Harnessing precision population health tools to advance primary health care

¹Amref Health Africa, Nairobi, Kenya

^&Corresponding author
Ndirangu Wanjuki, Amref Health Africa, Nairobi, Kenya

Perspective    

When I first came across the 2023 Universal Health Coverage (UHC) Report, I was struck by its stark message: 4.5 billion people still don´t have access to essential health services, and over a billion face financial ruin when they seek care [1]. As someone engaged in health systems, I believe there´s no better way to tackle this crisis than by focusing on primary health care (PHC), as the World Health Organization recommends. But the game-changer here is something I have defined as precision population health. Despite well-intentioned ongoing efforts, PHC frameworks often remain inefficient, frequently failing to optimize resources, leverage real-time impact data, or adapt to unique population needs. Without effectively responding to available impact data, interventions lack precision, leaving vulnerable groups underserved. Precision population health offers a meaningful shift, harnessing insights from impact data, ecosystem collaboration, lean models, strategic purchasing, public health security, and soft skills to deliver targeted, high-impact interventions that prioritize those most in need.

What is precision population health?

Precision population health is like tailoring a suit (the intervention or set of interventions) to ensure the perfect fit, with the suit's wearer being a demographic group such as women of reproductive age.

By definition, precision population health uses routine impact data to inform the design and deployment of resource-efficient strategies that equitably and sustainably improve health outcomes for at-risk demographic groups.

It is built on five principles: identifying at-risk demographic groups, using routine proximate impact data such as facility deaths or complications, ensuring actionable insights are strategically accessible through user-friendly platforms, driving lean ecosystem improvements, and promoting health equity by considering social factors such as gender, age, and economic status.

While precision medicine focuses on individual treatment (like using genetics to fight cancer), precision public health works to broadly protect populations based on disease risks among sub-groups [2,3]. Precision population health finds the sweet spot: it digs deep into specific demographics and puts people at the center, to target their unique challenges for the greatest possible equitable impact through resource-efficient system improvements. Precision population health originated from early population genomics work and now integrates social, behavioral, and environmental factors.

Precision population health builds on the WHO´s six building blocks as a complementary approach to health system optimisation. It employs targeted, data-driven interventions for at-risk populations to supplement, rather than replace, existing strategies. It enhances current systems to promote equity and improve outcomes. For example, how can health systems better deploy existing human resources to reach those most vulnerable and achieve greater precision in service delivery and impact?

Tools for precision population health

I propose six categories of precision population health tools: data science, ecosystem engagement, lean models, strategic purchasing, public health security, and soft skills and mindset. Technology plays a potentially transformative role as an enabler across all these categories. This article focuses on the first three. The tools highlighted in this article serve as a starter pack, encouraging practitioners to explore new tools, experiment with them, and share their insights.

1. Data analytics and data science - turning information to impact: this foundational category includes proximate impact analytics, predictive modelling, data visibility, information system interoperability, and data governance, as examples of data innovation tools.

Proximate analytics enables the use of routine health data to fine-tune health system strategies, even in resource-constrained settings. However, this potential is often hindered by challenges such as low reporting rates, incomplete data, and an over-reliance on input- and output-focused service statistics. Strengthening data completeness and quality, particularly for impact data such as maternal death reviews, is essential for generating meaningful insights that optimise impact pathways [4]. Predictive modelling, such as Kenya´s Antimicro.ai for antimicrobial resistance [5] and artificial intelligence (AI) solutions to combat malnutrition [6], leverages AI to improve health outcomes. Precision population health observatories can provide context-specific insights to enhance programs and achieve gains, such as those experienced in HIV programs [7]. System interoperability connects data across systems to reveal insights that strengthen health systems [8]. Health data governance is premised on global standards like ISO/IEC 27001 and 27799 [9].

2. Ecosystem engagement - health is everyone´s responsibility: this category includes ecosystem-wide diagnosis, coaching and mentoring, socio-ecological determinants, and co-creation with communities as examples made possible through the practice of adaptive leadership.

Adaptive leadership is key in countries facing tough health challenges, such as fiscal constraints amidst development financing landscape shifts, climate change impacts, and epidemiologic shifts. It drives ecosystem-wide learning by coaching people to build new skills and tackle complex problems through rigorous prioritization and action [10]. Tackling social and ecological determinants, such as ensuring high levels of girls' education in regions significantly affected by gender inequalities, requires cross-sector collaboration and integrated data. Notably, working directly with communities to design interventions improves equity and responsiveness.

3. Lean models - doing more with less: in health systems, efficiency isn´t just about cutting costs-it´s about eliminating waste and reaching the most underserved populations. Lean models do exactly this. Examples include tackling inequalities, eradicating poverty, delivering last-mile services, self-care, medicine access initiatives, virtual learning, primary care autonomy, genomic sequencing, disease elimination, water access, and addressing micronutrient deficiencies.

Addressing health inequalities means focusing on underserved populations informed by impact data, as seen in specific HIV program models [11]. It also requires fair resource distribution, local manufacturing, including micro-manufacturing [12], and poverty reduction efforts like Bangladesh Rehabilitation Assistance Committee (BRAC´s) Graduation Model implemented in 50 countries [13]. Reaching remote or hard-to-access areas relies on last-mile services, such as community case management [14,15], one-health mobile clinics [16], cost-effective movable clinics [17], intra-hospital oxygen systems [12], and digital health tools [18,19]. Self-care allows individuals to take charge of their health [20], while public-private partnerships enhance access to essential medicines like heat-stable carbetocin [21]. Virtual learning helps train health workers efficiently [22], and decentralizing decision-making improves the quality of primary healthcare [23,24]. Genomic sequencing enables precise diagnosis and treatment [25]. As with smallpox eradication, eliminating diseases depends on global collaboration and consistent funding [26,27]. In 2020, more than two billion people lacked safe water, highlighting the need for urgent investments [28]. Tackling micronutrient deficiencies like anaemia requires scalable solutions such as biofortified foods and multiple micronutrient powders [29].

Despite the promise of precision population health tools, implementation and scale-up remain hindered by inadequate South-to-South learning, which often fails to empower professionals in resource-limited settings on how to drive change. Strengthening knowledge exchange and equipping health workers with adaptive leadership skills are critical for effectively scaling and replicating these tools.

Concluding reflections

Precision population health reorients health systems on PHC principles, improving efficiency and driving progress toward UHC. To succeed, it is essential to use proven tools to decisively target vulnerable demographic groups with high-impact interventions while evading pitfalls like ethical data risks, leadership gaps, equity blindness, evidence-to-policy gaps, and metric chasing attributable to metric fixation, surrogation, and Goodhart´s law.

Practitioners and development agencies need to back government-led efforts to expand the application of precision population health tools, including by strengthening South-to-South learning. This is key to rethinking health systems as modern frameworks designed to tackle the uncertain and complex challenges of today and the future [30].

Future research on precision population health should focus on integrating artificial intelligence and machine learning for predictive modelling, expanding the application of genomic sequencing applications in resource-limited settings, developing precision population health observatories, strengthening multisectoral partnerships to address social determinants of health, and enhancing workforce capacity in adaptive leadership to scale the effective deployment of primary health care using precision population health tools.

Competing interests     

The authors declare no competing interests.

Authors' contributions     

All the authors read and approved the final version of this manuscript.

References