World Health Survey 2003 - Latvia

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系统的性能。无论卫生系统的类型如何，个人都会就机构对其需求的响应，在健康和非健康期望方面有所考量。然而，在许多国家，卫生系统未能有效运行，部分原因在于缺乏对卫生系统性能和不同服务提供者的信息。
世界卫生组织世界卫生调查的目的是向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、卫生系统的反应性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的方式，了解人们对健康状况的价值评估，测量卫生系统在响应方面的表现，并通过基于全国代表性人群的社区调查，收集关于医疗 encounters 的支付模式和程度的信息。此外，它还涉及医疗支出、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健康干预措施的覆盖率，具体包括额外的模块。
调查计划的目标包括：
1. 开发一种方法，以低成本提供有效、可靠且可比的信息，以补充常规卫生信息系统提供的信息。
2. 建立政策制定者所需的政策依据，以监测卫生系统是否实现既定目标，并评估额外投资于卫生是否达到预期成果。
3. 为政策制定者提供所需的证据，以便必要时调整其政策、战略和计划。
地理覆盖范围
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调查抽样框架必须涵盖该国合格人口的 100%，这意味着必须包括整个国家领土。这并不意味着每个省或地区都需要在调查样本中有所代表，而是意味着所有省或地区都必须有机会（已知概率）被纳入调查样本。
可能存在特殊情况，无法实现 100% 的国家覆盖率。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，例如特定的省或地区。例如，如果国家 X 的区域 D 的 3/4 由于战争而无法访问，则整个区域 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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WHS 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，这包括在调查期间可能因健康原因住院的人口：所有在住院时符合家庭成员定义的人员都包括在合格人口中。
如果随机选择的个人短期住院（例如，在医院的 3 天住院），则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住院（例如，过去 8 年一直在养老院），则调查员必须前往该机构对其进行访谈。
目标人群包括任何居住在私人家庭中的男性或女性成年人，年龄为 18 岁或以上。居住在集体宿舍、军事保留地或其他非家庭居住安排的人群不符合研究资格。在访问家庭时由于健康问题（如医院、临终关怀机构、养老院、养老院等）处于机构中的人将被访谈，无论是在机构中还是在返回家庭后（如果在此后的两周内），只要这是从第一次访问家庭后。
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个体都有已知的非零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进行多阶段集群抽样。
WHS 抽样框架应涵盖调查国家合格人口的 100%。这意味着该国中的每个合格人都有机会被纳入调查样本。这也意味着特定民族群体或地理区域可能不会被排除在抽样框架之外。
WHS 在每个国家的样本量是 5000 人（按国家单独考虑例外情况）。必须从抽样框架中抽取足够的人数，以考虑估计的非响应量（拒绝参与、空房屋等）。应使用对潜在非响应和空房屋的最高估计，以确保在调查期结束时达到所需的样本量。这一点非常重要，因为如果在数据收集结束时未达到所需的 5000 个样本量，则必须从抽样框架中随机选择额外的人员加入调查样本。这种情况既昂贵又技术复杂（如果出现这种情况，请咨询 WHO 抽样专家以获得协助），并且最好在数据收集开始前进行适当规划。
所有抽样步骤，包括分层理由、集群大小、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，都必须通报 WHO
分层
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分层是将人口划分为子组的过程。然后将在每个子组中分别进行抽样。选择层或子组是因为有证据表明它们与结果（例如健康、响应性、死亡率、覆盖率等）有关。选择的层将因国家而异，并反映当地情况。可以分层的因素示例包括地理（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），或在区域内存在卫生设施。
要使用的层必须由每个国家确定，并且必须明确说明选择理由。
分层在抽样第一阶段强烈推荐。一旦选择了层并证明了其合理性，则将在每个层中分别进行所有选择阶段。我们建议根据 3-5 个因素进行分层。理想情况下，层的数量应该是变量数量的一半（注意分层变量与层之间的区别，变量可以是性别、社会经济地位、省/地区等，而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）。
层应该在内部尽可能同质，在之间尽可能异质。这意味着层应该以这种方式制定，即属于层的个人在关键变量方面应该尽可能相似，并且尽可能不同于属于不同层的人。这最大限度地提高了分层在减少抽样方差方面的效率。
多阶段集群选择
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集群是人口中自然存在的单位或分组（例如，统计区域、城市、大学、省、医院等）；它是具有清晰、非重叠边界的行政单位。集群抽样之所以有用，是因为它避免了必须编制每名人口中每个人的详尽名单。集群应在内部尽可能异质，在之间尽可能同质（注意这与层的标准相反）。集群应尽可能小（即大型行政单位，如省或州不是好的集群），但不应如此小以至于同质。
在集群抽样中，从集群列表中随机选择一定数量的集群。然后，包括所选集群的所有成员或从他们中随机选择的一部分。多阶段抽样是集群抽样的一种扩展，其中从较大的集群到较小的集群选择集群层次结构。
为了进行多阶段抽样，需要知道抽样单位的人口规模。但是，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）的完整名单；为了能够在 TSU 中随机选择所有家庭，需要所有这些家庭的名单。这些信息可能来自最新的人口普查。如果上次普查已超过 3 年或提供的信息质量差或不可靠，则调查人员将负责对随机选择的最低抽样单位中的所有家庭进行普查。如果必要，必须为此步骤编制预算，并确保所有家庭都得到适当的统计，以便获得代表性样本。
最好尽可能在 PSU 中拥有尽可能多的集群。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，这会增加样本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 人的样本量，应该从样本中选取 100-200 个 PSU 集群。
概率抽样
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概率抽样意味着抽样框架中的每个个体都有已知的非零概率被选入调查样本。非概率抽样方法，如配额或便利抽样和随机游走，可能会将偏差引入调查，会使调查发现受到质疑，并且 WHO 不接受这些方法。
每个集群被选入调查样本的概率将与它的相对大小成比例。系统抽样
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系统抽样是从列表中以固定间隔有序抽样的方法，从随机选择的点开始。通常，系统抽样不在抽样第一阶段（PSU 选择）中使用，因为它会使抽样误差的估计变得困难。
系统抽样建议在 SSU、TSU 和家庭选择阶段的抽样中使用。系统抽样可以是线性的或循环的。
家庭选择
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家庭是获取个人的工具。家庭是抽样单位，个人是观察单位。虽然最好是从一个国家所有合格人员的列表中进行随机选择，但这种列表，除少数例外，通常不可用，因此我们必须使用最终的集群，即家庭，来获取我们的观察单位。
将从住宅单位的列表中选择家庭。不接受家庭选择方面的非概率方法，如随机游走。
此类列表通常来自人口登记、家庭清单、选民名单和普查名单。由于必须将所有家庭纳入抽样框架，因此必须评估选择家庭的方法：
- 自这些列表制作以来，人口发生了多大变化？
- 覆盖范围的完整性。是否存在未注册的人口（例如，贫民窟）
- 人口流动
- 注册的变化
质量
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几乎所有的列表都会出现常规问题。WHO 建议调查机构对随机选入调查样本的抽样单位中的所有家庭进行手动统计。
如果使用现有的列表或登记处，则必须对其质量进行详细分析，并必须对其进行更新，以确保没有家庭被排除在调查抽样框架之外。
家庭名单中选择个人
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调查样本中每个选定的家庭的成员都将登记在家庭名单上。家庭成员定义为通常住在家庭中、睡觉和共享餐食、将此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 商讨后，允许每个国家在标准定义上有所变化。
Kish 表格
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调查的受访者将在家庭所有合格成员中使用 Kish 表格进行选择。Kish 表格提供了一种方法，其中每个家庭的合格人员都有同等概率被选入调查样本。
这对于调查样本的代表性以及调查的完整性至关重要。必须正确实施 Kish 表格。
所有未正确实施 Kish 选择方法的访谈都将被拒绝。
Kish 技术允许对家庭中的所有人进行充分的代表性。
数据收集方式
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面对面 [f2f]
响应率
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对整个家庭进行适当和完整的统计和描述是调查过程的一个关键组成部分。
调查样本中所有选定的家庭的家庭名单必须完成，无论他们是否同意参与调查。
只有这样，我们才能收集估计调查非响应偏差所需的基本信息。
为了确保我们有足够的样本人数来进行精确估计，需要增加调查样本量以调整估计的非响应量是必要的。然而，这并没有考虑到非响应产生的偏差，因为非响应者通常与响应者在关键变量方面有所不同，这些关键变量与调查所研究领域的相关度较高。
因此，必须尽一切努力来最小化非响应，并在调查样本中尽可能采访更多的人。
拒绝转换方法、调查意识提高和召回的详细讨论可以在 WHS 调查手册中找到。
存在两种可能的非响应场景：
1) 访谈员完成家庭名单，随机选择的受访者拒绝参与。
2) 访谈员被拒绝进入家庭，无法填写家庭名单。
在第二种情况下，地点必须确保至少完成封面页的 00.1 和 00.3 页。
此外，如果从普查信息中可用，则应提供家庭中成年男性（18 岁或以上）和女性的数量以及他们的相应年龄。
重要的是要注意，家庭名单的完成服务于一个目的，即超越提供一个受访者选择的列表。在家庭名单和从地点收集的关于人口统计和其他信息方面，这些信息用于计算调查数据的统计分析的分母；如果没有家庭名单中的信息，我们就不能够确定国家中的健康相关结果率。