Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

This data presents national-level provisional maternal mortality rates based on a current flow of mortality and natality data in the National Vital Statistics System. Provisional rates which are an early estimate of the number of maternal deaths per 100,000 live births, are shown as of the date specified and may not include all deaths and births that occurred during a given time period (see Technical Notes). A maternal death is the death of a woman while pregnant or within 42 days of termination of pregnancy irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes. In this data visualization, maternal deaths are those deaths with an underlying cause of death assigned to International Statistical Classification of Diseases, 10th Revision (ICD-10) code numbers A34, O00–O95, and O98–O99. The provisional data include reported 12 month-ending provisional maternal mortality rates overall, by age, and by race and Hispanic origin. Provisional maternal mortality rates presented in this data visualization are for “12-month ending periods,” defined as the number of maternal deaths per 100,000 live births occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2020 would include deaths and births occurring from July 1, 2019, through June 30, 2020. Evaluation of trends over time should compare estimates from year to year (June 2020 and June 2021), rather than month to month, to avoid overlapping time periods. In the visualization and in the accompanying data file, rates based on death counts less than 20 are suppressed in accordance with current NCHS standards of reliability for rates. Death counts between 1-9 in the data file are suppressed in accordance with National Center for Health Statistics (NCHS) confidentiality standards. Provisional data presented on this page will be updated on a quarterly basis as additional records are received. Previously released estimates are revised to include data and record updates received since the previous release. As a result, the reliability of estimates for a 12-month period ending with a specific month will improve with each quarterly release and estimates for previous time periods may change as new data and updates are received.

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.

Data on death rates for suicide in the United States, by age, sex, race, and Hispanic origin. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Mortality File. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

This file contains the list of codes and general categories associated with the ICD10h (Historic cause of death coding and classification scheme for individual-level causes of death). ICD10h has been designed by the authors to aid the coding and classification of causes of death recorded on historic individual death records and associated files include a manual, a list of exemplar strings in the English language, and a categorisation for infant mortality. The ICD10h system is based on the 10th revision of the International Classification of Diseases - 2016 version (ICD10 - 2016), and combines ICD10 codes (without modification) with new codes for archaic/historic terms. The data was derived from the following projects/deposited data: Determining the Demography of Victorian Scotland Through Record Linkage, ESRC RES-000-23-0128 held at the Cambridge Group for the History of Population and Social Structure, University of Cambridge; P. Gunn and R. Kippen, ‘Household and Family Formation in Nineteenth-Century Tasmania, Dataset of 195 Thousand Births, 93 Thousand Deaths and 51 Thousand Marriages Registered in Tasmania, 1838-1899’, 2008.

The resource creation was supported by the following projects: Digitising Scotland/Scottish Health Informatics Project (funded by the ESRC); Studying Health in Port Cities (funded by The Netherlands Organisation for Scientific Research); The Great Leap (funded by COST-Action CA22116).

SHARING/ACCESS INFORMATION

This resource is available under a CC BY licence.

Recommended citation for this dataset: Historic cause of death coding and classification scheme for individual-level causes of death – Codes [https://doi.org/10.17863/CAM.109961]

Please see the associated resources: Historic cause of death coding and classification scheme for individual-level causes of death – manual [https://doi.org/10.17863/CAM.109960] Historic cause of death coding and classification scheme for individual-level causes of death – English language historic strings [https://doi.org/10.17863/CAM.109962] Historic cause of death coding and classification scheme for individual-level causes of death – Infant Categorisations [https://doi.org/10.17863/CAM.109963]

ICD10h is a research tool created to facilitate the study of historical cause of death records and should not be used for any official purpose. It is based on the International Classification of Diseases, 10th Revision (ICD-10) version 2016 (Geneva: World Health Organization 2016) but is not a recognised version or extension of ICD-10 and is not authorised by WHO. However we have consulted with WHO: they recognise that ICD10h is a useful academic methodology and have not raised any objections to its creation. Data coded using ICD10h are not directly comparable with data coded in ICD-10, and the underlying or primary cause of death derived using the ICD10h methodology may be different from the underlying cause derived in ICD-10 according to the WHO rules. Please note that ICD-10 version 2016 is not the most recent version of ICD-10; and that WHO now recommend the use of ICD-11; a more advanced and detailed classification.

DATA & FILE OVERVIEW

ICD10h_Masterlist.xlsx Excel file consisting of 3 worksheets: 1) ReadMe sheet 2) Masterlist
3) 2020to2024transfer

Separate csv files for 2) and 3) containing the same information.

This file builds on a previous, unpublished version of ICD10h (dating from 2020). The 2020to2024transfer file enables data coded to the earlier version to be updated to the current version.

METHODOLOGICAL INFORMATION

The data were hand-coded and subject to stringent algorithm-assisted tests.

DATA-SPECIFIC INFORMATION FOR: Masterlist

Number of variables: 10

Number of cases/rows: 14088

Variable List: IDMasterlist (a unique ID number for Masterlist table) ICD10h (ICD10h code) ICD10 (ICD10 code) ICD10_2levelCATEGORY (ICD10 first part of 2 level categorisation) ICD10_2levelCAUSE (ICD10 second part of 2 level categorisation) ICD10h_DESCRIPTION (ICD10h description - this differs from ICD10_2levelCAUSE only where there is a specific historical code) Histcat (category of general historical categorisation) DoNotUse (1=do not use for mortality coding – ICD10 asterisk codes) NotForUnderlying (1=do not use for underlying mortality codes) GenderSpecific (0=can be used for men or women; 1=use for men only; 2=use for women only)

DATA-SPECIFIC INFORMATION FOR: 2020to2024transfer

Number of variables: 4

Number of cases/rows: 13763

Variable List: ID2024_transfer (unique ID for 2020to2024transfer table) IDoct2020Masterlist (ID variable from the 2020 Masterlist) ICD10h_oct2020 (ICD10h from the October 2020 Masterlist) ICD10h2024 (ICD10h value from the current version of the Masterlist)

This table contains mortality indicators based on the number of deaths by cause of death for the period 1996-2019. The table contains figures on mortality per 10,000 (non-standardised, directly standardized and indirectly standardised), the CMF and SMR indices of respectively direct and indirect standardized mortality and years of life lost (YPLL, Years of Potential Life lost) per 10,000. At municipality level, the figures are only available for total men and women, four-year periods and some main groups of causes of death (Total, 2.1 Malignant neoplasms, 7 Diseases of the cardiovascular system, 8 Diseases of the respiratory organs, 17 External causes of death and all other causes of death together in one group) . At the GGD and national level, the figures are available for gender, individual years (in addition to the four-year periods) and with a more detailed classification of causes of death. The regional classification of 2020 has been used for all years. The figures in this table are based on the cause of death statistics of Statistics Netherlands (CBS). Data available from: 1996 Status of the figures: The figures in this table are final. Changes as of 23-12-2021: None, this is a new table. When will new numbers come out? New figures are released every two years. These are published in separate tables.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

This table contains the number of victims of suicide arranged by marital status, method, motives, age and sex. They represent the number deaths by suicide in the resident population of the Netherlands. The figures in this table are equal to the suicide figures in the causes of death statistics, because they are based on the same files. The causes of death statistics do not contain information on the motive of suicide. For the years 1950-1995, this information is obtained from a historical data file on suicides. For the years 1996-now the motive is taken from the external causes of death (Niet-Natuurlijke dood) file. Before the 9th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), i.e. for the years 1950-1978, it was not possible to code "jumping in front of train/metro". For these years 1950-1978 "jumping in front of train/metro" has been left empty, and it has been counted in the group "other method". Relative figures have been calculated per 100 000 of the corresponding population group. The figures are calculated based on the average population of the corresponding year. Data available from: 1950 Status of the figures: The figures up to and including 2021 are final. The figures of 2022 are provisional. Changes as of August 29th 2023: - The provisional figures for 2022 have been added. - Some final figures of 2021 were incorrect and have been revised. A small adjustment was made in the number of deceased women from 60 to 69 years. When will new figures be published: In the first quarter of 2024 the definitive figures for 2022 will be published.

Annual data on death registrations by single year of age for the UK (1974 onwards) and England and Wales (1963 onwards).

A dataset providing GP recorded coronary heart disease. Coronary heart disease (CHD) is the leading cause of death both in the UK and worldwide. It's responsible for more than 73,000 deaths in the UK each year. About 1 in 6 men and 1 in 10 women die from CHD. In the UK, there are an estimated 2.3 million people living with CHD and around 2 million people affected by angina (the most common symptom of coronary heart disease). CHD generally affects more men than women, although from the age of 50 the chances of developing the condition are similar for both sexes. As well as angina (chest pain), the main symptoms of CHD are heart attacks and heart failure. However, not everyone has the same symptoms and some people may not have any before CHD is diagnosed. CHD is sometimes called ischaemic heart disease.

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.

Background: Maternal, infectious, and non-communicable causes of death combinedly are a major health problem for women of reproductive age (WRA) in sub-Saharan Africa (SSA). Little is known about the relative risks of each of these causes of death in their combined form and their demographic impacts. The focus of studies on WRA has been on maternal health. The evolving demographic and health transitions in low- and middle-income countries (LMICs) suggest a need for a comprehensive approach to resolve health challenges of women beyond maternal causes.Methods: Deaths and person-years of exposure (PYE) were calculated by age for WRA within 15–49 years of age in the Kintampo Health and Demographic Surveillance System (KHDSS) area from January 2005 to December 2014. Causes of death were diagnosed using a standard verbal autopsy questionnaire and the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Identified causes of death were categorized into three broad areas, namely, maternal, infectious, and non-communicable diseases. Multiple decrements and associated single decrement life table methods were used.Results: Averting any of the causes of death was seen to lead to improved life expectancy, but eliminating infectious causes of death leads to the highest number of years gained. Infectious causes of death affected all ages and the gains in life expectancy, assuming that these causes were eliminated, diminished with increasing age. The oldest age group, 45–49, had the greatest gain in reproductive-aged life expectancy (RALE) if maternal mortality was eliminated.Discussion: This study demonstrated the existence of a triple burden. Infectious causes of death are persistently high while deaths from non-communicable causes are rising and the level of maternal mortality is still unacceptably high. It recommends that attention should be given to all the causes of death among WRA.

BackgroundCoronary atherosclerotic heart disease (coronary heart disease; CHD) is the leading cause of death in women worldwide, and the number of patients and deaths is increasing each year. Approximately 3.8 million women die from CHD every year globally. After menopause, estrogen levels decrease, and the risk of cardiovascular disease increases substantially; however, research on risk factors for CHD in postmenopausal women has been inconclusive.ObjectiveTo systematically evaluate the risk factors for CHD in postmenopausal women.MethodsThe PubMed, Embase, Web of Science, CINAHL, CBM, CNKI, and VIP databases were searched up to February 9, 2024, for studies on risk factors for CHD in postmenopausal women. Two researchers independently screened the literature, extracted data, and evaluated the quality of the included literature. STATA17.0 software was used for meta-analysis.ResultsNine studies involving 29,4103 patients were included. The results of the meta-analysis showed that history of six or more pregnancies (hazard ratio = 1.538, 95% confidence interval: 1.241%–1.906%) was significantly associated with risk of CHD (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)

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)

This table contains 26010 series, with data for years 1996 - 1996 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (170 items: Canada; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador ...), Sex (3 items: Both sexes; Females; Males ...), Selected causes of death (ICD-9) (17 items: Total; all causes of death; Colorectal cancer; Lung cancer; All malignant neoplasms (cancers) ...), Characteristics (3 items: Mortality; Low 95% confidence interval; mortality; High 95% confidence interval; mortality ...).

Abstract copyright UK Data Service and data collection copyright owner.The purpose of the Vital Statistics for England and Wales data is to record the numbers of conceptions, live births, stillbirths, deaths and causes of death for persons in England and Wales, by gender and age. Data are available at local authority, health authority and ward level. Individual studies in the series record various parts of these data. Changes have been made over time to the way in which the Office for National Statistics (ONS) collects vital statistics data, resulting in some variation in the content of later studies in the series. Further information may be found in the Key Population and Vital Statistics reports available from the ONS web site. During 2006, Sam Smith and colleagues at ESDS Government carried out work on various studies in the series prior to 2002, to improve the data format. The resulting files have been redeposited at the UKDA. More information is available in the documentation for the studies concerned. The data cover conceptions in 1994-1997, aggregated at ward level, which led to maternities or to abortions under the 1967 Act. They do not include conceptions resulting in spontaneous miscarriages during the first 23 weeks of gestation (data for these are not collected centrally) nor any illegal abortions. For the second edition (August 2006), work was carried out on the data by ESDS Government (see above) to produce more user-friendly tab-delimited ASCII files. The data are also now available in Excel, SPSS and Stata in addition to the previous ONS ITELite format. Main Topics: The data cover total number of conceptions to women of all ages, those aged under 20 years, and to girls under 16 years for each local authority ward in England and Wales. Tables include:Conw.94: conceptions for wards to 1996 boundariesConw.95: conceptions for wards to 1997 boundariesConw.96: conceptions for wards to 1998 boundariesConw.97: conceptions for wards to 1999 boundaries

Abstract

In 1985 the population and health observatory was established at Mlomp, in the region of Ziguinchor, in southern Senegal (see map). The objective was to complement the two rural population observatories then existing in the country, Bandafassi, in the south-east, and Niakhar, in the centre-west, with a third observatory in a region - the south-west of the country (Casamance) - whose history, ethnic composition and economic situation were quite different from those of the regions where the first two observatories were located. It was expected that measuring the demographic levels and trends on those three sites would provide better coverage of the demographic and epidemiological diversity of the country.

Following a population census in 1984-1985, demographic events and causes of death have been monitored yearly. During the initial census, all women were interviewed concerning the birth and survival of their children. Since 1985, yearly censuses, usually conducted in January-February, have been recording demographic data, including all births, deaths, and migrations. The completeness and accuracy of dates of birth and death are cross-checked against those of registers of the local maternity ward (_95% of all births) and dispensary (all deaths are recorded, including those occurring outside the area), respectively. The study area comprises 11 villages with approximately 8000 inhabitants, mostly Diola. Mlomp is located in the Department of Oussouye, Region of Ziguinchor (Casamance), 500 km south of Dakar.

On 1 January 2000 the Mlomp area included a population of 7,591 residents living in 11 villages. The population density was 108 people per square kilometre. The population belongs to the Diola ethnic group, and the religion is predominantly animist, with a large minority of Christians and a few Muslims. Though low, the educational level - in 2000, 55% of women aged 15-49 had been to school (for at least one year) - is definitely higher than at Bandafassi. The population also benefits from much better health infrastructure and programmes. Since 1961, the area under study has been equipped with a private health centre run by French Catholic nurses and, since 1968, a village maternity centre where most women give birth. The vast majority of the children are totally immunized and involved in a growth-monitoring programme (Pison et al.,1993; Pison et al., 2001).

Geographic coverage

The Mlomp DSS site, about 500 km from the capital, Dakar, in Senegal, lies between latitudes 12°36' and 12°32'N and longitudes 16°33' and 16°37'E, at an altitude ranging from 0 to 20 m above sea level. It is in the region of Ziguinchor, Département of Oussouye (Casamance), in southwest Senegal. It is locates 50 km west of the city of Ziguinchor and 25 kms north of the border with Guinea Bissau. It covers about half the Arrondissement of Loudia-Ouolof. The Mlomp DSS site is about 11 km × 7 km and has an area of 70 km2. Villages are households grouped in a circle with a 3-km diameter and surrounded by lands that are flooded during the rainy season and cultivated for rice. There is still no electricity.

Analysis unit

Individual

Universe

At the census, a person was considered a member of the compound if the head of the compound declared it to be so. This definition was broad and resulted in a de jure population under study. Thereafter, a criterion was used to decide whether and when a person was to be excluded or included in the population.

A person was considered to exit from the study population through either death or emigration. Part of the population of Mlomp engages in seasonal migration, with seasonal migrants sometimes remaining 1 or 2 years outside the area before returning. A person who is absent for two successive yearly rounds, without returning in between, is regarded as having emigrated and no longer resident in the study population at the date of the second round. This definition results in the inclusion of some vital events that occur outside the study area. Some births, for example, occur to women classified in the study population but physically absent at the time of delivery, and these births are registered and included in the calculation of rates, although information on them is less accurate. Special exit criteria apply to babies born outside the study area: they are considered emigrants on the same date as their mother.

A new person enters the study population either through birth to a woman of the study population or through immigration. Information on immigrants is collected when the list of compounds of a village is checked ("Are there new compounds or new families who settled since the last visit?") or when the list of members of a compound is checked ("Are there new persons in the compound since the last visit?"). Some immigrants are villagers who left the area several years before and were excluded from the study population. Information is collected to determine in which compound they were previously registered, to match the new and old information.

Information is routinely collected on movements from one compound to another within the study area. Some categories of the population, such as older widows or orphans, frequently move for short periods of time and live in between several compounds, and they may be considered members of these compounds or of none. As a consequence, their movements are not always declared.

Kind of data

Event history data

Frequency of data collection

One round of data collection took place annually, except in 1987 and 2008.

Sampling procedure

No samplaing is done

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires: - Household book (used to register informations needed to define outmigrations) - Delivery questionnaire (used to register information of dispensaire ol mlomp) - New household questionnaire - New member questionnaire - Marriage and divorce questionnaire - Birth and marital histories questionnaire (for a new member) - Death questionnaire (used to register the date of death)

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an out-migration event (OMG)

In the case of the village that was added (enumerated) in 2006, some individuals may have outmigrated from the original surveillance area and setlled in the the new village prior to the first enumeration. Where the records of such individuals have been linked, and indivdiual can legitmately have and outmigration event (OMG) forllowed by and enumeration event (ENU). In a few cases a homestead exit event (EXT) was followed by an enumeration event in these cases. In these instances the EXT events were changed to an out-migration event (OMG).

Response rate

On an average the response rate is about 99% over the years for each round.

Sampling error estimates

Not applicable

Data appraisal

CenterId Metric Table QMetric Illegal Legal Total Metric Rundate
SN012 MicroDataCleaned Starts 18756 2017-05-19 00:00
SN012 MicroDataCleaned Transitions 0 45136 45136 0 2017-05-19 00:00
SN012 MicroDataCleaned Ends 18756 2017-05-19 00:00
SN012 MicroDataCleaned SexValues 38 45098 45136 0 2017-05-19 00:00
SN012 MicroDataCleaned DoBValues 204 44932 45136 0 2017-05-19 00:00

About this dataset

Context

The dataset tabulates the population of Dead Lake township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Dead Lake township. The dataset can be utilized to understand the population distribution of Dead Lake township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Dead Lake township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Dead Lake township.

Key observations

Largest age group (population): Male # 65-69 years (71) | Female # 65-69 years (53). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Dead Lake township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Dead Lake township is shown in the following column.
• Population (Female): The female population in the Dead Lake township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Dead Lake township for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dead Lake township Population by Gender. You can refer the same here

The number of maternal deaths and maternal mortality rates for selected causes, 2000 to most recent year.