Recruiting hard-to-reach populations via respondent driven sampling for mobile phone surveys in Colombia: a qualitative study

BACKGROUND

Uptake of mobile phone surveys (MPS) is increasing in many low- and middle-income countries, particularly within the context of data collection on non-communicable diseases (NCDs) behavioural risk factors. One barrier to collecting representative data through MPS is capturing data from older participants.Respondent driven sampling (RDS) consists of chain-referral strategies where existing study subjects recruit follow-up participants purposively based on predefined eligibility criteria. Adapting RDS strategies to MPS efforts could, theoretically, yield higher rates of participation for that age group.

OBJECTIVE

To investigate factors that influence the perceived acceptability of a RDS recruitment method for MPS involving people over 45 years of age living in Colombia.

METHODS

An MPS recruitment strategy deploying RDS techniques was piloted to increase participation of older populations. We conducted a qualitative study that drew from surveys with open and closed-ended items, semi-structured interviews for feedback, and focus group discussions to explore perceptions of the strategy and barriers to its application amongst MPS participants.

RESULTS

The strategy's success is affected by factors such as cultural adaptation, institutional credibility and public trust, data protection, and challenges with mobile phone technology. These factors are relevant to individuals' willingness to facilitate RDS efforts targeting hard-to-reach people. Recruitment strategies are valuable in part because hard-to-reach populations are often most accessible through their contacts within their social network who can serve as trust liaisons and drive engagement.

CONCLUSIONS

These findings may inform future studies where similar interventions are being considered to improve access to mobile phone-based data collection amongst hard-to-reach groups.

Recruiting foreign-born individuals who have sought an abortion in the United States: Lessons from a feasibility study

Although studies have documented challenges people encounter when attempting to access abortion care in the United States, there is little research on the perspectives and experiences of foreign-born individuals, who may encounter unique barriers to accessing care. Since lack of data may be due to difficulty recruiting this population, we explored the feasibility of using social media to recruit foreign-born individuals who have sought an abortion into interviews to share their abortion experiences. Our target population was limited to English and Spanish-speakers due to budget constraints. As this recruitment method was unsuccessful, we attempted to recruit our target population through the crowdsourcing website, Amazon Mechanical Turk (mTurk) to take a one-time survey on their abortion experience. Both online recruitment methods yielded a significant number of fraudulent responses. Although we aimed to collaborate with organizations that work closely with immigrant populations, they were unavailable to assist with recruitment efforts at the time of the study. Future abortion research utilizing online methods to recruit foreign-born populations should consider incorporating information on their target populations' use of online platforms as well as cultural views on abortion in order to develop effective recruitment strategies.

Dried Blood Spot Tests for the Diagnosis and Therapeutic Monitoring of HIV and Viral Hepatitis B and C

Blood collected and dried on a paper card – dried blood spot (DBS) – knows a growing interest as a sampling method that can be performed outside care facilities by capillary puncture, and transported in a simple and safe manner by mail. The benefits of this method for blood collection and transport has recently led the World Health Organization to recommend DBS for HIV and hepatitis B and C diagnosis. The clinical utility of DBS sampling to improve diagnostics and care of HIV and hepatitis B and C infection in hard to reach populations, key populations and people living in low-income settings was highlighted. Literature about usefulness of DBS specimens in the therapeutic cascade of care – screening, confirmation, quantification of nucleic acids, and resistance genotyping -, was reviewed. DBS samples are suitable for testing antibodies, antigens, or nucleic acids using most laboratory methods. Good sensibility and specificity have been reported for infant HIV diagnosis and diagnosis of hepatitis B and C. The performance of HIV RNA testing on DBS to identified virological failure on antiretroviral therapy is also high but not optimal because of the dilution of dried blood in the elution buffer, reducing the analytical sensitivity, and because of the contamination by intracellular HIV DNA. Standardized protocols are needed for inter-laboratory comparisons, and manufacturers should pursue regulatory approval for in vitro diagnostics using DBS specimens. Despite these limitations, DBS sampling is a clinically relevant tool to improve access to infectious disease diagnosis worldwide.

Estimating uncertainty in respondent-driven sampling using a tree bootstrap method

Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.