Reproductive Health Survey 2005
Georgia, 2005
Identification
GEO_2005_RHS_v01_M
Reproductive Health Survey 2005
| Name | Country code |
|---|---|
| Georgia | GEO |
Other Household Health Survey [hh/hea]
Over the past several years, the United States Agency for International Development (USAID), the United Nations Population Fund (UNFPA), and other multilateral and bilateral donors have invested resources to improve access to family planning and other reproductive health services in Georgia.
Similar reproductive health survey (RHS) projects were conducted in seven other countries in Eastern Europe beginning in the mid-1990s. A major purpose of the RHS is to produce national estimates of factors related to pregnancy and fertility, such as sexual activity and contraceptive use; use of abortion and other medical services; and maternal and infant health.
Through funds provided by USAID and UNFPA, a nationwide Reproductive Health Surveys (RHS) was conducted in Georgia in 1999. A new cycle was implemented in March 2005, with field work ending in July of that year. A major function of successive cycles of the survey is to produce comparable time trend data. Generally, the 2005 survey was modeled after the 1999 RHS and other similar surveys conducted in the region with technical assistance from CDC. The proposed content of the second RHS was reviewed and commented upon by Georgian national experts, government representatives, and researchers from inside and outside governmental organizations, as well as donor agencies. A final panel of experts was set up to discuss the expanded draft questionnaire. Additional revisions were made in the months following that meeting, and the final questionnaire was pretested in December 2004.
The majority of the expert panel recommendations were addressed, with the exception of two: 1) a request for increase in the sample size, which was not possible due to budgetary constraints; and 2) the inclusion of a sample of men in the survey, which was deferred also for budgetary reasons and because it was judged to be less urgent (since a small scale survey of male respondents of reproductive age was scheduled to be fielded by a different Georgian agency in mid-2005).
Sample survey data [ssd]
Women aged 15-44 years
Scope
The questionnaire was designed to collect information on the following:
- Demographic characteristics
- Household assets (durable goods and dwelling characteristics)
- Fertility and child mortality
- Family planning and reproduction preferences
- Use of reproductive and child health care services
- Range and quality of maternity care services
- Use of preventive and curative health care services
- Reproductive health care expenditures
- Perceptions of health service quality
- Risky health behaviors (smoking and alcohol use)
- Young adult health education and behaviors
- Intimate partner violence
- HIV/AIDS and other STDs
Coverage
National, with tthe exception of the separatist regions of Abkhazia and South Ossetia.
Because the survey collected information from a representative sample of Georgian women aged 15-44 years, the data can be used to estimate percentages, averages, and other measures for the entire population of women of reproductive age residing in Georgian households in 2005.
Producers and sponsors
| Name | Affiliation |
|---|---|
| Centers for Disease Control and Prevention (CDC) | US Department of Health and Human Services |
| Georgian Centers for Disease Control (NCDC) | |
| Georgian Ministry of Health (MoLHSA) |
| Name |
|---|
| United States Agency for International Development |
| United Nations Population Fund |
Sampling
Similar to the 1999 RHS survey, the GERHS05 was a population-based probability survey consisting of face to face interviews with women of reproductive age (15-44 years) at their homes. The survey was designed to collect information from a representative sample of approximately 6,000 women of reproductive age throughout Georgia (excluding the separatist regions of Abkhazia and South Ossetia). The population from which the respondents were selected included all females between the ages of 15 and 44 years, regardless of marital status, who were living in households in Georgia during the survey period.
The current survey used a stratifi ed multistage sampling design that used the 2002 Georgia census as the sampling frame (State Department for Statistics, 2003). To better assist key stakeholders in assessing the baseline situation at a sub-national level, the sample was designed to produce estimates for 11 regions of the country. Census sectors were grouped into 11 strata, corresponding to Georgia’s administrative regions; three small regions, Racha-Lechkhumi, Kvemo Svaneti, and Zemo Svaneti were included in one stratum, identifi ed as the Racha-Svaneti stratum. Data are also representative for the urban-rural distribution of the population at the national level.
The first stage of the three stage sample design was selection of census sectors, with probability of selection proportional to the number of households in each of the 11 regional sectors. The first stage was accomplished by using a systematic sampling process with a random starting point in each stratum. During the fi rst stage, 310 census sectors were selected as primary sampling units (PSUs).
The overall sample consisted of 310 PSUs, and the target number of completed interviews was 6,200 for the entire sample, with an average of 20 completed interviews per PSU. The minimum acceptable number of interviews per stratum was set at 400, so that the minimum number of PSUs per stratum was set at 20. With these criteria, 20 PSUs were allocated to each stratum, which accounted for 220 of the available PSUs. The remaining 80 PSUs were distributed in the largest regions in order to obtain a distribution of PSUs approximately proportional to the distribution of households in the 2002 census. An additional 10 PSUs were added to the smallest stratum, Racha-Svaneti, to compensate for the considerable sparseness of women of reproductive age in this stratum.
Unlike the 1999 survey, a separate sample of internally displaced persons was not selected for the 2005 survey.
The sampling fraction ranges from 1 in 16 households in the Racha-Svaneti stratum (the least populated stratum) to 1 in 146 in Adjara. The ratio of households in the census to households in the sample is above 100.0, the region has been under-sampled, whereas if the ratio is less than 100.0, the region has been oversampled.
In the second stage of sampling, clusters of households were randomly selected from each census sector chosen in the first stage. Determination of cluster size was based on the number of households required to obtain an average of 20 completed interviews per cluster. The total number of households in each cluster took into account estimates of unoccupied households, average number of women aged 15–44 years per household, the interview of only one respondent per household, and an estimated response rate of 98%. In the case of households with more than one woman between the ages of 15 and 44, one woman was selected at random to be interviewed.
Of the 12,338 households selected in the household sample, 6,402 included at least one eligible woman (aged 15–44 years). Of these identified respondents,
6,376 women were successfully interviewed, yielding a response rate of 99%. Virtually all respondents who were selected to participate and who could be reached agreed to be interviewed and were very cooperative. Response rates did not vary signifi cantly by geographical location.
The purpose of the RHS is to produce statistical estimates that are nationally representative. National estimates are produced by devising a “sampling weight” for each respondent that adjusts for her probability of selection in the sample. The weights for the RHS were calculated as follows: First, the weight was adjusted to reflect the selection of only one eligible woman from each household with women of reproductive age. In cases where households included more than one eligible female respondent, the woman who was selected for interview received an additional weight. Second, the weight was adjusted to reflect that women residing in the regions with sparser populations were selected at higher rates (i.e., were oversampled) relative to those residing in regions with high
density of the population, who were under-sampled. Because the overall response rate (99%) was so high, no weighting was needed to adjust for the survey staff’s inability to locate some eligible women or for nonresponse among those who were located. After the weighted survey population distribution was broken down by five-year age-groups and by residence and was compared with the Census estimates, poststratification weights were not deemed to be necessary.
Survey instrument
The questionnaire, already refined during the first RHS in Georgia in 1999, was revised carefully and reviewed by a panel of Georgian experts; in subsequent meetings and informal consultations, CDC sought advice on how to design a more effective and useful survey instrument. As a result, the content of the questionnaire was expanded substantially and made more relevant for programmatic needs.
The questionnaire was designed to collect information on the following:
- Demographic characteristics
- Household assets (durable goods and dwelling characteristics)
- Fertility and child mortality
- Family planning and reproduction preferences
- Use of reproductive and child health care services
- Range and quality of maternity care services
- Use of preventive and curative health care services
- Reproductive health care expenditures
- Perceptions of health service quality
- Risky health behaviors (smoking and alcohol use)
- Young adult health education and behaviors
- Intimate partner violence
- HIV/AIDS and other STDs
The questionnaire was tested extensively, both before and during the pretest and prior to beginning the field work. Testing included practice field interviews and simulated interviews conducted by both CDC and NCDC staff. The questionnaire was translated into Georgian and Russian and back-translated into English.
The inclusion of life histories (marital history and pregnancy history) and the five-year month-by-month calendar of pregnancy, contraceptive use, and union status helped respondents accurately recall the dates of one event in relation to the dates of others they had already recorded.
Data collection
| Start | End |
|---|---|
| 2005-03 | 2005-07 |
Supervisors were trained to review and edit the questionnaires immediately after each interview; thus, if they noticed errors or omissions the interviewers or the respondents had made, the interviewers could make immediate corrections during short follow-up revisits. These edits reduced the item nonresponse rate for most questions to less than 2%.
Supervisors and field work coordinators spot-checked the quality of each interviewer's work often and carefully. This process of verifying fieldwork was a critical component of the overall quality control system.
The interviews were performed by 35 female interviewers trained in interview techniques, survey procedures, and questionnaire content. Interviewer training took place at the NCDC headquarters just before data collection began. 35 interviewers and supervisors were trained for seven full days in the classroom and another week in the field; the training was very interactive with several practical exercises that allowed for selection of the most highly qualified staff from an original pool of 55 trainees. At the end of the training period, seven teams were selected, each consisting of four female interviewers, one supervisor, and two drivers. Fieldwork was managed by staff of NCDC, with technical assistance from CDC, and lasted from March through July 2005. Each team was assigned several primary sampling units and traveled by car throughout the country on planned itineraries.
Although most interviews were conducted in Georgian, a Russian language questionnaire was also available. All interviewers were bilingual. Azeri-speaking health professionals facilitated interviews with monolingual Azeri respondents. Completed questionnaires were first reviewed in the field by team supervisors and then taken by the fieldwork coordinators to the National Center for Medical Statistics and Information, an NCDC-affiliated center, for data processing.
The field unit for GERHS05 consisted of two coordinators who divided the fieldwork assignments among the seven mobile teams of interviewers and supervisors. The field work coordinators and supervisors prepared interviewer assignments and were responsible for monitoring the progress of each interviewer, performing field observations, conducting in-person verifications of the interviewers’ work, and conducting refusal conversion efforts. Field supervisors were also responsible for analyzing each interviewer’s weekly production and quality of work, reviewing errors, and serving as the point of contact for the data entry supervisors. In addition, the supervisors were available to their interviewers as needed. The seven field supervisors made weekly progress reports to their assigned field work coordinator.
Interviewers were instructed to make several visits during the initial day of household contacts when selected respondents were absent; if they still could not complete the interviews, up to three additional visits on different days were scheduled. This reduced the overall nonresponse rate to very low levels-less than 1%.
Data processing
Legal ranges, pre-coded variables, consistency checks, and skips were programmed into the data entry software, so that data entry supervisors would notice errors or inconsistencies and could send problematic interviews back to the field for follow-up visits.
Data appraisal
The estimates for a sample survey are affected by two types of errors: non-sampling error and sampling error. Non-sampling error is the result of mistakes made in carrying out data collection and data processing, including the failure to locate and interview the right household, errors in the way questions are asked or understood, and data entry errors. Although intensive quality-control efforts were made during the implementation of the GERHS05 to minimize this type of error, non-sampling errors are impossible to avoid altogether and difficult to evaluate statistically.
Sampling error is a measure of the variability between an estimate and the true value of the population parameter intended to be estimated, which can be attributed to the fact that a sample rather than a complete enumeration was used to produce it. In other words, sampling error is the difference between the expected value for any variable measured in a survey and the value estimated by the survey. This sample is only one of the many probability samples that could have been selected from the female population aged 15–44 using the same sample design and projected sample size. Each of these samples would have yielded slightly different results from the actual sample selected. Because the statistics presented here are based on a sample, they may differ by chance variations from the statistics that would result if all women 15–44 years of age in Azerbaijan would have been interviewed.
Sampling error is usually measured in terms of the variance and standard error (square root of the variance) for a particular statistic (mean, proportion, or ratio). The standard error (SE) can be used to calculate confidence intervals (CI) of the estimates within which we can say with a given level of certainty that the true value of population parameter lies. For example, for any given statistic calculated from the survey sample, there is a 95 percent probability that the true value of that statistic will lie within a range of plus or minus two SE of the survey estimate. The chances are about 68 out of 100 (about two out of three) that a sample estimate would fall within one standard error of a statistic based on a complete count of the population. The estimated sampling errors for 95% confidence intervals (1.96 x SE) for selected proportions and sample sizes are shown in Table A.1 of the Final Report. The estimates in Table A.1 can be used to estimate 95% confidence intervals for the estimated proportions shown for each sample size. The sampling error estimates include an average design effect of 1.6, needed because the GERHS05 did not employ a simple random sample but included clusters of elements in the second stage of the sample selection.
The selection of clusters is generally characterized by some homogeneity that tends to increase the variance of the sample. Thus, the variance in the sample for the GERHS05 is greater than a simple random sample would be due to the effect of clustering. The design effect represents the ratio of the two variance estimates: the variance of the complex design using clusters, divided by the variance of a simple random sample using the same sample size (Kish L, 1967).
The weighted percentage distribution of women selected in the 2005 survey sample by 5-year age groups differs only slightly from the 2002 official estimates. The survey sample slightly over-represents young adult women (15- to 24-year-olds) and under-represents women aged 35–39 by less than two percentage points. However, after confidence intervals are taken into account, there are no significant differences except for a slight difference for 35- to 39–year-olds
(16.5% vs. 17.0%). The urban/rural distribution of the sample retains the same overrepresentation and under-representation for women aged 15–24 and 35–39 years, respectively, particularly in urban areas. Because the overall response rate was 99%, there is only one factor that may have contributed to these differences; official estimates reflect the age composition recorded in 2002, 3 years before the survey took place. Age projections made by CDC (which did not take out-migration into account) suggest that these small differences would disappear if the sample were to be compared with census projections.
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI_GEO_2005_RHS_v01_M_WBDG