Health-related social needs, medical financial hardship, and mortality risk among cancer survivors.

BACKGROUND: Cancer survivors may face challenges affording food, housing, and other living necessities, which are known as health-related social needs (HRSNs). However, little is known about the associations of HRSNs and mortality risk among adult cancer survivors. METHODS: Adult cancer survivors were identified from the 2013-2018 National Health Interview Survey (NHIS) and linked with the NHIS Mortality File with vital status through December 31, 2019. HRSNs, measured by food insecurity, and nonmedical financial worries (e.g., housing costs), was categorized as severe, moderate, and minor/none. Medical financial hardship, including material, psychological, and behavioral domains, was categorized as 2-3, 1, or 0 domains. Using age as the time scale, the associations of HRSNs and medical financial hardship and mortality risk were assessed with weighted adjusted Cox proportional hazards models. RESULTS: Among cancer survivors 18-64 years old (n = 5855), 25.5% and 18.3% reported moderate and severe levels of HRSNs, respectively; among survivors 65-79 years old (n = 5918), 15.6% and 6.6% reported moderate and severe levels of HRSNs, respectively. Among cancer survivors 18-64 years old, severe HRSNs was associated with increased mortality risk (hazards ratio [HR], 2.00; 95% confidence interval [CI], 1.36-2.93, p < .001; reference = minor/none) in adjusted analyses. Among cancer survivors 65-79 years old, 2-3 domains of medical financial hardship was associated with increased mortality risk (HR, 1.58; 95% CI, 1.13-2.20, p = .007; reference = 0 domain). CONCLUSIONS: HSRNs and financial hardship are associated with increased mortality risk among cancer survivors; comprehensive assessment of HRSN and financial hardship connecting patients with relevant services can inform efforts to mitigate adverse consequences of cancer.

Blood Profiles of Community-Dwelling People with Sarcopenia: Analysis Based on the China Health and Retirement Longitudinal Study.

INTRODUCTION: Routine blood factors can be economical and easily accessible candidates for sarcopenia screening and monitoring. The associations between sarcopenia and routine blood factors remain unclear. This study aimed to examine sarcopenia and blood factor associations based on a nation-wide cohort in China. METHODS: A total of 1,307 participants and 17 routine blood indices were selected from two waves (year 2011 and year 2015) of the China Health and Retirement Longitudinal Study (CHARLS). The diagnosis of sarcopenia was based on the criteria proposed by the Asian Working Group for Sarcopenia (AWGS 2019). Generalized mixed-effects models were performed for association analyses. A logistic regression (LR) model was conducted to examine the predictive power of identifying significant blood factors for sarcopenia. RESULTS: A higher sarcopenia risk was cross-sectionally associated with elevated blood concentrations of high-sensitivity C-reactive protein (hsCRP) (OR = 1.030, 95% CI [1.007, 1.053]), glycated hemoglobin (HbA1c) (OR = 1.407, 95% CI [1.126, 1.758]) and blood urea nitrogen (BUN) (OR = 1.044, 95% CI [1.002, 1.089]), and a decreased level of glucose (OR = 0.988, 95% CI [0.979, 0.997]). A higher baseline hsCRP value (OR = 1.034, 95% CI [1.029, 1.039]) and a greater over time change in hsCRP within 4 years (OR = 1.034, 95% CI [1.029, 1.039]) were associated with a higher sarcopenia risk. A higher BUN baseline value was related to a decreased sarcopenia risk over time (OR = 0.981, 95% CI [0.976, 0.986]), while a greater over time changes in BUN (OR = 1.034, 95% CI [1.029, 1.040]) and a smaller over time change in glucose (OR = 0.992, 95% CI [0.984, 0.999]) within 4 years were also related to a higher sarcopenia risk. LR based on significant blood factors (i.e., hsCRP, HbA1c, BUN, and glucose), and sarcopenia status in year 2015 yielded an area under the curve of 0.859 (95% CI: 0.836-0.882). CONCLUSION: Routine blood factors involved in inflammation, protein metabolism, and glucose metabolism are significantly associated with sarcopenia. In clinical practice, plasma hsCRP, BUN, blood sugar levels, sex, age, marital status, height, and weight might be helpful for sarcopenia evaluation and monitoring.

Associations of Medical Debt With Health Status, Premature Death, and Mortality in the US.

Importance: Medical debt is increasingly common in the US. Little is known regarding its association with population health. Objective: To examine the associations of medical debt with health status, premature death, and mortality at the county level in the US. Design, Setting, and Participants: This cross-sectional study was conducted at the US county level using 2018 medical debt data from the Urban Institute Debt in America project linked with 2018 data on self-reported health status and premature death from the County Health Rankings & Roadmaps and with 2015 to 2019 mortality data from the National Center for Health Statistics. Data analysis was performed from August 2022 to May 2023. Exposure: Share of population with any medical debt in collections and median amount of medical debt. Main Outcomes and Measures: Health status was measured as (1) the mean number of physically and mentally unhealthy days in the past 30 days per 1000 people, (2) the mean number of premature deaths measured as years of life lost before age 75 years per 1000 people, and (3) age-adjusted all-cause and 18 cause-specific mortality rates (eg, malignant cancers, heart disease, and suicide) per 100 000 person-years. Multivariable linear models were fitted to estimate the associations between medical debt and health outcomes. Results: A total of 2943 counties were included in this analysis. The median percentage of the county population aged 65 years or older was 18.3% (IQR, 15.8%-20.9%). Across counties, a median 3.0% (IQR, 1.2%-11.9%) of the population were Black residents, 4.3% (IQR, 2.3%-9.7%) were Hispanic residents, and 84.5% (IQR, 65.7%-93.3%) were White residents. On average, 19.8% (range, 0%-53.6%) of the population had medical debt. After adjusting for county-level sociodemographic characteristics, a 1-percentage point increase in the population with medical debt was associated with 18.3 (95% CI, 16.3-20.2) more physically unhealthy days and 17.9 (95% CI, 16.1-19.8) more mentally unhealthy days per 1000 people during the past month, 1.12 (95% CI, 1.03-1.21) years of life lost per 1000 people, and an increase of 7.51 (95% CI, 6.99-8.04) per 100 000 person-years in age-adjusted all-cause mortality rate. Associations of medical debt and elevated mortality rates were consistent for all leading causes of death, including cancer (1.12 [95% CI, 1.02-1.22]), heart disease (1.39 [95% CI, 1.21-1.57]), and suicide (0.09 [95% CI, 0.06-0.11]) per 100 000 person-years. Similar patterns were observed for associations between the median amount of medical debt and the aforementioned health outcomes. Conclusions and Relevance: These findings suggest that medical debt is associated with worse health status, more premature deaths, and higher mortality rates at the county level in the US. Therefore, policies increasing access to affordable health care, such as expanding health insurance coverage, may improve population health.

US Medicare Hospice and Palliative Medicine Physician Workforce and Service Delivery in 2008-2020.

CONTEXT: Despite clinical benefits of early palliative care, little is known about Medicare physician workforce specialized in Hospice and Palliative Medicine (HPM) and their service delivery settings. OBJECTIVES: To examine changes in Medicare HPM physician workforce and their service delivery settings in 2008-2020. METHODS: Using the Medicare Data on Provider Practice and Specialty from 2008 to 2020, we identified 2375 unique Medicare Fee-For-Service (FFS) physicians (15,565 physician-year observations) with self-reported specialty in "Palliative Care and Hospice". We examined changes in the annual number of HPM physicians, average number of Medicare services overall and by care setting, total number of Medicare FFS beneficiaries, and total Medicare allowed charges billed by the physician. RESULTS: The number of Medicare HPM physicians increased 2.32 times from 771 in 2008 to 1790 in 2020. The percent of HPM physicians practicing in metropolitan areas increased from 90% to 96% in 2008-2020. Faster growth was also observed in female physicians (52.4% to 60.1%). Between 2008 and 2020, we observed decreased average annual Medicare FFS beneficiaries (170 to 123), number of FFS services (467 to 335), and Medicare allowed charges billed by the physician ($47,230 to $37,323). The share of palliative care delivered in inpatient settings increased from 47% to 68% in 2008-2020; whereas the share of services delivered in outpatient settings decreased from 37% to 19%. CONCLUSION: Despite growth in Medicare HPM physician workforce, access is disproportionately concentrated in metropolitan and inpatient settings. This may limit receipt of early outpatient specialized palliative care, especially in nonmetropolitan areas.

Importance of Patient Health Insurance Coverage and Out-of-Pocket Costs for Genomic Testing in Oncologists' Treatment Decisions.

PURPOSE: Use of genomic testing, especially multimarker panels, is increasing in the United States. Not all tests and related treatments are covered by health insurance, which can result in substantial patient out-of-pocket (OOP) costs. Little is known about oncologists' treatment decisions with respect to patient insurance coverage and OOP costs for genomic testing. METHODS: We identified 1,049 oncologists who used multimarker tumor panels from the 2017 National Survey of Precision Medicine in Cancer Treatment. Separate multivariable ordinal logistic regressions examined associations of oncologist-, practice-, and area-level characteristics and oncologists' ratings of importance (very, somewhat, or a little/not important) of insurance coverage and OOP costs for genomic testing in treatment decisions, adjusting for oncologist years of experience, sex, race and ethnicity, specialty, use of next-generation sequencing (NGS) tests, region, tumor boards, patient insurance mix, and area-level socioeconomic characteristics. RESULTS: Among oncologists, 47.3%, 32.7%, and 20.0% reported that patient insurance coverage for genomic testing was very, somewhat, or a little/not important, respectively, in treatment decisions. In addition, 56.9%, 28.0%, and 15.2% reported that OOP costs for testing were very, somewhat, or a little/not important, respectively. In adjusted analyses, oncologists who used NGS tests were more likely to report patient insurance and OOP costs as important (odds ratio [OR], 2.00 [95% CI, 1.16 to 3.45] and OR, 2.12 [95% CI, 1.22 to 3.68], respectively) in treatment decisions compared with oncologists who did not use these tests, as were oncologists who treated solid tumors, rather than only hematological cancers. More years of experience and higher percentages of Medicaid or self-paid/uninsured patients in the practice were associated with reporting insurance coverage (OR, 1.43 [95% CI, 1.09 to 1.89]) and OOP costs (OR, 1.51 [95% CI, 1.13 to 2.01]) as important. Oncologists in practices with molecular tumor boards for genomic tests were less likely to report coverage (OR, 0.63 [95% CI, 0.47 to 0.85]) and OOP costs (OR, 0.72 [95% CI, 0.53 to 0.97]) as important than their counterparts in practices without these tumor boards. CONCLUSION: Most oncologists rate patient health insurance and OOP costs for genomic tests as important considerations in subsequent treatment recommendations. Modifiable factors associated with these ratings can inform interventions to support patient-physician decision making about care.

Assessment of Changes in Cancer Treatment During the First Year of the COVID-19 Pandemic in the US.

Importance: The COVID-19 pandemic led to disruptions in access to health care, including cancer care. The extent of changes in receipt of cancer treatment is unclear. Objective: To evaluate changes in the absolute number, proportion, and cancer treatment modalities provided to patients with newly diagnosed cancer during 2020, the first year of the pandemic. Design, Setting, and Participants: In this cohort study, adults aged 18 years and older diagnosed with any solid tumor between January 1, 2018, and December 31, 2020, were identified using the National Cancer Database. Data analysis was conducted from September 19, 2022, to July 28, 2023. Exposure: First year of the COVID-19 pandemic. Main Outcomes and Measures: The expected number of procedures for each treatment modality (surgery, radiotherapy, chemotherapy, immunotherapy, and hormonal therapy) in 2020 were calculated using historical data (January 1, 2018, to December 31, 2019) with the vector autoregressive method. The difference between expected and observed numbers was evaluated using a generalized estimating equation under assumptions of the Poisson distribution for count data. Changes in the proportion of different types of cancer treatments initiated in 2020 were evaluated using the additive outlier method. Results: A total of 3 504 342 patients (1 214 918 in 2018, mean [SD] age, 64.6 [13.6] years; 1 235 584 in 2019, mean [SD] age, 64.8 [13.6] years; and 1 053 840 in 2020, mean [SD] age, 64.9 [13.6] years) were included. Compared with expected treatment from previous years' trends, there were approximately 98 000 fewer curative intent surgical procedures performed, 38 800 fewer chemotherapy regimens, 55 500 fewer radiotherapy regimens, 6800 fewer immunotherapy regimens, and 32 000 fewer hormonal therapies initiated in 2020. For most cancer sites and stages evaluated, there was no statistically significant change in the type of cancer treatment provided during the first year of the pandemic, the exception being a statistically significant decrease in the proportion of patients receiving breast-conserving surgery and radiotherapy with a simultaneous statistically significant increase in the proportion of patients undergoing mastectomy for treatment of stage I breast cancer during the first months of the pandemic. Conclusions and Relevance: In this large national cohort study, a significant deficit was noted in the number of cancer treatments provided in the first year of the COVID-19 pandemic. Data indicated that this deficit in the number of cancer treatments provided was associated with decreases in the number of cancer diagnoses, not changes in treatment strategies.

The Association Between Allostatic Load and Frailty Trajectories Among Adults Aged 50+ Years: Mediating Role of Physical Activity.

OBJECTIVES: To identify frailty trajectories and examine its association with allostatic load (AL) and mediating effect of physical activity (PA). METHODS: This study included 8,082 adults from the English Longitudinal Study of Aging over Waves 4-9. AL was calculated by 14 biological indicators, and a 53-item frailty index was used to evaluate frailty. Frailty trajectories were classified by group-based trajectory modeling, and the mediated effect of PA was tested by causal mediation analysis. RESULTS: Four frailty trajectories were identified: "Robustness" (n = 4,437, 54.9%), "Incident prefrailty" (n = 2,061, 25.5%), "Prefrailty to frailty" (n = 1,136, 14.1%), and "Frailty to severe frailty" (n = 448, 5.5%). High baseline AL was associated with increased odds of "Incident prefrailty," "Prefrailty to frailty," and "Frailty to severe frailty" trajectories. PA demonstrated significant mediated effects in aforementioned associations. CONCLUSIONS: AL is significantly associated with the onset and progression of frailty, and such associations are partially mediated by PA.

Health-related quality of life among prostate cancer survivors with metastatic disease and non-metastatic disease and men without a cancer history in the USA.

BACKGROUND: Few studies have comprehensively compared health-related quality of life (HRQoL) between metastatic prostate cancer survivors, survivors with non-metastatic disease, and men without a cancer history. METHODS: We used the Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey (SEER-MHOS) data linkage to identify men aged ≥ 65 years enrolled in Medicare Advantage (MA) plans. Prostate cancer survivors were diagnosed between 1988 and 2017 and completed MHOS surveys between 1998 and 2019. We analyzed data from 752 metastatic prostate cancer survivors (1040 survey records), 19,583 localized or regional prostate cancer survivors (non-metastatic; 30,121 survey records), and 784,305 men aged ≥ 65 years without a cancer history in the same SEER regions (1.15 million survey records). We used clustered linear regressions to compare HRQoL measures at the person-level using the Veterans RAND 12 Item Health Survey (VR-12) T-scores for general health and physical and mental component summaries. RESULTS: Compared to men without a cancer history, prostate cancer survivors were older, more likely to be married, and had higher socioeconomic status. Compared to men without a cancer history, metastatic prostate cancer survivors reported lower general health (T-score differences [95% confidence interval]: - 6.26, [- 7.14, - 5.38], p < .001), physical health (- 4.33, [- 5.18, - 3.48], p < .001), and mental health (- 2.64, [- 3.40, - 1.88], p < .001) component summaries. Results were similar for other VR-12 T-scores. In contrast, non-metastatic prostate cancer survivors reported similar VR-12 T-scores as men without a cancer history. Further analyses comparing metastatic and non-metastatic prostate cancer survivors support these findings. CONCLUSION: Interventions to improve health-related quality of life for men diagnosed with metastatic prostate cancer merit additional investigation. IMPLICATIONS FOR CANCER SURVIVORS: Interventions to improve health-related quality of life for metastatic prostate cancer survivors merit additional investigation.

Medicaid expansion is associated with treatment receipt, timeliness, and outcomes among young adults with breast cancer.

Female breast cancer is a common cancer in young adults, an age group with the highest uninsured rate. Among 51 675 young adult women (ages 18-39 years) diagnosed with breast cancer between 2011 and 2018 in the National Cancer Database, we estimated changes in guideline-concordant treatment receipt, treatment timeliness, and survival associated with the Affordable Care Act Medicaid expansion. Of young adults with stage I-III estrogen receptor-positive or progesterone receptor-positive breast cancer, Medicaid expansion was associated with a net increase of 2.42 percentage points (95% confidence interval [CI] = 0.56 to 4.28 percentage points) in the percentage receiving endocrine therapy. Among all young adults with stage I-III breast cancer, Medicaid expansion was associated with a net reduction of 1.65 percentage points (95% CI = 0.08 to 3.22 percentage points) in treatment delays defined as treatment initiation of at least 60 days after diagnosis and a net increase of 1.00 percentage points (95% CI = 0.21 to 1.79 percentage points) in 2-year overall survival. Our study provides evidence of benefit in cancer care and outcomes from Medicaid expansion among the young adult population.

Changes in cancer diagnoses and stage distribution during the first year of the COVID-19 pandemic in the USA: a cross-sectional nationwide assessment.

BACKGROUND: The emergence of COVID-19 disrupted health care, with consequences for cancer diagnoses and outcomes, especially for early stage diagnoses, which generally have favourable prognoses. We aimed to examine nationwide changes in adult cancer diagnoses and stage distribution during the first year of the COVID-19 pandemic by cancer type and key sociodemographic factors in the USA. METHODS: In this cross-sectional study, adults (aged ≥18 years) newly diagnosed with a first primary malignant cancer between Jan 1, 2018, and Dec 31, 2020, were identified from the US National Cancer Database. We included individuals across 50 US states and the District of Columbia who were treated in hospitals that were Commission on Cancer-accredited during the study period. Individuals whose cancer stage was 0 (except for bladder cancer), occult, or without an applicable American Joint Committee on Cancer staging scheme were excluded. Our primary outcomes were the change in the number and the change in the stage distribution of new cancer diagnoses between 2019 (Jan 1 to Dec 31) and 2020 (Jan 1 to Dec 31). Monthly counts and stage distributions were calculated for all cancers combined and for major cancer types. We also calculated annual change in stage distribution from 2019 to 2020 and adjusted odds ratios (aORs) using multivariable logistic regression, adjusted for age group, sex, race and ethnicity, health insurance status, comorbidity score, US state, zip code-level social deprivation index, and county-level age-adjusted COVID-19 mortality in 2020. Separate models were stratified by sociodemographic and clinical factors. FINDINGS: We identified 2 404 050 adults who were newly diagnosed with cancer during the study period (830 528 in 2018, 849 290 in 2019, and 724 232 in 2020). Mean age was 63·5 years (SD 13·5) and 1 287 049 (53·5%) individuals were women, 1 117 001 (46·5%) were men, and 1 814 082 (75·5%) were non-Hispanic White. The monthly number of new cancer diagnoses (all stages) decreased substantially after the start of the COVID-19 pandemic in March, 2020, although monthly counts returned to near pre-pandemic levels by the end of 2020. The decrease in diagnoses was largest for stage I disease, leading to lower odds of being diagnosed with stage I disease in 2020 than in 2019 (aOR 0·946 [95% CI 0·939-0·952] for stage I vs stage II-IV); whereas, the odds of being diagnosed with stage IV disease were higher in 2020 than in 2019 (1·074 [1·066-1·083] for stage IV vs stage I-III). This pattern was observed in most cancer types and sociodemographic groups, although was most prominent among Hispanic individuals (0·922 [0·899-0·946] for stage I; 1·110 [1·077-1·144] for stage IV), Asian American and Pacific Islander individuals (0·924 [0·892-0·956] for stage I; 1·096 [1·052-1·142] for stage IV), uninsured individuals (0·917 [0·875-0·961] for stage I; 1·102 [1·055-1·152] for stage IV), Medicare-insured adults younger than 65 years (0·909 [0·882-0·937] for stage I; 1·105 [1·068-1·144] for stage IV), and individuals living in the most socioeconomically deprived areas (0·931 [0·917-0·946] for stage I; 1·106 [1·087-1·125] for stage IV). INTERPRETATION: Substantial cancer underdiagnosis and decreases in the proportion of early stage diagnoses occurred during 2020 in the USA, particularly among medically underserved individuals. Monitoring the long-term effects of the pandemic on morbidity, survival, and mortality is warranted. FUNDING: None.

Medicaid Expansion Associated With Increase In Palliative Care For People With Advanced-Stage Cancers.

Clinical guidelines have endorsed early palliative care for patients with advanced malignancies, but receipt remains low in the US. This study examined the association between Medicaid expansion under the Affordable Care Act and receipt of palliative care among patients newly diagnosed with advanced-stage cancers. Using the National Cancer Database, we found that the percentage of eligible patients who received palliative care as part of first-course treatment increased from 17.0 percent preexpansion to 18.9 percent postexpansion in Medicaid expansion states and from 15.7 percent to 16.7 percent, respectively, in nonexpansion states, resulting in a net increase of 1.3 percentage points in expansion states in adjusted analyses. Increases in receipt of palliative care associated with Medicaid expansion were largest for patients with advanced pancreatic, colorectal, lung, and oral cavity and pharynx cancers and non-Hodgkin lymphoma. Our findings suggest that increasing Medicaid coverage facilitates access to guideline-based palliative care for advanced cancer, and they provide additional evidence of benefit in cancer care from states' expansion of income eligibility for Medicaid.

Postoperative fasting plasma glucose and family history diabetes mellitus can predict post-transplantation diabetes mellitus in kidney transplant recipients.

PURPOSE: To explore whether glycated albumin (GA) or fasting plasma glucose (FPG), both routinely monitored during patients' hospital stay, can be used to predict post-transplantation diabetes mellitus (PTDM). METHODS: All kidney transplantation recipients (KTRs) from January 2017 to December 2018 were followed-up for 1 year. PTDM was diagnosed from day 45 post-operation to 1 year. When the completeness was above 80%, FPG or GA data on the day was selected, analyzed, and presented as range parameters and standard deviation (SD) and compared between PTDM and non-PTDM groups in fluctuation and stable periods. The predictive cut-off values were determined via receiver operating characteristic (ROC) analysis. The PTDM combined predictive mode, formed by the independent risk factors derived from logistic regression analyses, was compared with each independent risk factor with the independent ROC curve test. RESULTS: Among 536 KTRs, 38 patients developed PTDM up to 1 year post-operatively. The family history diabetes mellitus (OR, 3.21; P = 0.035), the FPG SD in fluctuation period >2.09 mmol/L (OR, 3.06; P = 0.002), and the FPG maximum in stable period >5.08 mmol/L (OR, 6.85; P < 0.001) were the PTDM independent risk factors. The discrimination of the combined mode (area under the curve = 0.81, sensitivity = 73.68%, and specificity = 76.31%) was higher than each prediction (P < 0.05). CONCLUSIONS: The FPG standard deviation during the fluctuation period, FPG maximum during the stable period, and family history diabetes mellitus predicted PTDM with good discrimination and potential routine clinical use.

Interpretable Machine Learning for Fall Prediction Among Older Adults in China.

INTRODUCTION: Falls in older adults are potentially devastating, whereas an accurate fall risk prediction model for community-dwelling older Chinese is still lacking. The objective of this study was to build prediction models for falls and fall-related injuries among community-dwelling older adults in China. METHODS: This study used data (Waves 2015 and 2018) from 5,818 participants from the China Health and Retirement Longitudinal Study. A total of 107 input variables at the baseline level were regarded as candidate features. Five machine learning algorithms were used to build the 3-year fall and fall-related injury risk prediction models. SHapley Additive exPlanations was used for the prediction model explanation. Analyses were conducted in 2022. RESULTS: The logistic regression model achieved the best performance among fall and fall-related injury prediction models with an area under the receiver operating characteristic curve of 0.739 and 0.757, respectively. Experience of falling was the most important feature in both models. Other important features included basic activity of daily living, instrumental activity of daily living, depressive symptoms, house tidiness, grip strength, and sleep duration. The important features unique to the fall model were house temperature, sex, and flush toilets, whereas lung function, smoking, and Internet access were exclusively related to the fall-related injury model. CONCLUSIONS: This study suggests that the optimal models hold promise for screening out older adults at high risk for falls in facilitated targeted interventions. Fall prevention strategies should specifically focus on fall history, physical functions, psychological factors, and home environment.

Age-stratified modifiable fall risk factors in Chinese community-dwelling older adults.

BACKGROUND: Fall incident is one of the major causes of mortality and injury in older adults. Modifiable fall risk factors are the targets for fall prevention. Since the status of some fall risk factors can change with age, insights into age-stratified fall risk factors can be beneficial for developing tailored fall prevention strategies for older adults at different ages. Therefore, the objective of this study was to identify fall risk factors in different age groups of older people. METHODS: The current study analysed data of 14,601 community-dwelling older Chinese (aged 65 years or above) recruited from the Chinese Longitudinal Healthy Longevity Survey (CLHLS, wave 2017-2018). 24 modifiable fall risk factors were selected from the CLHLS as candidate risk factors and multivariable logistic regression was used to identify significant risk factors associated with fall incidents by three age groups (65-79 years, 80-94 years, ≥95 years). RESULTS: Anxiety is identified across all age groups. Hearing impairment, stroke, rain/water leakage were found in both the 65-79 years and the 80-94 years old groups. Interactions between hearing and stroke and between hearing and rain /water leakage were found in these two groups, respectively. Medication use is a shared factor in both the 65-79 years and the ≥95 years old group. CONCLUSION: Modifiable fall risk factors varied among age groups, suggesting that customised fall prevention strategies can be applied by targeting at fall risk factors in corresponding age groups.

Association of rurality, type of primary caregiver and place of death with end-of-life medical expenditures among the oldest-old population in China.

BACKGROUND: Understanding whether the type of primary caregiver and end-of-life (EOL) care location are associated with EOL medical expenditures is crucial to inform global debates on policies for efficient and effective EOL care. This study aims to assess trends in the type of primary caregiver and place of death stratified by rural‒urban status among the oldest-old population from 1998-2018 in China. A secondary objective is to determine the associations between rurality, the type of primary caregiver, place of death and EOL medical expenditures.  METHODS: A total of 20,149 deaths of people aged 80 years or older were derived from the Chinese Longitudinal Health Longevity Survey (CLHLS). Cochran-Armitage tests and Cuzick's tests were used to test trends in the type of primary caregiver and place of death over time, respectively. Tobit models were used to estimate the marginal associations of rurality, type of primary caregiver, and place of death with EOL medical expenditures because CLHLS sets 100,000 Chinese yuan (approximately US$15,286) as the upper limit of the outcome variable.  RESULTS: Of the 20,149 oldest-old people, the median age at death was 97 years old, 12,490 (weighted, 58.6%, hereafter) were female, and 8,235 lived in urban areas. From 1998-2018, the prevalence of informal caregivers significantly increased from 94.3% to 96.2%, and home death significantly increased from 86.0% to 89.5%. The proportion of people receiving help from informal caregivers significantly increased in urban decedents (16.5%) but decreased in rural decedents (-4.0%), while home death rates significantly increased among both urban (15.3%) and rural (1.8%) decedents. In the adjusted models, rural decedents spent less than urban decedents did (marginal difference [95% CI]: $-229 [$-378, $-80]). Those who died in hospitals spent more than those who died at home ($798 [$518, $1077]). No difference in medical expenditures by type of primary caregiver was observed. CONCLUSIONS: Over the past two decades, the increases in informal caregiver utilization and home deaths were unequal, leading to substantially higher EOL medical expenditures among urban decedents and deceased individuals who died at hospitals than among their counterparts who lived in rural areas and died at home.

Rural-urban disparities in the unmet need for home visiting services among oldest-old in China: Changes over time and decomposition analysis.

PURPOSE: This study aims to assess trends in rural-urban disparities in the prevalence of unmet community-based home visiting services need and their contributing factors from 2005 to 2018 among oldest-old in China. METHODS: The Chinese Longitudinal Healthy Longevity Survey data of oldest-old collected with a targeted random-sampling approach from half of counties/cities from 23 provinces across China was used. Unmet need was measured as the differences between healthcare services expected and available. We used Cochran-Armitage tests to test linear trends in prevalence of unmet need. Average marginal differences were estimated to measure magnitude of rural-urban disparities in prevalence of unmet need. Changes in rural-urban disparities were decomposed using Blinder-Oaxaca Decomposition technique to logit models. All analysis was performed by Stata 15.0. RESULTS: From 2005-2018, decreased trends in prevalence of unmet need were observed (overall: 62.4% to 48.6%; rural: 65.9% to 47.3%; urban: 57.5% to 49.8%) (all ptrend < 0.001). In 2017/2018, urban oldest-old reported greater prevalence of unmet need (average marginal difference, 95% CI: 3.7% [0.4%-7.1%]); affluent oldest-old reported less unmet need than their peers. Oldest-old from Central and Western China reported greater prevalence of unmet need than their Eastern peers. Increases in income (percentages of explained change, overall: 21.3%; rural: 16.9%, urban: 36.9%) mainly contributed to decreased trends in prevalence of unmet need. CONCLUSIONS: Oldest-old with socioeconomic disadvantages or living in Central and Western China reported greater prevalence of unmet need. Policy efforts are warranted to ensure equitable access to home visiting services among those oldest-old.

Survival in Young Adults With Cancer Is Associated With Medicaid Expansion Through the Affordable Care Act.

PURPOSE: Medicaid expansion through the Affordable Care Act (ACA) has been shown to improve insurance coverage and early diagnosis of cancer in young adults (YAs); whether these improvements translate to survival benefits remains unknown. We examined the association between Medicaid expansion under the ACA and 2-year overall survival among YAs with cancer. METHODS: Using the National Cancer Database, we identified 345,413 YAs (age 18-39 years) diagnosed with cancer in 2010-2017. We applied the difference-in-differences (DD) method to estimate changes in 2-year overall survival after versus before Medicaid expansion in expansion versus nonexpansion states. RESULTS: Among all YAs, 2-year overall survival increased more in expansion states (90.39% pre-expansion to 91.85% postexpansion) than in nonexpansion states (88.98% pre-expansion to 90.07% postexpansion), resulting in a net increase of 0.55 percentage points (ppt; 95% CI, 0.13 to 0.96). The expansion-associated survival benefit was concentrated in patients with female breast cancer (DD, 1.20 ppt; 95%CI, 0.27 to 2.12) when stratifying by cancer type and in patients with stage IV disease (DD, 2.56; 95%CI, 0.36 to 4.77) when stratifying by stage. In addition, greater survival benefit associated with Medicaid expansion was observed among racial and ethnic minoritized groups (DD, 1.01 ppt; 95% CI, 0.14 to 1.87) as compared with non-Hispanic White peers (DD, 0.41 ppt; 95% CI, -0.06 to 0.87) and among patients with a Charlson comorbidity score of ≥ 2 (DD, 6.48 ppt; 95% CI, 0.81 to 12.16) than those with a comorbidity score of 0 (DD, 0.44 ppt; 95% CI, 0.005 to 0.87). CONCLUSION: Medicaid expansion under the ACA was associated with an improvement in overall survival among YAs with cancer, with survival benefits most pronounced among patients of under-represented race and ethnicity and patients with high-risk diseases.

Incarceration History and Health Insurance and Coverage Changes in the U.S.

INTRODUCTION: This study examines the association of incarceration history and health insurance coverage and coverage changes in the U.S. METHODS: Individuals with and without incarceration history were identified from the National Longitudinal Survey of Youth 1997 with follow-up through 2017-2018 (n=7,417). Generalized estimating equations were used to examine the associations between incarceration history and health insurance and coverage changes in the past 12 months. This study also assessed variation in associations by incarceration duration, frequency, and recency and reoffence history. Analysis was conducted in 2022. RESULTS: Individuals with incarceration history were more likely to be uninsured (AOR=1.69; 95% CI=1.55, 1.85) and to experience year-long uninsurance (AOR=1.34; 95% CI=1.12, 1.59) and were less likely to have stable health insurance coverage (AOR=1.30; 95% CI=1.08, 1.56) than individuals without incarceration history. Longer duration and more frequent incarcerations were associated with a higher likelihood of lack of and unstable insurance coverage and year-long uninsurance. CONCLUSIONS: People with an incarceration history had worse access to health insurance coverage. Targeted programs to improve health insurance coverage may reduce disparities associated with incarceration.

Access to Care Among Adults with Limited English Proficiency.

BACKGROUND: There are approximately 25.6 million individuals with limited English proficiency (LEP) in the USA, and this number is increasing. OBJECTIVE: Investigate associations between LEP and access to care in adults. DESIGN: Cross-sectional nationally representative survey. PARTICIPANTS: Adults with (n = 18,908) and without (n = 98,060) LEP aged ≥ 18 years identified from the 2014-2018 Medical Expenditure Panel Survey MAIN MEASURES: Associations between LEP and access to healthcare and preventive services were evaluated with multivariable logistic regression models, stratified by age group (18-64 and ≥ 65 years). The official government definition of LEP (answers "not at all/not well/well" to the question "How well do you speak English?") was used. Access to care included having a usual source of care (and if so, distance from usual source of care, difficulty contacting usual source of care, and provision of extended hours), visiting a medical provider in the past 12 months, having to forego or delay care, and having trouble paying for medical bills. Preventive services included blood pressure and cholesterol check, flu vaccination, and cancer screening. KEY RESULTS: Adults aged 18-64 years with LEP were significantly more likely to lack a usual source of care (adjusted odds ratios [aOR] = 2.48; 95% confidence interval [CI] = 2.27-2.70), not have visited a medical provider (aOR = 2.02; CI = 1.89-2.16), and to be overdue for receipt of preventive services, including blood pressure check (aOR = 2.00; CI = 1.79-2.23), cholesterol check (aOR = 1.22; CI = 1.03-1.44), and colorectal cancer screening (aOR = 1.58; CI = 1.37-1.83) than adults without LEP. Results were similar among adults aged ≥ 65 years. CONCLUSIONS: Adults with LEP had consistently worse access to care than adults without LEP. System-level interventions, such as expanding access to health insurance coverage, providing language services, improving provider training in cultural competence, and increasing diversity in the medical workforce may minimize barriers and improve equity in access to care.