Medicaid expansion in California and breast cancer incidence across neighborhoods with varying social vulnerabilities.

PURPOSE: To investigate changes in breast cancer incidence rates associated with Medicaid expansion in California. METHODS: We extracted yearly census tract-level population counts and cases of breast cancer diagnosed among women aged between 20 and 64 years in California during years 2010-2017. Census tracts were classified into low, medium and high groups according to their social vulnerability index (SVI). Using a difference-in-difference (DID) approach with Poisson regression models, we estimated the incidence rate, incidence rate ratio (IRR) during the pre- (2010-2013) and post-expansion periods (2014-2017), and the relative IRR (DID estimates) across three groups of neighborhoods. RESULTS: Prior to the Medicaid expansion, the overall incidence rate was 93.61, 122.03, and 151.12 cases per 100,000 persons among tracts with high, medium, and low-SVI, respectively; and was 96.49, 122.07, and 151.66 cases per 100,000 persons during the post-expansion period, respectively. The IRR between high and low vulnerability neighborhoods was 0.62 and 0.64 in the pre- and post-expansion period, respectively, and the relative IRR was 1.03 (95% CI 1.00 to 1.06, p = 0.026). In addition, significant DID estimate was only found for localized breast cancer (relative IRR = 1.05; 95% CI, 1.01 to 1.09, p = 0.049) between high and low-SVI neighborhoods, not for regional and distant cancer stage. CONCLUSIONS: The Medicaid expansion had differential impact on breast cancer incidence across neighborhoods in California, with the most pronounced increase found for localized cancer stage in high-SVI neighborhoods. Significant pre-post change was only found for localized breast cancer between high and low-SVI neighborhoods.

Malnutrition risk assessment using a machine learning-based screening tool: A multicentre retrospective cohort.

BACKGROUND: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. METHODS: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. RESULTS: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. CONCLUSION: MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.

Longitudinal Assessment of Association Between Tobacco Use and Tobacco Dependence Among Adults: Latent Class Analysis of the Population Assessment of Tobacco and Health Study Waves 1-4.

INTRODUCTION: With increasing tobacco product varieties, understanding tobacco use (TU) profiles and their associations with tobacco dependence (TD) has also become increasingly challenging. AIMS AND METHODS: We aimed to identify TU profiles and their associations with TD over time, and to identify subgroups with high risk of TD. We included 3463 adult recent tobacco users who had complete TU and TD data across waves 1-4 of the Population Assessment of Tobacco and Health (PATH) study. We used a composite index of TD and a summed TD score from an established 16-item TD measure. We applied a latent class analysis to identify TU profiles based on participants' usage of eight common tobacco product groups at each survey wave and to check the stability of the TU profiles over time. We then used generalized estimating equations regressions to evaluate the longitudinal TU-TD association, adjusting for potential confounders. RESULTS: We identified three distinct TU profiles that remained consistent across four survey waves: Dominant cigarette users (62%-68%), poly users with high propensity of using traditional cigarettes, e-cigarettes, and cigars (24%-31%), and dominant smokeless product users (7%-9%). Covariate-adjusted models showed that TD was significantly lower among the poly users and the dominant smokeless users, compared to that among the dominant cigarette users. CONCLUSIONS: Both TU profiles and their associations with TD were stable over time at the population level. Poly users and smokeless product users were consistently associated with lower TD than cigarette-dominant users, suggesting the need for tailored tobacco cessation interventions for users with different TU profiles. IMPLICATIONS: The finding of consistent TU profiles across four survey waves extends the current literature in capturing TU patterns in an evolving tobacco product landscape. The finding of the overall higher level of TD among the cigarette-dominant users compared to the other TU latent profiles (the Cig+eCig+Cigar dominant poly users and the dominant smokeless product users) can help identify high-risk groups for potential interventions. Our application of innovative statistical methods to high-quality longitudinal data from the PATH study helps improve the understanding of the dynamic TU-TD relationship over time.

Examining the role of healthcare access in racial/ethnic disparities in receipt of provider-patient discussions about smoking: A latent class analysis.

Using insurance as a single indicator of healthcare access in examining the association between race/ethnicity and healthcare encounter-based interventions for smoking may not be adequate. In this study, we assessed the role of healthcare access using multifactorial measures in accounting for racial/ethnic disparities in the receipt of provider-patient discussions, defined as either being asked about smoking or advised to quit smoking by providers. We identified adult current smokers from the 2015 National Health Interview Survey. We first conducted a latent class analysis (LCA) to identify the underlying patterns of healthcare access measured by 13 indicators of healthcare access and utilization. We then used a propensity score - based weighting approach to examine racial/ethnic disparities in receiving provider-patient discussions about smoking or quitting in stratified groups by the distinct healthcare access clusters. Out of the 4134 adult current smokers who visited a doctor or a healthcare provider during the past 12 months, 3265 (79.90%) participants were classified as having high healthcare access and 869 (20.10%) participants as having low healthcare access. Compared to non-Hispanic whites, Hispanics had significantly lower odds of being asked about smoking (OR 0.46, 95% CI (0.27-0.77)) and being advised to quit (OR 0.57, 95% CI (0.34-0.97)) in the low access group, but neither association was significant in the high access group. In addition to increasing health insurance coverage, reducing other healthcare access barriers for Hispanics will likely facilitate provider-patient discussion and promote tobacco cessation among Hispanic smokers.

Examining annual transitions in healthcare spending among U.S. medicare beneficiaries using multistate Markov models: Analysis of medicare current beneficiary survey data, 2003-2019.

Many studies have examined factors associated with individuals of high or low healthcare spending in a given year. However, few have studied how healthcare spending changes over multiple years and which factors are associated with the changes. In this study, we examined the dynamic patterns of healthcare spending over a three-year period, among a nationally representative cohort of Medicare beneficiaries in the U.S. and identified factors associated with these patterns. We extracted data for 30,729 participants from the national Medicare Current Beneficiary Survey (MCBS), for the period 2003-2019. Using multistate Markov (MSM) models, we estimated the probabilities of year-to-year transitions in healthcare spending categorized as three states (low (L), medium (M) and high (H)), or to the terminal state, death. The participants, 13,554 (44.1%), 13,715 (44.6%) and 3,460 (11.3%) were in the low, medium and high spending states at baseline, respectively. The majority of participants remained in the same spending category from one year to the next (L-to-L: 76.8%; M-to-M: 71.7%; H-to-H: 56.6 %). Transitions from the low to high spending state were significantly associated with older age (75-84, ≥85 years), residing in a long-term care facility, greater assistance with activities of daily living, enrollment in fee-for-service Medicare, not receiving a flu shot, and presence of specific medical conditions, including cancer, dementia, and heart disease. Using data from a large population-based longitudinal survey, we have demonstrated that MSM modelling is a flexible framework and useful tool for examining changes in healthcare spending over time.

Diversity within the neurosurgical oncology workforce in the United States: A cross-sectional study with proposed strategies to pave the path forward.

BACKGROUND: Improving and fostering diversity within the neurosurgical workforce has become a high priority. This cross-sectional study aims to provide data on the diversity of neurosurgical oncology faculty (NSOF) in the US. METHODS: All 115 neurosurgery (NS) Accreditation Council for Graduate Medical Education (ACGME) accredited programs were included in this study. The academic rank, academic and clinical title(s), gender, race, and hiring date of neurosurgical faculty with a primary focus on neurosurgical oncology (NSOF) were recorded. Geographical distribution and "top 10" programs were tabulated according to published data. Underrepresented minorities in medicine (URiM) faculty were identified according to the AAMC definition. RESULTS: The NSOF workforce constitutes 21% of the total NS faculty. Of these, 10.1% are women and 9.9% are URiM (P < .001). Currently, 58% of neurosurgery programs (NSP) do not have URiM and/or women NSOF. The top 10 ranked NSP, according to Blue Ridge Institute for Medical Research, had a significantly less URiM NSOF (P = .019) than nontop 10 ranked programs. There was a decreasing trend in the proportion of URiM at higher academic ranks (P = .019). All of the URiM department chairs (3/113)-all men-and 1/3 women department chairs nationwide subspecialized in neurosurgical oncology. CONCLUSIONS: Neurosurgical oncology is a sought-after subspecialty attracting a fifth of neurosurgeons practicing in ACGME-accredited training programs. Changing demographics and the benefits of workforce diversity represent a great opportunity for our field to continue leading inclusion efforts and attracting the best and brightest.

Current Neurosurgical Care in Ethiopia Using the Lens of the Lancet Global Health Commission on High-Quality Health Systems.

BACKGROUND: Neurosurgery is a rapidly developing specialty in Ethiopia. Previous global neurosurgery studies have highlighted the need for synchronizing workforce increase with improving quality, access, and capacity to provide neurosurgical care. OBJECTIVE: To evaluate Ethiopia's neurosurgical system and highlight the critical interventions required for the sustained development of Ethiopian neurosurgery as part of a high-quality health system (HQHS). METHODS: A comprehensive survey was sent to all practicing neurosurgeons. Public databases on Ethiopian census reports and current road infrastructure were used for spatial analysis of neurosurgical access. RESULTS: The survey response rate was 90% (45/50). Most respondents were men (95.6%), aged 30 to 40 years (82%), who worked at national referral hospitals (71%). The reported annual caseload per practicing neurosurgeon was >150 cases for 40% of urban and 20% of rural neurosurgeons. Head and spine neurotrauma and tumors were the most common neurosurgical indications. Computed tomography scanner was the most widely available diagnostic equipment (62%). 76% of respondents indicated the presence of postoperative rehabilitation care at their institutions. Thirteen percent and 27% of the nation lived within a 2-hour and 4-hour driving distance from a neurosurgical center, respectively. CONCLUSION: The results highlight the need for vital improvements in neurosurgical capacity to sustain progress toward HQHS. Promoting sustained development in all components of HQHS can be achieved by diversifying the workforce and training residency candidates committed to practicing in underserved regions. Additional strategies might include establishing a national registry for neurosurgical data and implementing policy changes conducive to improving perihospital care and other health system components.

Caring Together: Trajectories of Paid and Family Caregiving Support to Those Living in the Community With Dementia.

OBJECTIVES: Paid caregivers (e.g., home health aides) often work with family caregivers to support persons living with dementia at home. We identify (a) unique trajectories of paid and family caregiving support among persons living with dementia with high care needs and (b) factors associated with these trajectories. METHODS: We used group-based multiple trajectory modeling to identify distinct trajectories of paid and family caregiving hours among National Health and Aging Trends Study respondents with dementia who died or moved to a nursing home (n = 334, mean follow-up 5.5 years). We examined differences between trajectory groups and identified factors associated with group membership using generalized estimating equation modeling. RESULTS: A 3-group model best fit our data: (a) "low/stable care" (61.3% of respondents) with stable, low/no paid care and moderate family care, (b) "increasing paid care" with increasing, moderate paid and family care, and (c) "high family care" with increasing, high family care and stable, low paid care. While both the "increasing paid care" and "high family care" groups were more functionally impaired than the "low/stable care" group, the "high family care" group was also more likely to be non-White and experience multiple medical comorbidities, depression, and social isolation. DISCUSSION: Study findings highlight the importance of considering unique arrangements in dementia care. Receipt of paid care was not only determined by patient care needs. Creating equitable access to paid care may be a particularly important way to support both persons living with dementia and their family caregivers as care needs grow.

The Influence of Increasing Levels of Provider-Patient Discussion on Quit Behavior: An Instrumental Variable Analysis of a National Survey.

OBJECTIVE: We aimed to examine the influence of increasing levels of discussion (both asked and advised, either asked or advised but not both, and neither asked nor advised) on quit behavior. METHODS: We included 4133 adult current smokers from the 2015 National Health Interview Survey. The primary outcomes were quit intent and quit attempt, and the secondary outcomes were methods used for quitting. We used an instrumental variable analysis, as well as propensity score weighted and multivariable logistic regressions. RESULTS: Compared to no discussion, having both or only one discussion, respectively, increased quit intent (OR = 1.65, 95% CI = 1.63-1.66 and OR = 1.02, 95% CI = 0.99-1.05), quit attempt (OR = 1.76, 95% CI = 1.75-1.77 and OR = 1.60, 95% CI = 1.57-1.63). Among those who attempted to quit (n = 1536), having both or only one discussion increased the use of pharmacologic (OR = 1.99, 95% CI = 1.97-2.02 and OR = 1.56, 95% CI = 1.49-1.63) or behavioral (OR = 2.01, 95% CI = 1.94-2.08 and OR = 2.91, 95% CI = 2.74-3.08) quit methods. CONCLUSIONS: Increasing levels of provider-patient discussion encourages quit behavior, and should be an integral part of reducing the health and economic burden of smoking. Strategies that promote the adherence and compliance of providers to communicate with patients may help increase the success of smoking cessation.

Pre-hospital antiplatelet medication use on COVID-19 disease severity.

OBJECTIVE: To evaluate the association between pre-hospitalization antiplatelet medication use and COVID-19 disease severity. DESIGN: Retrospective cohort study. SETTING: Inpatient units at The Mount Sinai Hospital. PATIENTS: Adults age ≥18 admitted between March 1, 2020 and April 9, 2020 with confirmed COVID-19 infection with at least 28 days follow-up. MEASUREMENTS: We captured baseline demographic, pre-hospitalization antiplatelet medication use, and clinical encounter data for all patients who met inclusion criteria. The primary endpoint was peak score on a 6-point modified ordinal scale (MOS), which is based on World Health Organization blueprint R&S groups, used to grade severity of illness through clinical outcomes of interest. Scores indicate the following: 1 - COVID-19 infection not requiring hospitalization, 2 - requiring hospitalization but not supplemental oxygen, 3 - hospitalization requiring supplemental oxygen, 4 - hospitalization requiring high-flow nasal cannula (HFNC) or non-invasive positive pressure ventilation (NIPPV), 5 - hospitalization requiring intubation or extracorporeal membrane oxygenation (ECMO), 6 - death. Multivariable adjusted partial proportional odds model (PPOM) was performed to examine the association between pre-hospitalization antiplatelet medication use and likelihood of each MOS score. MAIN RESULTS: Of 762 people admitted with COVID-19, 239 (31.4%) used antiplatelet medications pre-hospitalization while 523 (68.6%) did not. Antiplatelet users were older and had more co-morbidities at baseline. Before adjusting for covariates, patients who used antiplatelet medications pre-hospitalization were more likely than non-users to have peak MOS score 6 (death, OR 1.75, 95% CI 1.21-2.52), peak MOS score ≥5 (intubation/ECMO or death, OR 1.4, 95% CI 1.00-1.98) and peak MOS score ≥4 (HFNC, NIPPV, intubation/ECMO or death, OR 1.40, 95% CI 1.01-1.94). On multivariable adjusted PPOM analysis controlling for 13 covariates, there were no longer any significant differences in peak MOS scores between users and non-users. CONCLUSIONS: After adjusting for covariates, pre-hospital antiplatelet use was not associated with COVID-19 severity in hospitalized patients.

Brain serotonin transporter binding, plasma arachidonic acid and depression severity: A positron emission tomography study of major depression.

BACKGROUND: Serotonin transporter (5-HTT) binding and polyunsaturated fatty acids (PUFAs) are implicated in major depressive disorder (MDD). Links between the two systems in animal models have not been investigated in humans. METHODS: Using positron emission tomography (PET) and [11C]DASB, we studied relationships between 5-HTT binding potential and plasma levels of PUFAs docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and arachidonic acid (AA) in medication-free MDD patients (n = 21). PUFAs were quantified using transesterification and gas chromatography. Binding potential BPP, and alternative outcome measures BPF and BPND, were determined for [11C]DASB in six a priori brain regions of interest (ROIs) using likelihood estimation in graphical analysis (LEGA) to calculate radioligand total distribution volume (VT), and a validated hybrid deconvolution approach (HYDECA) that estimates radioligand non-displaceable distribution volume (VND) without a reference region. Linear mixed models used PUFA levels as predictors and binding potential measures as outcomes across the specified ROIs; age and sex as fixed effects; and subject as random effect to account for across-region binding correlations. As nonlinear relationships were observed, a quadratic term was added to final models. RESULTS: AA predicted both 5-HTT BPP and depression severity nonlinearly, described by an inverted U-shaped curve. 5-HTT binding potential mediated the relationship between AA and depression severity. LIMITATIONS: Given the small sample and multiple comparisons, results require replication. CONCLUSIONS: Our findings suggest that AA status may impact depression pathophysiology through effects on serotonin transport. Future studies should examine whether these relationships explain therapeutic effects of PUFAs in the treatment of MDD.