BACKGROUND: Respondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings.
METHODS: We assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance - People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses.
RESULTS: The study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples.
CONCLUSIONS: In conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments.

OBJECTIVE: Addressing obstacles such as logistical complexities, social stigma, and the impact of historical traumas is essential for the successful inclusion of underrepresented groups in health research.
METHODS: This article reviews engagement and interview techniques used to ethically engage recently settled Afghan refugees in Oklahoma and rural Mexican-born women in Illinois in research. The paper concludes with a reflective discussion on the challenges and lessons learned.
RESULTS: Creative strategies to engage hard-to-reach populations in research included considering the participants\' socioeconomic and cultural contexts in their interactions and developing community partnerships to establish trust and obtain reliable data. Other engagement strategies were communicating in the participants\' preferred language, providing assistance with reading and responding to study questions for those with low literacy, employing research staff from the population of interest, and recruiting in specific locations where the populations of interest live.
CONCLUSIONS: Community engagement is essential at all stages of research for building trust in hard-to-reach populations, achieving inclusivity in health research, and ensuring that interventions are culturally sensitive and effective.

Respondent-driven sampling (RDS) is a peer chain-recruitment method for populations without a sampling frame or that are hard-to-reach. Although RDS is usually done face-to-face, the online version (WebRDS) has drawn a lot of attention as it has many potential benefits, despite this, to date there is no clear framework for its implementation. This article aims to provide guidance for researchers who want to recruit through a WebRDS.
Description of the development phase: guidance is provided addressing aspects related to the formative research, the design of the questionnaire, the implementation of the coupon system using a free software and the diffusion plan, using as an example a web-based cross-sectional study conducted in Spain between April and June 2022 describing the working conditions and health status of homecare workers for dependent people.
The application of the survey: we discuss about the monitoring strategies throughout the recruitment process and potential problems along with proposed solutions.
Under certain conditions, it is possible to obtain a sample with recruitment performance similar to that of other RDS without the need for monetary incentives and using a free access software, considerably reducing costs and allowing its use to be extended to other research groups.

Indian healthcare system is in immediate need of a new healthcare delivery model to increase healthcare accessibility and improve the health outcomes of the marginalized. Inaccessibility and underutilization of Primary Health Centers (PHCs) disproportionately affect people living in remote areas. It is thus imperative for the designers, engineers, health professionals, and policymakers to come together with a collaborative mindset to develop innovative interventions that sustainably manage the accessibility of PHCs at large, promote preventive health, and thus improve the health outcomes of hard-to-reach communities. This article examines the available literature on barriers to primary healthcare in Indian context, the reason of failure of PHCs and the way forward. The article further analysis literature on existing Mobile Medical Units (MMUs) as an alternate solution to conventional PHCs and attempt to extract the major lessons to propose a mobile Primary Health Center (mPHC) in contrast to the existing conventional static PHCs. The intention is to find out the research gaps in the existing literature and try to address the same for future researchers, designers, engineers, health professionals and policy makers to think forward to make this idea of a mobile Primary Health Center (mPHC), as the main delivery model to cater basic healthcare services to the underserved communities.

[This corrects the article DOI: 10.3389/fpubh.2023.1175482.].

Respondent-driven sampling (RDS) refers both to a chain-referral sampling method and an analytical model for analysing sampled data. Web-based respondent-driven sampling (webRDS) uses internet-based recruitment coupled with an electronic survey to carry out RDS studies; there is currently no commercially available webRDS solution. We designed and developed a webRDS solution to support a research study aimed at estimating conflict-attributable mortality in Yemen. Our webRDS solution is composed of an existing survey platform (i.e. ODK) and a bespoke RDS system. The RDS system is designed to administer and manage an RDS survey cascade and includes: (1) an application programming interface, (2) a study participant client, and (3) an administrator interface. We report here on the design of the webRDS solution and its implementation.
We consulted members of the Yemeni diaspora throughout the development of the solution. Technical obstacles were largely the result of: WhatsApp\'s policies on bulk messaging and automated messaging behaviour, the inherent constraints of SMS messaging, and SMS filtering behaviour. Language support was straight-forward yet time consuming. Survey uptake was lower than expected. Factors which may have impacted uptake include: our use of consumable survey links, low interest amongst the diaspora population, lack of material incentives, and the length and subject matter of the survey itself. The SMS/WhatsApp messaging integration was relatively complex and limited the information we could send potential participants.
Despite lower-than expected survey uptake we believe our webRDS solution provides efficient and flexible means to survey a globally diverse population.

Disseminated tuberculosis is frequently associated with delayed diagnosis and a poorer prognosis.
To describe case series of disseminated TB and diagnosis delay in a low TB burden country during the COVID-19 period.
We consecutively included all patients with of disseminated TB reported from 2019 to 2021 in the reference hospital of the Northern Crown of the Metropolitan Area of Barcelona. We collected socio-demographic information, clinical, laboratory and radiological findings.
We included all 30 patients reported during the study period-5, 9, and 16 in 2019, 2020, and 2021 respectively-20 (66.7%) of whom were male and whose mean age was 41 years. Twenty-five (83.3%) were of non-EU origin. The most frequent system involvement was central nervous system (N = 8; 26.7%) followed by visceral (N = 7; 23.3%), gastro-intestinal (N = 6, 20.0%), musculoskeletal (N = 5; 16.7%), and pulmonary (N = 4; 13.3%). Hypoalbuminemia and anemia were highly prevalent (72 and 77%). The median of diagnostic delay was 6.5 months (IQR 1.8-30), which was higher among women (36.0 vs. 3.5 months; p = 0.002). Central nervous system involvement and pulmonary involvement were associated with diagnostic delay among women. We recorded 24 cured patients, two deaths, three patients with post-treatment sequelae, and one lost-to-follow up. We observed a clustering effect of patients in low-income neighborhoods (p < 0.001).
There was a substantial delay in the diagnosis of disseminated TB in our study region, which might impacted the prognosis with women affected more negatively. Our results suggest that an increase in the occurrence of disseminated TB set in motion by diagnosis delay may have been a secondary effect of the COVID-19 pandemic.

Vaccines prevent 4-5 million deaths every year, but inequities in vaccine coverage persist among key disadvantaged subpopulations. Under-immunized subpopulations (e.g., migrants, slum residents) may be consistently missed with conventional methods for estimating immunization coverage and assessing vaccination barriers. Adaptive sampling, such as respondent-driven sampling, may offer useful strategies for identifying and collecting data from these subpopulations that are often \"hidden\" or hard-to-reach. However, use of these adaptive sampling approaches in the field of global immunization has not been systematically documented. We searched PubMed, Scopus, and Embase databases to identify eligible studies published through November 2020 that used an adaptive sampling method to collect immunization-related data. From the eligible studies, we extracted relevant data on their objectives, setting and target population, and sampling methods. We categorized sampling methods and assessed their frequencies. Twenty-three studies met the inclusion criteria out of the 3069 articles screened for eligibility. Peer-driven sampling was the most frequently used adaptive sampling method (57%), followed by geospatial sampling (30%), venue-based sampling (17%), ethnographic mapping (9%), and compact segment sampling (9%). Sixty-one percent of studies were conducted in upper-middle-income or high-income countries. Data on immunization uptake were collected in 65% of studies, and data on knowledge and attitudes about immunizations were collected in 57% of studies. We found limited use of adaptive sampling methods in measuring immunization coverage and understanding determinants of vaccination uptake. The current under-utilization of adaptive sampling approaches leaves much room for improvement in how immunization programs calibrate their strategies to reach \"hidden\" subpopulations.

The COVID-19 pandemic has further deepened socioeconomic and health inequities worldwide, especially among populations experiencing social vulnerability, such as international migrants. Sustained lockdowns and social distancing have raised challenges to conducting public health research with hard-to-reach populations. This study aims at exploring strategies to recruit \"hard-to-reach\" international migrants for qualitative public health research during the pandemic in Chile, based on the authors\' experience. A retrospective qualitative evaluation process was carried out on the recruitment processes of three qualitative research projects focused on international migrants in Chile. All projects were implemented during the COVID-19 pandemic, demanding complementary and flexible strategies: (i) social media; (ii) snowball sampling; (iii) referrals from social workers and pro-migrant and migrant-led organizations; (iv) vaccination centers and healthcare centers; and (v) community-based recruitment. The strategies are qualitatively evaluated around seven emerging qualitative categories: (i) feasibility during lockdown periods; (ii) speed of recruitment; (iii) geographical coverage; (iv) sample diversity; (v) proportion of successful interviews; (vi) ethical considerations; and (vii) cost. Engaging hard-to-reach international migrants in public health research during the pandemic required constantly adapting recruitment strategies. Furthermore, relying on strategies that were not only Internet-based promoted the participation of populations with limited access to the Internet and low-digital literacy.

Human trafficking has long-lasting implications for the well-being of trafficked people, families, and affected communities. Prevention and intervention efforts, however, have been stymied by a lack of information on the scale and scope of the problem. Because trafficked people are mostly hidden from view, traditional methods of establishing prevalence can be prohibitively expensive in the recruitment, participation, and retention of survey participants. Also, trafficked people are not randomly distributed in the general population. Researchers have therefore begun to apply methods previously used in public health research and other fields on hard-to-reach populations to measure the prevalence of human trafficking. In this topical review, we examine how these prevalence methods used for hard-to-reach populations can be used to measure the prevalence of human trafficking. These methods include network-based approaches, such as respondent-driven sampling and the network scale-up method, and venue-based methods. Respondent-driven sampling is useful, for example, when little information about the trafficked population has been produced and when an adequate sampling frame does not exist. The network scale-up method is unique in that it does not target the hidden population directly. The implications of our work internationally include the need for documenting and validating the various prevalence estimation methods in the United States in a more robust way than was done in existing efforts. In providing this roadmap for estimating the prevalence of human trafficking, our overarching goal is to promote the equitable treatment and overall well-being of the socially disadvantaged populations who disproportionately experience human trafficking.