This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Background: Substantial mortality occurs after hospital discharge in children younger than 5 years with suspected sepsis, especially in low-income countries. A better understanding of its epidemiology is needed for effective interventions to reduce child mortality in these countries. We evaluated risk factors for death after discharge in children admitted to hospital for suspected sepsis in Uganda, and assessed how these differed by age, time of death, and location of death. Methods: In this prospective observational cohort study, we recruited 0-60-month-old children admitted with suspected sepsis from the community to the paediatric wards of six Ugandan hospitals. The primary outcome was six-month post-discharge mortality among those discharged alive. We evaluated the interactive impact of age, time of death, and location of death on risk factors for mortality. Findings: 6,545 children were enrolled, with 6,191 discharged alive. The median (interquartile range) time from discharge to death was 28 (9-74) days, with a six-month post-discharge mortality rate of 5·5%, constituting 51% of total mortality. Deaths occurred at home (45%), in-transit to care (18%), or in hospital (37%) during a subsequent readmission. Post-discharge death was strongly associated with weight-for-age z-scores < -3 (adjusted risk ratio [aRR] 4·7, 95% CI 3·7–5·8 vs a Z score of >–2), referral for further care (7·3, 5·6–9·5), and unplanned discharge (3·2, 2·5–4·0). The hazard ratio of those with severe anaemia increased with time since discharge, while the hazard ratios of discharge vulnerabilities (unplanned, poor feeding) decreased with time. Age influenced the effect of several variables, including anthropometric indices (less impact with increasing age), anaemia (greater impact), and admission temperature (greater impact). Data Collection Methods: All data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). At admission, trained study nurses systematically collected data on clinical, social and demographic variables. Following discharge, field officers contacted caregivers at 2 and 4 months by phone, and in-person at 6 months, to determine vital status, post-discharge health-seeking, and readmission details. Verbal autopsies were conducted for children who had died following discharge. Data Processing Methods: For this analysis, data from both cohorts (0-6 months and 6-60 months) were combined and analysed as a single dataset. We used periods of overlapping enrolment (72% of total enrolment months) between the two cohorts to determine site-specific proportions of children who were 0-6 and 6-60 months of age. These proportions were used to weight the cohorts for the calculation of overall mortality rate. Z-scores were calculated using height and weight. Hematocrit was converted to hemoglobin. Distance to hospital was calculated using latitude and longitude. Extra symptom and diagnosis categories were created based on text field in these two variables. BCS score was created by summing all individual components. Abbreviations: MUAC -mid upper arm circumference wfa – weight for age wfl – weight for length bmi – body mass index lfa – length for age abx - antibiotics hr – heart rate rr – respiratory rate antimal - antimalarial sysbp – systolic blood pressure diasbp – diastolic blood pressure resp – respiratory cap - capillary BCS - Blantyre Coma Scale dist- distance hos - hospital ed - education disch - discharge dis -discharge fu – follow-up pd – post-discharge loc - location materl - maternal Ethics Declaration: This study was approved by the Mbarara University of Science and Technology Research Ethics Committee (No. 15/10-16), the Uganda National Institute of Science and Technology (HS 2207), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H16-02679). This manuscript adheres to the guidelines for STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). Study Protocol & Supplementary Materials: Smart Discharges to improve post-discharge health outcomes in children: A prospective before-after study with staggered implementation, NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. It includes deaths which occurred in hospital and deaths which occurred outside of hospital within 30 days (inclusive) of discharge. Deaths related to COVID-19 are excluded from the SHMI. The SHMI gives an indication for each non-specialist acute NHS trust in England whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected' (SHMI banding=1), 'as expected' (SHMI banding=2) or 'lower than expected' (SHMI banding=3) when compared to the national baseline. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided. The SHMI is composed of 142 different diagnosis groups and these are aggregated to calculate the overall SHMI value for each trust. The number of finished provider spells, observed deaths and expected deaths at diagnosis group level for each trust is available in the SHMI diagnosis group breakdown files. For a subset of diagnosis groups, an indication of whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected', 'as expected' or 'lower than expected' when compared to the national baseline is also provided. Details of the 142 diagnosis groups can be found in Appendix A of the SHMI specification. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells for England from March 2020 due to COVID-19 impacting on activity and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for this trust are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) has not submitted data to the Secondary Uses Service (SUS) since June 2022 due to an issue with their patient records system. This is causing a large shortfall in records with data only submitted for 4 months out of the 12 months in the current time period. Values for this trust should be viewed in the context of this issue. 5.There is a high percentage of invalid diagnosis codes for Milton Keynes University Hospital NHS Foundation Trust (trust code RD8). Values for this trust should therefore be interpreted with caution. 6. A number of trusts are currently engaging in a pilot to submit Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS), rather than the Admitted Patient Care (APC) dataset. As the SHMI is calculated using APC data, this does have the potential to impact on the SHMI value for these trusts. Trusts with SDEC activity removed from the APC data have generally seen an increase in the SHMI value. This is because the observed number of deaths remains approximately the same as the mortality rate for this cohort is very low; secondly, the expected number of deaths decreases because a large number of spells are removed, all of which would have had a small, non-zero risk of mortality contributing to the expected number of deaths. We are working to better understand the planned changes to the recording of SDEC activity and the potential impact on the SHMI. The trusts affected in this publication are: Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), Epsom and St Helier University Hospitals NHS Trust (trust code RVR), Frimley Health NHS Foundation Trust (trust code RDU), Imperial College Healthcare NHS Trust (trust code RYJ), Manchester University NHS Foundation Trust (trust code R0A), Norfolk and Norwich University Hospitals NHS Foundation Trust (trust code RM1), and University Hospitals of Derby and Burton NHS Foundation Trust (trust code RTG). 7. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

Status of COVID-19 cases in Ontario This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue: * Ontario COVID-19 testing percent positive by age group * Confirmed positive cases of COVID-19 in Ontario * Ontario COVID-19 testing metrics by Public Health Unit (PHU) * Ontario COVID-19 testing percent positive by age group * COVID-19 cases in hospital and ICU, by Ontario Health (OH) region * Cumulative deaths (new methodology) * Deaths Involving COVID-19 by Fatality Type For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data. Data includes: * reporting date * daily tests completed * total tests completed * test outcomes * total case outcomes (resolutions and deaths) * current tests under investigation * current hospitalizations * current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness * current patients in Intensive Care Units (ICUs) testing positive for COVID-19 * current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19 * current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness * current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19 * current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19 * Long-Term Care (LTC) resident and worker COVID-19 case and death totals * Variants of Concern case totals * number of new deaths reported (occurred in the last month) * number of historical deaths reported (occurred more than one month ago) * change in number of cases from previous day by Public Health Unit (PHU). This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations. ##Cumulative Deaths **Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool ** The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change. The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. ##Related dataset(s) * Confirmed positive cases of COVID-19 in Ontario

ObjectivesThe purpose of this study is to investigate the impact of COVID-19 on admission and in-hospital mortality of patients with acute myocardial infarction (AMI).MethodsWe constructed a dataset of monthly hospitalizations and mortality of inpatients with AMI from January 2017 to December 2021 utilizing the National Health Insurance Claims Data which covers nearly the entire population. Using an interrupted time series (ITS), we investigated how COVID-19 affected hospitalizations and in-hospital deaths of patients with AMI.ResultsDuring the study period, the average age of patients with AMI was 65.2–65.8 years, and the ratio of men to women was higher, with 73.0–75.3% of patients being men and 24.7–27.0% being women. ITS analysis showed that admission rates of patients with AMI decreased one per 100,000 population due to COVID-19 (P

The goal of the Chicago Women's Health Risk Study (CWHRS) was to develop a reliable and validated profile of risk factors directly related to lethal or life-threatening outcomes in intimate partner violence, for use in agencies and organizations working to help women in abusive relationships. Data were collected to draw comparisons between abused women in situations resulting in fatal outcomes and those without fatal outcomes, as well as a baseline comparison of abused women and non-abused women, taking into account the interaction of events, circumstances, and interventions occurring over the course of a year or two. The CWHRS used a quasi-experimental design to gather survey data on 705 women at the point of service for any kind of treatment (related to abuse or not) sought at one of four medical sites serving populations in areas with high rates of intimate partner homicide (Chicago Women's Health Center, Cook County Hospital, Erie Family Health Center, and Roseland Public Health Center). Over 2,600 women were randomly screened in these settings, following strict protocols for safety and privacy. One goal of the design was that the sample would not systematically exclude high-risk but understudied populations, such as expectant mothers, women without regular sources of health care, and abused women in situations where the abuse is unknown to helping agencies. To accomplish this, the study used sensitive contact and interview procedures, developed sensitive instruments, and worked closely with each sample site. The CWHRS attempted to interview all women who answered "yes -- within the past year" to any of the three screening questions, and about 30 percent of women who did not answer yes, provided that the women were over age 17 and had been in an intimate relationship in the past year. In total, 705 women were interviewed, 497 of whom reported that they had experienced physical violence or a violent threat at the hands of an intimate partner in the past year (the abused, or AW, group). The remaining 208 women formed the comparison group (the non-abused, or NAW, group). Data from the initial interview sections comprise Parts 1-8. For some women, the AW versus NAW interview status was not the same as their screening status. When a woman told the interviewer that she had experienced violence or a violent threat in the past year, she and the interviewer completed a daily calendar history, including details of important events and each violent incident that had occurred the previous year. The study attempted to conduct one or two follow-up interviews over the following year with the 497 women categorized as AW. The follow-up rate was 66 percent. Data from this part of the clinic/hospital sample are found in Parts 9-12. In addition to the clinic/hospital sample, the CWHRS collected data on each of the 87 intimate partner homicides occurring in Chicago over a two-year period that involved at least one woman age 18 or older. Using the same interview schedule as for the clinic/hospital sample, CWHRS interviewers conducted personal interviews with one to three "proxy respondents" per case, people who were knowledgeable and credible sources of information about the couple and their relationship, and information was compiled from official or public records, such as court records, witness statements, and newspaper accounts (Parts 13-15). In homicides in which a woman was the homicide offender, attempts were made to contact and interview her. This "lethal" sample, all such homicides that took place in 1995 or 1996, was developed from two sources, HOMICIDES IN CHICAGO, 1965-1995 (ICPSR 6399) and the Cook County Medical Examiner's Office. Part 1 includes demographic variables describing each respondent, such as age, race and ethnicity, level of education, employment status, screening status (AW or NAW), birthplace, and marital status. Variables in Part 2 include details about the woman's household, such as whether she was homeless, the number of people living in the household and details about each person, the number of her children or other children in the household, details of any of her children not living in her household, and any changes in the household structure over the past year. Variables in Part 3 deal with the woman's physical and mental health, including pregnancy, and with her social support network and material resources. Variables in Part 4 provide information on the number and type of firearms in the household, whether the woman had experienced power, control, stalking, or harassment at the hands of an intimate partner in the past year, whether she had experienced specific types of violence or violent threats at the hands of an intimate partner in the past year, and whether she had experienced symptoms of Post-Traumatic Stress Disorder related to the incidents in the past month. Variables in Part 5 specify the partner or partners who were responsible for the incidents in the past year, record the type and length of the woman's relationship with each of these partners, and provide detailed information on the one partner she chose to talk about (called "Name"). Variables in Part 6 probe the woman's help-seeking and interventions in the past year. Variables in Part 7 include questions comprising the Campbell Danger Assessment (Campbell, 1993). Part 8 assembles variables pertaining to the chosen abusive partner (Name). Part 9, an event-level file, includes the type and the date of each event the woman discussed in a 12-month retrospective calendar history. Part 10, an incident-level file, includes variables describing each violent incident or threat of violence. There is a unique identifier linking each woman to her set of events or incidents. Part 11 is a person-level file in which the incidents in Part 10 have been aggregated into totals for each woman. Variables in Part 11 include, for example, the total number of incidents during the year, the number of days before the interview that the most recent incident had occurred, and the severity of the most severe incident in the past year. Part 12 is a person-level file that summarizes incident information from the follow-up interviews, including the number of abuse incidents from the initial interview to the last follow-up, the number of days between the initial interview and the last follow-up, and the maximum severity of any follow-up incident. Parts 1-12 contain a unique identifier variable that allows users to link each respondent across files. Parts 13-15 contain data from official records sources and information supplied by proxies for victims of intimate partner homicides in 1995 and 1996 in Chicago. Part 13 contains information about the homicide incidents from the "lethal sample," along with outcomes of the court cases (if any) from the Administrative Office of the Illinois Courts. Variables for Part 13 include the number of victims killed in the incident, the month and year of the incident, the gender, race, and age of both the victim and offender, who initiated the violence, the severity of any other violence immediately preceding the death, if leaving the relationship triggered the final incident, whether either partner was invading the other's home at the time of the incident, whether jealousy or infidelity was an issue in the final incident, whether there was drug or alcohol use noted by witnesses, the predominant motive of the homicide, location of the homicide, relationship of victim to offender, type of weapon used, whether the offender committed suicide after the homicide, whether any criminal charges were filed, and the type of disposition and length of sentence for that charge. Parts 14 and 15 contain data collected using the proxy interview questionnaire (or the interview of the woman offender, if applicable). The questionnaire used for Part 14 was identical to the one used in the clinic sample, except for some extra questions about the homicide incident. The data include only those 76 cases for which at least one interview was conducted. Most variables in Part 14 pertain to the victim or the offender, regardless of gender (unless otherwise labeled). For ease of analysis, Part 15 includes the same 76 cases as Part 14, but the variables are organized from the woman's point of view, regardless of whether she was the victim or offender in the homicide (for the same-sex cases, Part 15 is from the woman victim's point of view). Parts 14 and 15 can be linked by ID number. However, Part 14 includes five sets of variables that were asked only from the woman's perspective in the original questionnaire: household composition, Post-Traumatic Stress Disorder (PTSD), social support network, personal income (as opposed to household income), and help-seeking and intervention. To avoid redundancy, these variables appear only in Part 14. Other variables in Part 14 cover information about the person(s) interviewed, the victim's and offender's age, sex, race/ethnicity, birthplace, employment status at time of death, and level of education, a scale of the victim's and offender's severity of physical abuse in the year prior to the death, the length of the relationship between victim and offender, the number of children belonging to each partner, whether either partner tried to leave and/or asked the other to stay away, the reasons why each partner tried to leave, the longest amount of time each partner stayed away, whether either or both partners returned to the relationship before the death, any known physical or emotional problems sustained by victim or offender, including the four-item Medical Outcomes Study (MOS) scale of depression, drug and alcohol use of the victim and offender, number and type of guns in the household of the victim and offender, Scales of Power and Control (Johnson, 1996) or Stalking and Harassment (Sheridan, 1992) by either intimate partner in the year prior to the death, a modified version of the Conflict Tactics Scale (CTS)

BackgroundSevere acute malnutrition (SAM) constitutes a substantial burden in African hospitals. Despite adhering to international guidelines, high inpatient mortality rates persist and the underlying contributing factors remain poorly understood.ObjectiveWe evaluated the 10-year trend (2011–2021) in clinical factors and outcomes among children with severe wasting and/or nutritional edema at Malawi’s largest nutritional rehabilitation unit (NRU).MethodsThis retrospective study analyzed trends in presentation and outcomes using generalized additive models. The association between clinical characteristics and mortality or readmission was examined and key features were also related to time to either mortality or discharge.Results1497 children (53%, females) were included. Median age at admission (23 months, IQR 14, 34) or anthropometry (i.e., weight-for-age, height-for-age and weight-for-height) did not change over the 10-years. But the prevalence of edema decreased by 40% whereas dehydration, difficulty breathing, and pallor became more common. Yearly HIV testing increased but positive-detection remained around 11%. Reporting of complete vaccination dropped by 49%, and no reduction in ‘watch’ antibiotic usage was detected. Overall admissions declined but mortality remained around 23% [95%CI; 21, 25], and deaths occurred earlier (5.6 days [95%CI; 4.6, 6.9] in 2011 vs. 3.5 days [95%CI; 2.5, 4.7] in 2021; p

Background: In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Methods: Four prospective cohort studies of children in two age groups (0–6 and 6–60 months) were conducted between 2012–2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. Findings: 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74–0.80) for 0-6-month-olds and 0.75 (95%CI 0.72–0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Data Processing Methods: The post-processed data files were created using R version 4.2.2. (R Foundation for Statistical Computing, Vienna, Austria) and briefly involved renaming columns from the different datasets so that they are consistent, converting categories coded as “unknown”, “don’t know”, or “missing” to NA, creating new columns, calculating z-scored variables, and converting relevant columns to factors or dates. Ethics Declaration: These studies were approved by the Mbarara University of Science and Technology (No. 15/10-16), the Uganda National Council for Science and Technology (HS 2207), and the University of British Columbia (H16-02679).

Background: The Smart Discharges studies in Uganda have enrolled over 10,000 children under-five with sepsis and have shown that death after hospital discharge occurs in 5-8% of patients, which is as common as death during the primary admission. The Smart Discharges evidence-based risk-prediction tool guides clinical interventions focused on education and post-discharge follow-up and improves healthcare-seeking behaviors and essential medical interventions among vulnerable children. Most importantly, these studies have preliminarily demonstrated that the prediction tool paired with these clinical interventions may substantially reduce post-discharge mortality up to 20-30%; however, these findings have not been validated outside of Uganda. The Smart Discharges project is now ready to expand the project borders and begin external validation research of the prediction tool in Rwanda. Objective(s): This study aims to: (1) characterize the epidemiology of post-discharge mortality among a representative cohort of 1000 children under 5 years of age from two hospitals in Rwanda; and (2) externally validate the Smart Discharges risk-prediction tool in a representative cohort of children from Rwanda. Methods: This study is a prospective observational cohort study that will be conducted between February 2022 and May 2023 at 2 hospitals in Northern and Central Rwanda, the University Teaching Hospital of Kigali (CHUK) in Nyarugenge District and Ruhengeri Referral Hospital in Musanze District. The study will enroll 1,000 children under 5 years of age between the two study sites. Following enrollment a research nurse will obtain and record clinical and demographic variables required for model validation including vital signs, oxygen saturation, anthropometric data, prior care seeking, co-morbidities and diagnoses. A rapid diagnostic test using blood, which will require a finger prick to collect < 0.5ml of blood, will be conducted to assess the patient's HIV status, malaria parasitemia, lactate, and hemoglobin (hemocue). All enrolled children will receive phone follow-up from study staff at 2-, 4- and 6 months following hospital discharge for research purposes. Verbal autopsies, often used in this context to determine cause of death, will be conducted for all children who die following discharge. Ethics Declaration: Institutional review boards at the University of British Columbia (H21-02795), the University of California San Francisco (21-34663), the University Teaching Hospital of Kigali (EC/CHUK/1/005/2022), and the University of Uganda (No 573/CMHS IRB/2022) approved the study. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

PurposeThis study aims to assess adherence to luteinising hormone-releasing hormone (LHRH) agonist treatment for prostate cancer (PC) in England, considering formulation-related differences, their impact on overall survival, and the association with changes in prostate-specific antigen (PSA) levels over time.MethodsIn this retrospective cohort study, utilising primary care data from the Clinical Practice Research Datalink (CPRD) Aurum database linked to Hospital Episode Statistics (HES) and Office for National Statistics (ONS) death registrations, we assessed male patients aged 40 and above diagnosed with PC and prescribed 1-, 3-, or 6-monthly LHRH agonist injections between January 2007 and December 2019. The primary objectives were to measure adherence through proportion of days covered (PDC) and characterize delayed injections, while secondary objectives included assessment of patient demographics, comorbidities, overall survival, and PSA levels. Descriptive statistics were employed, with follow-up restricted to one year for PSA and testosterone measurements due to data availability constraints.ResultsThe study included 32,777 patients with PC receiving LHRH agonists. Most patients (67%) were prescribed 3-monthly formulations, while only 2% received 6-monthly formulations. The mean age of the study population was 74.1 years. Over 80% of patients had at least one comorbidity, with hypertension being the most common. 94% of patients initially prescribed the 3-monthly or 6-monthly regimen remained on their original treatment, in contrast to only 38% for the 1-monthly formulation. Adherence analysis showed that 41.1% of 6-monthly injections were received without delay, compared with 67.9% for the 3-monthly and 77.3% for 1-monthly formulations. A large proportion of patients experienced delays of 14-27 days (32.0%, 33.4%, 54.2%) and over 27 days (39.6%, 48.3%, 46.6%) across the 1-, 3- and 6-monthly formulations respectively. The mean PDC ranged from 90-91% across the three formulation groups, with 89.9%, 84%, and 88.2% achieving ≥ 80% adherence for 3-monthly, 1-monthly, and 6-monthly respectively.ConclusionsThis study revealed substantial and consistent dosing delays in LHRH agonist prescriptions across all formulations within primary care settings in England. These delays can negatively affect the control of PC, potentially hindering disease management for affected patients. Future research with a larger population, encompassing a larger cohort using the 6-monthly formulation, is essential for a comprehensive evaluation of the impact of LHRH agonist injection delays on PC progression.