Assessing strategies to improve participation in mobile phone surveys for non- communicable diseases: Randomized studies from Bangladesh, Colombia, and Uganda

Assessing strategies to improve participation in mobile phone surveys for non- communicable diseases: Randomized studies from Bangladesh, Colombia, and Uganda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing strategies to improve participation in mobile phone surveys for non- communicable diseases: Randomized studies from Bangladesh, Colombia, and Uganda Katya Saksena, Gulam Muhammed Al Kibria, Andres Vecino-Ortiz, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9013567/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Mobile phone surveys (MPS) provide a cost-effective and logistically simpler alternative to household surveys for monitoring non-communicable diseases (NCDs) and their risk factors in low- and middle-income countries (LMICs). However, improving response and completion rates remains a challenge. This study aimed to assess strategies to optimize MPS for NCD risk factor surveillance. Methods In Bangladesh, Colombia, and Uganda, participants were randomly assigned to one of three study arms: 1) Interactive Voice Response (IVR) survey only, 2) IVR with pre-survey SMS notification, or 3) Calling-in, with a toll-free number. Contact, response, refusal, and cooperation rates were calculated and compared across arms using log-binomial regression models. Results A total of 301,024 (complete interviews: 630), 160,437 (complete interviews: 760), and 49,107 (complete interviews: 1,157) MPS calls were made in Bangladesh, Colombia, and Uganda, respectively. Across the three countries, the SMS pre-notification arm had significantly higher contact rates (risk ratios [RR] ranging from 1.60 to 1.86) and response rates (RR: 1.54–1.84) compared to the IVR-only arm. While the call-in arm had a low yield in Bangladesh and Colombia, it performed well in Uganda with the highest cooperation rate (RR 1.41, 95% CI: 1.32–1.52). No single strategy was favored by any demographic group. The call-in arm was costliest in Bangladesh ( $ 112.06 per completed survey) and Colombia ( $ 143.21), but most cost-effective in Uganda ( $ 3.97). Conclusions SMS pre-notifications and call-in strategies can improve MPS participation rates, but their effectiveness and cost-efficiency vary across contexts. Tailoring strategies to local preferences and contexts are crucial for optimizing MPS for NCD surveillance in LMICs. Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Figures Figure 1 Introduction Non-communicable diseases (NCDs) are responsible for deaths of more than 40 million people each year, and most of these deaths occur in low- and middle-income countries (LMIC) [ 1 , 2 ]. Several risk factors contribute to the development of NCDs, including physical inactivity, poor diet, excessive alcohol and tobacco smoking, and the burden of the most metabolic and behavioral risk factors has worsened in recent years [ 1 , 3 , 4 ]. Monitoring NCD and risk factor trends is important for informing interventional priorities, and several modalities exist to collect this information [ 5 ]. Although the World Health Organization (WHO) recommends a household survey with a STEPwise approach for NCD risk factor surveillance, the process can be logistically complicated, time-consuming, and expensive [ 5 , 6 ]. The widespread penetration of mobile technologies in LMICs (one mobile-cellular subscription on average per inhabitant, 2018) demonstrates the potential for mobile phone surveys (MPS) to be used to monitor a population’s health in such resource-limited settings [ 6 , 7 ]. The Bloomberg Philanthropy's Data for Health (D4H) initiative aims to explore innovative methods for public health data collection, namely mobile phone surveys (MPS), which have several modes of survey delivery and include short message service (SMS), interactive voice response (IVR), or computer assisted telephone interview (CATI) surveys. SMS surveys involve sharing questions and receiving answers via text-based SMS platforms. In IVR surveys, the participant receives a pre-recorded questionnaire and can answer questions using their device’s keypad, whereas the questions are asked by a live interviewer in the Computer Assisted Telephone Interview modality [ 7 ]. While SMS-based surveys have demonstrated good response and completion rates, they pose the concern of not representing populations with poor digital literacy [ 8 , 9 ]. This problem is overcome with modalities such as IVR and CATI. Though several of these survey processes have demonstrated promise, there remains a challenge to improve survey completeness, representativeness, and response rates [ 5 , 10 ]. Several studies have explored providing airtime incentives to overcome this problem and have found mixed results [ 11 – 13 ]. While they are generally looked upon favorably by survey participants, there are several data quality and ethical concerns that have been raised [ 6 , 14 ]. Multiple studies have established that the time of day, frequency, and timing of mHealth interventions often influence their level of acceptance and efficiency [ 7 , 8 , 12 , 15 ]. An alternative to combat low response and completion rates could be sharing pre-notifications or allowing participants to schedule a time to take the survey. Several studies have reported that with pre-notification SMS alerts improving response rates in studies conducted in developed countries [ 8 , 16 , 17 ]; however, their effect on response rates to MPS in LMIC’s hasn’t been explored extensively, especially comparing that with the survey methods that are already in place. Through this study, we aim to fill in some of the above-mentioned gaps and assess strategies to optimize MPS for NCD risk factor surveillance. Our study assessed several strategies to improve the contact, response, and cooperation rates using randomized controlled study designs implemented in Bangladesh, Colombia, and Uganda. We compared the representativeness and participation of ‘pre-survey SMS notification to call-in’ with IVR (control group) and ‘pre-survey SMS notification’ in these countries. Findings of this study will be helpful to design and implement MPS in LMICs. Methods Study design and participants In this randomized controlled micro-trial, participants were recruited through random digit dialing (RDD) [ 18 ]. RDD uses the known prefixes (typically three digits) for each mobile network operator (MNO) in each country to identify a digit sequence as the base and randomly generates the remaining digits to create a mobile phone number. In RDD, a large proportion of generated mobile phone numbers are inactive or not registered. A built-in randomization schema randomly assigned each dialed participant to one of the following three study arms (Supplemental Fig. 1): 1) Control: Only an IVR survey. 2) SMS: Participants were sent an SMS pre-notification, which included details of the sponsoring agency, requirements to receive an airtime incentive, and that an automated health survey would be shared with them in 5 minutes. 3) Call-in: The participants received a pre-notification message, which included the sponsoring agency, the incentive amount, and a toll-free phone number where participants could call to take the free IVR survey. If the participant did not call-in to the IVR survey, the reminder message was shared via SMS 24 hours later. Bangladesh, Colombia, and Uganda are three LMICs in South Asia, South America, and East Africa, respectively [ 19 ]. Mobile phone subscriptions in all three countries have increased in recent decades. For instance, the mobile phone subscription rates in Bangladesh, Colombia, and Uganda were 1, 6, and 1 per 100 people in 2000; and these rates increased to 105, 156, and 70 per 100 people in 2022, respectively [ 20 ]. Process Numbers were dialed by computer software connected to phone lines until a human respondent answered the call. Similarly, computer software shared SMS messages until a human respondent replied to them. Upon connecting to an active line, participants were asked to select the language they would prefer to take the survey. After a brief introduction, they were asked to enter their age. Participants were excluded from taking the survey if they entered an age less than 18. Aside from age, answering the phone, and consenting to the survey, there were no other inclusion or exclusion criteria. Once screened, respondents were asked whether they agreed to participate in the study. Respondents who chose to continue with the survey after hearing the brief introduction and procedures were asked to press a number on their keypad to indicate their willingness to continue (e.g. “Press 1 if you would like to participate in this survey. Press 3 if you do not want to participate in the survey”). Participants were asked to reconfirm their selection (e.g., “You have indicated that you do wish to complete the survey, press 1 to continue the survey or press 3 to end this survey”) to ensure that consent (or refusal) was not mistakenly provided. In Bangladesh and Uganda, the consent question was offered after establishing age-eligibility. Due to legal requirements, in Colombia, there was a two-step consent process at the start of the survey. Respondents who did not consent to participate were thanked for their time, and the survey ended. After this, respondents received the survey questions and answered a series of NCD modules whose order of presentation was randomized between participants. In all arms, the participants with incomplete surveys were called back twice. The first call-back attempt was made immediately, and the second attempt was made one hour later. In one arm of the study, where pre-notification SMS was shared, the message read– “Hello. You will receive an automated health survey from [Country Partner] in 5 min. You will get [incentive amount] airtime if you complete the 20 min survey.” The name of the country partner and incentive amount (in brackets) were adapted for each country. In another arm, the pre-notification SMS provided the same details and a toll-free number that the participants could dial if they were interested in taking the survey. The system would then call them back with the IVR survey immediately. In Bangladesh, the costs per text message and per minute of airtime were, respectively, $ 0.02 and $ 0.07. In Colombia, the respective costs were $ 0.07 and $ 0.09; in Uganda, they were $ 0.02 per text message and $ 0.13 per minute of airtime. Incentives were sent to all participants who completed the NCD survey. Participants received the airtime incentive in local currency: Bangladesh (50 Taka; $ 1.79 USD), Colombia (5000 Colombian Pesos; $ 1.60 USD ), and Uganda (5000 Ugandan Shillings; $ 1.91 USD). Outcomes We examined the contact, response, and cooperation rates by study arm (Supplemental Table 1), in each country, and as defined by the American Association for Public Opinion Research (AAPOR) [ 21 ]. We considered an interview complete if the participants answered at least four of the five modules. Those who responded to one out of three modules were considered partial interviews. When age-eligible participants did not indicate consent or terminate the survey before consenting, they were marked as refusals. Age-eligible participants who did not finish any NCD module despite providing consents were marked as break-offs. Participants who did not respond to the age question after survey initiation were marked as unknown. The contact rate is calculated by dividing the total number of potential eligible participants by the total number of complete and partial interviews, refusals, and other events. The total number of full and partial interviews divided by the total number of potential eligible participants yielded the response rate. The number of full interviews divided by the total of partial and full interviews, refusals, and other factors was the cooperation rate. Statistical Analysis The sample size for each arm was calculated assuming a baseline survey completion rate of 30%. To detect a difference of at least 10% between study arms at an alpha of 0.05 and power of 80%, we would need at least 376 participants to complete the survey in each arm. With the baseline survey completion rate assumption of 30%, this meant 1254 participants would have to consent to take the survey per study arm. With three study arms in this trial, this meant 3762 participants to be enrolled in each country. The distribution of sociodemographic variables among complete interviews was compared by study arm within each country using chi-squared tests. Contact, response, and cooperation rates were compared by study arm in each country. A priori, a n alpha of 0.05 was used for hypothesis testing. The control arm was selected as the reference category to which other arms of the study are compared. We conducted a log-binomial regression to assess the risk ratio and corresponding 95% Confidence Interval (95% CI) for each comparison. For the costing analysis, using an ingredients approach, all costs associated with inputs into making the IVR system functional were recorded. The USD provided values were the costs borne by the research arm and included the costs of the incentive, plus a small fee to send the incentive. These costs were used to assess the cost per completed NCD survey. R was used to analyze data. Results Table 1: Disposition codes by country and study arm Country Call type Control SMS Call-in Bangladesh Total Calls 153234 142408 5382 Complete Interview 387 (0.25) 240 (0.17) 3 (0.06) Partial Interview 183 (0.12) 105 (0.07) 0 (0%) Refusal/ Breakoffs 193 (0.13) 122 (0.09) 0 (0%) Unknown, picked up phone 15070 (9.83) 12181 (8.55) 4 (0.07) Unknown, didn’t pick up phone 92302 (60.2) 22910 (16.1) NA Unknown, no call-in NA NA 1943 (36.1) Ineligible- underage 660 (0.43) 594 (0.42) 0 (0%) Ineligible- unallocated 44439 (29.0) 271 (0.19) NA Ineligible- SMS did not go through NA 102071 (71.7) 3432 (63.8) Ineligible-IVR did not go through 0(0) 3914 (2.8) 0(0) Colombia Total Calls 94574 51829 14034 Complete Interview 377 (0.4) 376 (0.72) 7 (0.05) Partial Interview 53 (0.06) 44 (0.08) 0 (0) Refusal/ Breakoffs 30 (0.03) 26 (0.05) 0 (0) Unknown, picked up phone 28278 (29.9) 18158 (35.0) 15 (0.11) Unknown, didn’t pick up phone 20363 (21.5) 7920 (15.3) NA Unknown, no call-in NA NA 11026 (78.6) Ineligible- underage 47 (0.05) 31 (0.06) 0 (0%) Ineligible- unallocated 45426 (48.0) 14386 (27.8) 0 (0) Ineligible- SMS did not go through NA 10888 (21.0) 2986 (21.3) Ineligible-IVR did not go through 0 (0) 0 (0) 0 (0) Uganda Total Calls 14047 9295 25765 Complete Interview 385 (2.74) 387 (4.16) 385 (1.45) Partial Interview 113 (0.80) 108 (1.16) 28 (0.11) Refusal/ Breakoffs 124 (0.89) 146 (1.57) 27 (0.11) Unknown, picked up phone 2049 (14.6) 1634 (17.6) 425 (1.6) Unknown, didn’t pick up phone 3556 (25.3) 1748 (18.8) NA Unknown, no call-in NA NA 21382 (80.3) Ineligible- underage 222 (1.58) 175 (1.88) 33 (0.12) Ineligible- unallocated 7598 (54.1) 3888 (41.8) NA Ineligible- SMS did not go through NA 1209 (13.0) 3485 (13.1) Ineligible-IVR did not go through 0 (0) 0 (0) 0 (0) The total number of calls made in Bangladesh Colombia and Uganda was 301,034, 160,437, 49,979, respectively (see Figure 1 and Table 1). Although the distribution of disposition codes differed substantially by survey arm and country, most call attempts resulted in noncontact outcomes in all countries. In Bangladesh, only 0.2% of all call attempts resulted in a completed interview (n = 631), with the call-in arm producing only 3 completions. The predominant outcome in both control and SMS arms was unknown–no answer, accounting for 60.2% and 16.1% of calls, respectively. A large proportion of SMS calls failed due to messages not going through (71.7%). In Colombia, completion was similarly low (0.4%), ranging from 0.05% in the call-in arm to 0.72% in the SMS arm, and nearly half of the control arm calls were classified as unallocated. Uganda had the highest number of completed interviews across countries, approximately 385 completed surveys per arm, and had higher proportions of calls that resulted in an answered phone than the other two studied countries. Table 2: Demographic characteristics for complete surveys by study arm in each country Country Demographics Total Control SMS Call-in P-val Bangladesh 1 N 630 387 240 3 -- Age group 18-29 411(65.2%) 243(62.8%) 166(69.2%) 2(66.6%) .076 30-49 194(30.8%) 126(32.6%) 67(27.9) 1(33.3%) 50-69 21(3.3%) 15(3.88%) 6(2.5%) 0(0%) 70+ 4(0.63%) 3 (0.78%) 1(0.42) 0(0%) Sex Male 502(79.7%) 317(81.9%) 183(76.3%) 2 (66.6%) .69 Female 121(19.2) 66(17.1%) 54(22.5%) 1(33.3 %) Other 1(0.16%) 0 (0%) 1(0.42%) 0 (0%) Refused 6(0.95 %) 4(1.0%) 2(0.83%) 0 (0%) Education None 41(6.51%) 26(6.72%) 15(6.25) 0(0%) .24 Primary 79(12.5%) 47(12.1%) 31(12.9%) 1(33.3%) Secondary 197(31.3) 124 (32.0%) 72(30.0%) 1(33.3%) Tertiary 137(21.8%) 72(18.6%) 64(26.7%) 1(33.3%) Graduate 172(27.3%) 116(30.0%) 56(23.3%) 0(0%) Refused 4(0.63%) 2 (0.52%) 2(0.83%) 0(0%) Location Urban 325(51.6%) 197(50.9%) 125(52.1%) 3 (100%) .59 Rural 302(47.9%) 188(48.6%) 114(47.5%) 0(0%) Refused 3(0.48%) 2(0.52%) 1(0.42%) 0(0%) Colombia 1 N 760 377 376 7 -- Age group 18-29 315(41.5%) 137(36.3%) 173(46.0%) 5(71.4%) .003 30-49 331(43.6%) 168(44.6%) 162(43.9%) 1(14.3%) 50-69 98(12.9%) 60(15.9%) 37(9.8%) 1(14.3%) 70+ 16(2.1%) 12(3.2%) 4(1.1%) 0(0%) Sex Male 345(45.4%) 172(45.6%) 166 (44.1%) 7(100%) .39 Female 405(53.3%) 198(52.5%) 207(55.1%) 0(0%) Other 10(1.3%) 7(1.9%) 3(0.8%) 0(0%) Education None 48(6.32%) 27(7.2%) 21(5.59%) 0(0%) .112 Elementary 110(14.5%) 60(15.9%) 50(13.3%) 0(0%) High School 261(34.3%) 133(35.3%) 125(33.2%) 3(42.9%) Technical 208(27.4%) 87(23.1%) 119(31.7%) 2(28.6%) Graduate 133(17.5%) 70(18.6%) 61(16.22%) 2(28.6%) Location Rural 211(27.8%) 109 (28.9%) 101(26.9%) 1(14.3%) .53 Urban 549(72.2%) 268(71.1%) 275(73.1%) 6(85.7%) Uganda N 1157 385 387 385 -- Age group 18-29 742(64.1%) 240(62.3%) 235(60.7%) 267(69.4%) .26 30-49 359(31.0%) 124(32.2%) 131(33.9%) 104(27.0%) 50-69 46(3.98%) 18(4.68%) 17(4.39%) 11(2.86%) 70+ 10(0.86%) 3(0.78%) 4(1.03%) 3(0.78%) Sex Male 816(70.5%) 268(69.6%) 265(68.5%) 283(73.5%) .39 Female 333(28.8%) 113(29.4%) 118(30.5%) 102(26.5%) Other 8(0.7%) 4(1.03%) 4(1.03%) 0(0%) Education None 184(15.9%) 66(17.14%) 68(17.6%) 50(13.0%) .28 Primary 324(28.0%) 117(30.4%) 97(25.1%) 110(28.6%) O Level 363(31.4%) 116(30.1%) 116(30.0%) 131(34.0%) A Level 95(8.21%) 31(8.1%) 29(7.5%) 35(9.09%) University 173(15.0%) 51(13.3) 68(17.6%) 54(14.0%) Post Graduation 18(1.56%) 4(1.04%) 9(2.3%) 5(1.3%) Location Urban 522(45.11%) 175(45.5%) 183(47.3%) 164(42.6%) .42 Rural 635(54.9%) 210(54.5%) 204(52.7%) 221(57.4%) Notes: 1. chi-square test only for control and SMS arms; excludes refusals In Table 2, we reported the sociodemographic characteristics of the respondents according to study arm in each country. In Bangladesh, a majority of the respondents were 18-29-year-olds (65.2%), males (79.7%), and urban residents (51.6%); this pattern was observed across survey arms. Respondents from Uganda had a similar pattern, and respondents were largely 18–29-year-olds (64.1%) or males (70.5%). When we compared the study arms, no significant differences were observed for age, sex, education, or location (all p > .05). Among respondents from Colombia, the age distribution differed significantly across arms (p = .003), and younger respondents (i.e., 18-29-year-olds, 41.5%) or males (45.4%) were substantially lower than other two countries. Table 3: Study rates by country and study arm Country Rates Control SMS Call-in Bangladesh Contact Rate % 0.71% 1.31% 0.15% Risk ratio (95%CI) Ref. 1.86 (1.66, 2.09) 0.22 (0.07-0.68) P value -- <0.001 0.008 Response Rate % 0.53% 0.97% 0.15% Risk ratio (95%CI) Ref. 1.84 (1.61, 2.19) 0.29 (0.09-0.91) P value -- <0.001 0.033 Cooperation Rate % 50.7% 51.4% 100.00% Risk ratio (95%CI) Ref. 1.01 (0.91, 1.13) NA P value -- 0.905 NA Cost/completed survey $7.72 $32.65 $112.06 Call/completed survey 281 168 1794 Colombia Contact Rate % 0.94% 1.68% 0.06% Risk ratio (95%CI) Ref 1.79 (1.58, 2.04) 0.07 (0.03, 0.14) P value <0.001 <0.001 Response Rate % 0.88% 1.58% 0.06% Risk ratio (95%CI) Ref 1.81 (1.58, 2.07) 0.07 (0.03, 0.15) P value <0.001 <0.001 Cooperation Rate % 81.96% 84.3% 100% Risk ratio (95%CI) Ref. 1.03 (0.97, 1.09) NA P value 0.345 NA Cost/completed survey $9.54 $16.98 $143.21 Call/completed survey 130 71 1578 Uganda Contact Rate % 9.99% 15.9% 1.98% Risk ratio (95%CI) Ref 1.60 (1.44, 1.77) 0.20 (0.18, 0.22) P value <0.001 <0.001 Response Rate % 8.0% 12.3% 1.86% Risk ratio (95%CI) Ref 1.54 (1.37, 1.73) 0.23 (0.20, 0.26) P value <0.001 <0.001 Cooperation Rate % 61.9% 60.4% 87.5% Risk ratio (95%CI) Ref. 0.98 (0.89, 1.07) 1.41 (1.32, 1.52) P value -- .58 <0.001 Cost/completed survey $4.52 $4.80 $3.97 Call/completed survey 17 11 58 Table 3 shows the survey participation rates by study arms in all three countries. Overall, the SMS arm had higher contact and response rates than the control arm. The call-in arm had the lowest rates in Bangladesh and Colombia. For instance, in Bangladesh, compared to the control arm, the SMS arm demonstrated significantly higher contact (RR = 1.86; 95% CI: 1.66–2.09; p < .001) and response rates (RR = 1.84; 95% CI: 1.61–2.19; p < .001); however, cooperation rates were similar (51.4% vs. 50.7%) and the call-in arm showed very low contact (0.15%) or response (0.15%) rates. In this country, the cost per completed survey was lowest in the control arm ($7.72) and highest in the call-in arm ($112.06).Findings of Colombia were consistent with Bangladesh; the SMS arm had significantly higher contact (RR = 1.79; 95% CI: 1.58- 2.04, p < .001) and response rates (RR = 1.81; 95% CI: 1.58-2.07, p < .001) than the control arm. Cooperation was higher than 80% in both arms. The call-in arm had a response rate of only 0.06%, and the cost per completion ranged from $9.54 (control) to $143.21 (call-in). Uganda demonstrated the strongest performance across modes. Compared to the control arm, the SMS arm had higher contact (RR = 1.60; 95% CI: 1.44-1.77, p < .001) and response rates (RR = 1.54; 955 CI: 1.37-1.73, p < .001). Cooperation rates were comparable between control (61.9%) and SMS (60.4%) arms but substantially higher in the call-in arm (87.5%). Costs per completed survey were lower in all modes ($3.97–$4.80) than in the other two countries. Discussion We conducted these NCD surveys in Bangladesh, Colombia, and Uganda using RDD with participants randomized to one of three arms: IVR only; IVR with SMS pre-notification; and IVR with a toll-free number for calling-in shared via SMS. Our goal was to test which strategies could improve survey response and cooperation rates while being most cost-effective. Although the calling-in strategy was successful in Uganda, the low yield in Bangladesh and Colombia led us to prematurely stop this assessment in these regions. This resulted in a very few complete surveys and a high cost per completed survey for these study arms. Further research and an understanding of the local context in Uganda could help better understand the reason behind the success of the call-in strategy in this setting only. We also found a very high number of ineligible numbers dialed by the system in Bangladesh across the study arms, when compared to Colombia and Uganda. This may be due to the saturation of numbers with the selected MNO prefixes, resulting in calls to numbers that do not exist and hence are ineligible. An assessment of demographic features across the study arms in each country demonstrated no significant differences, except for a marginally higher number of younger individuals who completed the survey in the SMS arm in Colombia. Similar findings have been reported in previous studies, with the hypothesis that older adults (> 45 years) in Columbia are economically active and hence may not have the time to answer the survey [ 22 , 23 ]. We can infer that no specific MPS strategy was preferred by a particular demographic group or was able to successfully cater to hard-to-reach populations such as older women, individuals with low education levels or populations living in rural areas [ 23 ]. When we look at the contact rates across the three countries, we see lower values in Bangladesh than in Colombia and Uganda. This was similar in other previous studies [ 12 , 24 , 25 ]. This means that fewer individuals In Bangladesh picked up their phones when called to take the survey, and this may be attributed to a high incidence of spam calls in the region, which has resulted in mistrust and poor willingness to answer calls from unknown numbers [ 26 , 27 ]. While we see an improvement in the contact rate with a pre-notification SMS in Bangladesh, the cost per completed survey is still high and potentially not feasible for large-scale implementation. A comparison of survey rates across the study arms in the three countries showed us significant differences between the study arms and the control arm. The significantly higher response rates correlated with findings from similar studies that found an increase in responses when survey participants are notified about the survey in advance [ 16 , 17 ]. This may be due to several reason, including increased trust in the source of the survey, participants know what to expect when they receive the call, and they can set aside time for the task. Our results also showed us very high cooperation rates in the call-in arm due to low refusals and partially completed surveys. As participants dial the toll-free number to showcase their willingness towards survey participation, it serves as preliminary consent, and the few respondents lost during the survey are often due to age ineligibility. Apart from the survey rates, it is also crucial to assess the cost-effectiveness of these strategies. The cost incurred in each study arm was due to the cost of every call placed, SMS sent, and airtime incentive provided [ 11 – 13 ]. We see a relatively higher cost per completed survey in Colombia as compared to Uganda and Bangladesh. This could be attributed to a much longer informed consent question, which was mandated by law [ 28 ]. This not only drives up the airtime cost but may be associated with a high drop-out rate, which drives up the cost per completed survey. Apart from the survey modality, several other factors that are specific to the country context may have influenced these results. These include differences in mobile phone penetration rates, levels of digital literacy, and cultural appropriateness [ 29 – 33 ]. A qualitative assessment of user perceptions of MPS in Uganda also found that other factors, such as short survey lengths, a reliable and recognizable survey source, and evening survey calls, among several other factors, were conducive to participant willingness and could improve survey response rates [ 15 ]. Our results demonstrate that a specific mHealth survey strategy may be appropriate for a particular setting. For example, the call-in strategy is successful in Uganda and was the most cost-effective as well. In Bangladesh, while the SMS arm had the highest response rate, it was not financially feasible on a large scale; therefore, the IVR survey alone may be the most suitable strategy for Bangladesh. In Colombia, the SMS pre-notification arm had almost twice the response rate when compared to IVR alone. However, a cost-benefit assessment would help determine which of these would be apt for the local country context. One of the limitations of the study was that the call-in arms in Colombia and Bangladesh had to be prematurely stopped due to a low yield of responses. Hence, our understanding of the survey rates comparison is limited to one context–Uganda. The order of questions and the questions themselves were standardized in the survey. However, due to legal and regulatory reasons in some contexts, such as Colombia, the consent questions were altered and re-ordered. This may have altered the survey participation rates and limited the inference we can make. This study also has several strengths. The randomized study design and multi-country assessment broadened the scope of inferences we can make. It allows us to explore the effect of different mobile survey strategies and local country contexts. The use of standardized AAPOR definitions for disposition codes and survey rate calculations makes the study results comparable and easy to understand across contexts. Future studies can aim to further explore the effect of socio-cultural norms, gender dynamics, and in-country policy contexts on MPS rates. Conclusion Our study findings suggest that SMS pre-notifications and call-in strategies can be successful in increasing survey response and contact rates. However, the degree of effectiveness can vary depending on several factors specific to the local context and preferences of the participants. In some settings, call-in strategies can be very successful with high cooperation rates and cost-effectiveness. Any single strategy is not particularly favorable to a demographic group, and more research can be done to develop survey methods that can reach vulnerable populations that are often missed. MPS have shown promise in assessing health- related topics, such as NCDs and can facilitate timely, data-driven decision-making for improved health outcomes in LMICs. Declarations Ethical approval and consent to participate: Ethical approval was received from the institutional review boards of the institutions involved in the study, namely the Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, U.S; and Pontificia Universidad Javeriana, Bogotá, Colombia. All participants provided informed consent by pressing a number on their keypad. We only included adult individuals. All methods were carried out in accordance with relevant guidelines and regulations. Funding: Funding of the study was provided by Bloomberg Philanthropies Author Contribution DG did the conception and supervision of this study. GMAK and KS conducted literature review, analyzed data, and prepared the first draft. AVC, FRV, ER, CS, IAK, and ER contributed to the study design and administered the project. All other authors reviewed the manuscript and edited it. Final approval of the first draft was approved by all authors. Acknowledgement: The authors thank survey participants for their time Declarations Competing interests : The authors declare no competing interests. Data Availability Data will be available upon acceptance from the OpenICPSR repository database (https://www.openicpsr.org/openicpsr/.') References Naghavi, M. et al. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403 , 2100–2132. 10.1016/S0140-6736(24)00367-2 (2024). Vos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1204–1222. 10.1016/S0140-6736(20)30925-9 (2020). Forouzanfar, M. H. et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388 , 1659–1724. 10.1016/S0140-6736(16)31679-8 (2016). Ong, K. L. et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 402 , 203–234. 10.1016/S0140-6736(23)01301-6 (2023). Ali, J. et al. Ethics of mobile phone surveys to monitor non-communicable disease risk factors in low- and middle-income countries: A global stakeholder survey. Glob Public. Health . 14 , 1167–1181. 10.1080/17441692.2019.1566482 (2019). Ali, J. et al. Ethics Considerations in Global Mobile Phone-Based Surveys of Noncommunicable Diseases: A Conceptual Exploration. J. Med. Internet Res. 19 , e110. 10.2196/jmir.7326 (2017). Gibson, D. G. et al. Mobile Phone Surveys for Collecting Population-Level Estimates in Low- and Middle-Income Countries: A Literature Review. J. Med. Internet Res. 19 , e139. 10.2196/jmir.7428 (2017). Gibson, D. G. et al. Evaluation of Mechanisms to Improve Performance of Mobile Phone Surveys in Low- and Middle-Income Countries: Research Protocol. JMIR Res. Protoc. 6 , e81. 10.2196/resprot.7534 (2017). Kante, M. & Målqvist, M. Effectiveness of SMS-based interventions in enhancing antenatal care in developing countries: a systematic review. BMJ Open. 15 , e089671. 10.1136/bmjopen-2024-089671 (2025). Fernández-Niño, J. A. et al. A multi-country comparison between mobile phone surveys and face-to-face household surveys to estimate the prevalence of non-communicable diseases behavioural risk factors in low- and middle-income settings. BMJ Glob Health . 10 , e017785. 10.1136/bmjgh-2024-017785 (2025). Gibson, D. G. et al. Effect of airtime incentives on response and cooperation rates in non-communicable disease interactive voice response surveys: randomised controlled trials in Bangladesh and Uganda. BMJ Glob Health . 4 , e001604. 10.1136/bmjgh-2019-001604 (2019). Gibson, D. G. et al. Promised and Lottery Airtime Incentives to Improve Interactive Voice Response Survey Participation Among Adults in Bangladesh and Uganda: Randomized Controlled Trial. J. Med. Internet Res. 24 , e36943. 10.2196/36943 (2022). David, M. C. & Ware, R. S. Meta-analysis of randomized controlled trials supports the use of incentives for inducing response to electronic health surveys. J. Clin. Epidemiol. 67 , 1210–1221. 10.1016/j.jclinepi.2014.08.001 (2014). Rodriguez-Patarroyo, M. et al. Informed Consent for Mobile Phone Health Surveys in Colombia: A Qualitative Study. J. Empir. Res. Hum. Res. Ethics . 16 , 24–34. 10.1177/1556264620958606 (2021). Tweheyo, R., Selig, H., Gibson, D. G., Pariyo, G. W. & Rutebemberwa, E. User Perceptions and Experiences of an Interactive Voice Response Mobile Phone Survey Pilot in Uganda: Qualitative Study. JMIR Form. Res. 4 , e21671. 10.2196/21671 (2020). Dal Grande, E., Chittleborough, C. R., Campostrini, S., Dollard, M. & Taylor, A. W. Pre-Survey Text Messages (SMS) Improve Participation Rate in an Australian Mobile Telephone Survey: An Experimental Study. PloS One . 11 , e0150231. 10.1371/journal.pone.0150231 (2016). Steeh, C., Buskirk, T. D. & Callegaro, M. Using Text Messages in U.S. Mobile Phone Surveys. Field Methods . 19 , 59–75. 10.1177/1525822X06292852 (2007). Elliott, R. What is Random Digit Dialing? 29 Sept 2020 [cited 30 Aug 2022]. Available: https://www.geopoll.com/blog/what-is-random-digit-dialing/ World Bank. World Bank Country and Lending Groups. [cited 31 Aug 2023]. (2023). Available: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups The World Bank. Mobile cellular subscriptions (per 100 people). 29 Aug 2023 [cited 29 Aug 2023]. Available: https://data.worldbank.org/indicator/IT.CEL.SETS.P2 The American Association for Public Opinion Research. Standard Definitions: Final dispositions of case codes and outcome rates for surveys. 9th ed. (2016). Guzman-Tordecilla, D. N. et al. Examination of the demographic representativeness of a cross-sectional mobile phone survey in collecting health data in Colombia using random digit dialling. BMJ Open. 13 , e073647. 10.1136/bmjopen-2023-073647 (2023). Torres-Quintero, A. et al. Adaptation of a mobile phone health survey for risk factors for noncommunicable diseases in Colombia: a qualitative study. Glob Health Action . 13 , 1809841. 10.1080/16549716.2020.1809841 (2020). Ali, J. et al. Remote consent approaches for mobile phone surveys of non-communicable disease risk factors in Colombia and Uganda: A randomized study. Rashid TA, editor. PLOS ONE . 17 , e0279236. 10.1371/journal.pone.0279236 (2022). Labrique, A. et al. Improving success of non-communicable diseases mobile phone surveys: Results of two randomized trials testing interviewer gender and message valence in Bangladesh and Uganda. Pry JM, editor. PLOS ONE. ;18: e0285155. (2023). 10.1371/journal.pone.0285155 AAPOR Ad Hoc Committee. Spam Flagging and Call Blocking and Its Impact on Survey Research (Spam Flagging and Call Blocking and Its Impact on Survey Research, 2022). Kok, K. F. & Truecaller Insights Top 20 Countries Affected by Spam Calls & SMS in 2019. In: Truecaller Blog [Internet]. 3 Dec 2019 [cited 2 Feb 2022]. Available: http://truecaller.blog/2019/12/03/truecaller-insights-top-20-countries-affected-by-spam-calls-sms-in-2019/ Piper, D. Data Protection Laws in Colombia. (2025). Available: https://www.dlapiperdataprotection.com/index.html?t=law&c=CO Labrique, B. Mobile phone ownership and widespread mHealth use in 168,231 women of reproductive age in rural Bangladesh. J. Mob. Technol. Med. 1 , 26–26. 10.7309/jmtm.48 (2012). Greenleaf, A. R., Ahmed, S., Moreau, C., Guiella, G. & Choi, Y. Cell phone ownership and modern contraceptive use in Burkina Faso: implications for research and interventions using mobile technology. Contraception 99 , 170–174. 10.1016/j.contraception.2018.11.006 (2019). Inc, G. Disparities in Cellphone Ownership Pose Challenges in Africa. In: Gallup.com [Internet]. 17 Feb 2016 [cited 5 Apr 2022]. Available: https://news.gallup.com/poll/189269/disparities-cellphone-ownership-pose-challenges-africa.aspx Silver, L. Smartphone Ownership Is Growing Rapidly Around the World, but Not Always Equally. In: Pew Research Center [Internet]. 5 Feb 2019 [cited 22 Sept 2022]. Available: https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/ Kibria, G. M. A. & Nayeem, J. Trends and factors associated with mobile phone ownership among women of reproductive age in Bangladesh. Malta M, editor. PLOS Glob Public Health. ;3: e0001889. (2023). 10.1371/journal.pgph.0001889 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 16 May, 2026 Reviewers invited by journal 06 May, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 16 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9013567","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":635436701,"identity":"ed229843-f9e6-4d4c-8b3a-66a8380fdba3","order_by":0,"name":"Katya Saksena","email":"","orcid":"","institution":"Johns Hopkins University Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Katya","middleName":"","lastName":"Saksena","suffix":""},{"id":635436702,"identity":"09138d36-9234-4f57-8e60-653e1b03c341","order_by":1,"name":"Gulam Muhammed Al Kibria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACHsY2BgYDBh5+EOcBiEmsFhnJBgbGhgTitDCwgSgbgwPEapH3Odz2uKLAjsf4eI/5g4QKG2MG9sNHN+DTYni2sd3wjEEyj9mZM4YNCWfSzBh40tJu4NXSz9gm2WDAzGN2I8ewIbHtsA2DBJBNhJZ6HuMZxGqR520EaTnMYyAB0WJGUIsBz8F2wwaD4zwSZ44VzgD6xZiNkF/ke9KfPWz4U23P39684cOHChvDfvbDx/DbcgBdhA2fcrAtDYRUjIJRMApGwSgAAJFsSBq/vUhlAAAAAElFTkSuQmCC","orcid":"","institution":"Johns Hopkins University Bloomberg School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Gulam","middleName":"Muhammed Al","lastName":"Kibria","suffix":""},{"id":635436703,"identity":"099bccee-bc28-4198-a23d-6e3c67d5122a","order_by":2,"name":"Andres Vecino-Ortiz","email":"","orcid":"","institution":"Johns Hopkins University Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Andres","middleName":"","lastName":"Vecino-Ortiz","suffix":""},{"id":635436704,"identity":"802d76d3-d5e5-4749-9492-80312c46117e","order_by":3,"name":"Fernando Ruiz-Vallejo","email":"","orcid":"","institution":"Pontificia Universidad Javeriana","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Ruiz-Vallejo","suffix":""},{"id":635436705,"identity":"ad53f5c9-8028-427e-bfe6-1e3d95b59006","order_by":4,"name":"Carolina Saavedra","email":"","orcid":"","institution":"Pontificia Universidad Javeriana","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Saavedra","suffix":""},{"id":635436706,"identity":"55b7cfbd-2414-49d0-a6d2-4a0117af8b87","order_by":5,"name":"Iqbal Ansary Khan","email":"","orcid":"","institution":"Disease Control and Research","correspondingAuthor":false,"prefix":"","firstName":"Iqbal","middleName":"Ansary","lastName":"Khan","suffix":""},{"id":635436707,"identity":"7280d36f-f6b4-430a-8880-adb0d1994742","order_by":6,"name":"Elizeus Rutebemberwa","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Elizeus","middleName":"","lastName":"Rutebemberwa","suffix":""},{"id":635436708,"identity":"cad05ecc-762e-4b3e-aa30-ff980950af78","order_by":7,"name":"Dustin G Gibson","email":"","orcid":"","institution":"Johns Hopkins University Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Dustin","middleName":"G","lastName":"Gibson","suffix":""}],"badges":[],"createdAt":"2026-03-02 20:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9013567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9013567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109434788,"identity":"93e9158b-8966-4f48-a1ca-def570bb4aa3","added_by":"auto","created_at":"2026-05-18 05:55:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart depicting the number of calls made and loss of respondents at various stages in Bangladesh, Columbia, and Uganda\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9013567/v1/cf0235bcb8fbc8d164b9b468.jpg"},{"id":109434973,"identity":"614a8c26-f11f-41fa-ae07-f0f0b0a37f00","added_by":"auto","created_at":"2026-05-18 05:55:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":696297,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9013567/v1/b2d54456-964f-4a78-9ff5-e333a585ab99.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing strategies to improve participation in mobile phone surveys for non- communicable diseases: Randomized studies from Bangladesh, Colombia, and Uganda","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-communicable diseases (NCDs) are responsible for deaths of more than 40\u0026nbsp;million people each year, and most of these deaths occur in low- and middle-income countries (LMIC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Several risk factors contribute to the development of NCDs, including physical inactivity, poor diet, excessive alcohol and tobacco smoking, and the burden of the most metabolic and behavioral risk factors has worsened in recent years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Monitoring NCD and risk factor trends is important for informing interventional priorities, and several modalities exist to collect this information [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although the World Health Organization (WHO) recommends a household survey with a STEPwise approach for NCD risk factor surveillance, the process can be logistically complicated, time-consuming, and expensive [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe widespread penetration of mobile technologies in LMICs (one mobile-cellular subscription on average per inhabitant, 2018) demonstrates the potential for mobile phone surveys (MPS) to be used to monitor a population\u0026rsquo;s health in such resource-limited settings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The Bloomberg Philanthropy's Data for Health (D4H) initiative aims to explore innovative methods for public health data collection, namely mobile phone surveys (MPS), which have several modes of survey delivery and include short message service (SMS), interactive voice response (IVR), or computer assisted telephone interview (CATI) surveys. SMS surveys involve sharing questions and receiving answers via text-based SMS platforms. In IVR surveys, the participant receives a pre-recorded questionnaire and can answer questions using their device\u0026rsquo;s keypad, whereas the questions are asked by a live interviewer in the Computer Assisted Telephone Interview modality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile SMS-based surveys have demonstrated good response and completion rates, they pose the concern of not representing populations with poor digital literacy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This problem is overcome with modalities such as IVR and CATI. Though several of these survey processes have demonstrated promise, there remains a challenge to improve survey completeness, representativeness, and response rates [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Several studies have explored providing airtime incentives to overcome this problem and have found mixed results [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While they are generally looked upon favorably by survey participants, there are several data quality and ethical concerns that have been raised [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Multiple studies have established that the time of day, frequency, and timing of mHealth interventions often influence their level of acceptance and efficiency [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. An alternative to combat low response and completion rates could be sharing pre-notifications or allowing participants to schedule a time to take the survey. Several studies have reported that with pre-notification SMS alerts improving response rates in studies conducted in developed countries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; however, their effect on response rates to MPS in LMIC\u0026rsquo;s hasn\u0026rsquo;t been explored extensively, especially comparing that with the survey methods that are already in place. Through this study, we aim to fill in some of the above-mentioned gaps and assess strategies to optimize MPS for NCD risk factor surveillance. Our study assessed several strategies to improve the contact, response, and cooperation rates using randomized controlled study designs implemented in Bangladesh, Colombia, and Uganda. We compared the representativeness and participation of \u0026lsquo;pre-survey SMS notification to call-in\u0026rsquo; with IVR (control group) and \u0026lsquo;pre-survey SMS notification\u0026rsquo; in these countries. Findings of this study will be helpful to design and implement MPS in LMICs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eIn this randomized controlled micro-trial, participants were recruited through random digit dialing (RDD) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. RDD uses the known prefixes (typically three digits) for each mobile network operator (MNO) in each country to identify a digit sequence as the base and randomly generates the remaining digits to create a mobile phone number. In RDD, a large proportion of generated mobile phone numbers are inactive or not registered. A built-in randomization schema randomly assigned each dialed participant to one of the following three study arms (Supplemental Fig.\u0026nbsp;1):\u003c/p\u003e \u003cp\u003e1) Control: Only an IVR survey.\u003c/p\u003e \u003cp\u003e2) SMS: Participants were sent an SMS pre-notification, which included details of the sponsoring agency, requirements to receive an airtime incentive, and that an automated health survey would be shared with them in 5 minutes.\u003c/p\u003e \u003cp\u003e 3) Call-in: The participants received a pre-notification message, which included the sponsoring agency, the incentive amount, and a toll-free phone number where participants could call to take the free IVR survey. If the participant did not call-in to the IVR survey, the reminder message was shared via SMS 24 hours later.\u003c/p\u003e \u003cp\u003eBangladesh, Colombia, and Uganda are three LMICs in South Asia, South America, and East Africa, respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Mobile phone subscriptions in all three countries have increased in recent decades. For instance, the mobile phone subscription rates in Bangladesh, Colombia, and Uganda were 1, 6, and 1 per 100 people in 2000; and these rates increased to 105, 156, and 70 per 100 people in 2022, respectively [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcess\u003c/h3\u003e\n\u003cp\u003eNumbers were dialed by computer software connected to phone lines until a human respondent answered the call. Similarly, computer software shared SMS messages until a human respondent replied to them. Upon connecting to an active line, participants were asked to select the language they would prefer to take the survey. After a brief introduction, they were asked to enter their age. Participants were excluded from taking the survey if they entered an age less than 18. Aside from age, answering the phone, and consenting to the survey, there were no other inclusion or exclusion criteria.\u003c/p\u003e \u003cp\u003eOnce screened, respondents were asked whether they agreed to participate in the study. Respondents who chose to continue with the survey after hearing the brief introduction and procedures were asked to press a number on their keypad to indicate their willingness to continue (e.g. \u0026ldquo;Press 1 if you would like to participate in this survey. Press 3 if you do not want to participate in the survey\u0026rdquo;). Participants were asked to reconfirm their selection (e.g., \u0026ldquo;You have indicated that you do wish to complete the survey, press 1 to continue the survey or press 3 to end this survey\u0026rdquo;) to ensure that consent (or refusal) was not mistakenly provided. In Bangladesh and Uganda, the consent question was offered after establishing age-eligibility. Due to legal requirements, in Colombia, there was a two-step consent process at the start of the survey. Respondents who did not consent to participate were thanked for their time, and the survey ended. After this, respondents received the survey questions and answered a series of NCD modules whose order of presentation was randomized between participants.\u003c/p\u003e \u003cp\u003eIn all arms, the participants with incomplete surveys were called back twice. The first call-back attempt was made immediately, and the second attempt was made one hour later. In one arm of the study, where pre-notification SMS was shared, the message read\u0026ndash; \u003cem\u003e\u0026ldquo;Hello. You will receive an automated health survey from [Country Partner] in 5 min. You will get [incentive amount] airtime if you complete the 20 min survey.\u0026rdquo;\u003c/em\u003e The name of the country partner and incentive amount (in brackets) were adapted for each country. In another arm, the pre-notification SMS provided the same details and a toll-free number that the participants could dial if they were interested in taking the survey. The system would then call them back with the IVR survey immediately.\u003c/p\u003e \u003cp\u003eIn Bangladesh, the costs per text message and per minute of airtime were, respectively, \u003cspan\u003e$\u003c/span\u003e0.02 and \u003cspan\u003e$\u003c/span\u003e0.07. In Colombia, the respective costs were \u003cspan\u003e$\u003c/span\u003e0.07 and \u003cspan\u003e$\u003c/span\u003e0.09; in Uganda, they were \u003cspan\u003e$\u003c/span\u003e0.02 per text message and \u003cspan\u003e$\u003c/span\u003e0.13 per minute of airtime. Incentives were sent to all participants who completed the NCD survey. Participants received the airtime incentive in local currency: Bangladesh (50 Taka; \u003cspan\u003e$\u003c/span\u003e1.79 USD), Colombia (5000 Colombian Pesos; \u003cspan\u003e$\u003c/span\u003e1.60 USD ), and Uganda (5000 Ugandan Shillings; \u003cspan\u003e$\u003c/span\u003e1.91 USD).\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eWe examined the contact, response, and cooperation rates by study arm (Supplemental Table\u0026nbsp;1), in each country, and as defined by the American Association for Public Opinion Research (AAPOR) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe considered an interview complete if the participants answered at least four of the five modules. Those who responded to one out of three modules were considered partial interviews. When age-eligible participants did not indicate consent or terminate the survey before consenting, they were marked as refusals. Age-eligible participants who did not finish any NCD module despite providing consents were marked as break-offs. Participants who did not respond to the age question after survey initiation were marked as unknown.\u003c/p\u003e \u003cp\u003eThe contact rate is calculated by dividing the total number of potential eligible participants by the total number of complete and partial interviews, refusals, and other events. The total number of full and partial interviews divided by the total number of potential eligible participants yielded the response rate. The number of full interviews divided by the total of partial and full interviews, refusals, and other factors was the cooperation rate.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe sample size for each arm was calculated assuming a baseline survey completion rate of 30%. To detect a difference of at least 10% between study arms at an alpha of 0.05 and power of 80%, we would need at least 376 participants to complete the survey in each arm. With the baseline survey completion rate assumption of 30%, this meant 1254 participants would have to consent to take the survey per study arm. With three study arms in this trial, this meant 3762 participants to be enrolled in each country.\u003c/p\u003e \u003cp\u003eThe distribution of sociodemographic variables among complete interviews was compared by study arm within each country using chi-squared tests. Contact, response, and cooperation rates were compared by study arm in each country. \u003cem\u003eA priori, a\u003c/em\u003en alpha of 0.05 was used for hypothesis testing. The control arm was selected as the reference category to which other arms of the study are compared. We conducted a log-binomial regression to assess the risk ratio and corresponding 95% Confidence Interval (95% CI) for each comparison.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFor the costing analysis, using an ingredients approach, all costs associated with inputs into making the IVR system functional were recorded. The USD provided values were the costs borne by the research arm and included the costs of the incentive, plus a small fee to send the incentive. These costs were used to assess the cost per completed NCD survey. R was used to analyze data.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Disposition codes by country and study arm\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall-in\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBangladesh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Calls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e153234\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e142408\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5382\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplete Interview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e387 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e240 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartial Interview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e183 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e105 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRefusal/ Breakoffs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e193 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e122 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, picked up phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15070 (9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e12181 (8.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e4 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, didn\u0026rsquo;t pick up phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e92302 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e22910 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, no call-in\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1943 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- underage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e660 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e594 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- unallocated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e44439 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e271 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- SMS did not go through\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e102071 (71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3432 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible-IVR did not go through\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3914 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eColombia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Calls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e94574\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51829\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplete Interview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e377 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e376 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e7 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartial Interview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRefusal/ Breakoffs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e30 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e26 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, picked up phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e28278 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e18158 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e15 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, didn\u0026rsquo;t pick up phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20363 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e7920 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, no call-in\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e11026 (78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- underage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e47 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e31 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- unallocated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e45426 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e14386 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- SMS did not go through\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e10888 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2986 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible-IVR did not go through\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUganda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Calls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9295\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25765\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplete Interview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e385 (2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e387 (4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e385 (1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartial Interview\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e113 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e108 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e28 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRefusal/ Breakoffs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e124 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e146 (1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e27 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, picked up phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2049 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1634 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e425 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, didn\u0026rsquo;t pick up phone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3556 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1748 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown, no call-in\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e21382 (80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- underage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e222 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e175 (1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e33 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- unallocated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7598 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3888 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible- SMS did not go through\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1209 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3485 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneligible-IVR did not go through\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe total number of calls made in Bangladesh Colombia and Uganda was 301,034, 160,437, 49,979, respectively (see Figure 1 and Table 1). Although the distribution of disposition codes differed substantially by survey arm and country, most call attempts resulted in noncontact outcomes in all countries. In Bangladesh, only 0.2% of all call attempts resulted in a completed interview (n = 631), with the call-in arm producing only 3 completions. The predominant outcome in both control and SMS arms was unknown\u0026ndash;no answer, accounting for 60.2% and 16.1% of calls, respectively. A large proportion of SMS calls failed due to messages not going through (71.7%). In Colombia, completion was similarly low (0.4%), ranging from 0.05% in the call-in arm to 0.72% in the SMS arm, and nearly half of the control arm calls were classified as unallocated. Uganda had the highest number of completed interviews across countries, approximately 385 completed surveys per arm, and had higher proportions of calls that resulted in an answered phone than the other two studied countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Demographic characteristics for complete surveys by study arm in each country\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall-in\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-val\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"18\" style=\"width: 84px;\"\u003e\n \u003cp\u003eBangladesh\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e630\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e387\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e240\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e411(65.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e243(62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e166(69.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2(66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e30-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e194(30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e126(32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e67(27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e50-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e21(3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e15(3.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e6(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(0.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3 (0.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1(0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e502(79.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e317(81.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e183(76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2 (66.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e121(19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e66(17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e54(22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(33.3 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1(0.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1(0.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRefused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e6(0.95 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2(0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e41(6.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e26(6.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e15(6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e79(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e47(12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e31(12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e197(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e124 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e72(30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e137(21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e72(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e64(26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e172(27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e116(30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e56(23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRefused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(0.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2 (0.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2(0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e325(51.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e197(50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e125(52.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e302(47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e188(48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e114(47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRefused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3(0.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2(0.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1(0.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"15\" style=\"width: 84px;\"\u003e\n \u003cp\u003eColombia\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e760\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e377\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e376\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e315(41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e137(36.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e173(46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5(71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e30-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e331(43.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e168(44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e162(43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e50-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e98(12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e60(15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e37(9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e16(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e12(3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e345(45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e172(45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e166 (44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e405(53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e198(52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e207(55.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10(1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e7(1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3(0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e48(6.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e27(7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e21(5.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eElementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e110(14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e60(15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e50(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e261(34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e133(35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e125(33.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3(42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e208(27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e87(23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e119(31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2(28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e133(17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e70(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e61(16.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2(28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e211(27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e109 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e101(26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e549(72.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e268(71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e275(73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e6(85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"16\" style=\"width: 84px;\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1157\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e385\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e387\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e385\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e--\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e742(64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e240(62.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e235(60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e267(69.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e30-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e359(31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e124(32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e131(33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e104(27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e50-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e46(3.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e18(4.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e17(4.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e11(2.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e70+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e10(0.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3(0.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(1.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3(0.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e816(70.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e268(69.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e265(68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e283(73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e333(28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e113(29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e118(30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e102(26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e8(0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(1.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(1.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e184(15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e66(17.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e68(17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e50(13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e324(28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e117(30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e97(25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e110(28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eO Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e363(31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e116(30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e116(30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e131(34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eA Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e95(8.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e31(8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e29(7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e35(9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e173(15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e51(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e68(17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e54(14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003ePost Graduation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e18(1.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e4(1.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e9(2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5(1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e522(45.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e175(45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e183(47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e164(42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e635(54.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e210(54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e204(52.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e221(57.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: 1. chi-square test only for control and SMS arms; excludes refusals\u003c/p\u003e\n\u003cp\u003eIn Table 2, we reported the sociodemographic characteristics of the respondents according to study arm in each country. In Bangladesh, a majority of the respondents were 18-29-year-olds (65.2%), males (79.7%), and urban residents (51.6%); this pattern was observed across survey arms. Respondents from Uganda had a similar pattern, and respondents were largely 18\u0026ndash;29-year-olds (64.1%) or males (70.5%). When we compared the study arms, no significant differences were observed for age, sex, education, or location (all p \u0026gt; .05). Among respondents from Colombia, the age distribution differed significantly across arms (p = .003), and younger respondents (i.e., 18-29-year-olds, 41.5%) or males (45.4%) were substantially lower than other two countries. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Study rates by country and study arm\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall-in\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBangladesh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContact Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.86 (1.66, 2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.22 (0.07-0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.84 (1.61, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.29 (0.09-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCooperation Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e50.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e51.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e100.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.01 (0.91, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost/completed survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e$7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e$32.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e$112.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall/completed survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eColombia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContact Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.79 (1.58, 2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.07 (0.03, 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.81 (1.58, 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.07 (0.03, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCooperation Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e81.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e84.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.03 (0.97, 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost/completed survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e$9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e$16.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e$143.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall/completed survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUganda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContact Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e15.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.98%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.60 (1.44, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.20 (0.18, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e12.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e1.54 (1.37, 1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.23 (0.20, 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCooperation Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e61.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e60.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk ratio (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.98 (0.89, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.41 (1.32, 1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost/completed survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e$4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e$4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e$3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCall/completed survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 shows the survey participation rates by study arms in all three countries. Overall, the SMS arm had higher contact and response rates than the control arm. The call-in arm had the lowest rates in Bangladesh and Colombia. For instance, in Bangladesh, compared to the control arm, the SMS arm demonstrated significantly higher contact (RR = 1.86; 95% CI: 1.66\u0026ndash;2.09; p \u0026lt; .001) and response rates (RR = 1.84; 95% CI: 1.61\u0026ndash;2.19; p \u0026lt; .001); however, cooperation rates were similar (51.4% vs. 50.7%) and the call-in arm showed very low contact (0.15%) or response (0.15%) rates. In this country, the cost per completed survey was lowest in the control arm ($7.72) and highest in the call-in arm ($112.06).Findings of Colombia were consistent with Bangladesh; the SMS arm had significantly higher contact (RR = 1.79; 95% CI: 1.58- 2.04, p \u0026lt; .001) and response rates (RR = 1.81; 95% CI: 1.58-2.07, p \u0026lt; .001) than the control arm. Cooperation was higher than 80% in both arms. The call-in arm had a response rate of only 0.06%, and the cost per completion ranged from $9.54 (control) to $143.21 (call-in). Uganda demonstrated the strongest performance across modes. Compared to the control arm, the SMS arm had higher contact (RR = 1.60; 95% CI: 1.44-1.77, p \u0026lt; .001) and response rates (RR = 1.54; 955 CI: 1.37-1.73, p \u0026lt; .001). Cooperation rates were comparable between control (61.9%) and SMS (60.4%) arms but substantially higher in the call-in arm (87.5%). Costs per completed survey were lower in all modes ($3.97\u0026ndash;$4.80) than in the other two countries.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted these NCD surveys in Bangladesh, Colombia, and Uganda using RDD with participants randomized to one of three arms: IVR only; IVR with SMS pre-notification; and IVR with a toll-free number for calling-in shared via SMS. Our goal was to test which strategies could improve survey response and cooperation rates while being most cost-effective. Although the calling-in strategy was successful in Uganda, the low yield in Bangladesh and Colombia led us to prematurely stop this assessment in these regions. This resulted in a very few complete surveys and a high cost per completed survey for these study arms. Further research and an understanding of the local context in Uganda could help better understand the reason behind the success of the call-in strategy in this setting only.\u003c/p\u003e \u003cp\u003eWe also found a very high number of ineligible numbers dialed by the system in Bangladesh across the study arms, when compared to Colombia and Uganda. This may be due to the saturation of numbers with the selected MNO prefixes, resulting in calls to numbers that do not exist and hence are ineligible.\u003c/p\u003e \u003cp\u003eAn assessment of demographic features across the study arms in each country demonstrated no significant differences, except for a marginally higher number of younger individuals who completed the survey in the SMS arm in Colombia. Similar findings have been reported in previous studies, with the hypothesis that older adults (\u0026gt;\u0026thinsp;45 years) in Columbia are economically active and hence may not have the time to answer the survey [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We can infer that no specific MPS strategy was preferred by a particular demographic group or was able to successfully cater to hard-to-reach populations such as older women, individuals with low education levels or populations living in rural areas [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen we look at the contact rates across the three countries, we see lower values in Bangladesh than in Colombia and Uganda. This was similar in other previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This means that fewer individuals In Bangladesh picked up their phones when called to take the survey, and this may be attributed to a high incidence of spam calls in the region, which has resulted in mistrust and poor willingness to answer calls from unknown numbers [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While we see an improvement in the contact rate with a pre-notification SMS in Bangladesh, the cost per completed survey is still high and potentially not feasible for large-scale implementation.\u003c/p\u003e \u003cp\u003eA comparison of survey rates across the study arms in the three countries showed us significant differences between the study arms and the control arm. The significantly higher response rates correlated with findings from similar studies that found an increase in responses when survey participants are notified about the survey in advance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This may be due to several reason, including increased trust in the source of the survey, participants know what to expect when they receive the call, and they can set aside time for the task. Our results also showed us very high cooperation rates in the call-in arm due to low refusals and partially completed surveys. As participants dial the toll-free number to showcase their willingness towards survey participation, it serves as preliminary consent, and the few respondents lost during the survey are often due to age ineligibility.\u003c/p\u003e \u003cp\u003eApart from the survey rates, it is also crucial to assess the cost-effectiveness of these strategies. The cost incurred in each study arm was due to the cost of every call placed, SMS sent, and airtime incentive provided [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We see a relatively higher cost per completed survey in Colombia as compared to Uganda and Bangladesh. This could be attributed to a much longer informed consent question, which was mandated by law [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This not only drives up the airtime cost but may be associated with a high drop-out rate, which drives up the cost per completed survey.\u003c/p\u003e \u003cp\u003eApart from the survey modality, several other factors that are specific to the country context may have influenced these results. These include differences in mobile phone penetration rates, levels of digital literacy, and cultural appropriateness [\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A qualitative assessment of user perceptions of MPS in Uganda also found that other factors, such as short survey lengths, a reliable and recognizable survey source, and evening survey calls, among several other factors, were conducive to participant willingness and could improve survey response rates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results demonstrate that a specific mHealth survey strategy may be appropriate for a particular setting. For example, the call-in strategy is successful in Uganda and was the most cost-effective as well. In Bangladesh, while the SMS arm had the highest response rate, it was not financially feasible on a large scale; therefore, the IVR survey alone may be the most suitable strategy for Bangladesh. In Colombia, the SMS pre-notification arm had almost twice the response rate when compared to IVR alone. However, a cost-benefit assessment would help determine which of these would be apt for the local country context.\u003c/p\u003e \u003cp\u003eOne of the limitations of the study was that the call-in arms in Colombia and Bangladesh had to be prematurely stopped due to a low yield of responses. Hence, our understanding of the survey rates comparison is limited to one context\u0026ndash;Uganda. The order of questions and the questions themselves were standardized in the survey. However, due to legal and regulatory reasons in some contexts, such as Colombia, the consent questions were altered and re-ordered. This may have altered the survey participation rates and limited the inference we can make.\u003c/p\u003e \u003cp\u003eThis study also has several strengths. The randomized study design and multi-country assessment broadened the scope of inferences we can make. It allows us to explore the effect of different mobile survey strategies and local country contexts. The use of standardized AAPOR definitions for disposition codes and survey rate calculations makes the study results comparable and easy to understand across contexts. Future studies can aim to further explore the effect of socio-cultural norms, gender dynamics, and in-country policy contexts on MPS rates.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study findings suggest that SMS pre-notifications and call-in strategies can be successful in increasing survey response and contact rates. However, the degree of effectiveness can vary depending on several factors specific to the local context and preferences of the participants. In some settings, call-in strategies can be very successful with high cooperation rates and cost-effectiveness. Any single strategy is not particularly favorable to a demographic group, and more research can be done to develop survey methods that can reach vulnerable populations that are often missed. MPS have shown promise in assessing health- related topics, such as NCDs and can facilitate timely, data-driven decision-making for improved health outcomes in LMICs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval and consent to participate:\u003c/h2\u003e \u003cp\u003e Ethical approval was received from the institutional review boards of the institutions involved in the study, namely the Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, U.S; and Pontificia Universidad Javeriana, Bogot\u0026aacute;, Colombia. All participants provided informed consent by pressing a number on their keypad. We only included adult individuals. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eFunding of the study was provided by Bloomberg Philanthropies\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDG did the conception and supervision of this study. GMAK and KS conducted literature review, analyzed data, and prepared the first draft. AVC, FRV, ER, CS, IAK, and ER contributed to the study design and administered the project. All other authors reviewed the manuscript and edited it. Final approval of the first draft was approved by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003eThe authors thank survey participants for their time\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeclarations Competing interests\u003c/b\u003e: The authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be available upon acceptance from the OpenICPSR repository database (https://www.openicpsr.org/openicpsr/.')\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaghavi, M. et al. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e403\u003c/b\u003e, 2100\u0026ndash;2132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(24)00367-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(24)00367-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e396\u003c/b\u003e, 1204\u0026ndash;1222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(20)30925-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30925-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForouzanfar, M. H. et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990\u0026ndash;2015: a systematic analysis for the Global Burden of Disease Study 2015. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e388\u003c/b\u003e, 1659\u0026ndash;1724. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(16)31679-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(16)31679-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOng, K. L. et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e402\u003c/b\u003e, 203\u0026ndash;234. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(23)01301-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)01301-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, J. et al. Ethics of mobile phone surveys to monitor non-communicable disease risk factors in low- and middle-income countries: A global stakeholder survey. \u003cem\u003eGlob Public. Health\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 1167\u0026ndash;1181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/17441692.2019.1566482\u003c/span\u003e\u003cspan address=\"10.1080/17441692.2019.1566482\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, J. et al. Ethics Considerations in Global Mobile Phone-Based Surveys of Noncommunicable Diseases: A Conceptual Exploration. \u003cem\u003eJ. Med. Internet Res.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, e110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/jmir.7326\u003c/span\u003e\u003cspan address=\"10.2196/jmir.7326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibson, D. G. et al. Mobile Phone Surveys for Collecting Population-Level Estimates in Low- and Middle-Income Countries: A Literature Review. \u003cem\u003eJ. Med. Internet Res.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, e139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/jmir.7428\u003c/span\u003e\u003cspan address=\"10.2196/jmir.7428\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibson, D. G. et al. Evaluation of Mechanisms to Improve Performance of Mobile Phone Surveys in Low- and Middle-Income Countries: Research Protocol. \u003cem\u003eJMIR Res. Protoc.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, e81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/resprot.7534\u003c/span\u003e\u003cspan address=\"10.2196/resprot.7534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKante, M. \u0026amp; M\u0026aring;lqvist, M. Effectiveness of SMS-based interventions in enhancing antenatal care in developing countries: a systematic review. \u003cem\u003eBMJ Open.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, e089671. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2024-089671\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2024-089671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Ni\u0026ntilde;o, J. A. et al. A multi-country comparison between mobile phone surveys and face-to-face household surveys to estimate the prevalence of non-communicable diseases behavioural risk factors in low- and middle-income settings. \u003cem\u003eBMJ Glob Health\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, e017785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjgh-2024-017785\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2024-017785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibson, D. G. et al. Effect of airtime incentives on response and cooperation rates in non-communicable disease interactive voice response surveys: randomised controlled trials in Bangladesh and Uganda. \u003cem\u003eBMJ Glob Health\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, e001604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjgh-2019-001604\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2019-001604\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibson, D. G. et al. Promised and Lottery Airtime Incentives to Improve Interactive Voice Response Survey Participation Among Adults in Bangladesh and Uganda: Randomized Controlled Trial. \u003cem\u003eJ. Med. Internet Res.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, e36943. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/36943\u003c/span\u003e\u003cspan address=\"10.2196/36943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavid, M. C. \u0026amp; Ware, R. S. Meta-analysis of randomized controlled trials supports the use of incentives for inducing response to electronic health surveys. \u003cem\u003eJ. Clin. Epidemiol.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e, 1210\u0026ndash;1221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jclinepi.2014.08.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jclinepi.2014.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez-Patarroyo, M. et al. Informed Consent for Mobile Phone Health Surveys in Colombia: A Qualitative Study. \u003cem\u003eJ. Empir. Res. Hum. Res. Ethics\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 24\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1556264620958606\u003c/span\u003e\u003cspan address=\"10.1177/1556264620958606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTweheyo, R., Selig, H., Gibson, D. G., Pariyo, G. W. \u0026amp; Rutebemberwa, E. User Perceptions and Experiences of an Interactive Voice Response Mobile Phone Survey Pilot in Uganda: Qualitative Study. \u003cem\u003eJMIR Form. Res.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, e21671. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/21671\u003c/span\u003e\u003cspan address=\"10.2196/21671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDal Grande, E., Chittleborough, C. R., Campostrini, S., Dollard, M. \u0026amp; Taylor, A. W. Pre-Survey Text Messages (SMS) Improve Participation Rate in an Australian Mobile Telephone Survey: An Experimental Study. \u003cem\u003ePloS One\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, e0150231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0150231\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0150231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteeh, C., Buskirk, T. D. \u0026amp; Callegaro, M. Using Text Messages in U.S. Mobile Phone Surveys. \u003cem\u003eField Methods\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e, 59\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1525822X06292852\u003c/span\u003e\u003cspan address=\"10.1177/1525822X06292852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott, R. What is Random Digit Dialing? 29 Sept 2020 [cited 30 Aug 2022]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geopoll.com/blog/what-is-random-digit-dialing/\u003c/span\u003e\u003cspan address=\"https://www.geopoll.com/blog/what-is-random-digit-dialing/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank. World Bank Country and Lending Groups. [cited 31 Aug 2023]. (2023). Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups\u003c/span\u003e\u003cspan address=\"https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe World Bank. Mobile cellular subscriptions (per 100 people). 29 Aug 2023 [cited 29 Aug 2023]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/indicator/IT.CEL.SETS.P2\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/indicator/IT.CEL.SETS.P2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe American Association for Public Opinion Research. Standard Definitions: Final dispositions of case codes and outcome rates for surveys. 9th ed. (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzman-Tordecilla, D. N. et al. Examination of the demographic representativeness of a cross-sectional mobile phone survey in collecting health data in Colombia using random digit dialling. \u003cem\u003eBMJ Open.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, e073647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2023-073647\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2023-073647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres-Quintero, A. et al. Adaptation of a mobile phone health survey for risk factors for noncommunicable diseases in Colombia: a qualitative study. \u003cem\u003eGlob Health Action\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 1809841. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/16549716.2020.1809841\u003c/span\u003e\u003cspan address=\"10.1080/16549716.2020.1809841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, J. et al. Remote consent approaches for mobile phone surveys of non-communicable disease risk factors in Colombia and Uganda: A randomized study. Rashid TA, editor. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, e0279236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0279236\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0279236\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabrique, A. et al. Improving success of non-communicable diseases mobile phone surveys: Results of two randomized trials testing interviewer gender and message valence in Bangladesh and Uganda. Pry JM, editor. PLOS ONE. ;18: e0285155. (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0285155\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0285155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAAPOR Ad Hoc Committee. \u003cem\u003eSpam Flagging and Call Blocking and Its Impact on Survey Research\u003c/em\u003e (Spam Flagging and Call Blocking and Its Impact on Survey Research, 2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKok, K. F. \u0026amp; Truecaller Insights Top 20 Countries Affected by Spam Calls \u0026amp; SMS in 2019. In: Truecaller Blog [Internet]. 3 Dec 2019 [cited 2 Feb 2022]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://truecaller.blog/2019/12/03/truecaller-insights-top-20-countries-affected-by-spam-calls-sms-in-2019/\u003c/span\u003e\u003cspan address=\"http://truecaller.blog/2019/12/03/truecaller-insights-top-20-countries-affected-by-spam-calls-sms-in-2019/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiper, D. Data Protection Laws in Colombia. (2025). Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dlapiperdataprotection.com/index.html?t=law\u0026amp;c=CO\u003c/span\u003e\u003cspan address=\"https://www.dlapiperdataprotection.com/index.html?t=law\u0026amp;c=CO\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabrique, B. Mobile phone ownership and widespread mHealth use in 168,231 women of reproductive age in rural Bangladesh. \u003cem\u003eJ. Mob. Technol. Med.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 26\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7309/jmtm.48\u003c/span\u003e\u003cspan address=\"10.7309/jmtm.48\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenleaf, A. R., Ahmed, S., Moreau, C., Guiella, G. \u0026amp; Choi, Y. Cell phone ownership and modern contraceptive use in Burkina Faso: implications for research and interventions using mobile technology. \u003cem\u003eContraception\u003c/em\u003e \u003cb\u003e99\u003c/b\u003e, 170\u0026ndash;174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.contraception.2018.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.contraception.2018.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInc, G. Disparities in Cellphone Ownership Pose Challenges in Africa. In: Gallup.com [Internet]. 17 Feb 2016 [cited 5 Apr 2022]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://news.gallup.com/poll/189269/disparities-cellphone-ownership-pose-challenges-africa.aspx\u003c/span\u003e\u003cspan address=\"https://news.gallup.com/poll/189269/disparities-cellphone-ownership-pose-challenges-africa.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilver, L. Smartphone Ownership Is Growing Rapidly Around the World, but Not Always Equally. In: Pew Research Center [Internet]. 5 Feb 2019 [cited 22 Sept 2022]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/\u003c/span\u003e\u003cspan address=\"https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKibria, G. M. A. \u0026amp; Nayeem, J. Trends and factors associated with mobile phone ownership among women of reproductive age in Bangladesh. Malta M, editor. PLOS Glob Public Health. ;3: e0001889. (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pgph.0001889\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgph.0001889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9013567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9013567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMobile phone surveys (MPS) provide a cost-effective and logistically simpler alternative to household surveys for monitoring non-communicable diseases (NCDs) and their risk factors in low- and middle-income countries (LMICs). However, improving response and completion rates remains a challenge. This study aimed to assess strategies to optimize MPS for NCD risk factor surveillance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e In Bangladesh, Colombia, and Uganda, participants were randomly assigned to one of three study arms: 1) Interactive Voice Response (IVR) survey only, 2) IVR with pre-survey SMS notification, or 3) Calling-in, with a toll-free number. Contact, response, refusal, and cooperation rates were calculated and compared across arms using log-binomial regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 301,024 (complete interviews: 630), 160,437 (complete interviews: 760), and 49,107 (complete interviews: 1,157) MPS calls were made in Bangladesh, Colombia, and Uganda, respectively. Across the three countries, the SMS pre-notification arm had significantly higher contact rates (risk ratios [RR] ranging from 1.60 to 1.86) and response rates (RR: 1.54\u0026ndash;1.84) compared to the IVR-only arm. While the call-in arm had a low yield in Bangladesh and Colombia, it performed well in Uganda with the highest cooperation rate (RR 1.41, 95% CI: 1.32\u0026ndash;1.52). No single strategy was favored by any demographic group. The call-in arm was costliest in Bangladesh (\u003cspan\u003e$\u003c/span\u003e112.06 per completed survey) and Colombia (\u003cspan\u003e$\u003c/span\u003e143.21), but most cost-effective in Uganda (\u003cspan\u003e$\u003c/span\u003e3.97).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e SMS pre-notifications and call-in strategies can improve MPS participation rates, but their effectiveness and cost-efficiency vary across contexts. Tailoring strategies to local preferences and contexts are crucial for optimizing MPS for NCD surveillance in LMICs.\u003c/p\u003e","manuscriptTitle":"Assessing strategies to improve participation in mobile phone surveys for non- communicable diseases: Randomized studies from Bangladesh, Colombia, and Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 05:53:22","doi":"10.21203/rs.3.rs-9013567/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"209496768562652437834541709114717745929","date":"2026-05-19T16:02:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309736763792294343919132680231476550276","date":"2026-05-18T13:41:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50155883033259096709619275394705324290","date":"2026-05-17T04:43:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161176059805036773904341474382736983778","date":"2026-05-16T12:12:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T10:57:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T12:11:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T14:54:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T21:45:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-11T14:16:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38ac9af3-3281-49a4-a9ac-f5e13eaceaf4","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"209496768562652437834541709114717745929","date":"2026-05-19T16:02:01+00:00","index":79,"fulltext":""},{"type":"reviewerAgreed","content":"309736763792294343919132680231476550276","date":"2026-05-18T13:41:31+00:00","index":78,"fulltext":""},{"type":"reviewerAgreed","content":"50155883033259096709619275394705324290","date":"2026-05-17T04:43:16+00:00","index":77,"fulltext":""},{"type":"reviewerAgreed","content":"161176059805036773904341474382736983778","date":"2026-05-16T12:12:37+00:00","index":75,"fulltext":""},{"type":"reviewersInvited","content":"7","date":"2026-05-06T10:57:12+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67627547,"name":"Health sciences/Diseases"},{"id":67627548,"name":"Health sciences/Health care"},{"id":67627549,"name":"Physical sciences/Mathematics and computing"},{"id":67627550,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-18T05:53:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 05:53:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9013567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9013567","identity":"rs-9013567","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}