Lack of ownership of mobile phones could hinder the rollout of mHealth interventions in Africa

  1. Justin T Okano
  2. Joan Ponce
  3. Matthias Krönke
  4. Sally Blower

  • Curated by eLife

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    Evaluation Summary:

    This study used 2017-2018 Afrobarometer surveys of more than 45,000 individuals to examine the association between the ownership of mobile phones and proximity to a health clinic in 33 African countries. Findings show that about 40% of people own smartphones and those who live closer to health clinics are more likely to own a mobile phone. This manuscript will be of interest to all people who are involved in the design and implementation of mHealth interventions in Africa.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their names with the authors.)

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Abstract

Mobile health (mHealth) interventions, which require ownership of mobile phones, are being investigated throughout Africa. We estimate the percentage of individuals who own mobile phones in 33 African countries, identify a relationship between ownership and proximity to a health clinic (HC), and quantify inequities in ownership. We investigate basic mobile phones (BPs) and smartphones (SPs): SPs can connect to the internet, BPs cannot. We use nationally representative data collected in 2017–2018 from 44,224 individuals in Round 7 of the Afrobarometer surveys. We use Bayesian multilevel logistic regression models for our analyses. We find 82% of individuals in 33 countries own mobile phones: 42% BPs and 40% SPs. Individuals who live close to an HC have higher odds of ownership than those who do not (aOR: 1.31, Bayesian 95% highest posterior density [HPD] region: 1.24–1.39). Men, compared with women, have over twice the odds of ownership (aOR: 2.37, 95% HPD region: 1.96–2.84). Urban residents, compared with rural residents, have almost three times the odds (aOR: 2.66, 95% HPD region: 2.22–3.18) and, amongst mobile phone owners, nearly three times the odds of owning an SP (aOR: 2.67, 95% HPD region: 2.33–3.10). Ownership increases with age, peaks in 26–40 year olds, then decreases. Individuals under 30 are more likely to own an SP than a BP, older individuals more likely to own a BP than an SP. Probability of ownership decreases with the Lived Poverty Index; however, some of the poorest individuals own SPs. If the digital devices needed for mHealth interventions are not equally available within the population (which we have found is the current situation), rolling out mHealth interventions in Africa is likely to propagate already existing inequities in access to healthcare.

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  1. Version published to 10.7554/elife.79615 on eLife
  2. eLife

    Author Response

    Reviewer #3 (Public Review):

    The work is of general interest to audiences of public policy and public health. The data found some evidence that mobile health interventions may be affected by the type of mobile used but failed to substantiate the claim conclusively on how the lack of mobile ownership may hinder their rollout process. The claim about gender or geographic inequality must be elaborated in detail and many countries in developing countries are now connecting more users in rural areas through unconventional methods such as village phones instead of just mobile ownership.

    Strengths:

    The main strength of this paper is the usage of the cross-sectional data from the R7 Afrobarometer survey which is a newly available dataset and contains comprehensive data from more than 50 African countries. The usage of the Bayesian Logistic Regression (BLR) model produced some useful findings.

    Weakness:

    1. The authors have generalized a lot of things in a very simple manner. For example, they have assumed if participants have access to the internet means they own a smartphone and if they don't then they are basic phone users. It is possible a lot of smartphone owners do not have subscriptions to the internet due to the high cost of internet in African countries.

    We agree with the Reviewer that some smartphone owners may not have access to the internet due to the high cost of internet in African countries. Therefore, to estimate the percentage of SP owners who may not pay to access the internet, we looked at the frequency of access to the internet within this sub-group (Methods: lines 133-138). In the Afrobarometer surveys, participants were asked how often they accessed the internet; they were not asked to specify how they accessed the internet. We analyzed these data, stratified on the basis of the type of mobile phone that we assumed individuals owned (we assumed that an individual owned a smartphone if they reported that their mobile phone could access the internet, and that an individual owned a basic mobile phone if they reported that their mobile phone could not access the internet).

    Notably, we found that only 13% of individuals that we classified as SP owners (and 89% of individuals that we classified as owners of BP) reported that they never accessed the internet. We now include the results of this analysis in our revised manuscript (Results: lines 219-221); they are presented in Figure 1—figure supplement 2.

    Additionally, we now mention that in order to implement mHealth interventions that are based on smartphones, individuals will need to both own a smartphone and have financial means to access the internet.

    1. They have consistently talked about inequalities in gender, and rural-urban geographic regions based on the odds ratio derived from the BLR. A regression decomposition technique can quantify these differences more elaborately in detail.

    The purpose of our study was to determine – for 33 African countries – what proportion of people owned mobile phones (basic phones & smartphones) in each country, and if there were inequalities/inequities in the ownership of mobile phones based on: (i) gender, (ii) age, (iii) urban-rural residency, (iv) wealth, and (v) distance to a healthcare facility.

    We found a high ownership of mobile phone ownership that our results show varies substantially amongst the 33 countries. Additionally, by conducting a Bayesian Logistic Regression we have found that there are significant inequalities/inequities in all five variables. Additionally, we have identified substantial differences in the degree of these inequities in the 33 countries.

    We agree with the Reviewer that we have not explained why these inequalities exist, and that we could use a regression decomposition analysis to identify explanatory factors. We note that this is the next stage, and current focus, of our research. This next stage requires constructing new statistical models – and utilizing a different dataset – than the models that we present and the dataset that we utilize in our submitted manuscript. Consequently, conducting a regression decomposition analysis is beyond the scope of the present study: it will be an article in its own right.

    However, in response to this Comment, we have now added a description of potential factors that may explain inequalities in gender and rural-urban geographic regions (Discussion: lines 328-339). These factors have been identified in previous studies.

    1. They failed to explain why a lot of poor people own smartphones. This could be due to the usage of village phones (first implemented by Grameen Phone in Bangladesh). This has expanded in African countries as well where multiple users communicate through a community phone connecting more users in rural areas.

    We agree with the Reviewer. We now discuss the utilization of village phones in Africa, as well as other explanatory reasons for why a lot of poor people own smartphones (Discussion: lines 339-354).

    1. Basic phones may also be effective for mobile health interventions through voice-enabled systems and disseminating important messages to communities. (For e.g. there is extensive literature on how community-level messages, such as instructions on personal hygiene and usage of masks, were transmitted through basic phones during the beginning of covid19 in developing parts of Asia).

    We agree with the Reviewer that basic mobile phones may also be effective for mHealth interventions through voice-enabled systems and disseminating important messages to communities. We have added a paragraph (Discussion: lines 370-396) to discuss current mHealth interventions that are being utilized in Africa, including both those based on smartphones and those based on basic mobile phones.

    1. Further clarification of why lack of ownership of a mobile phone may propagate inequalities in health is needed beyond just simple associations. A latent factor may also cause these differences.

    We have added a paragraph (Discussion: lines 356-368) to discuss this topic.

  3. eLife

    Evaluation Summary:

    This study used 2017-2018 Afrobarometer surveys of more than 45,000 individuals to examine the association between the ownership of mobile phones and proximity to a health clinic in 33 African countries. Findings show that about 40% of people own smartphones and those who live closer to health clinics are more likely to own a mobile phone. This manuscript will be of interest to all people who are involved in the design and implementation of mHealth interventions in Africa.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their names with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    The objective of mobile phone (mHealth) interventions in African countries is to cost-effectively increase access to care and improve health. Due to resource constraints on the healthcare systems in many African countries, inaccessibility to healthcare is more noticeable in rural areas. While there is an increase in mHealth interventions in many African countries, it is salient to examine inequity in the distribution of smartphones that enable these interventions.

    Investigators used the 2017-2018 Afrobarometer data from 33 countries to estimate the percentage of the population with a mobile phone (smartphone or otherwise). The analyses were conducted at different levels: (1) among all 33 countries; (2) at the country level; and (3) at the sub-national level (within each country).

    The study is well designed, and the manuscript is clearly written. The findings are important from a policy and intervention perspective. This study shows that there are substantial inequities in smartphone ownership between and within African countries. These results have important implications for designing and rolling out mHealth interventions in African countries. This study shows that people who live in rural areas are less likely to own a smartphone and less likely to live close to a healthcare center. For mHealth intervention to work, individuals who are in high need of mHealth interventions would need to own mobile phones.

  5. eLife

    Reviewer #2 (Public Review):

    This work demonstrated inequities in basic mobile phone and smartphone ownership, with further analysis of discrepancies with respect to gender, distance to a hospital center, and urban/rural settings throughout 33 different countries in Africa. As telehealth innovations have increasingly become interventions of interest to increase access to care in rural and/or impoverished areas globally, it is imperative to understand the existing technological circumstances in the target area. This study demonstrated that while a majority of individuals own mobile phones, most do not own a smartphone, which would enable the greatest mHealth intervention. Importantly, there is significant variability in mobile phone and smartphone ownership throughout the countries analyzed. Significant additional findings include a gender disparity where men vs women, those in urban vs rural areas, those living in closer proximity to an HC vs those farther, and certain age groups have greater odds of owning a mobile phone.

    The data in this paper provide greater insight into the existing disparities that should be addressed for mHealth interventions in Africa, emphasizing the need to focus on region-specific issues with a focus on gender, age, and existing healthcare access. While studies have demonstrated similar concerns for exacerbation of inequities with poorly-planned mHealth interventions, others have focused on smaller, more specific target populations (Doyle et. al JMIR 2021; Kazi et.al Int. J. Med. Inform 2021). These findings add to the understanding of the broad technological landscape in Africa.

    This dataset encompasses a large sample size in countries representing the majority of the African population. The statistical analysis was meticulous and enabled insight into the nuances and variations within each country and region. The visualization of the data is strong, effectively and efficiently communicating the primary takeaways of this paper. The authors appropriately note that their findings do not demonstrate greater healthcare access and/or health outcomes in mobile phone or smartphone owners.

  6. eLife

    Reviewer #3 (Public Review):

    The work is of general interest to audiences of public policy and public health. The data found some evidence that mobile health interventions may be affected by the type of mobile used but failed to substantiate the claim conclusively on how the lack of mobile ownership may hinder their rollout process. The claim about gender or geographic inequality must be elaborated in detail and many countries in developing countries are now connecting more users in rural areas through unconventional methods such as village phones instead of just mobile ownership.

    Strengths:

    The main strength of this paper is the usage of the cross-sectional data from the R7 Afrobarometer survey which is a newly available dataset and contains comprehensive data from more than 50 African countries. The usage of the Bayesian Logistic Regression (BLR) model produced some useful findings.

    Weakness:

    1. The authors have generalized a lot of things in a very simple manner. For example, they have assumed if participants have access to the internet means they own a smartphone and if they don't then they are basic phone users. It is possible a lot of smartphone owners do not have subscriptions to the internet due to the high cost of internet in African countries.

    2. They have consistently talked about inequalities in gender, and rural-urban geographic regions based on the odds ratio derived from the BLR. A regression decomposition technique can quantify these differences more elaborately in detail.

    3. They failed to explain why a lot of poor people own smartphones. This could be due to the usage of village phones (first implemented by Grameen Phone in Bangladesh). This has expanded in African countries as well where multiple users communicate through a community phone connecting more users in rural areas.

    4. Basic phones may also be effective for mobile health interventions through voice-enabled systems and disseminating important messages to communities. (For e.g. there is extensive literature on how community-level messages, such as instructions on personal hygiene and usage of masks, were transmitted through basic phones during the beginning of covid19 in developing parts of Asia).

    5. Further clarification of why lack of ownership of a mobile phone may propagate inequalities in health is needed beyond just simple associations. A latent factor may also cause these differences.

  7. Version published to 10.1101/2022.05.10.22274883v1 on medRxiv