Tunisia - World Health Survey 2003

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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.

Type: 
Microdata
Acronym: 
WHS 2003
Languages Supported: 
English
Topics: 
Topic not specified
Geographical Coverage: 
Tunisia
Release Date: 
April 25, 2012

Last Updated

Last Updated: 
September 24, 2013

Harvest System ID

Harvest System ID: 
Microdata

Harvest Source ID

Harvest Source ID: 
2267
Funding Name, Abbreviation, Role: 
World Health Organization
Study Type: 
World Health Survey [hh/whs]
Unit of Analysis: 
Households and individuals
Primary Investigator Name, Affiliation: 
World Health Organization (WHO)
Sampling Procedure: 
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.
Response Rates: 
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.
Access Authority Name, Affiliation, Email: 

World Health Organization (WHO), [email protected]

Time Periods: 
August, 2017

No Visualizations Available.

Citation requirement is the way that the dataset should be referenced when cited in any publication. This will guarantee that the data producer gets proper credit, and that analytical results can be linked to the proper version of the dataset. The citation should include at least the primary investigator, the name and abbreviation of the dataset, the reference year, and the version number (and website address and date of download when the dataset was obtained on-line).

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.

FieldValue
Modified Date
2017-09-05
Release Date
Identifier
141584c4-a994-4621-bba8-d4c25f576511
License
License Not Specified
Contact Email
Rating: 
0
No votes yet
Acronym: 
WHS 2003
Type: 
Languages Supported: 
Access Authority Name, Affiliation, Email: 
World Health Organization (WHO), [email protected]
Response Rates: 
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.
Time Periods: 
August, 2017
Primary Investigator Name, Affiliation: 
World Health Organization (WHO)
Funding Name, Abbreviation, Role: 
World Health Organization
Unit of Analysis: 
Households and individuals
Geographical Coverage: 
Data Classification of a Dataset: 
Sampling Procedure: 
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.
Release Date: 
Wednesday, April 25, 2012
Last Updated Date: 
Tuesday, September 24, 2013
Harvest Source: 
Harvest System ID: 
2267
Citation Text: 
Citation requirement is the way that the dataset should be referenced when cited in any publication. This will guarantee that the data producer gets proper credit, and that analytical results can be linked to the proper version of the dataset. The citation should include at least the primary investigator, the name and abbreviation of the dataset, the reference year, and the version number (and website address and date of download when the dataset was obtained on-line).
Modified date: 
15972
Study Type: 
World Health Survey [hh/whs]
Primary Dataset: 
Yes
Mode of Data Collection: 

Face-to-face

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