Enhancing geographical access to cardiovascular disease healthcare services in Lagos State, Nigeria

1 Introduction

Cardiovascular diseases (CVDs) are the leading cause of global mortality and a significant contributor to disability [1]. According to the Global Burden of Disease Study 2019, the number of individuals affected by CVD nearly doubled, increasing from 271 million in 1990 to 523 million in 2019, while CVD-related deaths increased from 12.1 to 18.6 million during the same period [1]. This increasing burden makes CVD the most prominent disease worldwide [2]. Notably, over three-quarters of CVD-related deaths occur in low- and middle-income countries (LMICs) [3]. Unfortunately, individuals in these regions often lack access to primary healthcare programs for early detection and management of CVD risk factors [3], which is exacerbated by limited healthcare funding [4,5]. The most economically vulnerable populations bear the brunt of this burden, as CVDs and other non-communicable diseases (NCDs) contribute to poverty through significant healthcare expenses and high out-of-pocket spending [3].

One effective strategy to reduce the burden of CVDs is through early detection and treatment [3], achievable by improving spatial access to care facilities. The maximal covering location problem (MCLP) is a well-established optimization technique in facility location analysis that determines optimal locations for facilities to maximize coverage while considering capacity constraints and minimizing costs [6,7]. In healthcare contexts, MCLP has been widely applied, including for locating CVD care facilities [8,9]. Timely access to appropriate care facilities is crucial for effectively managing and treating CVDs [10]. Consequently, the location of these care facilities is critical for ensuring equitable access to quality healthcare services for affected populations.

Studies have applied MCLP across various healthcare domains, including primary care facilities [9], emergency services [11,12], and specialized care units [13,14]. Although research specifically focusing on MCLP applications for CVD care facility locations is limited, it is emerging. These studies aim to optimize the placement of CVD care facilities to maximize coverage and ensure accessibility for affected populations, addressing key themes such as demand and accessibility modeling, multi-objective optimization, capacity constraints, and resource allocation [9,15].

Kapwata and Manda [15] aimed to spatially evaluate the geographic healthcare access of individuals diagnosed with CVD in South Africa, focusing on their proximity to health facilities and assessing the density of the facility network. The spatial locations of healthcare facilities and the residences of participants diagnosed with CVD were integrated into a Geographic Information System (GIS) to define accessibility based on travel times and distances. In a facility assessment study conducted in Madhya Pradesh, India, Pakhare et al. [9] evaluated primary care facilities for their preparedness to manage CVD. The study identified critical gaps in essential resources needed for managing NCDs at the primary care level.

Similarly, due to the large geographical area and low population density of Hokkaido, Japan, leading to significant disparities in healthcare access, particularly for stroke treatment, Bando et al. [16] evaluated the balance of supply and demand for mechanical thrombectomy (MT) services to identify high-priority areas for enhancing stroke centers without increasing the number of facilities. Implementing a capacitated MCLP model, the coverage of demand points increased from approximately 53%–85% across different scenarios, and areas with low coverage were identified, highlighting the need for targeted enhancements in specific primary stroke centers (PSCs).

With the aim to optimize healthcare service delivery by addressing the challenges posed by congested environments and patient preferences in choosing healthcare facilities in Karachi, Pakistan, Pouraliakbari et al. [6] employed a modified maximal covering location–allocation problem (MCLAP) model that incorporates user choice behavior, allowing for a more realistic representation of how patients select healthcare providers based on factors like distance and service quality. The analysis reveals that incorporating user choice significantly impacts the effectiveness of location–allocation decisions, leading to improved access to healthcare services.

Even though the application of MCLP for locating CVD care facilities is an emerging research area within healthcare facility planning, the method has been found to confer advantages such as optimal distribution of facilities, improved access to care, efficient use of resources, and effective spatial planning [13,15,17]. Given these advantages in facility location planning and the limited funds available for healthcare in developing countries [18], there is a need to consider the adoption of MCLP for better care and outcomes related to CVDs.

Studies have been conducted on the location of health facilities in Lagos State, Nigeria, but none have considered the placement of healthcare facilities specifically for NCDs. Omogunloye et al. [19] investigated the spatial distribution and utilization of dental clinics in Lagos State using GIS techniques, concluding that the government should establish dental clinics outside general hospitals (GHs) to achieve a more even distribution, as existing clinics were clustered. Similarly, Olowofoyeku et al. [20] used GIS to examine the spatial distribution of primary healthcare (PHC) facilities in Ikorodu Local Government Area (LGA) of Lagos State for treating malaria and sickle cell disease. They found that facility coverage was 48% within a 2 km catchment radius and only 15% within a 1 km catchment of existing PHCs, suggesting a need to increase the number of optimally located facilities to reduce travel distances and expand coverage.

In light of Nigeria’s plans to adopt universal health coverage (UHC) as a policy, Obubu et al. [21] evaluated the existing human resource capacity and distribution of health facilities in Lagos State using descriptive statistics and discovered that the distribution was inequitable between rural and urban areas, and concluded that this disparity could negatively impact the UHC policy in the state. Akintuyi et al. [22], focusing on Surulere LGA of Lagos State, assessed the geographical distribution of healthcare facilities considering population, distance from water bodies, roads, and elevation using a fuzzy analytical hierarchy process. Based on proximity to roads or water bodies, their study suggested optimal locations for new primary healthcare centers to address the observed imbalance in existing facilities.

Consequently, this study explored the use of the MCLP in the location of CVD care facilities in Lagos State, Nigeria, to improve accessibility to care for the people of the state.

2 Materials and methods

2.2 Study area

Lagos State (Figure 1) is located in the southwestern part of Nigeria along the Gulf of Guinea. It is the second most populous state in the country [25], with a population exceeding 13 million [26]. Despite being the smallest state by area, covering only 3,577 km², Lagos contains 22% of Lagos is made up of lagoons and creeks [27]. Its significant economic and commercial importance extends beyond Nigeria, as it serves as a critical endpoint for three major trans-African highways [28]. As Nigeria’s financial capital, Lagos is highly urbanized and industrialized, contributing a significant portion of the national GDP. However, it also faces challenges such as a high prevalence of CVDs and related risk factors among its residents [29,30].

Figure 1

Map of Lagos State showing LGAs, the LASUTH, and GHs. Source: Authors.

3 Results

Out of a total of 6,780 cases, 51.2% were female, and 48.7% were male, with a majority of the cases occurring in individuals aged 50 years or older. When analyzing the relationship between the cases and the distances patients traveled, it was observed that the highest number of cases occurred at a distance of 7,000 m from LASUTH. Specifically, the 12th to 15th buffers, which represent a radius of 6–7.5 km, exhibited the highest concentration of cases. Subsequently, the number of cases decreased sharply and then stabilized after the 15th buffer. It was noted that approximately 70% of the cases were accounted for when the distance reached 10,000 m (Figure 2).

Figure 2

(a) Distances of the residence of cases to the care facility. (b) Buffers of the hospital and the corresponding cases. Source: Authors.

The findings from the maximum cover analysis, as presented in Table 1, indicate that within the 10,000 m distance threshold, LASUTH provides coverage for approximately 70% of the cases, with an average distance traveled of 9,038 m. However, to enhance coverage, the inclusion of a CVD care facility at GH, Isolo (as shown in Figure 3b), resulted in a decrease in the average distance traveled to 7,604 m and an increase in the percentage of patients covered to 79.7%. The addition of another CVD care facility at GH, Ile-Epo (as depicted in Figure 3c), further raised the percentage coverage of patients to 87.4% while reducing the average distance traveled by patients to 6,466 m.

Figure 3

Facility coverages of diagnosed CVD cases in Lagos State: (a) existing facility, (b) one additional facility, and (c) two additional facilities. Source: Authors.

However, considering that the majority of diagnosed cases of CVDs are concentrated in the central region of the state and that the utilization of healthcare facilities decreases as the distance increases, we examined the extent to which the current facility adequately serves the overall population of Lagos State. We also considered potential scenarios for enhancing access to CVD care facilities in the state, as the current number of cases may not accurately reflect the actual burden of the disease in the region.

Using the centroids of the 377 electoral wards in the state as reference points for the population, it was found that the facility located at LASUTH only served 40.3% of the population (Figure 4a), with an average travel distance of 18,788 m (Table 2). To improve the situation, the introduction of an additional facility at GH, Ikorodu (Figure 4b), resulted in a coverage increase of over 25% and a reduction of the average travel distance by more than 3 km. To further enhance accessibility, a third facility established at GH, Ajegunle (Figure 4c), reduced the travel distance by another 2 km, and increased coverage to 70.8%.

Figure 4

Facility coverages of the general population of Lagos State: (a) existing facility, (b) one additional facility, and (c) two additional facilities. Source: Authors.

4 Discussion

In some cases, specialized care and equipment are required for the treatment of certain cardiac ailments, and time is often of the essence. The distribution of cases around LASUTH mirrors what Oviasu [39] found in her study on the effect of travel time on patients’ use of the renal facility in Benin City, Nigeria. Oviasu [39] discovered that patients living far from the hospital are most affected by spatial accessibility to the renal facility within the University of Benin Teaching Hospital. This may have resulted in patients not seeking medical treatment early and thus resorting to alternative avenues for cure. Therefore, our study’s findings align with the established inverse relationship between the distance from a facility and its utilization, a phenomenon known as distance decay [40]. However, this situation is compounded by the fact that in Nigeria, most CVD care facilities are located within tertiary health facilities, which are few and far apart. Consequently, this may lead CVD patients to seek unorthodox care, as posited by Oviasu [39].

The number of diagnosed CVD cases peaked at a distance of 7,000 m from the existing CVD healthcare facility in Lagos State, gradually decreasing thereafter. This pattern indicates that the facility is not adequately serving diagnosed CVD cases, as usage declines with increasing distance. The observed trend in Figure 2a suggests that the 10,000 m mark (20th Buffer, Figure 2b) serves as a significant threshold for clinic attendance among diagnosed CVD patients in Lagos State. Furthermore, both extremes of the state recorded annual average cases of fewer than 20 throughout the study period.

Lagos State has 20 LGAs and 26 GHs [26,41], averaging 1.3 GHs per LGA. Refurbishing existing facilities and enhancing medical staffing and equipment to manage CVD cases could elevate patient coverage from 70 to 87.4% while decreasing the average travel distance from 9,038 to 6,466 m. Decentralizing chronic disease management, particularly for CVDs, presents a viable strategy to alleviate the strain on vulnerable healthcare systems [42]. The current 26 GHs in the state offer a significant opportunity for outreach programs that enable itinerant free-testing to facilitate early CVD detection, potentially mitigating the economic impact of these diseases. Peiris et al. [43] and Mensah et al. [44] indicate that a majority of CVD cases in LMICs affect individuals in their prime, resulting in substantial losses in workforce productivity and man-hours.

To enhance the number of CVD healthcare facilities in Lagos State, the addition of two more facilities at the GHs in Ikorodu and Ajegunle has resulted in a coverage of 70.8% for the entire state’s population, with an average travel distance of approximately 13,000 m. From an economic perspective, implementing these changes in the long term would likely be more advantageous for Lagos State. The introduction of these two new facilities is preferable to selecting the GHs in Isolo and Ile-Epo because they provide greater coverage, thereby enhancing accessibility and utilization of healthcare services. Moreover, due to their geographical locations, the CVD care facilities in Ikorodu and Ajegunle are situated closer to both ends of the state compared to Isolo and Ile-Epo. This improved accessibility has the potential to increase attendance at CVD clinics by residents in rural areas. For instance, the distance from Badagry to Ajegunle is more appealing for patients in Badagry than the distance from Badagry to Ikeja. This observation aligns with previous studies demonstrating a positive correlation between proximity to a facility and its usage [39,45].

In both scenarios, the selected locations for the additional CVD care facilities were more peripheral, which improved accessibility for a wider population. Peripheral locations often experience less traffic congestion compared to central areas, resulting in shorter travel times for users. This makes it easier for them to reach the facility quickly, especially during peak hours. Additionally, these locations facilitate access via multiple transportation modes, thereby increasing overall accessibility [46]. Furthermore, situating facilities in such areas helps integrate them into communities, fostering local engagement and ensuring that services meet the specific needs of those populations. This ultimately leads to better utilization of the facilities [47].

5 Conclusion

The study emphasizes the urgent need for enhanced access to CVD care facilities in Lagos State, Nigeria. It identifies an inverse relationship between the distance to existing facilities and the utilization of CVD care, suggesting that as distance increases, usage decreases. The research advocates for leveraging existing GHs in the state to establish new CVD care facilities. This approach aligns with the strategy of decentralizing chronic disease management, which is crucial for alleviating the burden on fragile healthcare systems. To improve accessibility and utilization, the study proposes the establishment of two additional CVD care facilities at GHs located in Ikorodu and Ajegunle. These locations are strategically chosen due to their proximity to underserved areas of the state compared to existing facilities. Overall, the findings offer valuable insights into optimal locations for CVD care facilities in Lagos State and underscore the importance of increasing coverage and minimizing average travel distances as priorities for health policy in the region.