included any study design, either quantitative, qualitative, or descriptive, that aimed to describe current strategies for birth and death notification via mobile devices; or to explore factors that influence the implementation of these strategies, including studies of acceptability or feasibility. For the primary objective , we included studies that compared birth and death notification via mobile devices with standard practice. For the secondary objectives, we included studies of birth and death notification via mobile device as long as we could extract data relevant to our secondary objectives. We included studies of all cadres of healthcare providers, including lay health workers; administrative, managerial, and supervisory staff; focal individuals at the village or community level; children whose births were being notified and their parents/caregivers; and individuals whose deaths were being notified and their relatives/caregivers. Data collection and analysis For the primary obj...
Background Ministries of health, donors, and other decision‐makers are exploring how they can use mobile technologies to acquire accurate and timely statistics on births and deaths. These stakeholders have called for evidence‐based guidance on this topic. This review was carried out to support World Health Organization (WHO) recommendations on digital interventions for health system strengthening. Objectives Primary objective: To assess the effects of birth notification and death notification via a mobile device, compared to standard practice. Secondary objectives: To describe the range of strategies used to implement birth and death notification via mobile devices and identify factors influencing the implementation of birth and death notification via mobile devices. Search methods We searched CENTRAL, MEDLINE, Embase, the Global Health Library, and POPLINE (August 2, 2019). We searched two trial registries (August 2, 2019). We also searched Epistemonikos for related systematic reviews and potentially eligible primary studies (August 27, 2019). We conducted a grey literature search using mHealthevidence.org (August 15, 2017) and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies in Web of Science and Google Scholar (May 15, 2020). We searched for studies published after 2000 in any language. Selection criteria For the primary objective, we included individual and cluster‐randomised trials; cross‐over and stepped‐wedge study designs; controlled before‐after studies, provided they have at least two intervention sites and two control sites; and interrupted time series studies. For the secondary objectives , we included any study design, either quantitative, qualitative, or descriptive, that aimed to describe current strategies for birth and death notification via mobile devices; or to explore factors that influence the implementation of these strategies, including studies of acceptability or feasibility. For the primary objective , we included studies that compared birth and death notification via mobile devices with standard practice. For the secondary objectives, we included studies of birth and death notification via mobile device as long as we could extract data relevant to our secondary objectives. We included studies of all cadres of healthcare providers, including lay health workers; administrative, managerial, and supervisory staff; focal individuals at the village or community level; children whose births were being notified and their parents/caregivers; and individuals whose deaths were being notified and their relatives/caregivers. Data collection and analysis For the primary obj...
The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis
Background Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis of smoking status ascertainment algorithms to describe the characteristics and validity of these algorithms. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We searched articles published from 1990 to 2022 in EMBASE, MEDLINE, Scopus, and Web of Science with key terms such as validity, administrative data, electronic health records, smoking, and tobacco use. The extracted information, including article characteristics, algorithm characteristics, and validity measures, was descriptively analyzed. Sources of heterogeneity in validity measures were estimated using a meta-regression model. Risk of bias (ROB) in the reviewed articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results The initial search yielded 2086 articles; 57 were selected for review and 116 algorithms were identified. Almost three-quarters (71.6%) of algorithms were based on EMR data. The algorithms were primarily constructed using diagnosis codes for smoking-related conditions, although prescription medication codes for smoking treatments were also adopted. About half of the algorithms were developed using machine-learning models. The pooled estimates of positive predictive value, sensitivity, and specificity were 0.843, 0.672, and 0.918 respectively. Algorithm sensitivity and specificity were highly variable and ranged from 3 to 100% and 36 to 100%, respectively. Model-based algorithms had significantly greater sensitivity (p = 0.006) than rule-based algorithms. Algorithms for EMR data had higher sensitivity than algorithms for administrative data (p = 0.001). The ROB was low in most of the articles (76.3%) that underwent the assessment. Conclusions Multiple algorithms using different data sources and methods have been proposed to ascertain smoking status in electronic health data. Many algorithms had low sensitivity and positive predictive value, but the data source influenced their validity. Algorithms based on machine-learning models for multiple linked data sources have improved validity.
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