Brain imaging in lung cancer staging: A real-world, multi-centre study of prevalence of brain metastases, impact on treatment and re-modelling of the NICE health economic analysis.

INTRODUCTION: In 2019, the National Institute for Health and Care Excellence (NICE) updated their recommendations with respect to brain imaging in the staging of non-small cell lung cancer (NSCLC) based on an analytic cost-effectiveness model using published data and modelling assumptions from committee experts. In this study, we aimed to re-run this model using real-world multi-centre UK data. MATERIALS AND METHODS: Retrospective data was collected on consecutive patients with radically treatable clinical stage II and III lung cancer from eleven acute NHS Trusts during the calendar year 01/01/2018 to 31/12/2018. Following a written application to the NICE lung cancer guideline committee, we were granted access to the NG122 brain imaging economic model for the purpose of updating the input parameters in line with the real-world findings from this study. RESULTS: A total of 444 patients had data for analysis. The combined prevalence of occult brain metastases was 6.2% (10/165) in stage II and 6% (17/283) in stage III, compared to 9.5% and 9.3% used in the NICE economic model. 30% of patients with clinical stage III NSCLC and occult BMs on pre-treatment imaging went onto complete the planned curative intent treatment of extracranial disease, 60% completed SRS to the brain and 30% completed WBRT. This compares to 0%, 10% and 0% in the NICE assumptions. The health economic analysis concluded that brain imaging was no longer cost-effective in stage II disease (ICERs £50,023-£115,785) whilst brain imaging remained cost-effective for stage III patients (ICERs 17,000-£22,173), with MRI being the most cost-effective strategy. CONCLUSION: This re-running of the NICE health economic model with real-world data strongly supports the NICE guideline recommendation for brain imaging prior to curative-intent treatment in stage III lung cancer but questions the cost-effectiveness of CT brain imaging prior to curative-intent treatment in stage II lung cancer.

Integrated care improves mental health in a medically underserved U.S.-Mexico border population.

INTRODUCTION: Chronic conditions, particularly diabetes, and related health conditions continue to be a major concern in the United States, especially in Hispanic populations. This study evaluated the effect of an integrated behavioral health care model, including promotoras(es), on a primarily Hispanic population living with diabetes. METHOD: Seven hundred fifty-six participants were enrolled in an intervention (n = 329) or comparison group (n = 427) and followed up for 12 months. We used a quasiexperimental design to compare participants who received coordinated integrated behavioral health care with those who received usual care from a federally qualified health center. The outcomes were HbA1c, blood pressure, body mass index, depressive symptoms (Patient Health Questionnaire-9), and quality of life (QoL). These outcomes were analyzed as continuous variables using linear regression with backward model selection. Longitudinal analyses were conducted using a likelihood-based approach to general linear mixed models. RESULTS: A total 563 intervention (n = 239) and comparison (n = 324) participants completed an end point assessment. After adjusting for important covariates, the intervention had a QoL score 5.36 points higher than the comparison participants on average after 12 months. The trajectories of QoL and Patient Health Questionnaire-9 scores differed over time, with intervention participants experiencing greater improvements. There were no statistically significant differences detected for other outcomes. DISCUSSION: Enabling access to services and providers to enhance participants' ability to manage their chronic disease led to positive impacts on mental health. The connection between QoL and diabetes has been of great interest to researchers, including the effects of relationships with promotoras(es). The impact of integrating care on QoL in this vulnerable population is discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Beyond CHNAS: Performance Measurement for Community Health Improvement.

RESEARCH OBJECTIVE: Non-profit hospitals are required to work with community organizations to prepare Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system's transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on monitoring collaborative implementation strategies. STUDY DESIGN: Based on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process. POPULATION STUDIED: U.S. hospitals. PRINCIPAL FINDINGS: Community health improvement processes benefit from a shared measurement system that indicate accountability for specific activities. Despite the importance of measurement and evaluation, existing community health improvement efforts often fall short in these areas. There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS's are less developed.Although all U.S. hospitals are familiar with performance measurement in their management, this familiarity does not seem to carry over to Community Benefit and CHNA efforts. Indeed, 5 of the 10 CHI processes we examined have some Accountable Care Organization (ACO) involvement, where population-health performance measures are commonplace. Yet this involvement is not mentioned in the CHNAs and ISs, nor are ACO data cited. CONCLUSIONS: Strengthening the CHNA regulations to require that hospitals report the evaluation measures they intend to monitor based on an established community health improvement model could help communities demonstrate impact. As in other areas of health care, performance measures should be tailored to implementation strategy, with clear indication of accountability, and move from outputs to process and outcome measures with established validity and reliability. IMPLICATIONS FOR POLICY OR PRACTICE: Although performance measurement is now commonplace throughout the health care system, the individuals who manage CHI processes may not be that familiar with this approach. This suggests that it is important to develop practitioners' knowledge and skills needed to use it population health data effectively.

Making Better Use of Population Health Data for Community Health Needs Assessments.

RESEARCH OBJECTIVE: Non-profit hospitals are required to work with community organizations to prepare a Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system's transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on measures for needs assessments and priority setting. STUDY DESIGN: Based on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process. POPULATION STUDIED: U.S. hospitals. PRINCIPAL FINDINGS: Census, American Community Survey, and similar data are available for smaller areas are used to describe the populations covered, and, to a lesser extent, to identify health issues where there are disparities and inequities.Common data sources for population health profiles, including risk factors and population health outcomes, are vital statistics, survey data including BRFSS, infectious disease surveillance data, hospital & ED data, and registries. These data are typically available only at the county level, and only occasionally are broken down by race, ethnicity, age, poverty.There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS's are less developed. CONCLUSIONS: The county is the unit of choice because most population health profile data are not available for sub-county areas, but when a hospital serves a population more broadly or narrowly defined, appropriate data are not available to set priorities or monitor progress.Measure definitions are taken from the original data sources, so comparisons across measures is difficult. Thus, although CHNAs cover many of the same topics, the measures used vary markedly. Using the same community health profile, e.g. County Health Rankings, would simplify benchmarking and trend analysis.Implications for Policy or Practice: It is important to develop population health data that can be disaggregated to the appropriate geographical level and to groups defined by race and ethnicity, socioeconomic status, and other factors associated with health outcomes.