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Background: Since society health is threatened by high risk diseases, populations exposed to these diseases, especially hidden populations, always attract the attention of researchers and policy makers in the field of public health. Conventional methods that are used by researchers for sampling and calculating population proportion estimation often lead to underestimation or overestimation of these proportions in the studied populations. Efficient sampling methods such as respondent-driven sampling (RDS) method have been introduction more than two decades ago. However, due to the unfamiliarity of researchers in this field with the technique of calculating estimations for samples in this method, this sampling method is less applied in estimating proportions of hidden populations. The main objective of the current study was to introduce estimators of population proportions for qualitative variables such as disease occurrence through estimates of the probability of intergroup and intragroup relations and respondents’ social network size. Methods: In the present study, by assuming the existence of reciprocal relationships in the population and sampling with replacement, the population proportions were computed through estimating the probability of intergroup and intragroup relations and social network size of respondents. Findings: Existing theories and computer simulations showed that estimators introduced for proportions of hidden populations were asymptotically unbiased and had a high rate of convergence. Conclusion: The lack of selection of a suitable sampling method and computing method for estimating proportions of hidden populations, which are exposed to high-risk diseases and are effective in health policies, will not provide acceptable results in achieving the objectives of this policy.

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