Malaria Indicator Survey 2017 - Malawi

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

The 2017 Malawi Malaria Indicator Survey (MMIS), a comprehensive, nationally-representative household survey, was designed in accord with the Roll Back Malaria Monitoring and Evaluation Working Group (RBM-MERG) guidelines. The primary objective of the 2017 MMIS project is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the 2017 MMIS collected information on mosquito nets, intermittent preventive treatment of malaria in pregnant women (IPTp), and care seeking behaviour and treatment of fever in children. Young children were also tested for anaemia and for malaria infection. Knowledge of malaria was assessed among interviewed women. The information collected through the 2017 MMIS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

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

National coverage

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

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

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

Sample survey data [ssd]

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

The 2017 MMIS followed a two-stage sample design and allows estimates of key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the 3 administrative regions in Malawi: Northern, Central, and Southern.

The first stage of sampling involved selecting sample points (clusters) from the sampling frame. Enumeration areas (EAs) delineated for the 2008 Population and Housing Census were used as the sampling frame. A total of 150 clusters were selected, with probability proportional to size, from the EAs covered in the 2008 Population and Housing Census. Of these clusters, 60 were in urban areas and 90 in rural areas. Urban areas were oversampled within regions to produce robust estimates for each area or domain.

The second stage of sampling involved systematic selection of households. A household listing operation was undertaken in all selected EAs between February and March 2017, and households to be included in the survey were randomly selected from these lists. Twenty-five households were selected from each EA, for a total sample size of 3,750 households. Because of the approximately equal sample sizes in each region, the sample is not self-weighting at the national level. Results shown in this report have been weighted to account for the complex sample design. See Appendix A for additional details on the sampling procedures.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. With the parent's or guardian's consent, children age 6-59 months were tested for anaemia and for malaria infection.

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

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

Face-to-face [f2f]

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

Data was primarily collected using three types of questionnaires: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire.

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

Data for the 2017 MMIS were collected through questionnaires programmed onto the CAPI application. The CAPI were programmed by ICF and loaded with the Household, Biomarker, and Woman’s Questionnaires. Using the cloud, the field supervisors transferred data on a daily basis to a central location for data processing in Lilongwe. To facilitate communication and monitoring, each field worker was assigned a unique identification number.

ICF provided technical assistance for processing the data using the Censuses and Surveys Processing (CSPro) system for data editing, cleaning, weighting, and tabulation. In the central office, data received from the field teams’ CAPI applications were registered and checked for any inconsistencies. Data editing and cleaning included an extensive range of structural and internal consistency checks. Any anomalies were communicated to team (field) supervisors so that the data processing teams could resolve data discrepancies.

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

A total of 3,750 households were selected for the sample, of which 3,735 were occupied at the time of fieldwork. Among the occupied households, 3,729 were successfully interviewed, yielding a total household response rate of 99.8%. In the interviewed households, 3,861 eligible women were identified as eligible for individual interview, and 3,860 women were successfully interviewed, yielding a response rate of 100%.

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

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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 2017 Malawi Malaria Indicator Survey (MMIS) to minimize this type of error, non-sampling 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 2017 MMIS 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 2017 MMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in SAS, using programs developed by ICF Macro. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions or ratios.

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
- Completeness of reporting
- Births by calendar years

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

摘要
---------------------------
2017年马拉维疟疾指标调查（MMIS），是一项全面的国家代表性家庭调查，其设计遵循了疟疾控制与监测评估工作组（RBM-MERG）的指南。2017年MMIS项目的首要目标是提供有关疟疾相关的基本人口和健康指标的最新估计。具体而言，2017年MMIS收集了有关蚊帐、孕妇间歇性预防性治疗疟疾（IPTp）、儿童发热的求医行为和治疗情况，以及儿童贫血和疟疾感染的信息。对受访女性的疟疾知识进行了评估。通过2017年MMIS收集的信息旨在协助政策制定者和项目管理者评估和设计旨在改善该国人口健康的计划和策略。
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岁女性
数据类型
---------------------------
样本调查数据 [ssd]
抽样程序
---------------------------
2017年MMIS采用了两阶段抽样设计，并允许对国家整体、城市和农村地区以及马拉维的3个行政区域（北部、中部和南部）的疟疾关键指标进行估计。抽样程序详见附录A的详细信息。
数据收集方式
---------------------------
面对面 [f2f]
研究工具
---------------------------
数据主要通过三种类型的问卷收集：家庭问卷、女性问卷和生物标志问卷。
数据清洗操作
---------------------------
2017年MMIS的数据通过编程到CAPI应用程序中的问卷收集。CAPI由ICF编程，并加载了家庭、生物标志和女性问卷。通过云服务，现场监督员每天将数据传输到利隆圭的数据处理中心。为了促进沟通和监控，每位现场工作人员都被分配了一个唯一的识别号码。
ICF提供了使用人口普查和调查处理系统（CSPro）进行数据处理的技术援助，包括数据编辑、清洗、加权和平板化。在中央办公室，从现场团队的CAPI应用程序接收的数据被登记并检查是否存在任何不一致之处。数据编辑和清洗包括广泛的结构和内部一致性检查。任何异常情况都会通知团队（现场）监督员，以便数据处理团队解决数据差异。
响应率
---------------------------
共选择了3,750户家庭作为样本，其中在实地工作期间有3,735户被占用。在这些被占用的家庭中，3,729户成功接受了访谈，总家庭响应率为99.8%。在受访的家庭中，确定了3,861名符合个人访谈条件的女性，其中3,860名女性成功接受了访谈，响应率为100%。
抽样误差估计
---------------------------
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。非抽样误差是实施数据收集和数据处理过程中出现的错误的结果，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2017年马拉维疟疾指标调查（MMIS）期间做出了众多努力以尽量减少此类错误，但非抽样误差是无法避免且难以进行统计分析的。
另一方面，抽样误差可以进行分析评估。2017年MMIS中选定的受访者样本只是从同一总体中可能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样本的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。尽管变异性程度并不完全清楚，但可以从调查结果中估计出来。
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，它是方差的平方根。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总体中的真实值。
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（大小和设计相同）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，2017年MMIS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SAS，通过ICF宏开发的程序进行计算。这些程序使用泰勒线性化方法进行方差估计，以对调查估计的均值、比例或比率进行估计。
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
- 符合条件和接受访谈的女性的年龄分布
- 报告的完整性
- 按日历年计算的出生人数
关于数据质量表的详细信息，请参阅调查最终报告的附录C。