The deadliest energy source worldwide is coal. It is estimated that there are roughly 33 deaths from brown coal (also known as Lignite) and 25 deaths from coal per terawatt-hour (TWh) of electricity produced from these fossil fuels. While figures take into account accidents, the majority of deaths associated with coal come from air pollution.

Air pollution deaths from fossil fuels

Air pollution from coal-fired plants has been of growing concern as it has been linked to asthma, cancer, and heart disease. Burning coal can release toxic airborne pollutants such as mercury, sulfur dioxide, nitrogen oxides, and particulate matter. Eastern Asia accounts for roughly 31 percent of global deaths attributable to exposure to fine particulate matter (PM2.5) generated by fossil fuel combustion, which is perhaps unsurprising given the fact China and India are the two largest coal consumers in the world.

Safest energy source

Clean and renewable energy sources are unsurprisingly the least deadly energy sources, with 0.04 and 0.02 deaths associated with wind and solar per unit of electricity, respectively. Nuclear energy also has a low death rate, even after the inclusion of nuclear catastrophes like Chernobyl and Fukushima.

The average number of deaths in the five-year average per 1000 units. Example: The number of 2003 corresponds to the average death rate of 1999-2003 in terms of the population of 1998.

In 2023, the mortality rate of children under five years of age in 336 monitoring sites in China was 6.2 per 1,000 children. In the past three decades, premature deaths of young people in China were substantially reduced, with the mortality rate of children under five dropping by almost 90 percent. Enhanced access to pediatric healthcare services Thanks to China's rapid transformation in the past few decades, the standard of medical services available to Chinese children has improved dramatically. Many children's hospitals throughout China's major cities, as well as a number of pediatric units in general hospitals, have reached highly sophisticated levels. Over the past decade, the number of pediatric ward beds and medical personnel in China has increased enormously, generally meeting the demand for children's care. The control of life-threatening diseases With a more robust healthcare system, many diseases that have long been threatening the lives of Chinese children have been brought under effective control, with the mortality rate from serious diseases such as neonatal tetanus dropping significantly in recent years. However, with disparities between the accessibility and quality of pediatric care in urban and rural areas, children in rural China usually have fewer treatment options when diagnosed with certain conditions. The mortality rates of serious illnesses such as childhood leukemia are often significantly higher in the countryside as a result.

The Global Subnational Infant Mortality Rates consists of estimates of infant mortality rates

for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first

birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per

10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator),

and a tabular dataset of the same and associated data. Over 10,000 national and subnational units are represented in the tabular and

grid datasets, while the shapefile uses approximately 1,000 units in order to protect the intellectual property of source datasets

for Brazil, China, and Mexico. This dataset is produced by the Columbia University Center for International Earth Science Information

Network (CIESIN).

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

Global Acute Myocardial Infarction Mortality Share by Country (Units (Deaths)), 2023 Discover more data with ReportLinker!

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

Statistical information on the number of babies born dead in the Republic of Lithuania according to the place of residence, sex, period and age of birth has been provided. The investigation shall cover babies born dead in the Republic of Lithuania whose mothers are permanent residents of the Republic of Lithuania. “Statistical observation unit” means a baby born dead. The time frame has been in place since 2000. The classifications used in the study are: Classification of World Countries and Territories (https://osp.stat.gov.lt/lt/web/guest/pasaulio-Sally-and-Territorial-Classification) Classification of administrative units and residential areas of the Republic of Lithuania (LR AVGVK 2018) (https://osp.stat.gov.lt/klasifikatoriai?portletFormName=FPKL500&classifier-version = 861 & tab = version) Classification of territorial statistical units (NUTS) (https://osp.stat.gov.lt/lt/web/guest/teritoriniu-statistical-unit-classifier-nuts)

Global Asthma Mortality Share by Country (Units (Deaths)), 2023 Discover more data with ReportLinker!

RBCs: red blood cellsa Categories are not mutually exclusive. Patients who received more than one type of blood product can be included under multiple categories.b Patients who did not receive RBCs, platelets, or plasma.In-hospital mortality rates and ICU mortality rates by type of blood product.

1. Mortality Rate by Major Cancers (by Gender), New Taipei City2. Unit: People/100,0003. For details on each field, please refer to the electronic file of gender data in New Taipei City (Website: http://www.bas.ntpc.gov.tw/home.jsp?idMTI5) or contact the Department of Budget, Accounting and Statistics for inquiry.

This dataset contains model-based county estimates for drug-poisoning mortality. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8). REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e. National Center for Health Statistics. County geog

Global Leukemia Mortality Share by Country (Units (Deaths)), 2023 Discover more data with ReportLinker!

Abstract

The 2001 Bangladesh Maternal Health Services and Maternal Mortality Survey (BMMS) is the first nationally representative sample survey designed to provide information on the level of maternal mortality, causes of maternal and nonmaternal deaths, and perception, experience, and utilization of maternal health care in Bangladesh.

The Bangladesh Maternal Health Services and Maternal Mortality Survey (BMMS) is intended to serve as a source of maternal health and maternal death data for policymakers and the research community. In general, the objectives of the BMMS are to: • Collect data at the national level, which will facilitate an assessment of the level of maternal mortality in Bangladesh; • Identify specific causes of maternal and nonmaternal deaths to adult women; • Collect data on women’s perception of and experience with antenatal, maternity, and emergency obstetrical care; • Measure indicators of utilization of maternal health services in Bangladesh.

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey Design

Administratively, Bangladesh is divided into six divisions. Each division is divided into districts (zilas), and then thanas or upazilas. Each urban area in a thana or upazila is divided into wards, and each ward is divided into mohallas; each rural area in a thana or upazila is divided into union parishadas, and each union is divided into mouzas.

Smaller subdivisions called enumeration areas (EAs) were created for the 1991 census based on the number of dwellings units. However, experience with the 1999-2000 Bangladesh Demographic and Health Survey (BDHS) showed that EA maps and sketch maps were not easily accessible. For this reason, EAs were not considered suitable as primary sampling units for the 2001 BMMS survey. Also, it was not feasible to obtain a computer file of the census information with the codes for the different subdivisions and their corresponding basic household or population information. However, the complete basic census information is available in published reports, from the division level down to the mouza level, but not at the EA level. Consequently, it was decided to make use of the published census reports and to use wards and unions as the primary sampling units. The second stage of sampling for urban areas involved selecting two mohallas in each ward, while for rural areas, two mouzas were selected in each selected union (with a few exceptions of one mouza or one mohalla per union or per ward, respectively). The third stage involved selecting households. In summary, in each division, the list of wards constituted the initial sample frame for urban areas and the list of unions was the sample frame for rural areas. A total of 808 primary sampling units were selected: 134 urban and 674 rural.

The field organizations (ACPR and Mitra and Associates) conducted a household listing operation in all of the sample points from November 2000 to April 2001. To obtain an accurate estimate of the maternal mortality rate at the national level (as well as to achieve other objectives of the survey), a stratified national sample of 104,323 households was systematically selected from a total of 1,616 clusters.

All ever-married women age 13-49 were eligible to be interviewed. It was expected that the sample would yield interviews with more than 100,000 ever-married women.

Note: See sample implementation table in APPENDIX D of the report which is presented in this documentation.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the BMMS: a Household Questionnaire, a Women’s Questionnaire (for ever-married women age 13-49), and a Verbal Autopsy Questionnaire (for deaths of women age 13-49).

The Household Questionnaire consisted of a schedule for listing all household members. For each listed person, the survey collected basic information such as age, sex, marital status, and education. Information was also collected on household characteristics such as type of housing, source of water, and availability of electricity. The Household Questionnaire asked about any deaths of household members in the three years preceding the survey. The information on age, sex, and marital status of household members was used to identify eligible respondents for the Women’s Questionnaire. The information about female adult deaths identified deaths for which the Verbal Autopsy Questionnaire was used.

The Women’s Questionnaire was administered to all ever-married women age 13-49 who were listed in the Household Questionnaire. These women were asked questions on the following topics: • Background characteristics (age, education, religion, etc.), • Reproductive history, • Use of family planning methods, • Information about siblings (to calculate the maternal mortality rate), • Knowledge of maternal health problems, • Antenatal, delivery, and postnatal care, • Experience with and treatment of maternal health problems during pregnancy, delivery, and after delivery, • Treatment-seeking behavior.

The Verbal Autopsy Questionnaire was used to collect information on causes of death for all female adult (age 13-49) deaths in the household in the three years preceding the survey. The questionnaire was both structured (precoded questions) and nonstructured (open-ended questions) in nature, and was answered by the most knowledgeable member of the household.

During the design of the questionnaires, input was sought from various organizations that were expecting to use the data. ORC Macro designed the questionnaires with assistance from JHU, ICDDR,B, and USAID. After preparation of the questionnaires in English, they were translated into Bengali. Then back-translations were done by people other than the initial translators, to verify the accuracy of the translations.

Cleaning operations

All questionnaires for the BMMS were returned to Dhaka for data processing at ACPR and Mitra and Associates. Data entry personnel were trained in Dhaka in February 2001 by ORC Macro data processing personnel. The processing operation consisted of office editing, coding of open-ended questions, data entry, and resolving inconsistencies found by the computer edit programs. The data were processed on microcomputers working in double shifts. The ISSA (Integrated System for Survey Analysis) program developed by MEASURE DHS+ was used during all stages of data entry and processing. Data processing commenced in mid-February 2001 and was completed by the end of August 2001.

Response rate

A total of 104,323 households were selected for the sample, of which 99,202 were successfully interviewed. The shortfall is primarily due to dwellings being vacant or the inhabitants being gone for an extended period at the time of the survey. Of the 100,379 households occupied, 99 percent were successfully interviewed. In these households, 106,789 women were identified as eligible for the individual interview (i.e., ever-married women age 13-49), and interviews were completed for 103,796, or 97 percent. The principal reason for nonresponse among eligible women was the failure to find them at home, despite repeated visits to the household. The refusal rate was low.

See summarized response rate by residence in Table 1.2 which is presented in the final report.

Sampling error estimates

Sampling error tables are presented in Appendix E of the final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by western calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: See these tables in APPENDIX C of the report which is presented in this documentation.

TABLE 4.8: Perinatal Statistics Report 2014: Size of Maternity Unit (Number of Live Births and Stillbirths Annually) by Number of Units: Total Births, Live Births, Mortality Rates, and Maternities, 2014. Published by Health Service Executive. Available under the license cc-by (CC-BY-4.0).Presents the distribution of TOTAL births for 2014 by Size of Maternity Unit. This table outlines data for total births, live births, stillbirths, early neonatal deaths and perinatal mortality rates. The Perinatal Statistics Report 2014 is a report on national data on Perinatal events in 2014. Information on every birth in the Republic of Ireland is submitted to the National Perinatal Reporting System (NPRS). All births are notified and registered on a standard four part birth notification form (BNF01) which is completed where the birth takes place. Part 3 of this form is sent to the HPO for data entry and validation. The information collected includes data on pregnancy outcomes (with particular reference to perinatal mortality and important aspects of perinatal care), as well as descriptive social and biological characteristics of mothers giving birth. See the complete Perinatal Statistics Report 2014 at http://www.hpo.ie/latest_hipe_nprs_reports/NPRS_2014/Perinatal_Statistics_Report_2014.pdf...

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

Avoidable mortality covers both preventable and treatable causes of mortality. Preventable mortality refers to mortality that can mainly be avoided through effective public health and primary prevention interventions (i.e. before the onset of diseases/injuries, to reduce incidence). Treatable mortality can mainly be avoided through timely and effective health care interventions, including secondary prevention and treatment (after the onset of diseases to reduce case-fatality). The total avoidable mortality includes a number of infectious diseases, several types of cancers, endocrine and metabolic diseases, as well as some diseases of the nervous, circulatory, respiratory, digestive, genitourinary systems, some diseases related to pregnancy, childbirth and the perinatal period, a number of congenital malformations, adverse effects of medical and surgical care, a list of injuries and alcohol and drug related disorders. The data are presented as standardised death rates, meaning they are adjusted to a standard age distribution in order to measure death rates independently of different age structures of populations. This approach improves comparability over time and between countries. The standardised death rates used here are calculated on the basis of the standard European population. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

Abstract

This dataset contains statistics on deaths in South Africa in 2008. The registration of deaths in South Africa is regulated by the Births and Deaths Registration Act, 51 of 1992. The South African Department of Home Affairs (DHA) is responsible for the registration of deaths in South Africa. The data is collected with two instruments: The death register and the medical certificate in respect of death. The staff of the DHA Registrar of Deaths section fills in the former while the medical practitioner attending to the death completes the latter. Causes of death are coded by the Department of Home Affairs according to the tenth revision of the International Classification of Diseases (ICD-10) ICD-10, as required by the World Health Organization for their member countries. The data is used by the Department of Home Affairs to update the Population Register. The forms are sent to Statistics South Africa (Stats SA) for their use for statistical purposes. From the two forms sent to Stats SA, the following data items of the deceased are extracted: place of residence, place of death, date of death, month and year of registration, sex, marital status, occupation, underlying cause of death, whether or not the death was certified by a medical practitioner, and whether or not the deceased died in a health institution or nursing home. From 1991 death notifications do not require data on population group, and therefore this dataset includes death data for all population groups. This dataset excludes 2010 deaths that were not registered, and late registrations which would not have been available to Stats SA in time for the production of the dataset.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all deaths that occurred in 2008 and registered at the Department of Home Affairs.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The data is collected with notification / death register / still birth instrument.

The number of potentially avoidable deaths from all causes by males, females and total people aged 0 to 74 years and their corresponding mortality rates/ratios with respective confidence intervals, …Show full descriptionThe number of potentially avoidable deaths from all causes by males, females and total people aged 0 to 74 years and their corresponding mortality rates/ratios with respective confidence intervals, 2010-14 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/. For information on the avoidable mortality concept, please refer to the Australian and New Zealand Atlas of Avoidable Mortality, available from: http://phidu.torrens.edu.au/. Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, and ABS Estimated Resident Population (ERP), 30 June 2010 to 30 June 2014. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

Abstract

The Government of The Gambia has always been committed to the “Health for All” year 2000 and beyond Alma Ata Declaration (1978) as well as other conventions such as the 1987 Global Conference on Safe Motherhood, the 1990 Convention on the Rights of the Child and the 1994 ICPD-Cairo Plan of Action, amongst others. A unique recommendation from all these conventions was the reduction of maternal mortality by half by the year 2000 and the provision of a comprehensive reproductive health programme using the life cycle approach from birth to death.

The 1987 conference on safe motherhood brought about increased awareness in the health sector on the issue of maternal mortality following which the “sisterhood” method of estimating levels of maternal mortality was first tested in The Gambia in 1987. This field test was done by the MRC field station located in one of the rural divisions of The Gambia and a total of 90 maternal deaths were identified. The lifetime risk of maternal death was estimated to be higher than one woman in twenty (Greenwood et al.). Subsequently, this revelation by the MRC study sparked a new impetus into the “silent epidemic” of maternal mortality following which the Department of State for Health through its MCH/FP programme commissioned a national survey in 1990. The results, which were quite startling, revealed a maternal mortality level of 1,050 per 100,000 live births nationally. There were variations between urban (600 per 100,000) and rural communities with trained birth attendants (894 per 100,000), and communities without trained birth attendants (1,600 per 100,000).

Recent isolated studies on maternal mortality have suggested a general decline in those areas. However, in the absence of a viable vital registration system in The Gambia, there has been a felt need to conduct another national survey, since the 1990 survey. Furthermore, the Department of State for Health’s proposed shift from MCH/FP service provision into a broad-focussed reproductive health programme also requires the availability of current baseline information and the identification of relevant process indicators, all of which justify the need to establish current levels of mortality and use of contraceptives.

It should be noted that current national policies and programmes continue to refer to data obtained from the 1990 maternal mortality study, the 1990 Gambia contraceptive prevalence and fertility determinants survey as well as the 1993 population and housing census as baseline benchmarks both for programme intervention and implementation. This long period to some extent renders the data quite obsolete and unsuitable for many national and development purposes. A simple compromise has been that of making comprehensive demographic, health and socio-economic projections. However, one important limitation of statistical projections is the period between the time the base data were collected and the time span of the projections. The probable margin of error in making projections with reference periods of eight or more years ago could be so large to warrant the acceptance of such projections within any reasonable statistical intervals.

Since there has been no comprehensive national survey on maternal, infant and child mortality during the past 10 years, and given that it would take a number of years before the final analyses of data obtained from the forthcoming census, it was found prudent to carry out a comprehensive study that would collect information on key reproductive health indicators. Furthermore, the complexity involved in studying maternal mortality compounded by its rarity of occurrence in the general population has necessitated conduction of a specialised study. Such a study would be useful in filling in the data deficiencies and providing baseline data for programme intervention and evaluation, especially in an era of a general shift of emphasis of population programmes from vertical family planning activities in favour of a more generally accepted concept - reproductive health.

Objectives of the survey: a) To establish current levels of maternal, peri-natal, neonatal and infant mortality rates. b) To establish the current levels of contraceptive prevalence rates and barriers to use. c) To elicit how the situation has improved or otherwise during the last ten years. d) Make practical recommendations to Department of State for Health for subsequent and long-term actions required.

Geographic coverage

National.

Analysis unit

• Households
• Women and men (both in child-bearing age)

Universe

The survey covered women age 15 to 49 years old and men age 18 years and over.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage stratified cluster sampling procedure was used for this study. The country is divided into 41 Districts and each of these districts was identified as a stratum. Stratification by districts increases the efficiency of the sample given the homogeneity of the districts. The sample size for the study was 4000 households and was based on the level of maternal mortality which was estimated at 1050 per 100,000 at the time of the study. According to WHO/UNICEF, 1997 publication on Sisterhood Method in Estimating Maternal Mortality, 4,000 households or less would be adequate for study of maternal mortality if the level of maternal mortality is at least 500 per 100,000.

Based on the Rule of Thumb, a 15 per cent sample of EAs (240) was selected for this study, which is also more than adequate for the study of other variables like contraceptive prevalence, infant mortality, fertility and its determinants. The selection of population elements were done at two stages; a representative sample of 240 Enumeration Areas (EAs) were randomly selected and allocated based on the Probability Proportional to the Size (PPS) of the district using random numbers. The EA is a cluster of settlements with an estimated population of 500 peoples.

A total of 4,000 households were then allocated to the districts with probability proportional to the size of each district. For the 240 selected EAs, a specified number of households were randomly selected for interview using a systematic sampling procedure. A complete listing of selected household members was done and all eligible male and female respondents were interviewed.

Sampling deviation

There were no discrepancies between the sample units obtained and the iniitial planned samples.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey tools included a compound and household schedules, female and male questionnaires. The compound and household schedules were used to collect information on local government area, health division and household number, together with residence, sex, age, education and eligibility status of the household members. The female and male questionnaires were administered to women aged 15-49 years and men aged 18 years and above respectively. The survey instruments were similar to the core modules of the Demographic and Health Survey questionnaires (Macro International), with adaptation to suit The Gambian needs. In addition a review of medical records in the three main hospitals in The Gambia (Royal Victoria Hospital, Farafenni Hospital and Bansang Hospital) was carried out in November 2001 to undertake first-hand assessment of the maternal mortality situation at the major referral facilities.

The Survey team with support and guidance of the Technical Team prepared the survey instruments by adapting the Demographic and Health survey modules. The main instruments for this study are: - Male questionnaire which was used to obtain information from males 18 years and above; - Female questionnaire, which obtained information from females, 15-49 years ; - Household questionnaire contains information on Local Government Area (LGA), Districts and Household numbers.

For each person listed on the household questionnaire, relationship to head of household, age, and sex are recorded.

The female questionnaire contains the following key information: - Respondent's background - Reproduction - Contraception - Marriage - Fertility preferences - Maternal mortality

The male questionnaire on the other hand, contained the following information: - Respondent's background - Contraception - Marriage - Maternal mortality

Response rate

All respondents with missing age were excluded from the model. There were about 15 percent of the responses with missing information on the deaths. Imputations were made to establish whether or not they qualified to be classified as maternal deaths. For instance, those missing sex of the sibling but had correctly answered maternal death-related questions, the sex was taken to be female and therefore included in the maternal mortality model. Responses with no information on the type of maternal death, but had indicated the death as having been as a result of complications of pregnancy or child birth, were imputed to be pregnancy-related. On the other hand, responses on symptoms before death were used to impute the type of death in case it was missing.

Data appraisal

All respondents with missing age were excluded from the model. There were about 15 percent of the responses with missing information on the deaths.