Dupilumab Improves Outcomes in Patients with Chronic Rhinosinusitis with Nasal Polyps and Coexisting Asthma Irrespective of Baseline Asthma Characteristics.

Purpose: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a predominantly type 2 inflammatory disease frequently coexisting with other type 2 conditions including asthma and non-steroidal anti-inflammatory drug-exacerbated respiratory disease (NSAID-ERD). Coexisting asthma leads to increased CRSwNP symptom burden. Dupilumab, a monoclonal antibody that blocks the shared receptor component for interleukin-4 and -13, demonstrated efficacy in adults with severe CRSwNP in the Phase 3 SINUS-24 (NCT02912468) and SINUS-52 (NCT02898454) studies, including in patients with coexisting asthma/NSAID-ERD. However, the impact of different asthma characteristics on dupilumab treatment in this population is unknown. We report CRSwNP and asthma outcomes with dupilumab in patients with CRSwNP and coexisting asthma according to baseline asthma characteristics. Methods: Change from baseline at Week 24 (pooled studies) and Week 52 (SINUS-52) in CRSwNP outcomes (nasal polyp score, nasal congestion, 22-item Sino-Nasal Outcome Test [SNOT-22], loss of smell score, University of Pennsylvania Smell Identification Test) and asthma outcomes (5-item Asthma Control Questionnaire [ACQ-5], pre-bronchodilator forced expiratory volume in 1 second [FEV1]) were analyzed post hoc for placebo and dupilumab 300 mg every 2 weeks according to baseline blood eosinophils ≥150/≥300 cells/µL, ACQ-5 scores <1.5/≥1.5, and FEV1 <80%. Results: In the pooled studies, 428/724 patients (59.1%) had coexisting asthma, of which 181/428 (42.3%) had coexisting NSAID-ERD. Dupilumab significantly improved all CRSwNP and asthma outcomes vs placebo at Week 24 (P < 0.001) regardless of baseline eosinophil or ACQ-5 category, or FEV1 <80%. Similar magnitude of improvement was seen at Week 52 (SINUS-52) and in patients with NSAID-ERD (pooled studies, Week 24). By Week 24, improvements with dupilumab exceeded the minimum clinically important differences for ACQ-5 and SNOT-22 in 35.2% to 74.2% and 72.0% to 78.7% of patients, respectively. Conclusion: Dupilumab improved CRSwNP outcomes in patients with CRSwNP and coexisting asthma, and improved asthma outcomes, regardless of differences in baseline asthma characteristics.

A Qualitative Process Evaluation of Participant Experiences in a Feasibility Randomised Controlled Trial to Reduce Indulgent Foods and Beverages.

There is a growing interest in the effects of ultra-processed/energy-dense nutrient-poor foods on health outcomes, and few interventions to reduce their consumption have been tested. We tested a simple intervention to help people reduce the indulgences they consume (energy-dense nutrient-poor (EDNP) foods). Herein, we report the qualitative findings to understand how participants reduced their consumption by exploring intervention fidelity and the factors affecting consumption. We conducted a qualitative descriptive study of 23 adults who had taken part in a feasibility randomised controlled trial that asked participants to say no to seven indulgences/week and record what they said no to. Data were collected using face-to-face semi-structured interviews and analysed thematically. A total of 23 adults with an average BMI of 30.8 kg/m2 took part. Participants liked the term indulgence, as they could apply it to their normal dietary intake and make small changes. They found self-monitoring what they said no to helpful and reported that emotional eating and habits affected consumption. They had difficulty overcoming these. As most people are consuming too many foods that are EDNP, this simple intervention of "Say No" seven times/week has the potential to be developed as a public health campaign.

Comparative effectiveness of sotrovimab and molnupiravir for preventing severe COVID-19 outcomes in non-hospitalised patients on kidney replacement therapy: observational cohort study using the OpenSAFELY-UKRR linked platform and SRR database

BackgroundPatients on kidney replacement therapy (KRT; dialysis and kidney transplantation) are at the highest risk of severe outcomes from COVID-19. Due to limited inclusion of patients on KRT in clinical trials, information is limited on the effectiveness of sotrovimab (a neutralising monoclonal antibody). We sought to address this by comparing its effectiveness against molnupiravir (an antiviral) in preventing severe COVID-19 outcomes in non-hospitalised adults with symptomatic COVID-19. MethodsWith the approval of NHS England we used routine clinical data from 24 million patients in England linked to the UK Renal Registry (UKRR) to identify patients on KRT, and data on antiviral treatments, COVID-19 test results, hospitalisation events and death from the OpenSAFELY-TPP data resource. Cox proportional hazards models (stratified for region) were used to estimate hazard ratios of sotrovimab vs. molnupiravir with regards to COVID-19 related hospitalisation or deaths in the subsequent 28 days (as the primary outcome). Further analyses were conducted using propensity score weighting (adjusted for region) and to investigate robustness of results with regards to different time periods, missing data, and adjustment variables. We also conducted a complementary analysis using data from patients in the Scottish Renal Registry (SRR) treated with sotrovimab or molnupiravir, following similar analytical approaches. ResultsAmong the 2367 renal patients treated with sotrovimab (n=1852) or molnupiravir (n=515) between December 16, 2021 and August 1, 2022 in England, 38 cases (1.6%) of COVID-19 related hospitalisations/deaths were observed during the 28 days of follow-up after treatment initiation, with 21 (1.1%) in the sotrovimab group and 17 (3.3%) in the molnupiravir group. In multiple-adjusted analysis sotrovimab was associated with substantially lower risk of 28-day COVID-19 related hospitalisation/death than treatment with molnupiravir (hazard ratio, HR=0.35, 95% CI: 0.17 to 0.71; P=0.004), with results remaining robust in sensitivity analyses. In the SRR cohort, there were 19 cases (1.9%) of COVID-19 related hospitalisations/deaths during the 28 days of follow-up after treatment initiation of sotrovimab (n=723) or molnupiravir (n=270). In multiple-adjusted analysis, sotrovimab showed a trend toward lower risk of 28-day COVID-19 related hospitalisation/death than treatment with molnupiravir (HR=0.39, 95% CI: 0.13 to 1.21; P=0.106). In both datasets, sotrovimab had no evidence of association with other hospitalisation/death compared with molnupiravir (HRs ranging from 0.73-1.29; P>0.05). ConclusionsIn routine care of non-hospitalised patients with COVID-19 on kidney replacement therapy, those who received sotrovimab had substantially lower risk of severe COVID-19 outcomes than those receiving molnupiravir.

Improving methods to measure comparable mortality cause (IMMCMC) gold standard verbal autopsy dataset.

OBJECTIVES: Gold standard cause of death data is critically important to improve verbal autopsy (VA) methods in diagnosing cause of death where civil and vital registration systems are inadequate or poor. As part of a three-country research study-Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) study-data were collected on clinicopathological criteria-based gold standard cause of death from hospital record reviews with matched VAs. The purpose of this data note is to make accessible a de-identified format of these gold standard VAs for interested researchers to improve the diagnostic accuracy of VA methods. DATA DESCRIPTION: The study was conducted between 2011 and 2014 in the Philippines, Bangladesh, and Papua New Guinea. Gold standard diagnoses of underlying causes of death for deaths occurring in hospital were matched to VAs conducted using a standardized VA questionnaire developed by the Population Health Metrics Consortium. 3512 deaths were collected in total, comprised of 2491 adults (12 years and older), 320 children (28 days to 12 years), and 702 neonates (0-27 days).

The contribution of pre-injury vulnerability to risk of psychiatric morbidity in adults injured in a road traffic crash: Comparisons with non-injury controls.

People who sustain injury in a road traffic crash (RTC) have significant risk of psychiatric morbidity, but effective screening for identifying at-risk individuals soon after the RTC is lacking. We investigated whether pre-injury vulnerability can assist as an early screen to manage this risk. We recruited 120 adults who sustained physical injury in a RTC and admitted to an emergency department (ED). They were comprehensively assessed for acute and long-term presence of psychiatric disorder/morbidity and disability over 12-months after the injury, with comparisons to a non-injury control. Propensity matching based on six pre-injury vulnerability factors (age, sex, education, socioeconomic status, prior mental health, prior physical health) with the control group was used to determine membership of high vulnerability (HV) and low vulnerability (LV) injury sub-groups. Compared to the LV sub-group and controls that had similar pre-injury vulnerability, the HV sub-group had a worrying post-RTC recovery profile, with significantly higher rates of long-term psychiatric morbidity (42.2% vs. 23.1% and 15.9% respectively, p = .002) including post-traumatic stress disorder and/or depression, and poorer psychological adjustment over the 12-months. In contrast, the HV and LV sub-groups were similar in injury-related characteristics and post-injury physical (pain, fatigue, physical functioning) and participation outcomes. Findings provide preliminary evidence that pre-injury vulnerability, primarily prior mental health status, is a promising screen for early identification of people at risk of psychiatric morbidity post-RTC. It is suggested this screen could be implemented in ED to prevent chronicity and improve recovery following a traumatic injury. Further research is warranted to enhance the screen's effectiveness.

OpenSAFELY: Risks of COVID-19 hospital admission and death for people with learning disabilities - a cohort study.

ObjectivesTo assess the association between learning disability and risk of hospitalisation and mortality from COVID-19 in England among adults and children. DesignWorking on behalf of NHS England, two cohort studies using patient-level data for >17 million people from primary care electronic health records were linked with death data from the Office for National Statistics and hospitalization data from NHS Secondary Uses Service using the OpenSAFELY platform. SettingGeneral practices in England which use TPP software. ParticipantsParticipants were males and females, aged up to 105 years, from two cohorts: (1) wave 1, registered with a TPP practice as of 1st March 2020 and followed until 31st August, 2020; (2) wave 2 registered 1st September 2020 and followed until 31st December 2020 (for admissions) or 8th February 2021 (for deaths). The main exposure group was people included on a general practice learning disability register (LDR), with a subgroup of people classified as having profound or severe learning disability. We also identified patients with Down syndrome and cerebral palsy (whether or not on the learning disability register). Main outcome measures(i) COVID-19 related death, (ii) COVID-19 related hospitalisation. Non-COVID-19 related death was also explored. ResultsIn wave 1, of 14,301,415 included individuals aged 16 and over, 90,095 (0.63%) were identified as being on the LDR. 30,173 COVID-related hospital admissions, 13,919 COVID-19 related deaths and 69,803 non-COVID deaths occurred; of which 538 (1.8%), 221 (1.6%) and 596 (0.85%) were among individuals on the LDR, respectively. In wave 2, 27,611 COVID-related hospital admissions, 17,933 COVID-19 related deaths and 54,171 non-COVID deaths occurred; of which 383 (1.4%), 260 (1.4%) and 470 (0.87%) were among individuals on the LDR. Wave 1 hazard ratios for individuals on the LDR, adjusted for age, sex, ethnicity and geographical location, were 5.3 (95% confidence interval (CI) 4.9, 5.8) for COVID-19 related hospital admissions and 8.2 (95% CI: 7.1, 9.4) for COVID-19 related death. Wave 2 produced similar estimates. Associations were stronger among those classed as severe-profound and among those in residential care. Down syndrome and cerebral palsy were associated with increased hazard of both events in both waves; Down syndrome to a much greater extent. Hazards of non-COVID-19 related death followed similar patterns with weaker associations. ConclusionsPeople with learning disabilities have markedly increased risks of hospitalisation and mortality from COVID-19. This raised risk is over and above that seen for non-COVID causes of death. Ensuring prompt access to Covid-19 testing and health care and consideration of prioritisation for COVID-19 vaccination and other targeted preventive measures are warranted.

Predicting COVID-19 related death using the OpenSAFELY platform

ObjectivesTo compare approaches for obtaining relative and absolute estimates of risk of 28-day COVID-19 mortality for adults in the general population of England in the context of changing levels of circulating infection. DesignThree designs were compared. (A) case-cohort which does not explicitly account for the time-changing prevalence of COVID-19 infection, (B) 28-day landmarking, a series of sequential overlapping sub-studies incorporating time-updating proxy measures of the prevalence of infection, and (C) daily landmarking. Regression models were fitted to predict 28-day COVID-19 mortality. SettingWorking on behalf of NHS England, we used clinical data from adult patients from all regions of England held in the TPP SystmOne electronic health record system, linked to Office for National Statistics (ONS) mortality data, using the OpenSAFELY platform. ParticipantsEligible participants were adults aged 18 or over, registered at a general practice using TPP software on 1st March 2020 with recorded sex, postcode and ethnicity. 11,972,947 individuals were included, and 7,999 participants experienced a COVID-19 related death. The study period lasted 100 days, ending 8th June 2020. PredictorsA range of demographic characteristics and comorbidities were used as potential predictors. Local infection prevalence was estimated with three proxies: modelled based on local prevalence and other key factors; rate of A&E COVID-19 related attendances; and rate of suspected COVID-19 cases in primary care. Main outcome measuresCOVID-19 related death. ResultsAll models discriminated well between patients who did and did not experience COVID-19 related death, with C-statistics ranging from 0.92-0.94. Accurate estimates of absolute risk required data on local infection prevalence, with modelled estimates providing the best performance. ConclusionsReliable estimates of absolute risk need to incorporate changing local prevalence of infection. Simple models can provide very good discrimination and may simplify implementation of risk prediction tools in practice.

Branched amphotericin functional poly(N-isopropyl acrylamide): an antifungal polymer.

Branched poly(N-isopropylacrylamide) was functionalized with Amphotericin B (AmB) at the chain ends to produce an antifungal material. The polymer showed antifungal properties against AmB-sensitive strains of Candida albicans, Fusarium keratoplasticum and Aspergillus flavus (minimal inhibitory concentration ranged from 5 to 500 µg ml-1) but was not effective against an AmB resistant strain of C. albicans nor against Candida tropicalis. The polymer end groups bound to the AmB target, ergosterol, and the fluorescence spectrum of a dye used as a solvatochromic probe, Nile red, was blue shifted indicating that segments of the polymer became desolvated on binding. The polymer was less toxic to corneal and renal epithelial cells and explanted corneal tissue than the free drug. Also, the polymer did not induce reactive oxygen species release from peripheral blood mononuclear cells, nor did it cause a substantial release of the proinflammatory cytokines, tumour necrosis factor-α and interleukin-1ß (at 0.5 mg ml-1).

OpenSAFELY: Do adults prescribed Non-steroidal anti-inflammatory drugs have an increased risk of death from COVID-19?

ImportanceThere has been speculation that non-steroidal anti-inflammatory drugs (NSAIDs) may negatively affect coronavirus disease 2019 (COVID-19) outcomes, yet clinical evidence is limited. ObjectiveTo assess the association between NSAID use and deaths from COVID-19 using OpenSAFELY, a secure analytical platform. DesignTwo cohort studies (1st March-14th June 2020). SettingWorking on behalf of NHS England, we used routine clinical data from >17 million patients in England linked to death data from the Office for National Statistics. ParticipantsStudy 1: General population (people with an NSAID prescription in the last three years). Study 2: people with rheumatoid arthritis/osteoarthritis. ExposuresCurrent NSAID prescription within the 4 months before 1st March 2020. Main Outcome and MeasureWe used Cox regression to estimate hazard ratios (HRs) for COVID-19 related death in people currently prescribed NSAIDs, compared with those not currently prescribed NSAIDs, adjusting for age, sex, comorbidities and other medications. ResultsIn Study 1, we included 535,519 current NSAID users and 1,924,095 non-users in the general population. The crude HR for current use was 1.25 (95% CI, 1.07-1.46), versus non-use. We observed no evidence of difference in risk of COVID-19 related death associated with current use (HR, 0.95, 95% CI, 0.80-1.13) in the fully adjusted model. In Study 2, we included 1,711,052 people with rheumatoid arthritis/osteoarthritis, of whom 175,631 (10%) were current NSAID users. The crude HR for current use was 0.43 (95% CI, 0.36-0.52), versus non-use. In the fully adjusted model, we observed a lower risk of COVID-19 related death (HR, 0.78, 95% CI, 0.65-0.94) associated with current use of NSAID versus non-use. Conclusion and RelevanceWe found no evidence of a harmful effect of NSAIDs on COVID-19 related deaths. Risks of COVID-19 do not need to influence decisions about therapeutic use of NSAIDs.

Robustness of the Tariff method for diagnosing verbal autopsies: impact of additional site data on the relationship between symptom and cause.

BACKGROUND: Verbal autopsy (VA) is increasingly being considered as a cost-effective method to improve cause of death information in countries with low quality vital registration. VA algorithms that use empirical data have an advantage over expert derived algorithms in that they use responses to the VA instrument as a reference instead of physician opinion. It is unclear how stable these data driven algorithms, such as the Tariff 2.0 method, are to cultural and epidemiological variations in populations where they might be employed. METHODS: VAs were conducted in three sites as part of the Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) study: Bohol, Philippines; Chandpur and Comila Districts, Bangladesh; and Central and Eastern Highlands Provinces, Papua New Guinea. Similar diagnostic criteria and cause lists as the Population Health Metrics Research Consortium (PHMRC) study were used to identify gold standard (GS) deaths. We assessed changes in Tariffs by examining the proportion of Tariffs that changed significantly after the addition of the IMMCMC dataset to the PHMRC dataset. RESULTS: The IMMCMC study added 3512 deaths to the GS VA database (2491 adults, 320 children, and 701 neonates). Chance-corrected cause specific mortality fractions for Tariff improved with the addition of the IMMCMC dataset for adults (+ 5.0%), children (+ 5.8%), and neonates (+ 1.5%). 97.2% of Tariffs did not change significantly after the addition of the IMMCMC dataset. CONCLUSIONS: Tariffs generally remained consistent after adding the IMMCMC dataset. Population level performance of the Tariff method for diagnosing VAs improved marginally for all age groups in the combined dataset. These findings suggest that cause-symptom relationships of Tariff 2.0 might well be robust across different population settings in developing countries. Increasing the total number of GS deaths improves the validity of Tariff and provides a foundation for the validation of other empirical algorithms.

Improving coverage of civil registration and vital statistics, Bangladesh.

PROBLEM: Bangladesh has no national system for registering deaths and determining their causes. As a result, policy-makers lack reliable and complete data to inform public health decisions. APPROACH: In 2016, the government of Bangladesh introduced a pilot project to strengthen the civil registration and vital statistics system and generate cause of death data in Kaliganj Upazila. Community-based health workers were trained to notify births and deaths to the civil registrar, and to conduct verbal autopsy interviews with family members of a deceased person. International experts in cause-of-death certification and coding trained master trainers on how to complete the international medical certificate of cause of death. These trainers then trained physicians and coders. LOCAL SETTING: Kaliganj Upazila has an estimated population of 304 600, and 5600 births and 1550 deaths annually. Health assistants and family welfare assistants make regular visits to households to track certain health outcomes. RELEVANT CHANGES: Following the start of the project in 2016, the number of births registered within 45 days rose from 873 to 4630 in 2018. The number of deaths registered within 45 days increased from 458 to 1404. During this period, health assistants conducted 7837 verbal autopsy interviews. Between January 2017 and December 2018, 105 master trainers and more than 7000 physicians were trained to complete the international medical certificate of cause of death and they completed more than 12 000 certificates. LESSONS LEARNT: Training community-based health workers, physicians and coders were successful approaches to improve death registration completeness and availability of cause-of-death data.

On the estimation of population cause-specific mortality fractions from in-hospital deaths.

BACKGROUND: Almost all countries without complete vital registration systems have data on deaths collected by hospitals. However, these data have not been widely used to estimate cause of death (COD) patterns in populations because only a non-representative fraction of people in these countries die in health facilities. Methods that can exploit hospital mortality statistics to reliably estimate community COD patterns are required to strengthen the evidence base for disease and injury control programs. We propose a method that weights hospital-certified causes by the probability of death to estimate population cause-specific mortality fractions (CSMFs). METHODS: We used an established verbal autopsy instrument (VAI) to collect data from hospital catchment areas in Chandpur and Comilla Districts, Bangladesh, and Bohol province, the Philippines, between 2011 and 2014, along with demographic covariates for each death. Hospital medical certificates of cause of death (death certificates) were collected and mapped to the corresponding cause categories of the VAI. Tariff 2.0 was used to assign a COD for community deaths. Logistic regression models were created for broad causes in each country to calculate the probability of in-hospital death, given a set of covariate values. The reweighted CSMFs for deaths in the hospital catchment population, represented by each hospital death, were calculated from the corresponding regression models. RESULTS: We collected data on 4228 adult deaths in the Philippines and 3725 deaths in Bangladesh. Short time to hospital and education were consistently associated with in-hospital death in the Philippines and absence of a disability was consistently associated with in-hospital death in Bangladesh. Non-communicable diseases (excluding stroke) and stroke were the leading causes of death in both the Philippines (33.9%, 19.1%) and Bangladesh (46.1%, 21.1%) according to the reweighted method. The reweighted method generally estimated CSMFs that fell between those derived from hospitals and those diagnosed by Tariff 2.0. CONCLUSIONS: Statistical methods can be used to derive estimates of cause-specific probability of death in-hospital for Bangladesh and the Philippines to generate population CSMFs. In regions where hospital death certification is of reasonable quality and routine verbal autopsy is not applied, these estimates could be applied to generate cost-effective and robust CSMFs for the population.

Assessing the quality of medical death certification: a case study of concordance between national statistics and results from a medical record review in a regional hospital in the Philippines.

BACKGROUND: Medical certificates of cause of death (MCCOD) issued by hospital physicians are a key input to vital registration systems. Deaths certified by hospital physicians have been implicitly considered to be of high quality, but recent evidence suggests otherwise. We conducted a medical record review (MRR) of hospital MCCOD in the Philippines and compared the cause of death concordance with certificates coded by the Philippines Statistics Authority (PSA). METHODS: MCCOD for adult deaths in Bohol Regional Hospital (BRH) in 2007-2008 and 2011 were collected and reviewed by a team of study physicians. Corresponding MCCOD coded by the PSA were linked by a hospital identifier. The study physicians wrote a new MCCOD using the patient medical record, noted the quality of the medical record to produce a cause of death, and indicated whether it was necessary to change the underlying cause of death (UCOD). Chance-corrected concordance, cause-specific mortality fraction (CSMF) accuracy, and chance-corrected CSMF were used to examine the concordance between the MRR and PSA. RESULTS: A total of 1052 adult deaths were linked between the MRR and PSA. Median chance-corrected concordance was 0.73, CSMF accuracy was 0.85, and chance-corrected CSMF accuracy was 0.58. 74.8% of medical records were deemed to be of high enough quality to assign a cause of death, yet study physicians indicated that it was necessary to change the UCOD in 41% of deaths, 82% of which required addition of a new UCOD. CONCLUSIONS: Medical records were generally of sufficient quality to assign a cause of death and concordance between the PSA and MRR was reasonably high, suggesting that routine mortality statistics data are reasonably accurate for describing population level causes of death in Bohol. While overall agreement between the PSA and MRR in major cause groups was sufficient for public health purposes, improvements in death certification practices are recommended to help physicians differentiate between treatable (immediate) COD and COD that are important for public health surveillance.

Comparing tariff and medical assistant assigned causes of death from verbal autopsy interviews in Matlab, Bangladesh: implications for a health and demographic surveillance system.

BACKGROUND: Deaths in developing countries often occur outside health facilities, making it extremely difficult to gather reliable cause of death (COD) information. Automated COD assignment using a verbal autopsy instrument (VAI) has been proposed as a reliable and cost-effective alternative to traditional physician-certified verbal autopsy, but its performance is still being evaluated. The purpose of this study was to compare the similarity of diagnosis by Medical Assistants (MA) in the Matlab Health and Demographic Surveillance System (HDSS) with the SmartVA Analyze 1.2 (Tariff 2.0) diagnosis. METHODS: This study took place between January 2011 and April 2014 in Matlab, Bangladesh. MA with 3 years of medical training assigned COD to Matlab residents by reviewing the information collected using the Population Health Metrics Research Consortium (PHMRC) long-form VAI. Smart VA Analyze 1.2 automatically assigned COD using the same questionnaire. COD agreement and cause-specific mortality fractions (CSMFs) were compared for MA and Tariff. RESULTS: Of the 4969 verbal autopsy cases reviewed, 4328 were adults, 296 were children, and 345 were neonates. Cohen's kappa was 0.38 (0.36, 0.40) for adults, 0.43 (0.38, 0.49) for children, and 0.27 (0.22, 0.33) for neonates. For adults, the top two COD for MA were stroke (29.6%) and ischemic heart diseases (IHD) (14.2%) and for Tariff these were stroke (32.0%) and IHD (14.0%). For children, the top two COD for MA were drowning (33.5%) and pneumonia (13.2%) and for Tariff these were also drowning (36.8%) and pneumonia (12.4%). For neonates, the top two COD for MA were birth asphyxia (41.2%) and meningitis/sepsis (22.3%) and for Tariff these were birth asphyxia (37.0%) and preterm delivery (30.9%). CONCLUSION: The CSMFs for Tariff and MA showed very close agreement across all age categories but some differences were observed for neonate preterm delivery and meningitis/sepsis. Given the known advantages of automated methods over physician certified verbal autopsy, the SmartVA software, incorporating the shortened VAI questionnaire and Tariff 2.0, could serve as a cost-effective alternative to Matlab MA to routinely collect and analyze verbal autopsy data in a HDSS to generate essential population level COD data for planning.

High-throughput detection and screening of plants modified by gene editing using quantitative real-time polymerase chain reaction.

Gene editing techniques are becoming powerful tools for modifying target genes in organisms. Although several methods have been developed to detect gene-edited organisms, these techniques are time and labour intensive. Meanwhile, few studies have investigated high-throughput detection and screening strategies for plants modified by gene editing. In this study, we developed a simple, sensitive and high-throughput quantitative real-time (qPCR)-based method. The qPCR-based method exploits two differently labelled probes that are placed within one amplicon at the gene editing target site to simultaneously detect the wild-type and a gene-edited mutant. We showed that the qPCR-based method can accurately distinguish CRISPR/Cas9-induced mutants from the wild-type in several different plant species, such as Oryza sativa, Arabidopsis thaliana, Sorghum bicolor, and Zea mays. Moreover, the method can subsequently determine the mutation type by direct sequencing of the qPCR products of mutations due to gene editing. The qPCR-based method is also sufficiently sensitive to distinguish between heterozygous and homozygous mutations in T0 transgenic plants. In a 384-well plate format, the method enabled the simultaneous analysis of up to 128 samples in three replicates without handling the post-polymerase chain reaction (PCR) products. Thus, we propose that our method is an ideal choice for screening plants modified by gene editing from many candidates in T0 transgenic plants, which will be widely used in the area of plant gene editing.

Performance of InSilicoVA for assigning causes of death to verbal autopsies: multisite validation study using clinical diagnostic gold standards.

BACKGROUND: Recently, a new algorithm for automatic computer certification of verbal autopsy data named InSilicoVA was published. The authors presented their algorithm as a statistical method and assessed its performance using a single set of model predictors and one age group. METHODS: We perform a standard procedure for analyzing the predictive accuracy of verbal autopsy classification methods using the same data and the publicly available implementation of the algorithm released by the authors. We extend the original analysis to include children and neonates, instead of only adults, and test accuracy using different sets of predictors, including the set used in the original paper and a set that matches the released software. RESULTS: The population-level performance (i.e., predictive accuracy) of the algorithm varied from 2.1 to 37.6% when trained on data preprocessed similarly as in the original study. When trained on data that matched the software default format, the performance ranged from -11.5 to 17.5%. When using the default training data provided, the performance ranged from -59.4 to -38.5%. Overall, the InSilicoVA predictive accuracy was found to be 11.6-8.2 percentage points lower than that of an alternative algorithm. Additionally, the sensitivity for InSilicoVA was consistently lower than that for an alternative diagnostic algorithm (Tariff 2.0), although the specificity was comparable. CONCLUSIONS: The default format and training data provided by the software lead to results that are at best suboptimal, with poor cause-of-death predictive performance. This method is likely to generate erroneous cause of death predictions and, even if properly configured, is not as accurate as alternative automated diagnostic methods.

Collecting verbal autopsies: improving and streamlining data collection processes using electronic tablets.

BACKGROUND: There is increasing interest in using verbal autopsy to produce nationally representative population-level estimates of causes of death. However, the burden of processing a large quantity of surveys collected with paper and pencil has been a barrier to scaling up verbal autopsy surveillance. Direct electronic data capture has been used in other large-scale surveys and can be used in verbal autopsy as well, to reduce time and cost of going from collected data to actionable information. METHODS: We collected verbal autopsy interviews using paper and pencil and using electronic tablets at two sites, and measured the cost and time required to process the surveys for analysis. From these cost and time data, we extrapolated costs associated with conducting large-scale surveillance with verbal autopsy. RESULTS: We found that the median time between data collection and data entry for surveys collected on paper and pencil was approximately 3 months. For surveys collected on electronic tablets, this was less than 2 days. For small-scale surveys, we found that the upfront costs of purchasing electronic tablets was the primary cost and resulted in a higher total cost. For large-scale surveys, the costs associated with data entry exceeded the cost of the tablets, so electronic data capture provides both a quicker and cheaper method of data collection. CONCLUSIONS: As countries increase verbal autopsy surveillance, it is important to consider the best way to design sustainable systems for data collection. Electronic data capture has the potential to greatly reduce the time and costs associated with data collection. For long-term, large-scale surveillance required by national vital statistical systems, electronic data capture reduces costs and allows data to be available sooner.