Objective: To evaluate the association between physical distancing interventions and incidence of coronavirus disease 2019 (covid-19) globally.
Design: Natural experiment using interrupted time series analysis, with results synthesised using meta-analysis.
Setting: 149 countries or regions, with data on daily reported cases of covid-19 from the European Centre for Disease Prevention and Control and data on the physical distancing policies from the Oxford covid-19 Government Response Tracker.
Participants: Individual countries or regions that implemented one of the five physical distancing interventions (closures of schools, workplaces, and public transport, restrictions on mass gatherings and public events, and restrictions on movement (lockdowns)) between 1 January and 30 May 2020.
Main outcome measure: Incidence rate ratios (IRRs) of covid-19 before and after implementation of physical distancing interventions, estimated using data to 30 May 2020 or 30 days post-intervention, whichever occurred first. IRRs were synthesised across countries using random effects meta-analysis.
Results: On average, implementation of any physical distancing intervention was associated with an overall reduction in covid-19 incidence of 13% (IRR 0.87, 95% confidence interval 0.85 to 0.89; n=149 countries). Closure of public transport was not associated with any additional reduction in covid-19 incidence when the other four physical distancing interventions were in place (pooled IRR with and without public transport closure was 0.85, 0.82 to 0.88; n=72, and 0.87, 0.84 to 0.91; n=32, respectively). Data from 11 countries also suggested similar overall effectiveness (pooled IRR 0.85, 0.81 to 0.89) when school closures, workplace closures, and restrictions on mass gatherings were in place. In terms of sequence of interventions, earlier implementation of lockdown was associated with a larger reduction in covid-19 incidence (pooled IRR 0.86, 0.84 to 0.89; n=105) compared with a delayed implementation of lockdown after other physical distancing interventions were in place (pooled IRR 0.90, 0.87 to 0.94; n=41).
Conclusions: Physical distancing interventions were associated with reductions in the incidence of covid-19 globally. No evidence was found of an additional effect of public transport closure when the other four physical distancing measures were in place. Earlier implementation of lockdown was associated with a larger reduction in the incidence of covid-19. These findings might support policy decisions as countries prepare to impose or lift physical distancing measures in current or future epidemic waves.
This systematic review and network meta-analysis synthesized evidence on the effects of third-wave cognitive behaviour therapies (3wCBT) on body weight, and psychological and physical health outcomes in adults with overweight or obesity. Studies that included a 3wCBT for the purposes of weight management and measured weight or body mass index (BMI) pre-intervention and ≥ 3 months post-baseline were identified through database searches (MEDLINE, CINAHL, Embase, Cochrane database [CENTRAL], PsycINFO, AMED, ASSIA, and Web of Science). Thirty-seven studies were eligible; 21 were randomized controlled trials (RCT) and included in the network meta-analyses. Risk of bias was assessed using RoB2, and evidence quality was assessed using GRADE. Random-effects pairwise meta-analysis found moderate- to high-quality evidence suggesting that 3wCBT had greater weight loss than standard behavioural treatment (SBT) at post-intervention (standardized mean difference [SMD]: −0.09, 95% confidence interval [CI]: −0.22, 0.04; N = 19; I2 = 32%), 12 months (SMD: −0.17, 95% CI: −0.36, 0.02; N = 5; I2 = 33%), and 24 months (SMD: −0.21, 95% CI: −0.42, 0.00; N = 2; I2 = 0%). Network meta-analysis compared the relative effectiveness of different types of 3wCBT that were not tested in head-to-head trials up to 18 months. Acceptance and commitment therapy (ACT)-based interventions had the most consistent evidence of effectiveness. Only ACT had RCT evidence of effectiveness beyond 18 months. Meta-regression did not identify any specific intervention characteristics (dose, duration, delivery) that were associated with greater weight loss. Evidence supports the use of 3wCBT for weight management, specifically ACT. Larger trials with long-term follow-up are needed to identify who these interventions work for, their most effective components, and the most cost-effective method of delivery.
People remain at risk of reinfection with hepatitis C virus (HCV), even after clearance of the primary infection. We identified factors associated with HCV reinfection risk in a large population-based cohort study in British Columbia, Canada, and examined the association of opioid substitution therapy and mental health counselling with reinfection.
We obtained data from the British Columbia Hepatitis Testers Cohort, which includes all individuals tested for HCV or HIV at the British Columbia Centre for Disease Control Public Health Laboratory during 1990–2013 (when data were available). We defined cases of HCV reinfection as individuals with a positive HCV PCR test after either spontaneous clearance (two consecutive negative HCV PCR tests spaced ≥28 days apart without treatment) or a sustained virological response (SVR; two consecutive negative HCV PCR tests spaced ≥28 days apart 12 weeks after completing interferon-based treatment). We calculated incidence rates of HCV reinfection (per 100 person-years of follow-up) and corresponding 95% CIs assuming a Poisson distribution, and used a multivariable Cox proportional hazards model to examine reinfection risk factors (age, birth cohort, sex, year of HCV diagnosis, HCV clearance type, HIV co-infection, number of mental health counselling visits, levels of material and social deprivation, and alcohol and injection drug use), and the association of opioid substitution therapy and mental health counselling with HCV reinfection among people who inject drugs (PWID).
5915 individuals with HCV were included in this study after clearance (3690 after spontaneous clearance and 2225 after SVR). 452 (8%) patients developed reinfection; 402 (11%) after spontaneous clearance and 50 (2%) who had achieved SVR. Individuals were followed up for a median of 5·4 years (IQR 2·9–8·7), and the median time to reinfection was 3·0 years (1·5–5·4). The overall incidence rate of reinfection was 1·27 (95% CI 1·15–1·39) per 100 person-years of follow-up over a total of 35 672 person-years, with significantly higher rates in the spontaneous clearance group (1·59, 1·44–1·76) than in the SVR group (0·48, 0·36–0·63). With the adjusted Cox proportional hazards model, we noted higher reinfection risks in the spontaneous clearance group (adjusted hazard ratio [HR] 2·71, 95% CI 2·00–3·68), individuals co-infected with HIV (2·25, 1·78–2·85), and PWID (1·53, 1·21–1·92) than with other reinfection risk factors. Among the 1604 PWID with a current history of injection drug use, opioid substitution therapy was significantly associated with a lower risk of reinfection (adjusted HR 0·73, 95% CI 0·54–0·98), as was engagement with mental health counselling services (0·71, 0·54–0·92).
The incidence of HCV reinfection was higher among HIV co-infected individuals, those who spontaneously cleared HCV infection, and PWID. HCV treatment complemented with opioid substitution therapy and mental health counselling could reduce HCV reinfection risk among PWID. These findings support policies of post-clearance follow-up of PWID, and provision of harm-reduction services to minimise HCV reinfection and transmission.
ObjectivesTo estimate the 11-year incidence trend of diabetic ketoacidosis (DKA) at and after the diagnosis of type 1 diabetes.Study designA retrospective cohort study using a population-based administrative cohort diagnosed with type 1 diabetes at 14 days, respectively, from diagnosis, identified using International Classification of Diseases, 9th and 10th editions codes. Incidence rate ratios were estimated using Poisson regression and DKA trends using Joinpoint regression analyses.ResultsThere were 1519 individuals (mean age at first-DKA, 12.6 ± 5.9 years; 50% male) with ≥1 DKA episode identified. Of 2615 incident cases of type 1 diabetes, there were 847 (32.4%; mean age, 9.9 ± 4.8 years; 52% male) episodes of DKA at the diagnosis of diabetes. Among prevalent cases of type 1 diabetes (1790 cases in 2002 increasing to 2264 in 2012), there were 1886 episodes of DKA after the diagnosis of diabetes (mean age at first DKA, 15.7 ± 5.2 years). The rates per 100 person-years of DKA at diabetes diagnosis (ranging from 24.1 in 2008 to 37.3 in 2006) and DKA after diabetes diagnosis (ranging from 4.9 in 2002 to 7.7 in 2008) remained stable. Females showed a 67% higher rate of incidence of DKA after the diagnosis of diabetes compared with their male counterparts (incidence rate ratio, 1.67; 95% CI, 1.50-1.86; P < .001), adjusted for the temporal trend by fiscal year. Younger age at diagnosis (<5 years) was associated with a greater risk of DKA at the time of diabetes diagnosis and older children (≥10 years) had a greater risk of DKA after the diagnosis of diabetes.ConclusionsThe risk of DKA at the time of diagnosis of diabetes was greater with younger age and the risk of DKA after the diagnosis of diabetes was higher in females and older children and youth.
Blood pressure abnormalities may play an important role in macrovascular damage in type 1 diabetes. Little is known about blood pressure abnormalities and macrovascular damage in children with type 1 diabetes.
Children with type 1 diabetes (n = 57) for a short (3 months‐2 years; n = 24) or long duration (≥5 years; n = 33) and a group of control children without diabetes (n = 29) completed 24‐h ambulatory blood pressure monitoring (ABPM). Carotid intima media thickness (cIMT), a subclinical indicator of atherosclerosis, was assessed by carotid ultrasound.
ABPM abnormalities were more prevalent (57% vs 24%, respectively), and daytime, nighttime and 24‐h systolic, diastolic, and mean arterial blood pressure indices were higher in children with type 1 diabetes compared to control children. The odds estimate of an ABPM abnormality was 6.68 (95% confidence interval: 1.95, 22.9; P = .003) in children with type 1 diabetes compared to controls after adjusting for age, sex, and BMI standardized for age and sex (zBMI). An interaction between ABPM and zBMI on cIMT was observed. In children with type 1 diabetes and ABPM abnormalities, every 1 SD increase in zBMI was associated with a 0.030 mm increase in cIMT (95% confidence interval: 0.002, 0.041; P = .031). This was not observed in control children with ABPM abnormalities or in children with normal ABPM, regardless of type 1 diabetes status.
Children with type 1 diabetes have a high prevalence of ABPM abnormalities independent of disease duration and this is related to early indicators of cardiovascular damage.
Indications for insulin pump therapy (IPT) in children with type 1 diabetes (T1D) are relatively non-specific and therefore subject to provider discretion. Health professionals' perceptions of which people will have difficulty with IPT, for example, those with higher hemoglobin A1c (HbA1c ), may not be correct. This study examined the effect of IPT on HbA1c , and the role of pre-pump HbA1c on this effect.
All children with T1D started on IPT at British Columbia Children's Hospital from January 2011 through June 2016 were included if they had HbA1c values available both before and after IPT (n = 125). Generalized estimating equations was used to estimate the effects of IPT on HbA1c , stratified by pre-pump HbA1c levels (good: <7.5% [<58 mmol/mol], moderate: 7.5%-9.0% [58-75 mmol/mol], poor: >9.0% [>75 mmol/mol]).
After adjusting for potential confounders, mean HbA1c decreased by 0.48% [5.2 mmol/mol] (95% confidence interval: -0.64, -0.33% [-7.0, -3.6 mmol/mol]; P < 0.0001) after IPT initiation. The adjusted mean HbA1c decreased by 0.14% [1.5 mmol/mol] (-0.35, 0.07% [-3.8, 0.8 mmol/mol]; P = 0.188), 0.54% [5.9 mmol/mol] (-0.74, -0.34% [-8.1, -3.7 mmol/mol]; P < 0.0001), and 1.08% [11.8 mmol/mol] (-1.69, -0.46% [-18.5, -5.0 mmol/mol]; P = 0.0006) after pump initiation in the good, moderate, and poor pre-pump metabolic control groups, respectively.
Pre-pump HbA1c appears to play a significant role in the effects of IPT on HbA1c , with the largest decrease in HbA1c seen in the poor pre-pump HbA1c group. Eligibility and consideration for IPT should be expanded to routinely include these children.
Summary Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries—Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5–23·9), representing an additional 7·61 million (7·20–8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0–8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0–24·0) and the death rate by 31·8% (30·1–33·3). Total deaths from injuries increased by 2·3% (0·5–4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2–15·1) to 57·9 deaths (55·9–59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000–289 000) globally in 2007 to 352 000 (334 000–363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8–148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2–40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2–36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990—neonatal disorders, lower respiratory infections, and diarrhoeal diseases—were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Funding Bill & Melinda Gates Foundation.