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Background In late 2021, the Omicron severe acute respiratory syndrome coronavirus 2 variant emerged and rapidly replaced Delta as the dominant variant. The increased transmissibility of Omicron led to surges in case rates and hospitalizations; however, the true severity of the variant remained unclear. We aimed to provide robust estimates of Omicron severity relative to Delta. Methods This retrospective cohort study was conducted with data from the British Columbia COVID-19 Cohort, a large provincial surveillance platform with linkage to administrative datasets. To capture the time of cocirculation with Omicron and Delta, December 2021 was chosen as the study period. Whole-genome sequencing was used to determine Omicron and Delta variants. To assess the severity (hospitalization, intensive care unit [ICU] admission, length of stay), we conducted adjusted Cox proportional hazard models, weighted by inverse probability of treatment weights (IPTW). Results The cohort was composed of 13 128 individuals (7729 Omicron and 5399 Delta). There were 419 coronavirus disease 2019 hospitalizations, with 118 (22%) among people diagnosed with Omicron (crude rate = 1.5% Omicron, 5.6% Delta). In multivariable IPTW analysis, Omicron was associated with a 50% lower risk of hospitalization compared with Delta (adjusted hazard ratio [aHR] = 0.50, 95% confidence interval [CI] = 0.43 to 0.59), a 73% lower risk of ICU admission (aHR = 0.27, 95% CI = 0.19 to 0.38), and a 5-day shorter hospital stay (aß = −5.03, 95% CI = −8.01 to −2.05). Conclusions Our analysis supports findings from other studies that have demonstrated lower risk of severe outcomes in Omicron-infected individuals relative to Delta.

BACKGROUND In late 2020, SARS-CoV-2 variants of concern (VOC) emerged and rapidly replaced the original strain globally. The increased transmissibility of these new variants led to increases in infections, hospitalisations and mortality, however investigation into the relative severity of VOCs remained difficult due to key confounding variables. OBJECTIVE We aimed to provide robust estimates of relative severity of all prevalent VOCs from during our study period in the context of vaccination rollout and heterogeneous social determinants of health (SDOH). METHODS We used a population-based retrospective cohort design with data from the British Columbia COVID-19 Cohort, a linked provincial surveillance platform. To assess the relative severity [hospitalisation, intensive care unit (ICU), death] of Gamma, Delta and Omicron infections during 2021 relative to Alpha, we used inverse probability treatment weighted Cox proportional hazards modelling. We also conducted a sub-analysis among unvaccinated individuals as assessed severity differed across VOC and SDOH. RESULTS We included 91,964 individuals infected with a SARS-CoV-2 VOC (20,487 Alpha, 15,223 Gamma, 49,161 Delta, 7,093 Omicron). Delta was associated with the most severe disease in terms of hospitalisation, ICU admissions and deaths (hospitalisation adjusted hazard ratio (aHR)=2.00; 95%CI: 1.92–2.08, ICU aHR=2.05; 95%CI: 1.91–2.20, death aHR=3.70; 95%CI: 3.23–4.25, relative to Alpha), followed by generally by Gamma and then Omicron and Alpha. The relative severity by VOC remained similar in the unvaccinated sub-analysis, although the proportion of Delta and Omicron infected individuals who were hospitalised was two times higher in unvaccinated individuals compared to those fully vaccinated. While exploring SDOHs, the proportion hospitalised was higher in areas with lower income across all VOCs, while in Alpha and Gamma infections, two VOCs which co-circulated, differential distributions of hospitalisations were found for among racially minoritised groups. CONCLUSIONS Our study provides robust severity estimates for the most prevalent VOCs during the COVID-19 pandemic in British Columbia, Canada. Relative to Alpha, we found Delta to be most severe, followed by Gamma and Omicron. This study sheds light on vaccination and SDOH as important factors while investigating VOC severity, with the latter being an area for future investigation.

In late 2021, the Omicron SARS-CoV-2 variant emerged and rapidly replaced Delta as the dominant variant globally. The increased transmissibility of the variant led to surges in case rates as well as increases in hospitalizations, however, the true severity of the variant remained unclear. We aimed to provide robust estimates of Omicron severity relative to Delta.This study was conducted using a retrospective cohort design with data from the British Columbia COVID-19 Cohort - a large provincial surveillance platform with linkage to administrative datasets. To capture the time of co-circulation with Omicron and Delta, December 2021 was chosen as the study period. We included individuals diagnosed with Omicron or Delta infection, as determined by whole genome sequencing (WGS). To assess the severity (hospitalization, ICU admission, length of stay), we conducted adjusted Cox proportional hazard models, weighted by inverse probability of treatment weights (IPTW), accounting for age, sex, underlying comorbidities, vaccination, sociodemographic status, and geographical variation.The cohort was composed of 13,128 individuals (7,729 Omicron and 5,399 Delta). There were 419 COVID-19 hospitalizations, with 118 (22%) among people diagnosed with Omicron (crude rate = 1.5% Omicron, 5.6% Delta). In multivariable IPTW analysis, Omicron was associated with a 50% lower risk of hospitalization compared to Delta (aHR = 0.50; 95%CI = 0·43-0.59), a 73% lower risk of ICU admission (aHR = 0.27; 95%CI = 0.19-0.38), and a 5 days shorter hospital stay on average (aß=-5.03; 95% CI=-8.01, -2.05).Our analysis supports findings from other studies demonstrating lower risk of severe outcomes in Omicron-infected individuals relative to Delta.

PURPOSE Bootstrapping can account for uncertainty in propensity score (PS) estimation and matching processes in 1:1 PS-matched cohort studies. While theory suggests that the classical bootstrap can fail to produce proper coverage, practical impact of this theoretical limitation in settings typical to pharmacoepidemiology is not well studied. METHODS In a plasmode-based simulation study, we compared performance of the standard parametric approach, which ignores uncertainty in PS estimation and matching, with two bootstrapping methods. The first method only accounted for uncertainty introduced during the matching process (the observation resampling approach). The second method accounted for uncertainty introduced during both PS estimation and matching processes (the PS reestimation approach). Variance was estimated based on percentile and empirical standard errors, and treatment effect estimation was based on median and mean of the estimated treatment effects across 1000 bootstrap resamples. Two treatment prevalence scenarios (5% and 29%) across two treatment effect scenarios (hazard ratio of 1.0 and 2.0) were evaluated in 500 simulated cohorts of 10 000 patients each. RESULTS We observed that 95% confidence intervals from the bootstrapping approaches but not the standard approach, resulted in inaccurate coverage rates (98%-100% for the observation resampling approach, 99%-100% for the PS reestimation approach, and 95%-96% for standard approach). Treatment effect estimation based on bootstrapping approaches resulted in lower bias than the standard approach (less than 1.4% vs 4.1%) at 5% treatment prevalence; however, the performance was equivalent at 29% treatment prevalence. CONCLUSION Use of bootstrapping led to variance overestimation and inconsistent coverage, while coverage remained more consistent with parametric estimation.

In observational studies, generalized propensity score (GPS)-based statistical methods, such as inverse probability weighting (IPW) and doubly robust (DR) method, have been proposed to estimate the average treatment effect (ATE) among multiple treatment groups. In this article, we investigate the GPS-based statistical methods to estimate treatment effects from two aspects. The first aspect of our investigation is to obtain an optimal GPS estimation method among four competing GPS estimation methods by using a rank aggregation approach. We further examine whether the optimal GPS-based IPW and DR methods would improve the performance for estimating ATE. It is well known that the DR method is consistent if either the GPS or the outcome models are correctly specified. The second aspect of our investigation is to examine whether the DR method could be improved if we ensemble outcome models. To that end, bootstrap method and rank aggregation method are used to obtain the ensemble optimal outcome model from several competing outcome models, and the resulting outcome model is incorporated into the DR method, resulting in an ensemble DR (enDR) method. Extensive simulation results indicate that the enDR method provides the best performance in estimating the ATE regardless of the method used for estimating GPS. We illustrate our methods using the MarketScan healthcare insurance claims database to examine the treatment effects among three different bones and substitutes used for spinal fusion surgeries. We draw conclusions based on the estimates from the enDR method coupled with the optimal GPS estimation method.

PurposePrescription opioids (POs) are widely prescribed for chronic non-cancer pain but are associated with several risks and limited long-term benefit. Large, linked data sources are needed to monitor their harmful effects. We developed and characterised a retrospective cohort of people dispensed POs.ParticipantsWe used a large linked administrative database to create the Opioid Prescribing Evaluation and Research Activities cohort of individuals dispensed POs for non-cancer pain in British Columbia (BC), Canada (1996–2015). We created definitions to categorise episodes of PO use based on a review of the literature (acute, episodic, chronic), developed an algorithm for inferring clinical indication and assessed patterns of PO use across a range of characteristics.Findings to dateThe current cohort includes 1.1 million individuals and 3.4 million PO episodes (estimated to capture 40%–50% of PO use in BC). The majority of episodes were acute (81%), with most prescribed for dental or surgical pain. Chronic use made up 3% of episodes but 88% of morphine equivalents (MEQ). Across the acute to episodic to chronic episode gradient, there was an increasing prevalence of higher potency POs (hydromorphone, oxycodone, fentanyl, morphine), long-acting formulations and chronic pain related indications (eg, back, neck, joint pain). Average daily dose (MEQ) was similar for acute/episodic but higher for chronic episodes. Approximately 7% of the cohort had a chronic episode and chronic pain was the characteristic most strongly associated with chronic PO use. Individuals initiating a chronic episode were also more likely to have higher social/material deprivation and previous experience with a mental health condition or a problem related to alcohol or opioid use. Overall, these findings suggest our episode definitions have face validity and also provide insight into characteristics of people initiating chronic PO therapy.Future plansThe cohort will be refreshed every 2 years. Future analyses will explore the association between POs and adverse outcomes.

Background & Aims We evaluated the effect of direct-acting antiviral (DAA)-induced sustained virologic response (SVR) on all-cause, liver- and drug-related mortality in a population-based cohort in British Columbia, Canada. Methods We used data from the British Columbia Hepatitis Testers Cohort, which includes people tested for HCV since 1990, linked with data on medical visits, hospitalizations, prescription drugs and mortality. We followed people who received DAAs and people who did not receive any HCV treatment to death or December 31, 2019. We used inverse probability of treatment weighting to balance the baseline profile of treated and untreated individuals and performed multivariable proportional hazard modelling to assess the effect of DAAs on mortality. Results Our cohort comprised 10,851 people treated with DAAs (SVR 10,426 [96%], no-SVR: 425) and 10,851 matched untreated individuals. Median follow-up time was 2.2 years (IQR 1.3–3.6; maximum 6.2). The all-cause mortality rate was 19.5/1,000 person-years (PY) among the SVR group (deaths = 552), 86.5/1,000 PY among the no-SVR group (deaths = 96), and 99.2/1,000 PY among the untreated group (deaths = 2,133). In the multivariable model, SVR was associated with significant reduction in all-cause (adjusted hazard ratio [aHR] 0.19; 95% CI 0.17–0.21), liver- (adjusted subdistribution HR [asHR] 0.22, 95% CI 0.18–0.27) and drug-related mortality (asHR 0.26, 95% CI 0.21–0.32) compared to no-treatment. Older age and cirrhosis were associated with higher risk of liver-related mortality while younger age, injection drug use (IDU), problematic alcohol use and HIV/HBV co-infections were associated with a higher risk of drug-related mortality. Conclusions DAA treatment is associated with a substantial reduction in all-cause, liver- and drug-related mortality. The association of IDU and related syndemic factors with a higher risk of drug-related mortality calls for an integrated social support, addiction, and HCV care approach among people who inject drugs. Lay summary We assessed the effect of treatment of hepatitis C virus infection with direct-acting antiviral drugs on deaths from all causes, liver disease and drug use. We found that treatment with direct-acting antiviral drugs is associated with substantial lowering in risk of death from all causes, liver disease and drug use among people with hepatitis C virus infection.

During 2017, opioids were associated with 47,600 deaths in the United States, approximately one third of which involved a prescription opioid (1). Amid concerns that overprescribing to patients with acute pain remains an essential factor underlying misuse, abuse, diversion, and unintentional overdose, several states have restricted opioid analgesic prescribing (2,3). To characterize patterns of opioid analgesic use for acute pain in primary care settings before the widespread implementation of limits on opioid prescribing (2,3), patients filling an opioid analgesic prescription for acute pain were identified from a 2014 database of commercial claims. Using a logistic generalized additive model, the probability of obtaining a refill was estimated as a function of the initial number of days supplied. Among 176,607 patients with a primary care visit associated with an acute pain complaint, 7.6% filled an opioid analgesic prescription. Among patients who received an initial 7-day supply, the probability of obtaining an opioid analgesic prescription refill for nine of 10 conditions was

Background Hepatitis C (HCV) affects diverse populations such as people who inject drugs (PWID), 'baby boomers,’ gay/bisexual men who have sex with men (gbMSM), and people from HCV endemic regions. Assessing HCV syndemics (i.e.relationships with mental health/chronic diseases) among subpopulations using Latent Class Analysis (LCA) may facilitate targeted program planning. Methods The BC Hepatitis Testers Cohort(BC-HTC) includes all HCV cases identified in BC between 1990 and 2015, integrated with medical administrative data. LCA grouped all BC-HTC HCV diagnosed people(n = 73,665) by socio-demographic/clinical indicators previously determined to be relevant for HCV outcomes. The final model was chosen based on fit statistics, epidemiological meaningfulness, and posterior probability. Classes were named by most defining characteristics. Results The six-class model was the best fit and had the following names and characteristics: ‘Younger PWID’ (n = 11,563): recent IDU (67%), people born >1974 (48%), mental illness (62%), material deprivation (59%). ‘Older PWID’ (n = 15,266): past IDU (78%), HIV (17%), HBV (17%) coinfections, alcohol misuse(68%). ‘Other Middle-Aged People’ (n = 9 019): gbMSM (26%), material privilege (31%), people born between 1965−1974 (47%). ‘ P eople of Asian backgrounds’ (n = 4718): East/South Asians (92%), no alcohol misuse (97%) or mental illness (93%), people born ‘Rural baby boomers’ (n = 20,401): rural dwellers (32%), baby boomers (79%), heterosexuals (99%), no HIV (100%). ‘Urban socially deprived baby boomers’ (n = 12,698): urban dwellers (99%), no IDU (100%), liver disease (22%), social deprivation (94%). Conclusions Differences between classes suggest variability in patients’ service needs. Further analysis of health service utilization patterns may inform optimal service layout.

Key Points Question Is long-term prescription opioid therapy for noncancer pain associated with hepatitis C virus (HCV) seroconversion among individuals who initially had no history of injection drug use? Findings In this cohort study of 382 478 individuals who had a baseline negative HCV test followed by at least 1 additional test, long-term prescription opioid therapy among those who were initially injection drug use–naive was associated with a statistically significant 3.2-fold higher risk of HCV seroconversion vs no long-term therapy. Overall, approximately 1% of exposed individuals seroconverted within 5 years. Meaning The findings of this study suggest that the risk of injection drug use initiation may be higher among people dispensed long-term prescription opioid therapy, leading to increased HCV seroconversion risk., Importance Initiation of injection drug use may be more frequent among people dispensed prescription opioid therapy for noncancer pain, potentially increasing the risk of hepatitis C virus (HCV) acquisition. Objective To assess the association between medically dispensed long-term prescription opioid therapy for noncancer pain and HCV seroconversion among individuals who were initially injection drug use–naive. Design, Setting, and Participants A population-based, retrospective cohort study of individuals tested for HCV in British Columbia, Canada, with linkage to outpatient pharmacy dispensations, was conducted. Individuals with an initial HCV-negative test result followed by 1 additional test between January 1, 2000, and December 31, 2017, and who had no history of substance use at baseline (first HCV-negative test), were included. Participants were followed up from baseline to the last HCV-negative test or estimated date of seroconversion (midpoint between HCV-positive and the preceding HCV-negative test). Exposures Episodes of prescription opioid use for noncancer pain were defined as acute (, This cohort study examines the incidence of hepatitis C seroconversion in individuals prescribed opioid therapy for noncancer pain.

ObjectiveTo assess the association between long term prescription opioid treatment medically dispensed for non-cancer pain and the initiation of injection drug use (IDU) among individuals without a history of substance use.DesignRetrospective cohort study.SettingLarge administrative data source (containing information for about 1.7 million individuals tested for hepatitis C virus or HIV in British Columbia, Canada) with linkage to administrative health databases, including dispensations from community pharmacies.ParticipantsIndividuals age 11-65 years and without a history of substance use (except alcohol) at baseline.Main outcome measuresEpisodes of prescription opioid use for non-cancer pain were identified based on drugs dispensed between 2000 and 2015. Episodes were classified by the increasing length and intensity of opioid use (acute (lasting Results59 804 participants (14 951 people from each opioid use category) were included in the matched cohort, and followed for a median of 5.8 years. 1149 participants initiated IDU. Cumulative probability of IDU initiation at five years was highest for participants with chronic opioid use (4.0%), followed by those with episodic use (1.3%) and acute use (0.7%), and those who were opioid naive (0.4%). In the inverse probability of treatment weighted Cox model, risk of IDU initiation was 8.4 times higher for those with chronic opioid use versus those who were opioid naive (95% confidence interval 6.4 to 10.9). In a sensitivity analysis limited to individuals with a history of chronic pain, cumulative risk for those with chronic use (3.4% within five years) was lower than the primary results, but the relative risk was not (hazard ratio 9.7 (95% confidence interval 6.5 to 14.5)). IDU initiation was more frequent at higher opioid doses and younger ages.ConclusionsThe rate of IDU initiation among individuals who received chronic prescription opioid treatment for non-cancer pain was infrequent overall (3-4% within five years) but about eight times higher than among opioid naive individuals. These findings could have implications for strategies to prevent IDU initiation, but should not be used as a reason to support involuntary tapering or discontinuation of long term prescription opioid treatment.

Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) estimating equations, have become popular in estimating average treatment effect (ATE) and average treatment effect among treated (ATT) in observational studies. Propensity score is the conditional probability receiving a treatment assignment with given covariates, and propensity score is usually estimated by logistic regression. However, a misspecification of the propensity score model may result in biased estimates for ATT and ATE. As an alternative, the generalized boosting method (GBM) has been proposed to estimate the propensity score. GBM uses regression trees as weak predictors and captures nonlinear and interactive effects of the covariate. For GBM-based propensity score, only IPW methods have been investigated in the literature. In this article, we provide a comparative study of the commonly used propensity score based methods for estimating ATT and ATE, and examine their performances when propensity score is estimated by logistic regression and GBM, respectively. Extensive simulation results indicate that the estimators for ATE and ATT may vary greatly due to different methods. We concluded that (i) regression may not be suitable for estimating ATE and ATT regardless of the estimation method of propensity score; (ii) IPW and stratification usually provide reliable estimates of ATT when propensity score model is correctly specified; (iii) the estimators of ATE based on stratification, IPW, and DR are close to the underlying true value of ATE when propensity score is correctly specified by logistic regression or estimated using GBM.

It is known that all agents that cause cancer (carcinogens) also cause a change in the DNA sequence. In order to identify such often subtle changes, we attempt to integrate multiple molecular profile data sets released by the International Cancer Genome Consortium (ICGC). The list of data sets includes matched gene and microRNA expression profiles, somatic copy number variation, DNA methylation, and protein expression profiles for lung adenocarcinoma patients receiving treatments. We consider both unsupervised and supervised learning techniques (clustering and penalized regression) to identify interesting molecular markers corresponding to each type of –omics profiles that can differentiate patients. Associations between important markers of 2 types have been studied. An adaptive ensemble binary regression model has been presented that uses the entirety of available –omics profiles leading to a more accurate clinical prognosis for the patients in the given sample. This integrated study provides a more com...

Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) estimating equations, have become popular in estimating average treatment effect (ATE) and average treatment effect among treated (ATT) in observational studies. Propensity score is the conditional probability receiving a treatment assignment with given covariates, and propensity score is usually estimated by logistic regression. However, a misspecification of the propensity score model may result in biased estimates for ATT and ATE. As an alternative, the generalized boosting method (GBM) has been proposed to estimate the propensity score. GBM uses regression trees as weak predictors and captures nonlinear and interactive effects of the covariate. For GBM-based propensity score, only IPW methods have been investigated in the literature. In this article, we provide a comparative study of the commonly used propensity score based methods for estimating ATT and ATE, and examine their performances when propensity score is estimated by logistic regression and GBM, respectively. Extensive simulation results indicate that the estimators for ATE and ATT may vary greatly due to different methods. We concluded that (i) regression may not be suitable for estimating ATE and ATT regardless of the estimation method of propensity score; (ii) IPW and stratification usually provide reliable estimates of ATT when propensity score model is correctly specified; (iii) the estimators of ATE based on stratification, IPW, and DR are close to the underlying true value of ATE when propensity score is correctly specified by logistic regression or estimated using GBM.

157 Background: Little is known about the prevalence of psychosocial and spiritual issues in patients being treated with palliative radiation therapy (PRT). This study assessed patients’ psychosocial and spiritual needs as interpreted by radiation oncologists at PRT consultation. Methods: This prospective, survey-based study assessed consecutive consults for PRT from May 19, 2014 to September 26, 2014 at 3 Boston-area community and academic hospitals. Consulting clinicians completed a survey to identify the presence of psychosocial and spiritual issues. 162 consecutive consults were surveyed with 139 psychosocial and spiritual assessments received (86% response rate). Psychosocial and spiritual domains include anxiety, depression, family conflict, family coping issues, personal worries, and spiritual need. Providers ranked the relevance (5-point Likert scale, not at all to extremely) of several palliative care issues, including psychosocial and spiritual issues. Results: Providers reported psychosocial or spiritual problems in 71.4% (n = 100) of consults. Psychosocial concerns were relevant in 68% of consults and spiritual concerns were relevant in 28%. The most frequent problems were anxiety (33.6%, n = 47) and personal issues (33.6%, n = 47), including financial worries. Univariate analysis showed association between psychosocial and spiritual concerns (p = 0.005). Providers treating patients less than 30 days from metastatic diagnosis more commonly reported spiritual concern (p = 0.020). Advanced care planning needs were also predictive of spiritual concerns (p = 0.026). At initial consult, 53% (n = 73) of providers ranked psychosocial problems as “Extremely” or “Very Important” to patient care. 10% of providers ranked spiritual problems as “Extremely” or “Very Important.” Conclusions: Providers noted relevance of psychosocial and spiritual issues to patient care. Given the prevalence of psychosocial and spiritual concerns in this population, it will be important for palliative providers to assess their patients routinely. The variability of predictors demonstrates the appropriateness of assessing all patients consulted for PRT for psychosocial and spiritual concerns.

Importance Osteoporosis medication treatment is recommended after hip fracture, yet contemporary estimates of rates of initiation and clinical benefit in the patient population receiving routine care are not well documented. Objectives To report osteoporosis treatment initiation rates between January 1, 2004, and September 30, 2015, and to estimate the risk reduction in subsequent nonvertebral fractures associated with treatment initiation in patients with hip fracture. Design, Setting, and Participants In this cohort study, data from a commercial insurance claims database from the United States were analyzed. Patients 50 years and older who had a hip fracture and were not receiving treatment with osteoporosis medications before their fracture were included. Exposure Prescription dispensing of an osteoporosis medication within 180 days of a hip fracture hospitalization. Main Outcomes and Measures Each initiation episode was matched with 10 nonuse episodes on person-time after the index hip fracture event to preclude immortal time bias and followed up for the outcome of nonvertebral fracture until change in exposure or a censoring event. An instrumental variable analysis using 2-stage residual inclusion method was conducted using calendar year, specialist access, geographical variation in prescribing patterns, and hospital preference. Results Among 97 169 patients with a hip fracture identified, the mean (SD) age was 80.2 (10.8) years, and 64 164 (66.0%) were women. A continuous decline over the study years was observed in osteoporosis medication initiation rates from 9.8% (95% CI, 9.0%-10.6%) in 2004 to 3.3% (95% CI, 2.9%-3.8%) in 2015. In the effectiveness analyses, the hospital preference instrumental variable had a stronger association with treatment (pseudoR2 = 0.20) than the other 3 instrumental variables (specialist access: pseudoR2 = 0.04; calendar year: pseudoR2 = 0.05; and geographic variation: pseudoR2 = 0.07). Instrumental variable analysis with hospital preference suggested a rate difference of 4.2 events (95% CI, 1.1-7.3) per 100 person-years in subsequent fractures associated with osteoporosis treatment initiation compared with nonuse in an additive hazard model. Conclusions and Relevance Low rates of osteoporosis treatment initiation after a hip fracture in recent years were observed. Clinically meaningful reduction in subsequent nonvertebral fracture rates associated with treatment suggests that improving prescriber adherence to guidelines and patient adherence to prescribed regimens may result in notable public health benefit.

A comparative study has been made by using generalized ratio and regression estimators of Brewer, Horvitz and Thompson and Cassel-Sarandal and Wretman estimators. The study also involves ratio and regression estimators along with the mean per unit estimator of equal probability sampling. Ranking is being done to see which population total variances are performing the best.