This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

Deaths by local authority of usual residence, numbers and standardised mortality ratios (SMRs) by sex.

SMR measures whether the population of an area has a higher or lower number of deaths than expected based on the age profile of the population (more deaths are expected in older populations). The SMR is defined as follows: SMR = (Observed no. of deaths per year)/(Expected no. of deaths per year).

SMRs are calculated using the previous year's mid-year population estimates. Live birth figures are used for calculations involving deaths under 1 year.

The age-standardised mortality rates in this release are directly age-standardised to the European Standard Population, which cover all ages and allows comparisons between populations with different age structures, including between males and females and over time.

Note: SMR and deaths by sex data only available since 2001.

Download from ONS website

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

Roads promote high levels of animal-vehicle collisions and have one of the most visible man-made impacts on wildlife. In Portugal, SW Europe, very few ecological studies have focused on the impacts from roads on vertebrates. Knowledge of the main factors driving the emergence of hotspots of vertebrate mortality is still scarce. A segment of a main road 26-km long was sampled by car at an average speed of 20 km/h every two weeks for two years (54 surveys) between 1995 and 1997, collecting all road-killed specimens found. We defined road sections with high collision rates, or vertebrate-mortality hotspots (VMH), by detecting clusters of animal collision locations. The analysis was conducted by comparing the spatial pattern of road kills with that expected in a random situation. In such a condition, the likelihood of collisions for each road section would show a Poisson distribution. Differences of explanatory variables between hotspots and low-mortality sections were evaluated with the Mann-Whitney U-test. Also, a direct-gradient analysis (Canonical Correspondence Analysis (CCA)) was executed with the mortality rates of the 24 most-killed species and the explanatory variables considered. A total of 2421 vertebrate road-killed specimens were collected, which corresponded to nearly 46 specimens per 0.5 km per year. Eighty non-domestic species were recorded. Several sections were defined as VMH, both for all observations and for each vertebrate class. Results suggested that some road sections should receive particular mitigation actions given that mortality hotspots may arise, particularly sections where montado is the dominant habitat …

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning from 1999 to 2015.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

1) Demographic traits These data are published data of age-specific mortality rates, age-specific lengths or weights, length and age at maturity, fecundity-length relationships, and egg size for 84 populations from 49 species of primarily commercial teleost fishes. The populations included are those for which all the life history traits under study have been estimated over a period shorter than 10 years. Traits were estimated from within the ten year window or averaged across it when data were available. Only studies in which reference population, sample size, techniques used for ageing fish and counting eggs, and models used for estimating mortality were reported are included. When only a size or age range was available, the midpoint between the extreme values was used. Raw data were converted into seven demographic traits: - Time-to-5%-survival (T.05): the time elapsed from sexual maturity until 95% of a cohort is dead. T.05 fwas estimated from an exponential mortality model, based on total mortality coefficients estimated by Virtual Population Analysis (age-structured model) in most cases or cohort analysis or catch curves. - Length-at-5%-survival (L.05). In fishes, adult size is difficult to measure because of their indeterminate growth. Adult size reported here is length at time-to-5%-survival. - Age at sexual maturity (Tm): median age at maturity was estimated directly from the data or by fitting a logistic curve to age-specific proportion mature data. When only an age range was available, the midpoint between minimum and maximum is reported. - Length at sexual maturity (Lm): median length at maturity was estimated as age at maturity. - Slope of the fecundity-length relationship (Fb): fish fecundity, defined as the number of eggs present in the ovaries immediately before spawning, is known to increase intraspecifically with the size of females. This increase is usually described by a power-law F = aLb. The exponent of this relationship, b (slope of the log-log fecundity-length regression), accounts for the increase in fecundity with size. - Fecundity at maturity (Fm): fecundity in the year of maturity was estimated from length at maturity, the fecundity-length relationship and the number of spawning bouts per year for batch spawners. - Egg volume (Egg): When information on egg size was unavailable in specific papers, values were borrowed from other studies, using the following criteria in the descending order: from the same period, the same population, the same species. In five species of Perciformes no estimate was available for any population, thus egg volume was estimated from other species of the same family. 2) Fishing pressure Three types of environments with low, moderate and high fishing pressure were defined. - To scale the pressure exerted by fishing to the natural population turn-over, it was expressed as the ratio of fishing mortality to natural mortality rates (F/M). Data were gathered from the literature together with demographic traits. Authors use the following methods to estimate natural mortality rates: intercept of a regression of total mortality on fishing effort, linear relationship known between estimates of natural mortality, growth parameters and the temperature, or multispecies models. Fishing mortality rates were estimated from Virtual Population Analysis or cohort analysis, or as the difference between total and natural mortality. Three levels of fishing pressure were defined: low fishing pressure (fishing mortality lower than natural mortality, F/M < 1), intermediate (1 <= F/M = 2).

This dataset contains counts of deaths for California residents by ZIP Code based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths of California residents. The data tables include deaths of residents of California by ZIP Code of residence (by residence). The data are reported as totals, as well as stratified by age and gender. 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.

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.

These indicators are designed to accompany the SHMI publication. The SHMI methodology does not make any adjustment for deprivation. This is because adjusting for deprivation might create the impression that a higher death rate for those who are more deprived is acceptable. Patient records are assigned to 1 of 5 deprivation groups (called quintiles) using the Index of Multiple Deprivation (IMD). The deprivation quintile cannot be calculated for some records e.g. because the patient's postcode is unknown or they are not resident in England. Contextual indicators on the percentage of provider spells and deaths reported in the SHMI belonging to each deprivation quintile are produced to support the interpretation of the SHMI. 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 from March 2020 due to COVID-19 impacting on activity for England 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) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 6. East Kent Hospitals University NHS Foundation Trust (trust code RVV) has a submission issue which is causing many of their patient spells to be duplicated in the HES Admitted Patient Care data. This means that the number of spells for this trust in this dataset are overstated by approximately 60,000, and the trust’s SHMI value will be lower as a result. Values for this trust should therefore be interpreted with caution. 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.

Number of infant deaths and infant mortality rates, by age group (neonatal and post-neonatal), 1991 to most recent year.

AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies. Methods For a given locality, year, and cause of death, the SMR is the ratio between the observed number of deaths (Om) and the number of expected deaths (Em): SMR = Om/Em (1) where Om should be an available observational data and Em is estimated as the weighted sum of age-specific population size for the given locality (ni) per age-specific death rates of the reference population (MRi): Em = sum(MRi x ni) (2) MRi could be provided by a public health organization or be estimated as the ratio between the age-specific number of deaths of reference population (Mi) to the age-specific reference population size (Ni): MRi = Mi/Ni (3) Thus, the value of Em is weighted by the age distribution of deaths and population size. SMR assumes value 1 when the number of observed and expected deaths are equal. Following eqns. (1-3), the SMR was computed for single years of the period 2009-2018 and for single cause of death as defined by the International ICD-10 classification system by using the following data: age-specific number of deaths by cause of reference population (i.e., Mi) from the Italian National Institute of Statistics (ISTAT, (http://www.istat.it/en/, last access: 26/01/2022)); age-specific census data on reference population (i.e., Ni) from ISTAT; the observed number of deaths by cause (i.e., Om) from ISTAT; the age-specific census data on population (ni); the SMR was estimated at three different level of aggregation: municipal, provincial (equivalent to the European classification NUTS 3) and regional (i.e., NUTS2). The SMR was also computed for the broad category of malignant tumors (i.e. C00-C979, hereinafter cancer macro-type C), and for the broad category of malignant tumor plus non-malignant tumors (i.e. C00-C979 plus D0-D489, hereinafter cancer macro-type CD). Lower 90% and 95% confidence intervals of 10-year average values were computed according to the Byar method.

Live births by usual residence of mother, and General Fertility Rates (GFR), and Deaths and Standardised Mortality Ratio (SMR) by ward and local authority.

The births and deaths data comes from ONS Vital Statistics Table 4.
Small area data is only available directly from ONS under licence.

The general fertility rate (GFR) is the number of live births per 1,000 women aged 15-44.

SMR measures whether the population of an area has a higher or lower number of deaths than expected based on the age profile of the population (more deaths are expected in older populations). The SMR is defined as follows: SMR = (Observed no. of deaths per year)/(Expected no. of deaths per year).

Rates are provisional, they are based on the GLA 2011 based SHLAA ward projections (standard) released in January 2012. At national level, however, they are based on the mid-year population estimates.

More information is on the ONS website.

This dataset presents the footprint of the percentage of live births that were of low birthweight. The data spans every two years between 2012-2016 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The Child and Maternal Health Indicators have been calculated from the Australian Institute of Health and Welfare (AIHW) National Mortality Database and Register of Births and National Perinatal Data Collection. This measure has been calculated with the numerator as the total number of low birthweight liveborn singleton babies, and the denominator as the total number of live births.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Child and Maternal Health Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• This dataset uses the World Health Organisation (WHO) definition of a low birthweight baby as weighing less than 2,500 grams.

• Data at the area level exclude births to Australian non-residents and women who could not be allocated because their usual residence was not stated or was not valid.

• Multiple births and stillbirths have been excluded.

• Percentage for an area are suppressed for publication and marked as 'NP' if the total number of liveborn singleton babies for the area is less than 100.

These indicators are designed to accompany the SHMI publication. As well as information on the main condition the patient is in hospital for (the primary diagnosis), the SHMI data contain up to 19 secondary diagnosis codes for other conditions the patient is suffering from. This information is used to calculate the expected number of deaths. 'Depth of coding' is defined as the number of secondary diagnosis codes for each record in the data. A higher mean depth of coding may indicate a higher proportion of patients with multiple conditions and/or comorbidities, but may also be due to differences in coding practices between trusts. Contextual indicators on the mean depth of coding for elective and non-elective admissions are produced to support the interpretation of the SHMI. 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 from March 2020 due to COVID-19 impacting on activity for England 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) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. There is a high percentage of invalid diagnosis codes for Chesterfield Royal Hospital NHS Foundation Trust (trust code RFS), Milton Keynes University Hospital NHS Foundation Trust (trust code RD8), and West Suffolk NHS Foundation Trust (trust code RGR). Values for these trusts should therefore be interpreted with caution. 6. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 7. East Kent Hospitals University NHS Foundation Trust (trust code RVV) has a submission issue which is causing many of their patient spells to be duplicated in the HES Admitted Patient Care data. This means that the number of spells for this trust in this dataset are overstated by approximately 60,000, and the trust’s SHMI value will be lower as a result. Values for this trust should therefore be interpreted with caution. 8. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

BackgroundGlobally, road traffic accidents (RTAs) cause over 1.35 million deaths each year, with an additional 50 million people suffering disabilities. Ethiopia has the highest number of road traffic accidents, with over 14,000 people killed and over 45,000 injured annually. This study aimed to assess survival status and predictors of mortality among road traffic accident adult patients admitted to intensive care units of Referral Hospitals in Tigray, 2024.MethodsAn institution-based retrospective follow-up study design was conducted from January 8, 2019, to December 11, 2023, on 333 patient charts. A bivariable Cox-regression analysis was performed to estimate crude hazard ratios (CHR). Subsequently, a multivariable Cox regression analysis was performed to estimate the Adjusted Hazard Ratios (AHR). Finally, AHR with p-value less than 0.05 was used to measure the association between dependent and independent variables.ResultThe incidence of mortality for road traffic accident victims, was 21 per 1000 person-days observation with (95% CI: 16, 27.6) and the median survival time was 14 days. The predictors of mortality in this study were the value of oxygen saturation on admission ≤ 89% (AHR = 4.9; 95%CI: 1.4–17.2), Intracranial hemorrhage (AHR = 3.3; 95% CI: 1.02–11), chest injury (AHR = 3.2; 95%CI: 1.38–7.59), victims with age catgories of 31–45 years (AHR = 0.3; 95% CI: 0.1–0.88) and 46–60 years (AHR = 0.22; 95% CI: 0.06–0.89).ConclusionA concerningly high mortality rate from car accidents were found in Referral Hospitals of Tigray. To improve the survival rates, healthcare providers should focus on victims with very low oxygen levels, head injuries, chest injuries, and older victims.

IntroductionContradictions remain on the impact of interhospital competition on the quality of care, mainly the mortality. The aim of the study is to evaluate the impact of interhospital competition on postoperative mortality after surgery for colorectal cancer in France.MethodsWe conducted a retrospective cross-sectional study from 2015 to 2019. Data were collected from a National Health Database. Patients operated on for colorectal cancer in a hospital in mainland France were included. Competition was measured using number of competitors by distance-based approach. A mixed-effect model was carried out to test the link between competition and mortality.ResultsNinety-five percent (n = 152,235) of the 160,909 people operated on for colorectal cancer were included in our study. The mean age of patients was 70.4 ±12.2 years old, and female were more represented (55%). A total of 726 hospitals met the criteria for inclusion in our study. Mortality at 30 days was 3.6% and we found that the mortality decreases with increasing of the hospital activity. Using the number of competitors per distance method, our study showed that a “highly competitive” and “moderately competitive” markets decreased mortality by 31% [OR: 0.69 (0.59, 0.80); p

High-risk human papillomavirus (hrHPV) infection is established as the major cause of invasive cervical cancer (ICC). However, whether hrHPV status in the tumor is associated with subsequent prognosis of ICC is controversial. We aim to evaluate the association between tumor hrHPV status and ICC prognosis using national registers and comprehensive human papillomavirus (HPV) genotyping.

In this nationwide population-based cohort study, we identified all ICC diagnosed in Sweden during the years 2002–2011 (4,254 confirmed cases), requested all archival formalin-fixed paraffin-embedded blocks, and performed HPV genotyping. Twenty out of 25 pathology biobanks agreed to the study, yielding a total of 2,845 confirmed cases with valid HPV results. Cases were prospectively followed up from date of cancer diagnosis to 31 December 2015, migration from Sweden, or death, whichever occurred first. The main exposure was tumor hrHPV status classified as hrHPV-positive and hrHPV-negative. The primary outcome was all-cause mortality by 31 December 2015. Five-year relative survival ratios (RSRs) were calculated, and excess hazard ratios (EHRs) with 95% confidence intervals (CIs) were estimated using Poisson regression, adjusting for education, time since cancer diagnosis, and clinical factors including age at cancer diagnosis and International Federation of Gynecology and Obstetrics (FIGO) stage.

Of the 2,845 included cases, hrHPV was detected in 2,293 (80.6%), and we observed 1,131 (39.8%) deaths during an average of 6.2 years follow-up. The majority of ICC cases were diagnosed at age 30–59 years (57.5%) and classified as stage IB (40.7%). hrHPV positivity was significantly associated with screen-detected tumors, young age, high education level, and early stage at diagnosis (p < 0.001). The 5-year RSR compared to the general female population was 0.74 (95% CI 0.72–0.76) for hrHPV-positive cases and 0.54 (95% CI 0.50–0.59) for hrHPV-negative cases, yielding a crude EHR of 0.45 (95% CI 0.38–0.52) and an adjusted EHR of 0.61 (95% CI 0.52–0.71). Risk of all-cause mortality as measured by EHR was consistently and statistically significantly lower for cases with hrHPV-positive tumors for each age group above 29 years and each FIGO stage above IA. The difference in prognosis by hrHPV status was highly robust, regardless of the clinical, histological, and educational characteristics of the cases. The main limitation was that, except for education, we were not able to adjust for lifestyle factors or other unmeasured confounders.

In conclusion, women with hrHPV-positive cervical tumors had a substantially better prognosis than women with hrHPV-negative tumors. hrHPV appears to be a biomarker for better prognosis in cervical cancer independent of age, FIGO stage, and histological type, extending information from already established prognostic factors. The underlying biological mechanisms relating lack of detectable tumor hrHPV to considerably worse prognosis are not known and should be further investigated.

Purpose:

To compile a comprehensive survival and HPV genotyping data and provide a large-scale population-based evaluation of the association between tumor high risk HPV status and prognosis of invasive cervical cancer.

This dataset (ccHPV_RelativeSurvival.dta) comprises 2845 invasive cervical cancer (ICC) cases diagnosed in Sweden during the years 2002-2011, and had valid human papillomavirus (HPV) results assessed from the formalin-fixed, paraffin-embedded (FFPE) blocks.

In order to control the risk of incidental disclosure of personal information, the data available here has been anonymized in the following manner: • The date of diagnosis has been moved to 2008-07-01 for all subjects. • Follow-up time has been censored at five years after diagnosis. • Age at diagnosis and follow-up time after diagnosis have been microaggregated in groups of five subjects (using function microaggregation in R package sdcMicro 2.5.9, available from https://cran.r-project.org/package=sdcMicro)

Analysis of the anonymized data replicates the results presented in main part of the study (Figures 2 & 3, Tables 1-3) with only minor numerical differences, with the following exceptions: • In Figure 2, relative survival can only be calculated up to five years after diagnosis. • In Table 1, the number of person years and the mean follow-up time differ considerably due to censoring; the distribution of subjects between age groups varies somewhat due to microaggregation. • In Figure 3, the excess hazard ratios for age groups 30-44 and 45-59 in Panel A shift noticeably, but without affecting the overall message (comparable reduced risk across all age strata).

The dataset includes 12 variables, eight of which are necessary for the analysis (core variables) and four of which are included for administrative purposes and convenience of coding the analysis (extra variables). Core variables: • dx_date: Date of diagnosis • age: Age (in years) at diagnosis • x_stage_group: International Federation of Gynecology and Obstetrics (FIGO) stage of tumor, IA; IB; II and III+ • edu_cat: Education (categorical, three levels): 1=low (less than high school); 2=middle (high school); 3=high (university exam and above); 99=missing • exit_new: End of follow-up (date) • censor_new: Censoring status: 1=death; 2=censored due to migration, loss of follow-up or end of study • final_type: Histological type of tumor: SCC=squamous cell carcinoma; AC=adenocarcinoma. • hr_hpv: High-risk HPV status of tumor (main exposure, binary): 0=hrHPV negative; 1= hrHPV positive

Extra variables: • entry: Entry date (copy of diagnosis date) • sex: Gender (all female, for linking to standard population mortality file): 2=female. • dx_year: Year of diagnosis (for linking to standard population mortality file)

Fitness consequences of early-life environmental conditions are often sex-specific, but corresponding evidence for invertebrates remains inconclusive. Here we use meta-analysis to evaluate sex-specific sensitivity to early-life nutritional conditions in insects. Using literature-derived data for 85 species with broad phylogenetic and ecological coverage, we show that females are generally more sensitive to food stress than males. Stressful nutritional conditions during development typically lead to female-biased mortality and thus increasingly male-biased sex ratios of emerging adults. We further demonstrate that the general trend of higher sensitivity to food stress in females can primarily be attributed to their typically larger body size in insects and hence higher energy needs during development. By contrast, there is no consistent evidence of sex-biased sensitivity in sexually size-monomorphic species. Drawing conclusions regarding sex-biased sensitivity in species with male-biased size dimorphism remains to wait for the accumulation of relevant data. Our results suggest that environmental conditions leading to elevated juvenile mortality may potentially affect the performance of insect populations further by reducing the proportion of females among individuals reaching reproductive age. Accounting for sex-biased mortality is therefore essential to understanding the dynamics and demography of insect populations, not least importantly in the context of ongoing insect declines. Methods Data collection These data were collected for a meta-analysis to assess sex-specific sensitivity to early-life nutritional conditions in insects. We made use of experimental case studies reporting sex ratios at adult emergence in conspecifics reared under two or more diet treatments (food quality or availability). We collated primary studies in two complementary ways. The majority of primary data sets for this synthesis were collected systematically by the lead author (T. Teder) from an extensive list of journals in the field of entomology, ecology and evolutionary biology, partly as a result of one-time retrospective screening (articles published before 2003) and partly as a result of continuous screening (articles published between 2004 and 2021) of journals' tables of contents. Our systematic screening meant that the journals' tables of contents were routinely examined, and all papers identified as potentially containing relevant data on the basis of article titles were subjected to full-text review. As data of this type are typically reported in tables and figures, their identification within articles was straightforward. To increase the amount of primary data, additional studies were identified by a thorough search in major literature databases (Google Scholar, Web of Science, Scopus, published until 2021). These complementary searches in the literature databases were undertaken to find relevant data in journals that remained uncovered by our main data collection method. Accordingly, while exploring the search results, we primarily focused on studies published in journals that were not subjected to systematic screening. The basic procedure for identifying relevant primary papers among search results was basically identical to that used when screening journals' contents: papers identified as potentially containing relevant data based on article titles were retrieved for full-text review. To minimize any search-related biases, we used only search queries that were strictly neutral concerning the focal questions of our study (i.e. sex-specific sensitivity to nutritional stress). Accordingly, our search queries included only combinations of very generic search terms: one of several synonyms of sex ratio ('sex ratio', 'proportion/percentage/fraction of males/females'), 'mortality' and one of particular insect order names ('Diptera', 'Hemiptera', 'Lepidoptera', 'Coleoptera', 'Orthoptera', etc., or 'insect*'). No restriction was set on the language or publication year of primary studies. As a major exception, we systematically ignored studies focusing on Hymenoptera and Thysanoptera during the process of data collection. These groups of insects have haplodiploid sex determination (males develop from unfertilized and females from fertilized eggs) which provides mothers with an efficient mechanism for manipulating offspring sex ratio. We also did not consider taxa regularly exhibiting asexual reproduction, such as aphids. Data extraction and criteria for eligibility For a study to be considered, it had to provide two types of information: i) sex ratios at adult emergence for multiple (two or more) diet treatments together with sample sizes, and ii) corresponding juvenile mortality rates. Typically, sex ratios in primary studies were reported as the proportion/percentage of males/females or the ratio of the two sexes at adult emergence (or, in a few cases, at the pupal stage). As sample sizes for sex ratio estimates were not always explicitly indicated, we applied various indirect approaches to derive them, most often combining information on sample sizes at the start of the experiment with data on mortality throughout juvenile stages. The combined juvenile mortality rate of the two sexes was used as a proxy for nutritional stress. Accordingly, our research relies on the premise that, within each primary study, food stress was most severe in treatments with the highest mortality rates and least pronounced in treatments with the lowest mortality rates. Both egg-to-adult and larval mortality rates (often reported as survival rates) were considered equally acceptable measures of juvenile mortality. In a few cases, we also accepted mortality rates estimated over a particular fixed part of the larval stage (two primary studies) or the pupal stage (three studies). We limited our inclusion criteria to studies where major external mortality agents – predators and parasitoids – were explicitly excluded. In all studies included, experimental treatments were applied to the F1 generation only, whereas their parents were maintained under identical conditions, excluding in this way any parental effect on sex ratios. Among-treatment differences in nutritional stress were solely due to variations in food quality (e.g., different host plants, different prey species, also different artificial diets) or food availability. Otherwise, the conditions were uniform within the experiments. Data from multifactorial experiments (e.g. those manipulating both diet and temperature) were divided into different data sets so that the environmental factor of our interest was allowed to vary while other factors were held constant. In some primary studies, food quality and amount were manipulated indistinguishably within the same experimental setup. Data extracted from different studies were always treated as different data sets. However, data from a single study could also be split into multiple primary data sets if obtained from different experiments or using different species/populations/genotypes. We deliberately did not consider studies in which diet treatments applied contained pesticides or their residues. WebPlotDigitizer 4.3 (A. Rohatgi; https://automeris.io/WebPlotDigitizer) was used to extract graphically presented data. One should note that the overwhelming majority of primary studies were conducted in contexts other than the focus of our synthesis: sex differences in stress responses per se were rarely addressed in these papers. Therefore, a considerable share of primary studies found, between-treatment differences in juvenile mortality were relatively small, indicating low variation in environmental stress levels. Naturally, in order to meaningfully evaluate sex-specific responses to food stress, there must be some variation in food stress across treatments. We therefore arbitrarily limited our main database to a subset of primary studies in which mortality rates across treatments had at least a 10 % difference (calculated as the difference between the maximum and minimum mortality rates across treatments). This way we ensured that growth conditions within studies were not "too similar" across treatments. Applying this threshold retained us altogether 125 primary data sets which formed the backbone of our analyses.

BackgroundA maternal mortality ratio is a sensitive indicator when comparing the overall maternal health between countries and its very high figure indicates the failure of maternal healthcare efforts. Cambodia, Laos, Myanmar, and Vietnam-CLMV countries are the low-income countries of the South-East Asia region where their maternal mortality ratios are disproportionately high. This systematic review aimed to summarize all possible factors influencing maternal mortality in CLMV countries.MethodsThis systematic review applied "The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Checklist (2020)", Three key phrases: "Maternal Mortality and Health Outcome", "Maternal Healthcare Interventions" and "CLMV Countries" were used for the literature search. 75 full-text papers were systematically selected from three databases (PubMed, Google Scholar and Hinari). Two stages of data analysis were descriptive analysis of the general information of the included papers and qualitative analysis of key findings.ResultsPoor family income, illiteracy, low education levels, living in poor households, and agricultural and unskilled manual job types of mothers contributed to insufficient antenatal care. Maternal factors like non-marital status and sex-associated work were highly associated with induced abortions while being rural women, ethnic minorities, poor maternal knowledge and attitudes, certain social and cultural beliefs and husbands’ influences directly contributed to the limitations of maternal healthcare services. Maternal factors that made more contributions to poor maternal healthcare outcomes included lower quintiles of wealth index, maternal smoking and drinking behaviours, early and elderly age at marriage, over 35 years pregnancies, unfavourable birth history, gender-based violence experiences, multigravida and higher parity. Higher unmet needs and lower demands for maternal healthcare services occurred among women living far from healthcare facilities. Regarding the maternal healthcare workforce, the quality and number of healthcare providers, the development of healthcare infrastructures and human resource management policy appeared to be arguable. Concerning maternal healthcare service use, the provisions of mobile and outreach maternal healthcare services were inconvenient and limited.ConclusionLow utilization rates were due to several supply-side constraints. The results will advance knowledge about maternal healthcare and mortality and provide a valuable summary to policymakers for developing policies and strategies promoting high-quality maternal healthcare.