Demographic and Health Survey 2018 - Nigeria

Abstract
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The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage
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National coverage

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

Universe
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The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-5 years resident in the household.

Kind of data
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Sample survey data [ssd]

Sampling procedure
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The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

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

Mode of data collection
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Computer Assisted Personal Interview [capi]

Research instrument
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Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The 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 Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations
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The processing of the 2018 NDHS data began almost immediately after the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NPC data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of April 2019.

Response rate
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A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, yielding a response rate of 99%. In the households interviewed, 42,121 women age 15-49 were identified for individual interviews; interviews were completed with 41,821 women, yielding a response rate of 99%. In the subsample of households selected for the male survey, 13,422 men age 15-59 were identified and 13,311 were successfully interviewed, yielding a response rate of 99%.

Sampling error estimates
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The estimates from a sample survey are affected by two types of errors: nonsampling errors and 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 2018 Nigeria Demographic and Health Survey (NDHS) to minimise 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 2018 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 among 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% 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 2018 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances 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.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal
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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
- Standardisation exercise results from anthropometry training
- Height and weight data completeness and quality for children
- Height measurements from random subsample of measured children
- Sibship size and sex ratio of siblings
- Pregnancy-related mortality trends
- Data collection period
- Malaria prevalence according to rapid diagnostic test (RDT)

Note: See detailed data quality tables in APPENDIX C of the report.

摘要
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2018年国家人口与健康调查（NDHS）的主要目标是提供最新的基本人口和健康指标估计值。具体而言，NDHS收集了有关生育、家庭规划方法的认知和使用、母乳喂养实践、妇女和儿童的营养状况、母婴健康、成人和儿童死亡率、妇女赋权、家庭暴力、女性生殖器切割、疟疾的流行情况，以及关于艾滋病/艾滋病病毒和其他性传播感染（STIs）的认知和行为，残疾，以及其他与健康相关的问题，如吸烟等信息。
通过2018年NDHS收集的信息旨在协助政策制定者和项目管理人员评估和设计旨在改善该国人口健康的计划和策略。2018年NDHS还提供了与尼日利亚可持续发展目标（SDGs）相关的指标。
地理覆盖范围
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全国覆盖
分析单元
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- 家庭
- 个人
- 0-5岁儿童
- 15-49岁妇女
- 15-49岁男性
总体
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调查涵盖了所有法定家庭成员（常住居民），所有居住在家庭中的15-49岁妇女，以及所有居住在家庭中的0-5岁儿童。
数据类型
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样本调查数据 [ssd]
抽样程序
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2018年NDHS使用的抽样框架是2006年由国家人口委员会进行的联邦共和国人口与住房普查（NPHC）。从行政上讲，尼日利亚分为各州。每个州被划分为地方政府区域（LGAs），每个LGA被划分为选区。除了这些行政单位外，在2006年NPHC期间，每个地方被划分为便于调查的称为普查计数区域（EAs）的便利区域。一级抽样单位（PSU），在2018年NDHS中被称为簇，是基于2006年EA普查框架中的EAs定义的。尽管2006年NPHC没有提供每个EA的家庭和人口数量，但已发布了774个LGAs的人口估计值。通过结合来自绘制每个EA的地图材料的资料和来自普查的LGA人口估计值，确定了EA列表，估计了家庭数量，并区分了调查样本框架中的城市或农村EA。在样本选择之前，所有地区根据预定的城市地区最小规模（截止点）分别单独划分为城市和农村地区；与2017年的官方定义一致，任何人口超过20,000的最小规模的地区都被划分为城市。
2018年NDHS的样本是在两阶段中选出的分层样本。通过将36个州和联邦首都特区分别划分为城市和农村地区来实现分层。总共确定了74个抽样层。在每个层中独立进行两阶段选择，通过在样本选择之前根据行政顺序对抽样框架进行排序和使用第一阶段中的大小成比例的概率选择来实现隐含分层。
有关样本选择的更多详细信息，请参阅最终报告的附录A。
数据收集方式
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计算机辅助个人访谈 [capi]
研究工具
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2018年NDHS使用了四个问卷：家庭问卷、妇女问卷、男性问卷和生物标志物问卷。这些问卷基于DHS项目标准的人口与健康调查（DHS-7）问卷，并进行了调整，以反映与尼日利亚相关的人口和健康问题。征求了代表政府各部和机构、非政府组织和国际捐助者的利益相关者的意见。此外，通过自我管理的调查员问卷收集了有关调查员的调查信息。
数据清洗操作
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2018年NDHS数据的处理在实地调查开始后几乎立即开始。在每个簇的数据收集完成后，所有电子数据文件都通过IFSS传输到阿布贾的国家人口委员会（NPC）中央办公室。这些数据文件被登记并检查是否存在不一致、不完整和异常。现场团队被提醒任何不一致和错误。在中央办公室进行的二级编辑涉及解决不一致和编码开放式问题。NPC数据处理员协调了在中央办公室的这项工作。将生物标志物纸质问卷与电子数据文件进行比较，以检查数据输入中是否存在任何不一致。数据输入和编辑使用CSPro软件包进行。数据的同时处理提供了明显的优势，因为它最大限度地提高了数据无错误和准确的可能性。及时生成现场检查表，以进行有效监控。数据二级编辑于2019年4月的第二周完成。
响应率
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总共选择了41,668个家庭作为样本，其中40,666个被占用。在占用的家庭中，40,427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42,121名15-49岁的妇女进行个别访谈；完成了41,821名妇女的访谈，响应率为99%。在为男性调查选择的子样本家庭中，确定了13,422名15-59岁的男性，其中13,311人接受了成功的访谈，响应率为99%。
抽样误差估计
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样本调查的估计受到两种类型误差的影响：非抽样误差和抽样误差。非抽样误差是由于在实施数据收集和数据处理过程中出现的错误而产生的结果，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，以及数据输入错误。尽管在实施2018年尼日利亚人口与健康调查（NDHS）期间采取了众多努力以最大限度地减少此类误差，但非抽样误差是不可避免的，并且难以从统计上进行评估。
另一方面，抽样误差可以通过统计方法进行评估。2018年NDHS中选出的受访者样本只是从同一人口中选出的许多样本之一，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的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变异性的度量。虽然变异的程度并不完全清楚，但可以从调查结果中估计。
抽样误差通常以特定统计量（平均值、百分比等）的标准误差来衡量，这是方差的平方根。标准误差可用于计算置信区间，在此区间内可以合理地假设人口的真实值。
例如，对于从样本调查中计算出的任何给定统计量，该统计量的值将在95%的所有可能样本的相同大小和设计中的标准误差的两倍范围内。
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，2018年NDHS样本是多层次分层设计的产物，因此有必要使用更复杂的公式。抽样误差使用ICF开发的程序在SAS中进行计算。这些程序使用泰勒线性化方法估计均值、比例或比率等调查估计值的方差。对于生育率和死亡率等更复杂的统计量的方差估计，使用Jackknife重复复制方法。
注：关于抽样误差估计的更详细描述见调查报告附录B。
数据评估
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数据质量表
- 家庭年龄分布
- 有资格和接受访谈的妇女年龄分布
- 有资格和接受访谈的男性年龄分布
- 报告的完整性
- 日历年度出生情况
- 死亡年龄报告（按天数）
- 死亡年龄报告（按月份）
- 人体测量学培训标准化练习结果
- 儿童身高和体重数据完整性和质量
- 随机子样本中测量儿童的身高测量
- 兄弟姐妹家庭规模和性别比
- 与妊娠相关的死亡率趋势
- 数据收集期间
- 根据快速诊断测试（RDT）的疟疾流行情况
注：请参阅报告中附录C的详细数据质量表。