Precision Population Health Tools

Dr. Ndirangu Wanjuki, MPH, MB CHB, MBA

Precision Population Tools is the third piece in a series advocating for health systems redesign. The first article, accessible here, introduced the Health Systems 2.0 framework, rooted in three theoretical foundations to inform health systems evolution. The second article, available here, outlined eight practices for development practitioners to actualise the Health Systems 2.0 philosophy.

Idea in Brief

The 2023 Universal Health Coverage (UHC) global monitoring report highlights the critical need to reorient health systems through a primary health care (PHC) approach to accelerate progress toward the 2030 UHC goals. This article proposes precision population health as a compelling pathway to achieving this reorientation. It defines precision population health, explores its potential to address systemic inefficiencies, introduces practical tools for its implementation and concludes by highlighting common pitfalls to avoid.

This article explores precision population health, a practice introduced in the article eight practices for development practitioners. It outlines the foundational pillars or principles of precision population health and provides examples of tools that practitioners can deploy to reorient health systems toward a PHC approach.

Precision population health, as I see it, is grounded in five foundational pillars:

Identifying at-risk demographic groups,
Utilizing routine proximate impact data to generate actionable insights
Ensuring that insights are strategically accessible through user-friendly platforms
Driving lean ecosystem improvements informed by such insights, and
Relentlessly promoting health equity by addressing social factors, including gender, age, and economic status.

The tools of precision population health can be grouped into six thematic areas:

Data analytics and governance
Ecosystem engagement
Lean models
Strategic purchasing
Public health security, and
Soft skills and mindsets.

This article focuses on the first three categories, with the remaining three to be discussed in a subsequent publication. The examples provided here are intended as a starting point to inspire practitioners to innovate and share additional tools, fostering collaboration and learning. This is especially important in resource-constrained settings, where the thoughtful application of precision population health can significantly enhance health outcomes and system efficiency.

Development agencies and philanthropic organizations should prioritize supporting government-led efforts to scale precision population health tools and improve local system efficiency and resilience.

What is Precision Population Health?

Achieving UHC by 2030 demands a decisive reorientation of health systems, shaped by the primary health care (PHC) approach championed by the World Health Organization (WHO) ^[1]. Precision population health provides a strategic framework for this reorientation.

Precision Population Health
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.

The family of precision health includes the youngest sibling, precision medicine; the middle sibling, precision population health; and the oldest sibling, precision public health. As discussed by various authors, the related disciplines of precision medicine and precision public health aim to replace one-size-fits-all strategies with tailored approaches ^{[2, 3, 4, 5, 6]}. Precision medicine focuses on individualizing treatments using genetic or molecular data, primarily in clinical settings. Precision public health broadly safeguards entire populations by targeting sub-groups based on epidemiologic risk, often in contexts like public health security or communicable disease elimination. As aptly defined above, precision population health applies a depth approach, systemically focusing on specific demographic subgroups as a means to optimize PHC for advancing UHC.

Precision population health is grounded in five foundational pillars: identifying at-risk demographic groups, using routine proximate impact data such as facility deaths or complications, ensuring 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.

To contextualize the definition of precision population health and provide practical insights into its application, consider the example of children under five who are particularly vulnerable to pneumonia.

1. Demographic group: Targeting a specific at-risk demographic subgroup ensures the efficient allocation of limited resources. For example, children under the age of five face a heightened risk of mortality due to pneumonia caused by Streptococcus pneumoniae or Haemophilus influenzae.

2. Proximate population health data: Effective action should be directly guided by insights from causal analysis of proximate population health or impact data. For example, monthly data on facility-based pneumonia deaths, sourced from routine information systems, can provide valuable interventional guidance.

3. Visibility of precision insights: Insights are helpful only when accessible to stakeholders who can act on them. This can be achieved by translating data into formats like dashboards, infographics, or simplified insights hosted in government-owned observatories and integrated with routine health information systems.

4. Lean ecosystem improvement: Streamlining systems and operations by eliminating redundancies and promoting efficient, data-driven strategies is crucial in resource-limited settings. For example, reducing childhood pneumonia risk and mortality may require improving access to vaccines through targeted outreach, enabling community health workers to provide pre-referral antibiotics, improving access to medical oxygen through intra-hospital piping, and offering virtual continuing medical education informed by child mortality reviews. Additional lean approaches could include strategic purchasing actions, such as integrating pneumonia treatment into health insurance and leasing cold chains to reduce upfront costs and maintenance expenses.

5. Health equity: Ensuring health equity in initiatives demands prioritizing underserved areas by providing, for example, oxygen plants and mobile clinics for vaccination to tackle childhood pneumonia, along with other vaccine-preventable diseases. Aligning with UHC 2030, interventions must integrate equity by addressing social and ecological determinants of health, including gender, age, economic status, and access to clean water.

Tools of Precision Population Health

Precision population health tools can be classified into six categories: data analytics and governance, ecosystem engagement, lean models, strategic purchasing, public health security, and soft skills and mindsets. This article focuses on the first three categories, with the remaining three to be detailed in a follow-up piece. Across all these categories, technology plays a potentially vital role as an enabler ^{[7, 8, 9]}. The tools highlighted here serve as a starter pack, encouraging practitioners to explore new tools, experiment with them, and share their insights to catalyse a movement toward health system efficiency.

1. Data Analytics and Governance

In this first category of precision population health tools, we examine proximate analytics, predictive modelling, data visibility, interoperability, and security as examples of data analytics and governance tools.

1.1 Proximate Analytics

Imagine managing a company for five years without insight into profitability—it would likely collapse. Yet, health systems often rely on data collected every few years, such as demographic surveys, to guide strategies. Precision population health addresses this using routine data sources such as District Health Information Software, Electronic Medical Records, and Health Management Information Systems to generate proximate health impact measures. For example, when skilled delivery attendance exceeds 90%, the facility maternal mortality rate can predict maternal mortality trends.

Analysing monthly associations between metrics like these and health system actions, such as maternal care quality, reveals actionable insights. This methodology, already applied in maternal death reviews, can be extended to other population groups, enabling causal pattern identification. Regular analyses of such data, spanning primary care units to national levels, can ensure that health system strategies remain relevant to PHC principles. This approach can help avoid the inefficient equilibrium trap described by Prof. Heifetz [10] and contextualized for the health sector by Wanjuki^[11], enabling stakeholders to directly assess the effectiveness of their interventions.

When designing programs, consider these precision population health questions:

What population-level outcome (X) should change over five to 10 years?
What routine metric (Y) serves as a good enough monthly proxy for X?
How can the team identify impactful interventions (A, B, C, D, E…) to sustainably change X using proxy impact data (Y) and inputs from experts and communities?
Which interventions (e.g., B, D, E) should be prioritized to optimize resources for equitable improvement in X, tracked via Y?

1.2 Predictive Modelling

Artificial Intelligence (AI), including Machine Learning (ML), holds unique potential in disease prevention and treatment in resource-limited settings. AI-powered systems can improve responses to climate change-related challenges, exemplified by Kenya’s Antimicro.ai, an open-source platform analysing 850,000 antibacterial samples across 83 countries, 341 bacterial species, and 38 drugs. Antimicro.ai predicts bacterial resistance, enabling precise antibiotic use and improved outcomes while informing policies for antibiotic use for specific populations. Despite such success, regions like sub-Saharan Africa lack sufficient AI-ready health data, limiting AI’s potential. Only 2% of antimicrobial resistance data used by Antimicro.ai comes from Africa. Robust AI-ready datasets and a continent-specific language model are vital to fully leverage AI^[12].

1.3 Data Visibility

In healthcare, the value of data emerges when it strengthens health systems to deliver impact. Data must be accurate, timely, visible, and easily interpretable for health systems to realize this value. Health observatories enable user-friendly visibility by gathering and analysing health data to track trends and guide policies. Prominent examples include the WHO Global Health Observatory and the African Health Observatory. Typically, observatories present data on inputs (e.g., health budgets), processes (e.g., healthcare workers), and outputs or outcomes (e.g., mortality rates). Observatories can be enhanced by integrating actionable precision insights – in which case we can refer to them as precision population health observatories.

For instance, precision population health observatories in maternal and newborn health could track deaths while suggesting precision or lean ecosystem improvements based on analytics. Governments and development agencies should invest in precision population health observatories tailored to country contexts to strengthen policy and programming, allowing health systems to track progress and adapt strategies accordingly. Precision population health observatories can enable countries to achieve what HIV programs have accomplished by deploying widely visible care cascades to support nations such as Botswana, Eswatini, Rwanda, Tanzania, and Zimbabwe in achieving epidemic control and meeting the ‘95-95-95’ HIV targets ^[13].

By integrating with existing routine information systems through interoperability, precision population health observatories can deliver insights into various population health challenges such as infectious diseases, cardiometabolic disorders, maternal and child health, and neglected tropical diseases, enabling health systems to accelerate progress towards UHC.

1.4 Data Interoperability

Clive Humby’s 2006 phrase, “Data is the new oil”, highlights the potential of refined data. Humby likened data to crude oil, which is valuable only when processed into actionable products. Tools such as proximate population health data analytics, predictive modelling, and precision population health data visibility refine raw data into meaningful insights. The full potential of data lies in achieving interoperability by linking diverse systems.

In the health sector, interoperability can enable the integration of platforms like the District Health Information Software, Health Management Information Systems, and Community Health Information Systems. This integration transitions data analysis from describing ‘what’ to explaining ‘why’ and determining ‘so what’. In Kenya, for instance, linking electronic community health information systems to health facility medical records through standardisations such as universal patient identifiers can enable client cascades that track screening, referrals, and chronic disease care. These cascades, backed by policy frameworks, can support continuous quality improvement, akin to the previously mentioned HIV programme success ^[13].

Similarly, integrating Kenya Medical Supplies Authority’s i-LMIS with county inventory systems has enhanced disease control by enabling real-time data sharing. Its reverse logistics model identifies unused malaria nets in low-demand areas, redistributing them to high-need regions and optimising malaria prevention efforts [14].

Beyond the traditional health systems, interoperability can facilitate the integration of data from social and ecological sectors, including meteorology, education, and economics. For example, integrating health and climate data can accelerate malaria control efforts. Achieving meaningful interoperability requires robust policies that foster cross-sectoral data sharing, enabling countries to bridge gaps in connectivity after years of investment in data refinement.

1.5 Data Security

In the health sector, data security, privacy, and ethical use are vital for leveraging data to improve health outcomes while safeguarding trust. Health systems must adopt global standards like ISO/IEC 27001 for risk mitigation and ISO/IEC 27799 for healthcare-specific protections ^[15]. These frameworks provide the foundations for robust policies, regular audits, and oversight mechanisms for secure data handling.

Data security measures include encryption, firewalls, two-factor authentication, and data retention policies. Encryption protects sensitive details like patient records and payment data, ensuring security against breaches. Ethical use involves handling health data responsibly for defined purposes such as treatment or research (with consent). Governance protocols, anonymization and ethical training are applied for accountability.

2. Ecosystem Engagement

This second category of precision population health tools includes examples of ecosystem engagement tools, namely ecosystem-wide diagnosis, coaching and mentoring, addressing socio-ecological determinants, and collaborating with communities for co-creation and co-monitoring.

2.1 Ecosystem-wide Diagnosis

Countries facing complex or adaptive health challenges — such as increasing cardiometabolic diseases, stagnant maternal health outcomes, teenage pregnancies, persistently low coverage of financial protection, and the impacts of climate change within constrained fiscal spaces — require ecosystem-wide approaches guided by adaptive leadership. This type of leadership unites diverse stakeholders with varying interests and motivations to enable systemic learning and evolution. The process begins with stakeholder mapping and perspective analysis to identify roles, points of alignment, and conflicts related to the adaptive challenge. Key stakeholders may include government bodies, development agencies, healthcare providers, community leaders, patient groups, civil society, and private-sector entities.

Establishing a ‘holding environment,’ as conceptualized by Professor Heifetz ^[10], such as a well-facilitated multi-stakeholder working group, creates a controlled space for productive discomfort. This environment fosters constructive dialogue, conflict resolution, learning, innovation, experimentation, and collaborative problem-solving among stakeholders.

Positive deviance analysis is valuable for identifying successful areas within the ecosystem — such as hospitals, regions, or countries excelling in addressing the challenge — providing a solid foundation for replication, scaling, and new innovations. By fostering purposeful multi-sectoral collaboration, ecosystem-wide diagnosis integrates diverse perspectives into coordinated actions, achieving sustainable improvements in population health and advancing collaboration in PHC.

2.2 Coaching and Mentoring

Building on ecosystem-wide diagnosis, adaptive leaders coach individuals to adapt to changes that disrupt their familiar realities and push them outside their comfort zones to solve complex problems. Coaching involves guiding people to face difficult truths, including losses and power shifts often associated with change. For example, advancing malaria control may require delegating case management to community health workers, potentially reducing client flow and income for laboratory staff. Coaching fosters a culture of observation, experimentation, and decentralized problem-solving by strengthening leadership capacity at all levels, aligning with Prof. Heifetz’s principle of ‘giving the work back to the people,’ thereby enhancing resilience within the ecosystem ^[10]. Leaders employ skills like presence, self-awareness, empathy, and accountability to create supportive environments where constructive conflict drives innovation.

2.3 Tackling Social and Ecological Determinants

Social and ecological determinants are important across precision population health pillars, especially demographics, analytics, and health equity. Analysing data that integrates these determinants enables actionable insights and fosters meaningful cross-sector collaboration with ministries such as education, water, and gender, as well as civil society. For instance, causal analyses of maternal health facility mortality data can identify factors influencing complications, such as education, economic stability, and gender dynamics. Multi-sectoral partnerships can address these barriers through targeted solutions, like school retention programs for girls and pro-poor social health insurance schemes. Additionally, integrating epidemiologic and meteorological data can enhance control of climate-sensitive diseases like malaria. As climate change expands as a significant social determinant of health, data-driven, coordinated multi-sectoral action will become increasingly essential.

2.4 Co-Creation and Co-Monitoring with Communities

Precision population health emphasises targeted interventions tailored to specific demographics, improving responsiveness, implementation efficiency, and equity in outcomes. Co-designing and co-monitoring interventions with individuals with lived experience ensures programmes address real needs, leaving out any superfluous elements. During implementation, co-monitoring through qualitative methods — such as focus groups — captures insights on key service attributes from both users and non-users of services. These attributes include access, equity, safety, quality, efficiency, and responsiveness, all assessed from the user’s perspective, facilitating meaningful, people-centred refinement of interventions. This approach enhances health equity by reducing exclusion risks, a precision population health pillar, by prioritising solutions that meet the needs of those most at risk.

3. Lean Models

Effective health systems must deliver scalable, context-specific services that extend to the last mile, encompassing preventive care, diagnosis, and treatment. In line with the spirit of precision population health, for these services to be accessible and affordable at scale in low-resource settings, they must follow a lean, minimalist design, precisely tailored to meet communities’ distinct needs, well-supported by sound strategic purchasing decisions, and aimed at systems strengthening so that gains are sustainable in the medium and long term.

This section examines examples of lean models, namely tackling health inequalities, poverty elimination via the Graduation approach, last-mile services, self-care interventions, access to medicines, virtual learning for health workers, health facility autonomy, genomic sequencing, disease elimination, safe drinking water access, and micronutrient supplementation.

3.1 Tackling Health Inequalities

Addressing health inequalities is crucial for effective health systems in sub-Saharan Africa, where disparities are significant. Equity measurement using disaggregated data — by geography, gender, disability, and income — can guide the design of interventions for underserved populations.

HIV programmes have employed equity-focused strategies, prioritising vulnerable groups such as residents of urban informal settlements, youth, and sex workers. For example, South Africa’s She Conquers initiative was successful through data-driven actions, community engagement, and strong leadership ^[16]. Similarly, MTV’s Shuga empowers adolescent girls and young women to prevent HIV ^[17]. These initiatives, combined with pre-exposure prophylaxis and harm reduction programmes, have established HIV prevention and care as effective large-scale efforts. Beyond HIV, Rwanda’s inclusive health insurance system improves maternal health and provides financial protection for disadvantaged groups.

Equity-focused interventions can be categorised into four approaches: individual-level graduation strategies, equitable resource allocation at the meso level, last-mile service delivery, and local manufacturing. Kenya’s Equalisation Fund exemplifies equitable resource allocation to previously marginalised counties. Last-mile service delivery, described elsewhere in this article, ensures services reach underserved populations such as those living in remote areas. Local manufacturing — such as Kenya’s Universal Corporation Limited ^[18] and micro-manufacturing of pressure swing adsorption (PSA) plants for oxygen in remote areas ^[19] — enhances access to essential medical supplies and strengthens health system resilience.

When supported by robust policies and regulations, these interventions can enable countries to achieve meaningful progress towards health equity.

3.2 Graduation Approach to Eliminate Poverty

Extreme poverty significantly hinders health equity. BRAC’s Graduation model, a highly regarded development strategy, disrupts poverty cycles through four key interventions: immediate support — cash or food aid and health services to address urgent needs; income generation — training and assets for micro businesses; financial inclusion — financial literacy, savings, and responsible borrowing for resilience; and social empowerment — confidence building, community integration, and life skills for independence.

In Bangladesh, 95% of participants achieved sustainable livelihoods, with earnings rising by 37%, savings increasing nine-fold, and food security improving. These outcomes persisted for over seven years after the intervention ^[20]. Applied in nearly 50 countries, the Graduation approach links poverty reduction to better health outcomes, including nutrition and psychosocial well-being. As a scalable, evidence-based tool, it addresses poverty — one of the most critical health determinants — and holds immense potential for resource-constrained settings.

3.3 Last-mile Services

Precision preventive and therapeutic interventions focus on delivering tailored services to underserved communities. For example, Amref Health Africa’s collaboration with civil society organisations, the Global Fund, and Kenya’s Ministry of Health empowers community health workers to provide home-based malaria diagnosis and treatment. This model, extendable to maternal health, demonstrates adaptability for various health needs ^{[21, 22]}. Similarly, with USAID funding, Amref and the Turkana County Government have deployed One Health outreach clinics for nomadic pastoralists. These clinics integrate human and animal health services, including vaccinations, tailored to the unique needs of these populations ^[23].

As another example, Kenya’s investment in bulk oxygen storage and PSA plants, supported by the Global Fund, strengthens its oxygen supply chain. Hospitals distribute oxygen directly to patient bedsides via intra-hospital piping, significantly improving access, particularly for children with pneumonia ^[19].

Digital health innovations also offer worthwhile last-mile solutions, with telemedicine, AI-powered tools, mobile health apps, and virtual communities addressing care gaps effectively. For instance, in Kenya, Jacaranda Health employs AI in local languages to guide pregnant women to timely care ^[24]. Similarly, portable AI-enabled digital X-rays expedite tuberculosis diagnosis ^[7]. Integrated into solar-powered mobile clinics, as seen during the COVID-19 pandemic, such technologies enhance access to preventive and diagnostic care in remote areas ^[25].

3.4 Self-Care Interventions

As defined by WHO, self-care is the ability of individuals, families, and communities to manage health, prevent disease, and cope with illness and disability, either independently or with minimal support from health workers. It includes evidence-based medicines, devices, diagnostics, and digital tools that can be accessed without formal health services. The COVID-19 pandemic highlighted its importance through practices such as masking, distancing, and handwashing. With half the global population lacking access to essential health services, WHO advocates self-care interventions to strengthen PHC and achieve UHC ^[26].

Self-care provides a precise and scalable model for improving health outcomes and reducing costs in resource-limited settings. It empowers individuals to make informed health decisions, adopt healthier lifestyles, use preventive measures, adhere to medications, recognise illness symptoms, and seek timely care. Self-monitoring tools, such as home-use glucose and blood pressure devices, further enhance disease management.

Governments and development agencies can advance self-care through supportive policies and programmes offering accessible, accurate information. Mobile platforms, social media, community health promoters, and local radio are effective in educating individuals on self-care practices. Scaling up self-care initiatives provides a sustainable pathway for addressing complex issues such as non-communicable diseases and improving population health in resource-limited settings ^[27].

3.5 Medicine Access Initiatives

In low-resource settings, poor health outcomes often arise from limited access to essential medicines. For instance, heat-stable carbetocin, a synthetic oxytocin analogue that retains efficacy without refrigeration, shows promise in reducing maternal mortality from postpartum haemorrhage. A WHO-led study across ten countries demonstrated its potential ^[28]. However, as a patented drug, its high cost limits adoption in countries with constrained fiscal spaces.

To address this, Ferring Pharmaceuticals partnered with UNITAID’s Medicines Patent Pool in April 2024 under a conditional licensing agreement ^[29]. This arrangement enables affordable carbetocin production through sublicensing in select nations. The initiative’s impact is already notable. In Kenya, prices dropped from Ksh1,444 ($11.18) to Ksh92 ($0.71) per dose, contributing to declining maternal mortality rates, as reflected in monthly facility data.

Countries with constrained fiscal spaces should collaborate with stakeholders to replicate such initiatives for other costly but life-saving medicines. Development agencies should facilitate negotiations between pharmaceutical companies, WHO, UNITAID, and governments to design and implement similar strategies to enhance access to essential medicines.

3.6 Virtual Learning Platforms for Health Workers

Virtual learning is an educational approach that replaces or complements traditional classrooms with digital tools like live video, pre-recorded lectures, and mobile content, offering flexibility for broad access and engagement across diverse settings. During the COVID-19 pandemic, governments leveraged virtual learning to equip frontline health workers with life-saving skills [https://amrefuk.org/our-work/health-worker-training/leap]. Amref Health Africa, supported by development agencies such as the Global Fund, used platforms such as Leap [https://leaphealthmobile.com/] and Jibu [https://icd.amref.org/jibu/] to train health workers across Africa. In Kenya, a two-month Leap campaign educated 70,000 community health workers on COVID-19 prevention, while Ethiopia’s Ministry of Health also used Leap to train 40,000 Health Extension Workers in a month ^[30].

Virtual learning enables the swift, cost-effective delivery of Continuing Medical Education, building technical and behavioural skills tailored to specific health challenges based on local context. At less than 1% of the cost of traditional training, this approach exemplifies precision population health. Development practitioners should partner with governments, professional associations, and regulatory agencies to deliver accredited virtual learning sessions that address adaptive challenges in the health sector.

3.7 Primary Care Unit Autonomy

Decentralising decision-making in resource-limited settings strengthens PHC financing by enabling primary care units to address local needs autonomously. By generating and managing funds independently, these units ensure predictable financing aligned with healthcare priorities, promoting sustainable PHC models.
In Kenya, Facility Improvement Fund laws allow public health facilities to retain revenues from user fees, insurance, and donor contributions. This autonomy facilitates local quality improvements while avoiding delays associated with central treasury processes. The funds are used to procure essential supplies, pay casual staff, and implement facility-specific upgrades, effectively mitigating bureaucratic inefficiencies typical of centralised systems.

Early Facility Improvement Fund achievements highlight key success factors, such as effective engagement of county assemblies, robust legal and governance frameworks, digital revenue tracking systems, stakeholder collaboration, capacity building, and inter-county learning ^{[31, 32, 33]}.

3.8 Genomic Sequencing

Genomic sequencing enables precise diagnosis and treatment of hereditary and complex diseases by tailoring care to genetic profiles, whether individual (precision medicine) or population-based (precision population health).

This approach holds promise for diseases such as breast cancer in Black women, who face elevated risks for aggressive subtypes like triple-negative breast cancer, yet remain underrepresented in genetic research. A study of 40,000 women of African descent identified 12 loci linked to breast cancer, including three specific to triple-negative cases. Eight percent of participants carried high-risk variants, increasing their risk by 4.2 times. These findings inform predictive tools, such as polygenic risk scores, enabling earlier detection and targeted therapies ^{[34, 35]}.

Addressing funding gaps, building local genetics and bioinformatics expertise, and ensuring ethical genomic data use are essential to leveraging Africa’s genetic diversity for improved population health outcomes ^[36]. Collaborative initiatives, such as H3Africa, are advancing genomic research to illuminate genetic and environmental factors to enhance population health.

3.9 Elimination of Selected Diseases

Eliminating specific diseases represents a pinnacle in preventive health. The eradication of smallpox, declared by WHO in 1980, saved millions of lives, alleviated suffering, and delivered economic returns 130 times its $300 million cost. Dr William Foege, a key strategist in this effort, highlighted essential lessons, including truth-seeking, coalition-building, political will, cultural respect, and health equity ^[37].

Polio eradication demonstrates the power of precision tools such as surveillance and genomic sequencing, supported by robust global coalitions. Similarly, combating diseases whose epidemiology is worsened by climate change requires thoughtful collaboration. Malaria, a climate-sensitive disease causing over 600,000 annual deaths — 94% in sub-Saharan Africa — represents a key elimination target ^[38]. By 2050, an additional 608 million Africans could face malaria risk due to climate change ^[39]. Yet, eradication is achievable.

Forty-four countries, including five in Africa – Algeria, Cape Verde, Mauritius, Seychelles, and Egypt – have successfully eliminated malaria through leadership, innovation, and coalitions. Effective interventions include indoor residual spraying, insecticidal nets, chemoprophylaxis, intermittent preventive treatment during pregnancy, rapid diagnostics, and Artemisinin-based therapies. Emerging technologies offer additional promise, such as malaria vaccines, gene drives, Wolbachia-based vector modifications, AI tools, resilient supply chains, and optimised human resources.

Achieving polio and malaria elimination and subsequent eradication, alongside addressing other climate-sensitive diseases, requires sustained funding and coordinated global action. Initiatives like Roll Back Malaria must integrate elimination goals with climate adaptation strategies to maintain political commitment. Unified efforts can deliver profound health and economic benefits, safeguarding future generations.

3.10 Access to Safe Drinking Water

In 2020, one in four people globally — approximately two billion individuals — lacked access to safely managed drinking water, with rural and fragile populations most affected. Sub-Saharan Africa alone accounts for half of the global population without basic drinking water services ^[40]. At current rates, global coverage will reach only 81% by 2030, leaving 1.6 billion people without safe water.

Ensuring access to safe drinking water is critical for preventing waterborne diseases, improving nutrition and school attendance, and enhancing health outcomes ^[41]. The economic benefits are equally compelling, with every dollar invested in safe water access generating up to $4 in returns by lowering healthcare costs and increasing productivity ^[42].

Through government-led frameworks and multi-sectoral collaborations, health systems must prioritise investments in water systems as a fundamental health issue, without which we cannot meaningfully expect to get close to UHC. These efforts are even more urgent in the face of climate change, which threatens to reverse progress. As Dr Maria Neira, Director of the Department of Environment, Climate Change and Health, observed: “Climate change is eating into those achievements. We must accelerate our efforts to ensure every person has reliable access to safe drinking water—a human right, not a luxury” ^[43].

3.11 Micronutrient Supplementation

Micronutrient deficiencies, particularly iron deficiency anaemia, profoundly affect health in resource-limited settings, especially among children as well as pregnant and postpartum women. Globally, anaemia impacts 40% of children aged 6–59 months, 37% of pregnant women, and 30% of women aged 15–49 years (273 million), predominantly in low- and lower-middle-income countries ^{[44, 45]}. In 2019, anaemia caused 50 million years of healthy life lost, mainly due to dietary iron deficiency and diseases such as malaria ^[46].

Precision Population Health offers tailored solutions, such as biofortified crops like iron-enriched beans and vitamin A-rich sweet potatoes in Uganda, led by HarvestPlus working with local governments and farmer cooperatives. A randomised trial found biofortified beans reduced anaemia among women by 19% within a year ^[47]. Another effective intervention is micronutrient supplementation using Multiple Micronutrient Powders, which enhance children’s diets in areas with limited nutritious food.

Evidence confirms Multiple Micronutrient Powders reduce anaemia by 18%, iron deficiency by 53%, and improve haemoglobin levels, with a favourable safety profile ^[48]. The WHO recommends Multiple Micronutrient Powders for food fortification for infants and children aged 6–23 months in populations with high rates of anaemia and nutrient deficiencies.

Common Traps in the Practice of Precision Population Health

Precision population health holds considerable promise, yet practitioners face several pitfalls that can derail even well-intentioned initiatives.

One key pitfall is metric chasing.

For example, prioritising high rates of facility-based deliveries may overshadow the broader goal of improving maternal health outcomes, diverting attention from the quality of care. Metric chasing stems from phenomena such as surrogation (metrics replacing strategic objectives) ^[49], metric fixation (confusing metrics with actual performance) ^[50], and Goodhart’s Law (metrics losing validity when used as sole indicators) ^[51]. To avoid this, practitioners should adopt sets of metrics aligned with desired outcomes and involve communities in monitoring efforts. Tools like the HIV care ‘95-95-95’ cascade provide stakeholders with insights into service fidelity from a continuum of care perspective and should be replicated in other health thematic areas.

Another common pitfall is addressing adaptive challenges as technical problems.

This often occurs when health professionals assume leadership roles without the skills to distinguish between these fundamentally different challenges. Misclassifying adaptive challenges leads to leadership failures, as it ignores the complexities of broader systems ^[10]. In under-resourced regions like sub-Saharan Africa, investments in adaptive leadership training, mentorship, and coaching can significantly enhance leadership capacity.

A third trap is problem paralysis.

This is where over-analysis stifles decision-making and action, leading to wasted resources, missed opportunities, and demoralised teams. In the climate-health nexus, extensive modelling and data generation often fail to produce actionable priorities, such as eliminating malaria or scaling climate-resilient health infrastructure. Leaders can mitigate this by clarifying objectives, setting decision timeframes, engaging stakeholders, and adopting a learning approach that prioritises progress over perfection.

The evidence-to-policy gap also poses challenges.

Producing evidence of effective solutions does not guarantee adoption by governments. Successful uptake requires engaging governments in the evidence-generation process, fostering co-ownership, and mobilising implementation resources. As discussed earlier, ecosystem engagement tools are vital for bridging this gap, as linking evidence to policy is an adaptive — not technical — challenge.

Precision approaches further risk exacerbating health inequities.

Marginalised groups often lack access to advanced tools like AI. For instance, while these tools can guide pregnant women to care, those in remote areas without connectivity are often excluded. In such contexts, community health workers conducting household visits offer more equitable solutions, underscoring the importance of people-centred design.

Risks of surveillance and data misuse

Lastly, the data-driven nature of precision approaches introduces risks of surveillance and data misuse, raising ethical concerns about privacy and trust ^[52]. Robust data governance policies are essential to safeguard patient data, ensure transparency and accountability, fostering trust.

By recognising and addressing these challenges with context-appropriate tools, practitioners can effectively navigate the complexities of health systems and avoid inefficient equilibria.

Concluding Reflections

This article examines three categories of precision population health tools: Data Analytics and Governance, Ecosystem-wide Engagement, and Lean Models. A forthcoming article will explore three additional categories: Strategic Purchasing, Public Health Security, and Soft Skills and Mindsets.

The series of articles aims to inspire the reimagination of health systems for the 21st century, as argued by Wanjuki ^[53]. Health systems in low-resource settings, constrained by fiscal challenges, demographic and epidemiological shifts, and the growing impacts of climate change, must prioritise contextual relevance and efficiency. Achieving UHC requires scalable and cost-effective precision population health tools to address inefficiencies in health systems.

This article outlined tools to mitigate systemic inefficiencies. Readers are encouraged to adopt these tools where applicable, pursue context-specific solutions to advance the UHC agenda, and contribute to this field by sharing insights and additional precision population health strategies.

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