World Health Survey 2003 - Zimbabwe

Abstract
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Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to:
1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems.
2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes.
3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage
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The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit
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Households and individuals

Universe
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The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data
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Sample survey data [ssd]

Sampling procedure
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SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which increases sample variance and effectively reduces our estimating power. WHO requires an absolute maximum of 50 respondents per PSU, and ideally would suggest 20-30. This means that for a sample size of 5000 respondents, 100- 200 PSU clusters should be taken into the sample. Calculating that, roughly, one fifth of the total number of PSU clusters in a country will be randomly selected into the survey sample, the sampling frame should consist of 500-1000 PSU clusters.

PROBABILITY SAMPLING

Probability sampling means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. Non-probability methods of sampling such as quota or convenience sampling and random walk, may introduce bias into the survey, will throw survey findings into question, and are not accepted by WHO.

The probability of selection into the survey sample for each cluster will be proportional to its relative size. Systematic Sampling Systematic sampling is the ordered sampling at fixed intervals from a list, starting from a randomly chosen point. Typically, systematic sampling is not used at the first stage of sampling (selection of PSUs) because it renders the estimation of sampling error difficult.

Systematic sampling is recommended at the SSU, TSU, and household selection stages of sampling. Systematic sampling may be linear or circular.

SELECTION OF HOUSEHOLDS

The Household is a device used to get at the individual. The household is the sampling unit while the individual is the observational unit. While it would be preferable to randomly select from a list of all eligible persons in a country, such lists, with a few exceptions, are not available, so we must employ a final cluster, the household, to get at our observational units.

Households will be selected from lists of dwelling units. Non-probabilistic methods of household selection such as the random walk are not acceptable. Such lists are typically available from population registries, household listings, voter lists and census list. As it is essential to include all households in the sampling frame, an assessment of the methodology employed to select households must be made:
- How much has the population changed since these lists were made?
- Completeness of coverage. Are there unregistered populations (e.g. slums)
- Population shifts
- Changes in Registry

QUALITY

Almost all lists will suffer from routine problems. WHO recommends that survey institutions manually enumerate all the households in the sampling units randomly selected into the survey sample. If existing lists or registries will be used, then a detailed analysis of their quality must be made and they must be updated to ensure that there is no exclusion of households from the survey sampling frame.

SELECTION OF INDIVIDUALS FROM HOUSEHOLD ROSTER

All members of each household selected into the survey sample will be enumerated on the household roster. A member of the household is defined as someone who usually stays in the household, sleeps and shares meals, who has that address as primary place of residence, or who spends more than 6 months a year living there. Country-specific variations in this standard definition are allowed in consultation with WHO.

KISH TABLES

The respondent for the survey will be selected among all eligible members of the household using Kish tables. Kish tables provide a method by which each eligible person in a household has an equal probability of selection into the survey sample. It is extremely important for the representativeness of the survey sample and the integrity of the survey that the Kish tables are properly implemented. All interviews where the Kish selection method is not properly implemented will be rejected. The Kish technique allows adequate representation for all the persons in the household.

Mode of data collection
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Face-to-face [f2f]

Response rate
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The proper and complete enumeration and description of the entire household is a critical component of the survey process. The household roster must be completed for all households selected randomly into the survey sample, whether they agreed to participate in the survey or not. It is only in this way that we can collect the basic information required to estimate the non-response bias in the survey.

The requirement of augmenting the survey sample size to adjust for estimated non-response is necessary to ensure that we have adequate persons in the sample to have the power to make precise estimates. This does not, however, account for the bias that is created by non-response, since non-responders are often different from responders with respect to key variables that are linked to the domains under study in the survey. All effort, therefore, must be made to minimise non-response, and to interview as many people in the survey sample as possible. A detailed discussion of refusal conversion methods, survey awareness raising, and call-backs is found in the WHS Survey Manual.

There are two possible scenarios of non-response:
1) The interviewer completes the household roster and the randomly chosen respondent refuses to participate.
2) The interviewer is refused access to the household and is unable to fill in the household roster.
In second scenario, sites must ensure that, at least, pages 00.1 and 00.3 of the Coversheet are completed for the household. In addition, if available from census information, the number of adult (18 years of age or older) males and females in the household, and their respective ages should be provided. It is important to note that the completion of the household roster serves a purpose above and beyond providing a list from which a respondent will be selected.

The demographic and other information collected in the household roster and requested from sites serves to calculate the denominators for statistical analysis of the survey data; without the information in the household roster, we would not be able to determine the health-related outcome rates in your country.

摘要
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各国在健康结果方面存在差异，部分原因在于各自卫生体系的运行方式。无论卫生体系的类型如何，个人都会对机构如何回应其需求持有健康和非健康期望。然而，在许多国家，卫生体系的有效性并不高，这在很大程度上是由于缺乏对卫生体系绩效以及不同服务提供者的信息。

世界卫生组织全球卫生调查的目的是为国家卫生信息系统提供经验数据，以便更好地监测人民的健康状况、卫生系统的响应能力和健康相关参数的测量。

调查的整体目标在于审视人群报告其健康状况的方式，了解人们对健康状况的价值观，测量卫生系统在响应能力方面的绩效，并通过基于全国代表性人口社区调查收集有关健康接触的支付方式和程度的详细信息。此外，它还涉及各种领域，如卫生支出、成人死亡率、出生史、各种风险因素、主要慢性健康状况的评估以及卫生干预的覆盖范围，具体包括额外的模块。

调查项目的目标是：
1. 开发一种提供有效、可靠且可比信息的方法，以低成本补充常规卫生信息系统提供的信息。
2. 构建政策制定者监控卫生体系是否达到预期目标以及评估额外投资于卫生是否实现预期成果所需的证据基础。
3. 为政策制定者提供必要的证据，以便根据需要调整其政策、战略和计划。

地理覆盖范围
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调查抽样框架必须覆盖国家的全部合格人口，这意味着必须包括整个国家领土。这并不意味着每个省份或地区都需要在调查样本中有所代表，而是所有地区都必须有机会（已知概率）被纳入调查样本。

可能存在特殊情况，导致无法实现100%的国家覆盖。由于可访问性或冲突等原因，某些国家的一些地区可能无法包括在内。所有此类例外情况都必须与WHO抽样专家讨论。如果必须排除任何地区，则必须构成一个连贯的区域，例如一个特定的省份或地区。例如，如果国家X的地区D的3/4由于战争而无法访问，则整个地区D将被排除在分析之外。

分析单元
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家庭和个人

总体
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全球卫生调查将包括在调查期间不在国外的所有男性和女性成年人（18岁及以上）。应注意的是，这包括在调查期间因健康原因被机构化的人群：所有在机构化时符合家庭成员定义的人都被纳入合格人群。

如果随机选择的个人短期机构化（例如，在医院的3天停留），调查员必须在个人返回时返回家庭进行访谈。如果随机选择的个人长期机构化（例如，在过去8年中一直在养老院），调查员必须前往该机构对其访谈。

目标人群包括任何18岁或以上、居住在私人家庭中的成年人，无论是男性还是女性。在集体宿舍、军事保留地或其他非家庭居住安排中的群体将不符合研究资格。在访问家庭时因健康原因（如医院、临终关怀院、养老院、老年人之家等）在机构中的人将在机构或返回家庭后（如果这是在首次访问家庭后的两周内）接受访谈。

数据类型
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样本调查数据 [ssd]

抽样程序
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WHS 抽样指南

WHS 程序中的调查必须采用概率抽样设计。这意味着抽样框架中的每个人都有已知的且非零的概率被选入调查样本。虽然单阶段随机抽样在可行时是理想的，但人们认识到大多数地点将执行多阶段聚类抽样。

WHS 抽样框架应覆盖调查国家中所有合格人口的100%。这意味着国家中的每个合格人都有机会被纳入调查样本。这也意味着某些民族群体或地理区域不应从抽样框架中排除。

WHS 在每个国家中的样本量是5000人（按国家考虑例外情况）。必须从抽样框架中抽取足够的人员，以考虑到估计的非响应量（拒绝参与、空房子等）。应使用对潜在非响应和空房子的最高估计，以确保在调查期末达到所需的样本量。这一点非常重要，因为如果在数据收集结束时，所需的5000人样本量尚未达到，则必须从抽样框架中随机选择额外的人员进入调查样本。这种情况既昂贵又技术复杂（如果出现这种情况，请咨询WHO抽样专家以获得帮助），并且最好在数据收集开始之前进行适当的规划。

所有抽样步骤，包括分层的理由、聚类大小、选择概率、选择每个阶段的权重以及用于随机化的计算机程序，都必须通知WHO。

分层

分层是将人口划分为子群的过程。然后在每个子群中进行单独抽样。选择层或子群是因为有证据表明它们与结果（例如健康、响应能力、死亡率、覆盖率等）相关。选择的层将因国家而异，反映当地条件。以下是一些可以分层的因素示例：地理（例如，北方、中部、南方）、城市化水平（例如，城市、农村）、社会经济区域、省份（特别是如果卫生管理主要属于省级当局的管辖权），或区域内是否存在卫生设施。必须由每个国家确定要使用的层，并明确说明选择理由。

强烈建议在抽样第一阶段进行分层。一旦选择了层并进行了说明，将在每个层中单独进行所有选择阶段。我们建议根据3-5个因素进行分层。理想的层是数量减半（注意分层变量，例如性别、社会经济地位、省份/地区等，与层之间的区别，层是变量类别的组合，例如男性、高社会经济地位、Xingtao省将是一个层）。

层应在内部尽可能同质，在之间尽可能异质。这意味着层应制定得尽可能使属于同一层的人在对关键变量的关系上尽可能相似，与属于不同层的人尽可能不同。这最大限度地提高了分层在减少抽样方差方面的效率。

多阶段聚类选择

聚类是在人口中自然发生的单位或分组（例如，统计区域、城市、大学、省份、医院等）；它是一个具有清晰、非重叠边界的行政级别单位。聚类抽样是有用的，因为它避免了必须编制人口中每个人的详尽清单。聚类应在内部尽可能异质，在之间尽可能同质（请注意，这与层的标准相反）。聚类应尽可能小（即大型行政单位，如省份或州，不是好的聚类），但不能太小以至于同质。

在聚类抽样中，从聚类列表中随机选择一定数量的聚类。然后，包括所选聚类的所有成员或从中随机选择的一部分成员。多阶段抽样是聚类抽样的扩展，其中选择一个从大到小的聚类层次结构。

为了执行多阶段抽样，需要知道抽样单位的种群大小。然而，对于高于基本单位的最低抽样单位，需要所有基本单位（家庭）的完整清单；为了能够在TSU中随机选择所有家庭，需要所有这些家庭的清单。这些信息可能来自最近的人口普查。如果上次普查已超过3年或其提供的信息质量差或不可靠，则调查人员将负责在随机选择的最低抽样单位中编制所有家庭的清单。如果需要此步骤，则必须为此步骤进行预算，并确保所有家庭都得到适当的编制，以便获得代表性样本。

最好是尽可能在PSU中有尽可能多的聚类。这样做的原因是，每个PSU中的受访者数量越少，聚类效应就越低，这会增加抽样方差并有效地降低我们的估计能力。WHO要求每个PSU的绝对最大受访者为50人，理想情况下建议为20-30人。这意味着对于5000名受访者的样本量，应将100-200个PSU聚类纳入样本。计算大约五分之一的该国PSU聚类总数将被随机选入调查样本，抽样框架应包括500-1000个PSU聚类。

概率抽样

概率抽样意味着抽样框架中的每个人都有已知的且非零的概率被选入调查样本。如配额抽样、便利抽样和随机游走等非概率抽样方法可能会引入调查偏差，将使调查发现受到质疑，并且不被WHO接受。

每个聚类的调查样本选择概率与其相对大小成比例。系统抽样系统抽样是从列表中以固定间隔按顺序抽样的抽样。通常，系统抽样不在抽样第一阶段（PSU的选择）中使用，因为它使得抽样误差的估计变得困难。

系统抽样建议在SSU、TSU和 household 选择阶段进行抽样。系统抽样可以是线性的或循环的。

household 的选择

household 是用来获取个体的工具。household 是抽样单位，而个体是观察单位。虽然最好是从一个国家所有合格人员列表中随机选择，但此类列表（除少数例外外）是不可用的，因此我们必须使用最终的聚类，即 household，来获取我们的观察单位。

将从住宅单位列表中选择 household。不接受 household 选择中的非概率方法，如随机游走。此类列表通常来自人口登记、household 列表、选民名单和普查清单。由于必须包括抽样框架中的所有 household，因此必须评估选择 household 所采用的方法：
- 自这些列表编制以来，人口发生了多大变化？
- 覆盖范围的完整性。是否有未登记的群体（例如，贫民窟）
- 人口流动
- 注册的变化

质量

几乎所有列表都会出现常规问题。WHO建议调查机构手动编制随机选入调查样本的抽样单位中的所有 household 的清单。如果使用现有的列表或登记册，则必须对其质量进行详细分析，并必须对其进行更新，以确保没有 household 被排除在调查抽样框架之外。

从 household 名册中选择 individual

每个选入调查样本的 household 的所有成员都将被列入 household 名册。家庭成员被定义为通常住在 household 中、睡觉和共进餐、将该地址作为主要居住地或每年在那里居住超过6个月的人。与WHO协商后，允许在国家层面对此标准定义进行特定变化。

KISH 表

调查的受访者将在 household 的所有合格成员中使用 KISH 表进行选择。KISH 表提供了一种方法，使 household 中的每个合格人员都有同等的机会被选入调查样本。对于调查样本的代表性以及调查的完整性，正确实施 KISH 表至关重要。所有未正确实施 KISH 选择方法的访谈将被拒绝。KISH 技术允许对 household 中所有人员提供充分的代表性。

数据收集方式
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面对面 [f2f]

响应率
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