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In resource-poor settings, lack of awareness and low demand for services constitute important barriers to expanding the coverage of effective interventions. In India, childhood immunization is a priority health strategy with suboptimal uptake.
To assess study feasibility and key implementation outcomes for the Tika Vaani model, a new approach to educate and empower beneficiaries to improve immunization and child health.
A cluster-randomized pilot trial with a 1:1 allocation ratio was conducted in rural Uttar Pradesh, India, from January to September 2018. Villages were randomly assigned to either the intervention or control group. In each participating village, surveyors conducted a complete enumeration to identify eligible households and requested participation before randomization. Interventions were designed through formative research using a social marketing approach and delivered over 3 months using strategies adapted to disadvantaged populations: (1) mobile health (mHealth): entertaining educational audio capsules (edutainment) and voice immunization reminders via mobile phone and (2) face-to-face: community mobilization activities, including 3 small group meetings offered to each participant. The control group received usual services. The main outcomes were prespecified criteria for feasibility of the main study (recruitment, randomization, retention, contamination, and adoption). Secondary endpoints tested equity of coverage and changes in intermediate outcomes. Statistical methods included descriptive statistics to assess feasibility, penalized logistic regression and ordered logistic regression to assess coverage, and generalized estimating equation models to assess changes in intermediate outcomes.
All villages consented to participate. Gaps in administrative data hampered recruitment; 14.0% (79/565) of recorded households were nonresident. Only 1.4% (8/565) of households did not consent. A total of 387 households (184 intervention and 203 control) with children aged 0 to 12 months in 26 villages (13 intervention and 13 control) were included and randomized. The end line survey occurred during the flood season; 17.6% (68/387) of the households were absent. Contamination was less than 1%. Participation in one or more interventions was 94.0% (173/184), 78.3% (144/184) for the face-to-face strategy, and 67.4% (124/184) for the mHealth strategy. Determinants including place of residence, mobile phone access, education, and female empowerment shaped intervention use; factors operated differently for face-to-face and mHealth strategies. For 11 of 13 intermediate outcomes, regression results showed significantly higher basic health knowledge among the intervention group, supporting hypothesized causal mechanisms.
A future trial of a new intervention model is feasible. The interventions could strengthen the delivery of immunization and universal primary health care. Social and behavior change communication via mobile phones proved viable and contributed to standardization and scalability. Face-to-face interactions remain necessary to achieve equity and reach, suggesting the need for ongoing health system strengthening to accompany the introduction of communication technologies.
International Standard Randomized Controlled Trial Number (ISRCTN) 44840759; https://doi.org/10.1186/ISRCTN44840759
Expanding coverage of effective interventions is a critical challenge for many low- and middle-income countries (LMICs). In addition to technical improvements in service delivery, improving coverage often hinges critically on enhanced awareness and demand for services on the part of beneficiaries. Furthermore, in settings of low literacy, deep poverty, and poor access to information, behavior change is extremely challenging.
Immunization is a priority health strategy for LMIC policy makers seeking to advance the 2030 United Nations (UN) Sustainable Development Goals (SDGs) due to its inherent value in reducing the burden of disease and its potential role as a lever for health system strengthening. Immunization reaches more households than any other health service, bringing communities into regular contact with the health system [
In India, the government has prioritized immunization, making remarkable gains in recent years. However, coverage continues to fall short of the target to fully immunize 90% of India’s infants against 7 vaccine-preventable diseases by 2020 [
The widespread availability of mobile phones in LMICs has stimulated interest in the potential of mobile health (mHealth) interventions to achieve health objectives. A recent systematic review found that mHealth interventions can improve maternal and neonatal service delivery and that text-based vaccination reminders are associated with improved vaccination coverage [
We developed the
In keeping with the plan for the main study, this pilot study adopted a cluster-randomized design with a 1:1 allocation ratio. A cluster design was chosen owing to the nature of the study interventions: face-to-face interventions are structured around communities rather than individuals, whereas mHealth interventions have a possibility of contamination. Clusters were rural villages in a district of Uttar Pradesh (UP), India. Villages were randomly assigned to either the intervention or control group (CG). The protocol was registered in a WHO International Clinical Trials Registry Platform-compliant registry (ISRCTN44840759 doi.org/10.1186/ISRCTN44840759). There were no important changes to methods after trial commencement. We originally sought to register the trial in the Clinical Trials Registry–India (CTRI), which is free of charge and has as the mission to enroll all clinical trials conducted in India. The CTRI took several months to follow-up; in the interim, we applied to a different registry. Owing to the delay caused by waiting and changing registries, the trial was registered shortly after patient enrollment was completed.
India’s most populous state of over 200 million residents, UP is an area of national focus due to lagging health and development indicators. Hardoi (population 4 million; under-5 mortality rate 118 per 1000; cf. UP under-5 mortality rate 90 per 1000, India under-5 mortality rate 57.3 per 1000) [
Villages (clusters) were eligible for inclusion if they had less than 4000 inhabitants and were located in Bawan Block, Hardoi, UP. In participating villages, interventions were offered to all residents. Participants in the baseline survey were consenting primary caregivers of children aged between 0 and 12 months residing in a study village. We excluded those who were not able to understand and speak Hindi or Urdu or those who did not intend to reside in the village for the study duration (6 months). The same individuals were approached for the end line survey.
The survey sampling unit was the household. We conducted a door-to-door census of the village and cross-checked administrative records from the Anganwadi workers (AWW) and Accredited Social Health Activists (ASHA) to identify all households containing a child in the age range of 0 to 12 months within each village. These households constituted our primary target group.
From January 1, 2017, to January 10, 2018, we conducted formative research using a social marketing approach to inform intervention design [
The study interventions took place over a 3-month period and offered social and behavior change communication (SBCC) for members of the general public in rural Indian villages, addressing topics related to child health. The primary target group was families residing in a selected village with a child in the age range of 0 to 12 months. Although vaccination was the primary focus of the study, the SBCC interventions addressed additional areas stipulated to be co-delivered with immunization during Village Health and Nutrition Days (VHNDs), such as health education related to health care entitlements; prevention, recognition, and management of common infectious diseases (diarrhea, pneumonia, dengue, and chikungunya); nutrition; and water, sanitation, and hygiene (WASH).
SBCC materials were delivered through 2 channels: (1) mHealth: educational audio capsules in entertaining formats (edutainment) and voice reminders for immunization sessions broadcast via mobile phone and (2) face-to-face: community mobilization activities, consisting of 1 large introductory meeting to introduce the project to communities and 3 small meetings offered to each participant covering specific themes. For the mHealth component, push messages (automated dial outs) and voice-based reminders were privileged owing to low education level and comfort with technology. For the face-to-face component, small group meetings were held separately for men and women and in different geographical locations within villages to ensure ease of communication. mHealth vaccination reminders were based on the child’s birthdate and offered only to the target group; however, other interventions (mHealth edutainment and face-to-face meetings) were open to all village residents. Community workers (AWW and ASHAs) were encouraged to participate and received advance access to intervention materials. All interventions were free of charge to end users. The CG received standard GoI health and welfare services.
The mHealth strategy (Tika Vaani SBCC Version 1.5, released on July 7, 2017) was designed and delivered by Gram Vaani, a social tech startup incubated out of the Indian Institute of Technology Delhi, using the Mobile Vaani Interactive Voice Response System. Access was free and open to anyone who called the number. The participants could give a missed call to access the platform, and as a result receive a callback enabling them to access all content, record any queries or feedback, or be connected to a live expert. To simplify access, consenting households in intervention villages with children aged less than 12 months at baseline received automated outbound calls twice per week. In total, 26 content pieces were offered. In addition, child vaccination reminders were sent to target group households. Small group meetings lasting approximately 1 hour involving 2 trained facilitators with a minimum of 12 years of education were held once per month and open to all village residents. The access number was shared at each meeting. A video describing Mobile Vaani is available [
The pilot study considered a range of implementation outcomes (
Outcome variables and data sources for the Tika Vaani social and behavior change communication pilot study.
Outcomes | Definition | Approach | Analysis sample | Data sources | |
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Feasibility of the future main study | Ex-ante success criteria Recruitment Randomization Retention Contamination |
Quantb | IGc and CGd |
Project records (all) IVRe platform HHf surveys (contamination) |
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Uptake (adoption) | Participation in Small group meetings mHealthg |
Quant | IG |
Project records (meetings) IVR platform (mHealth) |
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Acceptability and appropriateness | Perception among stakeholders that an intervention is agreeable, suitable, relevant, useful, and credible | Mixed methodsh | IG | Refer to the study by Pérez et al (unpublished data, 2020) |
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Fidelity | Ability to deliver the interventions as planned | Mixed methods | IG (and CG) | Refer to the study by Pérez et al (unpublished data, 2020) |
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Coverage | The degree to which a population eligible to benefit from an intervention actually receives it | Quant | IG |
HH surveys Project records (meetings) IVR platform (mHealth) |
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Adequacy of the program theory | Intermediate outcomes reflecting changes in knowledge, attitudes, and practices of end users | Quant | IG and CG | HH surveys |
aOutcomes and definitions adapted from the study by Peters et al [
bQuant: quantitative.
cIG: intervention group.
dCG: control group.
eIVR: interactive voice response.
fHH survey: household survey.
gmHealth: mobile health.
hMixed methods: quantitative and qualitative.
We established ex-ante criteria for the feasibility of the main study related to recruitment, randomization, retention, and contamination. We were concerned about contamination among treatment groups for mHealth services, as the phone number is easily shared. We viewed a contamination proportion exceeding 15% as a threat to the feasibility of adopting a cluster-randomized design with village as the unit of randomization and geographical distances between villages (mean 15 km; range 5 km-50 km) similar to those in the pilot. As health interventions must achieve sufficient uptake to impact population health, we also established minimum criteria for participation in the new interventions.
We present quantitative findings for 2 secondary outcomes: (1) coverage or the extent to which the interventions reached specific populations and (2) adequacy of the program theory. We constructed a logic model describing the hypothesized program impact pathway and mechanisms of action (
Conceptual model of the intervention. ASHAs: Accredited Social Health Activists; AWWs: Anganwadi Workers; CIHR: Canadian Institutes for Health Research; IC-IMPACTS: the India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability; WASH: water, sanitation, and hygiene.
Quantitative data were collected from the following sources:
Face-to-face surveys: Interviewer-administered household surveys were conducted in all participating study villages. Surveys were administered at baseline before random group assignment and approximately 5 months later following interventions at end line.
Project administrative records: Standardized forms to assess delivery of study procedures and interventions were maintained by field staff to facilitate structured observation and data capture.
Interactive voice response (IVR) system: The IVR system automatically recorded all calls to the platform. IVR data can be disaggregated by various fields including caller phone number, date, time, frequency, duration (seconds), call type, content type, and user characteristics. IVR data were linked to phone numbers provided by target households during the baseline survey to map calls sent and/or received.
We measured the use of the IVR through the number and duration of calls from a unique phone number. We considered that an mHealth item was
Although the pilot was a cluster-randomized two-group study, the study size was based on the rate of contamination among controls. We estimated the required sample size needed for the CG using methods for a one-group descriptive study. We assumed that the true proportion of contamination (calls originating from controls) was 10%, that contamination was most likely to arise from parents of young children, and that there would be 20 households with children aged less than 12 months per village. On the basis of these inputs and using a binomial (
The sampling frame was informed by the 2011 census [
Villages were assigned to either intervention or control using simple randomization with a 1:1 allocation following a computer-generated randomization schedule. The random allocation sequence was generated at the Centre de recherche du Centre hospitalier de l’Université de Montréal by a professional statistician (MPS) using commands for random samples and permutations in the R environment for statistical computing [
Field team leaders enrolled clusters by contacting village officials in person to explain study aims and activities and request consent to participate. Subsequently, in each participating village, surveyors conducted a complete enumeration to identify all households with children in the target age range and directly approached all such households to request participation in the baseline survey and pilot study. Consent was sought before randomization. No advertisements were used for recruitment, and no incentives or rewards were offered for participation. Surveyors communicated group assignments personally to households.
Due to the nature of the interventions, neither participants nor those delivering interventions were blinded to the group assignment. We hired independent surveyors at end line to assess study outcomes. These surveyors were not informed about study aims or group assignments.
We used counts, frequencies, and proportions to summarize categorical data, and means and standard deviations for continuous variables. We assessed bivariate associations using univariable logistic regression for continuous variables and the chi-square test for categorical variables.
We studied the degree to which target beneficiaries (IG households with a child aged less than 12 months at baseline) received the interventions.
To investigate patterns of uptake, we developed separate models for each intervention component: immunization reminders (mHealth), edutainment capsules (mHealth), and small group meetings (face-to-face). Outcomes were modeled as binary (0 uptake vs 1 or more instances of uptake). We followed guidance for the use of logistic regression in small data sets [
To study the determinants of intensity of participation, we repeated analyses specifying ordered logistic regression models for 2 outcomes: (1) the number of mHealth items heard and (2) the number of small group meetings attended. We tested the proportional odds assumption using an approximate likelihood ratio test [
We studied intervention impact on intermediate outcomes using generalized estimating equations (GEE): (1) we used the differences-in-differences method to study changes in variables measuring immunization knowledge in the 2 study groups between the baseline survey and the end line survey using unadjusted regression coefficients (with their 95% CIs) for the interaction between group (intervention or control) and time period (end line or baseline) [
Feasibility outcomes were assessed using the intention-to-treat (ITT) sample; no clusters and no participants were excluded. Analyses of intervention uptake and coverage used the ITT IG; no clusters and no participants randomized to the IG were excluded. To assess the program theory, we analyzed intermediate outcomes using the sample that participated at both baseline and end line, for which 0 clusters, 68 households, and 69 caregivers were lost to follow-up, which is equivalent to an observational sample. For 2 households, missing data on caste were imputed based on the locality of residence within the village. There were no other missing data. Analyses were conducted in Stata 15 (Stata Corporation).
Permission was granted by the Institutional Committee for Ethics and Review of Research, Indian Institute of Health Management Research, Jaipur, on January 10, 2017, and by the Comité d’éthique de la recherche du Centre hospitalier de l’Université de Montréal (Research Ethics Committee of the University of Montreal Hospital) on January 11, 2017 (Reference number 16.084). All participants provided written, in-person informed consent. After completing the study, we offered CG residents access to the mHealth interventions.
The baseline survey and recruitment took place from January 19 to February 19, 2018. We approached 29 villages and 100% (29/29) consented to participate. Recruitment of individual participants was complicated by gaps in administrative data, as 13.9% (79/565) of recorded households were in fact nonresident. Only 1.4% (8/565) of the candidate households did not consent to participate. A total of 391 (185
Characteristics of the participating individuals (
Flow diagram of the parallel group cluster trial. ITT: intention-to-treat.
Baseline characteristics of participating households, by treatment group.
Variablea | Intervention (n=184), n (%) | Control (n=203), n (%) | All participants (n=387), n (%) | |
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(Q1) Lowest | 48 (26.1) | 30 (14.8) | 78 (20.2) |
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(Q2) | 37 (20.1) | 40 (19.7) | 77 (19.9) |
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(Q3) | 37 (20.1) | 41 (20.2) | 78 (20.2) |
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(Q4) | 28 (15.2) | 49 (24.1) | 77 (19.9) |
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(Q5) Highest | 34 (18.5) | 43 (21.2) | 77 (19.9) |
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Hindu | 181 (98.4) | 176 (86.7) | 357 (92.3) |
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Muslim | 3 (1.6) | 27 (13.3) | 30 (7.8) |
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General | 38 (20.7) | 37 (18.2) | 75 (19.4) |
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Other backward caste | 89 (48.4) | 80 (39.4) | 169 (43.7) |
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Scheduled caste | 57 (31.0) | 86 (42.4) | 143 (37.0) |
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None (0) | 62 (33.7) | 75 (37.0) | 137 (35.4) |
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Primary (1-8) | 85 (46.2) | 84 (41.4) | 169 (43.7) |
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Secondary (9-12) or more | 37 (20.1) | 44 (21.7) | 81 (20.9) |
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None (0) | 29 (15.8) | 26 (12.8) | 55 (14.2) |
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Primary (1-8) | 90 (48.9) | 99 (48.8) | 189 (48.8) |
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Secondary (9-12) or more | 65 (35.3) | 78 (38.4) | 143 (37.0) |
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No | 142 (77.2) | N/Ae | N/A |
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Yes | 42 (22.8) | N/A | N/A |
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No | 163 (88.6) | 203 (100.0) | 366 (94.6) |
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Yes | 21 (11.4) | 0 (0.0) | 21 (5.4) |
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No | 11 (6.0) | 15 (7.4) | 26 (6.7) |
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Yes | 173 (94.0) | 188 (92.6) | 361 (93.3) |
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No | 106 (57.6) | 134 (66.0) | 240 (62.0) |
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Yes | 78 (42.4) | 69 (34.0) | 147 (38.0) |
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No | 68 (37.0) | 76 (37.4) | 144 (37.2) |
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Yes | 116 (63.0) | 127 (62.6) | 243 (62.8) |
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No | 162 (88.0) | 166 (81.8) | 328 (84.8) |
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Yes | 22 (12.0) | 37 (18.2) | 59 (15.3) |
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Cannot access | 90 (48.9) | 88 (43.4) | 179 (45.0) |
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Can use easily | 94 (51.1) | 115 (56.7) | 209 (54.0) |
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No | 47 (25.5) | 55 (271) | 102 (26.4) |
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Yes | 137 (745) | 148 (72.9) | 285 (73.6) |
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Rarely | 27 (14.7) | 30 (14.8) | 57 (14.7) |
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When needed | 102 (55.4) | 106 (52.2) | 208 (53.8) |
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Almost daily | 55 (29.9) | 67 (33.0) | 122 (31.5) |
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Lowest | 83 (45.1) | 94 (46.3) | 177 (45.7) |
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Average | 74 (40.2) | 84 (41.4) | 158 (40.8) |
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Highest | 27 (14.7) | 25 (12.3) | 52 (13.4) |
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No | 111 (60.3) | 107 (52.7) | 218 (56.3) |
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Yes | 73 (39.7) | 96 (47.3) | 169 (43.7) |
aBaseline data are presented for the intention-to-treat sample of 387 households (184 IG and 203 CG).
bThis is the religion of the household head.
cCaste categories from most to least advantaged: general, other backward caste, and scheduled caste. The scheduled tribe category is missing, as there are no tribes in the study area.
dHH: household.
eN/A: not applicable.
Baseline characteristics of participating villages, by treatment group.
Variablea | Intervention | Control | All | |
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Total | 974 (673.84) | 1129 (1056.51) | 1051 (871.79) |
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Total 0-6 years | 166 (111.05) | 188 (171.36) | 177 (141.95) |
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Total SCc | 225 (194.85) | 422 (484.76) | 323 (375.62) |
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No electricity | 1 (7.7) | 2 (15.4) | 3 (11.5) |
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Less than 6 hours | 2 (15.4) | 0 (0.0) | 2 (7.7) |
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More than 6 hours | 10 (76.9) | 11 (84.6) | 21 (80.8) |
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Number of Muslim HH per village | 31 (61.39) | 98 (168.96) | 65 (129.25) |
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Number of eligible HHe per village | 14 (7.39) | 16 (12.46) | 15 (10.07) |
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% poorf (Q1+Q2) per village | 44.6 (0.19) | 34.8 (0.23) | 39.7 (0.21) |
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% better off (Q4+Q5) per village | 39.3 (0.26) | 47.5 (0.28) | 43.4 (0.27) |
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% SC per village | 31.0 (0.31) | 41.5 (0.34) | 36.3 (0.32) |
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% of mothers with 0 schooling per village | 32.4 (0.17) | 41.0 (0.24) | 36.7 (0.21) |
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% of fathers with 0 schooling per village | 14.6 (0.12) | 9.8 (0.09) | 12.2 (0.11) |
aBaseline data are presented for the intention-to-treat sample of 26 villages (13 IG and 13 CG) containing 387 households (184 IG and 203 CG).
bData from the 2011 Census of India.
cSC: scheduled caste (least privileged).
dHH: household.
eEligible household: at least one child aged less than 12 months at baseline.
fPoor versus better off households based on asset indices (wealth quintiles).
Ex-ante criteria were fully satisfied (
Primary outcomes.
Primary outcomesa,b | Ex-ante criteria | Ex-post results | |
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Recruitment and randomization (villages) | 70% of villages approached will agree to participate and accept randomization | 100% (29/29 villages) agreedb |
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Recruitment and randomization (households) | In participating villages, 70% of eligible households will agree to participate and accept randomization | 98.0% (387/395 households contacted) agreedb |
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Retention (households) | 50% of households participating in the baseline survey will agree to participate in the end line survey | 82.2% (318/387) enrolled households agreedb and 2.1% (8/387) households refused |
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Contamination | Contamination proportion between treatment groups should be <15% | 0.6% (1/166 control end line respondents called); 0.07% (1/1310 unique callers to IVR system from a control village)c |
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50% of households recruited to the study will participate | 94.0% (173/184) of households participated | |
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mHealthd interventions | Either by listening to ≥1 mHealth item | 67.4% (124/184) listened to ≥1 mHealth item |
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Small group meetings | Or by attending ≥1 small group meeting | 78.3% (144/184) attended ≥1 meeting |
aFeasibility outcomes were computed using the intention-to-treat (ITT) sample of 387 households (184 IG and 203 CG). Uptake was computed using the ITT intervention group sample (184 households).
bSee flow diagram (
cSee
dmHealth: mobile health.
Uptake of the 3 intervention channels (mHealth vaccination reminders, mHealth edutainment capsules, and face-to-face small group meetings) differed among user segments (
The ownership of a mobile phone was common among IG households (173/184, 94.0%) and a critical precondition for uptake of both mHealth strategies. Owing to the very few (n=11) households without a mobile phone and the prognostic importance of this variable, effect size estimates for mobile phone ownership are unreliable. However, estimates for other variables are, in principle, unbiased:
mHealth audio vaccination reminders were accessed by 62.5% (115/184) of households. In addition to mobile phone ownership, 2 factors predicted higher uptake: high maternal education (secondary 9 years or higher vs none; OR 4.45, 95% CI 1.17-16.88;
mHealth edutainment capsules were accessed by 60.3% (111/184) of households. In addition to mobile phone ownership, intervention uptake was predicted by high (as compared with low) women’s empowerment (OR 3.29, 95% CI 1.28-8.47;
Face-to-face small group meetings were attended by 78.3% (144/184) of households. Living far from the meeting site reduced the uptake of small meetings (OR 0.07, 95% CI 0.02-0.33;
We also studied factors shaping the intensity of uptake. In modeled analyses, the number of mHealth items heard was influenced by 3 factors: mother’s possession of a mobile phone, mother’s ease of phone access, and women’s empowerment. The number of small group meetings attended was influenced by 2 factors: living far from the meeting site and women’s empowerment (
Determinants of mobile health intervention uptake.
Variablea,b,c,d | Vaccination reminders | Edutainment | |||||||
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OR (95% CI) | OR (95% CI) | |||||||
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Poorest (Q1; reference) | —e |
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— | — | ||||
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(Q2) | 0.51 (0.12-2.15) | .36 | 0.42 (0.12-1.52) | .19 | ||||
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(Q3) | 0.56 (0.21-1.51) | .26 | 0.78 (0.22-2.71) | .69 | ||||
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(Q4) | 1.15 (0.36-3.64) | .83 | 0.74 (0.28-1.93) | .54 | ||||
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Highest (Q5) | 0.43 (0.09-2.11) | .30 | 1.24 (0.40-3.92) | .71 | ||||
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General (reference) | — | — | — | — | ||||
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Other backward caste | — | — | 1.15 (0.36-3.67) | .81 | ||||
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Scheduled caste | — | — | 2.79 (0.95-8.21) | .06 | ||||
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None (reference) | — | — | — | — | ||||
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Primary | 0.80 (0.26-2.50) | .70 | 1.21 (0.53-2.79) | .65 | ||||
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Secondary or higher | 4.45 (1.17-16.88) | .03 | 1.95 (0.56-6.80) | .29 | ||||
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None (reference) | — | — | — | — | ||||
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Primary | 2.01 (0.60-6.70) | .26 | 1.15 (0.45-2.94) | .77 | ||||
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Secondary or higher | 2.01 (0.62-6.47) | .24 | 1.52 (0.52-4.44) | .45 | ||||
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Yes versus no | 23.90 (5.09-112.1) | .001 | 16.80 (4.27-66.18) | .001 | ||||
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Yes versus no | — | — | 0.29 (0.12-0.71) | .01 | ||||
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Yes versus no | 1.21 (0.5-2.61) | .64 | — | — | ||||
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Easy versus no access | 3.55 (1.08-11.71) | .04 | — | — | ||||
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No versus yes | 0.82 (0.27-2.55) | .74 | — | — | ||||
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Lowest (reference) | — | — | — | — | ||||
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Average | — | — | 0.96 (0.4-2.09) | .91 | ||||
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Highest | — | — | 3.29 (1.28-8.47) | .01 |
aAnalyses based on the intention-to-treat intervention group sample comprising 184 households.
bEstimates produced using Firth logistic regression with cluster bootstrapped standard errors (1000 iterations).
cWe present the full models implemented for each outcome. Potential determinants with no evidence of association at the
dCaste categories from most to least advantaged: general, other backward caste, and scheduled caste. The scheduled tribe category is missing, as there are no tribes in the study area.
e—: empty cells signify that variables were not included in models. Please see the
fHH: household.
Determinants of face-to-face intervention uptake.
Variablea,b,c,d | Small group meetings | ||||
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OR (95% CI) | ||||
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Poorest (Q1; reference) | —e | — | ||
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(Q2) | 0.67 (0.12-3.69) | .64 | ||
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(Q3) | 0.60 (0.11-3.28) | .55 | ||
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(Q4) | 0.50 (0.08-3.00) | .45 | ||
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Highest (Q5) | 0.68 (0.16- 2.88) | .60 | ||
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None (reference) |
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Primary | 0.62 (0.11-3.33) | .58 | ||
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Secondary or higher | 0.41 (0.04- 3.87) | .44 | ||
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None (reference) | — | — | ||
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Primary | 2.76 (0.32-23.70) | .35 | ||
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Secondary or higher | 2.84 (0.25-31.73) | .40 | ||
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No (reference) | — | — | ||
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Yes | 0.07 (0.02-0.33) | .001 | ||
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Yes versus no | 2.19 (0.6-8.03) | .24 | ||
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Yes versus no | 0.41 (0.08-2.12) | .29 | ||
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No versus yes | 1.79 (0.2-15.63) | .60 | ||
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Yes versus no | 0.69 (0.16-3.08) | .63 |
aAnalyses based on the intention-to-treat intervention group sample comprising 184 households.
bEstimates produced using the Firth logistic regression with cluster bootstrapped standard errors (1000 iterations).
cWe present the full models implemented for each outcome. Potential determinants with no evidence of association at the
dHH: household.
e—: empty cells signify that variables were not included in models.
Immunization knowledge was low at baseline in both study groups. For 3 of the 4 indicators studied, knowledge improved in the IG at end line (
For 8 of 9 intermediate outcomes, the regression results showed significantly higher basic health knowledge among the IG at end line (
Proportion of correct responses on intermediate outcomes related to immunization knowledge, by study group.
Outcomea | Baseline | End line | |||||
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Treated, n (%) | Control, n (%) | Treated, n (%) | Control, n (%) | |||
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Yes | 49 (26.6) | 70 (34.5) | .095 | 102 (66.7) | 74 (44.6) | <.001 |
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Correct | 2 (1.1) | 3 (1.5) | .734 | 30 (19.6) | 6 (3.6) | .001 |
“ |
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Correct | 21 (11.4) | 29 (14.3) | .400 | 42 (27.5) | 34 (20.5) | .144 |
“ |
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True | 112 (60.9) | 119 (58.6) | .652 | 110 (71.9) | 95 (57.2) | .006 |
aAll responses are binary.
b
cThis is self-assessed knowledge of the schedule from birth to 5 years.
dThe correct response is
eThe correct response is a vaccine dose.
fThe correct response is
Impact of the intervention on intermediate outcomes related to immunization knowledge.
Outcomea | Model 0b | Model 1c | ||
|
OR (95% CI) | OR (95% CI) | ||
Knows immunization schedule from birth to 5 years | 7.87 (1.90-32.49) | .004 | 8.40 (2.05-34.35) | .003 |
Knows how many times to vaccinate by age 5 | 3.52 (2.08-5.98) | .001 | 4.21 (2.25-7.85) | .001 |
“On the vaccination card, what does each box represent?” | 1.84 (1.12-3.03) | .016 | 2.00 (1.18-3.40) | .011 |
“Children with a minor illness should be vaccinated” | 1.53 (0.72-3.28) | .27 | 1.54 (0.71-3.34) | .27 |
aThese are differences-in-differences estimates of intervention impact.
bModel 0=unadjusted.
cModel 1=adjusted for variables imbalanced at the time of randomization (wealth index and cell network).
Estimated probability of correct responses for intermediate outcomes reflecting basic health knowledge, intervention group versus controls.
Outcomesa | Model 0b | Model 1c | |||||||
|
OR (95% CI) | OR (95% CI) | |||||||
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Has heard of | 4.60 (2.68-7.89) | .001 | 4.98 (2.89-8.56) | .001 | ||||
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Can state signs | 3.36 (1.58-7.13) | .002 | 3.67 (1.54-8.74) | .003 | ||||
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Can state how to prevent | 4.12 (1.94-8.74) | .001 | 5.09 (2.16-12.02) | .001 | ||||
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Has heard of | 1.24 (0.72-2.13) | .442 | 1.20 (0.74-1.96) | .456 | ||||
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Can state signs | 4.14 (1.64-10.44) | .003 | 2.81 (1.48-5.32) | .002 | ||||
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Can state how to prevent | 3.71 (2.06-6.67) | .001 | 3.82 (2.20-6.61) | .001 | ||||
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Has heard of | 3.80 (2.35-6.10) | .001 | 3.97 (2.57-6.13) | .001 | ||||
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Can state how it is transmitted | 3.61 (2.13-6.12) | .001 | 3.94 (2.45-6.31) | .001 | ||||
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Can state how to prevent | 3.30 (1.97-5.53) | .001 | 3.53 (2.19-5.67) | .001 |
aThese are basic health topics other than immunization, evaluated only at study end line.
bModel 0=unadjusted.
cModel 1=adjusted for wealth index, maternal education, paternal education, caste, and women’s empowerment.
We conducted a pilot trial of SBCC interventions focusing on immunization and other basic themes important for child and family health. Interventions were delivered through small face-to-face meetings and via mobile phones using pushed audio messages and other strategies suitable for disadvantaged populations. The pilot trial aimed to assess the feasibility of a future planned main study and to draw lessons to optimize delivery of the interventions at scale.
This paper offers 4 salient findings: First, all ex-ante feasibility criteria related to recruitment, randomization, retention, and contamination were satisfied, providing compelling evidence that the planned future main trial is feasible as planned. Uptake of interventions (adoption) was near universal (50% ex-ante vs 94% in practice), demonstrating strong interest and acceptability. Second, analyses of uptake and use demonstrated that intervention use was shaped by social determinants but that the chosen combination of strategies reached all population groups, even the most vulnerable. Third, constellations of determinants differed by intervention delivery channel. Ownership of a mobile phone was critical for participation in mHealth (vaccination reminders and edutainment) interventions, whereas distance from place of residence to the meeting site was important for small group meetings. mHealth vaccination reminders were taken up preferentially by more educated women and those with easy phone access within the household, whereas mHealth edutainment capsules were favored by more empowered women and by lower caste groups, for whom the content was likely novel and useful. Face-to-face meetings were the most equitable intervention channel; participation was equal or higher among those with greater needs. Women’s empowerment was an important transversal determinant, increasing uptake and intensity for all interventions. Fourth, we found that the study interventions lead to measurable improvements in basic health knowledge, supporting the potential for impact at scale. Changes in intermediate outcomes are consistent with the intervention theory of change.
At least five important caveats should also be considered. First, an important potential bias relevant for the future definitive trial relates to vaccination coverage assessment. As documented in our pilot study and elsewhere, the population denominator used in administrative estimates is often inaccurate (and the reported number of doses unreliable) [
We highlight 3 insights relevant to the future definitive trial and other studies: The first relates to adapting mHealth interventions for highly disadvantaged populations. In rural India, mobile phone penetration and infrastructure is increasing rapidly, reducing barriers to delivering mHealth interventions. Our experience demonstrates that mHealth interventions can achieve reach and improve knowledge even in highly underprivileged populations, but that technical delivery and content must be substantially adapted. Although mHealth interventions using SMS have shown promise [
A novel SBCC intervention model using face-to-face and mHealth approaches is feasible to evaluate in a future randomized trial and has the potential to strengthen the delivery of immunization and universal primary health care. The interventions achieved widespread reach in a highly disadvantaged population and showed early evidence of impact on participants’ knowledge, supporting the intervention theory of change. Behavior change communication via mobile phones proved viable and contributed to standardization and scalability. Face-to-face interactions remain necessary to achieve equity and reach, suggesting the need for ongoing health system strengthening to accompany the introduction of promising mobile phone technologies.
Supplementary methods and results.
Assistant Nurse Midwives
Accredited Social Health Activists
Anganwadi Workers
control group
Clinical Trials Registry–India
Government of India
generalized estimating equations
intervention group
intention-to-treat
interactive voice response
low- and middle-income country
mobile health
randomized controlled trial
social and behavior change communication
Sustainable Development Goals
United Nations
Uttar Pradesh
Village Health and Nutrition Day
water, sanitation, and hygiene
The authors gratefully acknowledge the support of IC-IMPACTS (the India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability), Grand Challenges Canada (Award # R-ST-POC-1707-06282), and the Canadian Institutes for Health Research (Award # 342296), who funded this study. The study sponsors played no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The authors would like to thank Aaditeshwar Seth, Rohit Singh, Vijay Sai Pratap, Sangeeta Saini, Dinesh Rautela, and the entire Gram Vaani team (Gurgaon, India) for expert leadership of the mHealth interventions. Valuable contributions to research design and instruments were made by Claudia Bojanowski, Alexandra Otis, Myriam Cielo Perez, and Dr Isabelle Michaud-Letourneau (Université de Montréal), and Frédérique Sauvé (McGill University). Dr Bhupendra Tripathi (Bill & Melinda Gates Foundation) has been a constant source of guidance and encouragement. At the state and district levels, the authors would like to thank Mr Pankaj Kumar (Mission Director, National Health Mission, UP), Dr Arun Chaturvedi (State EPI Officer, UP), Dr Ved Prakash (General Manager, Routine Immunization, UP), Dr SK Rawat (Chief Medical Officer, Hardoi), Dr PN Chaturvedi (Former Chief Medical Officer, Hardoi), Dr Vijay Singh (District Immunization Officer, Hardoi), Dr Prakash Kumar Chaurashiya (Former District Program Officer, Hardoi), Mrs Buddhi Mishra (District Program Officer, Hardoi), Mr CK Shrivastav (Assistant Program Officer, Hardoi), Dr Prabhakar Tripathi (Medical Officer In-charge, Bawan Block, Hardoi), and Ms Gayatri Tiwari (Block Program Manager, Bawan Block, Hardoi). Study interventions were codeveloped and implemented in partnership with communities. The authors thank Mr Ajit Solanki (Chief Executive Officer of Jagriti Foundation) and the entire
Finally, the authors express heartfelt gratitude to the residents of Hardoi district for collaborating to improve basic health conditions for their children, families, and communities.
MJ designed the study, acquired the funding, performed the statistical analyses and wrote the draft manuscript; DC contributed to intervention design, oversaw study implementation, contributed to data management, and revised the manuscript; GK had primary responsibility for data management, contributed to the statistical analyses, and revised the manuscript; MPS, AM, SH, and AN advised on study methodology and revised the manuscript.
All authors gave final approval of the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
None declared.