Analysis of Access to Prescription Data Management Programs Data for Research.
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ABSTRACT:
SUBMITTER: Lee VA
PROVIDER: S-EPMC9218843 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data. METHODS:In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements. RESULTS:Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility. CONCLUSIONS:Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.
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Project description:Prescription drug monitoring programs (PDMPs) are a crucial component of federal and state governments' response to the opioid epidemic. Evidence about the effectiveness of PDMPs in reducing prescription opioid-related adverse outcomes is mixed. We conducted a systematic review to examine whether PDMP implementation within the United States is associated with changes in 4 prescription opioid-related outcome domains: opioid prescribing behaviors, opioid diversion and supply, opioid-related morbidity and substance-use disorders, and opioid-related deaths. We searched for eligible publications in Embase, Google Scholar, MEDLINE, and Web of Science. A total of 29 studies, published between 2009 and 2019, met the inclusion criteria. Of the 16 studies examining PDMPs and prescribing behaviors, 11 found that implementing PDMPs reduced prescribing behaviors. All 3 studies on opioid diversion and supply reported reductions in the examined outcomes. In the opioid-related morbidity and substance-use disorders domain, 7 of 8 studies found associations with prescription opioid-related outcomes. Four of 8 studies in the opioid-related deaths domain reported reduced mortality rates. Despite the mixed findings, emerging evidence supports that the implementation of state PDMPs reduces opioid prescriptions, opioid diversion and supply, and opioid-related morbidity and substance-use disorder outcomes. When PDMP characteristics were examined, mandatory access provisions were associated with reductions in prescribing behaviors, diversion outcomes, hospital admissions, substance-use disorders, and mortality rates. Inconsistencies in the evidence base across outcome domains are due to analytical approaches across studies and, to some extent, heterogeneities in PDMP policies implemented across states and over time.
| S-EPMC7947593 | biostudies-literature
Project description:Background and aimsIn the US, benzodiazepine overdose deaths increased at an alarming rate in the past two decades. Benzodiazepines were also the most common drugs involved in prescription opioid overdose deaths. Benzodiazepine prescribing has been monitored by Prescription Drug Monitoring Programs (PDMPs), but little was known about whether PDMPs reduced drug overdose deaths involving benzodiazepines.Design and methodsThis study used a difference-in-difference design with state-quarter aggregate data on drug overdose deaths. The primary data source was Mortality Multiple Cause Files in 1999-2016. Three age-adjusted rates of drug overdose deaths were examined, including those involving benzodiazepines, those involving prescription opioids, and those involving both benzodiazepines and prescription opioids. The policy variables included PDMP data access for benzodiazepines and mandatory use of PDMP data for benzodiazepines. Linear multivariable regressions were used to assess the associations of PDMP policies specific to benzodiazepines with drug overdose death rates, controlling for other state-level policy and socioeconomic factors, state and time fixed effects, and state-specific time trends.ResultsNo significant associations were found between PDMP data access for benzodiazepines and changes in drug overdose death rates involving benzodiazepines and/or prescription opioids. Similarly, no significant associations were found between mandatory use of PDMP data for benzodiazepines and changes in drug overdose death outcomes.Discussion and conclusionsThis study suggested no evidence that PDMP policies specific to benzodiazepines were associated with reduction in benzodiazepine overdose death rates. Future research is warranted to examine detailed features of PDMPs and continuously monitor the impacts of PDMP policies on benzodiazepine-related consequences.
| S-EPMC6641857 | biostudies-literature
Project description:IntroductionThe objective of this scoping review is to describe the extent and nature of research studies based on linked prescription drug monitoring program (PDMP) data; defined as PDMP data linked to other clinical, administrative or public health data sets. The population is prescribed and dispensed controlled substances. The concept is analysis of linked PDMP data to other clinical, administrative or public health data sets. The context is the USA.Methods and analysisThe scoping review will be conducted with guidance from the latest version of the JBI Manual for Evidence Synthesis, using the framework as outlined by Arksey and O'Malley. Search strategies will be peer-reviewed according to the Peer Review of Electronic Search Strategies (PRESS) guidelines. For transparency and reproducibility, we will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews reporting guidelines in reporting results. Two reviewers will independently screen titles and abstracts, then independently review full text to select papers or studies for inclusion. When consensus cannot be reached with discussion, a third reviewer will resolve the conflicts. From our included studies, we will extract variables describing aspects of population, concept and context (USA).Ethics and disseminationEthical approval was not required for this review. This scoping review entails analysis of previously published, peer-reviewed research. We intend to publish findings in a peer-reviewed journal.
| S-EPMC9013995 | biostudies-literature
Project description:AimTo identify better performing iCCM programs in sub-Saharan Africa (SSA) and identify factors associated with better performance using routine data.MethodsWe examined 15 evaluations or studies of integrated community case management (iCCM) programs in SSA conducted between 2008 and 2013 and with information about the program; routine data on treatments, supervision, and stockouts; and, where available, data from community health worker (CHW) surveys on supervision and stockouts. Analyses included descriptive statistics, Fisher exact test for differences in median treatment rates, the Kruskal-Wallis test for differences in the distribution of treatment rates, and Spearman's correlation by program factors.ResultsThe median percent of annual expected cases treated was 27% (1-74%) for total iCCM, 37% (1-80%) for malaria, 155% (7-552%) for pneumonia, and 27% (1-74%) for diarrhoea. Seven programs had above median total iCCM treatments rates. Four programs had above median treatment rates, above median treatments per active CHW per month, and above median percent of expected cases treated. Larger populations under-five targeted were negatively associated with treatment rates for fever, malaria, diarrhea, and total iCCM. The ratio of CHWs per population was positively associated with diarrhoea treatment rates. Use of rapid diagnostic tests (RDTs) was negatively associated with treatment rates for pneumonia. Treatment rates and percent of annual expected cases treated were equivalent between programs with volunteer CHWs and programs with salaried CHWs.ConclusionsThere is large variation in iCCM program performance in SSA. Four programs appear to be higher performing in terms of treatment rates, treatments per CHW per month, and percent of expected cases treated. Treatment rates for diarrhoea are lower than expected across most programmes. CHWs in many programmes are overtreating pneumonia. Programs targeting larger populations under-five tend to have lower treatment rates. The reasons for lower pneumonia treatment rates where CHWs use RDTs need to be explored. Programs with volunteer CHWs and those with salaried CHWs can achieve similar treatment rates and percent of annual expected cases treated but to do so volunteer programs must manage more CHWs per population and salaried CHWs must provide more treatments per CHW per month.
| S-EPMC4267092 | biostudies-literature
Project description:BACKGROUND AND AIMS:Prescription drug monitoring programs (PDMP), defined as state-level databases used in the United States that collect prescribing information when controlled substances are dispensed, have varied substantially between states and over time. Little is known about the combinations of PDMP features that, collectively, may produce the greatest impact on prescribing and overdose. We aimed to (1) identify the types of PDMP models that have developed from 1999 to 2016, (2) estimate whether states have transitioned across PDMP models over time and (3) examine whether states have adopted different types of PDMP models in response to the burden of opioid overdose. METHODS:A latent transition analysis of PDMP models based on an adaptation of nine PDMP characteristics classified by prescription opioid policy experts as potentially important determinants of prescribing practices and prescription opioid overdose events. RESULTS:We divided the time-period into three intervals (1999-2004, 2005-09, 2010-16), and found three distinct PDMP classes in each interval. The classes in the first and second interval can be characterized as 'no/weak', 'proactive' and 'reactive' types of PDMPs, and in the third interval as 'weak', 'cooperative' and 'proactive'. The meaning of these classes changed over time: until 2009, states in the 'no/weak' class had no active PDMP, whereas states in the 'proactive' class were more likely to proactively provide unsolicited information to PDMP users, provide open access to law enforcement, and require more frequent data reporting than states in the 'reactive' class. In 2010-16, the 'weak' class resembled the 'reactive' class in previous intervals. States in the 'cooperative' class in 2010-16 were less likely than states in the 'proactive' class to provide unsolicited reports proactively or to provide open access to law enforcement; however, they were more likely than those in the 'proactive' class to share PDMP data with other states and to report more federal drug schedules. CONCLUSIONS:Since 1999, US states have tended to transition to more robust classes of prescription drug monitoring programs. Opioid overdose deaths in prior years predicted the state's prescription drug monitoring program class but did not predict transitions between prescription drug monitoring program classes over time.
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Project description:BackgroundTo advance research on malaria, the outputs from existing studies and the data that fed into them need to be made freely available. This will ensure new studies can build on the work that has gone before. These data and results also need to be made available to groups who are developing public health policies based on up-to-date evidence. The Malaria Atlas Project (MAP) has collated and geopositioned over 50,000 parasite prevalence and vector occurrence survey records contributed by over 3,000 sources including research groups, government agencies and non-governmental organizations worldwide. This paper describes the results of a project set up to release data gathered, used and generated by MAP.MethodsRequests for permission to release data online were sent to 236 groups who had contributed unpublished prevalence (parasite rate) surveys. An online explorer tool was developed so that users can visualize the spatial distribution of the vector and parasite survey data before downloading it. In addition, a consultation group was convened to provide advice on the mode and format of release for data generated by MAP's modelling work. New software was developed to produce a suite of publication-quality map images for download from the internet for use in external publications.ConclusionMore than 40,000 survey records can now be visualized on a set of dynamic maps and downloaded from the MAP website on a free and unrestricted basis. As new data are added and new permissions to release existing data come in, the volume of data available for download will increase. The modelled data output from MAP's own analyses are also available online in a range of formats, including image files and GIS surface data, for use in advocacy, education, further research and to help parameterize or validate other mathematical models.
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