https://orcid.org/0000-0002-5847-1425
Figures
Abstract
Introduction
Vaccine-preventable diseases are the public health problems in Africa, although vaccination is an available, safe, simple, and effective method prevention. Technologies such as mHealth may provide maternal access to health information and support decisions on childhood vaccination. Many studies on the role of mHealth in vaccination decisions have been conducted in Africa, but the evidence needs to provide conclusive information to support mHealth introduction. This study provides essential information to assist planning and policy decisions regarding the use of mHealth for childhood vaccination.
Methods
We conducted a systematic review and meta-analysis for studies applying mHealth in Africa for vaccination decisions following the Preferred Reporting Items for Systematic and Meta-Analysis [PRISMA] guideline. Databases such as CINAHL, EMBASE, PubMed, PsycINFO, Scopus, Web of Science, Google Scholar, Global Health, HINARI, and Cochrane Library were included. We screened studies in Endnote X20 and performed the analysis using Revman 5.4.1.
Results
The database search yielded 1,365 articles [14 RCTs and 4 quasi-experiments] with 21,070 participants satisfied all eligibility criteria. The meta-analysis showed that mHealth has an OR of 2.15 [95% CI: 1.70–2.72; P<0.001; I2 = 90%] on vaccination rates. The subgroup analysis showed that regional differences cause heterogeneity. Funnel plots and Harbord tests showed the absence of publication bias, while the GRADE scale showed a moderate-quality body of evidence.
Conclusion
Although heterogeneous, this systematic review and meta-analysis showed that the application of mHealth could potentially improve childhood vaccination in Africa. It increased childhood vaccination by more than double [2.15 times] among children whose mothers are motivated by mHealth services. MHealth is more effective in less developed regions and when an additional incentive party with the messaging system. However, it can be provided at a comparably low cost based on the development level of regions and can be established as a routine service in Africa.
Citation: Gilano G, Sako S, Molla B, Dekker A, Fijten R (2024) The effect of mHealth on childhood vaccination in Africa: A systematic review and meta-analysis. PLoS ONE 19(2): e0294442. https://doi.org/10.1371/journal.pone.0294442
Editor: Olushayo Oluseun Olu, World Health Organization, SOUTH SUDAN
Received: June 2, 2023; Accepted: October 31, 2023; Published: February 21, 2024
Copyright: © 2024 Gilano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: mHealth, Mobile Health; RD, Risk Difference; WHO, World Health Organization; UNICEF, United Nations International Child Emergency Fund; OR, Odds Ratio; USA, United States of America
Introduction
Vaccination is the safest, simplest, and most effective strategy to confer immunity against nearly 20 childhood diseases [1]. Vaccines signal the body to prepare against diseases before they come in contact with them and can save millions of lives [2].
As stipulated by the Centers for Disease Control and Prevention [CDC], childhood vaccination prevents 4 million deaths every year. This is shown by the global reduction of deaths due to vaccination-preventable diseases from 12.5 to 5.3 million from 1990 to 2018 [3]. Vaccination is expected to save 14 and 19 million children from Hepatitis B and measles, respectively. However, vaccination coverage is decreasing currently and was only 81% in 2021 throughout the globe [4]. Consequently, 25 million more children do not receive vaccination routinely, and 18 million receive zero doses [1, 2, 4]. Due to limited childhood vaccination, Africa has a high child mortality rate of 1 in 9 compared to 1 in 199 in developed countries [3].
According to the 2021 World Health Organization [WHO] report, the high child mortality in Africa is attributable to vaccine-preventable diseases in more than half of cases [5, 6]. The reasons for this are manifold, but people living in the Sub-Saharan region have high missed vaccination opportunities because of individual factors, for instance, sociodemographic variables, family’s financial capacity, place of birth, and upbringing [7]. This means individuals and combined factors contribute to the low vaccination rates and associated high childhood mortality.
In recent years, there has been a rapid increase in applications of mHealth to increase the rate of childhood vaccination in many countries throughout the globe [8–10]. Africa is one of the continents where mHealth has been tested as an intervention to improve vaccination rates [9, 11, 12]. In Ethiopia, completeness and timeliness of vaccination were improved through text message reminders [12] and the number of revisits for vaccination increased in Tanzania because of Short Message Service [SMS] text reminders [13]. Additionally, a mobile phone-based platform improved the childhood vaccination rate in Kenya [14]. At the same time, another study in Nigeria showed an enhancement in the overall number of childhood vaccinations due to SMS reminders [9]. Furthermore, other studies showed an increased number of vaccination visits due to mHealth in Benin [15], Kenya [11, 16], Burkina Faso [17], Nigeria [18], Ghana [19], and Zimbabwe [20]. The above evidence shows reminders and vaccination information, which can be delivered through mobile phones, can potentially improve childhood vaccination [21].
Many challenges in Africa affect routine vaccinations and the application of mHealth [22, 23]. Africa is behind in the global vaccination rate for different reasons. For instance, maternal education of any level [primary, secondary, or tertiary] [21], and political instability through civil war [24]. Dependency on unsustainable multinational foreign aid, which usually leads to unprecedented financial restraints [25], low uptake of vaccination in Muslim families, which represent a significant part of African populations [26], and lack of transportation to health centers in rural areas are some of the challenges in addition to economic factors and false beliefs regarding vaccines safety [27–29].
The mHealth-related challenges include poor usability, lack of system integration, poor data security and privacy, poor network access, and poor reliability [30]. In addition, other challenges such as low literacy, cultural constraints to accept mHealth, and healthcare-seeking behavior are prominent in Africa [15, 19, 31, 32]. Additional challenges are related to poor standardization, a challenging regulatory framework, and poor overall health system readiness [33]. These findings indicate that the implementation of mHealth in Africa needs multifactorial considerations.
Facilitators of childhood vaccination include positive clinical encounters, free vaccination service policy, optimal vaccine, and device supply chain system, adequate knowledge of vaccination benefits and efficacy, vaccination outreaches, and provision of incentives to caregivers [34, 35]. Another study identified early childhood vaccination facilitators’ rural communities as a reminder/recall system and positive parents’ relationships with providers [36].
In conclusion, there are many challenges and opportunities regarding the application of mHealth to improve childhood vaccination. In the presence of challenges, the existing evidence is inconclusive and inconsistent if mHealth can improve childhood vaccination in challenging African contexts. For this, we summarized barriers and facilitators of mHealth to improve childhood vaccination and provided conclusive evidence. The summarized evidence can support the implementation of mHealth to improve vaccination rates and thus support planning and policy decisions on mHealth.
Methods
Study design
A systematic review and meta-analysis informed by Preferred Reporting Items for Systematic and Meta-analysis [PRISMA] standard guideline was employed.
Inclusion criteria
The included have the following features:
- Studies published from 2000 to 2023 were considered as mHealth is a newly emerging technology in Africa
- Population: women with children aged <36 months from African countries
- Interventions: used mHealth as an intervention [all types of service such as voice calls/message, video call/message, and text messages delivered through mobile phone]. The studies may or may not have other interventions to mHealth, and our study targets the mentioned interventions only.
- Comparison: studies with groups of mothers who do not receive mHealth promotion to take and use the usual care or standard care without any interference
- Outcome: primary endpoints of rate, coverage, timeliness, and vaccination completion compared with controls receiving standard care.
- The secondary outcome was the difference among sample sizes, regions, and designs.
- Designs: all trials, experimental and longitudinal cohort studies reported in the English language
Exclusion criteria
Studies that
- Lack of clarity in the method section [unknown design]
- Are study protocols and review articles
- Have a baseline difference between groups
Search strategy and information source
We conducted a multi-step search strategy informed by the eligibility criteria outlined above. The databases included are CINAHL, EMBASE, PubMed, PsycINFO, Scopus, Web of Science, Google Scholar, Global Health, HINARI, and Cochrane Library. Additionally, the following source was explored: WHO website, mHealth Alliance, arxiv, AAS Open Research, Advance: a SAGE Preprints Community, AfricArxiv, AMRC Open Research, and International Development Research Centre reports, profit and nonprofit organizational websites, and the WHO International Clinical Trials Registry Portal [37].
Key search terms.
Telehealth, telemedicine, telenursing, mobile app*, remote consultation, cellular phone, mHealth, wireless communication, mobile technology*, smartphone app*, vaccination, Immunization, child, and childhood
The search strategies for some databases were provided as a supplemental material [S1 File].
Two independent investigators who followed the PRISMA guidelines searched all articles that fulfilled the eligibility criteria.
Study selection, quality appraisal, and data extraction
Endnote X20 was used to organize the articles and remove duplicates. After removing duplicates, the remaining papers were passed through title, abstracts, and full-text screening. A third observer decided on any disagreement between the two screening observers. The Joanna Briggs Institute [JBI] critical appraisal checklist was used to check the qualities of articles by informing eligibility criteria [38]. Two separate investigators conducted the final review and determined bias using the Cochrane tool [39], such as random sequence generation, allocation concealment, blinding of participants, blinding of personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias. Using this, all studies were classified as having low, unclear, or high bias. The author’s name, year of publication, year of study, study design, study area, response rate, sample size, study quality score, and percentage were captured in a spreadsheet. The authors of the studies were contacted for any data that needed to be included.
We conducted database and manual searches between 23 January and 4th of March 2023. We found 1365 mother-children pair studies that were further assessed for eligibility criteria. Of these, 416 were excluded due to duplications. Then 949 studies were enrolled, and 866 were excluded by title or abstract. We excluded 36 studies by accessibility full-text. The included studies were further screened, and studies were additionally excluded due to ineligible continent [n = 11], population [n = 5], design [n = 2], methods [n = 1], reviews [n = 6], and protocols [n = 4] [Fig 1].
Ethical approval and consent to participate
This study is a systematic review and metanalysis based on the protocol registered in PROPERO. The registration number is PROSPERO: CRD42023415956. Using these does not have ethical risks. As part of a big project on the effect of mobile health on maternal and child health service uptaking, Arba Minch University IRB approved its awareness (AMU-IRB/1326/2022). All steps and activities are performed according to the available international guidelines.
Statistical methods and analysis
The Revman 5.4.1, STATA 14, Comprehensive Meta-Analysis [CMA] interval prediction, and GRADEPro online software were applied for a detailed examination of the effect size, and the childhood vaccination data were presented using Forest Plots. The heterogeneity [I2] and the between-studies variance [τ2] were quantified. We applied a p-value of less than 0.05 to declare associations [40]. The initial p<0.1 and I2>50% decide the selection [random or fixed effect]. Subgroup analysis was conducted considering sample size, region, and design. The Egger regression asymmetry test and the Cochrane Collaboration Risk of Bias [CCRB] were used to check publication bias at p<0.05 [41], In contrast, missing studies were estimated using Duval and Tweedie’s ‘trim and fill method [42]. We used flow diagrams to display PRISMA steps, funnel plots to display publication biases, graphs to display the risk of bias, and forest plots to show effect size.
Analyses of sensitivity
We checked the effect or influence of specific studies on the mean effect size and used a subgroup analysis to examine the observed heterogeneity. Model selection, risk of bias criteria, and loss to follow-up ≤25% were also considered [43].
Results
Characteristics of the included studies
Eighteen articles fulfilled the eligibility criteria out of 1,365 screened studies. Table 1 summarizes some characteristics of the included studies. The included studies account for a total sample size of 21070 [11246 mHealth and 9824 usual care], of which 11861 [6686 interventions and 5175 controls] used childhood vaccination [59.45% versus 52.67%]. Of 18 articles, only four are quasi-experimental studies [9, 44–46], while the remaining 14 are randomized controlled trials [11, 12, 16, 17, 20, 47–54]. Nigeria accounts for most studies [n = 8] [9, 18, 45, 46, 48, 51, 53, 54] followed by Kenya [n = 3] [11, 16, 47], Ethiopia [n = 2] [12, 50], Zimbabwe [n = 1] [20], South Africa [n = 1] [44], Ghana [n = 1] [49], Côte d’Ivoire [n = 1] [52], and Burkina Faso [n = 1] [17]. Nine studies were conducted in urban, seven in rural, and two in mixed settings [rural and urban]. The study population is mostly mother-child pairs except three caregivers included studies [Table 1] [11, 20, 54].
Assessing the risk of bias
Fig 1 summarizes the quality assessment parameters of the included studies. Of 18 studies, 15 showed no selection bias [randomization and allocation], while two had high concerns and one had some concerns [9, 46]. Seven studies were rated to have high or some concerns regarding blinding of participants, researchers, and outcome assessors [11, 12, 16, 17, 44, 45, 48], five had incomplete outcome data biases [11, 20, 44, 51, 52], and four had other biases [Table 2] [17, 44, 46, 54] [Fig 2]. Based on the Cochrane risk of bias judgment tool, the risk of bias observed is presented [Fig 3].
Childhood vaccination
Except for one study [50], all mHealth studies showed an improvement in childhood vaccination outcomes compared to usual care. Some studies had additional intervention arms, among which three had incentive intervention arms [11, 16, 49]. In all cases, monetary incentives groups had significantly better vaccination rates than SMS alone. However, the SMS intervention indicated pentavalent, polio, and measles vaccination improvement. The average effect size using the random-effects model [OR = 2.15 [95% CI: 1.70–2.72; P<0.001; I2 = 90%; RD = 0.11, 95%CI: 0.07–0.16]. The confidence interval of this effect size does not perfectly overlap the actual effect size [0.81, 5.74] [Fig 4], which might show some uncertainty in the estimation of the effect size [Fig 4]. Despite these variations, mHealth has been shown to significantly improve childhood vaccination completion compared to standard of care [Fig 5].
Sensitivity test
The mean heterogeneity [I2] of 90% and tau2 of 0.20 indicate that the effect size significantly varies. A one-by-one exclusion sensitivity analysis shows that the results are stable, and no study causes an excessive change by its presence or absence in the analysis and does not influence the effect size significantly. An examination of the student zed residuals revealed that none of the studies had a value greater than ± 2.9913, and our review does not indicate outliers. According to Cook’s distances, none of the studies could be considered overly influential. Neither the rank correlation nor the regression test showed any funnel plot asymmetry [Fig 6]. The Harbord test also suggested the absence of publication bias [P = 0.591].
Certainty of the evidence
We checked the quality of evidence for this systematic review and meta-analysis with the GRADEpro online application on a four-grade scale of very low, low, moderate, and high [Fig 7]. The overall quality of the body of evidence is found to be reasonable [Fig 8]. The meta-regression was conducted to see the impact of variables on the pooled effect size. The effect of each sample size and follow-up time were regressed on the effect size and were not statistically significant in indicating the reverse relationships [p = 0.947 for follow-up time and p = 0.960].
Subgroup analysis
Analysis by regions.
A subgroup analysis by African regions [Western, Southern, and Easter] indicates that there is a significant effect on the application of mHealth to the improvement of childhood vaccination [OR = 2.15; 95%CI: 1.70–2.72; p = 0.22; I2 = 34.7%]. It showed that regional variation is a source of outcome heterogeneity [Fig 9].
Eight studies reported pentavalent-3 vaccine as an outcome and showed a significant pentavalent-3 vaccination improvement among children [OR = 2.21, 95% CI: 1.62–3.02; P<0.002; I2 = 81%; RD = 0.11, 95%CI: 0.07–0.15] with variation among outcomes [Fig 10]. It has a higher risk difference [RD] that is equal to the RD of the overall analysis. This shows a high variation in pentavalent outcomes [Fig 10].
Three studies reported polio vaccination and showed a significant change in mHealth intervention groups compared to usual care [OR = 2.27; 95%CI: 1.51–3.40; p<0.30; I2 = 18] [Fig 11].
Six studies also reported Measles vaccination as an outcome and showed a significant improvement through the mHealth intervention [OR = 2.19, 95% CI: 1.63–2.96; P<0.001; I2 = 80%] [Fig 12].
Additionally, three studies used incentive [monetary] and reported significant improvement in childhood vaccination [OR = 1.59, 95% CI: 1.22–2.08; P<0.47; I2 = 0%] compared to controls and SMS-only intervention [Fig 13].
Discussion
This review showed that the overall childhood vaccination is 2.15 times higher among mHealth-used groups. Eight studies sought the effect of mHealth on pentavalent [penta-3] vaccination in African countries [9, 11, 12, 20, 46, 47, 52, 53]. All these studies reported a significant improvement in penta-3 vaccination rates positively. Previous systematic reviews reported consistent findings in childhood penta-3 vaccination [55, 56]. This evidence suggests that mHealth implementation should be considered to improve pentavalent vaccination in African countries [Fig 10]. Four studies with poliovirus vaccination outcomes reported that mHealth interventions significantly improved childhood polio vaccination rates [9, 20, 44, 49]. Other evidence shows that the mHealth intervention improves polio vaccination rates along with other vaccinations and indicates the potential success of mHealth implementation [55–57] [Fig 11]. The six studies that reported mHealth application to improve measles vaccination showed a significant improvement in measles vaccination rates [9, 11, 12, 16, 52, 53]. Other systematic reviews and meta-analysis studies also showed the potential of mHealth in improving measles vaccination [55, 56]. Overall, childhood measles vaccinations were enhanced with the application of mobile technology in Africa [Fig 12].
In addition to reminders, three studies [11, 16, 49] sought improvement in childhood vaccination using monetary incentives. In all cases, financial incentives improved vaccination revisits among the mothers who received economic incentives for their last visits compared to the usual care. Although there is no previous systematic review evidence on financial-based studies, the current information indicates that this will improve childhood vaccination rates [Fig 13]. Using mHealth was stated to be cheaper, cost-saving, and life-saving mechanism [58]. Evidence suggests that incentives might provide an additional dimension to mHealth to get more effect [11]. However, applying low-cost simple mobile phones to use mHealth has been as effective as using incentives while incentives are additional costs for the governments and might not be sustainable in low-economy regions [59].
Overall, the timeliness, rate, and dropout rate of vaccinations improved through mHealth assistance. However, there is a challenging heterogeneity in the observed effect size, which led us to examine settings, sample size, and varying duration of experiments. Socio-demographic challenges such as educational status and living conditions may also contributed to heterogeneity [54, 60]. The subgroup analysis by region showed that there is a substantial variation in effect size among regions. All diagnostic methods applied to identify the heterogeneity showed that none of the included studies are overly influential and underlines the stability of the findings. One of the studies among the 18 appeared to have a contradicting finding after text reminder intervention in Ethiopia [50]. However, examining the evidence showed that mothers have many reasons for reduced visits for childhood vaccination, including waiting time, as caregivers are too busy, the absence of health professionals to provide vaccination on specific days, and the lack of the vaccine itself on some occasions. This indicates that the study in Ethiopia was conducted in a more challenging environment. It was also noticed that resource limitations such as the limited number of service providers and limited access to vaccine resources could still hinder childhood vaccination even in the presence of successful simple technologies such as mHealth.
The effect of mHealth on childhood vaccination is among the winning ideas throughout the globe especially, in developing countries. There is a positive relationship between childhood vaccination and mobile health applications globally [21, 32, 58, 61, 62]. Africa as one of the underdeveloped continents has the highest rate of unvaccinated children. A new UNICEF report shows that 12.7 million children were under-vaccinated in 2021, including 8.7 million who never started vaccination [zero-dose children] [63]. The application of mHealth has shown a potential implication of increasing childhood vaccination in the African continent [8, 14, 64]. The lack of summarized evidence on the effect of mHealth on childhood vaccination in the continent was covered in this review. Thus, this review is current, and comprehensive to include previous, and the most current evidence to assist planning and policy decisions.
The strengths and limitations of this study
This review’s main strength is extracting, presenting, and evaluating the evidence using standard guidelines. Publication bias, quality of evidence, and source of variation of outcomes were addressed using the guidelines. However, there are still some limitations. Many studies are from Western and Eastern Africa, while the other regions contribute markedly fewer studies. Additional studies have either an unclear or a high risk of bias, such as blinding participants, researchers, data collectors, and loss of follow-up. This increases the risk of overall biases in outcome evaluation. However, GRADEPro [Fig 7], which we used to examine the quality of the body, considered the existence of those issues and concluded that bias is not the concern.
Conclusions
The application of mHealth increased childhood vaccination by more than double among participants in the included studies in Africa. This shows a high potential of improving childhood vaccination in the African continent. Although there are some variations based on the level of development of regions and the inclusion of additional incentives in some studies, it is still productive in improving childhood vaccination in all regions. It improved coverage, timeliness, and completeness and reduces the dropout rates aspect of the vaccination. Based on this evidence the Global Vaccine Action Plan, which states all individuals and communities should enjoy life that is free from vaccine-preventable diseases [65], can be achieved by applying mHealth in Africa. This is also an opportunity that the WHO African region plans, which focus on increasing vaccination coverage, interrupting poliovirus transmission, eliminating measles, and controlling other vaccine-preventable diseases [66], can be achieved by applying mHealth. Additionally, this review contains conclusive evidence that can support continental and international efforts to use mHealth to improve vaccination.
Acknowledgments
The authors are thankful to Maastricht University for providing support to access various databases in the review process.
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