Big Data analytical implications for programming on Adolescent Sexual Health and Reproductive Rights - RADAF
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Big Data analytical implications for programming on Adolescent Sexual Health and Reproductive Rights

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Big Data analytical implications for programming on Adolescent Sexual Health and Reproductive Rights

BY SANYA SRIVASTAVA   16 October 2020

Big data is an all-inclusive approach to managing, processing and analysing huge volumes of data for providing insights for better decision making power (Wamba et al., 2015); in many cases, big data applications have assisted in reducing the cost of healthcare (Akinnagbe, et al., 2018). Information and Communications Technology (ICT) and data-driven approaches to the development of a robust health information system, and by extension, welfare, prove to be solutions to these inequities and exclusions within SRHR. Although application of big data and business intelligence is still in its infancy in Botswana; big data analytics is emerging in Botswana and can be a big weapon to improve information dissemination and access to healthcare with respect to SRHR.

Africa (statistics relating to Botswana were not available; from here on Africa is being used as a proxy for Botswana as an implicit assumption) as a continent has taken the lead in the worldwide shift from fixed phones to mobile phones (Essoungou, A.-M., 2010), making it easier to collect data on people’s behaviour. With the increase in the use of social media and mobile phones in Africa, there is a crucial need to increase the effort on how to use big data emanating from social media network and mobile phones to facilitate systemic changes.

Implications of integrating Big Data Analytics

  • The move towards data-driven development and the integration of ICT and big data technologies in the ‘advancement’ SRHR is to digitise the supply side of reproductive services while continuing the use of market-driven language, government officials position technology as the way to bridge the gap between the demand (unmet need for reproductive services) and supply of these services.
  • Data gathered from increased use of social networks (Facebook, Twitter, WhatsApp, WeChat etc.), mobile devices and the internet can be used for disease surveillance and to understand the narrative on SRHR. This data will be helpful in gauging conversations around SRH issues/ relationships, knowledge on sex, pregnancy and contraception to pin point gaps and leverage evidence to drive policy. Additionally, by collecting data on maternal health in the ante-natal and postnatal periods, these initiatives can improve accessibility, improve quality of care, and create more complete government databases to monitor family planning and population control.
  • Bringing in Artificial Intelligence in program interventions communicates reproductive and sexual health information to adolescents. The Artificial Intelligence (AI) design can be in the form of a digital avatar who intends to be a virtual companion for young rural women. This may be advantageous in contexts where consulting with others is highly stigmatised.
  • Conversations on SRHR are restricted to contraception and issues of consent, overlooking the need for a deeper understanding of men’s needs and perspectives as a prerequisite for men to support women achieving sexual and reproductive health and rights; crunching huge amounts of social media data on the same can help trigger behaviour change through Digital Marketing. (Quilt.AI, 2020)
  • This will provide a deeper understanding of the perceptions and behaviour of adolescent boys online. Based on this, once can devise behaviour change strategies, or ‘nudges’, for online interventions to influence their attitudes and behaviour towards safe sex, objectification of women, and an overall reduction of misogyny and entitlement. 

Outcomes

  • The adolescents and young people, primarily aged 10-19, will benefit directly from program activities using big data as an intervention. The people (youths and adults)  will indirectly benefit from the program because of their contact with people who are directly involved in the program or because of their contact with materials developed by the program (curricula, online interventions, etc.).
  • This will prove to be beneficial for research, advocacy policy and practice for inclusive and accountable health delivery system and greater equality for young women and men through the active participation of young people in national processes that design, deliver and evaluate projects that address data gaps in health service provision (including life skills and sexuality education) and to work alongside government and other organisations to share results.
  • A significant increase in access to services will be geographically homogeneous due to the decentralized scale up of services through health and community structures at all levels.

Potential Challenges (Mewa, T., 2020)

  • Often there is no comprehensible information provided to the patient on how and where their data is stored. In an environment where misinformation, stigmatisation, and the potential for ostracization are widespread, building data on an individual’s reproductive health choices entrenches the vulnerabilities and barriers women face in accessing these services.
  •  If the only data input into AI revolves around issues of fertility, reproductive health, AI will inherently result in the prioritisation of fertility over other aspects of reproductive health, thereby restricting access to them. Moreover, if AI rapidly propagates in the healthcare area without oversight and regulation along with digital literacy training, this has the potential to leave many women vulnerable due to lack of attention to potential violations of data privacy.

References

1. Akinnagbe, A., Amitha Peiris, K.D. & Akinloye, O., 2018. Prospects of Big Data Analytics in Africa Healthcare System. https://www.researchgate.net/publication/325039878_Prospects_of_Big_Data_Analytics_in_Africa_Healthcare_System.

 

2. Anon, 2008. Welcome to ACHAP. Available at: http://www.achap.org/ppay.php#:~:text=Young people constitute almost half,23.4%) of the population

 

3. Essoungou, A.-M., 2010. A social media boom begins in Africa | Africa Renewal. United Nations. Available at: https://www.un.org/africarenewal/magazine/december-2010/social-media-boom-begins-africa.

 

4. Kajala, E. & Charles, 2017. OI Engine, an innovation management software built on design thinking. OpenIDEO. Available at: https://challenges.openideo.com/challenge/reproductive-health/ideas/using-evidence-to-improve-the-sexual-and-reproductive-health-and-rights-of-adolescents-and-young-people-in-zimbabwe.

 

5. Mewa, T., 2020. Country case-study: sexual and reproductive rights in India. Privacy International. Available at: https://privacyinternational.org/long-read/3863/country-case-study-sexual-and-reproductive-rights-india#tech

 

6. Quilt.AI, 2020. Talking to Young Men About Sexual and Reproductive Health & Rights. Quilt.AI. Available at: https://www.quilt.ai/post/talking-to-young-men-about-sexual-reproductive-health-rights.

 

7. United Nations Population Fund- East and Southern Africa (ESARO, UNFPA), 2016. HIV and SRHR linkages infographic snapshot.
 https://esaro.unfpa.org/sites/default/files/pubpdf/Botswana_HIVSRHR_infographic_snapshot_en.pdf.

 

8. Wamba, S.F. et al., 2015. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, pp.234–246

 

9. World Health Organisation (WHO), 2018. Adolescent Health in Botswana.
https://www.afro.who.int/sites/default/files/2019-08/29 Botswana AH24022019.pdf.
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