Appendix 1. Sampling Procedure. Discussion In general, the goal of the sampling procedure was to select women for inclusion such that it would provide a representative cross-section of the target population within the Marz (region). Methodological concerns include selection criteria that might result in a biased selection of women such that their use/knowledge of family planning methods is different (higher or lower) than the target population or selects them from specific socio- demographic groups. Logistical considerations include the efficient use of limited personnel resources; limited time for implementation, and the availability of the required information/resources to carry out the process. Thus the two primary factors driving the decision for an acceptable sampling strategy are the methodological rigor and the feasibility/cost of implementation. The methods used must assure sufficient sample size for detecting urban/rural differences, and differences among regions receiving the supplemental campaigns from those receiving only the national campaign. Sufficient numbers were sampled so as to allow for those declining to participate in the study, those selected but disqualified, and those lost to follow-up over the duration of the study. In terms of sampling strategy, three basic approaches were considered: multi-stage cluster sampling (probability proportional to size/PPS), random digit dialing, and random household selection. After an assessment of the extant data underlying each of the approaches and reviewing methods used for similar rigorous studies in the country immediately prior to this project, cluster sampling was selected. Cluster Sampling Cluster sampling is an efficient use of resources of particular utility when viable sampling frames for simple random sampling (SRS) are not available. Such is the case in Armenia where the population has undergone tremendous transition in recent years, including large-scale migration/emigration, further complicated by the renaming of Appendix 1 streets and addresses at the time of independence, and the destruction of several towns and villages due to an earthquake in 1988. Two extant data sources, both presumed to be equitably distributed throughout the population in question and thus suitable for use in selecting clusters, are medical rosters and administrative rosters. With the collapse of the Soviet Union and consequently the Armenian economy, the medical system remains largely in disarray. While adults may forgo medical treatment, the pediatric component of the medical system remains the most viable. Its rosters of children have been used as the basis for cluster sampling in the past with good success. The administrative district roster, in theory, offers an advantage that it is geographically based. Each district headquarters has a record of each address (household) within the administrative district. This could provide a non-biased and non-medically influenced selection of households. The reality is, however, that these records are poorly maintained, are incomplete, and have not been updated to reflect the changes in street/village names or the destruction of some towns/ villages. This introduces unknown and potentially systematic biases in selection, which cannot be adjusted for. Therefore, this promising approach is not as desirable as the clinic rosters. In opting to use the pediatric clinic rosters, the potential bias toward the selection of households forgoing family planning must be addressed. The very fact that the household has a child may imply a set of family planning practices different from the general population. Thus, selecting a household for inclusion in every cluster on this basis would bias the sample with respect to a key outcome of interest - family planning practices. To assure that the cluster selection criteria do not preferentially select for or against users of family planning methods, the cluster starting point was offset an Nth unit away from the address randomly selected from the children’s roster. The methodology still permitted the referent household to be included in the sample through the procedures for completing the cluster without introducing a bias or counter-bias with respect to the presence of young children in the home. Appendix 1 In addition, the typical cluster sampling procedure of selecting adjacent households was further modified to assure that proximal households were not included as the literature clearly shows that neighbors can influence each other's use of health care and this may include family planning choices. The process used reflects a cluster sampling methodology that attempts to more closely mimic a simple random sample design by increasing the catchment area of the cluster. This approach presumably increasing the within-cluster heterogeneity and preserves the power of the sample size. Random Digit Dialing. Random Digit Dialing (RDD) has been used in Yerevan with limited success. The poor condition of the phone system (such that it can be difficult to call/connect to certain parts/districts of the city where the phone system is extremely poor) as well as the socio- economic bias introduced by selecting for the presence of a phone introduce significant biases of concern for this study. Furthermore, this strategy is not feasible for the outlying regions where access to phone service is even more limited. It would not be prudent to mix sampling methodologies between Yerevan and the regions without some understanding of the differing biases that might be introduced. Additionally, RDD selection for a survey that requires a face-to-face interview would add a significant unnecessary administrative burden. Random Selection While the gold standard for selection, simple random selection (SRS) is virtually impossible in Armenia, as no viable, unbiased, complete sampling frames currently exist. Attempts at approaching this ideal would be extremely costly and time consuming with little appreciable gain in analytic power over the PPS cluster sampling. Sample size The use of PPS sampling in this case is based on several assumptions. 1. Patterns of emigration are uniform across Armenia. 2. A cluster size of 6 coupled with skipping adjacent households effectively minimizes heterogeneity bias. Appendix 1 3. The population is uniformly distributed across geographic areas with respect to key socio-demographic variables. Extant data and prior research indicate that these assumptions are reasonable for Armenia at this time. The sample size of 1212 is comparable to similar studies and supported by simple sample size calculations (5), which indicate a minimum of 200 observations for each comparison unit [n= 2z2pq/d2 where z = 1.96, p=0.4 (based on prior research and erring toward a more conservative sample size estimate), q = 0.6, and d = 0.1 (minimal practically significant difference to detect)]. The methods used assured sufficient sample size for detecting urban/rural differences, and differences among regions receiving the supplemental campaigns from those receiving only the national campaign. Sufficient numbers were sampled so as to allow for those declining to participate in the study, those selected but disqualified, and those lost to follow-up over the duration of the study. Sampling Procedures Preparatory Phase For this study, the cluster size was set at 6. This is a reasonably small cluster size (minimizes homogeneity bias) and represents the total number of interviews expected from one interviewer per full day. 1. For each region (city in the case of Yerevan), a list of all pediatric polyclinics (or equivalent) was compiled. The population served by each Polyclinic was tabulated in an ordered manner. A random starting point was selected and then cluster starting points systematically drawn from the ordered list (presented later in this Appendix). The number of clusters to be drawn form each policlinic was influenced by the size of the population served by the clinic. Thus, some polyclinics had more cluster starting points than others. Similarly, starting points near the geographic boundary of a polyclinic catchment area, could lead to a cluster drawn from households residing in several polyclinic catchment areas. Appendix 1 2. When visiting the polyclinic for its records, those clinics selected more than once had that many of its districts randomly sampled. As districts (physician/nurse team) usually serve similar sized populations, the listing of districts were randomly selected in lieu of the more intensive process of weighted sampling described for the selection of polyclinics. 3. For each selected district, a random address was selected from the roster using a random number generator and the number of addresses in the roster. For purposes of this study, the roster of registered births for 1997 was used. 4. A list of the 84 cluster starting points was produced for Yerevan. A similar process was used to sample from the intervention regions (Lori & Vayots Dzor). Here, the organization was slightly different, with 2-6 shrjans (districts) in each region. Identification of the cities/regional centers/villages to be selected within each of these marzes was performed as the first level of selection. After that, the number of the clusters that should be taken from each city/regional center/village within the shrjan, based on the population, was enumerated. Smaller towns and villages did not have a polyclinic, but a smaller, comparable unit such as an ambulatory clinic or a FAP (see Table 1 in this Appendix). 5. A database of the starting addresses was created to facilitate administration of the survey. Implementation Phase The interviewer proceeded to the cluster start address and then counted off the offset (5 houses/units to the right/up). The interviewers were working individually and were completing 6 surveys within the cluster following the protocol below. 1. If there was at least one eligible, willing respondent at the address, the interviewers completed the survey. Appendix 1 2. Once a survey was completed or if the household yielded no respondents, the next household was selected as follows: Direction of movement: Direction of movement for the cluster was decided by a coin toss (heads = right/up, tails = left/down).