The Cholera-Hospital-Based-Intervention-for-7-Days (CHoBI7) is a handwashing with soap and water treatment intervention program delivered by a health promoter bedside in a health facility and through home visits to diarrhea patients and their household members during the 7 days after admission to a health facility. In a randomized controlled trial among cholera patient households in Bangladesh, the 7-day CHoBI7 program resulted in a significant reduction in cholera among household members of cholera patients and sustained improvements in drinking water quality and handwashing with soap practices 12 months post-intervention. In an effort to take this intervention to scale across Bangladesh in partnership with the Bangladesh Ministry of Health and Family Welfare, this study evaluates the feasibility and acceptability of mobile health  programs as a low-cost, scalable approach for CHoBI7 program delivery.Formative research for the development of the CHoBI7 mHealth intervention included 40 semi-structured interviews, 4 mHealth workshops, 2 group discussions, and a pilot study of 52 households to assess the feasibility and acceptability of the developed mHealth program. Thematic analysis of the interviews and group discussions was conducted by two individuals separately based on emergent themes, and then themes were compared and discussed.A theory- and evidence-based approach using qualitative research methods was implemented to design the CHoBI7 mHealth program. Semi-structured interviews with government stakeholders identified perceptions and preferences for scaling the CHoBI7 mHealth program. Group discussions and semi-structured interviews with diarrhea patients and their family members identified beneficiary perceptions of mHealth and preferences for CHoBI7 mHealth program delivery. mHealth workshops were conducted as an interactive approach to draft and refine mobile message content based on stakeholder preferences. The pilot findings indicate that the CHoBI7 mHealth program has high user acceptability and is feasible to deliver to diarrhea patients that present at health facilities for treatment in Bangladesh. Both text and voice messages were recommended for program delivery. Dr. Chobi, the sender of mHealth messages, was viewed as a credible source of information that could be shared with others.This study presents a theory- and evidence-based approach that can be implemented for the development of future water, sanitation, and hygiene mHealth programs in low-resource settings.The online version of this article (10.1186/s12889-019-7144-z) contains supplementary material, which is available to authorized users. Diarrhea is a leading cause of death in young children globally, causing an estimated 500,000 deaths annually . Lack ofPhone-based reminders are an emerging low-cost intervention approach that has been shown to lead to significantly improved case management and disease prevention practices –15. FurtMost WASH programs focus solely on educational modules related to diarrhea prevention, and do not tailor their interventions to their target population . This apHousehold members of diarrhea patients are at a much higher risk of developing diarrheal diseases (> 100 times for cholera) than the general population during the 1-week period after the diarrhea patient presents at a health facility –34. ThisV. cholerae. Furthermore, the 7-day CHoBI7 intervention led to significant sustained improvements in household stored drinking water quality and observed handwashing with soap practices up to 12-months post intervention  and [text] messages are good. Many people can learn from this and by saying this information is from the hospital, we can let other people know about this information as well.It was really good to get a call after coming from hospital.CHoBI7 mHealth messages were well-received by group discussion and pilot interview participants. CHoBI7 voice and text messages served as important reminders for patients and their household members to perform the recommended behaviors after returning from the health facility. Participants liked that the messages were coming from the hospital, and mentioned this brought credibility to the messages being sent:A few participants said CHoBI7 messages would be better understood and well-received through in-person visits or a combination of in-person visits and phone calls, with one participant mentioning that not all problems can be solved over the phone. A few participants recommended pictures or videos be added to mobile phone messages.Government stakeholders recommended a wide range of individuals to be the sender for CHoBI7 mobile messages including the Prime Minster, the Health Minister, a parliament member, a health care provider, a high level person from icddr,b, a socialite, or celebrities such as a cricket player or singer. A female voice was said to be attractive for message delivery. One respondent mentioned that the visibility of the person sending the messages was very important, and that the government is planning for people to see the person who is sending voice and text messages in future programs.Two characters were developed to deliver the mHealth messages: Dr. Chobi and Aklima. Dr. Chobi is a doctor at icddr,b hospital who called and texted participants to share information and reminders on handwashing with soap and water treatment behaviors. She was sometimes also called “Dr. Chobi Apa,” meaning “Sister Dr. Chobi.” Dr. Chobi introduced Aklima, a woman who brought her child to a health facility for diarrhea treatment, and who learned proper handwashing with soap and water treatment behaviors from Dr. Chobi. Aklima shared how she used Dr. Chobi’s advice to successfully keep her family safe from diarrheal disease. While Dr. Chobi was introduced as a guide, Aklima served as a peer role model for the participants designed to target descriptive norms—they could learn anything she could learn and also keep their families safe and healthy.I could feel her [Dr. Chobi’s] words from my heart.There was a positive response for Dr. Chobi:Participants in the first group discussion helped determine Dr. Chobi’s age and sex. In the first group discussion, a recording was provided of a male and female voice for Dr. Chobi. Participants liked both voices. Participants in the first group discussion were also shown a photo of a female Dr. Chobi in her 20s and a second image in her 30s. The majority of participants preferred the image of the woman in her 30s, stating she had more gravitas. Based on the feedback from the first group discussion, the Dr. Chobi in her 30s was selected as the character for the CHoBI7 program, and images of her were showed to participants in the pilot.Participants said Dr. Chobi’s name was easy to remember, and thought that all of the CHoBI7 program messages should come from her. Participants also recommended that her name should show up on the caller ID when phone messages were sent. Voice messages were viewed as a more effective way to popularize Dr. Chobi because individuals could interact directly with her and hear her voice. Participants in the pilot requested weekly sessions with Dr. Chobi to discuss their health problems.Aklima was a patient like me and it is good that she is taking help from Chobi apa [sister] like us … She should promote all this knowledge to others.Aklima was also well-received, with one participant stating “I am Aklima—it’s me, not Aklima”, and another stating “It’s my life story.” Participants recommended Aklima share her knowledge with others:Voice and text messages were recommended by all government stakeholders for CHoBI7 program delivery, except for one who recommended mobile messages however did not specify a type. Voice messages were recommended because they could be more easily understood than text messages by individuals that could not read, with non-literate individuals described as the ones needing the CHoBI7 program messages the most. Respondents recommended voice messages come as calls without requiring files to be opened. Text messages had been used in previous government programs, and were said to have a positive impact and be important. Phonetic Bangla was recommended for text messages because many individuals were stated to not have smart phones.and text messages be sent:Both voice and text [messages] are good. From calls we can learn and if sometimes we forget, we can see the [text] message or tell others to read them for us. That is why both together are good.The response was positive for both voice and text messages, with some participants recommending both voice I can show this [text] message to 10 more people.Voice messages were viewed as better than text messages for understanding message content. While the ability to save text messages was mentioned as an advantage over voice messages that could not be saved. Text messages were also described as better for sharing message content with others:Some participants said text messages were faster to read and access than voice messages. However, text messages were problematic for those that were not literate. Some participants said no one in their home could read messages aloud to them. While other participants mentioned that household members could read them CHoBI7 program text messages.In the first group discussion, phonetic Bangla (Bangla written in English letters) was used to send CHoBI7 mHealth messages to participants based on the recommendations from government stakeholder interviews. Some Bangla words spelled using English characters such as the word for diarrhea, “patla paikhana”, were challenging for participants to understand. Participants recommended all text messages be in Bangla script so messages were more easily understood.If the phone [voice message] comes first and the message [text message] comes later this would be good. Then if you cannot remember the content of the call, you can read the message [text message].Participants recommended having text messages summarizing the content of voice messages (summary text messages), so if a voice call was dropped individuals could still receive CHoBI7 messages:I feel so happy when Chobi apa [sister] says-yes! You are right [IVR quiz message response].The CHoBI7 IVR quiz questions had a positive response:Participants enjoyed taking these quizzes, and mentioned discussing the content of these IVR quiz messages with neighbors. Some participants described previous experiences where money was “cut” from their phones when they replied to text messages from health information lines and herbal companies. Participants mentioned being comfortable submitting quiz IVR responses because Dr. Chobi stated it was free to reply during these messages.A few participants mentioned a preference for direct phone calls over recorded voice messages. One participant expressed concern that some people may not receive CHoBI7 voice calls because they will think they are advertisements from different companies. Some participants had full inboxes and could not receive CHoBI7 text messages, or needed to delete CHoBI7 text messages to free space in their inboxes. Additional challenges included lost phones, broken phones that didn’t allow participants to view messages, not knowing how to open text messages, and not checking text messages regularly. Some of the participants in the first group discussion mentioned that voice messages were too long and should be shorten.During their hospital stay they will give 80% of their time to health related issues but in their house they may give 1% of their time …[give] a few messages … what you want to emphasize.For the CHoBI7 mHealth program, government stakeholders recommended giving only a few key messages, and recommended that these messages be very specific and based on scientific evidence. This was considered important given the limited time people would likely give for the CHoBI7 program once they returned from the hospital:Sending messages out to everyone would be a wastage of money … [this] should be for times like during diarrhea outbreaks and should be areas-based [and] people-based not general.Government stakeholders recommended that CHoBI7 mHealth messages be integrated with other health care messages, such as those on antenatal care. Government stakeholders also recommended tailoring CHoBI7 mHealth messages to high-risk populations to ensure the messages were relevant to those receiving them. Delivering mobile messages during diarrhea outbreaks was given as an example:However, it was mentioned that individuals should be practicing handwashing with soap always not only during outbreaks.Everybody will see these messages and learn from them. They would then be free from cholera and other diseases.Message content was found to be clear and participants said messages from Dr. Chobi were stated to be culturally appropriate. Mobile messages from the CHoBI7 program were considered to be important for spreading awareness and improving health:These are not bad words, and people will be more careful after reading the text message. . Mobile messages are also sent out using Bangla script based on the recommendations from the first group discussion. It was found that Bangla script can be viewed on feature phones without smart phone capabilities.Based on the recommendations of government stakeholders and participants in the first group discussion, a female health care provider, Dr. Chobi, was selected to be the sender of voice and text messages for the CHoBI7 mHealth program. Given the iterative nature of message development, using the voice of a government official was explored, however, was found not to be feasible because of the repeated recordings of voice messages that was needed. A celebrity was also found to not be a feasible option for the same reason and because of the high cost for their time.Promoters at health facilities ask diarrhea patients and their family members to save the number for Dr. Chobi in their phones upon enrollment in the CHoBI7 mHealth program. In addition, all text messages show Dr. Chobi as the sender. This was included to ensure individuals receiving calls and text messages from the program know the sender. A tutorial is also given by health promoters in the health facility on how to open text messages and respond to an IVR quiz message to respond to concerns about difficulties opening text messages and responding to quizzes.In an effort to increase access to mHealth messages among female caregivers, we added a module delivered by a promoter in the health facility during the patient’s stay in the hospital for illness on mHealth message delivery and access. During this module the promoter starts by asking diarrhea patients and their family members which phones in the household should receive CHoBI7 mHealth messages. The health promoter then recommends mobile messages be sent to the phone of the primary caregiver of the child under 5 years in the home, when possible, explaining the importance of caregivers receiving these messages for the health of their children (targeting nurture). Promoters also explain that a female health provider will be contacting them by phone, never a male. This module was included to try to alleviate concerns from husbands about voice and text messages being sent to their wife’s phone, and based on recommendations to send phone messages to the female household members.All CHoBI7 text and voice messages state “Please share this message” to promote message sharing among those in the household without personal mobile phones or not receiving program messages. This was added to address challenges with message sharing identified in the pilot. Voice and text messages are sent at 5 PM, this was recommended as a time when most individuals were home. CHoBI7 voice and text messages are sent to households at least once every 2 weeks for 1 year, based on the recommendations from interviews. Voice messages were shortened to approximately 2 minutes based on feedback from the first group discussion. This message length was reported to be fine in the second group discussion. All IVR quiz messages state “There is no charge for your reply” based on pilot participant concerns that a fee would be charged for responding to IVR messages.A theory- and evidence-based approach using qualitative research methods was conducted to design the CHoBI7 mHealth program. The formative research findings have shown that the CHoBI7 mHealth program has high user acceptability and is feasible to deliver to diarrhea patients that present at health facilities for treatment in Bangladesh. Semi-structured interviews with government stakeholders identified their perceptions and preferences for scaling the CHoBI7 mHealth program. mHealth workshops were an interactive approach to draft and refine mobile message content based on stakeholder preferences. Group discussions and semi-structured interviews with diarrhea patients and their family members identified beneficiary perceptions of mHealth and preferences for delivery of the CHoBI7 mHealth program. The pilot of the CHoBI7 mHealth program allowed for an assessment of program feasibility and to identify potential barriers to successful program implementation. Applying the IBM-WASH framework to the development of the CHoBI7 mHealth program allowed us to gain an understanding of the multi-level, multidimensional contextual, psychosocial, and technological factors that were influencing program delivery and the targeted WASH behaviors. This is the first published study, to our knowledge, to conduct formative research to design a WASH mHealth intervention delivered directly to households to increase handwashing with soap and water treatment behavior.Through government stakeholders interviews we identified contextual and technological factors that will be important to consider for scaling the CHoBI7 mHealth program across Bangladesh. Integration of the CHoBI7 program into existing mHealth programs being delivered by the government, such as mobile messaging promoting child and maternal health and health call centers was recommended by government stakeholders. This could help to reduce the cost of program implementation. Government stakeholders also stated the potential for implementation of the CHoBI7 mHealth program without external donor support, and recommended the CHoBI7 program be included in the upcoming National Operational Plan for the CDC Department. Inclusion of the CHoBI7 program in the National Operational Plan and government funding without external donor support will be crucial to the sustainability of this program if taken to scale across Bangladesh.Government stakeholders also recommended delivering CHoBI7 mobile messages to high risk populations during the times when they were most susceptible, such as diarrhea outbreaks. This is consistent with the CHoBI7 program approach to initially deliver mobile messages in the health facility to diarrhea patients and their family members when they are at highest risk for diarrhea. This high-risk time is when WASH messages are likely to resonate the most with individuals and facilitate sustained behavior change , 40.There was a positive response for Dr. Chobi as the sender of the CHoBI7 voice and text messages. She was viewed as a credible source of information, with participants reporting they could share the messages that they received from her with others since she was a doctor at the well-known “Cholera Hospital”. Aklima was also well liked, with participants identifying with her as also being a mother who came with her child to the health facility for diarrhea treatment. Through the formative research it emerged that it was important to have the sender of text and voice messages be a known individual and female. This is consistent with a previous study in Ghana that found a positive response with a female voice being used for IVR message delivery . In BangBoth text and voice messages were considered to be important by beneficiaries and government stakeholders for delivery of the CHoBI7 mHealth program. Voice messages were seen as beneficial because they could be understood by those that could not read. While text messages were viewed as being valuable because they could be saved and viewed later and shared messages with others. Text messages were also stated to serve as important reminders to perform the promoted WASH behaviors. This finding lead to the delivery of voice messages followed by a summary text message in the CHoBI7 mHealth program. Most mHealth programs rely on either text or voice message, and rarely employ both . Future Gender norms around mobile phone access and message sharing emerged as important contextual and technological factors for delivery of the CHoBI7 mHealth program. Some female household members did not have consistent access to mobile phones, and reported that male household members were not always sharing CHoBI7 mHealth messages. Female participants emphasized the importance of receiving program messages since they were often the ones responsible for taking care of young children, and wanted to keep their child healthy. This is an important challenge to address, particularly if no home visits are conducted to reinforce mHealth messages, and given our setting where female household members have lower rates of phone ownership than male household members. In Bangladesh, it is estimated that 82% of male adults are mobile owners compared to only 55% of adult females . This geAn important contextual factor for delivery of text messages for the CHoBI7 program is the literacy rate in the target population. In Bangladesh, the literacy rate for females over 15 years of age is 70%, and 76% for males . FurtherThis is the first study, to our knowledge, to use IVR for sending quiz questions to assess knowledge of WASH practices in a low and middle-income country setting. IVR quizzes were well-received by both group discussions and pilot interview participants, with some participants even reporting sharing the content of quiz questions with neighbors. Previous studies have found that two-way text messaging was associated with improved medication adherence practices compared to one-way texting , 54. TheThe use of mHealth for promoting WASH behaviors presents a low-cost approach for program delivery that does not involve the cost of frequent in-person visits by health promoters. The VIAMO platform was 0.028 USD per minute for recorded voice messages and 0.041 USD per text message in Bangladesh at the time the pilot study was conducted. Therefore, if text and voice messages are delivered every 2 weeks over a 1 year period, the cost of message delivery would be less than 2 USD per phone. Previous formative research from the Aponjon mHealth program found that the majority of pregnant women and new mothers were willing to pay 0.025 USD per mobile message . SimilarmHealth messages have the potential to serve as important reminders to facilitate WASH behavior change that do not require home visits. However, there are no published studies to date, to our knowledge, that have evaluated the impact of a WASH mHealth programs on WASH behavior change. Previously, mHealth RCTs in low- and middle-income countries have focused mostly on reminders for medication adherence and immunization visits –15. Our This study has several strengths. First, the engagement of both government stakeholders and beneficiaries during the formative research that informed intervention development. Through engaging both of these key stakeholders, both scalability and acceptability of the CHoBI7 mHealth program were assessed. Second, the 7-month length of our formative research phase, which allowed for intervention development through an iterative process where changes were made to CHoBI7 mHealth messages based on findings from the pilot study. Third, the use of both group discussions and semi-structured interviews, which provided complementary information. Fourth, the use of mHealth workshops, which were valuable to discuss findings from semi-structured interviews and group discussions, and to draft and refine mobile health message content. Fifth, the use of health theory for intervention development.One limitation is that this study focused only on households that reported mobile phone ownership, and therefore is not generalizable to households sharing phones with other individuals outside the home. In our current RCT, over 90% of diarrhea patient households screened for eligibility reported household phone ownership . Future studies should evaluate the impact of the CHoBI7 mHealth program on households that report sharing a mobile phone. A second limitation is that the pilot study was conducted only in Dhaka, Bangladesh. Future studies should pilot the CHoBI7 mHealth program across all divisions of Bangladesh. A third limitation is that messages were not sent out using the government’s mobile platform. The VIAMO platform was used given the iterative nature of message development. Future work should use the government’s mobile platform.This study presents a theory- and evidence-based approach that can be used for the development of future WASH mHealth programs. The CHoBI7 mHealth program presents a promising scalable approach for which mobile messages can be sent to a large number of households at minimal cost and can serve as valuable cues to action to facilitate WASH behavior change. The ongoing RCT of the CHoBI7 mHealth program is the first to evaluate the impact of voice and text message reminders sent to households promoting handwashing with soap and water treatment behaviors. The evidence generated by this study will be used by the Bangladesh Ministry of Health and Family Welfare to determine the feasibility of scaling the CHoBI7 mHealth program across Bangladesh.Additional file 1:Table S1. Overview of Intervention Activities by Arm. Table S2. Mobile Messages Played During the First Group Discussion. Table S3. Overview of CHoBI7 Mobile Health Program Development: Themes, Formative Research Findings, and Intervention Components. (DOCX 38 kb)


METHODS/STUDY POPULATION: Participant recruitment programs across the consortium are inconsistent in staffing, program services, and program goals. The participant recruitment program at the University of Michigan’s (U-M) Michigan Institute for Clinical & Health Research (MICHR) provides expertise, tools, and resources to facilitate participant recruitment in clinical and health research studies. RESULTS/ANTICIPATED RESULTS: We will explain our program infrastructure, staffing, services, and discuss how we maintain an engaged registry with over 27,000 participants interested in research studies at U-M. DISCUSSION/SIGNIFICANCE OF IMPACT: Proper recruitment into clinical trials results in findings that are relevant for genetic, cultural, linguistic, racial/ethnic, gender, and age differences . We hope to share our best practices that aid in the development and success of participant recruitment across the CTSA Consortium.OBJECTIVES/SPECIFIC AIMS: Across the Clinical and Translational Science Award (CTSA) Consortium, participant recruitment into clinical trials is essential to advance science. Without proper participant recruitment, clinical trials do not result in gains in scientific knowledge, wastes time, funds, and other resources (Mahon


Provision of evidence-based smoking cessation treatment may contribute to health disparities if barriers to treatment are greater for more disadvantaged groups. We describe and evaluate the public health impact of a novel outreach program to improve access to smoking cessation treatment in Ontario, Canada.We partnered with Public Health Units (PHUs) located across the province to deliver single-session workshops providing standardized evidence-based content and 10 weeks (2007–2008) or 5 weeks (2008–2016) of nicotine replacement therapy (NRT). Participants completed a baseline assessment and were followed up by phone or e-mail at 6 months. We used the RE-AIM  framework to evaluate the public health impact of the program from 2007 to 2016. Given the iterative design and changes in implementation over time, data is presented annually or bi-annually.reach was estimated to be 0.1–0.3% of eligible smokers in Ontario. Participants were older, smoked more heavily, had a lower household income, were more likely to be female and be diagnosed with a mood or anxiety disorder, and less likely to have a postsecondary degree compared to average Ontario smokers eligible for participation. The intervention was effective; at 6-month follow-up 22–33% of respondents reported abstinence from smoking. Adoption by PHUs was 81% by the second year of operation and remained high (72–97%) thereafter, with the exception of 2009–2010 (33–56%) when the program was temporarily unavailable to PHUs due to lack of funding. Implementation at the organizational level was not tracked; however, at the individual level, approximately half of participants used most or all of the NRT received. On average, maintenance of the program was high, with PHUs conducting workshops for 7 of the 10 years (2007–2016) and 4 of the 5 most recent years (2012–2016).There were 26,122 enrollments from 2007 to 2016. Between 31 and 442 workshops were held annually. The annual The smoking cessation program had a high rate of adoption and maintenance, reached smokers over a large geographic area, including individuals more likely to experience disparities, and helped them make successful quit attempts. This novel model can be adopted in other jurisdictions with limited resources. Despite the availability of smoking cessation treatments that increase long-term quit success , 2, a maIn 2005, the Smoking Treatment for Ontario Patients (STOP) program was established to distribute free NRT to smokers wanting to quit across the province, including rural and remote areas. For more than a decade, the STOP program has made use of and evaluated various methods for distribution of free NRT including large-scale mail-outs  as well In 2006, the STOP program partnered with Ontario’s Public Health Units (PHUs) to pilot a program offering individual or group counselling and NRT. PHUs are official health agencies that administer public health programs and services to the community. Every urban and rural municipality (and its surrounding area) in Ontario is served by a PHU, so they have wide reach across the province. The program provided eligible smokers in the local catchment area with up to 10 weeks of NRT and multiple counseling sessions with a PHU staff member trained in smoking cessation counselling. Though all 36 PHUs in Ontario were invited, only 12 implemented the program, as many did not have the resources or capacity to participate. At the end of the pilot program, feedback from the PHUs that participated highlighted benefits of the program including increased access to cessation treatment and increased demand for existing cessation services. However, many PHUs did not have the time or resources available to handle the additional workload on an ongoing basis. The funder was willing to provide some but not all the resources necessary for such a program. This required innovation and adaptation of the intervention to fit the setting without losing the active ingredients which were access to NRT, brief behavioural support and self-help material.Mobile and pop-up clinics have been utilized as an innovative method to bring healthcare services to local communities to reduce or eliminate financial and other access barriers to health care among underserved communities and disadvantaged populations such as the homeless, uninsured and those living in rural or remote communities –21. TheyThe decision was supported by the absence of evidence that group cessation support is less effective than individual support and evidence that is more effective than self-help alone , 31. TheThe objective of this paper is two-fold: (1) describe the use of a theory-guided approach to the implementation of a smoking cessation outreach program and (2) evaluate its public health impact over 10 years (2007–2016) using the RE-AIM framework . RecogniA continuous quality improvement approach, with regular evaluation and feedback, was used to inform the iterative design of the program and its implementation. We based our activities on implementation best practices described by Fixen et al. . This inFor installation and early implementation of the intervention STOP program staff (research assistants) would travel to communities across Ontario to deliver workshops in partnership with PHUs. A combination of NRT and behavioural intervention was provided during these single-session workshops. Providing the workshop in a group setting allowed treatment to be provided to up to 50 individuals in a brief period of time, requiring less time and fewer resources than individual support. STOP and the PHU arranged a mutually convenient time and date for the workshop. The PHU was responsible for locating and booking an appropriate venue, promoting the workshop, and screening prospective workshop participants for eligibility. STOP provided PHUs with template recruitment posters and ads (on air and in print) to advertise the workshop, and PHUs provided a telephone number individuals could call for screening and registration. STOP program staff travelled to the workshop venue and were responsible for setting up and conducting the workshop, ensuring consent and baseline assessment forms were completed, delivering the psychoeducational presentation, and handing out NRT kits. The STOP program was also responsible for conducting follow-up surveys and evaluating treatment outcomes and participant satisfaction.Starting in 2011, in order to increase the number of workshops and ensure more flexibility in scheduling, we adapted the program so workshops could be conducted without STOP program staff in attendance. A PHU staff member was able to facilitate the workshop if they had completed our TEACH  accredited certificate program in cessation counselling, offered by the Centre for Addiction and Mental Health and University of Toronto , or equiThe STOP program continues to provide ongoing support to PHUs running workshops by providing training in program implementation as needed and working to identify the number of facilitators that are TEACH-trained who can deliver workshops in their communities. This is a necessary step given staff turnover. STOP also continues to conduct evaluations of the program and follow-up of participants to track outcomes, and regularly provides feedback to individual PHUs about their participant enrollment and outcomes. See Fig. .The workshops were typically 2–3 h long. At the beginning of the workshop, participants were asked to provide written informed consent and complete a baseline assessment questionnaire. A 1-h presentation followed that covered various aspects of smoking cessation, including behavioural strategies for quitting and proper use of NRT. After the presentation, each participant had a brief 5- to 15-min consultation with STOP/PHU staff to ensure consent and assessment forms were completed, confirm eligibility, and select type of NRT from the options available. Selection of NRT was based on participant preference and previous use of NRT, along with program recommendations based on number of cigarettes smoked per day. Participants received a kit consisting of a supply of free NRT, an evidence-based self-help booklet on quitting smoking, a list of frequently asked questions about NRT, a list of additional smoking cessation resources, and STOP program contact information for questions and inquiriesInitially, a 10-week supply of monotherapy  was provided as per manufacturers’ recommended duration of use at the time. Quit rates after providing 10 weeks of NRT were similar to those observed after providing 5 weeks of mail-out NRT in Ontario . TherefoThe evaluation timeframe was January 29, 2007 (first workshop) to December 31, 2016.participants or individuals should not be taken to mean unique individuals. Though this evaluation focuses on PHUs, the primary implementers of STOP on the Road workshops, participants that attended workshops through other community health providers in Ontario  are included in the sample.Workshop participants were required to be residents of Ontario; at least 18 years old; smoke 10 or more cigarettes per day; be willing to set a quit date within 30 days; and not have any medical contraindication to NRT . Individuals were able to enroll in more than one workshop over the ten-year period, thus any reference to the number of The baseline assessment questionnaire collected data on participant demographics, smoking characteristics, and medical/psychiatric history. All participants who provided contact information were invited at 6 months after the end of treatment to complete a follow-up survey and were asked about quit attempts made and current smoking status. Follow-up was conducted either by telephone or email.Given the iterative design and changes over time, data are presented annually or bi-annually. Missing data were not imputed and analyses were conducted with complete cases. Data were analyzed using SPSS version 24 .Reach refers to the number and proportion of eligible individuals that are recruited to participate in an intervention and how representative they are of the target population [pulation . Reach wpulation –39. To cTo determine reach in rural areas, we used Rurality Index of Ontario (RIO2008) scores  to classEffectiveness is the extent to which an intervention achieves its intended outcomes [outcomes . The effadoption assesses the number, proportion and representativeness of settings and staff willing to implement a program [Similar to the individual-level measure of reach,  program . Adoptioimplementation refers to the extent that an intervention is consistently delivered as intended, as well as the time and costs involved, while at the individual level it refers to participant adherence [At the organizational level, dherence . With reMaintenance at the individual level is an assessment of long-term outcomes (6 months or longer) [ longer)  and was n = 25,369) attended a workshop through a PHU, 2.5% (n = 652) through another health related organization, and for 0.4% (n = 101) type of organization was unknown. See Table There were a total of 26,122 enrollments in a smoking cessation workshop from January 2007 to December 2016. Of these, 98.2% n = 25,9 attendeTable See Table The number and proportion of PHUs that offered a workshop is presented by year in Table Though not formally assessed, an overview of some time and costs involved are described. Time needed to set-up and run a workshop was approximately 3 h. Additional time required included: (1) PHU staff: time required to find and book an appropriate venue, as well as recruit and screen participants; and (2) STOP staff: travel to workshops by car or airplane, ranging from locations within the same city to as far within the province as approximately 1500 km. STOP has consistently covered advertisement, room rental and refreshment costs (if needed) for workshops, in addition to the cost of NRT kits. STOP was previously required to also budget for travel expenses incurred, but this has been eliminated with the new implementation model. The new implementation model does require a budget for mail out of NRT kits for a small number of PHUs that cannot dispense NRT. Personnel costs for STOP included one full-time project coordinator to support the PHUs in planning the workshops and ensuring the NRT and other materials were sent to each health unit in advance of the workshop. The coordinator also updated the content of the workshop based on feedback from PHU staff, participant evaluations and recent evidence. Earlier iterations also hired research assistants to assist with workshop delivery (two STOP staff were present at each workshop) and telephone follow-up calls. The only costs PHUs invested were personnel costs for time needed for training, implementation of the workshop  and communication with STOP staff.Consistent delivery of the intervention was encouraged, but not monitored or assessed formally. Contents of the NRT kits were identical across all sites and they were delivered to partnering organizations already pre-assembled and sealed. In addition, presentation slides and accompanying speaker’s notes were provided in a format that could not be modified (PDF file).At the individual level, amount of the NRT used (adherence) varied . At the At the individual level, maintenance of the intervention effect is reported above as the abstinence rate at 6-month follow-up. At the organizational level, on average, PHUs offered workshops for 7 of the 10 years (2007–2016) and 4 of the 5 most recent years (2012–2016). The number of workshops per year has also increased  estimated. Finally, our evaluation employed only quantitative methods and a mixed methods approach could have enriched our understanding and appraisal of the intervention on each dimension .To our knowledge, this is the first report to describe the use of implementation science models and evaluation for public health impact of an outreach program to provide evidence-based smoking cessation treatment to smokers wanting to quit. This model can be adopted in other jurisdictions where there is a gap in smoking cessation services. Additional research is needed to identify and address the limitations of this program and assess the cost-effectiveness and compare it with other programs.


Unspent Transaction Outputs (UTXOs) are the internal mechanism used in many cryptocurrencies to represent coins. Such representation has some clear benefits, but also entails some complexities that, if not properly handled, may leave the system in an inefficient state. Specifically, inefficiencies arise when wallets (the software responsible for transferring coins between parties) do not manage UTXOs properly when performing payments. In this paper, we study three cryptocurrencies: Bitcoin, Bitcoin Cash and Litecoin, by analysing the state of their UTXO sets, that is, the status of their sets of spendable coins. These three cryptocurrencies are the top-3 UTXO-based cryptocurrencies by market capitalization. Our analysis shows that the usage of each cryptocurrency presents some differences, and led to different results. Furthermore, it also points out that the management of the transactions has not always been performed efficiently and therefore, the current state of the UTXO sets is far from ideal. Once a transaction is included in the ledger, the transaction can no longer be modified. Transactions aim to modify account balances.How such accounts are stored and their balances modified is implemented in entirely different ways depending on the specific blockchain technology used. The two main approaches are Unspent Transaction Output (UTXO)-based and account-based, and the most relevant cryptocurrencies for each category are Bitcoin and Ethereum, respectively.In the UTXO-based approach, currency units can be identified as coins represented by a specific data-structure known as UTXO. As the coins of the system, the set of all UTXOs represents the total currency supply. Each coin contains essentially two different pieces of data: ownership data , and the amount it represents . Therefore, in this model coins do not have a fixed amount: a single coin may represent just the smallest unit of the currency  up to the total currency supply (e.g. a unique coin with 21 million bitcoins).On the other hand, in the account-based approach, the currency is spread among the multiple accounts of the system, each of which has a specific balance. The sum of all accounts’ balances is the total amount of the cryptocurrency in circulation. With this approach, the system has to track the balance of every account, even if such balance is zero. Notice that in this case, the data required to operate the cryptocurrency is bound to the total number of accounts rather than the total number of coins.Both models present their benefits and problems in terms of efficiency, scalability, decentralization and privacy.One of the benefits of the UTXO model is its atomicity. Transactions can be processed in parallel for different UTXOs. There is no need to order transactions since coins involved in one transaction do not affect other coins of the system. An entity may own different coins and they can all be used alongside. The UTXO model also entails some benefits regarding privacy. Coins are attached to an address, that can be different for each coin. Although such approach may be also used in the account-based approach, this model suggests holding a single address per user, to minimize the number of zero-balance accounts the system has to keep track of. By contrast, a UTXO approach may discard the UTXO representing a coin once such coin has been spent. Furthermore, atomic transactions involving inputs from different parties are also easier to perform with a UTXO model than with an account-based model .A pays an amount x to user B, the account-based model can perform such operation efficiently but the UTXO approach may generate some inefficiencies if A does not have a coin with the exact amount x. For instance, if A has a coin with an amount greater than x, the transaction will increase the number of coins in the system since the initial coin from A will be transformed in two new coins, a coin of x-value for B and another coin for the remainder value to A (the change). On the other hand, if A did not have a coin with amount greater or equal than x, she will have to collect multiple coins to make the payment. Such complexities that arise in a simple payment in the UTXO approach become more complex when other considerations enter into the equation. For instance, in order to reward miners, transactions include a fee and such fee is related to the size of the transaction. The key point here is that such fee is independent of the value the transaction is transferring, so surprisingly, transferring 1000 bitcoins may incur the same fees as transferring 0.00001 bitcoins. This fact has huge implications regarding the use of coins. Note that such idea indicates that when performing a payment you will pay a fee depending on the total number of coins you use in that payment. So if you have to collect multiple coins to achieve the x-value of your payment, you will have to pay higher fees than if you had a single coin. Notice, however, that unless you have the exact value, after the payment you will have a coin with a value containing the change. After multiple payments with all your bigger coins, you will end up with multiple small coins. In the following payment, you do not have any other option that collecting multiple coins to perform the payment, paying a higher fee for that transaction.However, the UTXO approach comes with some shortcomings, its complexity being the main one. When a user needs to know the total value she owns, it is much easier to check her balance directly than to collect all the coins she owns and count them. Furthermore, for a simple payment transaction where user The complexity even goes further when the coins that are generated in a transaction end up with a value that is lower than the fee needed to spend them. In this case, the newly created coins will probably not be spent, since it is more expensive to spend the coin than not using the coin at all. In that situation, the system becomes inefficient since it has to track the status of that particular coin, that may never be spent due to an economic disincentive to do so.To avoid such inefficiencies and to minimize the fees that users pay for their transactions, wallets should perform accurate management of the UTXOs that they use for their payments. However, as we will see in the analysis presented in this paper, such efficient management has not always been performed by all wallets.1 The paper provides insightful details about both the current and the evolution of the status of the UTXO sets of those coins. This analysis allows also to compare the usage of the three cryptocurrencies. Moreover, the paper further analyses one of the problems of UTXO-based cryptocurrencies: the existence of outputs not worth spending. We propose two metrics to evaluate whether an output is worth spending and analyse the UTXO sets of the three above-mentioned coins in terms of these metrics.The main contribution of this paper is an analysis of the UTXO sets of the three most capitalized UTXO-based cryptocurrencies: Bitcoin, Bitcoin Cash and Litecoin.The rest of the paper is organized as follows. Section 2 provides an overview of the UTXO set concept. Then, §3 describes the contents of the current UTXO sets of Bitcoin, Bitcoin Cash and Litecoin. After that, §4 describes the metrics used to evaluate if outputs in the UTXO set are worth spending, that is, dust and unprofitability. Section 5 analyses the UTXO sets taking into account the previously defined metrics and considering both the current state and their evolution over time. Finally, §6 presents the related work and §7 the conclusions of the paper.2.outpoint, a two field data structure containing the transaction ID and output index that created them, and they store two data fields: the amount they are holding, and the locking script (scriptPubKey) that specifies the conditions under which they can be redeemed.In a UTXO-based cryptocurrency, the Unspent Transaction Output (UTXO) set is the subset of transaction outputs that have not been spent at a given point in time. Whenever a new transaction is created, funds are redeemed from existing UTXOs, and new ones are created. Basically, transactions consume UTXOs (in their inputs) and generate new ones (in their outputs). Therefore, transactions produce changes in the UTXO set. Regarding UTXOs, they can be identified by their The main purpose of the UTXO set is speeding up the transaction validation process. When a new block is appended to the blockchain, full nodes update their vision of the UTXO set, removing the outputs that have been spent in the block and adding the newly generated ones. Being all unspent transactions outputs stored in the set, there is no need to scan throughout the blockchain to check for double-spends, but just check if the inputs of a transaction can be found in the set.The format, storage requirements and implementation of the UTXO set may differ depending on the specific implementation. However, their content must be consistent between all the implementations, since all of them need to be able to verify every transaction. For our analysis, we have chosen the Bitcoin Core implementation. However, equivalent results can be obtained by any other implementation that follows the UTXO set approach. The main benefit of such a choice is that Bitcoin Core’s implementation is also followed by wallets used in Bitcoin Cash and Litecoin so that the same analysis tool can be used for all three of them. In such implementation, the UTXO set is stored in the chainstate, a LevelDB database that provides persistent key-value storage. Apart from the UTXO set, the chainstate database stores two additional values: the block height at which the set is updated and an obfuscation key that is used to mask UTXO data ,3. Such STATUS  [2 STATUS works with a chainstate folder as input and can perform two different types of analysis: a UTXO-based analysis where each output is independent of the rest, and a transaction-based analysis where outputs belonging to the same transactions are aggregated. Moreover, comparative analysis between different states of the UTXO set can be also performed by using several chainstate snapshots. Our analysis has been complemented using BlockSci [In order to perform the analysis included in the paper, we have created txo Set) , an openBlockSci  to obtai3.In this section, we provide an overview of the UTXO sets of Bitcoin, Bitcoin Cash and Litecoin, the three UTXO-based cryptocurrencies with the highest market capitalization. Results included in this section correspond to the UTXO set as it was on 6 February 2018 and were made using the STATUS tool.x, y) in the plot shows the probability y that a given variable (depicted in the x-axis label) will take a value less than or equal to x.Unless the contrary is stated, all the plots included in this section show cumulative distribution functions. Therefore, a point .Bitcoin Cash is a fork of the Bitcoin blockchain created on August 2017 as a consequence of the disagreements between the community about how to handle scaling problems in Bitcoin. Bitcoin and Bitcoin Cash share the same blockchain history up to block 478 558, where they forked and each coin followed a different path. On February 2018, Bitcoin and Bitcoin Cash still shared 32.3 million UTXOs , which, software fork4 of the original Bitcoin Core client created in 2011. Litecoin mainly differs from Bitcoin in the average time between blocks (2.5 min instead of 10), the hashing algorithm (Scrypt instead of SHA256) and the increased maximum amount of coins (84 instead of 21 million).On the contrary, Litecoin is a coin whose chain is totally independent of the Bitcoin blockchain. However, the source code of Litecoin is a 5 .The distributions of the number of UTXOs per transaction are very skewed . Even whRegarding the transaction height of the UTXOs in the sets , some di3 satoshis (4% of the UTXOs), followed by 104 and 1 . This can be seen in the plots of 6 and 108 .Bitcoin and Litecoin also present clear differences regarding the amounts stored in the UTXOs. 66.7% of the UTXOs in Litecoin store the smallest unit of value possible, that is, just 1 litoshi. By contrast, just 1.4% of Bitcoin’s UTXOs store 1 satoshi. Moreover, the vast majority of UTXOs are smaller than one bitcoin/litecoin . However, both currencies have one thing in common: the preference for output amounts that are powers of 10. In Bitcoin, the most common amount is 10The previous figures draw attention to the fact that there are a lot of UTXOs with an amount equal to 1. To further explore this phenomenon, 6 In all the analysed coins, P2PKH is the most common script type, representing 81% and 94% of the UTXOs in Bitcoin and Bitcoin Cash, respectively, and 97% of them in Litecoin. It is worth mentioning that there are no segregated witness scripts in Bitcoin Cash, since such kinds of scripts do not exist in the coin. Litecoin has a significant amount less of both multisignature scripts and P2PK scripts using compressed keys.c), it is surprising to see that all multisig UTXOs in the snapshot were created in a very short time: 99% of them were created before block 753 252 and 96% of them between blocks 743 428 and 753 252. All existing non-standard UTXOs were only created during the first 487 470 blocks. P2PKH and P2PK UTXOs were mostly created in the very beginning (as most of the coins).Regarding Litecoin c, it is 4.An interesting type of output included in the UTXO set is that whose economic value is small enough to pose a problem when it has to be spent. Since such UTXOs are in the set, they occupy space and thus convey a cost to the system. However, the cost of spending them is so high with respect to its value, that the owner has no direct economic incentive to do so.dust output as an output that costs more in fees to spend than the value of the output. To compute the cost of spending an output, both its size and the size of the input from the transaction that will spend it, are required. Since the input that spends a given UTXO is not yet in the blockchain Bitcoin Core sets such value to 148 regardless of the type of outputs that will generate the input. These 148 bytes are based on the most common type of Bitcoin outputs, P2PKH and account for the outpoint (32 + 4), the length of the script in bytes (1), the signature (1 + 72), the public key (1 + 33) and the sequence number (4).Bitcoin Core currently  defines α is the segwit discount factor. If the output is not a native segwit script, then α is 1. However, if it is indeed a segwit output, the size of the input script needed to redeem it is discounted by a factor α = 4.Therefore, a given UTXO out is considered dust by the Bitcoin Core client with respect to a fee-per-byte rate 𝔣 with the following definition:The definition of dust, as interpreted by the Bitcoin Core, has two main limitations. First, it assumes a fixed transaction input size of 148. Although the exact content of the input of a UTXO will never be, by definition, known to the client, in fact, the size of an input can usually be predicted with high accuracy. Second, it considers both the sizes of the output and the input. Although this may be useful in some cases, we claim that since the transaction containing the output is already in the blockchain, its size should not be taken into account when analysing the dust problem .unprofitable output as the output of a transaction that holds less value than the fee necessary to be spent, taking into account only the size of the input that will be needed to spend it.Therefore, we define an low), that will take into account the minimum size of the input; and an estimation of unprofitability (is_unprofitableest), that tries to estimate as accurately as possible the real unprofitable rates taking into account data available in the blockchain.However, it is not always possible to exactly determine the size of the input of a certain UTXO. Depending on the type of the script found in the UTXO, we will be able to accurately determine the size that its input will have, or we will have some uncertainty about it. Therefore, we will consider two different metrics for unprofitability: a lower bound on unprofitability (is_unprofitablelow and unprofitableest), it is important to recall that the amount of fees a transaction has to pay to be included in a new block depend on two factors: the fee-per-byte rate, 𝔣, that the network is expecting at the time of creating the transaction and the size of the transaction. Section 4.1 explains what is the fee-per-byte rate and shows its value over the past years. Regarding the size of the transaction, the definition of dust uses a fixed size, but our unprofitability approach takes into account the minimum size of the input. Section 4.2 explains what are the minimum inputs of UTXOs depending on their type. Finally, the estimation of unprofitability needs to take into account the sizes of variable parameters. Section 4.3 presents the estimations of those sizes made from blockchain data.In order to identify the three types of outputs  per byte is a highly variable factor that depends on the transaction backlog (i.e. how many transactions are pending to be included in new blocks and how much are those transactions willing to pay to be included in a block).7 that paid as much as 85.9 BTC in fees. In Litecoin, the highest peak is found in block 158 277, that has a single non-coinbase transaction8 paying 100 LTC as fees.4.2.minimum-input of a UTXO as the smallest size input that spends such UTXO. The size of such minimum-input, along with the value held in the output and the fee rate, will determine whether a UTXO may be flagged as unprofitable.Unprofitability is defined considering the size of the minimum-input of each UTXO. In order to label the outputs in the UTXO set as unprofitable, we need an estimation of the size of data required to spend such output. To identify such minimum information, we can consider an already standard transaction with its inputs and its outputs and enough fees to be relayed. Then, we define the To measure the size of such minimum-input, we need to review the structure of a Bitcoin transaction. As depicted in prev_tx_id, pev_out_index and nSequence. Therefore, for every UTXO, its minimum-input will be at least 40 bytes long independently of its type. On the other hand, the content and length of the fields scriptSig and scriptSig length depend on the UTXO type, specified in the field scriptPubKey of the UTXO.Depending on the UTXO type, its minimum-input size will be different. Such measure can be split in two parts: fixed size and variable size. Regarding the fixed size, as depicted in The different types of outputs, with their corresponding size, can be classified as follows:Pay-to-PubKey (P2PK) outputs. The minimum-input of this type of UTXO specifies just a digital signature to redeem the output and the scriptSig includes the following data:PUSH sig (1 byte)+sig (71 bytes)r and s components [r parameter. Since we are defining the minimum possible input to be created, 71-byte signatures are considered. Hence, the scriptSig for a P2PK UTXO will be 72 bytes long and scriptSig len field will be 1 byte long, resulting in a minimum-input size of 73 bytes.Bitcoin uses DER encoded ECDSA signatures in the scripts of its transactions, which can be between 71 and 73 bytes long depending on their Pay-to-PubkeyHash (P2PKH) outputs. For this UTXO to be redeemed, both a signature (sig) and a public key (pk) are required in the scriptSig, as shown below:PUSH sig (1 byte)+sig (71 bytes)+PUSH pk (1 byte)+pk (33-65 bytes)—Uncompressed keys: such keys were used, by default, in the first versions of the Bitcoin Core client, and they are 65 bytes long.—Compressed keys: in 2012, Bitcoin Core started using this more efficient type of keys, which are almost half size of the previous ones (33 bytes), and therefore make smaller scripts.So, the size for the scriptSig varies from 106 to 138 and therefore, the scriptSig length field will be 1 byte long, resulting in a total minimum-input size between 107 and 139 bytes.Regarding the signatures, the same assumption as for P2PK outputs applies, that is, 71-byte length can be considered. Regarding public keys used by Bitcoin, they can either be compressed or uncompressed, which will significantly vary their size:Pay-to-multisig (P2MS) outputs. The size of the minimum-input to redeem such a script varies highly depending on the number of signatures required, which ranges up to 20 (20-of-20 multisig),9 so the scriptSig for redeeming such output is as follows:OP_0 (1 byte)+(PUSH sig (1 byte)+sig (71 bytes)) *required_signatures (1-20)scriptSig field will range between 73 and 1441 bytes, making the scriptSig len field range between 1 and 2 bytes, so the total minimum-input size will be between 74 and 1443.Thus, the size of the Pay-to-ScriptHash (P2SH) outputs. Unlike any previous output type, input size created from P2SH outputs cannot be straightforwardly defined in advance. P2SH outputs hide the actual input script behind a hash, to make smarter outputs, by making them smaller and thus allowing the payer to pay lower fees. However, the scripts held by those UTXOs give us no clue about how the minimum-input should be build. Therefore, we have set the minimum-input size to just the fixed size (40 bytes) plus an additional byte for the script length, resulting in 41 bytes.Pay-to-Witness-Public-Key-Hash (P2WPKH) and Pay-to-Witness-Script-Hash (P2WSH) outputs. These new types of outputs are redeemed with empty input scripts. Data that prove ownership are moved into the witness and get a discount when computing the size of the input. Therefore, native segwit outputs will need the 40 fixed length bytes, the script size (1) and the discounted witness size.Native The witness script needed to redeem a P2WPKH script isPUSH sig (1 byte)+sig (71 bytes)+PUSH pk (1 byte)+pk (33 bytes)Note that, in contrast with P2PKH scripts, here the public keys will always be found in a compressed form, as only this format is accepted in P2WPKH scripts. Therefore, P2PKH scripts will always be 106 bytes, and their inputs will need 40 + 1 + 106/4 = 68 bytesThe witness script to redeem a P2WSH output is equivalent to the input script of a P2SH output. However, P2WSH are not affected by the 520-byte push limit of P2SH scripts.4.3.As introduced in the last section, the inputs of some of the output script types contain values whose size cannot be known before actually seeing the input. However, the moment an output of such type is spent, we can observe the content of the corresponding input script and, therefore, compute its size. Yet whenever the output is spent, it is no longer an Unspent Transaction Output (UTXO), and it is thus no longer the subject of our analysis. On the other hand, we can indeed use historic data about outputs that have already been spent to estimate the input sizes of unspent outputs of the same type.10 The estimation has also been performed for both Bitcoin Cash and Litecoin and the obtained values have been used in the analysis performed in §5. However, only the analysis for the Bitcoin blockchain will be broken down into parts due to space limitations.In this section, we provide an estimation of the variable-size parameters of input scripts based on the real data that already appears in the Bitcoin blockchain. Specifically, we analyse the sizes of public keys in P2PKH inputs, the sizes of P2SH inputs, the sizes of non-standard inputs and the sizes of P2WSH witnesses.Public keys can be found either in a compressed or uncompressed format. Public keys may be found both in input and output scripts: they may appear in P2PK and multisig outputs or in inputs spending P2PKH outputs. In our analysis, we have focused on checking public keys in inputs spending P2PKH outputs, since these are the ones that will affect our profitability analysis. The first compressed public key in a P2PKH input can be found in block 158 216.11 The input spends an output in block 158 214.12The use of compressed or uncompressed public keys highly depends on the height of the block: whereas there were no compressed public keys in the beginning, nowadays its use is prevalent. For this reason, we are interested in knowing how likely it is for a P2PKH output at a given height to be spent by an input with a compressed or uncompressed public key. P2SH inputs must contain the redeem script (whose hash matches the one specified in the UTXO) and any data that are required to make the redeem script evaluate to true. Both items must be taken into account when computing the size of the input.Multisig scripts are by far the most common script encapsulated within P2SH, and they account for 85.6% of the P2SH redeem scripts in the blockchain. Because native multisig outputs are costly, it is common to encapsulate them in a P2SH, transferring the fees to the redeemer of the output. The size of a P2SH multisig input is determined by the number of allowed signers (that determines the number of public keys) and the number of required signers (that determines the number of signatures). Although a wide variety of encapsulated multisig scripts can be found in the blockchain, 2-of-2 and 2-of-3 multisig account for 97, 85% of multisig scripts , plus the marker (0 × 00) and two data pushes.13 The average size of P2SH scripts with non-standard redeem scripts is 169.98 bytes, although most of them (66.06%) are just 11 or 12 bytes. The largest input script of this type is 9 319 bytes long.Non-standard redeem scripts may be of arbitrary length. However, the redeem script has to be pushed to the stack, so it is affected by the maximum stack element size of 520 bytes.Inputs of P2SH scripts with nested P2PK scripts may be either 108 or 140 bytes long (assuming 71-byte signatures), depending on whether they use compressed or uncompressed public keys. Nevertheless, data show that almost all of them have compressed public keys (there are just six of such scripts in the blockchain using uncompressed public keys). Similarly, inputs of P2SH scripts with nested P2PKH scripts may be either 132 or 164 bytes long, although all of them used compressed keys.OP_0 and the other asking for the preimage of a 152-byte length value. These make input sizes of 25 and 178 bytes, respectively.Finally, there are 82 redeem scripts that contain hash puzzles, that is, they ask for the preimage of a hash. We have found there are just two different versions of these scripts, one asking for the preimage of In contrast with compressed and uncompressed public key usage, whose behaviour highly depends on the block height, we have found a small correlation between P2SH input script sizes and the block height where they are found . There anon-standard outputs may contain scripts of arbitrary length. Since such inputs do not have a predefined structure, we resort to analysing the sizes of inputs spending non-standard outputs in the blockchain. We have found 220 758 of such inputs in the blockchain, which correspond to 78 different input scripts. The average size of non-standard input scripts is 1.04 bytes . As a matter of curiosity, the input script OP_TRUE is found in 219 191 inputs.Inputs spending These 220 758 non-standard inputs are found in just 143 different blocks, so data is too disperse to evaluate its size with respect to block height.α = 4.As in P2SH inputs, P2WSH inputs must also include the redeem script with a matching hash and any data needed to redeem it. However, instead of including the redeem script in the input itself, the redeem script is found in the witness and thus gets discounted by a factor We have found 215 742 P2WSH inputs with an average witness size of 251.6 bytes. From block ≈ 488 000 onwards, the plot shows an stable trend. Of the redeem scripts, 97% are 252, 253 or 254 bytes long, and correspond to 2-of-3 multisignature scripts. As P2SH input scripts, witness length is not clearly correlated with block height . The fir5.unprofitable_low) and estimation of unprofitability (unprofitable_est). After that, we analyse how unprofitability has evolved over time.In previous sections, we have seen that the two parameters that affect the amount of fees needed to spend a transaction are the size of the transaction and the current fee-per-byte rate. In this section, we first evaluate the current status of the UTXO sets of Bitcoin, Bitcoin Cash and Litecoin regarding the parameters previously defined: dust, a minimum bound on unprofitability , we have chosen the smallest possible value . On the contrary, for unprofitable_est we have considered exact input sizes when known and resorted to estimations based on blockchain data when a variable-length parameter is needed, as described in §4.3. As explained in the previous section, the use of compressed or uncompressed public keys was highly dependant on the block height. On the contrary, P2SH, non-standard and P2WSH sizes do not show that tendency . For a  compare  and 12. As depicted in a shows the fraction of UTXOs that are considered dust, unprofitablelow and unprofitableest in the Bitcoin UTXO set . For a fee-per-byte as small as 116 satoshi/byte onwards, more than the 50% of UTXOs (30.2 million outputs) from the set can be considered dust, whereas the same 50% size for unprofitable_low is reached for 232 satoshi/byte onward. Note that 5 617 outputs will always be considered dust and unprofitable, regardless of the fee rate, because they have an amount of 0.Figure 13unprofitable_est present a very similar distribution, whereas the unprofitable_low always shows, as expected, a lower amount of unprofitable outputs. However, the similar trend shown by dust and unprofitable_est may lead to misinterpretation: although the overall distribution is indeed similar, the individual estimations are very different, but overestimations in one kind of samples compensate for underestimations of the other kind. For P2PKH outputs (which represent 81% of the set), dust always considers size of 182 bytes (148 of the input and 34 of the output). On the contrary, unprofitability will only take into account the size of the input. For the lower bound, it will be either 147 or 179 . For the estimation, it will be any value between 148 and 180 and will depend on the average public key size found for outputs of the same height. Therefore, on one hand, dust will always estimate higher sizes than both unprofitability metrics and, on the other hand, the two unprofitability distributions are very similar, with the estimation offering slightly higher sizes than the minimum , while the same can be seen for unprofitable_low UTXO for a rate of 204 satoshi/byte onward.Figure 13c shows, as expected, how those dust and unprofitable UTXOs represent a negligible amount from the total value of the UTXO set, that is, the total number of bitcoins in circulation.Finally, from an economic point of view, figure 13a shows the fraction of UTXOs that are considered dust, unprofitablelow and unprofitableest in the Litecoin UTXO set. It is worth noting that 67% of the UTXOs are considered dust and unprofitable for a fee-per-byte as small as 1 litoshi/byte. Those UTXOs have output amounts of just 1 litoshi . Therefore, the three metrics (dust and the two unprofitability versions) consider all those UTXOs unprofitable at the same fee rate. From 300 litoshi/byte onwards, the same scenario previously seen in Bitcoin is shown: dust estimates bigger sizes for P2PKH, whereas unprofitable_est estimates bigger sizes for P2SH outputs.Figure 14i recall c. Therefunprofitable_est showing very similar distributions although individual samples’ estimations do differ.Finally, Bitcoin Cash dust and unprofitability analysis  shows liIn conclusion, we have seen that the three analysed coins exhibit lots of UTXOs that can be considered dust/non-profitable, and they account for an important part of the UTXO set in terms of size. However, their value is negligible.5.1.low and unprofitableest) is far from being negligible. In this regard, we have analysed how such data have evolved since the early days of Bitcoin. To perform such analysis, we have focused on the unprofitableest metric since we think it is the most accurate one.We have seen how for the most recent analysed UTXO set snapshot, namely data from 6 February 2018, the percentage of data that fall into any of the three categories  stands out compared to the values from the other two. The explanation of such a huge difference is in the content of that snapshot: the 400K snapshot has an outstanding number of UTXOs holding exactly 1000 satoshis, whereas in the 450 K and 500 K snapshots the UTXOs are more uniformly distributed. In fact, the 54.1% of the unprofitable UTXOs located in the 0–50 range for the 400K snapshot hold exactly 1000 satoshi, whereas for the 450 K and 500 K they represent 18.35% and 14.21%, respectively.The percentage of unprofitable outputs per snapshot is almost always increasing at any point in the chart, as we can see depicted in figure 17a, we see how the accumulation of unprofitable outputs in the lower fee rates tends to increase over time, which increases the number of outputs that will be hardly spent. Such accumulation is mainly with regard to P2PKH outputs, which as we have already seen in §3 represented around 81% of the outputs in the set.Analysing the general trend of the data for figure 17b).Similarly, we have analysed the evolution of the UTXO sets of Litecoin by studying 13 snapshots, from block 100 000 up until block 1 300 000 stepping every 100 000 blocks. Unprofitability is very stable, with most snapshots  showing very similar distributions b.We have omitted the figures related to Bitcoin Cash since they share the same history with Bitcoin (the snapshots up until 450K are thus exactly the same), and therefore do not provide any new information.6.As we have seen, the characteristics of the UTXO set can be a key point in cryptocurrencies like Bitcoin, Litecoin and Bitcoin Cash. The size and performance of this set have a direct impact on how the system will perform, and it is thus a focus area where to improve the scalability and efficiency of these cryptocurrencies. For example, transaction generation performance in Bitcoin is greatly influenced by the size of the UTXO set .We can currently find typical statics and simple visualizations of the UTXO set of Bitcoin ,11, but The relevance of the UTXO is not new, concerns about its size, composition and performance have been around for some time . These c15 But such strategies are not easy to generalize. A consolidation will not always reduce the fees for a given user, specially if we cannot anticipate future fee rates. On the other hand, some user will need to maintain a minimum number of UTXOs to be able to generate transactions in parallel. Furthermore, such strategies can undermine the privacy requirements of some users. Given these, sometimes conflicting, constraints and the unpredictability of future fee rates, there is currently no actual strategy for UTXO consolidation.From the user point of view, a strategy of consolidating UTXOs in order to prevent the creation of dust and unprofitable UTXOs in the future (in case of higher fees) has always been considered .15 But scoin selection decision performed by wallets [bitcoinj library [An important process that directly impacts (and is influenced by) the UTXO set composition and size is the  wallets . Coin se wallets . For ins wallets , several wallets  , we have found many outputs whose properties  make them non-economically rational to spend, because spending them results in losing money. These UTXOs represent an important part of the UTXO sets of the three coins  and occupy also a significant amount of space (similar in percentage). However, they are just a very small amount of the current coin supply (0.01% for Bitcoin and Bitcoin Cash and a negligible percentage of litecoins).These results indicate the importance of designing proper coin selection strategies in UTXO-based cryptocurrencies. If these strategies are not properly designed and applied, the UTXO sets may end up full of outputs not worth spending. In turn, maintaining these UTXO sets becomes costly, and thus its management may grow into a problem for users with low computational resources available. As a result, incorrect UTXO set management threatens the scalability and the decentralization of UTXO-based cryptocurrencies. Therefore, our work motivates the importance of designing proper coin selection strategies.This work opens many lines of further research. On the one hand, by describing the composition and properties of the UTXO sets, our analysis can be used to optimize the management of the sets. On the other hand, our work is the first step towards solving the problem of not-worth-spending UTXOs. With this regard, we envision the design of both strategies to disincentivize the creation of dust UTXOs and to incentivize dust consolidation.


The 2015 earthquake in Nepal caused massive damages and triggered relief activities to minimize human suffering. The post-earthquake nutrition and food security situation in the hardest hit areas remains uncertain.Two national cross-sectional surveys were conducted in 2014 and 2016 among households (HH) with pre-school aged children or newly married women. Of the 21 village development committees (VDCs) included in this sample, 7 fell within “earthquake-affected” areas. This paper presents data from 982 HH, 1015 women, and 883 children from 2014 and 1056 HH, 1083 women, and 998 children from 2016 living in these areas, with longitudinal overlap of about 55%. Prevalence estimates and 95% confidence intervals were calculated, and logistic regression was used to calculate p-values, both using robust estimates of standard errors to account for clustering.From 2014 to 2016, child wasting (weight-for-height z score <-2) fell from 4.5% (95% CI 3.3%– 6.1%) to 2.1% (1.4%– 3.1%) and food insecurity  dropped from 17.6% (11.7%– 25.6%) to 12.4% (6.9%– 21.2%). Child stunting prevalence remained similar at both time-points. Improvements were also evident in dietary diversity and breastfeeding indicators.Nutrition and food security conditions remained comparable or improved one year after the earthquake despite evidence of structural and other damage. Livelihood resilience to shocks and/or effective nutrition, food or health interventions may have helped buffer the impact on nutrition, although this hypothesis requires further exploration. The 2015 Nepal earthquake and its subsequent series of aftershocks caused widespread damage and loss of life. In its immediate aftermath, around 9,000 people died, 22,000 were injured, 2.6 million displaced, and over 750,000 houses were damaged . More thAt the request of the Government of Nepal, humanitarian clusters were organised to coordinate emergency responses across different sectors. Upon evaluation of immediate damages, the government identified 31 districts as “earthquake-affected”, among which 14 were “crisis-hit” and were prioritised to receive aid and rehabilitation assistance . The Nutpost hoc sample of areas classified by the government as “earthquake-affected” from 2014 to 2016 (2) examine in greater detail the shocks experienced by households, potential risk factors for malnutrition, and indicators of household resilience in the same sample.Reconstruction efforts following the earthquake were reportedly slow and inconsistent, partly because financial resources were scarce, and disbursement was slow , 8. HoweThis analysis uses data from two rounds of the PoSHAN Community Study, a nationally representative, mixed-longitudinal sequence of annual surveys from 2013 through 2016, designed to assess household agricultural practices, socioeconomic status, food security, and diet, health, and nutritional status of preschool children and women of reproductive age . Data frThe design of the overall study is described in detail in previous papers –13. FollFig 1) [post hoc “earthquake-affected” sample. We focused on the subset of affected areas because prior studies had found greater adverse effects on nutrition closer to earthquake epicenters and because of the desire to better understand the outcomes that may have been influenced by relief and rehabilitation activities [Following the earthquake, the Government of Nepal categorised 31 out of 75 districts as “earthquake-affected,” and prioritised a subset of 14 districts, termed “severely-hit” or “crisis-hit”, for relief assistance. Our national sample included seven VDC’s across the Hills (n = 4) and Mountains (n = 3) from districts that had been categorised as earthquake-affected, including in Solukhumbu, Rasuwa, Sindhupalchok, Arghakhanchi, Lamjung, Kathmandu and Ramechhap  , and theIn both rounds, households were also asked to recall any shocks experienced the previous year, including death of a family member, having a family member suffer injury or morbidity, job loss, business loss, structural/house damage, crop loss, and livestock or poultry loss. A post-earthquake module administered only in 2016 also asked the households that had reported a shock whether the shock was due to the earthquake and the degree to which they had recovered from each shock .Anthropometric measures were taken in triplicate by trained staff. Weight was measured to the nearest 100 g using a digital scale . Supine length was measured to the nearest 0.1 cm for infants 0–23 months and standing height for children 24–59 months, accepting the median of triplicate readings as the true value. Weight and length/height measures were converted to z-scores of weight-for-height using the WHO growth standards, with a cut-off of <-2 defining moderate to severe, and <-3 as severe wasting. Height-for-age z-scores were computed, with a cut-off <-2 used to define stunting .In each national round, blood was obtained in a random ~25% subsample of infants <6 months of age by heel stick, and children ≥ 6 months and women by finger stick for haemoglobin (Hb) analysis using a portable haemoglobinometer . Hb concentrations were adjusted for altitude as recommended by WHO . AnaemiaTeams consisting of three interviewers and one supervisor were trained, standardised and assigned to complete field work in one VDC. Forms were collated and transported to Kathmandu for double-entry of data.Proportions of households experiencing various types of shocks and perceived degree of recovery were calculated. Prevalence of wasting, stunting, food security and crop production were estimated together with their associated standard errors and confidence intervals using robust estimates of standard errors to account for clustering. Logistic regression with robust standard errors was conducted to test for statistical significance of post-earthquake changes in household food security, dietary diversity and anthropometry scores from 2014 to 2016. For households with longitudinal data from both time points, those reporting each type of shock and perceived recovery were stratified by their pre-earthquake socio-economic status. Cochran–Armitage tests for trend were conducted to test for linear trends across strata. Stata version 15.1  was used to conduct all statistical analyses.Signed or verbal informed consent, due to illiteracy in the population, was obtained from head of households, newly married women, and mothers or caregivers of children <5 prior to all interviews. Ethical approval for the PoSHAN Community Studies was obtained annually from the Nepal Health Research Council (Reg. No.: 91/2016), an autonomous body under the MoHP, and the Institutional Review Board at the Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (IRB number: 4937).The funders of this study had no role in the study design, or the collection, analysis, and interpretation of data, in writing of the report, or the decision to submit the paper for publication. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.S1 Fig. In 2016, 12,143 households were screened, and 5,109 of the 5,173 eligible households consented. These households included 5,568 children <5 years with approximately 14%, 25%, and 61% assessed in the Mountains, Hills and Terai, respectively. The 2014 cross-sectional sample included 982 households with 1015 women and 883 children under five years (Table 1). Just under 10% of women were less than 20 years old and 21.7% did not have formal education. About two-thirds of household heads were male, 23.1% were engaged in business or self-employment, 20% were engaged in agriculture/livestock rearing, and just over half of households owned livestock. Few before-after differences in demographic or socioeconomic characteristics were apparent. Households assessed in both 2014 and 2016  tended to have more educated mothers, were more likely to own livestock, and to have a household head engaged in business or trading compared with those with only pre-earthquake data (S1 Table).The study flow diagram is presented in ve years . The posFig 2) but mean weight-for-height z-scores did not change from -0.4 (-0.5 –-0.3) to -0.3 (-0.4 –-0.1). Severe wasting (<-3 z-scores) were also similar from 0.5% (0.1–1.6) to 0.2% (0.0–1.1). The prevalence of under-five stunting did not change over time, nor did mean height-for-age z-scores (HAZ): -1.1 (95% CI -1.3 –-0.8) vs. -1.0 (-1.2 –-0.7). There was no suggestion of any deterioration in any age subgroup from 2014 vs. 2016 (S2 Table). The prevalence of anaemia in children under five years increased from 2014 to 2016 from 37.7% (95% CI 28.4–48.0) to 45.5% (36.4–55.0) but this change was not statistically significant. Similarly, there was no difference in women’s anaemia rates over time: 27.7% (95% CI 16.5–42.6) in 2014 vs. 27.9% (20.0–37.5) in 2016.The prevalence of wasting declined significantly from 2014 to 2016  came from Gokarneshwor, a peri-urban VDC located in the Kathmandu Valley (Gokarneshwor), that had contributed 48.9% of all households to the 2014 earthquake-affected sample. Children of households that moved were also better off nutritionally prior to the earthquake compared with those who remained, although only the 0.17 z-score increment in weight-for-height was statistically significant (p<0.01).The characteristics of households included in the pre-earthquake sample who were accounted for post-earthquake are compared with characteristics of those who were censored due to having moved away during the peri-quake period Table 2). Nearly half of all sampled households in 2016 in affected VDCs reported damage to their house compared with <2% during the year prior to the earthquake, and most damage was attributed to the quake. Other shocks with increased occurrence reported following the vs. a year before included crop or animal losses (each experienced by about a fifth of households), and business loss (experienced by nearly 10% of households). Over 6% of households lost a family member in the year following the earthquake, a rate that was about double the year before the quake, and 16% reported injury. Among households with longitudinal data, those in the higher wealth quintiles faced more business failures, while households in lower wealth quintiles reported more structural damages, and animal and crop losses . More than 60% of households described having had at least partial recovery from loss of livestock (S5 Table). Nearly 90% of those who experienced housing or structural damage reported partial or non-recovery (S6 Table). Similar proportions (42–44%) of households reported receiving remittances before vs. earthquake, though the amount received was slightly higher in 2016, the increase was not significant (p = 0.16) . Dietary quality also improved, with the proportion of women with minimum dietary diversity score of ≥ 5, rising from 42.8% to 50.6% (p<0.001) after the earthquake (Table 1). The proportion of predominantly breastfed infants exhibited an increasing trend, from 39.7% (95% CI 19.8–63.8) in 2014 to 48.7% (32.5–65.2) in 2016, although the difference was not statistically significant. A rise was also seen in the proportion of infants who were breastfed within an hour of birth (p<0.01) from 37.2% (95% CI 25.1–51.1%) of children in 2014 to 52.5% (39.4–65.2%) in 2016 (Table 1). We also observed significant improvements in certain handwashing behaviours and water treatment (S7 Table). An examination of the proportion of households producing major staple crops and the amounts produced in prior years suggested that proportion of households growing each type of crop decreased in 2016 compared with prior years, but the absolute amount produced by those who grew was similar .Household food insecurity significantly decreased (p<0.01) from 17.6% (95% CI 11.7–25.6) in 2014 to 12.4% (6.9–21.2) in 2016 Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.