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Please enable JavaScript in your web browser. Sitemap Products Map. Your Cart. Contact Us. While it would be ideal to study visits that were only intended to renew a prescription, billing data do not contain sufficient detail. To assess any changes in hospitalization rates, we will analyse the number of hospital admissions for relevant diagnoses i. To define urban and rural areas, we will divide BC Local Health Areas LHAs based on population density using existing publically available data [ 31 ].
Income level will be derived from Statistics Canada data, as described above. For each pharmacy and pharmacist in our sample, we will calculate average prescribing volumes over the study period by dividing the total number of prescriptions by the number of months.
We will use the complete population-based cohort of prescriptions to detail the following descriptive characteristics of adaptations for the first year of the program:. The concentration of adaptations amongst particular pharmacists, particular pharmacies and particular geographic regions;.
The cost of professional services fees for the adaptation program. Using Interrupted Time Series Analysis, we will study longitudinal changes in drug utilization and costs, medication adherence, and ambulatory care visits and hospitalizations during each study month [ 33 ]. Our models will take the following form to model each measure in month i:. Where time represents the month in study time i. As the monthly observations may be correlated over time, we will control for autocorrelation using appropriate adjustments in a generalised least squares model, or a similar model for correlated data [ 33 ].
We will use the resulting model estimates to calculate both absolute and percentage changes in the outcomes to make the results easily interpretable [ 34 ]. As the policy may have had differential uptake rates amongst particular pharmacists, we will also explore constructing a "control" cohort for this analysis in several ways.
For example, we will explore the utility of stratifying our sample into two groups: patients that visited a pharmacy that adapts prescriptions, and patients that did not. This would allow us to more specifically identify an effect by having a control series in all our time series regressions and perhaps to determine if "early adoptors" are representative of the pharmacist population in general. Alternately, we may explore using propensity score matching to pair individuals who had a prescription adapted with otherwise similar individuals who did not [ 35 ] or instrumental variables analysis.
We will use multi-level modelling techniques to determine what factors at the region, pharmacy, patient and prescription levels are associated with prescription adaptations and renewals. In particular, we will investigate systematic differences in how the policy has affected patients of different age, sex and socioeconomic status.
For example, we know that drug use varies by sex; for instance, there are higher rates of antidepressant use amongst women in Canada [ 36 ]. Thus, it may be that this policy has differential effects on men and women. Further, one identified goal of the policy was to increase access to medicines in rural communities. Thus, we will investigate rural region as a key factor. By using a hierarchical model to model the probability of an adaptation or renewal, we can estimate the effect of different region, pharmacy, and patient characteristics while controlling for the clustering of observations within these different levels.
For the determinants associated with adaptation, we will use the dataset containing all prescriptions for drugs potentially subject to adaptation. For renewals, we will use the dataset of potentially renewable prescriptions described above. Our model will contain a hierarchical structure of 4 levels and include the following variables:.
When modelling, we will test intra-cluster correlation coefficients to determine which levels of the hierarchical structure are important. In both analyses, we will control for clustering at each level by appropriately including random effects terms in the model. As the primary goal of our modelling strategy is to test for differences between types of LHAs i. Our models will thus have the following general structure to model the probability of adaptation or renewal:.
Using a prospective population-based cohort study and interrupted time series analyses, we plan to take advantage of a natural experiment in policy change with respect to pharmacist adaptation and renewal. Short of a randomized trial, our proposed approach has perhaps the highest level of internal validity that could be brought to bear to answer such an important policy question. Our analysis will benefit from having comprehensive population-based data on every prescription for the majority of the BC population, something that is not possible in other Canadian jurisdictions with similar policies in place.
Despite its novelty and strengths, we recognize several limitations. First, while our study methodology is based on a strong quasi-experimental research design, individual patients could not feasibly be randomized to treatment as in a controlled trial. While it might have been possible to randomize the pharmacists themselves to when they could begin using their advance prescribing privileges, this was not part of the policy implementation.
Second, our study will be limited to administrative health data, and as such will not contain detailed clinical data that might be obtained through medical records. Third, we will have limited measures regarding the appropriateness of prescribing and patient or physician or pharmacist satisfaction. Finally, we will not have access to data on over-the-counter medications use.
This will only be a potential issue for the few drug classes where there are over-the-counter equivalents, such as acid-suppressing drugs. However, as many individuals have low deductibles under BC's Fair PharmaCare program, the prescription alternative will be less costly for many individuals with regular drug use. In a recent survey of Canadian policymakers, many respondents from across the country ranked the issue of prescribing privileges as one of their most pressing policy questions [ 37 ].
No matter the results of our study, they will be important for policymakers. If we find the policy is safe and effective and cost-neutral, it will provide justification for widespread adoption elsewhere and provide justification for it's continuation in BC.
If it leads to substantially increased drug costs or health care use, it may need to be significantly altered or abandoned. However, our study findings will make such decisions evidence-based, and help inform other healthcare jurisdictions. Law, Morgan and Majumdar declare that they have no competing interests. Both Drs. All authors contributed to the conception and design of the study. ML drafted the manuscript and all authors participated in revisions for intellectual content.
All authors read and approved the final manuscript. Michael Law. The authors thank Tracey Ma for her assistance with preparing this manuscript. The funders had no role in the collection, analysis and interpretation of data; in the writing of the protocol; and in the decision to submit the protocol for publication.
National Center for Biotechnology Information , U.
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