Identifying Non-Working Phone Numbers in Africa for Response Rate Calculations

Lau, Charles; Tada, Nicolas di

doi:10.29115/sp-2018-0020

Cited by 7 publications

(6 citation statements)

References 1 publication

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“…As a result, our response, contact and refusal rates are likely inflated, with no effect on the cooperation rates. Future studies could consider re-contacting a sample of these unascertainable numbers to estimate the percentage of working and non-working numbers 37. Second, our survey was available in only three out of six major Ugandan language groups 38.…”

Section: Discussionmentioning

confidence: 99%

Effect of airtime incentives on response and cooperation rates in non-communicable disease interactive voice response surveys: randomised controlled trials in Bangladesh and Uganda

et al. 2019

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BackgroundThe global proliferation of mobile phones offers opportunity for improved non-communicable disease (NCD) data collection by interviewing participants using interactive voice response (IVR) surveys. We assessed whether airtime incentives can improve cooperation and response rates for an NCD IVR survey in Bangladesh and Uganda.MethodsParticipants were randomised to three arms: a) no incentive, b) 1X incentive or c) 2X incentive, where X was set to airtime of 50 Bangladesh Taka (US$0.60) and 5000 Ugandan Shillings (UGX; US$1.35). Adults aged 18 years and older who had a working mobile phone were sampled using random digit dialling. The primary outcomes, cooperation and response rates as defined by the American Association of Public Opinion Research, were analysed using log-binomial regression model.ResultsBetween 14 June and 14 July 2017, 440 262 phone calls were made in Bangladesh. The cooperation and response rates were, respectively, 28.8% (353/1227) and 19.2% (580/3016) in control, 39.2% (370/945) and 23.9% (507/2120) in 50 Taka and 40.0% (362/906) and 24.8% (532/2148) in 100 Taka incentive groups. Cooperation and response rates, respectively, were significantly higher in both the 50 Taka (risk ratio (RR) 1.36, 95% CI 1.21 to 1.53) and (RR 1.24, 95% CI 1.12 to 1.38), and 100 Taka groups (RR 1.39, 95% CI 1.23 to 1.56) and (RR 1.29, 95% CI 1.16 to 1.43), as compared with the controls. In Uganda, 174 157 phone calls were made from 26 March to 22 April 2017. The cooperation and response rates were, respectively, 44.7% (377/844) and 35.2% (552/1570) in control, 57.6% (404/701) and 39.3% (508/1293) in 5000 UGX and 58.8% (421/716) and 40.3% (535/1328) in 10 000 UGX groups. Cooperation and response rates were significantly higher, respectively in the 5000 UGX (RR 1.29, 95% CI 1.17 to 1.42) and (RR 1.12, 95% CI 1.02 to 1.23), and 10 000 UGX groups (RR 1.32, 95% CI 1.19 to 1.45) and (RR 1.15, 95% CI 1.04 to 1.26), as compared with the control group.ConclusionIn two diverse settings, the provision of an airtime incentive significantly improved both the cooperation and response rates of an IVR survey, with no significant difference between the two incentive amounts.Trial registration numberNCT03768323.

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Section: Discussionmentioning

confidence: 99%

Effect of airtime incentives on response and cooperation rates in non-communicable disease interactive voice response surveys: randomised controlled trials in Bangladesh and Uganda

et al. 2019

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“…Despite including only numerically feasible phone numbers in our sample frame, we still expected a notable amount of nonresponse. To improve the estimate of the percent of phone numbers that would be classified as noncontact, we conducted “pulsing” [ 34 ]. Before creating the sample, 3 supervisors called 100 phone numbers that were randomly sampled and excluded from the LeCellPHIA sample frame.…”

Section: Methodsmentioning

confidence: 99%

Participatory Surveillance of COVID-19 in Lesotho via Weekly Calls: Protocol for Cell Phone Data Collection

Greenleaf¹,

Mwima²,

Lethoko³

et al. 2021

JMIR Res Protoc

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Background The increase in cell phone ownership in low- and middle-income countries (LMIC) has created an opportunity for low-cost, rapid data collection by calling participants on their cell phones. Cell phones can be mobilized for a myriad of data collection purposes, including surveillance. In LMIC, cell phone–based surveillance has been used to track Ebola, measles, acute flaccid paralysis, and diarrheal disease, as well as noncommunicable diseases. Phone-based surveillance in LMIC is a particularly pertinent, burgeoning approach in the context of the COVID-19 pandemic. Participatory surveillance via cell phone could allow governments to assess burden of disease and complements existing surveillance systems. Objective We describe the protocol for the LeCellPHIA (Lesotho Cell Phone PHIA) project, a cell phone surveillance system that collects weekly population-based data on influenza-like illness (ILI) in Lesotho by calling a representative sample of a recent face-to-face survey. Methods We established a phone-based surveillance system to collect ILI symptoms from approximately 1700 participants who had participated in a recent face-to-face survey in Lesotho, the Population-based HIV Impact Assessment (PHIA) Survey. Of the 15,267 PHIA participants who were over 18 years old, 11,975 (78.44%) consented to future research and provided a valid phone number. We followed the PHIA sample design and included 342 primary sampling units from 10 districts. We randomly selected 5 households from each primary sampling unit that had an eligible participant and sampled 1 person per household. We oversampled the elderly, as they are more likely to be affected by COVID-19. A 3-day Zoom training was conducted in June 2020 to train LeCellPHIA interviewers. Results The surveillance system launched July 1, 2020, beginning with a 2-week enrollment period followed by weekly calls that will continue until September 30, 2022. Of the 11,975 phone numbers that were in the sample frame, 3020 were sampled, and 1778 were enrolled. Conclusions The surveillance system will track COVID-19 in a resource-limited setting. The novel approach of a weekly cell phone–based surveillance system can be used to track other health outcomes, and this protocol provides information about how to implement such a system. International Registered Report Identifier (IRRID) DERR1-10.2196/31236

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“…RenderX nonresponse. To improve the estimate of the percent of phone numbers that would be classified as noncontact, we conducted "pulsing" [34]. Before creating the sample, 3 supervisors called 100 phone numbers that were randomly sampled and excluded from the LeCellPHIA sample frame.…”

Section: Xsl • Fomentioning

confidence: 99%

Participatory Surveillance of COVID-19 in Lesotho via Weekly Calls: Protocol for Cell Phone Data Collection (Preprint)

Greenleaf¹,

Mwima²,

Lethoko³

et al. 2021

Preprint

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BACKGROUND The increase in cell phone ownership in low- and middle-income countries (LMIC) has created an opportunity for low-cost, rapid data collection by calling participants on their cell phones. Cell phones can be mobilized for a myriad of data collection purposes, including surveillance. In LMIC, cell phone–based surveillance has been used to track Ebola, measles, acute flaccid paralysis, and diarrheal disease, as well as noncommunicable diseases. Phone-based surveillance in LMIC is a particularly pertinent, burgeoning approach in the context of the COVID-19 pandemic. Participatory surveillance via cell phone could allow governments to assess burden of disease and complements existing surveillance systems. OBJECTIVE We describe the protocol for the LeCellPHIA (Lesotho Cell Phone PHIA) project, a cell phone surveillance system that collects weekly population-based data on influenza-like illness (ILI) in Lesotho by calling a representative sample of a recent face-to-face survey. METHODS We established a phone-based surveillance system to collect ILI symptoms from approximately 1700 participants who had participated in a recent face-to-face survey in Lesotho, the Population-based HIV Impact Assessment (PHIA) Survey. Of the 15,267 PHIA participants who were over 18 years old, 11,975 (78.44%) consented to future research and provided a valid phone number. We followed the PHIA sample design and included 342 primary sampling units from 10 districts. We randomly selected 5 households from each primary sampling unit that had an eligible participant and sampled 1 person per household. We oversampled the elderly, as they are more likely to be affected by COVID-19. A 3-day Zoom training was conducted in June 2020 to train LeCellPHIA interviewers. RESULTS The surveillance system launched July 1, 2020, beginning with a 2-week enrollment period followed by weekly calls that will continue until September 30, 2022. Of the 11,975 phone numbers that were in the sample frame, 3020 were sampled, and 1778 were enrolled. CONCLUSIONS The surveillance system will track COVID-19 in a resource-limited setting. The novel approach of a weekly cell phone–based surveillance system can be used to track other health outcomes, and this protocol provides information about how to implement such a system. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/31236

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Identifying Non-Working Phone Numbers in Africa for Response Rate Calculations

Cited by 7 publications

References 1 publication

Effect of airtime incentives on response and cooperation rates in non-communicable disease interactive voice response surveys: randomised controlled trials in Bangladesh and Uganda

Effect of airtime incentives on response and cooperation rates in non-communicable disease interactive voice response surveys: randomised controlled trials in Bangladesh and Uganda

Participatory Surveillance of COVID-19 in Lesotho via Weekly Calls: Protocol for Cell Phone Data Collection

Participatory Surveillance of COVID-19 in Lesotho via Weekly Calls: Protocol for Cell Phone Data Collection (Preprint)

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