Demographic and Health Survey 2016 - Nepal

Abstract
---------------------------

The primary objective of the 2016 Nepal Demographic and Health Survey (NDHS) is to provide up-to-date estimates of basic demographic and health indicators. The NDHS provides a comprehensive overview of population, maternal, and child health issues in Nepal. Specifically, the 2016 NDHS:
- Collected data that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and for the country’s seven provinces
- Collected data that allowed for calculation of adult and maternal mortality rates at the national level
- Explored the direct and indirect factors that determine levels and trends of fertility and child mortality
- Measured levels of contraceptive knowledge and practice
- Collected data on key aspects of family health, including immunization coverage among children, prevalence and treatment of diarrhea and other diseases among children under age 5, maternity care indicators such as antenatal visits and assistance at delivery, and newborn care
- Obtained data on child feeding practices, including breastfeeding
- Collected anthropometric measures to assess the nutritional status of children under age 5 and women and men age 15-49
- Conducted hemoglobin testing on eligible children age 6-59 months and women age 15-49 to provide information on the prevalence of anemia in these groups
- Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviors and condom use
- Measured blood pressure among women and men age 15 and above
- Obtained data on women’s experience of emotional, physical, and sexual violence

The information collected through the 2016 NDHS is intended to assist policymakers and program managers in the Ministry of Health and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population. The 2016 NDHS also provides data on indicators relevant to the Nepal Health Sector Strategy (NHSS) 2016-2021 and the Sustainable Development Goals (SDGs).

Geographic coverage
---------------------------

National coverage

Analysis unit
---------------------------

- Household
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-49

Universe
---------------------------

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-49 years resident in the household.

Kind of data
---------------------------

Sample survey data [ssd]

Sampling procedure
---------------------------

The sampling frame used for the 2016 NDHS is an updated version of the frame from the 2011 National Population and Housing Census (NPHC), conducted by the Central Bureau of Statistics (CBS).

The sampling frame contains information about ward location, type of residence (urban or rural), estimated number of residential households, and estimated population. In rural areas, the wards are small in size (average of 104 households) and serve as the primary sampling units (PSUs). In urban areas, the wards are large, with average of 800 households per ward. The CBS has a frame of enumeration areas (EAs) for each ward in the original 58 municipalities. However, for the 159 municipalities declared in 2014 and 2015, each municipality is composed of old wards, which are small in size and can serve as EAs.

The 2016 NDHS sample was stratified and selected in two stages in rural areas and three stages in urban areas. In rural areas, wards were selected as primary sampling units, and households were selected from the sample PSUs. In urban areas, wards were selected as PSUs, one EA was selected from each PSU, and then households were selected from the sample EAs.

For further details on sample design, see Appendix A of the final report.

Mode of data collection
---------------------------

Face-to-face [f2f]

Research instrument
---------------------------

Six questionnaires were administered in the 2016 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, the Fieldworker Questionnaire, and the Verbal Autopsy Questionnaire (for neonatal deaths). The first five questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nepal. The Verbal Autopsy Questionnaire was based on the recent 2014 World Health Organization (WHO) verbal autopsy instruments (WHO 2015a).

Cleaning operations
---------------------------

The processing of the 2016 NDHS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the New ERA central office in Kathmandu. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The biomarker paper questionnaires were compared with the electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The secondary editing of the data was completed in the second week of February 2017. The final cleaning of the data set was carried out by The DHS Program data processing specialist and was completed by the end of February 2017.

Response rate
---------------------------

A total of 11,473 households were selected for the sample, of which 11,203 were occupied. Of the occupied households, 11,040 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 13,089 women age 15-49 were identified for individual interviews; interviews were completed with 12,862 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 4,235 men age 15-49 were identified and 4,063 were successfully interviewed, yielding a response rate of 96%.

Response rates were lower in urban areas than in rural areas. The difference was slightly more prominent for men than for women, as men in urban areas were often away from their households for work.

Sampling error estimates
---------------------------

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Non-sampling errors result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding 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 2016 Nepal DHS (NDHS) 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 2016 NDHS 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.

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. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2016 NDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal
---------------------------

Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Completeness of reporting
- Births by calendar years
- Reporting of age at death in days
- Reporting of age at death in months
- Sibling size and sex ratio of siblings
- Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

摘要
---------------------------
2016年尼泊尔人口与卫生调查（NDHS）的主要目标是提供最新的人口与健康基本指标估计值。NDHS全面概述了尼泊尔的人口、孕产妇和儿童健康问题。具体而言，2016年NDHS：
- 收集了数据，允许在国家层面、城市和农村地区以及该国七个省份计算关键人口指标，特别是生育率和5岁以下死亡率
- 收集了数据，允许在国家层面计算成年人和孕产妇死亡率
- 探讨了决定生育率和儿童死亡率水平和趋势的直接和间接因素
- 测量了避孕知识的普及程度和实践情况
- 收集了关于家庭健康关键方面的数据，包括儿童免疫接种覆盖率、5岁以下儿童中腹泻和其他疾病的患病率和治疗情况、孕产妇保健指标，如产前检查和分娩时的帮助，以及新生儿护理
- 收集了关于儿童喂养实践的数据，包括母乳喂养
- 收集了5岁以下儿童和15-49岁男女的营养状况评估的人体测量数据
- 对6-59个月龄的合格儿童和15-49岁的妇女进行了血红蛋白测试，以提供这些群体贫血患病率的信息
- 收集了关于妇女和男子对性传播疾病和艾滋病/HIV的知识和态度的数据，并通过探讨高风险行为和避孕套使用评估了潜在暴露于HIV感染风险
- 测量了15岁及以上男女的血压
- 获得了关于妇女经历情感、身体和性暴力的数据
通过2016年NDHS收集的信息旨在协助政策制定者和卫生部门及其他组织的项目管理人员设计、评估旨在改善国家人口健康的计划和策略。2016年NDHS还提供了与尼泊尔卫生部门战略（NHSS）2016-2021年和可持续发展目标（SDGs）相关的指标数据。
地理覆盖范围
---------------------------
全国覆盖
分析单元
---------------------------
- 家庭
- 个人
- 0-5岁儿童
- 15-49岁妇女
- 15-49岁男子
总体
---------------------------
调查涵盖了所有法定家庭成员（常住居民），以及居住在家庭中的15-49岁妇女和15-49岁男子。
数据类型
---------------------------
样本调查数据 [ssd]
抽样程序
---------------------------
2016年NDHS使用的抽样框架是2011年国家人口和住房普查（NPHC）框架的更新版本，由国家统计局（CBS）实施。
抽样框架包含关于区位置、居住类型（城市或农村）、估计的住宅家庭数量和估计人口的信息。在农村地区，区规模较小（平均104户），作为一级抽样单位（PSU）。在城市地区，区规模较大，平均每个区有800户。国家统计局为原始的58个市每个区都有一份人口普查区（EA）的框架。然而，对于2014年和2015年宣布的159个市，每个市都由小型旧区组成，这些旧区规模较小，可以作为EA。
2016年NDHS样本在农村地区分两个阶段进行分层抽样，在城市地区分三个阶段进行分层抽样。在农村地区，区被选为一级抽样单位，从样本PSU中选择家庭。在城市地区，区被选为PSU，从每个PSU中选择一个EA，然后从样本EA中选择家庭。
有关样本设计的更多详细信息，请参阅最终报告的附录A。
数据收集方式
---------------------------
面对面 [f2f]
研究工具
---------------------------
2016年NDHS中使用了六份问卷：家庭问卷、妇女问卷、男子问卷、生物标志物问卷、调查员问卷和口头尸检问卷（用于新生儿死亡）。前五份问卷基于DHS Program的标准人口与健康调查（DHS-7）问卷，并根据尼泊尔的相关人口与健康问题进行了调整。口头尸检问卷基于世界卫生组织（WHO）最近的2014年口头尸检工具（WHO 2015a）。
清洗操作
---------------------------
2016年NDHS数据的处理与实地工作同时开始。在每个簇的数据收集完成后，所有电子数据文件通过IFSS传输到加德满都的新ERA中央办公室。这些数据文件被注册并检查不一致性、不完整性和异常值。生物标志物纸质问卷与电子数据文件进行了比较，以检查数据录入中是否存在任何不一致。数据录入和编辑使用CSPro软件包进行。数据的二级编辑在2017年2月的第二周完成。数据集的最终清洗由DHS Program数据处理专家进行，并在2017年2月底完成。
响应率
---------------------------
共选择了11,473个家庭作为样本，其中11,203个被占用。在占用的家庭中，11,040个家庭成功接受了访谈，响应率为99%。在访谈的家庭中，确定了13,089名15-49岁的妇女进行个别访谈；完成了12,862名妇女的访谈，响应率为98%。在为男性调查选择的子样本家庭中，确定了4,235名15-49岁的男子，其中4,063名成功接受了访谈，响应率为96%。城市地区的响应率低于农村地区。对于男性来说，差异略大于女性，因为城市地区的男性经常因工作而远离家庭。
抽样误差估计
---------------------------
样本调查的估计值受两种类型的误差的影响：非抽样误差和抽样误差。非抽样误差源于在实施数据收集和数据处理过程中犯下的错误，例如未能找到和访谈正确的家庭、访谈员或受访者对问题的误解以及数据录入错误。尽管在实施2016年尼泊尔人口与健康调查（NDHS）期间做出了大量努力来最大限度地减少此类错误，但非抽样误差是不可避免的，且难以进行统计评估。
另一方面，抽样误差可以通过统计方法进行评估。2016年NDHS中选定的受访者样本只是从同一人口中可能选择的许多样本之一，使用相同的设计和预期规模。这些样本中的每一个都会产生与实际选定样本结果略有不同的结果。抽样误差是衡量所有可能样本之间差异的一种度量。虽然变异程度并不完全清楚，但可以从调查结果中进行估计。
抽样误差通常以特定统计量（均值、百分比等）的标准误差来衡量，它是方差的平方根。标准误差可用于计算置信区间，其中可以合理地假设真实值落在该区间内。例如，从任何给定样本调查计算出的任何给定统计量的值将在95%的所有可能样本（大小和设计相同）的标准误差范围内。
如果受访者样本被选为简单随机样本，则可以使用简单的公式来计算抽样误差。然而，2016年NDHS样本是多阶段分层设计的产物，因此有必要使用更复杂的公式。抽样误差使用ISSA或SAS计算，使用ICF开发的程序。这些程序使用泰勒线性化方法估计调查估计的方差，这些估计值是均值、比例或比率。对于更复杂的统计量，如生育率和死亡率，使用Jackknife重复复制法进行方差估计。
抽样误差估计的更详细描述见调查最终报告的附录B。
数据评估
---------------------------
数据质量表
- 家庭年龄分布
- 合格和接受访谈的妇女年龄分布
- 合格和接受访谈的男子年龄分布
- 报告的完整性
- 日历年度出生情况
- 报告死亡时的年龄（天数）
- 报告死亡时的年龄（月份）
- 兄弟姐妹的大小和性别比
- 与妊娠相关的死亡率趋势

调查最终报告附录C中提供了数据质量表的详细信息。