Sociodemographic and geographic factors impacting radiotherapy recommendation, initiation, and completion for patients with cancer.

BACKGROUND: More than half of patients with cancer receive radiotherapy, which often requires daily treatments for several weeks. The impact of geographic and sociodemographic factors on the odds of patients with cancer being recommended radiotherapy, starting radiotherapy, and completing radiotherapy is not well understood. METHODS: This was a retrospective patient cohort study that included patients diagnosed with one of the 10 most common solid cancers from January 1, 2018, to December 31, 2021, in the National Cancer Database. The primary predictor was radial distance from a patient's home to their cancer treatment hospital. Other covariates included baseline patient characteristics (age, sex, comorbidities, metastatic disease, cancer site), sociodemographic characteristics (race, ethnicity, median income quartile, insurance status), geographic region, and facility type. The three primary outcomes were being recommended radiotherapy, starting recommended radiotherapy, and completing radiotherapy. RESULTS: Of the 3,068,919 patients included, patients living >50 miles away had lower odds of being recommended radiotherapy than those living <10 miles away. Compared to White patients, Asian and Hispanic patients had lower odds of being recommended radiotherapy, and Black patients had lower odds of starting recommended radiotherapy. Uninsured patients, those with Medicaid or Medicare, and patients in lower median income quartiles had lower odds of starting or completing radiotherapy. CONCLUSIONS: Geographic and sociodemographic factors impact access to radiotherapy at different levels in cancer care and understanding these factors could aid policymakers and practices in identifying and supporting at-risk patients.

The Decline of Small Practice in Colorectal Surgery: Practice Consolidation From 2015-2022.

INTRODUCTION: Physicians have gravitated toward larger group practice arrangements in recent years. However, consolidation trends in colorectal surgery have yet to be well described. Our objective was to assess current trends in practice consolidation within colorectal surgery and evaluate underlying demographic trends including age, gender, and geography. METHODS: We performed a retrospective cross-sectional study using the Center for Medicare Services National Downloadable File from 2015 to 2022. Colorectal surgeons were categorized by practice size and by region, gender, and age. RESULTS: From 2015 to 2022, the number of colorectal surgeons in the United States increased from 1369 to 1621 (+18.4%), while the practices with which they were affiliated remained relatively stable (693-721, +4.0%). The proportion of colorectal surgeons in groups of 1-2 members fell from 18.9% to 10.7%. Conversely, those in groups of 500+ members grew from 26.5% to 45.2% (linear trend P < 0.001). The midwest region demonstrated the highest degree of consolidation. Affiliations with group practices of 500+ members saw large increases from both female and male surgeons (+148.9% and +86.9%, respectively). New surgeons joining the field since 2015 overwhelmingly practice in larger groups (5.3% in groups of 1-2, 50.1% in groups of 500+). CONCLUSIONS: Colorectal surgeons are shifting toward larger practice affiliations. Although this change is happening across all demographic groups, it appears unevenly distributed across geography, gender, and age. New surgeons are preferentially joining large group practices.

Benchmarking the Radiation Oncology Alternative Payment Model: Changes in Medicare Reimbursement for 16 Common Radiation Therapy Treatment Courses.

Radiation oncology (RO) has seen declines in Medicare reimbursement (MCR) in the past decade under the current fee-for-service model. Although studies have explored decline in reimbursement at a per-code level, to our knowledge there are no recent studies analyzing changes in MCR over time for common RO treatment courses. By analyzing changes in MCR for common treatment courses, our study had 3 objectives: (1) to provide practitioners and policymakers with estimates of recent reimbursement changes for common treatment courses; (2) to provide an estimate of how reimbursement will change in the future under the current fee-for-service model if current trends continue; and (3) to provide a baseline for treatment episodes in the event that the episode-based Radiation Oncology Alternative Payment Model is eventually implemented. Specifically, we quantified inflation- and utilization-adjusted changes in reimbursement for 16 common radiation therapy (RT) treatment courses from 2010 to 2020. Centers for Medicare & Medicaid Services Physician/Supplier Procedure Summary databases were used to obtain reimbursement for all RO procedures in 2010, 2015, and 2020 for free-standing facilities. Inflation-adjusted average reimbursement (AR) per billing instance was calculated for each Healthcare Common Procedure Coding System code using 2020 dollars. For each year, the billing frequency of each code was multiplied by the AR per code. Results were summed per RT course per year, and AR for RT courses were compared. Sixteen common RO courses for head and neck, breast, prostate, lung, and palliative RT were analyzed. AR decreased for all 16 courses from 2010 to 2020. From 2015 to 2020, the only course that increased in AR was palliative 2-dimensional 10-fraction 30 Gy, which increased by 0.4%. Courses using intensity modulated RT saw the largest AR decline from 2010 to 2020, ranging from 38% to 39%. We report significant declines in reimbursement from 2010 to 2020 for common RO courses, with the largest declines for intensity modulated RT. Policymakers should consider the significant cuts to reimbursement that have already occurred when considering future reimbursement adjustment under the current fee-for-service model or when considering mandatory adoption of a new payment system with further cuts and the negative effect of such cuts on quality and access to care.

A cross-sectional analysis of trends in dermatology practice size in the United States from 2012 to 2020.

Physicians are trending towards practice consolidation nationally; however, changes in dermatology practice size remain to be assessed. The objective of this study was to analyze trends in dermatology practice size from 2012 to 2020 using a large-scale Medicare physician database. We performed a retrospective cross-sectional analysis using 2012 and 2020 data obtained from the Physician Compare Database. Responses from dermatologists were analyzed for trends in practice size, with a sub-analysis to examine differences among different regions, gender, and years of experience. The proportion of dermatologists in solo practice decreased from 26.1% in 2012 to 15.6% in 2020 (p < 0.001). Dermatologists were 40% less likely to be practicing in solo practice and 36% more likely to be in a practice with 10 or more members in 2020 (p < 0.001). These findings were consistent among all regions and genders examined. Additionally, in 2020, dermatologists with 30 or more years in practice were 7.5 times more likely to be in solo practice compared to dermatologists with 0-9 years in practice (p < 0.001). There is a trend of dermatologists working for larger practices, which is consistent with a larger nationwide trend of expanding physician practices. This shift in practice settings should be closely monitored to analyze the effect on healthcare efficiency, cost, and delivery.

Measuring expertise in identifying interictal epileptiform discharges.

OBJECTIVE: Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS: A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS: Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE: Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.

Decreases in Radiation Oncology Medicare Reimbursement Over Time: Analysis by Billing Code.

PURPOSE: Radiation oncology (RO) has seen declines in Medicare reimbursement (MCR). However, there are no recent studies analyzing the contributions of specific billing codes to overall RO reimbursement. We compared total MCR for specific Healthcare Common Procedure Coding System (HCPCS) codes in 2019 with MCR for those codes in 2010 and 2015, corrected for inflation, to see how the same basket of RO services in 2019 would have been reimbursed in 2010 and 2015 (adjusted MCR). METHODS AND MATERIALS: The Centers for Medicare & Medicaid Services Physician/Supplier Procedure Summary database was used to obtain MCR data for RO HCPCS codes in 2010, 2015, and 2019. For each code, the total allowed charge was divided by the number of submitted claims to calculate the average MCR per claim in 2010, 2015, and 2019. The 2019 billing frequency for each code was then multiplied by the inflation-adjusted average MCR for those codes in 2010 and 2015 to determine what the MCR would have been in 2010 and 2015 using 2019 dollars and utilization rates. Results were compared with actual 2019 MCR to calculate the projected difference. RESULTS: Total inflation-adjusted RO MCR was $2281 million (M), $1991 M, and $1848 M in 2010, 2015, and 2019 respectively. This represents a cut of $433 M (19%) and $143 M (7%) from 2010 and 2015, respectively, to 2019. After utilization adjustment, total reimbursement was $2534 M, $2034 M, and $1848 M for 2010, 2015, and 2019, respectively, representing a cut of $686 M (27%) and $186 M (9%) from 2010 and 2015, respectively, to 2019. Intensity modulated radiation therapy (IMRT) treatment delivery and planning accounted for $917 M (36%), $670 M (33%), and $573 M (31%) of the adjusted MCR in 2010, 2015, and 2019, respectively. CONCLUSIONS: Medicare reimbursement decreased substantially from 2010 to 2019. A decline in IMRT treatment reimbursement was the primary driver of MCR decline. When considering further cuts, policymakers should consider these trends and their consequences for health care quality and access.

Gastroenterology Practice Consolidation Between 2012 and 2020.

INTRODUCTION: Multiple studies have indicated physicians are practicing medicine in increasingly larger groups. However, specialty-specific data on the extent of consolidation are lacking for many specialties, including gastroenterology. We aim to determine the extent of consolidation for gastroenterology in recent years. METHODS: The Physician Compare database was used to gather information at both an individual and group level. This information included location and number of providers for each group. Cochran-Armitage tests were used to test for differences between practice sizes in 2012 and 2020. RESULTS: Between 2012 and 2020, the number of physicians increased from 12,766 to 13,934, while the total number of practices decreased from 4517 to 3865. The total number of physicians who practice in groups of less than 9 physicians decreased by 23.9%, while the total number of physicians in practices of 100 + increased by 16.8%. DISCUSSION: Significant consolidation has occurred in the field of gastroenterology in every geographic region of the USA. The causes of consolidation are multi-faceted and include the legislative environment, private equity and hospital acquisition of private groups, individual physician lifestyle preferences, and economic benefits of economies of scale. However, the consequences of consolidation are still unclear. CONCLUSION: Over the last eight years, gastroenterologists have been practicing in increasingly larger groups. This trend has been consistent in each area of the country. Future research should focus on the impact of consolidation on patient care and physician wellbeing.

Current trends in U.S. general surgery practice consolidation.

BACKGROUND: Consolidation of physician practices is well-documented in recent years, yet minimal data exist regarding consolidation in general surgery. This study evaluates current trends in general surgery practice consolidation. METHODS: Data were obtained through the CMS Physician Compare database. Surgeons and practices were categorized by size, and trends were analyzed using the Cochran-Armitage test. Data were stratified by US region. RESULTS: From 2012 to 2020, practicing general surgeons increased from 20,044 to 20,637 (+3%). Unique general surgery practices declined from 8178 to 6489 (-21%). The percentage of surgeons in practices of 1 or 2 declined from 19% to 12%, while surgeons in groups of 500 or more grew from 20% to 31%. Tests for trends towards consolidation at both the individual surgeon and unique practice levels were significant (p < .001). The Midwest region demonstrated the highest degree of consolidation. CONCLUSION: Consistent with trends in medicine overall, general surgery is experiencing substantial practice consolidation.

Group Practice Size Consolidation in Emergency Medicine.

STUDY OBJECTIVE: Practice consolidation is common and has been shown to affect the quality and cost of care across multiple health care delivery settings, including hospitals, nursing homes, and physician practices. Despite a long history of large practice management group formation in emergency medicine and intensifying media attention paid to this topic, little is known about the recent practice consolidation trends within the specialty. METHODS: All data were obtained from the Centers for Medicare and Medicaid Services Physician Compare database, which contains physician and group practice data from 2012 to 2020. We assessed practice size changes for both individual emergency physicians and groups. RESULTS: From 2012 to 2020, the proportion of emergency physicians in groups sized less than 25 has decreased substantially from 40.2% to 22.7%. Physicians practicing in groups of more than or equal to 500 physicians increased from 15.5% to 24%. CONCLUSION: Since 2012, we observed a steady trend toward increased consolidation of emergency department practice with nearly 1 in 4 emergency physicians nationally working in groups with more than 500 physicians in 2020 compared with 1 in 7 in 2012. Although the relationship between consolidation is likely to draw the most attention from policymakers or payers seeking to negotiate prices in the near term and advance payment models in the long term, greater attention is required to understand the effects of practice consolidation on emergency care.

ICU delirium burden predicts functional neurologic outcomes.

BACKGROUND: We investigated the effect of delirium burden in mechanically ventilated patients, beginning in the ICU and continuing throughout hospitalization, on functional neurologic outcomes up to 2.5 years following critical illness. METHODS: Prospective cohort study of enrolling 178 consecutive mechanically ventilated adult medical and surgical ICU patients between October 2013 and May 2016. Altogether, patients were assessed daily for delirium 2941days using the Confusion Assessment Method for the ICU (CAM-ICU). Hospitalization delirium burden (DB) was quantified as number of hospital days with delirium divided by total days at risk. Survival status up to 2.5 years and neurologic outcomes using the Glasgow Outcome Scale were recorded at discharge 3, 6, and 12 months post-discharge. RESULTS: Of 178 patients, 19 (10.7%) were excluded from outcome analyses due to persistent coma. Among the remaining 159, 123 (77.4%) experienced delirium. DB was independently associated with >4-fold increased mortality at 2.5 years following ICU admission (adjusted hazard ratio [aHR], 4.77; 95% CI, 2.10-10.83; P < .001), and worse neurologic outcome at discharge (adjusted odds ratio [aOR], 0.02; 0.01-0.09; P < .001), 3 (aOR, 0.11; 0.04-0.31; P < .001), 6 (aOR, 0.10; 0.04-0.29; P < .001), and 12 months (aOR, 0.19; 0.07-0.52; P = .001). DB in the ICU alone was not associated with mortality (HR, 1.79; 0.93-3.44; P = .082) and predicted neurologic outcome less strongly than entire hospital stay DB. Similarly, the number of delirium days in the ICU and for whole hospitalization were not associated with mortality (HR, 1.00; 0.93-1.08; P = .917 and HR, 0.98; 0.94-1.03, P = .535) nor with neurological outcomes, except for the association between ICU delirium days and neurological outcome at discharge (OR, 0.90; 0.81-0.99, P = .038). CONCLUSIONS: Delirium burden throughout hospitalization independently predicts long term neurologic outcomes and death up to 2.5 years after critical illness, and is more predictive than delirium burden in the ICU alone and number of delirium days.

Does the sequence of high-dose rate brachytherapy boost and IMRT for prostate cancer impact early toxicity outcomes? Results from a single institution analysis.

BACKGROUND: We present the first report comparing early toxicity outcomes with high-dose rate brachytherapy (HDR-BT) boost upfront versus intensity modulated RT (IMRT) upfront combined with androgen deprivation therapy (ADT) as definitive management for intermediate risk or higher prostate cancer. METHODS AND MATERIALS: We reviewed all non-metastatic prostate cancer patients who received HDR-BT boost from 2014 to 2019. HDR-BT boost was offered to patients with intermediate-risk disease or higher. ADT use and IMRT target volume was based on NCCN risk group. IMRT dose was typically 45 Gy in 25 fractions to the prostate and seminal vesicles ± pelvic lymph nodes. HDR-BT dose was 15 Gy in 1 fraction, delivered approximately 3 weeks before or after IMRT. The sequence was based on physician preference. Biochemical recurrence was defined per ASTRO definition. Gastrointestinal (GI) and Genitourinary (GU) toxicity was graded per CTCAE v5.0. Pearson Chi-squared test and Wilcoxon tests were used to compare toxicity rates. P-value < 0.05 was significant. RESULTS: Fifty-eight received HDR-BT upfront (majority 2014-2016) and 57 IMRT upfront (majority 2017-2018). Median follow-up was 26.0 months. The two cohorts were well-balanced for baseline patient/disease characteristics and treatment factors. There were differences in treatment sequence based on the year in which patients received treatment. Overall, rates of grade 3 or higher GI or GU toxicity were <1%. There was no significant difference in acute or late GI or GU toxicity between the two groups. CONCLUSION: We found no significant difference in GI/GU toxicity in intermediate-risk or higher prostate cancer patients receiving HDR-BT boost upfront versus IMRT upfront combined with ADT. These findings suggest that either approach may be reasonable. Longer follow-up is needed to evaluate late toxicity and long-term disease control.

Quantitative Analysis of Practice Size Consolidation in Radiation Oncology: A Trend Toward Bigger and Fewer Practices.

PURPOSE: There is evidence of practice consolidation in US health care in recent years. To our knowledge, a detailed quantitative study of recent changes in radiation oncology practice size has not been performed. We aim to evaluate radiation oncology practice size changes between 2012 and 2020 in the United States. MATERIALS AND METHODS: Using the Medicare Physician Compare Database, we identified practices employing radiation oncologists using their taxpayer identification number and individual radiation oncologists using their national provider identifier. We grouped individual radiation oncologists into categories by practice size (which includes the number of physicians of all specialties) and compared the number of radiation oncologists in each category between 2012 and 2020. Further analyses by US geographic census region, single-specialty practice, academic practice, and high- and low-population density areas were performed. RESULTS: Between 2012 and 2020, the total number of practicing radiation oncologists increased by 9%, and the number of practices employing radiation oncologists decreased by 11.5%. The number of radiation oncologists in practices of size 1 to 2, 3 to 9, 10 to 24, and 25 to 49 decreased by 3.7%, 4.7%, 4.9%, and 2%, respectively, and the number of radiation oncologists in practices of size 50 to 99, 100 to 499, and 500+ increased by 1.4%, 2.1%, and 11.8%, respectively (all 500+ practices are multispecialty groups). The increase in practice size was significant in all regions, for single-specialty and multispecialty practices, academic and nonacademic practices, and for practices in high-, middle-, and low-population density areas (P < .05 for all comparisons). The proportion of single-specialty practices has decreased significantly (P < .001), and the proportion of academic practices increased significantly (P = .004). Additionally, the proportion of practices and physicians in high- and low-population density regions remained stable during this period (P > .05). CONCLUSIONS: Our analysis suggests that practice size consolidation has occurred within the US radiation oncology workforce from 2012 to 2020. The impact of this consolidation on quality, cost, and patient access deserves further attention.

Orthopaedic Group Practice Size Is Increasing.

PURPOSE: To analyze recent trends in orthopaedic surgery consolidation and quantify these changes temporally and geographically from 2012 to 2020. METHODS: We performed a retrospective cross-sectional analysis of orthopaedic surgeon practice size in the United States using 2012 and 2020 data obtained from the Physician Compare database. RESULTS: Although we observed an increase from 21,216 unique orthopaedic surgeons in 2012 to 21,553 in 2020 (1.6% increase), the number of practices experienced a large decrease from 7,299 practices in 2012 to 5,829 in 2020 (20.1% decrease). The proportion of orthopaedic surgeons working in solo practices decreased from 13.2% (2,790) in 2012 to 7.4% (1,595) in 2020, and the proportion of orthopaedic surgeons working in groups sized 2 to 24 decreased from 35.3% (7,482) in 2012 to 22.2% (4,775) in 2020. In contrast, groups sized 25 to 99 have grown from 20.7% (4,387) of all orthopaedic surgeons to 23.4% (5,048) in 2020. Groups sized 100 to 499 have increased from 16.9% (3,593) in 2012 to 24.1% (5,190) in 2020, whereas groups sized 500 or greater have grown from 14% (2,964) in 2012 to 22.9% (4,945) in 2020. The number of unique group practices showed a significant decrease in the number of solo groups, which comprised 43.8% (3,200) of the total number of individual practices in 2012, decreasing to 32% (1,886) in 2020. All other groups increased in number and proportionally from 2012 to 2020. CONCLUSIONS: This study shows that over the period from 2012 to 2020, there has been a substantial trend of orthopaedic surgeons shifting to increasing practice sizes, potentially indicating that more orthopaedic surgeons are working for large health care organizations rather than small independent practices. CLINICAL RELEVANCE: The impact of these changes should be examined to determine large-scale effects on patient care, payment models, access, and outcomes, along with physician compensation, lifestyle, and satisfaction.

Night-to-night variability of sleep electroencephalography-based brain age measurements.

OBJECTIVE: Brain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient's BAI. METHODS: 86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured. RESULTS: The within-patient night-to-night standard deviation in BAI was 7.5 years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0 years, respectively. CONCLUSIONS: Averaging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level. A brain age risk lookup table of results provides thresholds above which a patient has a high probability of excess BAI. SIGNIFICANCE: With increasing ease of EEG acquisition, including wearable technology, BAI has the potential to track brain health and detect deviations from normal physiologic function. The measure of night-to-night variability and how this is reduced by averaging across multiple nights provides a basis for using BAI in patients' homes to identify patients who should undergo further investigation or monitoring.

Burst Suppression: Causes and Effects on Mortality in Critical Illness.

BACKGROUND: Burst suppression in mechanically ventilated intensive care unit (ICU) patients is associated with increased mortality. However, the relative contributions of propofol use and critical illness itself to burst suppression; of burst suppression, propofol, and critical illness to mortality; and whether preventing burst suppression might reduce mortality, have not been quantified. METHODS: The dataset contains 471 adults from seven ICUs, after excluding anoxic encephalopathy due to cardiac arrest or intentional burst suppression for therapeutic reasons. We used multiple prediction and causal inference methods to estimate the effects connecting burst suppression, propofol, critical illness, and in-hospital mortality in an observational retrospective study. We also estimated the effects mediated by burst suppression. Sensitivity analysis was used to assess for unmeasured confounding. RESULTS: The expected outcomes in a "counterfactual" randomized controlled trial (cRCT) that assigned patients to mild versus severe illness are expected to show a difference in burst suppression burden of 39%, 95% CI [8-66]%, and in mortality of 35% [29-41]%. Assigning patients to maximal (100%) burst suppression burden is expected to increase mortality by 12% [7-17]% compared to 0% burden. Burst suppression mediates 10% [2-21]% of the effect of critical illness on mortality. A high cumulative propofol dose (1316 mg/kg) is expected to increase burst suppression burden by 6% [0.8-12]% compared to a low dose (284 mg/kg). Propofol exposure has no significant direct effect on mortality; its effect is entirely mediated through burst suppression. CONCLUSIONS: Our analysis clarifies how important factors contribute to mortality in ICU patients. Burst suppression appears to contribute to mortality but is primarily an effect of critical illness rather than iatrogenic use of propofol.

Excess brain age in the sleep electroencephalogram predicts reduced life expectancy.

The brain age index (BAI) measures the difference between an individual's apparent "brain age" (BA; estimated by comparing EEG features during sleep from an individual with age norms), and their chronological age (CA); that is BAI = BA-CA. Here, we evaluate whether BAI predicts life expectancy. Brain age was quantified using a previously published machine learning algorithm for a cohort of participants ≥40 years old who underwent an overnight sleep electroencephalogram (EEG) as part of the Sleep Heart Health Study (n = 4877). Excess brain age (BAI >0) was associated with reduced life expectancy (adjusted hazard ratio: 1.12, [1.03, 1.21], p = 0.002). Life expectancy decreased by -0.81 [-1.44, -0.24] years per standard-deviation increase in BAI. Our findings show that BAI, a sleep EEG-based biomarker of the deviation of sleep microstructure from patterns normal for age, is an independent predictor of life expectancy.