Abstract

The 2005 Armenia Demographic and Health Survey (2005 ADHS) is the second in a series of nationally representative sample surveys designed to provide information on population and health issues in Armenia. As in the 2000 ADHS, the primary goal of the 2005 survey was to develop a single integrated set of demographic and health data pertaining to the population of the Republic of Armenia. In addition to integrating measures of reproductive, child, and adult health, another feature of the 2005 ADHS survey is that the majority of data are presented at the marz (region) level.

The 2005 ADHS was conducted by the National Statistical Service (NSS) and the MOH of the Republic of Armenia from September through December 2005. ORC Macro provided technical support for the survey through the MEASURE DHS project. MEASURE DHS is a worldwide project, sponsored by the United States Agency for International Development (USAID), with a mandate to assist countries in obtaining information on key population and health indicators. USAID/Armenia provided funding for the survey, while the United Nations Children’s Fund (UNICEF)/Armenia and the United Nations Population Fund (UNFPA)/Armenia supported the survey through in-kind contributions.

The 2005 ADHS collected national- and regional-level data on fertility and contraceptive use, maternal and child health, adult health, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. Data are presented by marz wherever sample size permits.

The 2005 ADHS results are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of and health services for the people of Armenia. The 2005 ADHS also contributes to the growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-49

Kind of data

Sample survey data

Sampling procedure

The sample was designed to permit detailed analysis-including the estimation of rates of fertility, infant/child mortality, and abortion-for the national level, for Yerevan, and for total urban and total rural areas separately. Many indicators can also be estimated at the regional (marz) level.

A representative probability sample of 7,565 households was selected for the 2005 ADHS sample. The sample was selected in two stages. In the first stage, 308 clusters were selected from a list of enumeration areas in a subsample from a master sample that was designed from the 2001 Population Census. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey.

All women age 15-49 who were either permanent residents of the households in the 2005 ADHS sample or visitors present in the household on the night before the survey were eligible to be interviewed. Interviews were completed with 6,566 women. In addition, in a subsample of one-third of all the households selected for the survey, all men age 15-49 were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey. Interviews were completed with 1,447 men.

Note: See detailed summarized sample implementation tables in APPENDIX A of the report which is presented in this documentation.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2005 ADHS: a Household Questionnaire, a Women’s Questionnaire, and a Men’s questionnaire. The Household and Individual Questionnaires were based on model survey instruments developed in the MEASURE DHS program and on questionnaires used in the 2000 ADHS. The model questionnaires were adapted for use by experts from the NSS and MOH. Input was also sought from a number of non-governmental organizations. The questionnaires were developed in English and translated into Armenian. The Household and Individual Questionnaires were pretested in June 2005.

The Household Questionnaire was used to list all usual members of and visitors to the selected households and to collect information on the socioeconomic status of the household. The first part of the Household Questionnaire collected information on the age, sex, educational attainment, and relationship to the household head of each household member or visitor. This information provides basic demographic data for Armenian households. It also was used to identify the women and men who were eligible for the individual interview (i.e., women and men age 15-49). In the second part of the Household Questionnaire, there were questions on housing characteristics (e.g., flooring material, source of water, type of toilet facilities), on ownership of a variety of consumer goods, and other questions relating to the socioeconomic status of the household. In addition, the Household Questionnaire was used to record height and weight measurements of women, men, and children under age five; hemoglobin measurement of women and children under age five; and blood pressure measurement of women and men.

The Women’s Questionnaire obtained data from women age 15-49 on the following topics: • Background characteristics • Pregnancy history • Antenatal, delivery, and postnatal care • Knowledge, attitudes, and use of contraception • Reproductive and adult health • Health care utilization • Vaccinations, birth registration, and health of children under age five • Episodes of diarrhea and respiratory illness of children under age five • Breastfeeding and weaning practices • Marriage and recent sexual activity • Fertility preferences • Knowledge of and attitude toward HIV/AIDS and other sexually transmitted infections

The Men’s Questionnaire, administered to men age 15-49, focused on the following topics: • Background characteristics • Health and health care utilization • Marriage and recent sexual activity • Attitudes toward and use of condoms • Knowledge of and attitude toward HIV/AIDS and other sexually transmitted infections • Attitudes toward women’s status

Response rate

A total of 7,565 households were selected for the sample, of which 7,003 were occupied at the time of fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. Of the occupied households, 96 percent were successfully interviewed.

In these households, 6,773 women were identified as eligible for the individual interview, and interviews were completed with 97 percent of them. Of the 1,630 eligible men identified, 89 percent were successfully interviewed. Response rates are almost identical in urban and rural areas.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the report which is presented this documentation.

Sampling error estimates

Estimates derived from a sample survey are affected by two types of errors: 1) non-sampling errors, and 2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and 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 2005 Armenia DHS (2005 ADHS) to minimize this type of error, non-sampling 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 2005 ADHS 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 percent 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 2005 ADHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use a more complex formula. The computer software used to calculate sampling errors for the 2005 ADHS is the sampling error module in ISSA (Integrated System for Survey Analysis). This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. Another approach, the Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed

Recent papers postulate that epifaunal organisms use artificial structures as stepping-stones to spread to areas that are too distant to reach in a single generation. With thousands of artificial structures present in the North Sea, we test the hypothesis that these structures are connected by water currents and act as an interconnected reef. Population genetic structure of the Blue mussel, Mytilus edulis was expected to follow a pattern predicted by particle tracking models (PTM). Correlation between population genetic differentiation, based on microsatellite markers, and particle exchange was tested. Specimens of M. edulis were found at each location, although the PTM indicated that locations >85 km offshore were isolated from coastal sub-populations. Fixation coefficient FST correlated with the number of arrivals in the PTM. However, the number of effective migrants per generation as inferred from coalescent simulations did not show a strong correlation with the arriving particles. Isolation by distance analysis showed no increase in isolation with increasing distance and we did not find clear structure among the populations. The marine stepping-stone effect is obviously important for the distribution of M. edulis in the North Sea and it may influence ecologically comparable species in a similar way. In the absence of artificial shallow hard substrates, M. edulis would be unlikely to survive in offshore North Sea waters. Although we found an indication that FST was lower between connected locations, isolation by distance analysis showed no increase in isolation with increasing distance. Finally, we did not find clear structure among the populations. Methods

Mytilus edulis population genetics

Samples were collected at 27 locations (henceforth named sub-populations) between April 2014 and January 2016: from coastal sub-populations during low tide, by commercial and scientific divers from oil and gas platforms and wind farm foundations and during inspection, repair and maintenance work on buoys and offshore installations from structures lifted out of the water (Table 1, Figure 1). Sampling depth varied between 0 and 27 meters. Two likely outgroup sub-populations corresponding to peripheral populations of the species M. edulis (Denmark) and M. galloprovincialis (Portugal) were included; from a harbour in Lisbon, Portugal and from mussel longlines in the Limfjorden in Denmark. Geographic distances between sub-populations (excluding outgroups) varied from 17 km (Scheveningen and Q13-A) to 1,105 km (Blyth and Sylt) with an average of 348 km.

Between 50 and approximately 100 individuals were sampled randomly at every location and stored at -20°C or in 70% ethanol to be transported to the laboratory. Then, all samples were cleaned of marine growth and placed at -80°C for long-term storage.

Molecular methods

From each sample, between 24 and 67 specimens were selected randomly and genomic DNA was isolated from the adductor muscles by using the 96 well genomic DNA extraction kit according to the manufacturer’s protocol (FAVORGEN Biotech Corp). DNA concentration was measured on a Nanodrop and diluted to 10 ng/μl. Two multiplex sets of four markers were used (set1: Med367, Med379, Med722 and Med733; set2: Med737, Med740, Med747 and Me15/16). Markers were used individually in the PCR, pooled per set and analysed on an ABI3730 DNA Analyser. Seven markers were microsatellite loci as described in Lallias et al. (2009) and the Me15/16 locus targeted a part of the adhesive protein gene which partially discriminates between M. edulis, M. galloprovincialis and M. trossulus (Inoue, Waite, Matsuoka, Odo, & Harayama, 1995). Marker information, PCR conditions and multiplex conditions are indicated in Table 2. The GeneScan 500 LIZ marker was used as internal marker. Allele calling was performed in Genemapper v3.7 (Applied Biosystems 2004).

Connectivity calculated by particle tracking models

The transport of mussel larvae between sampled sub-populations in the North Sea was modelled using two Delft3D software modules: FLOW and PART (Deltares, 2016b, 2016a). The PART module was able to simulate mid-field water quality and particle tracking, based on a hydrodynamic forcing output from the other Delft3D module, FLOW.

Hydrodynamical model

The hydrodynamical model applied was Delft3D-FLOW, which solves the unsteady shallow-water equations in three dimensions. The model incorporates a large number of processes, such as wind shear, wave forces, tidal forces, density-driven flows, stratification, atmospheric pressure changes, air temperature and the exposure and inundation of intertidal flats. The large number of processes included in this module means that Delft3D-FLOW can be applied to a wide range of environments (e.g. river, estuarine, coastal and marine areas; Lesser et al. 2004). Flow equations were solved on a curvilinear grid consisting of 8,710 computational elements (Roelvink, Jeuken, van Holland, Aarninkhof, & Stam, 2001). The vertical resolution of the model was ten water layers using a sigma-coordinated approach (i.e., proportional to water depth; Stelling and van Kester 1994). Hydrodynamic transport was computed using detailed bathymetry and open boundary forcing based on tidal constituents. The model was forced using meteorological data from the High Resolution Limited Area Model (KNMI, 2015), which comprised two horizontal wind velocity components (at 10 m above mean sea level) and other atmospheric variables such as air pressure and temperature, archived every 6 h. The freshwater discharges from 18 rivers were included in the model; seven of these discharges varied temporally (daily averages) and 11 were constant (based on long-term averages). This model is described in detail in Erftemeijer et al. (2009).

Two grid lay-outs covering the southern North Sea (including the Wadden Sea) were used in this study: a moderately fine grid (ZUNOGROF) and a domain decomposition model grid (ZUNO-DD) with a much higher grid resolution in the Dutch coastal zone and the Wadden Sea. The subdomains of the ZUNO DD model are displayed in Figure 2 using different colours and are used to increase spatial resolution for hydrodynamic results in these areas. In coastal and inshore (estuarine, lagoonal) systems, the spatial variability in hydrodynamic forcing is much higher. To enable the simulation of these processes, the areas near the Dutch coast and in the Wadden Sea have been given a much higher horizontal grid density, so with smaller grid cells per surface unit. This provides for a much better simulation of the hydrodynamic processes in such areas. These spatial differences in resolution in the Dutch coastal waters and the Wadden Sea have been given a different colouring in Figure 2.

Particle transport model

Particle tracking models (PTM) are often used in environmental modelling (e.g., North et al. 2008; Broekhuizen et al. 2011; Postma et al. 2013). Here, the Delft3D-PART module was used to calculate larval transport across the southern North Sea. Delft3D-PART is a random walk particle tracking model, based on the principle that movement of dissolved substances in water can be described by a limited but potentially large number of discrete particles that are subject to advection due to currents and by horizontal and vertical dispersion. The movement of the particles in the model consists of two steps: advection, which is driven by the FLOW results, and dispersion, which is a stochastic random walk process. In addition, the horizontal and vertical movement of the particles can be adjusted to account for swimming preferences or changes in buoyancy. Particle tracking allows water quality processes to be described in a detailed spatial pattern, resolving sub-grid concentration distributions. Delft3D-PART is shown to be locally mass conservative (Postma et al., 2013).

Modelling set up

DELFT3D-FLOW calculated the hydrodynamic conditions for the southern North Sea for two consecutive years, 2004 and 2005. Meteorological conditions are important to determine the geographic destination of particles such as larvae, superimposed on the regular transport conditions in the southern North Sea. At the time of analysis, there were no hydrodynamic modelling results available for the years of sampling, 2014 to 2016. Instead, two model runs from 2004 and 2005 were used. Although there is large variation in climatic forcing between years, we chose these two years because of the overall comparable (southern) North Sea wind patterns during the months we used for PTM, i.e. March to June. The comparability especially regards wind directions and wind force. We could only roughly assess this comparability from meteorological data available to us (Royal Dutch Meteorological Institute); we did not do a detailed analysis. We assume that through this approach the differences between the hydrodynamic circulation patterns in the (southern) North Sea between these two periods is less than when random years would have been used. For the DELFT3D-PART model, each sampling location acted as an origin point of larvae and the density of the larvae throughout the consecutive months at the other locations (destinations) was calculated. Larval release was started at day 59 (1 March) of each year, from the top layer in the model. Both the timing and the period of particle release was based on the general reproductive timing and pelagic larval stage period of M. edulis. Mussels start spawning early spring, based on temperature of the water. Variation of spawning timing may result of the variation in temperature increase between years; in the southern North Sea this usually starts in March, and peaks in April/May or even later (De Vooys, 1999). Time from spawning of mussel larvae to settlement is around three months (Widdows, 1991). It was further assumed that the larvae are largely passive and that they are transported only by advection and

Abstract

The DHS is intended to serve as a primary source for international population and health information for policymakers and for the research community. In general, DHS has four objectives: - To provide participating countries with a database and analysis useful for informed choices, - To expand the international population and health database, - To advance survey methodology, and - To help develop in participating countries technical skills and resources necessary to conduct demographic and health surveys.

Apart from estimating fertility and contraceptive prevalence rates, DHS also covers the topic of child health, which has become the focus of many development programs aimed at improving the quality of life in general. The Indonesian DHS survey did not include health-related questions because this information was collected in the 1987 SUSENAS in more detail and with wider geographic coverage. Hence, the Indonesian DHS was named the "National Indonesian Contraceptive Prevalence Survey" (NICPS).

The National Indonesia Contraceptive Prevalence Survey (NICPS) was a collaborative effort between the Indonesian National Family Planning Coordinating Board (NFPCB), the Institute for Resource Development of Westinghouse and the Central Bureau of Statistics (CBS). The survey was part of an international program in which similar surveys are being implemented in developing countries in Asia, Africa, and Latin America.

The 1987 NICPS was specifically designed to meet the following objectives: - To provide data on the family planning and fertility behavior of the Indonesian population necessary for program organizers and policymakers in evaluating and enhancing the national family planning program, and - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and availability of contraception.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The 1987 NICPS sample was drawn from the annual National Socioeconomic Survey (popularly called SUSENAS) which was conducted in January and February 1987. Each year the SUSENAS consists of one set of core questions and several modules which are rotated every three years. The 1987 SUSENAS main modules covered household income, expenditure, and consumption. In addition, in collaboration with the Ministry of Health, information pertaining to children under 5 years of age was collected, including food supplement patterns, and measurement of height, weight, and arm circumference. In this module, information on prenatal care, type of birth attendant, and immunization was also asked.

This national survey covered over 60,000 households which were scattered in almost all of the districts. The data were collected by the "Mantri Statistik", a CBS officer in charge of data collection at the sub-district level. All households covered in the selected census blocks were listed on the SSN 87-LI form. This form was then used in selecting samples for each of the modules included in the SUSENAS. This particular form was also used to select the sample households in the 1987 NICPS.

Sample selection in the 1987 SUSENAS utilized a multistage sampling procedure. The first stage consisted of selecting a number of census blocks with probability proportional to the number of households in the block. Census blocks are statistical areas formed before the 1980 Population Census and contain approximately 100 households. At the second stage, households were selected systematically from each sampled census block.

Selection of the 1987 NICPS sample was also done in two stages. The first stage was to select census blocks from the those selected in the 1987 SUSENAS. At the second stage a number of households was selected systematically from the selected census block.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire was used to record all members of the selected households who usually live in the household. The questionnaire was utilized to identify the eligible respondents in the household, and to provide the numerator for the computation of demographic measurements such as fertility and contraceptive use rates.

The individual questionnaire was used for all ever-married women aged 15-49, and consisted of the following eight sections:

Section 1 Respondent's Background

This part collected information related to the respondent and the household, such as current and past mobility, age, education, literacy, religion, and media exposure. Information related to the household includes source of water for drinking, for bathing and washing, type of toilet, ownership of durable goods, and type of floor.

Section 2 Reproduction

This part gathered information on all children ever born, sex of the child, month and year of birth, survival status of the child, age when the child died, and whether the child lived with the respondent. Using the information collected in this section, one can compute measures of fertility and mortality, especially infant and child mortality rates. With the birth history data collected in this section, it is possible to calculate trends in fertility over time. This section also included a question about whether the respondent was pregnant at the time of interview, and her knowledge regarding women's fertile period in the monthly menstrual cycle.

Section 3 Knowledge and Practice of Family Planning

This section is one of the most important parts of the 1987 NICPS survey. Here the respondent was asked whether she had ever heard of or used any of the family planning methods listed. If the respondent had used a contraceptive method, she was asked detailed questions about the method. For women who gave birth to a child since January 1982, questions on family planning methods used in the intervals between births were also asked. The section also included questions on source of methods, quality of use, reasons for nonuse, and intentions for future use. These data are expected to answer questions on the effectiveness of family planning use. Finally, the section also included questions about whether the respondent had been visited by a family planning field worker, which community-level people she felt were most appropriate to give family planning information, and whether she had ever heard of the condom, DuaLima, the brand being promoted by a social marketing program.

Section 4 Breastfeeding

The objective of this part was to collect information on maternal and child health, primarily that concerning place of birth, type of assistance at birth, breastfeeding practices, and supplementary food. Information was collected for children born since January 1982.

Section 5 Marriage

This section gathered information regarding the respondent's age at first marriage, number of times married, and whether the respondent and her husband ever lived with any of their parents. Several questions in this section were related to the frequency of sexual intercourse to determine the respondent's risk of pregnancy. Not all of the data collected in this section are presented in this report; some require more extensive analysis than is feasible at this stage.

Section 6 Fertility Preferences

Intentions about having another child, preferred birth interval, and ideal number of children were covered in this section.

Section 7 Husband's Background and Respondent's Work

Education, literacy and occupation of the respondent's husband made up this section of the questionnaire. It also collected information on the respondent's work pattern before and after marriage, and whether she was working at the time of interview.

Section 8 Interview Particulars

This section was used to record the language used in the interview and information about whether the interviewer was assisted by an interpreter. The individual questionnaire also included information regarding the duration of interview and presence of other persons at particular points during the interview. In addition to the questionnaires, two manuals were developed. The manual for interviewers contained explanations of how to conduct an interview, how to carry out the field activity, and how to fill out the questionnaires. Since information regarding age was vital in this survey, a table to convert months from Javanese, Sundanese and Islamic calendar systems to the Gregorian calendar was attached to the 1987 NICPS manual for the interviewers.

Response rate

The NICPS covered a sample of nearly 15,000 households to interview 11,884 respondents. Respondents for the individual interview were ever-married women aged 15-49. During the data collection, 14,141 out of the 14,227 existing households and 11,884 out of 12,065 eligible women were successfully interviewed. In general, few problems were encountered during interviewing, and the response rate was high--99 percent for households and 99 percent for individual respondents.

Note: See APPENDIX A in the report for more information.

Sampling error estimates

The results from sample surveys are affected by two types of errors: (1) non-sampling error and (2) sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and