Life expectancy is an estimate of how long a person would live, on average.

Life expectancy is affected by many factors such as: • Socioeconomic status, including employment, income, education and economic wellbeing. • The quality of the health system and the ability of people to access it; health behaviors such as tobacco and excessive alcohol consumption, poor nutrition and lack of exercise. • Social factors; genetic factors; and environmental factors including overcrowded housing, lack of clean drinking water and adequate sanitation, etc.

With the help of the above-mentioned factors, I tried to analyse t the data and come up with measurable solutions to improve the Life Expectancy.

In this essay, we ask whether the distributions of life expectancy and mortality have become generally more unequal, as many seem to believe, and we report some good news. Focusing on groups of counties ranked by their poverty rates, we show that gains in life expectancy at birth have actually been relatively equally distributed between rich and poor areas. Analysts who have concluded that inequality in life expectancy is increasing have generally focused on life expectancy at age 40 to 50. This observation suggests that it is important to examine trends in mortality for younger and older ages separately. Turning to an analysis of age-specific mortality rates, we show that among adults age 50 and over, mortality has declined more quickly in richer areas than in poorer ones, resulting in increased inequality in mortality. This finding is consistent with previous research on the subject. However, among children, mortality has been falling more quickly in poorer areas with the result that inequality in mortality has fallen substantially over time. We also show that there have been stunning declines in mortality rates for African Americans between 1990 and 2010, especially for black men. Finally we offer some hypotheses about causes for the results we see, including a discussion of differential smoking patterns by age and socioeconomic status.

This table shows three variants of healthy life expectancy: -Life expectancy in perceived good health. -Life expectancy without reported physical limitations. -Life expectancy without reported chronic diseases. -Life expectancy in good mental health In addition, data on mortality probabilities and total life expectancy are presented. Total life expectancy indicates the number of years that a person of a given age is expected to live. In the table, the data on (healthy) life expectancy can be broken down into the following characteristics: -Gender -Age -Income The standardized disposable household income allocated to individuals is used as an indicator of socio-economic status. The figures in the publication relate to the average over the years 2004 up to and including 2007, the average over the years 2007 up to and including 2010, the average over the years 2011 up to and including 2014 and the average over the years 2014 up to and including 2017. Data available from 2004/2007 up to and including 2017. Status of the figures: The figures in this table are final Changes as of 21 December 2022: None, this table has been discontinued. When will new numbers come out? Not applicable anymore. This table is followed by the Healthy life expectancy table; income and wealth. See section 3.

Background: Child malnutrition is not only common in developing countries but also an important issue faced by developed countries. This study aimed to explore the influence and degree of childhood starvation on the health of the elderly, which provides a reference for formulating health-related policies under the concept of full lifecycle health.Methods: Based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2008, 2011, and 2014, this study took a total of 13,185 elderly people aged 65–99 years as the target population. By IMaCH software, with gender and income level as the control variables, the average life expectancy and healthy life expectancy of the elderly were measured. The x2test was used to explore the differences in the socioeconomic status of elderly people with or without starvation in childhood. Statistical differences between average life expectancy and healthy life expectancy were analyzed by rank tests.Results: (1) The results showed that there was a statistically significant difference in age, gender, residency, education level, and income level between the groups with or without starvation (P < 0.05). (2) Transition probabilities in health–disability, health–death, and disability–death all showed an upward trend with age (P < 0.05), where the elderly who experienced starvation in childhood were higher than those without such an experience (P < 0.05). However, the probability of disability–health recovery showed a downward trend with age (P < 0.05), in which the elderly who experienced starvation in childhood were lower than those without starvation (P < 0.05). (3) For the elderly who experienced starvation in childhood, the health indicators of the average life expectancy, healthy life expectancy, and healthy life expectancy proportion accounted for the remaining life were lower than those of the elderly without childhood starvation (P < 0.05).Conclusions: The average life expectancy and healthy life expectancy of the elderly with childhood starvation are lower than those without childhood starvation. It shows that the negative impact of childhood starvation on health through the life course till old age has a persistent negative cumulative effect on the quantity and quality of life. Therefore, it is important to pay attention to the nutritional status of children in poor families from the perspective of social policymaking.

Abstract The issue of social inequalities is a subject of recurrent studies and remains relevant due to the growing trend of these inequalities over the years. This study proposes the creation of the Health Inequality Index (HII) composed of health indicators – Mean life span and Mean Potential Years of Life Lost (PYLL) – and socioeconomic indicators of income, schooling, and population living in poverty in the city of Natal – the State Capital of Rio Grande do Norte, Brazil. Therefore, a probabilistic linkage was made between mortality and socioeconomic databases in order to capture the census tracts of households with death records from 2007 to 2013. The authors used the Principal Component Factor Analysis to calculate the index. The Health Inequality Index showed areas with worse socioeconomic and health conditions located in the suburban areas of the city, with differences between and within the districts. The difference in the mean life span between the districts of Natal arrives at 25 years, and the worst district has mortality rates comparable to poor African countries. Public policymakers can use the index to prioritize actions aimed at reducing or eliminating health inequalities.

Death rate has been age-adjusted by the 2000 U.S. standard populaton. All-cause mortality is an important measure of community health. All-cause mortality is heavily driven by the social determinants of health, with significant inequities observed by race and ethnicity and socioeconomic status. Black residents have consistently experienced the highest all-cause mortality rate compared to other racial and ethnic groups. During the COVID-19 pandemic, Latino residents also experienced a sharp increase in their all-cause mortality rate compared to White residents, demonstrating a reversal in the previously observed mortality advantage, in which Latino individuals historically had higher life expectancy and lower mortality than White individuals despite having lower socioeconomic status on average. The disproportionately high all-cause mortality rates observed among Black and Latino residents, especially since the onset of the COVID-19 pandemic, are due to differences in social and economic conditions and opportunities that unfairly place these groups at higher risk of developing and dying from a wide range of health conditions, including COVID-19.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Age-Adjusted Death Rates

Death Rates and Life Expectancy in the United States, 2011-2013

By Health [source]

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How to use the dataset

In order to use this dataset, start by selecting a particular set of variables to investigate. You can choose from Measure Names (e.g., Death Rates or Life Expectancy), Race (e.g., All Races), Sex (Male/Female) and Year (2011-2013). Once you have selected your desired variables, you can begin analyzing the data by looking at mortality rates and life expectancy averages amongst different populations in the United States over time.

You may also wish to perform more detailed analyses such as identifying trends or examining correlations between features, regional disparities in mortality rates or changes in average life expectancies over time. If so, you can do so by creating line graphs plotted against one or more independent variables such as Race and Sex to see how demographics impact these statistics overall and on a yearly basis using the Year variable computed from July 1st 2010 estimates

Research Ideas

• Analyzing mortality and life expectancy trends among certain races and sexes over time.
• Examining the effects of different socioeconomic factors on death rates and life expectancies.
• Making predictions about future mortality rates and average life expectancies with machine learning algorithms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: rows.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------------| | Measure Names | The type of measure being reported. (String) | | Race | The race of the population being reported. (String) | | Sex | The gender of the population being reported. (String) | | Year | The year the data was collected. (Integer) | | Average Life Expectancy | The average life expectancy of the population being reported. (Float) | | Mortality | The mortality rate of the population being reported. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

Life expectancy worldwide has seen significant improvements over the past three decades, with notable variations across regions. In 2021, a child born in the Americas could expect to live an average of **** years, compared to ** years in 1990. However, the COVID-19 pandemic caused a universal decline in life expectancy from 2019 to 2021, affecting all World Health Organization regions. Regional disparities and global trends While global life expectancy has generally increased over time, stark regional differences persist. ****** consistently reports the lowest life expectancy, with **** years in 2021. In fact, the twenty countries with the lowest life expectancy in the world are all found in ******, with **** and ******* reporting the lowest life expectancies at just ** years. In contrast, the *************** now has the highest life expectancy, reaching **** years in 2021. These disparities reflect broader socioeconomic factors, with low-income countries facing challenges such as limited healthcare access and higher rates of infectious diseases. Impact of health issues on life expectancy Various health issues contribute to differences in life expectancy across countries and regions. Mental health has emerged as a significant concern, with a survey of 31 countries identifying it as the biggest health problem facing people in these countries in 2024. The COVID-19 pandemic not only directly impacted life expectancy but also exacerbated mental health issues worldwide. Additionally, non-communicable diseases play a crucial role in determining life expectancy. In 2021, ********************** was the leading cause of death globally, highlighting the importance of addressing chronic health conditions to improve overall life expectancy.

Descriptive statistics for SMA-level LE, De-FLE, LE with Dementia, and regional variables (n = 333).

BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

ObjectiveDrawing from the extended Grossman health capital and demand theory and the life course theory, this study examined whether childhood SES has direct and significant correlation with health in middle and old age in a specific historical context in China.MethodsA sample of 9,861 respondents was obtained from the China Health and Retirement Longitudinal Study (CHARLS). Childhood SES was measured by objective indices of recall. Health was assessed by self-reported, physician diagnosis and the Center for Epidemiologic Studies Depression Scale (CESD). The Propensity Score Matching (PSM) was used to estimate the treatment effect between childhood SES and later life health. The Karlson-Holm-Breen (KHB) method was employed to examine the associative mediation effects.ResultsCompared to respondents with low SES in childhood, respondents with high SES in childhood had, on average, 5.1% more likely to report their health as good, an average 2.4% lower prevalence of chronic diseases and an average 7.6% lower in the score of depression in middle and old age. The indirect relationships of childhood health, adulthood SES and adulthood lifestyle with health in middle and old age were all significant. SES upward mobility in adulthood can diminish the association between childhood disadvantage and poor health in middle and old age.ConclusionThe health effects of childhood SES can persist into middle and old age, this is more noticeable in rural areas, particularly in females. The critical period, cumulative risk and social mobility models produce synergistic effects in China. Our results also promote a paradigm shift in health interventions from old age to early life for health-vulnerable populations.

Economically Active Population Survey (EAPS): Activity and unemployment rates for population 16 to 64 years of age by sex and Autonomous Community. Quarterly. Autonomous Communities and Cities.

In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

Use of absolute and derived values in assessing Population health and the activities of healthcare

Submitted by Riya Patil & Rutuja Sonar, to Moldoev Murzali Ilyazovich Osh state University

ABSTRACT

In contrast, derived values involve the use of statistical techniques to calculate indirect indicators from absolute values. These include metrics like disability-adjusted life years (DALYs), quality-adjusted life years (QALYs), and health-adjusted life expectancy (HALE). Derived values are instrumental in understanding the broader context of population health, as they often combine both mortality and morbidity data to reflect the overall burden of disease.

In healthcare institutions, these values are integral in guiding resource allocation, evaluating the effectiveness of interventions, and shaping policies aimed at improving health outcomes. While absolute values provide essential raw data, derived values offer nuanced insights into the quality and long-term impact of healthcare services. Together, they form a comprehensive approach to measuring and improving population health, helping healthcare institutions prioritize actions and allocate resources more effectively.

This paper explores the role of absolute and derived values in assessing population health and their relevance to healthcare institutions, examining how both types of values support decision-making and influence health policy.

Keywords: Population health, absolute values, derived values, healthcare institutions, mortality rates, morbidity, Disability-Adjusted Life Years (DALYs), Quality-Adjusted Life Years (QALYs), Health-Adjusted Life Expectancy (HALE), health policy, healthcare interventions.

INTRODUCTION

Use of Absolute and Derived Values in Assessing Population Health and the Activities of Healthcare Institutions**

Population health is a key focus of public health systems and healthcare institutions worldwide. Assessing the health of a population requires robust metrics to understand the current state of health, identify risks, and track trends over time. One of the essential tools in evaluating population health is the use of **absolute values** and **derived values**. These metrics offer complementary insights into both the health status of individuals within a population and the effectiveness of healthcare interventions.

**Absolute values** are straightforward measures that provide direct data points, such as the total number of people suffering from a specific disease, the number of hospital admissions, or the total expenditure on healthcare services. These values are critical for understanding the scale of health issues and resource needs within a community.

**Derived values**, on the other hand, are ratios or indices calculated from absolute values. They allow for more meaningful comparisons across populations, time periods, or geographical areas. Examples include rates such as morbidity or mortality rates, life expectancy, and disease prevalence, which are essential for assessing public health outcomes and guiding healthcare policy and decision-making.

By integrating both absolute and derived values, healthcare institutions can gain a comprehensive picture of population health, identify areas for improvement, allocate resources more efficiently, and track the effectiveness of healthcare initiatives. This approach helps ensure that healthcare systems are responsive to the needs of the population and can adapt to emerging health challenges.

METHODOLOGY

Method and analysis which is performed by the google worksheet and google forms

Absolute Values in Assessing Population Health:

Absolute values refer to raw, unadjusted data points that provide a direct measure of a population's health status. These values are fundamental for initial assessments, as they provide baseline data for various health indicators.

Definition and Examples

Absolute values refer to concrete figures that represent the total counts or occurrences of specific health events or conditions. For example:

Total Mortality Rate: The number of deaths in a population over a specific time period (e.g., deaths per 100,000 people).

Prevalence Rates: The proportion of individuals in a population diagnosed with a specific condition at a particular time (e.g., diabetes prevalence).

Incidence Rates: The number of new or newly diagnosed cases of a disease over a given period (e.g., cancer incidence).

Life Expectancy: The average number of years a person is expected to live based on current mortality rates.

Use in Population Health

Health Monitoring: Absolute values allow public health authorities to monitor trends in population health, such as increases in mortality or the spread of disease.

Resource Allocation: These values help in determining the burden of disease in different populations, aiding in the efficient distribution of healthcare resources.

Derived Values in Assessing Population Health

Derived values involve the use of mathematical formulas or statistical techniques to adjust or combine absolute values to create composite indices or ratios that provide deeper insights into health outcomes and healthcare activities.

Definition

Derived values are statistical measures that offer context to absolute

by relating them to population characteristics. Common examples include:

Age-Standardized Mortality Rate: Adjusts the mortality rate for differences in the age structure of different populations, allowing comparisons between populations with different age distributions.

Disability-Adjusted Life Years (DALY): A composite measure that combines years of life lost due to premature death and years lived with disability. DALY provides a more comprehensive understanding of the burden of disease.

Quality-Adjusted Life Years (QALY): A measure used to evaluate the effectiveness of healthcare interventions by combining quantity and quality of life.

Health Inequality Index: Derived by comparing health disparities between different subgroups within a population.

Use in Population Health

Risk Assessment: Derived values like DALYs or QALYs enable healthcare providers and policymakers to assess the relative impact of different diseases or health conditions on the population’s overall health.

Health Outcomes Comparison: Derived values facilitate comparisons across different populations or regions, adjusting for factors like age, gender, or socioeconomic status.

Policy and Program Evaluation: Derived values are used to evaluate the effectiveness of public health interventions or healthcare programs, such as whether a vaccination program reduces disease burden over time.

Significance

Contextualizing Health Trends: Absolute values alone may not offer a clear picture. For instance, while an increase in the number of cancer cases might be alarming, derived values like the cancer incidence rate allow us to understand if the increase is due to an actual rise in cases or simply a result of population growth.

Comparative Analysis: Derived values are essential when comparing different populations or regions. For example, comparing the infant mortality rate in different countries provides insights into healthcare system performance, whereas absolute numbers may mislead without considering population size differences.

Evaluating Healthcare Efficiency: Derived values such as cost-effectiveness or patient outcomes per healthcare dollar provide insights into the efficiency of healthcare institutions. This helps identify areas of improvement in resource allocation and delivery of services.

Policy and Planning: Derived values play a crucial role in informing public health policies and healthcare strategies. For example, the quality-adjusted life year (QALY), derived from health outcome measures, is commonly used in health economics to assess the effectiveness of medical treatments and interventions.

Conclusion

Both absolute and derived values are integral to assessing population health and healthcare institution activities. Absolute values provide raw data, while derived values allow for deeper analysis, trends, and comparisons, giving a more comprehensive picture of health outcomes and healthcare performance.

REFERENCE

1.Kindig D, Stoddart G (March 2003). "What is population health?". American Journal of Public Health. 93 (3): 380–3. doi:10.2105/ajph.93.3.380. PMC 1447747. PMID 12604476.

2. McGinnis JM, Williams-Russo P, Knickman JR (2002). "The case for more active policy attention to health promotion". Health Aff (Millwood). 21 (2): 78–93. doi:10.1377/hlthaff.21.2.78. PMID 11900188.. See also National Academies Press free publication: The Future of Public Health in the 21st Century.

3. World Health Organization. 2006. Constitution of the World Health Organization – Basic Documents, Forty-fifth edition, Supplement, October 2006.

4. Jeffery RW. 2001. Public health strategies for obesity treatment and prevention. American Journal of Health Behavior 25:252–259.

5. Buunk BP, Verhoeven K. 1991. Companionship and support at work: a microanalysis of the stress-reducing features of social interactions. Basic and Applied Social Psychology 12:243–258.

6. CDC. 2001. a. CDC FactBook 2000/2001: Profile of the Nation's Health. Atlanta, GA: CDC.

7. What is the WHO definition of health? from the Preamble to the Constitution of WHO as adopted by the

Abstract

The Afghanistan Mortality Survey (AMS) 2010 was designed to measure mortality levels and causes of death, with a special focus on maternal mortality. In addition, the data obtained in the survey can be used to derive mortality trends by age and sex as well as sub-national estimates. The study also provides current data on fertility and family planning behavior and on the utilization of maternal and child health services.

OBJECTIVES

The specific objectives of the survey include the following: - National estimates of maternal mortality; causes and determinants of mortality for adults, children, and infants by age, sex, and wealth status; and other key socioeconomic background variables; - Estimates of indicators for the country as a whole, for the urban and the rural areas separately, and for each of the three survey domains of North, Central, and South, which were created by regrouping the eight geographic regions; - Information on determinants of maternal health; - Other demographic indicators, including life expectancy, crude birth and death rates, and fertility rates.

ORGANIZATION OF THE SURVEY

The AMS 2010 was carried out by the Afghan Public Health Institute (APHI) of the Ministry of Public Health (MoPH) and the Central Statistics Organization (CSO) Afghanistan. Technical assistance for the survey was provided by ICF Macro, the Indian Institute of Health Management Research (IIHMR) and the World Health Organization Regional Office for the Eastern Mediterranean (WHO/EMRO). The AMS 2010 is part of the worldwide MEASURE DHS project that assists countries in the collection of data to monitor and evaluate population, health, and nutrition programs. Financial support for the survey was received from USAID, and the United Nations Children’s Fund (UNICEF). WHO/EMRO’s contribution to the survey was supported with funds from USAID and the UK Department for International Development and the Health Metrics Network (DFID/HMN). Ethical approval for the survey was obtained from the institutional review boards at the MoPH, ICF Macro, IIHMR, and the WHO.

A steering committee was formed to coordinate, oversee, advise, and make decisions on all major aspects of the survey. The steering committee comprised representatives from various ministries and key stakeholders, including MoPH, CSO, USAID, ICF Macro, IIHMR, UNICEF, UNFPA, WHO, and local and international NGOs. A technical advisory group (TAG) made up of experts in the field of mortality and health was also formed to provide technical guidance throughout the survey, including reviewing the questionnaires, the tabulation plan for this final report, the final report, and the results of the survey.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

The AMS 2010 is the first nationwide survey of its kind. A nationally representative sample of 24,032 households was selected. All women age 12-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey. The survey was designed to produce representative estimates of indicators for the country as a whole, for the urban and the rural areas separately, and for each of the three survey domains, which are regroupings of the eight geographical regions. The compositions of the domains are given below: - The North, which combines the Northern region and the North Eastern region, consists of nine provinces: Badakhshan, Baghlan, Balkh, Faryab, Jawzjan, Kunduz, Samangan, Sari Pul, and Takhar. - The Central, which combines the Western region, the Central Highland region, and the Capital region, consists of 12 provinces: Badghis, Bamyan, Daykundi, Farah, Ghor, Hirat, Kabul, Kapisa, Logar, Panjsher, Parwan, and Maydan Wardak. - The South, which combines the Southern region, the South Eastern region, and the Eastern region, consists of 13 provinces: Ghazni, Hilmand, Kandahar, Khost, Kunar, Laghman, Nangarhar, Nimroz, Nuristan, Paktika, Paktya, Uruzgan, and Zabul.

The sample for the AMS 2010 is a stratified sample selected in two stages from the 2011 Population and Housing Census (PHC) preparatory frame obtained from the Central Statistics Organization (CSO). Stratification was achieved by separating each domain into urban and rural areas. Because of the low urban proportion for most of the provinces, the combined urban areas of each domain form a single sampling stratum, which is the urban stratum of the domain. On the other hand, the rural areas of each domain are further split into strata according to province; that is, the rural areas of each province form a sampling stratum. In total, 34 sampling strata have been created after excluding the rural areas of Hilmand, Kandahar, and Zabul from the domain of the south. Among the 34 sampling strata, 3 are urban strata, and the remaining 31 are rural strata, which correspond with the total number of provinces and their rural areas. Samples were selected independently in each sampling stratum by a twostage selection process. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels within a sampling stratum, by sorting the sampling frame according to administrative units at different levels within each stratum, and by using a probability proportional to size selection at the first stage of sampling.

The primary sampling unit was the enumeration area (EA). After selection of the EA and before the main fieldwork, a household listing operation was carried out in the selected EAs to provide the most updated sampling frame for the selection of households in the second stage. The household listing operation consisted of (1) visiting each of the 751 selected EAs, (2) drawing a location map and a detailed sketch, and (3) recording on the household listing forms all structures found in the EA and all households residing in the structure with the address and the name of the household head. The resulting lists of households serve as the sampling frame for the selection of households at the second stage of sampling. In the second stage of sampling, a fixed number of 32 households was selected randomly in each selected cluster by an equal probability systematic sampling technique. The household selection procedure was carried out at the IIHMR office in Kabul prior to the start of fieldwork. An Excel spreadsheet prepared by ICF Macro to facilitate the household selection was used. A level of non response, or refusals on the part of households and individuals, had already been taken into consideration in the sample design and sample calculation.

The survey interviewers interviewed only pre-selected households, and no replacements of pre-selected households were made during the fieldwork, thus maintaining the representativeness of the final results from the survey for the country. Interviewers were also trained to optimize their effort to identify selected households and to ensure that individuals cooperated to minimize non-response. It is important to note here that interviewers in the AMS were not remunerated according to the number of questionnaires completed but given a daily per diem for the number of days they spent in the field; in addition, it is also important to note that respondents were neither compensated in any way for agreeing to be interviewed nor coerced into completing an interview.

For security reasons, the rural areas of Kandahar, Hilmand, and Zabul, which constitute less than 9 percent of the population, were excluded during sample design from the sample selection; however, the urban areas of these provinces were included. Of the 751 EAs that were included in the sample, 34 EAs (5 urban and 29 rural) were not surveyed. Six of the selected EAs in Ghazni, 16 in Paktika, 1 in Uruzgan, 3 in Kandahar, 3 in Daykundi, and 2 in Faryab were not surveyed because of the security situation. In addition, two EAs from Badakshan and one from Takhar were not surveyed because base maps from the CSO were unavailable. The non-surveyed EAs-which were primarily in rural areas-represent 4 percent of the total population of the country,

Table 1.1 - Sample coverage (Percentage of the population represented by the sample surveyed in the Afghanistan Mortality Survey, Afghanistan 2010) Region / Urban / Rural / Total North / 97 / 98 / 98 Central / 100 / 98 / 99 South / 94 / 63 / 66 Total / 98 / 84 / 87

Overall, approximately 13 percent of the country was not surveyed; most of these areas were in the South zone. As shown in Table 1.1, the survey covered only 66 percent of the population in the South zone. Sample weights were adjusted accordingly to take into account those EAs that were selected but not completed for security or other reasons.

Overall, the AMS 2010 covered 87 percent of the population of the country, 98 percent of the urban population and 84 percent of the rural population. Nevertheless, the lack of total coverage and the disproportionate exclusion of areas in the South, and particularly the rural South, should be taken into consideration when interpreting national level estimates of key demographic indicators and estimates for the South zone and regions within. For this reason key indicators will be presented for all Afghanistan and Afghanistan excluding the South zone. Despite these exclusions, the AMS is the most comprehensive mortality survey conducted in Afghanistan in the last few decades in terms of geographic coverage of the country.

Throughout this report, numbers in the tables reflect weighted numbers unless indicated otherwise. In most cases, percentages based on 25-49 cases are shown in parentheses and percentages based on fewer than 25 unweighted cases are suppressed and replaced with an asterisk, to caution readers when interpreting data that a percentage may not

Each component’s factor loadings/correlations and variable importance in the projection.

Note: All percentages in bracket are age-adjusted percentages. Death rate is calculated per 1,000 person-months of observation.

Economically Active Population Survey (EAPS): Activity rates by studies in progress, sex and age group. Quarterly. National.

Economically Active Population Survey (EAPS): Unemployment rates by level of education attained, sex and age group. Annual. National.

Abstract

The IDHS is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health.

The main objective of 2007 IDHS was to provide detailed information on population, family planning, and health for policymakers and program managers. The 2007 IDHS was conducted in all 33 provinces in Indonesia. The survey collected information on respondents’ socioeconomic background, fertility levels, marriage and sexual activity, fertility preferences, knowledge and use of family planning methods, breastfeeding practices, childhood and adult mortality including maternal mortality, maternal and child health, and awareness and behavior regarding HIV/AIDS and other sexually-transmitted infections.

The 2007 IDHS was specifically designed to meet the following objectives: - Provide data concerning fertility, family planning, maternal and child health, maternal mortality, and awareness of AIDS/STIs to program managers, policymakers, and researchers to help them evaluate and improve existing programs; - Measure trends in fertility and contraceptive prevalence rates, analyze factors that affect such changes, such as marital status and patterns, residence, education, breastfeeding habits, and knowledge, use, and availability of contraception.; - Evaluate achievement of goals previously set by the national health programs, with special focus on maternal and child health; - Assess men’s participation and utilization of health services, as well as of their families; - Assist in creating an international database that allows cross-country comparisons that can be used by the program managers, policymakers, and researchers in the area of family planning, fertility, and health in general.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Kind of data

Sample survey data

Sampling procedure

Administratively, Indonesia is divided into 33 provinces. Each province is subdivided into districts (regency in areas mostly rural and municipality in urban areas). Districts are subdivided into subdistricts and each subdistrict is divided into villages. The entire village is classified as urban or rural.

The 2007 IDHS sample is designed to provide estimates with acceptable precision for the following domains: - Indonesia as a whole; - Each of 33 provinces covered in the survey, and - Urban and rural areas of Indonesia

The census blocks (CBs) are the primary sampling unit for the 2007 IDHS. The sample developed for the 2007 National Labor Force Survey (Sakernas) was used as a frame for the selection of the 2007 IDHS sample. Household listing was done in all CBs covered in the 2007 Sakernas. This eliminates the need to conduct a separate household listing for the 2007 IDHS.

A minimum of 40 CBs per province has been imposed in the 2007 IDHS design. Since the sample was designed to provide reliable indicators for each province, the number of CBs in each province was not allocated proportional to the population of the province nor proportional by urban-rural classification. Therefore, a final weighing adjustment procedure was done to obtain estimates for all domains.

The 2007 IDHS sample is selected using a stratified two-stage design consisting of 1,694 CBs. Once the number of households was allocated to each province by urban and rural areas, the number of CBs was calculated based on an average sample take of 25 selected households. All evermarried women age 15-49 and all unmarried persons age 15-24 in these households are eligible for individual interview. Eight households in each CB selected for the women sample were selected for male interview.

Note: See detailed description of sample design in APPENDIX B of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2007 IDHS used three questionnaires: the Household Questionnaire (HQ), the Ever-Married Women’s Questionnaire (EMWQ) and the Married Men’s Questionnaire (MMQ). In consultation with BKKBN and MOH, BPS made a decision to base the 2007 IDHS survey instruments largely on the questionnaires used in the 2002-03 IDHS to facilitate trend analysis. Input was solicited from other potential data users, and several modifications were made to optimize the draft 2007 IDHS instruments to collect the needs for population and health data. The draft IDHS questionnaires were also compared with the most recent version of the standard questionnaires used in the DHS program and minor modifications incorporated to facilitate international comparison.

The HQ was used to list all the usual members and visitors in the selected households. Basic information collected on each person listed includes: age, sex, education, and relationship to the head of the household. The main purpose of the HQ was to identify women and men who were eligible for the individual interview. Information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, construction materials used for the floor and outer walls of the house, and ownership of various durable goods were also recorded in the HQ. These items reflect the household’s socioeconomic status.

The EMWQ was used to collect information from all ever-married women age 15-49. These women were asked questions on the following topics:: - Background characteristics (marital status, education, media exposure, etc.) - Knowledge and use of family planning methods - Reproductive history and fertility preferences - Antenatal, delivery and postnatal care - Breastfeeding and infant feeding practices - Vaccinations and childhood illnesses - Practices related to the malaria prevention - Marriage and sexual activity - Woman’s work and husband’s background characteristics - Infant’s and children’s feeding practices - Childhood mortality - Awareness and behavior regarding AIDS and other sexually transmitted infections (STIs) - Sibling mortality, including maternal mortality.

The MMQ was administered to all currently married men age 15-54 living in every third household in the IDHS sample. The MMQ collected much of the same information included in the EMWQ, but was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition and maternal mortality. Instead, men were asked about their knowledge and participation in health-care-seeking practices for their children.

Cleaning operations

All completed questionnaires for the IDHS, accompanied by their control forms, were returned to the BPS central office in Jakarta for data processing. This consisted of office editing, coding of openended questions, data entry, verification, and editing computer-identified errors. A team of 42 data entry clerks, data editors and data entry supervisors processed the data. Data entry and editing was carried using a computer package program called CSPro, which was specifically designed to process DHS-type survey data. During the preparation of the data entry programs, a BPS staff spent several weeks at ORC Macro offices in Calverton, Maryland. Data entry and editing activities, which began in September, 2007 were completed in March 2008.

Response rate

In general, the response rates for both the household and individual interviews in the 2007 IDHS are high. A total of 42,341 households were selected in the sample, of which 41,131 were occupied. Of these households, 40,701 were successfully interviewed, yielding a household response rate of 99 percent.

In the interviewed households, 34,227 women were identified for individual interview and of these completed interviews were conducted with 32,895 women, yielding a response rate of 96 percent. In a third of the households, 9,716 eligible men were identified, of which 8,758 were successfully interviewed, yielding a response rate of 90 percent. The lower response rate for men was due to the more frequent and longer absence of men from the household.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2007 Indonesia Demographic and Health Survey (IDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2007 IDHS is only one of many 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 differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall.