Misclassification matrix for adult gold high quality diagnoses. Misclassification table for gold standard 1 and 2 deaths that compares the underlying cause of death assigned by study physicians with the underlying cause of death assigned by the Philippine Statistics Authority. (XLSX 15 kb)

BackgroundWhile Denmark is well known for its plethora of registers. Many studies are conducted on research databases that only cover parts of Denmark, and regional differences could potentially threaten these studies’ external validity. The aim of this study was to assess sociodemographic and health related homogeneity of the five Danish regions.MethodsWe obtained descriptive data for the five Danish regions, using publicly available data sources: Statbank Denmark, the Danish Ministry of Economic Affairs, and Medstat.dk. These data sources comprise aggregate data from four different nationwide registers: The Danish National Patient Register, The Danish Civil Registration System, The Danish Register of Medicinal Product Statistics, and The Danish National Health Service Register for Primary Care. We compared the Danish regions regarding demographic and socioeconomic characteristics, health care utilization, and use of medication. For each characteristic, one-year prevalence was obtained and analyses were performed for 2013 and 2008 to account for possible change over time.ResultsIn 2013, 5,602,628 persons were living in Denmark. The mean age was 40.7 years in the entire Danish population and ranged between 39.6 to 42.4 years in the five regions (coefficient of variation between regions [CV] = 0.028). The proportion of women in Denmark was 50.4% (CV = 0.009). The proportion of residents with low education level was 28.7% (CV = 0.051). The annual number of GP contacts was 7.1 (range: 6.7–7.4, CV = 0.040), and 114 per 1,000 residents were admitted to the hospital (range: 101–131, CV = 0.107). The annual number of persons redeeming a prescription of any medication was 723 per 1,000 residents (range: 718–743, CV = 0.016). Analyses for 2008 showed comparable levels of homogeneity as for 2013.ConclusionsWe found substantial homogeneity between all of the five Danish regions with regard to sociodemographic and health related characteristics. Epidemiologic studies conducted on regional subsets of Danish citizens have a high degree of generalizability.

Distribution of CRV by absolute values and percentage on census, province of registration and place of birth. National. Distribution of CRV by provinces of registration and place of birth.

The total number of deaths per 1000 children aged 1-4 years old. The data is sorted by both sex and total and includes a range of values from 1955 to 2019. This data is sourced from the UN Inter-Agency Group for Child Mortality Estimation. The UN IGME uses the same estimation method across all countries to arrive at a smooth trend curve of age-specific mortality rates. The estimates are based on high quality nationally representative data including statistics from civil registration systems, results from household surveys, and censuses. The child mortality estimates are produced in conjunction with national level agencies such as a country’s Ministry of Health, National Statistics Office, or other relevant agencies.

Abstract

SAVVY is a demographic surveillance system built around vital events monitoring. It operates in a similar way to existing Health and Demographic Surveillance (HDSS) but is distributed across the country and sampled to generate estimates that are nationally representative. The system is based on a periodic census of the sample population that provides information on population age, sex, household characteristics and migration. During the year, community key informants report births and deaths and probable cause of death is determined through verbal autopsy.

Geographic coverage

SAVVY is part of the Sentinel Panel of Districts (SPD), a nationally-representative sample of 23 districts (plus an additional 4) in Mainland Tanzania for health monitoring, evaluation and research. Attention: the totality of distrcits has been reached only in March 2014!

Universe

Resident population (nationally representative), longitudinal.

Kind of data

Event/transaction data [evn]

Sampling procedure

A two-stage probability sampling approach was employed. District sampling aims to permit disaggregation of results by residence (urban/rural) as well as by zone. Within selected districts, enumeration areas were randomly selected from the national master sample frame, to yield a total sample of 167,000 households comprising about 800,000 individuals (~2% of Mainland Tanzania population).

SAVVY data collection is grouped into three categories: census enumeration, birth and death notifications, and VA interviews. During initial setup of the SAVVY arm, baseline censuses were conducted in all districts enumerating all households within the selected enumeration areas and captured a snapshot of the population. Each household was visited and family structure data were collected including details of the head of household, each member's name, gender, occupation, and education. Follow up questions were asked for female household members on number of children. During baseline census, retrospective death events of the past 12 months were also collected. A notification system of vital events was set up following the baseline censuses. Each birth or death event occurring in SAVVY enumeration areas triggered a notification message sent by a community key informant using a mobile phone. In addition to reporting of vital events, SAVVY also promotes vital registration through use of government registers provided by the Registration Insolvency and Trusteeship Agency (RITA).

SAVVY started with baseline enumeration censuses in March 2011 and continued in phases until it reached a full scale of all 23 districts in March 2014. Follow-up enumeration censuses will be conducted from 2015. Monitoring of vital events and conducting verbal autopsy (VA) interviews in enumeration areas began shortly after commencement of baseline censuses and is done prospectively. FBIS data collection began in January 2010 and is conducted regularly on monthly basis from all health facilities in SPD districts.

Research instrument

Census enumeration, birth and death notifications, and VA interviews.

Data collection instruments include two registers (births, deaths) and three questionnaires (household census, and verbal autopsy questionnaires for neonates, children and adults). The household census questionnaire includes household identification, location, household members, dates of birth, highest educational attainment, occupation and births in the past twelve months. The births and deaths registers record individual and household identity, location and date of event. The verbal autopsy questionnaires have an identification section; history of chronic illness; verbal account of the events leading to death; symptoms checklist; lifestyle (use of alcohol, drugs and smoking), and sequential use of health services prior to death.

Each death notification event is followed by a VA interview with the head of household or a person who took care of the deceased. Interviewers use the three standard World Health Organisation’s 2002 VA questionnaires: for newborns (0-28 days), children (29 days -14 years) and adults (15 years and above).9 These questionnaires are designed to collect background information of the deceased including their age, sex, marital status, and health data prior to death. Other information collected in verbal autopsy interviews include history of chronic illness, a narrative account of events leading to death, symptom checklist and duration, lifestyle (use of alcohol, drugs and smoking) and a sequence of use of health services prior to death. All information on verbal autopsy interviews (those captured retrospectively and prospectively during baseline census) are sent to trained physicians in order to establish a probable cause of death. Each death is coded independently using the World Health Organisation International Classification of Diseases and Health Related Conditions version 10 (ICD 10).

Response rate

Number of districts 23 districts Total Population 644,217 people Males (%) 48% Population rural (%) 70%

Distribution of CRV by province of birth and province of registration. National. Distribution of CRV by provinces of birth and registration.

Bolivia BO: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data was reported at 176.000 Ratio in 2012. This records a decrease from the previous number of 396.000 Ratio for 2008. Bolivia BO: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data is updated yearly, averaging 339.500 Ratio from Dec 1994 (Median) to 2012, with 4 observations. The data reached an all-time high of 396.000 Ratio in 2008 and a record low of 176.000 Ratio in 2012. Bolivia BO: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bolivia – Table BO.World Bank.WDI: Social: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.;The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;;

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

Benin BJ: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data was reported at 512.000 Ratio in 2014. This records a decrease from the previous number of 541.000 Ratio for 2006. Benin BJ: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data is updated yearly, averaging 526.500 Ratio from Dec 1992 (Median) to 2014, with 4 observations. The data reached an all-time high of 693.000 Ratio in 1996 and a record low of 317.000 Ratio in 1992. Benin BJ: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Benin – Table BJ.World Bank.WDI: Social: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.;The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;;

This data contains provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. Counts for the most recent final annual data are provided for comparison. National provisional counts include deaths occurring within the 50 states and the District of Columbia as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation (see Technical notes) resulting in an underestimate relative to final counts. To address this, methods were developed to adjust provisional counts for reporting delays by generating a set of predicted provisional counts (see Technical notes). Starting in June 2018, this monthly data release will include both reported and predicted provisional counts.

The provisional data include: (a) the reported and predicted provisional counts of deaths due to drug overdose occurring nationally and in each jurisdiction; (b) the percentage changes in provisional drug overdose deaths for the current 12 month-ending period compared with the 12-month period ending in the same month of the previous year, by jurisdiction; and (c) the reported and predicted provisional counts of drug overdose deaths involving specific drugs or drug classes occurring nationally and in selected jurisdictions. The reported and predicted provisional counts represent the numbers of deaths due to drug overdose occurring in the 12-month periods ending in the month indicated. These counts include all seasons of the year and are insensitive to variations by seasonality. Deaths are reported by the jurisdiction in which the death occurred.

Several data quality metrics, including the percent completeness in overall death reporting, percentage of deaths with cause of death pending further investigation, and the percentage of drug overdose deaths with specific drugs or drug classes reported are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical notes). Reporting of the specific drugs and drug classes involved in drug overdose deaths varies by jurisdiction, and comparisons of death rates involving specific drugs across selected jurisdictions should not be made (see Technical notes). Provisional data will be updated on a monthly basis as additional records are received.

Technical notes

Nature and sources of data

Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from state vital registration offices through the Vital Statistics Cooperative Program (VSCP).

The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death.

Provisional death counts presented in this data visualization are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2017 would include deaths occurring from July 1, 2016, through June 30, 2017. The 12-month ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. Counts for the 12-month period ending in the same month of the previous year are shown for comparison. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12-month ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Methods to adjust provisional counts have been developed to provide predicted provisional counts of drug overdose deaths, accounting for delayed reporting (see Percentage of records pending investigation and Adjustments for delayed reporting).

Provisional data are based on available records that meet certain data quality criteria at the time of analysis and may not include all deaths that occurred during a given time period. Therefore, they should not be considered comparable with final data and are subject to change.

Cause-of-death classification and definition of drug deaths
Mortality statistics are compiled in accordance with World Health Organization (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regulations on use of the classification. Causes of death for data presented in this report were coded according to ICD guidelines described in annual issues of Part 2a of the NCHS Instruction Manual (2).

Drug overdose deaths are identified using underlying cause-of-death codes from the Tenth Revision of ICD (ICD–10): X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined). Drug overdose deaths involving selected drug categories are identified by specific multiple cause-of-death codes. Drug categories presented include: heroin (T40.1); natural opioid analgesics, including morphine and codeine, and semisynthetic opioids, including drugs such as oxycodone, hydrocodone, hydromorphone, and oxymorphone (T40.2); methadone, a synthetic opioid (T40.3); synthetic opioid analgesics other than methadone, including drugs such as fentanyl and tramadol (T40.4); cocaine (T40.5); and psychostimulants with abuse potential, which includes methamphetamine (T43.6). Opioid overdose deaths are identified by the presence of any of the following MCOD codes: opium (T40.0); heroin (T40.1); natural opioid analgesics (T40.2); methadone (T40.3); synthetic opioid analgesics other than methadone (T40.4); or other and unspecified narcotics (T40.6). This latter category includes drug overdose deaths where ‘opioid’ is reported without more specific information to assign a more specific ICD–10 code (T40.0–T40.4) (3,4). Among deaths with an underlying cause of drug overdose, the percentage with at least one drug or drug class specified is defined as that with at least one ICD–10 multiple cause-of-death code in the range T36–T50.8.

Drug overdose deaths may involve multiple drugs; therefore, a single death might be included in more than one category when describing the number of drug overdose deaths involving specific drugs. For example, a death that involved both heroin and fentanyl would be included in both the number of drug overdose deaths involving heroin and the number of drug overdose deaths involving synthetic opioids other than methadone.

Selection of specific states and other jurisdictions to report
Provisional counts are presented by the jurisdiction in which the death occurred (i.e., the reporting jurisdiction). Data quality and timeliness for drug overdose deaths vary by reporting jurisdiction. Provisional counts are presented for reporting jurisdictions based on measures of data quality: the percentage of records where the manner of death is listed as “pending investigation,” the overall completeness of the data, and the percentage of drug overdose death records with specific drugs or drug classes recorded. These criteria are defined below.

Percentage of records pending investigation

Drug overdose deaths often require lengthy investigations, and death certificates may be initially filed with a manner of death “pending investigation” and/or with a preliminary or unknown cause of death. When the percentage of records reported as “pending investigation” is high for a given jurisdiction, the number of drug overdose deaths is likely to be underestimated. For jurisdictions reporting fewer than 1% of records as “pending investigation”, the provisional number of drug overdose deaths occurring in the fourth quarter of 2015 was approximately 5% lower than the final count of drug overdose deaths occurring in that same time period. For jurisdictions reporting greater than 1% of records as “pending investigation” the provisional counts of drug overdose deaths may underestimate the final count of drug overdose deaths by as much as 30%. Thus, jurisdictions are included in Table 2 if 1% or fewer of their records in NVSS are reported as “pending investigation,” following a 6-month lag for the 12-month ending periods included in the dashboard. Values for records pending investigation are updated with each monthly release and reflect the most current data available.

Percent completeness

NCHS receives monthly counts of the estimated number of deaths from each jurisdictional vital registration offices (referred to as “control counts”). This number represents the best estimate of how many

Bhutan BT: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data was reported at 325.000 Ratio in 2011. This records a decrease from the previous number of 482.000 Ratio for 2005. Bhutan BT: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data is updated yearly, averaging 403.500 Ratio from Dec 1994 (Median) to 2011, with 4 observations. The data reached an all-time high of 605.000 Ratio in 1994 and a record low of 240.000 Ratio in 2000. Bhutan BT: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bhutan – Table BT.World Bank.WDI: Social: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.;The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;;

Live births and stillbirths annual summary statistics, by sex, age of mother, whether within marriage or civil partnership, percentage of non-UK-born mothers, birth rates and births by month and mothers' area of usual residence.

Annual UK and constituent country figures for births, deaths, marriages, divorces, civil partnerships and civil partnership dissolutions.

Abstract

The key objective of every census is to count every person (man, woman, child) resident in the country on census night, and also collect information on assorted demographic (sex, age, marital status, citizenship) and socio-economic (education/qualifications; labour force and economic activity) information, as well as data pertinent to household and housing characteristics. This count provides a complete picture of the population make-up in each village and town, of each island and region, thus allowing for an assessment of demographic change over time.

With Vanuatu, as many of her Pacific island neighbours increasingly embracing a culture of informed, or evidence-based policy development and decision-making, national census databases, and the possibility to extract complex cross-tabulations as well as a host of important sub-regional and small-area relevant information, are essential to feed a growing demand for data and information in both public and private sectors.

Educational, health and manpower planning, for example, including assessments of future demands for staffing, facilities, and programmed budgets, would not be possible without periodic censuses, and Government efforts to monitor development progress, such as in the context of its Millennium Development Goal (MDG) commitments, would also suffer greatly, if not be outright impossible, without reliable data provided by regular national population counts and updates.

While regular national-level surveys, such as Household Income and Expenditure Surveys, Labour force surveys, agriculture surveys and demographic and health surveys - to name but just a few - provide important data and information across specific sectors, these surveys could not be sustained or managed without a national sampling frame (which a census data provides). And the calculation and measurement of all population-based development indicators, such as most MDG indicators, would not be possible without up-to-date population statistics, which usually come from a census or from projections and estimates that are based on census data.

With most of this information now already 9 years old (and thus quite outdated), and in the absence of reliable population-register type databases, such as those provided from well-functional civil registration (births and deaths) and migration-recording systems, the 2009 Vanuatu census of population and housing, will provide much needed demographic, social and economic statistics that are essential for policy development, national development planning, and the regular monitoring of development progress.

Apart from achieving its general aims and objectives in delivering updated population, social and economic statistics, the 2009 census also represented a major national capacity building exercise, with most Vanuatu National Statistics Office (VNSO) staff who were involved with the census, having no prior census experience. Having been carefully planned and resourced, all 2009 census activities have potentially provided very useful (and desired) on-the-job-training for VNSO staff, right across the spectrum of professional rank and responsibilities. It also provided for short-term overseas training and professional attachments (at SPC or ABS, or elsewhere) for a limited number of professional staff, who subsequently mentored other staff in the Vanuatu National Statistics Office (VNSO).

With some key senior VNSO members involved with the 1999 census, they provided a wealth of experience that was available in-house and not to mention the ongoing surveys such HIES and Agriculture Census that the office has conducted before the census proper. The VNSO has also professional officers who have qualified in the fields of Population and Demography who had manned the project, and with this type of resources, we managed to conduct yet another successful project of the 2009 census.

While some short-term census advisory missions were fielded from SPC Demography/ Population programme staff, standard SPC technical assistance policy arrangements could not cater for long-term, or repeated in-country assignments. However, other relevant donors were invited for the longer-term attachments of TA expertise to the VNSO.

Geographic coverage

The 2009 Population and Housing Census Geographical Coverage included:

• National (Vanuatu)
• Provinces (Torba, Sanma, Penama, Malampa, Shefa, tafea)
• Inhabited Islands (From Hiu, Torres Islands to Aneityum, Southern Islands)
• Ennumeration Areas (EA assigned to each enumerator)
• Villages / Towns
• Household or Dwelling

Analysis unit

The Unit Analysis of the 2009 Population and Housing Census included: - Household - Person (Population)

Universe

The census covered all households and individuals throguhout Vanuatu

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire basically has 5 sections; the geographical identifiers, the general population questions and education, labour force questions, the women and fertility questions and the housing questions.The geographical identifiers include the Village name, GPS code, EA number, household number and the Enumerator ID.The Person questions contain the person demographics including the education level and labour force status. A section on fertility for women in the reproductive age is also included. All have been guided by 'skip patterns' to guide the flow of questions asked.Household questions contained the basic description of the house materials, tenure, access to water and sanitation, energy, durables, use of treated mosquito nest and internet access.

Cleaning operations

In the Census proper, the Optical Character Recognition (OCR) system (ReadSoft Application System) was used to capture information from the completed forms. The captured data were then exported to MS Access database system for further editing and cleaning before the final data is transferred to CSPro for more editing and quality checks before the data was finalised. All system files and data files were stored in the server under 2009PopCensus folder. Three temporary data operators were hired to do the job, under the supervision of Rara Soro, the system analyst for VNSO. No data was stored in work stations, because all data were directly written to the DATA folder in the server.

Range checks and basic checks (online edits) were built in the manual data entry system, while the complex edits were written in a separate batch edit program. If the system encounter and error during data entry, an error message will be displayed and the data operator cannot proceed unless the error displayed is fixed. e.g Males + Females = Total Persons. Please re-enter. It was strongly recommended to the data operators not to make up answers but consult the supervisor if he/she cannot fix it. Listed below are the checks that were built into the data entry system.

01 Person 1 must be the head of household 02 Sex against relationship 03 Age against date of birth 04 Marital status - Married people should be age 15+ 05 Spouse should be married 06 P9, P10, P11 against village enumerated 07 Never been to school but can use internet - Is this possible 08 Check for multiple head or spouse in the household 09 Husband and wife of same sex 10 Total persons match total people in personal form 11 Total children born and live in household (F2a) against total persons total 12 Age difference of head and child is less than 13 13 Total children born (F4) against total alive(F2) + total died(F3)

A separate batch edit program was developed for further data cleaning. All online edits were also re-written in this program to make sure that all errors flagged out during data entry were fixed. Some of the errors detected are not really errors, but still requires double checking, and if the answer recorded is the correct answer, don't change it. The batch edit was performed on each batch, and also on the concatenated batch. Below is the summary list of errors generated from manual data entry data before batch editing.

MDE Error message summary
Age does not match date of birth 272 Total children born and living in household (F2a) > total in 1
Attend school full-time in P12 but also working 16
Too young for highest education recorded 14
Highest education completed does not match with grade currently attending 80

Age had the highest errors rate, and this is due to an error in the logic statement, otherwise all ages that do not match their date of birth are corrected during data entry.

The Data capturing (Scanning) and Editing process took about 6 months to be completed but then more checks were made after that to finalise the dataset before publishing the results.

During re-coding of zero's and blanks, a couple of batch edit statement written in the batch edit program were wrong, and it created errors in the scanned data. The batch edit was suppose to recode only those people that didn't answer questions P19, P23 - P25, but instead it recoded valid codes as well to blanks. This was only picked up when tables were generated and numbers were found to be so much different in manual data entry and scanned data. Another batch edit program was developed to recode and fix this problem.

Data appraisal

Household characteristics and basic demographic variables for the census data was used in comparision with the 1999 census data to determine the accuracy of the pilot data. Some of the key indicators used for comparision are the household size, sex ratio, educational attainment, employment status. A pyramid was also used

The main characteristic of the city of Charleville (Ardennes), concerning the history of its population, is the realization by the municipal authorities of a nominative, spatial and annual census of the inhabitants, undertaken from the end of the 17th century, and until at the beginning of the 20th century.
This gives the possibility of exploiting Carolopolitan data within a geographic information system (GIS). Thanks to this GIS, to its informal quality and volume, it is possible to reconstruct in 3D the city of 1834, and to make full use of the methodological environment known as "BIM" (or Building Information Model). BIM will make it possible to exploit the architectural and demographic composition of each plot, block, dwelling in this city, from its genesis to the present day, like a real 3D GIS.
Spatial analyzes built from cartographic and topographic data, analyzed, vectorized (2D and 3D), georeferenced and linked to the occupation data of the city of Charleville.

Data collected, digitized and structured from 01/15/2016 to 01/04/2021 in the context of:
- Axis 1 of the Roland Mousnier Center (UMR 8596), Sorbonne University / CNRS
- the C2EP2 Project funded by the Sorbonne University "Emergencies" call for projects (2019-21) for the web and BIM parts (CSTB partnership)
- scientific and technical collaboration (partnership agreement) between the National Archives and the Roland Mousnier Center.

Creative Commons License
Dataset "Analyzes of the urban fabric (2D and 3D) of Charleville from 1724 to 1876.". by Sylvain Rassat, Roland Mousnier Center, CNRS, Departmental Archives, National Archives is made available under the terms of the Creative Commons Attribution - Share under the Same Conditions 4.0 International license.
Based on a Source Link work.
Permissions beyond the scope of this license can be obtained at https://www.researchgate.net/profile/Sylvain_Rassat.
(https://cesium.cstb.fr/Apps/cnrs/MN_Charleville3D.html)

Distribution of CRV by province of registration and province of birth. National.

BackgroundIn much of sub-Saharan Africa, health facilities serve as the primary source of routine vital statistics. These passive surveillance systems, however, are plagued by infrequent and unreliable reporting and do not capture events that occur outside of the formal health sector. Verbal autopsies (VA) have been utilized to estimate the burden and causes of mortality where civil registration and vital statistics systems are weak, but VAs have not been widely employed in national surveillance systems. In response, we trained lay community health workers (CHW) in a rural sub-county of western Uganda to conduct VA interviews in order to assess the feasibility of leveraging CHW to measure the burden of disease in resource limited settings.Methods and findingsTrained CHWs conducted a cross-sectional survey of the 36 villages comprising the Bugoye sub-county to identify all deaths occurring in the prior year. The sub county has an estimated population of 50,249, approximately one-quarter of whom are children under 5 years of age (25.3%). When an eligible death was reported, CHWs administered a WHO 2014 VA questionnaire, the results of which were analyzed using the InterVA-4 tool. To compare the findings of the CHW survey to existing surveillance systems, study staff reviewed inpatient registers from neighboring referral health facilities in an attempt to match recorded deaths to those identified by the survey. Overall, CHWs conducted high quality VA interviews on direct observation, identifying 230 deaths that occurred within the sub-county, including 77 (33.5%) among children under five years of age. More than half of the deaths (123 of 230, 53.5%) were reported to have occurred outside a health facility and thus would not be captured by passive surveillance. More than two-thirds (73 of 107, 68.2%) of facility deaths took place in one of three nearby hospitals, yet only 35 (47.9%) were identified on our review of inpatient registers. Consistent with previous VA studies, the leading causes of death among children under five years of age were malaria (19.5%), prematurity (19.5%), and neonatal pneumonia (15.6%). while among adults, HIV/AIDS-related deaths illness (13.6%), pulmonary tuberculosis (11.4%) and malaria (8.6%) were the leading causes of death. No child deaths identified from inpatient registers listed HIV/AIDS as a cause of death despite 8 deaths (10.4%) attributed to HIV/AIDS as determined by VA.ConclusionsLay CHWs are able to conduct high quality VA interviews to capture critical information that can be analyzed using standard methodologies to provide a more complete estimate of the burden and causes of mortality. Similar approaches can be scaled to improve the measurement of vital statistics in order to facilitate appropriate public health interventions in rural areas of sub-Saharan Africa.

Information on the fetal death data tape file was abstracted from the Report of Fetal Death forms received in all the States and the District of Columbia, with a record on the data file for each report of a fetal death received. The data is provided to the National Center for Health Statistics (NCHS) through the Vital Statistics Cooperative Program by the registration offices of all States, the District of Columbia, and New York City. Data from New York, excluding New York City, were submitte d in machine readable form. All other 1992 data were coded and keyed by the U.S. Bureau of the Census. Fetal death data are limited to deaths occurring within the United States to U.S. residents and nonresidents. Fetal deaths occurring to U.S. citizens outside the United States are not included in this data file. In NCHS tabulations by place of residence, fetal deaths to nonresidents of the United States are excluded. The foreign resident records can be identified by code 4 in tape location 7 of the data tape. In addition, the majority of fetal death tables published by NCHS include only those fetal deaths with stated or presumed gestation of 20 weeks or more (see the Technical Appendix). Those records identified with a 2 in tape location 5 are included in these tabulations. All other records are excluded. Effective January 1, 1989, a revised U-S. Standard Report of Fetal Death replaced the 1978 revision. The 1989 revision provides a wide variety of new information on maternal and fetal health characteristics. Questions on complications of labor and delivery and congenital anomalies of fetus were changed from an open-ended question to a checkbox format to improve reporting of information. Several new items were added that improve the data files value for monitoring and research of factors affecting fetal mortality. The Office of Management and Budget revised its designation of metropolitan statistical areas based on figures from the 1990 Census. Effective with the 1990 data file, NCHS has been using these new definitions and codes as indicated in the listing of 320 Metropolitan Statistical Areas (MSAS), Primary Metropolitan Statistical Areas (PMSAS), and New England County Metropolitan Ar eas (NEaSS) included in this documentation. There are also 20 Consolidated Metropolitan Statistical Areas (mSAS), which are made up of PMSAS. Other geographic changes based on the 1990 Census will be implemented later. NCHS has adopted a new policy on release of vital statistics unit record data files. This new policy was implemented with the 1989 vital event files to prevent the inadvertent disclosure of individuals and institutions. As a result, this file does not contain the actual day of the death. The geographic detail is also restricted-only counties and cities of 100,000 or more population based on the 1980 Census as well as metropolitan areas of 100,000 or more population based on the 1990 Census, are identified. NOSB = Note to Users: This CD is part of a collection located in the Data Archive at the Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items may be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Courts of Petty Sessions, known since 1971 as Magistrates' Courts, have dealt with a very large range of "minor" court matters. The types of cases heard, which have changed and increased over time, fall within four broad jurisdictions: criminal, civil, licensing and family law. Apart from a large number of tribunals, Courts of Petty Sessions/Magistrates Courts provide the lowest level of redress in civil and criminal matters. The County Court, the Supreme Court and various Commonwealth courts have heard and determined more serious criminal cases and larger civil disputes. The licensing jurisdiction since 1886 has comprised non-liquor licensing matters only.

Successive Justices' Acts, and more recently Magistrates' Court Acts, have required the clerk or registrar of each Court to make and keep a register of all convictions, orders and other proceedings of the Court. This register is the authoritative record of the Court. Until about 1888 this record was known as a Cause List Book.

Initially, most clerks maintained a single register for all or most of the Court's business. This series comprises a Court Register which includes a mixture of cases from the various jurisdictions. Subsequently Clerks of Court were instructed to create separate registers for certain types of cases. Some Courts also began to maintain additional registers for different types of cases. Typically, separate registers have been established for the following cases:

Register Used For

Adoption of Children Adoption of children (1928 to 1958)
Civil/Summons Cases brought to court by summons
Commonwealth Commonwealth jurisdiction (from 1915)
Family Law Commonwealth family law jurisdiction (from 1975)
Licence Liquor (pre 1886) and non-liquor licence applications
Maintenance Maintenance cases (1928 to 1975)
Police/Arrest Cases brought to court by police arrest
Quasi Criminal cases brought by summons
Special Complaints Civil cases where Court determines redress (1928 to 1979)

Where courts have subdivided the registration of cases, each Register has been allocated a different Victorian Public Record Series (VPRS) number and the type of register has been included in the series title.

Court Registers are generally in a common format, giving details of the case number, the name of the prosecutor or informant (in a criminal matter), complainant (in a civil matter), or applicant (in a licensing matter), the name of the accused or defendant, how the case came to the court (arrest, warrant, summons etc), the fees or court costs accrued, a description of the charge, cause or proceeding, the decision or order and any remarks. The column for remarks was often used to record the payment of fines and fees. In order to authenticate entries made in the register the presiding officer(s) of the court signed the register at the end of each day.

Abstract

The Viet Nam Multiple Indicator Cluster Survey (MICS) was carried by General Statistics Office of Viet Nam (GSO) in collaboration with Viet Nam Committee for Population, Family and Children (VCPFC). Financial and technical support by the United Nations Children's Fund (UNICEF).

In the World Summit for children held in New York in 1990, the Government of Vietnam committed itself to the implementation of the World Declaration and Plan of Action for children.

In implementation of directive 34/1999/CT-TTg on 27 December 1999 on promoting the implementation of the end-decade goals for children, reviewing the National Plan of Action for children, 1991-2000 and designing the National Plan of Action for children, 2001-2010, in the framework of the “Development of Social Indicators” project, the General Statistical Office (GSO) has chaired and coordinated with the Viet Nam Committee for the Protection and Care for Children (CPCC) to conduct the survey evaluating the end- decade goals for children, 1991-2000 (MICS). MICS has covered a sample size of 7628 households in 240 communes and wards representing the whole country, the urban area, the rural area and the 8 geographical areas in 61 towns/provinces. Field activities to collect data lasted 2 months, May- June/2000. The survey was technically supported by statisticians from EAPRO, UNICEF regional offices, UNICEF Hanoi on sample and questionnaire designing, data input software, not least the software analyzing and calculating the estimates generalizing the results of survey.

Survey Objectives: The end-decade survey on children is aimed at. · Providing up-to-date and reliable data to analyse the situation of children and women in 2000. · Providing data to assess the implementation of the World summit goals for children and of the National Plan of Action for Vietnamese Children, 1991-2000. · Serving as a basis (with baseline data and information) for development of the National Plan of Action for Children, 2001-2010. · Building professional capacity in monitoring, managing and evaluating all the goals of child protection, care and education at all levels.

Geographic coverage

The 2000 MICS of Vietnam was a nationally representative sample survey.

Analysis unit

Households, Women, Child.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Viet Nam Multiple Indicator Cluster Survey (MICSII) was designed to provide reliable estimates on a large number of indicators on the situation of children and women at the national level, for urban and rural areas, and for 8 regions: Red River Delta, North West, North East, North Central Coast, South Central Coast, Central Highlands, South East, and Mekong River Delta. Regions were identified as the main sampling domains and the sample was selected in two stages: At the first stage, 240 EAs are sellected. After a household listing was carried out within the selected enumeration areas, a systematic sample of 1/3 of households in each EA was drawn. The survey managed to visit all of 240 selected EAs during the fieldwork period. The sample was stratified by region and is not self-weighting. For reporting national level results, sample weights are used.

Sampling deviation

No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for MICS in Vietnam are based on the New York UNICEF module questionnaires with some modifications and additions to fit in with Vietnam's context and to evaluate the goals set out in the National Plan of Action. The questionnaires have been arranged in such a way as to prevent the loss of questionnaire sheets and to facilitate the logic control between the items in the modules. Questionnaires include 3 sections. Section 1: general questions to be administered to families and family members. Section 2: questions for child bearing-age women (aged 15-49). Section 3: for children under 5.

Section 1: Household questionnaire Part A: Household information panel Part B: Household listing form Part C: Education Part D: Child labour Part E: Maternal mortality Part F: Water and sanitation Part G: Salt iodization

Section 2: Questionnaire for child bearing-age women Part A: Child mortality Part B: Tetanus toxoid (TT) Part C: Maternal and newborn health Part D: Contraceptive use Part E: HIV/AIDS

Section 3: Questionnaire for children under five Part A:Birth registration and early learning Part B: Vitamin A Part C: Breastfeeding Part D: Care of illness Part E: Malaria Part F: Immunization Part G: Anthropometry

Apart from the questionnaires to collect information at family level, questionnaires are also designed to gather information at community level supplementary to some indicators that can not have data collected at family level. The information garnered includes local population, socio-economic and physical conditions, education, health and progress of projects/plans of actions for children.

Cleaning operations

To minimize the errors made by data entry staff members, all the records were double- entered by two different members. Any error detected between the two entries was re-checked to find out which one is wrong. Data cleaning started in to early September. This process was closely observed to ensure the accuracy, quality and practicality of all the data collected.

To minimize the errors due to wrong statements of respondents or wrong registration by interviewers, a cleaning programme was used to check the consistency and logic in the items of questionnaires and between the questionnaires. The cleaning programme printed out all the errors, then questionnaires were checked by qualified officials.

Response rate

8356 households were selected for the sample. Of these all were found to be occupied households and 8355 were successfully interviewed for a response rate of 100%. Within these households, 10063 eligible women aged 15-49 were identified for interview, of which 9473 were successfully interviewed (response rate 94.1%), and 2707 children aged 0-4 were identified for whom the mother or caretaker was successfully interviewed for 2680 children (response rate 99%).

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the MICS - 3 to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors can be evaluated statistically. The sample of respondents to the MICS - 3 is only one of many possible samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that different somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability in the results of the survey between all possible samples, and, although, the degree of variability is not known exactly, it can be estimated from the survey results. The sampling errors are measured in terms of the standard error for a particular statistic (mean or percentage), which is the square root of the variance. Confidence intervals are calculated for each statistic within which the true value for the population can be assumed to fall. Plus or minus two standard errors of the statistic is used for key statistics presented in MICS, equivalent to a 95 percent confidence interval.

If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the MICS - 3 sample is the result of a two-stage stratified design, and consequently needs to use more complex formulae. The SPSS complex samples module has been used to calculate sampling errors for the MICS - 3. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. This method is documented in the SPSS file CSDescriptives.pdf found under the Help, Algorithms options in SPSS.

Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and include the following:

Age distribution of the household population Age distribution of eligible women and interviewed women Age distribution of eligible children and children for whom the mother or caretaker was interviewed Age distribution of children under age 5 by 3 month groups Age and period ratios at boundaries of eligibility Percent of observations with missing information on selected variables Presence of mother in