Publications

BACKGROUND

To explore the perspectives of nursing home (NH) residents with diabetes and their doctors regarding the burdens of living with diabetes and diabetes treatments.

METHODS

Qualitative study of nursing home residents aged 65 and older with diabetes (n = 14) and nursing home physicians (n = 9) at a Department of Veterans Affairs nursing home (known as the Community Living Center). A semi-structured interview was used to elicit nursing home residents' and physicians' perspectives on the burden of diabetes and diabetes treatments. Transcripts were analyzed using constant comparative methods.

RESULTS

The mean age of the nursing home residents was 74; Most (93%) were male and 50% self-identified themselves as white. The mean age of nursing home physicians was 39 and 55% were geriatricians. Dietary restrictions, loss of independence and fingersticks/insulin were noted to be the most burdensome aspects of diabetes. Nursing home residents with a more positive outlook were generally more engaged in their care, while nursing home residents with a more pessimistic outlook were less engaged, allowing their physicians to assume complete control of their care. While physicians noted the potential negative impact of dietary restrictions, nursing home residents' comments suggest that physicians underestimate the burden of dietary restrictions.

CONCLUSIONS

Veterans Affairs nursing home residents were substantially burdened by their diabetes treatments, especially dietary restrictions and fingerstick monitoring. Since there is little evidence that dietary restrictions improve outcomes, fewer dietary restrictions may be appropriate and lead to lower treatment burdens for nursing home residents with diabetes.

OBJECTIVE

To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.

METHODS

We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.

RESULTS

According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.

CONCLUSIONS

mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

Socioeconomic position (SEP) is an important risk factor for malaria, but there is no consensus on how to measure SEP in malaria studies. We evaluated the relative strength of four indicators of SEP in predicting malaria risk in Nagongera, Uganda. A total of 318 children resident in 100 households were followed for 36 months to measure parasite prevalence routinely every 3 months and malaria incidence by passive case detection. Household SEP was determined using: 1) two wealth indices, 2) income, 3) occupation, and 4) education. Wealth Index I (reference) included only asset ownership variables. Wealth Index II additionally included food security and house construction variables, which may directly affect malaria. In multivariate analysis, only Wealth Index II and income were associated with the human biting rate, only Wealth Indices I and II were associated with parasite prevalence, and only caregiver's education was associated with malaria incidence. This is the first evaluation of metrics beyond wealth and consumption indices for measuring the association between SEP and malaria. The wealth index still predicted malaria risk after excluding variables directly associated with malaria, but the strength of association was lower. In this setting, wealth indices, income, and education were stronger predictors of socioeconomic differences in malaria risk than occupation.

INTRODUCTION

Ipilimumab is a fully human monoclonal antibody that blocks Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) to potentiate antitumor T cell response. Ipilimumab is approved for the treatment of advanced melanoma based on improved overall survival. Clinical trials of ipilimumab in patients with metastatic castrate-resistant prostate cancer (mCRPC) have demonstrated some clinical activity, but have largely been disappointing to date.

AREAS COVERED

Results of key clinical studies of ipilimumab in the treatment of prostate cancer, including clinical efficacy and toxicities, are summarized.

EXPERT OPINION

There is likely a clinical benefit to ipilimumab in a subset of mCRPC patients. The development of biomarkers for enrichment treatment strategies that select for patients most likely to benefit from ipilimumab is a top priority. Similarly, an understanding of the factors predictive of toxicity will be important in the development of future treatment approaches.