Anupama Kalsekar
Eli Lilly and Company
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Featured researches published by Anupama Kalsekar.
Biological Psychiatry | 2006
Thomas Roth; Savina A. Jaeger; Robert Jin; Anupama Kalsekar; Paul E. Stang; Ronald C. Kessler
BACKGROUND Little is known about the population prevalence of sleep problems or whether the associations of sleep problems with role impairment are due to comorbid mental disorders. METHODS The associations of four 12-month sleep problems (difficulty initiating or maintaining sleep, early morning awakening, nonrestorative sleep) with role impairment were analyzed in the National Comorbidity Survey Replication controlling 12-month DSM-IV anxiety, mood, impulse-control, and substance disorders. The WHO Composite International Diagnostic Interview was used to assess sleep problems and DSM-IV disorders. The WHO Disability Schedule-II (WHO-DAS) was used to assess role impairment. RESULTS Prevalence estimates of the separate sleep problems were in the range 16.4-25.0%, with 36.3% reporting at least one of the four. Mean 12-month duration was 24.4 weeks. All four problems were significantly comorbid with all the 12-month DMS-IV disorders assessed in the survey (median OR: 3.4; 25(th)-75(th) percentile: 2.8-3.9) and significantly related to role impairment. Relationships with role impairment generally remained significant after controlling comorbid mental disorders. Nonrestorative sleep was more strongly and consistently related to role impairment than were the other sleep problems. CONCLUSIONS The four sleep problems considered here are of public health significance because of their high prevalence and significant associations with role impairment.
PharmacoEconomics | 2010
Lizheng Shi; Jinan Liu; Yordanka Koleva; Vivian Fonseca; Anupama Kalsekar; Manjiri Pawaskar
The primary objective of this review was to identify and examine the literature on the association between medication adherence self-reported questionnaires (SRQs) and medication monitoring devices. The primary literature search was performed for 1980–2009 using PubMed, PubMed In Process and Non-Indexed, Ovid MEDLINE, Ovid MEDLINE In-Process, PsycINFO (EBSCO), CINAHL (EBSCO), Ovid HealthStar, EMBASE (Elsevier) and Cochrane Databases and using the following search terms: ‘patient compliance’, ‘medication adherence’, ‘treatment compliance’, ‘drug monitoring’, ‘drug therapy’, ‘electronic’, ‘digital’, ‘computer’, ‘monitor’, ‘monitoring’, ‘drug’, ‘drugs’, ‘pharmaceutical preparations’, ‘compliance’ and ‘medications’. We identified studies that included SRQs and electronic monitoring devices to measure adherence and focused on the SRQs that were found to be moderately to highly correlated with the monitoring devices.Of the 1679 citations found via the primary search, 41 full-text articles were reviewed for correlation between monitoring devices and SRQs. A majority (68%) of articles reported high (27%), moderate (29%) or significant (12%) correlation between monitoring devices (37 using Medication Event Monitoring System [MEMS®] and four using other devices) and SRQs (11 identified and numerous other unnamed SRQs). The most commonly used SRQs were the Adult/Pediatric AIDS Clinical Trial Group (AACTG/ PACTG; 24.4%, 10/41) followed by the 4-item Morisky (9.8%, 4/41), Brief Medication Questionnaire (9.8%, 4/41) and visual analogue scale (VAS; 7.3%, 3/41). Although study designs differed across the articles, SRQs appeared to report a higher rate of medication adherence (+14.9%) than monitoring devices.In conclusion, several medication adherence SRQs were validated using electronic monitoring devices. A majority of them showed high or moderate correlation with medication adherence measured using monitoring devices, and could be considered for measuring patient-reported adherence prospectively.
Health and Quality of Life Outcomes | 2010
Lizheng Shi; Jinan Liu; Vivian Fonseca; Philip Walker; Anupama Kalsekar; Manjiri Pawaskar
PurposeIt is vital to understand the associations between the medication event monitoring systems (MEMS) and self-reported questionnaires (SRQs) because both are often used to measure medication adherence and can produce different results. In addition, the economic implication of using alternative measures is important as the cost of electronic monitoring devices is not covered by insurance, while self-reports are the most practical and cost-effective method in the clinical settings. This meta-analysis examined the correlations of two measurements of medication adherence: MEMS and SRQs.MethodsThe literature search (1980-2009) used PubMed, OVID MEDLINE, PsycINFO (EBSCO), CINAHL (EBSCO), OVID HealthStar, EMBASE (Elsevier), and Cochrane Databases. Studies were included if the correlation coefficients [Pearson (rp) or Spearman (rs)] between adherences measured by both MEMS and SRQs were available or could be calculated from other statistics in the articles. Data were independently abstracted in duplicate with standardized protocol and abstraction form including 1) first authors name; 2) year of publication; 3) disease status of participants; 4) sample size; 5) mean age (year); 6) duration of trials (month); 7) SRQ names if available; 8) adherence (%) measured by MEMS; 9) adherence (%) measured by SRQ; 10) correlation coefficient and relative information, including p-value, 95% confidence interval (CI). A meta-analysis was conducted to pool the correlation coefficients using random-effect model.ResultsEleven studies (N = 1,684 patients) met the inclusion criteria. The mean of adherence measured by MEMS was 74.9% (range 53.4%-92.9%), versus 84.0% by SRQ (range 68.35%-95%). The correlation between adherence measured by MEMS and SRQs ranged from 0.24 to 0.87. The pooled correlation coefficient for 11 studies was 0.45 (p = 0.001, 95% confidence interval [95% CI]: 0.34-0.56). The subgroup meta-analysis on the seven studies reporting rp and four studies reporting rs reported the pooled correlation coefficient: 0.46 (p = 0.011, 95% CI: 0.33-0.59) and 0.43 (p = 0.0038, 95% CI: 0.23-0.64), respectively. No differences were found for other subgroup analyses.ConclusionMedication adherence measured by MEMS and SRQs tends to be at least moderately correlated, suggesting that SRQs give a good estimate of medication adherence.
Patient Preference and Adherence | 2010
Machaon Bonafede; Anupama Kalsekar; Manjiri Pawaskar; Kimberly M Ruiz; Amelito M Torres; Karen R Kelly; Suellen Curkendall
Objective: To describe insulin persistence among patients with type 2 diabetes initiating insulin therapy with basal insulin or insulin mixtures and determine factors associated with nonpersistence. Research design and methods: The Thomson Reuters MarketScan® databases were used to retrospectively analyze insulin-naïve patients with type 2 diabetes by initiating insulin therapy. Insulin use was described using a variety of measures. The persistence to insulin was described using both a gap-based measure and the number of claims measure. Results: Patients in the basal insulin cohort (N = 15,255) primarily used insulin analogs (88.1%) and vial and syringe (97%). Patients in the mixture cohort (N = 2,732) were more likely to initiate on human insulin mixtures (62.5%) and vial and syringe (68.1%). Average time between insulin refills was 80 and 71 days for basal and mixture initiators, respectively. Nearly, 75% of basal insulin initiators and 65% of insulin mixture initiators had a 90-day gap in insulin prescriptions. More than half of all the patients had at least one insulin prescription per quarter. Patients initiating with insulin analogs were more likely to be persistent compared with those initiating with human insulin across both cohorts and measures of persistence (P < 0.001). Conclusion: Persistence to insulin therapy is poorer than one would anticipate, but appears to be higher in users of insulin analogs and insulin mixtures.
Behavioral Sleep Medicine | 2010
Kathleen Foley; Khaled Sarsour; Anupama Kalsekar; James K. Walsh
Medical claims and survey data were used to evaluate patients with sleep disturbance lasting 1 year or more, and to identify subtypes of sleep disturbance using latent class analysis. Four subtypes were identified from the 1,374 patients. Subtypes differed on the number of sleep disturbance symptoms, presence of non-restorative sleep and comorbidities, degree of daytime impairment, and insomnia severity. The results from this study suggest that patient-reported symptoms of sleep disturbance, the frequency of symptoms, functional impairment, and comorbid conditions are important elements in distinguishing among groups of patients with varying degrees of sleep problems. These data provide evidence that the Insomnia Severity Index (ISI) varies accordingly with the frequency and resulting impairment of symptoms captured in the 4 clusters.
BMC Endocrine Disorders | 2011
Machaon Bonafede; Anupama Kalsekar; Manjiri Pawaskar; Kimberly M Ruiz; Amelito M Torres; Karen R Kelly; Suellen Curkendall
BackgroundThe objective of this study was to characterize insulin use and examine factors associated with persistence to mealtime insulin among patients with type 2 diabetes (T2D) on stable basal insulin therapy initiating mealtime insulin therapy.MethodsInsulin use among patients with T2D initiating mealtime insulin was investigated using Thomson Reuters MarketScan® research databases from July 2001 through September 2006. The first mealtime insulin claim preceded by 6 months with 2 claims for basal insulin was used as the index event. A total of 21 months of continuous health plan enrollment was required. Patients were required to have a second mealtime insulin claim during the 12-month follow-up period. Persistence measure 1 defined non-persistence as the presence of a 90-day gap in mealtime insulin claims, effective the date of the last claim prior to the gap. Persistence measure 2 required 1 claim per quarter to be persistent. Risk factors for non-persistence were assessed using logistic regression.ResultsPatients initiating mealtime insulin (n = 4752; 51% male, mean age = 60.3 years) primarily used vial/syringe (87%) and insulin analogs (60%). Patients filled a median of 2, 3, and 4 mealtime insulin claims at 3, 6, and 12 months, respectively, with a median time of 76 days between refills. According to measure 1, persistence to mealtime insulin was 40.7%, 30.2%, and 19.1% at 3, 6, and 12 months, respectively. Results for measure 2 were considerably higher: 74.3%, 55.3%, and 42.2% of patients were persistent at 3, 6, and 12 months, respectively. Initiating mealtime insulin with human insulin was a risk factor for non-persistence by both measures (OR < 0.80, p < 0.01). Additional predictors of non-persistence at 12 months included elderly age, increased insulin copayment, mental health comorbidity, and polypharmacy (p < 0.05 for all).ConclusionsMealtime insulin use and persistence were both considerably lower than expected, and were significantly lower for human insulin compared to analogs.
Behavioral Sleep Medicine | 2013
Leah Kleinman; Daniel J. Buysse; Gale Harding; Kenneth L. Lichstein; Anupama Kalsekar; Thomas Roth
This article describes qualitative research conducted with patients with clinical diagnoses of insomnia and focuses on the development of a conceptual framework and endpoint model that identifies a hierarchy and interrelationships of potential outcomes in insomnia research. Focus groups were convened to discuss how patients experience insomnia and to generate items for patient-reported questionnaires on insomnia and associated daytime consequences. Results for the focus group produced two conceptual frameworks: one for sleep and one for daytime impairment. Each conceptual framework consists of hypothesized domains and items in each domain based on patient language taken from the focus group. These item pools may ultimately serve as a basis to develop new questionnaires to assess insomnia.
Pharmacoepidemiology and Drug Safety | 2014
Anita Chawla; Daniel S. Mytelka; Stephan McBRIDE; Dave Nellesen; Benjamin R. Elkins; Daniel E. Ball; Anupama Kalsekar; Adrian Towse; Louis P. Garrison
To evaluate the advantages and disadvantages of pre‐approval requirements for safety data to detect cardiovascular (CV) risk contained in the December 2008 U.S. Food and Drug Administration (FDA) guidance for developing type 2 diabetes drugs compared with the February 2008 FDA draft guidance from the perspective of diabetes population health.
Clinical Medicine Insights: Endocrinology and Diabetes | 2009
Jayne Palmer; Anupama Kalsekar; Kristina S. Boye; Gordon Goodall
Objectives There is an established causal link between obesity and cardiovascular outcomes. The aim of this review was to determine whether an independent relationship exists between anthropometric measurements of weight (typically body mass index [BMI]) and cardiovascular outcomes (e.g. angina, myocardial infarction, congestive heart failure, stroke, and mortality due to cardiovascular disease) in the general population and in patients with type 2 diabetes. Methods A review of the medical literature published between 1988 and May 2008 was conducted using the PubMed, EMBASE, Cochrane and Center for Review and Dissemination databases. Studies longer than 12 months, with ≥500 adult subjects and published in English were included. Results In studies conducted in general populations there was an overall trend towards increased risk for adverse cardiovascular outcomes with increasing BMI. The nature and strength of this relationship varied according to the measurement used (e.g. BMI, waist circumference, waist-to-hip ratio) and the population studied, with notable differences observed in Asian/Asia-Pacific compared with European or North American-based studies. However, data from diabetes-specific populations are limited. Conclusions In general, the degree of being overweight or obese was associated with an elevated risk of adverse cardiovascular events and mortality. Although inextricable links exist between obesity, type 2 diabetes and cardiovascular disease in the general population, the extent to which findings can be extrapolated to a diabetes-specific population is limited.
Sleep Medicine | 2010
Khaled Sarsour; Charles M. Morin; Kathleen Foley; Anupama Kalsekar; James K. Walsh