Malaria Indicator Survey 2022 - Liberia

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

The 2022 Liberia Malaria Indicator Survey (LMIS) was implemented by the National Malaria Control Program (NMCP) in collaboration with the Liberia Institute of Statistics and Geo-Information Services (LISGIS) and with technical assistance from ICF. The U.S. President’s Malaria Initiative (PMI) and the Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM) provided financial support. Data collection took place from October 4 to December 13, 2022.

The primary objective of the 2022 LMIS was to provide up-to-date estimates of basic demographic and health indicators for malaria. Specifically, the LMIS collected information on vector control interventions such as mosquito nets, intermittent preventive treatment of malaria in pregnant women, and care seeking for and treatment of fever in children. Also, young children were tested for malaria infection and anemia.

The information collected through the LMIS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population.

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

National coverage

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

- Household
- Individual
- Woman age 15-49

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

Sample survey data [ssd]

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

The LMIS followed a two-stage sample design and was intended to allow estimates of key indicators for the following domains:
• National level
• Urban and rural areas
• Geographical regions, consisting of the following groups of counties:
- Greater Monrovia
- North Western: Bomi, Grand Cape Mount, and Gbarpolu counties
- South Central: Montserrado (excluding Greater Monrovia district), Margibi, and Grand Bassa counties
- North Central: Bong, Nimba, and Lofa counties
- South Eastern A: River Cess, Sinoe, and Grand Gedeh counties
- South Eastern B: River Gee, Grand Kru, and Maryland counties

The first stage involved selecting sample points (clusters) consisting of enumeration areas (EAs) delineated within the sampling frame. A total of 150 clusters were randomly selected using probability proportional to size. Of these clusters, 70 were in urban areas and 80 in rural areas.

A household listing operation was undertaken by LISGIS in all of the selected EAs from August to September 2022 using a tablet computer-based CSPro application. The updated lists of households served as a sampling frame for the selection of households in the second stage. In the second stage, 30 households per cluster were systematically selected, resulting in a total sample size of 4,500 households. In instances where EAs were large (greater than 300 households), segmentation was carried out; one segment at random was selected for the survey, and 30 households were selected systematically from the segment. GPS points were also collected during the listing operation in order to verify that the listing took place in the correct locations.

Because of the approximately equal sample size in each region, the sample was not self-weighting at the national level.

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

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

Computer Assisted Personal Interview [capi]

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

Three questionnaires were used in the 2022 LMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires were based on The DHS Program’s model questionnaires and were adapted to reflect the population and health issues relevant to Liberia. Country-specific topics included questions about the 2021 mass insecticide-treated net (ITN) distribution campaign, the acceptability of the new malaria vaccine, and mass drug administration (seasonal malaria chemoprevention for children). The questionnaires were prepared in English, with some text adapted to Liberian English. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer assisted personal interviewing (CAPI) for data collection purposes.

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

The processing of the 2022 LMIS data began immediately after the start of fieldwork. As data collection was ing completed in each cluster, all electronic data files were transferred via SyncCloud to the NMCP central office in Monrovia. Data files were registered and checked for inconsistencies, incompleteness, and outliers. e field teams were alerted of any inconsistencies and errors. Secondary editing carried out in the central office involved resolving inconsistencies and coding open-ended questions. Data entry and editing were carried out using the CSPro software package. Concurrent processing of the data offered a distinct advantage because it maximized the likelihood of the data being error-free and accurate. Secondary editing of the data was completed in January 2023.

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

A total of 4,486 households were selected for the survey, of which 4,338 were occupied and 4,306 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 4,598 women age 15–49 were identified for individual interviews. Interviews were completed with 4,513 women, yielding a response rate of 98%.

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

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 in 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 2022 Liberia Malaria Indicator Survey (2022 LMIS) 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 2022 LMIS 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. 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 2022 LMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2022 LMIS is an SAS program. This program uses the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.

Sampling errors tables are presented in Appendix B of the final report.

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

Data Quality Tables

- Household age distribution
- Age distribution of eligible and interviewed women
- Age displacement at ages 14/15
- Age displacement at ages 49/50
- Live births by years preceding the survey
- Completeness of reporting
- Observation of mosquito nets
- Number of enumeration areas completed by month and region
- Positive rapid diagnostic test (RDT) results by month and region
- Concordance and discordance between rapid diagnostic test (RDT) and microscopy results
- Concordance and discordance between national and external quality control laboratories

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

摘要
---------------------------
2022年利比里亚疟疾指标调查（LMIS）由国家疟疾控制计划（NMCP）实施，与利比里亚统计局和地理信息服务院（LISGIS）合作，并得到ICF的技术援助。美国总统疟疾倡议（PMI）和全球抗击艾滋病、结核病和疟疾基金（GFATM）提供了财务支持。数据收集于2022年10月4日至12月13日进行。

2022年LMIS的主要目标是提供疟疾基本人口健康指标的最新估计。具体而言，LMIS收集了关于蚊子网、孕妇疟疾间歇性预防治疗以及儿童发热寻求护理和治疗的信息。此外，对幼儿进行了疟疾感染和贫血的检测。

通过LMIS收集的信息旨在协助政策制定者和项目管理者设计、评估旨在改善该国人口健康的计划和战略。

地理覆盖范围
---------------------------
全国覆盖

分析单元
---------------------------
- 家庭
- 个人
- 15-49岁女性

数据类型
---------------------------
样本调查数据 [ssd]

抽样程序
---------------------------
LMIS遵循两阶段样本设计，旨在允许对以下领域的关键指标进行估计：
• 国家层面
• 城市和农村地区
• 地理区域，包括以下县群：
- 大蒙罗维亚
- 西北部：博米、大卡波马特和加巴尔波卢县
- 南部中部：蒙塞拉多（不包括大蒙罗维亚区）、马吉比和格兰德巴萨县
- 北部中部：邦、尼姆巴和洛法县
- 东南部A：河塞斯、锡诺埃和格兰德吉赫县
- 东南部B：河吉、格兰德库鲁和马里兰州

第一阶段涉及选择样本点（聚类），这些聚类由抽样框架内划定的统计区域组成。总共随机选择了150个聚类，其中70个位于城市地区，80个位于农村地区。

LISGIS在所有选定的统计区域从2022年8月到9月进行了家庭清单操作，使用基于平板电脑的CSPro应用程序。更新的家庭清单作为第二阶段选择家庭的数据框架。在第二阶段，每个聚类系统地选择了30个家庭，从而总共样本量为4,500个家庭。在统计区域较大的情况下（超过300个家庭），进行了分割；随机选择一个分割区域进行调查，并从该区域系统地选择30个家庭。在清单操作期间还收集了GPS点，以验证清单是在正确位置进行的。

由于每个地区的样本量大致相等，因此样本在国家层面上不具有自加权性。

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
---------------------------
计算机辅助个人访谈 [capi]

研究工具
---------------------------
2022年LMIS使用了三个问卷：家庭问卷、女性问卷和生物标志物问卷。问卷基于DHS项目的模型问卷，并调整为反映利比里亚的人口和健康问题。特定于国家的话题包括关于2021年大规模杀虫剂处理蚊帐（ITN）分发活动、新疟疾疫苗的接受度和大规模药物发放（儿童季节性疟疾化学预防）的问题。问卷以英语准备，其中一些文本调整为利比里亚英语。家庭和女性问卷被编程到平板电脑中，以便进行数据收集的计算机辅助个人访谈（CAPI）。

数据清理操作
---------------------------
2022年LMIS数据的处理始于现场工作开始后。在完成每个聚类的数据收集后，所有电子数据文件通过SyncCloud传输到蒙罗维亚的NMCP中央办公室。数据文件被注册并检查不一致性、不完整性和异常值。现场团队被通知任何不一致和错误。在中央办公室进行的二级编辑涉及解决不一致性和编码开放式问题。数据录入和编辑使用CSPro软件包进行。数据的同时处理提供了一个显著的优势，因为它最大限度地提高了数据无错误和准确的可能性。数据二级编辑于2023年1月完成。

响应率
---------------------------
总共选择了4,486个家庭进行调查，其中4,338个被占用，4,306个被成功访谈，响应率为99%。在访谈的家庭中，确定了4,598名15-49岁的女性进行个人访谈。完成了4,513名女性的访谈，响应率为98%。

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，以及数据录入错误。尽管在实施2022年利比里亚疟疾指标调查（2022年LMIS）期间采取了众多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的，并且难以从统计上进行评估。

另一方面，抽样误差可以统计评估。2022年LMIS中选定的受访者样本只是从同一人口中、使用相同设计和预期规模可以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（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，其中可以合理地假设人口的真实值会落在该区间内。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%的所有可能相同大小和设计的样本中落在该统计量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，2022年LMIS样本是多层次分层设计的结果，因此有必要使用更复杂的公式。用于计算2022年LMIS抽样误差的计算机软件是SAS程序。该程序使用泰勒线性化方法对调查估计进行方差估计，这些估计是均值、比例或比率。

抽样误差表在最终报告的附录B中提供。

数据评估
---------------------------
数据质量表

- 家庭年龄分布
- 有资格和被访谈女性的年龄分布
- 14/15岁年龄位移
- 49/50岁年龄位移
- 调查前几年的活产
- 报告的完整性
- 蚊帐观察
- 每月和地区的统计区域完成数量
- 每月和地区的阳性快速诊断测试（RDT）结果
- 快速诊断测试（RDT）和显微镜结果之间的一致性和不一致性
- 国家和外部质量控制实验室之间的一致性和不一致性

有关数据质量表的详细信息，请参阅最终报告的附录C。