Malaria Indicator Survey 2019 - Ghana

Abstract
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The primary objective of the survey is to provide current estimates of key malaria indicators. Specific objectives were:
▪ To measure the extent of ownership and use of mosquito bed nets
▪ To assess coverage of intermittent preventive treatment to protect pregnant women
▪ To identify practices and specific medications used for treating malaria among children under age 5
▪ To measure indicators of behaviour change communication messages, knowledge, and practices regarding malaria
▪ To measure the prevalence of malaria and severe anaemia among children age 6-59 months

The findings from the 2019 GMIS will assist policymakers and programme managers in evaluating and designing programmes and strategies for improving malaria control interventions in Ghana.

Geographic coverage
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National coverage

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

Universe
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The survey covered all de jure household members (usual residents), women age 15-49 years and children age 6-59 months resident in the household.

Kind of data
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Sample survey data [ssd]

Sampling procedure
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The sample for the 2019 GMIS was designed to provide estimates of key malaria indicators for the country as a whole, for urban and rural areas separately, and for each of the 10 administrative regions (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West) as defined in the Ghana 2010 Population and Housing Census (PHC).

The sampling frame used for the 2019 GMIS is the frame of the 2010 PHC, conducted in Ghana by GSS. In 2019, Ghana created six new regions, resulting in a total of 16 regions and 260 administrative districts; however, during survey design, the new administrative boundaries were not available. The 2019 GMIS sampling frame is therefore based on the 10 regional boundaries defined according to the 2010 PHC. The frame is a complete list of all census enumeration areas (EAs) created for the PHC. An EA is the smallest geographic area that can be easily canvassed by an enumerator during an enumeration exercise. The sampling frame contains information about EA location, type of residence (urban or rural), the estimated number of residential households, and the estimated population.

The 2019 GMIS sample was stratified and selected from the sampling frame in two stages. In the first stage, 200 EAs (97 in urban areas and 103 in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. In the second stage of selection, a fixed number of 30 households was selected from each cluster to make up a total sample size of 6,000 households.

For further details on sample design, 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 types of questionnaires were used for the 2019 GMIS: the Household Questionnaire, the Woman’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. The questionnaires were adapted to reflect issues relevant to Ghana. Modifications were determined after a series of meetings with various stakeholders from the NMCP and other government ministries and agencies, nongovernmental organisations, and international partners. The Household and Woman’s Questionnaires in English and four local Ghanaian languages (Akan, Dagbani, Ewe, and Ga) were programmed into tablet computers, which enabled the use of computer-assisted personal interviewing for the survey. The Biomarker Questionnaire, also translated into four local languages, was filled out on hard copy and entered into the CAPI system when complete.

Cleaning operations
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Data for the 2019 GMIS were collected through questionnaires programmed into the CAPI application. The CAPI application was programmed by The DHS Program and loaded into the computers along with the Household, Biomarker, and Woman’s Questionnaires. Using the Internet File Streaming System (IFSS) developed by The DHS Program, the field supervisors transferred data on a daily basis to a central location for data processing in the GSS office located in Accra. To facilitate communication and monitoring, each fieldworker was assigned a unique identification number.

The Census and Survey Processing (CSPro) program was used for data editing, cleaning, weighting, and tabulation. Data received from the field teams’ CAPI applications were registered and checked for any inconsistencies and outliers at the GSS Head Office. Data editing and cleaning included an extensive range of structural and internal consistency checks. All anomalies were communicated to field teams, which resolved data discrepancies. The corrected results were maintained in master CSPro data files and then used in producing tables for the final report.

Sampling error estimates
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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 2019 Ghana Malaria Indicator Survey (GMIS) 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 2019 GMIS 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% 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 2019 GMIS 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 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.

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
- Completeness of reporting
- Births by calendar years
- Number of enumeration areas completed by month, according to region, Ghana MIS 2019
- Percentage of children age 6-59 months classified as having malaria according to rapid diagnostic test (RDT), by month and region, Ghana MIS 2019
- Number of children age 6-59 months measured for malaria via rapid diagnostic test (RDT), by month and region (unweighted), Ghana MIS 2019

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

摘要
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本调查的主要目标是为关键疟疾指标提供当前估计值。具体目标包括：
▪ 测量蚊帐拥有和使用程度
▪ 评估保护孕妇的间歇性预防治疗覆盖率
▪ 确定用于治疗5岁以下儿童疟疾的实践和特定药物
▪ 测量与疟疾相关的行为改变沟通信息、知识和实践指标
▪ 测量6至59个月儿童中疟疾和严重贫血的患病率

2019年GMIS的调查结果将协助政策制定者和项目管理者评估和设计改进加纳疟疾控制干预措施的项目和策略。
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至5岁儿童
- 15至49岁妇女

总体
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本调查覆盖了所有法定家庭成员（常住居民）、15至49岁的妇女和居住在家庭中的6至59个月儿童。
数据类型
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样本调查数据 [ssd]
抽样程序
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2019年GMIS的样本设计旨在为国家整体、城市和农村地区分别，以及每个定义在2010年加纳人口和住房普查（PHC）中的10个行政区域（西部、中部、大阿克拉、沃尔特、东部、阿散蒂、布隆阿哈福、北部、上东和上西）提供关键疟疾指标估计值。

2019年GMIS使用的抽样框架是2010年PHC的框架，该框架由GSS在加纳进行。2019年，加纳创建了六个新地区，从而使行政区总数达到16个，行政区域总数达到260个；然而，在调查设计期间，新的行政边界尚不可用。因此，2019年GMIS的抽样框架基于根据2010年PHC定义的10个区域边界。该框架是PHC为普查创建的所有人口普查区（EAs）的完整清单。EAs是在普查作业期间可以被计数员轻松调查的最小地理区域。抽样框架包含有关EAs位置、居住类型（城市或农村）、估计的住宅户数和估计人口的信息。

2019年GMIS样本分为两个阶段进行分层和选择。在第一阶段，根据EAs规模按比例选择200个EAs（城市地区97个，农村地区103个），并在每个抽样层中进行独立选择。在选择的第二阶段，从每个集群中选择了固定数量的30户家庭，以形成一个总共6,000户家庭的样本量。

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
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计算机辅助个人访谈 [capi]
研究工具
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2019年GMIS使用了四种类型的问卷：家庭问卷、妇女问卷、生物标志物问卷和调查员问卷。问卷被改编以反映与加纳相关的问题。在一系列与NMCP和其他政府部門和机构、非政府组织以及国际合作伙伴的利益相关者会议之后，确定了修改。家庭和妇女问卷用英语和四种当地加纳语（阿坎语、达加班语、埃维语和加语）编程到平板电脑中，这使得调查可以使用计算机辅助个人访谈。生物标志物问卷也翻译成四种当地语言，以硬拷贝形式填写，并在完成时输入到CAPI系统中。
清洗操作
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2019年GMIS的数据通过编程到CAPI应用程序中的问卷进行收集。CAPI应用程序由DHS项目编程，并加载到计算机中，包括家庭、生物标志物和妇女问卷。使用DHS项目开发的互联网文件流系统（IFSS），现场监督员每天将数据传输到位于阿克拉的GSS办公室进行数据处理。为了促进沟通和监控，每个现场工作人员都被分配了一个唯一的识别号码。

使用人口普查和调查处理（CSPro）程序进行数据编辑、清洗、加权和平板整理。从现场团队CAPI应用程序接收到的数据在GSS总部进行了登记，并检查了任何不一致和异常。数据编辑和清洗包括广泛的结构和内部一致性检查。所有异常都通报给了现场团队，这些团队解决了数据差异。修正后的结果保存在主CSPro数据文件中，然后用于制作最终报告中的表格。
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。非抽样误差是实施数据收集和数据处理中犯错误的结果，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2019年加纳疟疾指标调查（GMIS）期间做出了巨大努力以最大限度地减少此类错误，但非抽样误差是无法避免且难以从统计上进行评估的。

另一方面，抽样误差可以统计评估。2019年GMIS中选定的受访者样本只是从同一总体中按相同设计和预期规模选出的许多样本之一。这些样本中的每一个都会产生与实际样本选择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指标。尽管变异程度无法确切知道，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，这是方差的平方根。标准误差可以用来计算置信区间，在这个区间内可以合理地假设总体真实值。

例如，对于从样本调查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加减两倍标准误差的范围内。

如果受访者样本是按简单随机样本选择的，则可以使用简单的公式来计算抽样误差。然而，2019年GMIS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进行方差估计，用于调查估计的均值、比例或比率。

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
- 合格和接受访谈的妇女年龄分布
- 报告的完整性
- 日历年度出生
- 根据地区按月完成的人口普查区数量，加纳MIS 2019
- 根据快速诊断测试（RDT）按月和地区分类的6-59个月儿童疟疾患病率
- 按月和地区（未加权）通过快速诊断测试（RDT）测量的6-59个月儿童疟疾病例数，加纳MIS 2019

有关数据质量表的详细信息，请参阅最终报告附录C。