OBJECTIVE
To test for differential weight loss response to Low-Fat (LF) vs. Low-Carbohydrate (LC) diets by insulin resistance status with emphasis on overall quality of both diets.
METHODS
Sixty-one adults, BMI 28-40 kg/m2, were randomized in a 2X2 design to LF or LC by insulin resistance status in this pilot study. Primary outcome was 6-month weight change. Participants were characterized as more insulin resistant (IR) or more insulin sensitive (IS) by median split of baseline insulin-area-under-the-curve from an oral glucose tolerance test. Intervention consisted of 14 one-hour class-based educational sessions.
RESULTS
Baseline % carb:% fat:% protein was 44:38:18. At 6m the LF group reported 57:21:22 and the LC group reported 22:53:25 (IR and IS combined). Six-month weight loss (kg) was 7.4 ± 6.0 (LF-IR), 10.4 ± 7.8 (LF-IS), 9.6 ± 6.6 (LC-IR), and 8.6 ± 5.6 (LC-IS). No significant main effects were detected for weight loss by diet group or IR status; no significant diet X IR interaction. Significant differences in several secondary outcomes were observed.
CONCLUSION
Substantial weight loss was achieved overall, but a significant diet X IR status interaction was not observed. Opportunity to detect differential response may have been limited by the focus on high diet quality for both diet groups and sample size.
6500 Background: Low-income and minority populations have less activation in their cancer care, lower health-related quality of life, greater acute care use and total costs of care than affluent and white populations. Community-based interventions are needed to improve patient experiences and quality of cancer care equitably among these populations. We used community-based participatory methods to refine a previously tested intervention for use in urban communities. The intervention, LEAPS, uses community health workers trained to activate patients in discussions with their cancer clinicians regarding advance care planning and symptom-burden and to connect patients with community-based resources to overcome social determinants of health. We conducted a randomized controlled trial of LEAPS in collaboration with an employer-union health plan in Atlantic City, NJ and Chicago, IL. Members of the employer-union health plan with newly diagnosed with hematologic and solid tumor cancers were randomized to the 6-month LEAPS intervention. The objective of the study was to determine whether LEAPS improved quality of life (primary). Secondarily, we evaluated the effect of LEAPS on patient activation, acute care use, and total costs of care. Methods: We used generalized linear regression models to evaluate differences in quality of life and patient activation scores between groups from baseline to 4- and 12-months post-enrollment and regression models offset for length of follow-up to compare emergency department use, hospitalizations, and total costs of care. Results: A total of 160 patients were consented and randomized into the study (80 intervention; 80 control). There were no differences in demographic or clinical factors across groups. The majority were non-White (74%), female (53%), mean age 57 years with breast (31%) or lung cancer (21%) and Stage 3 or 4 (63%) disease. At 4- and 12-months follow-up, the intervention group had greater improvements in quality of life overtime as compared to the control group (difference in difference: 11.5 p < 0.001) and greater change in patient activation overtime (difference in difference: 11.9 (p < 0.001)). At 12-months follow-up there were no differences in emergency department use (0.44 (0.71) versus 0.73 (0.22) p = 0.22) however intervention group participants had fewer hospitalizations (1.55 (0.86) vs. 2.29 (1.31), p = 0.002) and lower median total costs of care ($72,585 vs. $153,980, p = 0.04). Conclusions: Integrating community-based interventions into clinical cancer care delivery for low-income and minority populations can significantly improve patient activation, reduce hospitalizations and total costs of care. These interventions may represent a sustainable resource to facilitate equitable, value-based cancer care. Clinical trial information: NCT03699748.
Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied missing data methods resulted in biased and inefficient estimates, and we observed large variation in performance across MI-based strategies. Based on our findings, we recommend 1) estimating the PS after applying MI to impute missing confounders; 2) conducting PSM within each imputed dataset followed by averaging the treatment effects to arrive at one summarized finding; 3) a bootstrapped-based variance to account for uncertainty of PS estimation, matching, and imputation; and 4) inclusion of key auxiliary variables in the imputation model.
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