Avascular necrosis in sickle cell (homozygous S) patients: Predictive clinical and laboratory indices

Background: Pathogenetic mechanism as well as laboratory and clinical correlates of osteonecrosis in sickle cell have not been fully investigated. The aim of this study is to investigate the predictive value of the steady state white cell and platelet count as well as the frequency of bone pain crisis per annum to detect sickle cell patients who will eventually develop avascular necrosis (AVN).Patients and Methods: A 5 year retrospective analysis of 122 homozygous S (HbSS) patients, aged 6‑49 years (mean age 24.7 ± 7 years), out of which 16 patients (13.1%) had developed AVN within the years under review.Results: The prevalence of AVN in sickle cell patients was determined to be 13.1 per 1000. The steady state white cell count, platelet count, frequency of bone pain crisis and hematocrit, was compared in patients that develop AVN and those who had not over the period. Only the steady state platelet count was found to differ significantly (P = 0.011) between these two patient groups and to correlate positively (Pearson correlation coefficient = −0.251) with development of AVN. The hematocrit, white cell count, and frequency of bone pain crisis were found neither to differ significantly nor correlate with the development of AVN.Conclusion: In conclusion, patients with a raised steady state platelet count may have a higher tendency to develop AVN and may require closer orthopedic review and prophylactic intervention.Key words: Avascular necrosis, homozygous S, platelet count, sickle cell anemia, white cell coun

A process pattern model for tackling and improving big data quality

Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality. The process pattern model is defined as a proven series of activities, aimed at improving the data quality given a certain context, a particular objective, and a specific set of initial conditions. Four different patterns are derived to deal with the variations in data quality of datasets. Instead of having to find the way to improve the quality of big data for each situation, the process model provides data users with generic patterns, which can be used as a reference model to improve big data quality

High DNA Methylation Pattern Intratumoral Diversity Implies Weak Selection in Many Human Colorectal Cancers

It is possible to infer the past of populations by comparing genomes between individuals. In general, older populations have more genomic diversity than younger populations. The force of selection can also be inferred from population diversity. If selection is strong and frequently eliminates less fit variants, diversity will be limited because new, initially homogeneous populations constantly emerge.Here we translate a population genetics approach to human somatic cancer cell populations by measuring genomic diversity within and between small colorectal cancer (CRC) glands. Control tissue culture and xenograft experiments demonstrate that the population diversity of certain passenger DNA methylation patterns is reduced after cloning but subsequently increases with time. When measured in CRC gland populations, passenger methylation diversity from different parts of nine CRCs was relatively high and uniform, consistent with older, stable lineages rather than mixtures of younger homogeneous populations arising from frequent cycles of selection. The diversity of six metastases was also high, suggesting dissemination early after transformation. Diversity was lower in DNA mismatch repair deficient CRC glands, possibly suggesting more selection and the elimination of less fit variants when mutation rates are elevated.The many hitchhiking passenger variants observed in primary and metastatic CRC cell populations are consistent with relatively old populations, suggesting that clonal evolution leading to selective sweeps may be rare after transformation. Selection in human cancers appears to be a weaker than presumed force after transformation, consistent with the observed rarity of driver mutations in cancer genomes. Phenotypic plasticity rather than the stepwise acquisition of new driver mutations may better account for the many different phenotypes within human tumors

All-cause and cause-specific mortality of different migrant populations in Europe

This study aimed to examine differences in all-cause mortality and main causes of death across different migrant and local-born populations living in six European countries. We used data from population and mortality registers from Denmark, England & Wales, France, Netherlands, Scotland, and Spain. We calculated age-standardized mortality rates for men and women aged 0–69 years. Country-specific data were pooled to assess weighted mortality rate ratios (MRRs) using Poisson regression. Analyses were stratified by age group, country of destination, and main cause of death. In six countries combined, all-cause mortality was lower for men and women from East Asia (MRRs 0.66; 95 % confidence interval 0.62–0.71 and 0.76; 0.69–0.82, respectively), and Other Latin America (0.44; 0.42–0.46 and 0.56; 0.54–0.59, respectively) than local-born populations. Mortality rates were similar for those from Turkey. All-cause mortality was higher in men and women from North Africa (1.09; 1.08–1.11 and 1.19; 1.17–1.22, respectively) and Eastern Europe (1.30; 1.27–1.33 and 1.05; 1.01–1.08, respectively), and women from Sub-Saharan Africa (1.34; 1.30–1.38). The pattern differed by age group and country of destination. Most migrants had higher mortality due to infectious diseases and homicide while cancer mortality and suicide were lower. CVD mortality differed by migrant population. To conclude, mortality patterns varied across migrant populations in European countries. Future research should focus both on migrant populations with favourable and less favourable mortality pattern, in order to understand this heterogeneity and to drive policy at the European level

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Myomectomy in a case of von Willebrand’s disease in a low resource setting

Von Willebrand’s disease (vWD) is an inherited bleeding disorder with an estimated prevalence of 1% in the general population. It is caused by deficiency or dysfunction of von Willebrand’s factor. Surgical procedure on patients with vWD is usually associated with increased haemorrhage.Keywords: Clotting Factors, Coagulation Disorder, Fibroids, von Willebrand’s Facto

The GRAIDS trial:a cluster randomised controlled trial of computer decision support for the management of familial cancer risk in primary care

The objective was to evaluate the effect of an assessment strategy using the computer decision support system (the GRAIDS software), on the management of familial cancer risk in British general practice in comparison with best current practice. The design included cluster randomised controlled trial, and involved forty-five general practice teams in East Anglia, UK. Randomised to GRAIDS (Genetic Risk Assessment on the Internet with Decision Support) support (intervention n=23) or comparison (n=22). Training in the new assessment strategy and access to the GRAIDS software (GRAIDS arm) was conducted, compared with an educational session and guidelines about managing familial breast and colorectal cancer risk (comparison) were mailed. Outcomes were measured at practice, practitioner and patient levels. The primary outcome measure, at practice level, was the proportion of referrals made to the Regional Genetics Clinic for familial breast or colorectal cancer that were consistent with referral guidelines. Other measures included practitioner confidence in managing familial cancer (GRAIDS arm only) and, in patients: cancer worry, risk perception and knowledge about familial cancer. There were more referrals to the Regional Genetics Clinic from GRAIDS than comparison practices (mean 6.2 and 3.2 referrals per 10 000 registered patients per year; mean difference 3.0 referrals; 95% confidence interval (CI) 1.2–4.8; P=0.001); referrals from GRAIDS practices were more likely to be consistent with referral guidelines (odds ratio (OR)=5.2; 95% CI 1.7–15.8, P=0.006). Patients referred from GRAIDS practices had lower cancer worry scores at the point of referral (mean difference -1.44 95% CI -2.64 to -0.23, P=0.02). There were no differences in patient knowledge about familial cancer. The intervention increased GPs' confidence in managing familial cancer. Compared with education and mailed guidelines, assessment including computer decision support increased the number and quality of referrals to the Regional Genetics Clinic for familial cancer risk, improved practitioner confidence and had no adverse psychological effects in patients. Trials are registered under N0181144343 in the UK National Research Register