Zambia ZM: Cause of Death: by Injury: % of Total data was reported at 10.200 % in 2016. This records an increase from the previous number of 10.000 % for 2015. Zambia ZM: Cause of Death: by Injury: % of Total data is updated yearly, averaging 9.350 % from Dec 2000 (Median) to 2016, with 4 observations. The data reached an all-time high of 10.200 % in 2016 and a record low of 5.700 % in 2000. Zambia ZM: Cause of Death: by Injury: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank.WDI: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries.; ; Derived based on the data from WHO's Global Health Estimates.; Weighted average;

Zambia ZM: Lifetime Risk Of Maternal Death data was reported at 1.258 % in 2015. This records a decrease from the previous number of 1.317 % for 2014. Zambia ZM: Lifetime Risk Of Maternal Death data is updated yearly, averaging 2.639 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 3.637 % in 1990 and a record low of 1.258 % in 2015. Zambia ZM: Lifetime Risk Of Maternal Death data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank.WDI: Health Statistics. Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average;

Zambia ZM: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data was reported at 79.000 NA in 2015. This records an increase from the previous number of 76.000 NA for 2014. Zambia ZM: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data is updated yearly, averaging 38.000 NA from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 79.000 NA in 2015 and a record low of 27.000 NA in 1990. Zambia ZM: Lifetime Risk of Maternal Death: 1 in: Rate Varies by Country data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank: Health Statistics. Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Weighted average;

Zambia ZM: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 17.400 NA in 2016. This records a decrease from the previous number of 17.600 NA for 2015. Zambia ZM: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 20.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 24.400 NA in 2000 and a record low of 17.400 NA in 2016. Zambia ZM: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

BackgroundTrauma is a major global public health issue, with an annual death toll of approximately 5 million, disproportionately affecting low- and middle-income countries. Zambia bears a significant burden of trauma-related mortalities, contributing to 7% of all annual deaths and 1 in 5 premature deaths in the country. Despite the significant burden of trauma in our country, few studies have been conducted, with most focusing on high-population centers, and there is a lack of epidemiological data on trauma-related deaths in our region. Therefore, our aim was to estimate the proportion of deaths caused by injuries at Livingstone University Teaching Hospital, a tertiary hospital located in Zambia’s southern province.MethodsWe conducted a retrospective cross-sectional study from June 22, 2020, to June 22, 2021, among 956 individuals from 1 month old (29 days of age) to 100 years. Demographic and clinical data were collected from patient’s records from Accident and Emergency department. Data analysis included descriptive statistics, chi square, mann-whitney test and multivariable logistic using forward stepwise generalized linear model equations (GLM) to identified factors associated with mortality, with a significance level set at p < 0.05. Data were analyzed using STATA version 15.ResultsAmong the study participants, the median age was 26 years (interquartile range (IQR) 15, 37) and the majority were males (74.2%, n = 709). Prevalence of mortality was 1.0% (n = 10). The deaths were caused by burns (60%, n = 6), violence (30%, n = 3), and traffic accidents (10%, n = 1). Among those who died, the majority of the trauma occurred at home (90%, n = 9), followed by road (10%, n = 1) and were as a result of burns (60%, n = 6) and community violence (30%, n = 3). Survivors had significantly higher treatment costs (ZMK 9,837 vs. ZMK 6,037, p

Zambia ZM: Number of Maternal Death data was reported at 1,400.000 Person in 2015. This records a decrease from the previous number of 1,500.000 Person for 2014. Zambia ZM: Number of Maternal Death data is updated yearly, averaging 2,200.000 Person from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 2,700.000 Person in 1998 and a record low of 1,400.000 Person in 2015. Zambia ZM: Number of Maternal Death data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank.WDI: Health Statistics. A maternal death refers to the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes.; ; WHO, UNICEF, UNFPA, World Bank Group, and the United Nations Population Division. Trends in Maternal Mortality: 1990 to 2015. Geneva, World Health Organization, 2015; Sum;

Abstract

The main objectives of the 2010 Census of Population and Housing were: • To provide accurate and reliable information on the size, composition and distribution of the population of Zambia at the time of the census; • To provide information on the demographic and socioeconomic characteristics of the population of Zambia at the lowest administrative level - the ward; • To provide indicators for measuring progress towards national and international development goals in a timely and user friendly manner; • To provide information on the number and characteristics of households engaged in agriculture and other economic activities; • To provide an accurate sampling frame and sample weights for future inter-censal household and population based surveys; • To provide information identifying the number of eligible voters for the 2011 General Elections; • To provide a census that meets national and international standards and allows for comparability with other censuses; • To provide information on the housing characteristics of the population.

Universe

Census Enumerators went out visiting all buildings in Zambia whether completed, incomplete, abandoned, habitable and inhabitable for the purpose of identifying characteristics of all buildings, households and other human aspects. All persons who lived in the buildings were counted and detailed information pertaining to their characteristics obtained.

Sampling procedure

The Census mapping methodology in 2010 was Geographic Information System (GIS) driven with the use of Satellite Imagery in urban areas and Global Positioning System (GPS) in rural areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2010 Census used a single questionnaire to capture individual, household and housing characteristics from the population. The 2010 Census differs from the 2000 Census by including questions on deaths of Household Members during the 12 months period prior to the census enumeration, as well as cause of death for all reported deaths.

Included for the first time were questions on maternal deaths to women aged 12-49 years during the reference period (12 months prior to the Census). Questions were asked of female household members aged 12-49 years that were reported to have died during the reference period (12 months prior to the census), whether the death had occurred while the woman was pregnant, during childbirth or six weeks after the end of a pregnancy, regardless of the outcome of the pregnancy. Another new addition was the question on whether one was an Albino or not.

Cleaning operations

In April 2011, the Central Statistical Office started the data capture and processing of the 2010 Census questionnaires. Scanning of the 2010 Census questionnaires started in April 2011 and was successfully concluded in August 2011. The data capture used Optical Mark Reading (OMR) and Intelligent Character Recognition (ICR) technology in order to speed up the processing time. Data verification and development of edit and imputation specifications and programmes started in May and was completed in November 2011.

Data appraisal

Methods of evaluation applied were:

• Direct Method: Post Enumeration Survey (PES)- a sample of households is revisited after the census and data are again collected but on a smaller scale and later compared with that collected during the actual census. • Indirect Method: Comparison of data using both internal and external consistency checks. Internal consistency checks compare relationships of data within the same census data, whereas external consistency checks compare census data with data generated from other sources.

Coverage errors: • Omission or duplication of individuals, households, or housing units resulting in under or over enumeration. • Lack of accessibility or cooperation with respondents. • Lack of proper boundary descriptions on maps. Coverage errors can be measured by examining certain statistics such as growth rate, age composition, child woman ratio and dependency ratio.

Content errors: Content errors refer to instances where characteristics such as age, sex, marital status, economic activity, etc. of a person enumerated in a census or survey are incorrectly reported or tabulated. • Content errors are caused by either a respondent giving a wrong response or by an enumerator recording an incorrect response. • 2010 census errors were estimated by the use of the Myers' Index, Sex Ratios, Age Ratios and Population Pyramids.

For findings, please refer to the presentation on census data evaluation provided as external resources.

WHO: COVID-2019: Number of Patients: Death: To-Date: Zambia data was reported at 4,069.000 Person in 24 Dec 2023. This stayed constant from the previous number of 4,069.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Death: To-Date: Zambia data is updated daily, averaging 3,716.000 Person from Jan 2020 (Median) to 24 Dec 2023, with 1451 observations. The data reached an all-time high of 4,069.000 Person in 24 Dec 2023 and a record low of 0.000 Person in 02 Apr 2020. WHO: COVID-2019: Number of Patients: Death: To-Date: Zambia data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued).

The human immune virus (HIV) is a viral infection that destroys the human immune system resulting in acquired immunodeficiency syndrome (AIDS). If untreated, it can reduce the cluster of CD4 positive T-cells and increases the HIV viral load, thus causing AIDS. The Zambia HIV prevalence rate is among the highest in the sub-Saharan region. According to WHO, HIV/AIDS is a major cause of death in Zambia, with about a million deaths attributed to HIV/AIDS-related causes. With no HIV vaccine readily available and no permanent cure for HIV/AIDS, the antiretroviral (ARV) drug that slows the spread of the virus remains the only option. The ARV shuts down viral reproduction as well as reduces the immune suppression caused by HIV. Taking a combination of three ARV drugs from different classes suppresses the reproduction of the virus. The administration of ARV has challenges of Transmitted Drug Resistance Mutation strains (TDRMs) in the treatment of HIV naïve patients. In this article, we formulate a technique for determining an optimal ARV combination using Bayesian statistical methods. The proposed technique assist the medical personnel responsible in deciding the optimal ARV combination per patient in the presence of TDRMs test. We developed a transition probability matrix chart for each combination. Using the data from Zambia, we demonstrate the computation process and provide an interpretation of the obtained results. The findings from the analysis indicate that the probability of patients remaining on first baseline combinations namely, 1, 2, 3, 4, 5 and 6 are: 0.96, 0.99, 0.97, 0.91, 0.96, and 0.96 respectively. The probabilities obtained can be used to choose an optimal ARV combination in the presence of Transmitted Drug Resistance Mutation Strains because you can isolate the particular drugs which the patient is resistance.

Hospital-based recruitment of females seeking termination of pregnancy or post-abortion care at a Zambian government health facility. The research used an innovative mixed methods interview which combined quantitative and qualitative techniques in one interview. Each participant was interviewed by two research assistants (RAs). One RA led the interview, using a conventional interview schedule in the manner of a qualitative semi-structured interview, while the second RA listened and, where possible, completed the quantitative ‘data sheet’. When the first RA has completed the qualitative part of the interview, interviewer two took over and asked the participant any remaining questions not yet answered on the data sheet. This technique allowed us to capture both the individual fine-grained narratives, which are not easily captured in a questionnaire-type survey, especially on such a sensitive area, as well as survey data. Rather than conducting an in-depth qualitative interview and a survey, our method reduced the burden on the respondent, avoiding repetition of questions and reducing the time taken. The quantitative data was used to establish the distribution of out-of-pocket expenses, for women and their households, incurred using hospital-based safe abortion and PAC services. Qualitative data established the range of reasons why women sought abortion, and why they used or did not use safe abortion services, and explored the social costs and benefits of their trajectories, and the policy implications.

Unsafe abortion is a significant, preventable, cause of maternal mortality and morbidity and is both a cause and a consequence of poverty. Unsafe abortion is the most easily prevented cause of maternal death. Post-abortion care (PAC) is a strategy to address the problem of the outcomes of unsafe abortion.This research aims to establish how investment in safe abortion services impacts on the socio-economic conditions of women and their households, and the implications for policy-making and service provision in Zambia. The microeconomic impact of out-of-pocket health expenditure for reproductive health and abortion care, have received little attention.The data available for sub-Saharan Africa are particularly scanty and poor quality. The approach is multi-disciplinary, with primary data collection of both qualitative and quantitative data, including a quantitative survey and in-depth qualitative interviews with women who have sought PAC, and policymaker interviews. Zambia's relatively liberal legal context, and the existence of PAC provision facilitates research on issues related to abortion which can have broader lessons for developments elsewhere in the region.The majority of women seeking abortion-related care in Zambia do so for PAC following an unsafe abortion, and have not accessed safe abortion services.This demands better understanding and analysis.

Large herbivore migrations are imperiled globally; however, the factors limiting a population across its migratory range are typically poorly understood. Zambia’s Greater Liuwa Ecosystem (GLE) contains one of the largest remaining blue wildebeest (Connochaetes taurinus taurinus) migrations, yet the population structure, vital rates, and limiting factors are virtually unknown. We conducted a long-term demographic study of GLE wildebeest from 2012–2019 of 107 collared adult females and their calves, 7,352 herd observations, 12 aerial population surveys, and concurrent carnivore studies. We applied methods of vital rate estimation and survival analysis within a Bayesian estimation framework. From herd composition observations, we estimated rates of fecundity, first-year survival, and recruitment as 68%, 56%, and 38% respectively, with pronounced inter-annual variation. Similar rates were estimated from calf detections with collared cows. Adult survival rates declined steadily from 91% at age 2 years to 61% at age 10 years thereafter dropping more sharply to 2% at age 16 years. Predation, particularly by spotted hyena, was the predominant cause of death for all wildebeest ages and focused on older animals. Starvation only accounted for 0.8% of all unbiased known natural causes of death. Mortality risk differed substantially between wet and dry season ranges, reflecting strong spatio-temporal differences in habitat and predator densities. There was substantial evidence that mortality risk to adults was 27% higher in the wet season, and strong evidence that it was 45% higher in the migratory range where predator density was highest. The estimated vital rates were internally consistent, predicting a stable population trajectory consistent with aerial estimates. From essentially zero knowledge of GLE wildebeest dynamics, this work provides vital rates, age structure, limiting factors, and a plausible mechanism for the migratory tendency, and a robust model-based foundation to evaluate the effects of potential restrictions in migratory range, climate change, predator-prey dynamics, and poaching. Methods See journal paper.

Univariate analysis with factors associated with death.

ZM：死因：按非传染病分类：占总量百分比在12-01-2016达29.200%，相较于12-01-2015的28.300%有所增长。ZM：死因：按非传染病分类：占总量百分比数据按年更新，12-01-2000至12-01-2016期间平均值为26.200%，共4份观测结果。该数据的历史最高值出现于12-01-2016，达29.200%，而历史最低值则出现于12-01-2000，为16.100%。CEIC提供的ZM：死因：按非传染病分类：占总量百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的赞比亚 – 表 ZM.世行.WDI：卫生统计。

Time to death for persons with TB in Zambian hospitals (2019), n = 10,987.

ZM：死因：按传染病以及母体、胎儿期及营养状况分类：占总量百分比在12-01-2016达60.600%，相较于12-01-2015的61.600%有所下降。ZM：死因：按传染病以及母体、胎儿期及营养状况分类：占总量百分比数据按年更新，12-01-2000至12-01-2016期间平均值为64.400%，共4份观测结果。该数据的历史最高值出现于12-01-2000，达78.200%，而历史最低值则出现于12-01-2016，为60.600%。CEIC提供的ZM：死因：按传染病以及母体、胎儿期及营养状况分类：占总量百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的赞比亚 – Table ZM.World Bank.WDI：卫生统计。

Baseline characteristics, also noted separately for patients who were HIV infected and HIV uninfected.