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The pandemic has shed harsh light on the need for a live minimum dataset

Health and social care are highly co-dependent—we must invest in the social care workforce or risk the collapse of the whole system, write Adam L Gordon and Andrew Elder

Effective healthcare for older people lies at the heart of equitable, effective, and ethical healthcare for all, writes Adam Gordon

AbstractObjectiveTo examine the associations between stopping treatment with opioids, length of treatment, and death from overdose or suicide in the Veterans Health Administration.DesignObservational evaluation.SettingVeterans Health Administration.Participants1 394 102 patients in the Veterans Health Administration with an outpatient prescription for an opioid analgesic from fiscal year 2013 to the end of fiscal year 2014 (1 October 2012 to 30 September 2014).Main outcome measuresA multivariable Cox non-proportional hazards regression model examined death from overdose or suicide, with the interaction of time varying opioid cessation by length of treatment (≤30, 31-90, 91-400, and >400 days) as the main covariates. Stopping treatment with opioids was measured as the time when a patient was estimated to have no prescription for opioids, up to the end of the next fiscal year (2014) or the patient’s death.Results2887 deaths from overdose or suicide were found. The incidence of stopping opioid treatment was 57.4% (n=799 668) overall, and based on length of opioid treatment was 32.0% (≤30 days), 8.7% (31-90 days), 22.7% (91-400 days), and 36.6% (>400 days). The interaction between stopping treatment with opioids and length of treatment was significant (P<0.001); stopping treatment was associated with an increased risk of death from overdose or suicide regardless of the length of treatment, with the risk increasing the longer patients were treated. Hazard ratios for patients who stopped opioid treatment (with reference values for all other covariates) were 1.67 (≤30 days), 2.80 (31-90 days), 3.95 (91-400 days), and 6.77 (>400 days). Descriptive life table data suggested that death rates for overdose or suicide increased immediately after starting or stopping treatment with opioids, with the incidence decreasing over about three to 12 months.ConclusionsPatients were at greater risk of death from overdose or suicide after stopping opioid treatment, with an increase in the risk the longer patients had been treated before stopping. Descriptive data suggested that starting treatment with opioids was also a risk period. Strategies to mitigate the risk in these periods are not currently a focus of guidelines for long term use of opioids. The associations observed cannot be assumed to be causal; the context in which opioid prescriptions were started and stopped might contribute to risk and was not investigated. Safer prescribing of opioids should take a broader view on patient safety and mitigate the risk from the patient’s perspective. Factors to address are those that place patients at risk for overdose or suicide after beginning and stopping opioid treatment, especially in the first three months.

Debates over the evolution of hominin bipedalism, a defining human characteristic, revolve around whether early bipeds walked more like humans, with energetically efficient extended hind limbs, or more like apes with flexed hind limbs. The 3.6 million year old hominin footprints at Laetoli, Tanzania represent the earliest direct evidence of hominin bipedalism. Determining the kinematics of Laetoli hominins will allow us to understand whether selection acted to decrease energy costs of bipedalism by 3.6 Ma. Using an experimental design, we show that the Laetoli hominins walked with weight transfer most similar to the economical extended limb bipedalism of humans. Humans walked through a sand trackway using both extended limb bipedalism, and more flexed limb bipedalism. Footprint morphology from extended limb trials matches weight distribution patterns found in the Laetoli footprints. These results provide us with the earliest direct evidence of kinematically human-like bipedalism currently known, and show that extended limb bipedalism evolved long before the appearance of the genus Homo. Since extended-limb bipedalism is more energetically economical than ape-like bipedalism, energy expenditure was likely an important selection pressure on hominin bipeds by 3.6 Ma.

To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with [greater than or equal to]1 opioid prescriptions. This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling [greater than or equal to]1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile. The training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age [greater than or equal to]65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000). Machine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.

BackgroundHealth care systems struggle to identify risk factors for suicide. Adverse social determinants of health (SDH) are strong predictors of suicide risk, but most electronic health records (EHR) do not include SDH data.ObjectiveTo determine the prevalence of SDH documentation in the EHR and how SDH are associated with suicide ideation and attempt.DesignThis cross-sectional analysis included EHR data spanning October 1, 2015–September 30, 2016, from the Veterans Integrated Service Network Region 4.ParticipantsThe study included all patients with at least one inpatient or outpatient visit (n = 293,872).Main MeasurementsAdverse SDH, operationalized using Veterans Health Administration (VHA) coding for services and International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes, encompassed seven types (violence, housing instability, financial/employment problems, legal problems, familial/social problems, lack of access to care/transportation, and nonspecific psychosocial needs). We defined suicide morbidity by ICD-10 codes and data from the VHA’s Suicide Prevention Applications Network. Logistic regression assessed associations of SDH with suicide morbidity, adjusting for socio-demographics and mental health diagnoses (e.g., major depression). Statistical significance was assessed with p < .01.Key ResultsOverall, 16.4% of patients had at least one adverse SDH indicator. Adverse SDH exhibited dose-response-like associations with suicidal ideation and suicide attempt: each additional adverse SDH increased odds of suicidal ideation by 67% (AOR = 1.67, 99%CI = 1.60–1.75; p < .01) and suicide attempt by 49% (AOR = 1.49, 99%CI = 1.33–1.68; p < .01). Independently, each adverse SDH had strong effect sizes, ranging from 1.86 (99%CI = 1.58–2.19; p < .01) for legal issues to 3.10 (99%CI = 2.74–3.50; p < .01) for non-specific psychosocial needs in models assessing suicidal ideation and from 1.58 (99%CI = 1.10–2.27; p < .01) for employment/financial problems to 2.90 (99%CI = 2.30–4.16; p < .01) for violence in models assessing suicide attempt.ConclusionsSDH were strongly associated with suicidal ideation and suicide attempt even after adjusting for mental health diagnoses. Integration of SDH data in EHR could improve suicide prevention.

The purpose was to observe current incidence and trends of hand and wrist injuries presenting to U.S. emergency departments (EDs) over a decade. The National Electronic Injury Surveillance System (NEISS) was queried for hand and wrist injuries from January 2009–December 2018. Descriptive analyses were used to report injury types to the hand and wrist. Incidence, age, gender, race, injury location, and type of injury were recorded. Linear regression analyses were used to assess changes in trends over time. A p value <0.05 was statistically significant. In total, 649,131 cases of hand and wrist injuries were identified in the NEISS from 2009 to 2018, correlating to 25,666,596 patients nationally. Incidence rates for finger, hand, and wrist were 450, 264, and 182 per 100,000 people. The estimated number of patients per year declined by 8.6% from 2009 to 2018. Male adults (aged 18–39) were the most frequent demographic. Total national estimates of hand (−8.2%; p = 0.001), wrist (−6.1%; p = 0.007), and finger (−9.9%; p < 0.001) injuries declined over the study period. The most common injuries were lacerations (36.5%), fractures (19.9%), strains/sprains (12.3%), and contusions/abrasions (12.1%) which significantly declined over the study period. The overall admission rate was 1.8%. The estimated annual number of hand/wrist injuries presenting to US EDs was 2.6 million with gradual decline over the decade. Hand injury registries could assist in quality improvement measures targeted toward increased efficiency and resource allocation and education.