Health-related quality of life predicted subsequent health care resource utilization in patients with active cancer

Healthcare utilization in cancer survivors: six-month longitudinal cohort data

PURPOSE

To describe healthcare utilization and reasons for delaying medical care and to identify factors that influence high healthcare utilization and care delay among cancer survivors.

METHODS

Baseline (n = 991) and 6 month follow-up data (n = 777) were collected among breast, prostate, and colorectal cancer survivors from 32 US cancer centers. Participants completed surveys on healthcare utilization (e.g., number of visits to specific providers) and delay of medical care. We categorized participants as high or low users based on median number of visits. We used logistic regression models to examine factors that predicted high healthcare utilization or delay.

RESULTS

Survivors reported a median of 10.5 visits to healthcare providers and 28% reported ever delaying medical care over 6 months. Compared to prostate cancer survivors, breast and colorectal survivors were 2.4 times more likely (CI = 1.2-4.8) and 4 times more likely (CI = 2.2-7.3) to be high healthcare users, respectively. A higher quality of life score predicted high healthcare utilization (OR = 2.4, CI = 2.0-2.8) and delay of medical care (OR = 1.8, CI = 1.5-2.2). Black survivors were 1.5 times more likely than White survivors to be high healthcare users (CI = 1.1-2.0) and respondents reporting a race category other than White or Black were 1.8 times more likely to delay care (CI = 1.3-2.5). Lower levels of self-efficacy predicted greater healthcare use (OR = 0.7, CI = 0.6-0.8) and delay (OR = 0.6, CI = 0.5-0.7).

CONCLUSION

Our findings suggest that race, education, marital status, cancer type, time since diagnosis, quality of life, and self-efficacy are associated with both high healthcare utilization and delay among cancer survivors.

Food insecurity status is of added value in explaining poor health: a cross-sectional study among parents living in disadvantaged neighbourhoods in the Netherlands

OBJECTIVES

The aim of this study was to examine the added value of food insecurity in explaining poor physical and mental health beyond other socioeconomic risk factors.

DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES

Data for this cross-sectional study were collected using questionnaires with validated measures for food insecurity status and health status, including 199 adult participants with at least 1 child living at home, living in or near disadvantaged neighbourhoods in The Hague, the Netherlands. To assess the added value of food insecurity, optimism-corrected goodness-of-fit statistics of multivariate regression models with and without food insecurity status as a covariate were compared.

RESULTS

In the multivariable models explaining poor physical health (Physical Component Summary: PCS) and mental health (Mental Component Summary: MCS), from all included socioeconomic risk factors, food insecurity score was the most important covariate. Including food insecurity score in those models led to an improvement of explained variance from 6.3% to 9.2% for PCS, and from 5.8% to 11.0% for MCS, and a slightly lower root mean square error. Further analyses showed that including food insecurity score improved the discriminative ability between those individuals most at risk of poor health, reflected by an improvement in C-statistic from 0.64 (95% CI 0.59 to 0.71) to 0.69 (95% CI 0.62 to 0.73) for PCS and from 0.65 (95% CI 0.55 to 0.68) to 0.70 (95% CI 0.61 to 0.73) for MCS. Further, explained variance in these models improved with approximately one-half for PCS and doubled for MCS.

CONCLUSIONS

From these results it follows that food insecurity score is of added value in explaining poor physical and mental health beyond traditionally used socioeconomic risk factors (ie, age, educational level, income, living situation, employment status and migration background) in disadvantaged communities. Therefore, routine food insecurity screening may be important for effective risk stratification to identify populations at increased risk of poor health and provide targeted interventions.

Quality of Life and Psychological Distress of Lung Cancer Patients Undergoing Chemotherapy

Background

Patients with lung cancer often experience multiple symptoms associated with both the disease itself and the treatment. The disease and therapy-related adverse effects may lead to poor quality of life (QoL) and increased psychological distress. The aim of this study was to investigate the QoL and psychological distress of patients with lung cancer. The relationship between these two aspects was also an area of focus.

Methods

This was a quantitative descriptive study. Data collection was done using a self-complementary tool. The data were collected between February and March 2020. The sample consisted of 135 patients with lung cancer who were undergoing chemotherapy in 1-day clinic in Athens (a sample of convenience).

Results

Regarding the QoL of our sample, we observed that the mean score of the physical health component of SF-12 was 38.17 ± 9.94 and of the mental health component was 45.63 ± 11.80. As regards the psychological distress of our sample, we observed that the mean score for depression was 4.55 ± 5.04, for anxiety was 3.84 ± 4.17 and for stress was 5.21 ± 5.01.

Conclusion

As is clear from the results, lung cancer patients reported poor QoL and increased rates of psychological distress.

Systematic Review and Meta-Analysis of Community- and Choice-Based Health State Utility Values for Lung Cancer

BACKGROUND

Using appropriate health state utility values (HSUVs) is critical for economic evaluation of new lung cancer interventions, such as low-dose computed tomography screening and immunotherapy. Therefore, we provide a systematic review and meta-analysis of community- and choice-based HSUVs for lung cancer.

METHODS

On 6 March 2017, we conducted a systematic search of the following databases: Embase, Ovid MEDLINE, Web of Science, Cochrane CENTRAL, Google Scholar, and the School of Health and Related Research Health Utility Database. The search was updated on 17 April 2019. Studies reporting mean or median lung cancer-specific HSUVs including a measure of variance were included and assessed for relevance and validity. Studies with high relevance (i.e. community- and choice-based) were further analysed. Mean HSUVs were pooled using random-effects models for all stages, stages I-II, and stages III-IV. For studies with a control group, we calculated the disutility due to lung cancer. A sensitivity analysis included only the methodologically most comparable studies (i.e. using the EQ-5D instrument and matching tariff). Subgroup analyses were conducted by time to death, histology, sex, age, treatment modality, treatment line, and progression status.

RESULTS

We identified and analysed 27 studies of high relevance. The pooled HSUV was 0.68 (95% confidence interval [CI] 0.61-0.75) for all stages, 0.78 (95% CI 0.70-0.86) for stages I-II, and 0.69 (95% CI 0.65-0.73) for stages III-IV (p = 0.02 vs. stage I-II). Heterogeneity was present in each pooled analysis (p < 0.01; I2 = 92-99%). Disutility due to lung cancer ranged from 0.11 (95% CI 0.05-0.17) to 0.27 (95% CI 0.18-0.36). In the sensitivity analysis with the methodologically most comparable studies, stage-specific HSUVs varied by country. Such studies were only identified for Canada, China, Spain, the UK, the USA, Denmark, Germany, and Thailand. In the subgroup analysis by time to death, HSUVs for metastatic non-small-cell lung cancer ranged from 0.83 (95% CI 0.82-0.85) at ≥ 360 days from death to 0.56 (95% CI 0.46-0.66) at < 30 days from death. Among patients with metastatic non-small-cell lung cancer, HSUVs were lower for those receiving third- or fourth-line treatment and for those with progressed disease. Results of subgroup analyses by histology, sex, age, and treatment modality were ambiguous.

CONCLUSIONS

The presented evidence supports the use of stage- and country-specific HSUVs. However, such HSUVs are unavailable for most countries. Therefore, our pooled HSUVs may provide the best available stage-specific HSUVs for most countries. For metastatic non-small-cell lung cancer, adjusting for the decreased HSUVs in the last year of life may be considered, as may further stratification of HSUVs by treatment line or progression status. If required, HSUVs for other health states may be identified using our comprehensive breakdown of study characteristics.