Optimising care for uncomplicated type 2 diabetes mellitus in Lagos, Nigeria: cost and benefit estimates using real

Discussion This study evaluated the cost and effectiveness of type 2 diabetes care in Lagos State, Nigeria. A retrospective analysis of patient records showed that current care costs US$1413 per patient annually—72% lower than the US$5128 needed for optimal care. Medications accounted for over 97% of total costs. The findings reveal significant gaps in care quality, limiting effective diabetes management. While best practice care could reduce diabetes-related strokes and MI at an incremental cost of US$61 492 per averted case, it would require an additional US$2.1 billion annually. This estimate represents the aggregate financial gap in direct medical spending needed to achieve guideline-concordant care across the entire system, combining current expenditures by government, insurers and households. Our findings of an average actual cost of US$1413 represents 73% of the annual per capita income which stood at US$1930 in 2023,41 and a state level extrapolated annual equivalent of US$817 million (if all DM patients accessed care). These figures are higher in absolute terms compared with previous Nigerian studies but slightly lower as a percentage of per capita income. For instance, based on a study conducted in 2012 at a tertiary facility in the Niger Delta region of Nigeria, Suleiman and Festus reported an average direct cost of illness per patient with type 2 diabetes US$284.57 per patient, amounting to an annual national direct cost of US$1.6 billion.42 Similarly, Abdulganiyu and Fola (2014) in their 2010 study at a tertiary institution in North-Eastern Nigeria estimated an annual average direct cost of illness of approximately US$320, representing 88% of the annual per capita income and an annual national burden of about US$1.5 billion.43 Suleiman et al’s study conducted in 2004 at a tertiary facility in South-West Nigeria reported an average direct cost of illness of US$262.22 amounting to 84% of annual per capita income and an annual national direct cost of US$1.1 billion, based on a prevalence estimate of 3%.44 Variations in the costs can be attributed to several factors, including inflationary influences associated with the temporal gap across the studies, methodological heterogeneities, differences in study settings (eg, tertiary facilities vs primary and secondary facilities), and case mix variations.13 45 46 Although our mean cost estimate is higher than the estimates from the previous studies, our population level cost burden is smaller than their estimates because we report single state-level cost burden while they report national-level extrapolations. The finding of a cost of over US$61 000 per complication averted is substantial but this is in fact an overestimation, considering that the current study has only included MI and strokes. Left out in this analysis are numerous other diabetes complications avertable by optimum sugar control, including retinopathies, nephropathies, foot ulcers, and neuropathies. Studies have shown that excess costs of such diabetes complications are significant and the highest average cost ratios were recorded for nephropathy, diabetic foot and acute stroke while lowest cost ratios were recorded for retinopathy, ketoacidosis and hypertension.13 Further, mortalities averted were not considered, neither were productivity losses associated with diabetes morbidity and mortalities. Incorporating these elements in an appropriately designed economic evaluation model is likely to return economically more favourable results. Furthermore, our primary model of cost gap assumes a scenario of 100% compliance with the guidelines for all patients with DM, including taking the prescribed medications for 365 days of the year, but this is rarely achieved in real-world settings. Evidence from a systematic literature review from high-income countries shows adherence to DM medications to be between 36% and 93%.47 Poorer adherence is reported in LMICs due to a multiplicity of factors extending beyond cost of services48 to include cost of reaching the health facility, cultural beliefs and lack of awareness.9 To reflect these realities, we conducted scenario analyses at 75% and 50% adherence levels, which reduced the mean per-patient annual journey costs to US$3847 and US$2565, respectively, with corresponding statewide annual investment requirements of US$1.4 billion and US$665 million. We find significant cost variations across facility types and insurance status. Care costs are higher in private and secondary facilities than in public and primary centres. Since medications account for most expenses, adjusting prescription patterns is key to improving efficiency. Insured patients face higher costs, likely due to providers prescribing costlier drugs, knowing that coverage is available, in addition to insured patients’ greater adherence to care. This aligns with existing evidence that provider decisions in LMICs are influenced by cost considerations.49 Policies promoting prescription of affordable, quality-assured generics over expensive branded drugs are needed to control DM care costs. Our results here surpass and reinforce the findings from a similar study earlier conducted by our team on hypertension care using patients drawn from the same Banigbe22 used in the present study and the same reference resource price data. The paper reported an average investment gap of US$120 per patient and a statewide annual investment requirement of about US$300 million to implement complete care journeys for all patients with hypertension, translating to US$5000 to US$13 000 per saved life year.38 Collectively, these studies highlight deficiencies in care for NCDs generally and the substantial resources needed to optimise this care in Lagos State. In our study sample, 5% received insulin therapy. Poor access to insulin has been recognised as a major challenge to management of diabetes in LMICs, and the low 5% insulin use in our study may, at least in part, reflect this.50 While country differences exist, estimates from Basu et al suggest that an average of 12.7% of patients with type 2 DM in sub-Saharan Africa will receive insulin therapy in an ideal scenario of universal access to therapy, compared with the present average use of 1.8% with limited access to insulin.50 The latest report by the Access to Medicines Foundation showed only 29 of the 108 countries scoped have all the insulins from WHO’s essential medicines list registered and only one of those is a low-income country while no insulins were found to be registered at all in 24 countries.51 Increasing access to human as well as analogue insulins should be a global health priority to foster universal access to high quality diabetic care. Our regression analysis identified frequency of consultations, health insurance coverage, age, and insulin use as significant determinants of blood glucose control—findings that are consistent with broader evidence from LMICs.52 53 Regular consultations are often constrained by OOP costs and limited health workforce availability leading to poor continuity of care. Insurance coverage reduces financial barriers and enhances treatment adherence, but coverage levels remain low in Nigeria and many LMICs. Insulin use was associated with poorer control in our cohort, implying that, on average, patients on insulin had higher blood sugar compared with those not taking insulin. This associative finding must not be interpreted to mean that insulin causes a rise in sugar levels. It rather reflects the conventional clinical practice of prescribing insulin for patients with more advanced diseases or poorly controlled sugar. Age-related differences suggest younger patients may face additional challenges with adherence, potentially linked to occupational, social, or cultural barriers. While the evidence on the relationship between gender and glycaemic control has been mixed, our finding of no relationship between gender and sugar control aligns with studies that conclude that gender alone is not a strong predictor, but interacts with socioeconomic, cultural and behavioural factors.52 Overall, these determinants highlight the need for system-level interventions to address the multilayered barriers to effective glycaemic control in LMIC contexts. Policy implications Our findings indicate a critical underfunding for diabetes care and NCDs in Lagos generally. The high prevalence of uncontrolled blood glucose levels and the significant cost burden of medications highlight substantial gaps in the current care and funding landscape. Enhanced financial support from both governmental and non-governmental sources is essential therefore to improve healthcare infrastructure, provide necessary medications, and support comprehensive diabetes management programmes. Yet, we also calculate that meeting the funding requirement for optimum care by direct financial investment alone is neither feasible nor even efficient. This necessitates the exploration of more efficient (1) medicine procurement processes and (2) cheaper DM care delivery models. Medicines, constituting most of the care costs, present a critical point for cost-reduction interventions. First, we recommend bulk purchasing practices to leverage economies of scale and reduce the per-unit cost of medications making them more affordable for patients.54 Shared procurement platforms, possibly coordinated at a regional or national level, are strongly encouraged to enhance purchasing power and streamline the supply chain.55 56 Further, the government should engage in strategic negotiations with pharmaceutical companies to lower prices while promoting the use of generic drugs. Ultimately, the government must prioritise enhancement of the local production capacity for diabetes medications and other healthcare resources. In this regard, the recently launched Presidential Initiative for Unlocking the Healthcare Value Chain (PVAC)57 which aims to boost local production of pharmaceuticals and medical supplies in Nigeria is a welcome initiative.58 Beyond recommending policies that promote the prescription of affordable, quality-assured generics over expensive branded drugs, there is also a need to incentivise physicians toward appropriate prescribing practices. Strategies may include: (1) integrating clinical decision support systems into prescribing platforms to guide evidence-based choices (where electronic prescription platforms exist); (2) incorporating rational prescribing into performance-based financing and value-based care models; (3) linking insurance reimbursements to adherence to national and state treatment guidelines and (4) providing continuous medical education and peer-review feedback on prescribing patterns. Such interventions could help align physician behaviour with cost-effective diabetes management while safeguarding patient outcomes. We advocate for the implementation of more efficient service delivery approaches, such as group therapy sessions.59 Group therapy provides peer support and learning and fosters a supportive community environment that can improve patient adherence to treatment regimens59 while reducing the strain on healthcare providers by allowing them to manage multiple patients simultaneously. Additionally, governments at all levels must recognise prevention as a cornerstone of diabetes care policy to reduce the incidence of diabetes. Public health initiatives aimed at promoting healthy lifestyles, such as regular physical activity, balanced diets and weight management, and early detection through targeted screening programmes have been shown to be not only effective but cost-effective and cost-saving.60 Our study highlights the need for innovative (digital) health solutions to enhance diabetes care efficiency and effectiveness in Lagos State. From a health system perspective, we recommend investments in digital platforms such as electronic health records that have the capacity to improve medication targeting and evidence-based prescription practices and track patient adherence to follow-ups,61 as well as tools linking providers and patients to digital drug supply chain systems to streamline procurement, ensuring consistent medication availability and better affordability.56 Equally recommended are mobile health apps that can empower patients with personalised reminders, glucose monitoring support and health education, fostering proactive self-management.62 The findings underscore the urgent need for policymakers to intensify health insurance expansion interventions in the State. Health insurance schemes should be designed to cover essential diabetes care services and medications, thereby reducing OOP expenses for patients and improving overall health outcomes. Considering the documented evidence of disproportionate economic burden of diabetes care on the poorest populations, policymakers should urgently operationalise the Vulnerable Group Fund to cover the most vulnerable patients, as stipulated in the 2022 National Health Insurance Act of Nigeria. Lastly, expanding fiscal space for NCD care in Nigeria is crucially recommended to manage the rising economic burden. Beyond increasing health insurance and budget allocation, innovative financing like sin taxes should be explored. The existing 10 Naira per litre tax policy on sugary drinks is short of constitutional ring-fencing for health. An earlier political economy analysis showed strong pro-NCD momentum, which NCD advocates can leverage for legislative amendments to allocate these funds for NCD care.63 Raising the tax to the WHO-recommended 20% could further support prevention and treatment while discouraging unhealthy consumption. Strengths and limitations This study leverages a comprehensive dataset from diverse LSHS facilities, ensuring a robust patient sample. Using actual patient records and price data enhances realism and generalisability, avoiding modelling limitations. The longitudinal approach captures treatment changes over time, refining care and cost analysis. Mixed-effects regression provides nuanced insights into blood glucose control, considering individual and facility-level variations. This study has some limitations. First, by excluding patients under 30, we aimed to capture type 2 diabetes; however, some adults with long-standing or late onset type 1 diabetes may still be included, which could introduce misclassification bias. On the flip side, we could have missed some young-onset type 2 diabetes patients in the population (if any). Lack of data on the prevalence of these categories of patients in this setting limits our understanding of the extent to which this is a problem for our analyses.1 The retrospective data collection and reliance on health facility records may introduce biases due to incomplete or inaccurate records. Clinical data were from 2019, while cost data were based on 2023 prices. Although key variables were adjusted for, unmeasured factors such as socioeconomic status, medication adherence and lifestyle may influence outcomes. Some patients may have visited other facilities or used unrecorded treatments. The baseline visit was set as the first visit during the study, though many patients had been receiving care earlier. The time of initial diagnosis or first-ever visit was unknown and not included in the analysis. Findings are specific to Lagos and may not fully apply to regions with different healthcare systems. Moreover, by excluding patients with complications, we limit the generalisability of the findings to the broader DM population. Our extrapolation approach assumes structural similarity between the study cohort and the broader adult diabetic population, but this may not fully hold due to selection and exclusion criteria. Our extrapolation results therefore must be taken for what it is—indicative, rather than inferential, estimates to illustrate potential system-level implications. Lastly, the risk evaluation derives from Bolden-Albala et al.37 This is applicable to our study to the extent that it provides evidence of the risks of cardiac events and strokes in diabetics with controlled and uncontrolled sugar over a 7-year period, using a mixed population that includes patients of black race. However, its applicability is substantially limited by differences in the study population biology, lifestyles, socioeconomic profiles and broader healthcare system designs.