Formula for converting median and interquartile range (IQR) into mean and standard deviation (SD).

This layer gives the results of a detailed investigation into the background concentrations of selected trace elements in Canterbury's major soil groups from samples taken between 28/2/2006 and 16/3/2006. Canterbury soil groups identified by the Land Resource Inventory (LRI) and Canterbury Soils (CS) datasets were used in this investigation and are retained in this layer. A total of 90 sample sites were distributed across these soil groups; 17 in the Christchurch urban area and 73 through-out the rest of Canterbury. From these samples concentrations of; Arsenic, Boron, Cadmium, Chromium, Copper, Lead, Manganese, Mercury, Nickel, and Zinc were measured in mg/kg. Level 2 gives the maximum concentration values of the above trace elements measured in each soil group plus half the interquartile range (buffer). It is recommended new soil sample results be compared against both "Trace Elements Level 1" and "Level 2" to assess whether the site is contaminated. For a detailed account of the site selection and sampling method employed in this investigation and recommend user guidelines please see Report No. R07/1 "Background concentrations of selected trace elements in Canterbury soils" prepared for Environment Canterbury by Tonkin and Taylor Ltd, July 2006.

BackgroundTuberculosis (TB) remains a significant public health challenge, particularly among vulnerable populations like children. This is especially true in Sub-Saharan Africa, where the burden of TB in children is substantial. Zambia ranks 21st among the top 30 high TB endemic countries globally. While studies have explored TB in adults in Zambia, the prevalence and associated factors in children are not well documented. This study aimed to determine the prevalence and sociodemographic, and clinical factors associated with active TB disease in hospitalized children under the age of 15 years at Livingstone University Teaching Hospital (LUTH), the largest referral center in Zambia’s Southern Province.MethodsThis retrospective cross-sectional study of 700 pediatric patients under 15 years old, utilized programmatic data from the Pediatrics Department at LUTH. A systematic sampling method was used to select participants from medical records. Data on demographics, medical conditions, anthropometric measurements, and blood tests were collected. Data analysis included descriptive statistics, chi-square tests, and multivariable logistic regression to identify factors associated with TB.ResultsThe median age was 24 months (interquartile range (IQR): 11, 60) and majority were male (56.7%, n = 397/700). Most participants were from urban areas (59.9%, n = 419/700), and 9.2% (n = 62/675) were living with HIV. Malnutrition and comorbidities were present in a significant portion of the participants (19.0% and 25.1%, respectively). The prevalence of active TB cases was 9.4% (n = 66/700) among hospitalized children. Persons living with HIV (Adjusted odds ratio (AOR) of 6.30; 95% confidence interval (CI) of 2.85, 13.89, p< 0.001), and those who were malnourished (AOR: 10.38, 95% CI: 4.78, 22.55, p< 0.001) had a significantly higher likelihood of developing active TB disease.ConclusionThis study revealed a prevalence 9.4% active TB among hospitalized children under 15 years at LUTH. HIV status and malnutrition emerged as significant factors associated with active TB disease. These findings emphasize the need for pediatric TB control strategies that prioritize addressing associated factors to effectively reduce the burden of tuberculosis in Zambian children.

Distribution of individual studies by outcome, randomization and blinding.

This record contains raw data related to article “Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

Abstract

Background and aims: Autoimmune hepatitis (AIH) is a rare chronic liver disease of unknown aetiology; the risk of hepatocellular carcinoma (HCC) remains unclear and risk factors are not well-defined. We aimed to investigate the risk of HCC across a multicentre AIH cohort and to identify predictive factors.

Methods: We performed a retrospective, observational, multicentric study of patients included in the International Autoimmune Hepatitis Group Retrospective Registry. The assessed clinical outcomes were HCC development, liver transplantation, and death. Fine and Gray regression analysis stratified by centre was applied to determine the effects of individual covariates; the cumulative incidence of HCC was estimated using the competing risk method with death as a competing risk.

Results: A total of 1,428 patients diagnosed with AIH from 1980 to 2020 from 22 eligible centres across Europe and Canada were included, with a median follow-up of 11.1 years (interquartile range 5.2-15.9). Two hundred and ninety-three (20.5%) patients had cirrhosis at diagnosis. During follow-up, 24 patients developed HCC (1.7%), an incidence rate of 1.44 cases/1,000 patient-years; the cumulative incidence of HCC increased over time (0.6% at 5 years, 0.9% at 10 years, 2.7% at 20 years, and 6.6% at 30 years of follow-up). Patients who developed cirrhosis during follow-up had a significantly higher incidence of HCC. The cumulative incidence of HCC was 2.6%, 4.6%, 5.6% and 6.6% at 5, 10, 15, and 20 years after the development of cirrhosis, respectively. Obesity (hazard ratio [HR] 2.94, p = 0.04), cirrhosis (HR 3.17, p = 0.01), and AIH/PSC variant syndrome (HR 5.18, p = 0.007) at baseline were independent risk factors for HCC development.

Conclusions: HCC incidence in AIH is low even after cirrhosis development and is associated with risk factors including obesity, cirrhosis, and AIH/PSC variant syndrome.

Impact and implications: The risk of developing hepatocellular carcinoma (HCC) in individuals with autoimmune hepatitis (AIH) seems to be lower than for other aetiologies of chronic liver disease. Yet, solid data for this specific patient group remain elusive, given that most of the existing evidence comes from small, single-centre studies. In our study, we found that HCC incidence in patients with AIH is low even after the onset of cirrhosis. Additionally, factors such as advanced age, obesity, cirrhosis, alcohol consumption, and the presence of the AIH/PSC variant syndrome at the time of AIH diagnosis are linked to a higher risk of HCC. Based on these findings, there seems to be merit in adopting a specialized HCC monitoring programme for patients with AIH based on their individual risk factors.

We provide the raw data used for the following article:

Vitacca M, Malovini A, Balbi B, Aliani M, Cirio S, Spanevello A, Fracchia C, Maniscalco M, Corica G, Ambrosino N, Paneroni M. Minimal Clinically Important Difference in Barthel Index Dyspnea in Patients with COPD. "Int J Chron Obstruct Pulmon Dis". 2020 Oct 21;15:2591-2599. doi: 10.2147/COPD.S266243. PMID: 33116476; PMCID: PMC7585803.

Abstract Background: The Barthel Index dyspnea (BId) is responsive to physiological changes and pulmonary rehabilitation in patients with chronic obstructive pulmonary disease (COPD). However, the minimum clinically important difference (MCID) has not been established yet. Aim: To identify the MCID of BId in patients with COPD stratified according to the presence of chronic respiratory failure (CRF) or not. Materials and methods: Using the Medical Research Council (MRC) score as an anchor, receiver operating characteristic curves and quantile regression were retrospectively evaluated before and after pulmonary rehabilitation in 2327 patients with COPD (1151 of them with CRF). Results: The median post-rehabilitation changes in BId for all patients were -10 (interquartile range = -17 to -3, p<0.001), correlating significantly with changes in MRC (r = 0.57, 95% CI = 0.53 to 0.59, p<0.001). Comparing different methods of assessment, the MCID ranged from -6.5 to -9 points for patients without and -7.5 to -12 points for patients with CRF. Conclusion: The most conservative estimate of the MCID is -9 points in patients with COPD, without and -12 in those with CRF. This estimate may be useful in the clinical interpretation of data, particularly in response to intervention studies.

BackgroundAn increase in the prevalence of HIV drug resistance (HIVDR) has been reported in recent years, especially in persons on non-nucleoside reverse transcriptase inhibitors (NNRTIs) due to their low genetic barrier to mutations. However, there is a paucity of epidemiological data quantifying HIVDR in the era of new drugs like dolutegravir (DTG) in sub-Saharan Africa. We, therefore, sought to determine the prevalence and correlates of viral load (VL) suppression in adult people with HIV (PWH) on a fixed-dose combination of tenofovir disoproxil fumarate/lamivudine/dolutegravir (TLD) or tenofovir alafenamide/emtricitabine/dolutegravir (TAFED) and describe patterns of mutations in individuals failing treatment.MethodsWe conducted a cross-sectional study among 384 adults living with HIV aged ≥15 years between 5th June 2023 and 10th August 2023. Demographic, laboratory and clinical data were collected from electronic health records using a data collection form. Viral load suppression was defined as plasma HIV-1 RNA VL of

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administration’s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984–2018. The source data used to generate this product were downloaded from the Chesapeake Bay Program’s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984–2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs east–west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBP’s “Problem†and “Qualifier†flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

Supplementary figure 1Rank abundance distributions for habitats at three taxonomic levelsSuppl_fig_1.pdfSupplementary figure 2Evenness and species richness of the four habitats at three taxonomic levels.Suppl_fig_2.pdfSupplementary figure 3Distribution of p-values from Mantel test for Spearman correlation between dissimilarity matrices representing different taxonomic and numerical levels. A-C, Correlation between taxonomic levels at different numerical resolutions. D-F, Correlation between proportional abundance data and higher levels of numerical transformation. Filled points represent median p-values across 1000 subsampling iterations, empty points are outliers that lie beyond 1.5 times the interquartile range from the upper quartile.Suppl_fig_3.pdfSupplementary figure 4NMDS ordination of a double-standardized subsample of the total dataset comparing individual habitats along the depth- and salinity gradient for species and families using proportional abundances and presence/absence ...

BackgroundRheumatic and musculoskeletal disorders (RMDs) are associated with cardiovascular diseases (CVDs), with hypertension being the most common. We aimed to determine the prevalence of high blood pressure (HBP), awareness, treatment, and blood pressure control among patients with RMDs seen in a Rheumatology clinic in Uganda.MethodsWe conducted a cross-sectional study at the Rheumatology Clinic of Mulago National Referral Hospital (MNRH), Kampala, Uganda. Socio-demographic, clinical characteristics and anthropometric data were collected. Multivariable logistic regression was performed using STATA 16 to determine factors associated with HBP in patients with RMDs.ResultsA total of 100 participants were enrolled. Of these, majority were female (84%, n = 84) with mean age of 52.1 (standard deviation: 13.8) years and median body mass index of 28 kg/m2 (interquartile range (IQR): 24.8 kg/m2–32.9 kg/m2). The prevalence of HBP was 61% (n = 61, 95% CI: 51.5–70.5), with the majority (77%, n = 47, 95% CI: 66.5–87.6) being aware they had HTN. The prevalence of HTN was 47% (n = 47, 37.2–56.8), and none had it under control. Factors independently associated with HBP were age 46-55years (adjusted prevalence ratio (aPR): 2.5, 95% confidence interval (CI): 1.06–5.95), 56–65 years (aPR: 2.6, 95% CI: 1.09–6.15), >65 years (aPR: 2.5, 95% CI: 1.02–6.00), obesity (aPR: 3.7, 95% CI: 1.79–7.52), overweight (aPR: 2.7, 95% CI: 1.29–5.77).ConclusionThere was a high burden of HBP among people with RMDs in Uganda with poor blood pressure control, associated with high BMI and increasing age. There is a need for further assessment of the RMD specific drivers of HBP and meticulous follow up of patients with RMDs.

This data supports a meta-analysis investigating ecological impacts of intense lawn management (mowing). Raw data on invertebrate abundance and temperature data was collected by Léonie Carignan-Guillemette (2018) and Caroline Turcotte (2017) under the supervision of Raphaël Proulx and Vincent Maire (refer to Appendix S1 within related publication for more information). Other data was gathered and processed according to the following: We searched the Scopus database on 8 February, 2019 with the following combinations of keywords: (lawn OR turf) AND mowing AND (urban OR city). Generally, studies were ineligible when: full-text of the article was not available even after contacting the authors; mowing was incidental to the study and not an experimental factor; response variables were not ecologically relevant; confounding factors (e.g. fertilisation) could not be isolated; a non-urban context was used; or simulated data were presented. We extracted the mean and statistical variation (standard deviation or standard error) for each response variable in control (less-intensively mown) and treatment (intensively mown) groups. Reported data were used when available. Otherwise, data were extracted from published figures using the Web Plot Digitizer tool. Where summary data on median, and interquartile range was presented, mean and standard deviation was estimated. Variables with multi-temporal data (e.g. soil moisture) were summarised using the mean and pooled standard deviation to provide an aggregated value per site per year. Where seasonal trends were evident in raw multi-temporal data (e.g. soil temperature), data was detrended using a polynomial function and analysis applied to the residuals.

Study methodology in individual Qualifying Notices (QN).

BackgroundAlthough the survival of preterm neonates has improved, thanks to advanced and specialized neonatal intensive care, it remains the main reason for neonatal admission, death, and risk of lifelong complication. In this study, we assessed time to death and its predictors among preterm neonates admitted to neonatal intensive care units (NICU) at public hospitals in southern Ethiopia.MethodsA hospital based retrospective cohort was conducted among preterm neonates admitted to NICU at public hospitals in west Guji and Borena zones, Oromia National Regional State, southern Ethiopia. Simple random sampling technique was used to select records of preterm neonates admitted to both major hospitals in the study area. Data on neonatal condition, obstetric information, and status at discharge were collected from admission to discharge by trained research assistant through review of their medical records. Kaplan Meir curve and Log rank test were used to estimate the survival time and compare survival curves between variables. Cox-Proportional Hazards model was used to identify significant predictors of time to death at p

BackgroundBirthweight has an impact on newborn’s future health outcomes. Maternal factors, including age, delivery mode, HIV status, gestational age, parity and obstetric complications (preeclampsia or eclampsia [PE], antepartum hemorrhage [APH] and sepsis), however, have been shown as risk factors of low birthweight (LBW) elsewhere. For data-guided interventions, we aimed to identify predictors of LBW and compare newborn birthweights between different groups of maternal factors at Rev. John Chilembwe Hospital in Phalombe district, Malawi.MethodsUsing a retrospective record review study design, we extracted data from maternity registers of 1244 women and their newborns from October, 2022 to March, 2023. Data were skewed. Median test was used to compare median birthweights. Chi-square or Fisher’s exact tests were used to compare proportions of LBW among different groups of maternal factors. Multivariable logistic regression with stepwise, forward likelihood method was performed to identify predictors of LBW.ResultsMedian birthweight was 2900.00g (interquartile range [IQR]: 2600.00g to 3200.00g). Prevalence of LBW was 16.7% (n = 208). Proportions of LBW infants were higher in women with PE, APH, including women with sepsis than controls (10 [47.6%] of 21 vs 7 [58.3%] of 12 vs 191 [15.8%] of 1211, p < .001). Lower in term and postterm than preterm (46 [5.5%] of 835 vs 2 [3.7%] of 54 vs 160 [45.1%] of 355, p < .001). The odds of LBW infants were higher in preterm than term (AOR = 13.76, 95%CI: 9.54 to 19.84, p < .001), women with PE (AOR = 3.88, 95%CI: 1.35 to 11.18, p = .012), APH, including women with sepsis (AOR = 6.25, 95%CI: 1.50 to 26.11, p = .012) than controls.ConclusionPrevalence of LBW was high. Its predictors were prematurity, PE, APH and sepsis. Interventions aimed to prevent these risk factors should be prioritized to improve birthweight outcomes.

The characteristics of each group by group-based trajectory modeling.

Sociodemographic and clinical characteristics of the study participants.

Studies conflict on the significance of burn-induced coagulopathy. We posit that burn-induced coagulopathy is associated with injury severity in burns. Our purpose was to characterize coagulopathy profiles in burns and determine relationships between % total burn surface area (TBSA) burned and coagulopathy using the International Normalized Ratio (INR). Burned patients with INR values were identified in the TriNetX database and analyzed by %TBSA burned. Patients with history of transfusions, chronic hepatic failure, and those on anticoagulant medications were excluded. Interquartile ranges for INR in the burned study population were 1.2 (1.0–1.4). An INR of ≥ 1.5 was used to represent those with burn-induced coagulopathy as it fell outside the 3rd quartile. The population was stratified into subgroups using INR levels

Sepsis is the leading cause of global death with the highest burden found in sub-Saharan Africa (sSA). The Universal Vital Assessment (UVA) score is a validated resource-appropriate clinical tool to identify hospitalized patients in sSA who are at risk of in-hospital mortality. Whether a decrease in the UVA score over 6 hours of resuscitation from sepsis is associated with improved outcomes is unknown. We aimed to determine (1) the association between 6-hour UVA score and in-hospital mortality, and (2) if a decrease in UVA score from admission to 6 hours was associated with improved in-hospital mortality. We analyzed data from participants with severe sepsis aged ≥14 years enrolled at the Mbarara Regional Referral Hospital in Uganda from October 2014 through May 2015. Among 197 participants, the median (interquartile range) age was 34 (27–47) years, 99 (50%) were female and 116 (59%) were living with HIV. At 6 hours, of the 65 participants in the high-risk group, 28 (43%) died compared to 28 (30%) of 94 in the medium-risk group (odds ratio [OR] 0.56, 95% confidence interval [CI] 0.29,1.08, p = 0.086) and 3 (9%) of 33 in the low-risk group (OR 0.13, 95% CI 0.03, 0.42, p = 0.002). In a univariate analysis of the 85 participants who improved their UVA risk group at 6 hours, 20 (23%) died compared to 39 (36%) of 107 participants who did not improve (OR 0.54, 95% CI 0.27–1.06, p = 0.055). In the multivariable analysis, the UVA score at 6 hours (adjusted OR [aOR] 1.26, 95%CI 1.10–1.45, p

Dataset contains patient identification from 1 to 82 (ID), steatosis grade (Steatosis), inflammation grade (Inflammation), fibrosis stage (Fibrosis), point shear wave elasticity (pSWE), μn = mean intensity normalized by its maximal value (munMean), 1/α = reciprocal of the scatterer clustering parameter (ialphaMean), k = coherent-to-diffuse signal ratio (kMean), 1/(k + 1) = diffuse-to-total signal power ratio (ikappaMean), mean intensity normalized by its maximal value inter-quartile range (munIQR), reciprocal of the scatterer clustering parameter inter-quartile range (ialphaIQR), coherent-to-diffuse signal ratio inter-quartile range (kIQR), diffuse-to-total signal power ratio inter-quartile range (ikappaIQR),total attenuation coefficient slope (TotalACS), local attenuation coefficient slope (LocalACS). (XLSX)

Data are presented with mean (SD) unless stated otherwise.Abbreviations: HAM-D17/HAM-D6 – Hamilton depression scale with 17 or 6 Items; BDI – Beck’s Depression Inventory II; WHO-5 – World Health Organisation’s well being index; IQR – interquartile range; Buschke’s SRT – Buschke’s Selective Reminding Test; RCFT – Rey’s Complex Figure Test; VO2 max – maximal oxygen uptake; QUICKI – Quantitative insulin sensitivity index; hsCRP – high sensitive C reactive Protein.