Abstract
Background
In the face of rapid urbanization, developing countries like Nigeria are witnessing a surge in rural-urban migration, with a predominant concentration in low-income neighborhoods known as slums. The provision of appropriate health services in slums is limited due to their unplanned nature. Similarly, access and utilization of health services are constrained by many factors, especially the unavailability of formal healthcare providers in urban slums. This study investigated challenges and opportunities to the provision and use of health services in urban slums in two states in southeast Nigeria. The study is anchored in Andersen’s Behavioral Model of Health Services Use (1995).
Methods
A cross-sectional survey was conducted in four urban slums in Onitsha in Anambra state, and Enugu in Enugu state. Eligible formal and informal health providers and householders were randomly selected for the interviews. A total of 255 health providers and 1025 primary caregivers in households were randomly selected from the households and interviewed using different structured pre-tested questionnaires for the consumers and providers, respectively. Univariate, bivariate, and multiple regression analyses were used to analyze the data.
Results
The major perceived challenge to accessing healthcare was poverty, since many people stated that they were unable to pay for health services (80%). Other challenges were health system factors such as the lack of drugs in government facilities (52%) and the high cost of treatment (76%). The major challenge to the provision of health services was poor availability of quality medicines (14.9%) and lack of supportive supervision of health workers (9.4%). In multiple regression analysis, the type of health provider’s facility (OR -1.69, CI 0.07–0.51) and training received on the type of health services they provide (OR -0.91, CI 0.21–0.79) were significantly associated with health workers’ provision of healthcare services. The consumers’ utilization of healthcare services was explained by employment status (OR 0.53, CI 1.02–2.83). Respondents suggested that subsidizing or providing free healthcare services, improving the capacity of informal health providers, and the quality of health services provided by the existing health facilities are opportunities for improving health service utilization by urban slum dwellers.
Conclusion
The provision and utilization of appropriate health services in urban slums is severely constrained by multiple, interconnected barriers. Addressing these through targeted financial protection, provider regulation, slum-specific infrastructure and improving the capacity of informal healthcare providers is essential to bridge the gap between demand and supply gap and improve healthcare access and quality in these underserved communities.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-025-13914-z.
Keywords: Healthcare access, Demand and supply side challenges, Urban slum, Nigeria
Background
About one billion people worldwide live in slums. Slum dwellers are individuals who live in conditions lacking one or more of these essentials: stable and adequate housing, access to public health services, easy access to safe drinking water, sufficient living space, and property security.
Confronted by numerous health challenges and often lacking access to even a single government health facility, urban slum dwellers present a unique set of healthcare needs [1]. In addition to inadequate health infrastructure, lack of safe water, suitable food, and other items, living in marginal and poor urban areas is also accompanied by various crimes and social deprivation [2].
Enhancing the health of slum dwellers requires an understanding of both the barriers to accessing and providing quality healthcare services, as well as the opportunities to enhance the delivery and utilization of such services [3]. Urban slum dwellers have limited healthcare choices, leading to poor access and adoption of both formal and informal health providers. There is then a need to highlight their challenges, as studies have shown that the non-recognition of slums in official discourses limits their consideration for essential public service provision and increases inequities, which are major drivers of poor health outcomes [1, 4, 5].
In seeking to understand the barriers and solutions to ensure that there are optimal health services in urban slums, there is a need to move beyond only demand-side challenges that are prevalent in most studies [6, 7], but rather integrate demand and supply-side approaches that are more likely to give a holistic understanding of healthcare access.
Understanding the challenges and barriers to providing and accessing healthcare services in urban slums is critical to identifying the interventions and solutions that can be offered, especially for the most disadvantaged groups, such as urban slum dwellers. Hence, having a comprehensive view of the challenges, both of health and its determinants, and paying attention to the factors beyond the health sector, such as security, finances, registration of informal health providers with appropriate government agencies, etc., would lead to better intervention choices for health provision for slum dwellers.
Hence, this paper provides new knowledge on the enabling and constraining demand and supply factors that determine the pattern of provision and use of health services in urban slums. This study is anchored in Andersen’s Behavioral Model of Health Services Use (1995) [8], which posits that health service utilization and provision are shaped by predisposing, enabling, and need factors at both individual and contextual levels. In urban slums, contextual enabling factors (e.g., cost, facility availability) and provider-level enabling factors (e.g., training, registration) are hypothesized to interact with individual predisposing factors (e.g., employment) to determine access and quality. This framework guides our dual demand-supply analysis and the development of composite Utilization and Provision Indices. The study also provides information on the potential solutions to the identified barriers, and this will help policymakers, program managers and non-governmental organizations develop and implement interventions that will improve healthcare delivery appropriately to specific demographic categories, such as urban slums.
Theoretical framework for the study
Adapted from Andersen’s Behavioral Model of Health Services Use (Version 4, 1995) [8]
]The components of this framework have been described above. It was chosen because it has been widely validated, models both demand-side (utilization) and supply-side (provision) determinants and has contextual (community-level) and provider-level factors.
Methods
The cross-sectional quantitative study was conducted in two cities, in two states in southeast Nigeria. They were Enugu city in Enugu State and Onitsha city in Anambra State. Enugu and Anambra States were purposively selected based on their retention of large expanses of long-existing urban slums and their proximity to each other and to the research team. Furthermore, Onitsha in Anambra State has the largest open drug markets in Nigeria, a situation that exacerbates fake and adulterated drugs alongside thriving informal health service providers [9]. Enugu state has an estimated population of over 4.4 million as of 2016,10 while Anambra State’s projected population as of 2022 is 5.9 million [10]. Enugu and Onitsha have several slums across their busy areas, and the majority (over 90%) of slum dwellers are ethnically diverse migrant populations seeking economic opportunities in the surrounding cities [11]. The slums are characterized by overcrowding, poor sanitation, a polluted environment, and poor infrastructure [12]. Slum dwellers lack many urban basic amenities, such as standard public schools, good roads, and health centers in both cities, as they are informal dwellers; hence, they were not in the original plan of the city [12].
Sampling and sample size
The study has two major population groups: primary caregivers i.e., mostly wives/mothers or grandmothers living in poor urban areas in the study cities. The second population group was the heads of health facilities and individual providers (if operating alone), comprising both formal and informal healthcare providers, who ordinarily live and provide health services in the selected slums. The formal healthcare providers are those skilled providers working in the selected public primary healthcare facilities, while the informal healthcare providers are the patent medicine vendors, bone setters and traditional birth attendants.
For the consumers’ survey, a minimum sample size of 1,025households (512 per state) was determined using the guidelines outlined in the National Demographic Health Survey (NDHS) listing manual (2012) [13], whilst for the providers’ survey, a sample size of 255 facilities was determined using the guidelines outlined in the NDHS manual (2012) [13]. A stratified, two-stage cluster design was used to select the respondents: first was the selection of 8 urban slums stratified by State. The eight informal settlements were purposively selected based on the size of the settlement, availability of different informal health providers, and a functional public primary health center close to the informal settlement. Enugu urban informal settlements included in the study were Afia Nine, Ugbo Oghe, Ngenevu, and Ikilike, while in Anambra state, the informal settlements included were Okpoko 4, Okpoko 5, Ibollo, and Prison Marine. The second stage was the selection of households within each urban slum cluster. A complete household listing and mapping exercise was conducted in each selected cluster before the main fieldwork, serving as the sampling frame for household selection. Equal probability systematic sampling was done to select a fixed number of households per cluster. There was only one public primary health center in each slum, and it was selected for the formal providers survey, while all the mapped informal providers in the selected slum clusters were included in the survey.
Data collection
Data was collected using two pre-tested interviewer-administered questionnaires developed by the study team [14]: one for the providers and another for the consumers. Information was collected from 1025 randomly selected household respondents and 255 healthcare providers.
The consumer questionnaire collected from the households elicited information on the socio-demographic characteristics of respondents, their perceived enablers of and challenges with accessing quality health care services, and their suggested solutions to improving the quality of healthcare services they receive in the urban slums.
The provider questionnaire, collected at the health facilities, elicited information on the socio-demographic characteristics of both formal and informal health providers in the urban slums, the training status for the healthcare services they provide, the registration with any government agency, the number of staff they have in their facilities, their perceived enablers of and challenges with providing quality health care services and their suggested solutions to improving the quality of healthcare services they provide to urban slum dwellers. The questionnaire has been attached as a supplementary file.
Data analysis
Univariate analysis to describe the background characteristics of respondents and perceived challenges and solutions to the identified challenges, whilst cross-tabulations and regression analyses were undertaken to explore statistically significant associations between dependent and independent variables.
Dependent variables
The dependent variables in this study are defined by the outcomes: healthcare service utilization, healthcare service provision, perceived challenges to utilizing and providing healthcare, and perceived solutions to improve utilization and provision of healthcare services.
Independent variables
The explanatory variables are hypothesized to cause or explain the outcome variable. These include type of health provider (formal/informal), training status of providers on the services they provide (trained/ untrained), registration with government regulatory agencies (yes/no), respondents staff size (1–3/4–6/≥7), household size (< 5 persons/ ≥5 persons), age group (≤/25–34/35–44/≥45), education (primary, secondary, tertiary), marital status (never married/ever or currently married), employment status (unemployed/employed) and type of health facility mostly utilized by households (formal/informal/both). Principal component analysis was used to create a variable for the analysis.
Results
The results are presented for the 1025 household respondents and 255 healthcare providers.
Table 1 shows that the mean age of the respondents is 32 years and they were mostly the female head of household who are the primary caregivers (95%). Most of the respondents had finished senior secondary school (60.3%) and were employed.
Table 1.
Socio-demographic characteristics of consumers
| Variables (N = 1,025) | n (%) |
|---|---|
| Respondents gender | |
| Male | 42 (4.1) |
| Female | 983 (95.9) |
| Age group (years) | |
| ≤ 24 | 135 (13.2) |
| 25–34 | 522 (50.9) |
| 35–44 | 291 (28.4) |
| ≥ 45 | 77 (7.5) |
| Mean age of respondents = 32years | |
| Marital status | |
| Divorced/separated | 21 (2.0) |
| Living with spouse | 918 (89.6) |
| Never married | 49 (4.8) |
| Widowed | 37 (3.6) |
| Status in the household | |
| Male head of household | 36 (3.5) |
| Female head of household | 491 (47.9) |
| Wife | 459 (44.8) |
| Grandmother | 2 (0.2) |
| Representative of household | 37 (3.6) |
| Formal education | 1017 (99.2) |
| Highest completed level of education | |
| Some primary | 7 (0.7) |
| Primary | 110 (10.7) |
| Junior secondary | 123 (12.0) |
| Senior secondary | 618 (60.3) |
| Teachers Training College | 23 (2.2) |
| University | 121 (11.8) |
| Others (OND, HND) | 15 (1.5) |
| Employment status | |
| Employed | 827 (87.7) |
| Unemployed | 116 (12.3) |
Table 2 shows that both study cities have almost same number of health provider respondents, with a majority of the respondents being informal healthcare providers (93.3%) and private owners of facilities (94.1%). Amongst the providers, patent medicine vendors are the most (56.9%), and most have received some form of training for the services they provide (78%).
Table 2.
Socio-demographic characteristics of providers (N = 255)
| Variables | n (%) |
|---|---|
| State | |
| Anambra | 128 (50.2) |
| Enugu | 127 (49.8) |
| Respondent’s facility type | |
| Formal | 17 (6.7) |
| Informal | 238 (93.3) |
| Ownership of facility | |
| Private | 240 (94.1) |
| Public | 15 (5.9) |
| Status of respondent in a facility | |
| Head | 213 (83.5) |
| Representative of head | 42 (16.5) |
| Type of service provider | |
| Herbal home | 41 (16.1) |
| Laboratory | 4 (1.6) |
| Nursing/maternity home | 15 (5.9) |
| Patent medicine vendor | 145 (56.9) |
| Pharmacy | 7 (2.7) |
| Primary Health Center | 13 (5.1) |
| Private clinic/practice | 6 (2.4) |
| Spiritual homes | 3 (1.2) |
| Others | 21 (8.2) |
| Received training for the type of services provided | 199 (78.0) |
| Registered with any government agency/body | 105 (41.2) |
Table 3 shows that the demand-side challenges identified are mostly the inability of clients to afford the type of services they need (80%) and high cost of drugs and commodities (66%), as well as lack of supervision of healthcare providers have been identified as supply-side challenges to provision of health services.
Table 3.
Demand- and supply-side perceived challenges with access to and provision of quality health care services in selected urban slums in Enugu and Onitsha cities
| Variables | n (%) |
|---|---|
| Demand-side challenges ( N = 1025) | |
| High cost of treatment | 785 (76.6) |
| No availability of government health facilities | 351 (34.2) |
| Lack of money to pay for treatment | 685 (66.8) |
| Lack of drugs in government facilities | 539 (52.6) |
| Difficulty with transportation to health facilities | 165 (16.1) |
| Distance from the health service | 19 (1.9) |
| Long waiting times in health facilities | 9 (0.9) |
| Poor patients/clients with no money to pay for health services | 205 (80.4) |
| Others (Poor attitude of health workers, untrained health workers, PHCs not always open) | 109 (10.6) |
| Supply-side challenges (N-255) | |
| Lack of supportive supervision | 24 (9.4) |
| Low level of availability of good-quality drugs | 38 (14.9) |
| Insecurity of the health facility | 26 (10.2) |
| Lack of good access road | 82 (32.2) |
| Harassment by government officials | 10 (7.9) |
| High cost of services | 170 (66.7) |
| Lack of basic amenities (such as electricity, water, toilet) | 31 (12.2) |
| Others (e.g., attitude of providers) | 29 (11.4) |
Table 4 shows that there is a statistically significant relationship between the high cost of treatment and healthcare service utilization index (0.001). Other variables that were found to be statistically significant include non-availability of government health facilities (0.004) and lack of money to pay for treatment (0.000). There is a statistically significant relationship between patients with no money to pay for services (0.00), low level of availability of good quality drugs (0.02), high cost of providing healthcare services (0.00) and healthcare service provision index.
Table 4.
Demand- and supply-side challenges associated with healthcare service utilization and provision
| Demand-side variables | High cost of treatment | t-test | p-value |
|---|---|---|---|
| Healthcare service utilization index | Mean = 0.06 | 3.36 | 0.00* |
| Non availability of government health facilities | |||
| Mean = -0.1236 | -2.86 | 0.00* | |
| Lack of money to pay for treatment | |||
| Mean = 0.0805 | 3.68 | 0.00* | |
| Lack of drugs in government facilities | -1.15 | 0.25 | |
| Mean = -0.0340 | |||
| Difficulty with transportation to health facilities | |||
| Mean= -0.0493 | -0.69 | 0.48 | |
| Others (Poor attitude of health workers, untrained health workers, PHCs not always open) | |||
| Mean = -0.0343 | -0.379 | 0.71 | |
| Supply-side variables | Poor patients/clients with no money to pay for services | t-test | p-value |
| Mean | |||
| Healthcare service provision index | -0.80 | -11.31 | 0.00* |
| Lack of supportive supervision | |||
| Mean = -0.09 | -1.51 | 0.13 | |
| Low level of availability of good quality drugs | |||
| Mean = -0.15 | -2.28 | 0.02* | |
| Insecurity at health facilities | -1.52 | 0.13 | |
| Mean= -0.10 | |||
| High cost of health services | -8.72 | 0.00* | |
| Mean = -0.67 | |||
| Harassment by government officials | |||
| Mean = -0.04 | -0.62 | 0.54 | |
| Lack of basic amenities in facilities | |||
| Mean = -0.12 | -1.84 | 0.07* | |
Table 5 shows that some perceived demand side challenges were found to be statistically significant with the following sociodemographic variables: gender (0.000), age (0.000), level of education (0.000) and employment status (0.003).
Table 5.
Relationship between demographic characteristics of respondents and demand side challenges to health care utilization in selected urban slums
| Demographic characteristics | High cost of treatment | No availability of government health facilities | Lack of money to pay for treatment | Lack of drugs in government facilities | Difficulty with transportation to health facilities | Others e.g. (Poor attitude of health workers, untrained staff, etc.) |
|---|---|---|---|---|---|---|
| n(%) | n(%) | n(%) | n(%) | n(%) | n(%) | |
| Gender | ||||||
| Female | 753(95.9) | 327(93.2) | 655(95.6) | 512(95.0) | 163(98.8) | 105(96.3) |
| Male | 32(4.1) | 24(6.8) | 30(4.4) | 27(5.0) | 2(1.2) | 4(3.7) |
| χ2 (p value) | 0.01(0.95) | 10.19(0.00)* | 0.42(0.52) | 2.41(0.12) | 4.17(0.04)* | 0.06(0.81) |
| Formal Education | ||||||
| Yes | 780(99.4) | 348(99.1) | 679(99.1) | 535(99.3) | 163(98.8) | 107(98.2) |
| No | 5(0.6%) | 3(0.9) | 6(0.9) | 4(0.7) | 2(1.2) | 2(1.8) |
| χ2 (p value) | 0.89(0.35) | 0.04(0.85) | 0.24(0.62) | 0.02(0.88) | 0.47(0.49) | 1.75(0.19) |
| Current marital status | ||||||
|
Divorced/ separated |
20(2.5) | 7(2.0) | 16(2.3) | 8(1.50) | 6(3.6) | 1(0.9) |
| Living with spouse | 704(89.7) | 323(92.0) | 606(88.5) | 484(89.8) | 145(87.9) | 100(91.7) |
| Never married | 34(4.3) | 12(3.4) | 33(4.8) | 25(4.6) | 7(4.2) | 6(5.5) |
| Widowed | 27(3.4) | 9(2.6) | 30(4.4) | 22(4.1) | 7(4.2) | 2(1.8) |
| χ2 (p value) | 5.77(0.12) | 4.06(0.26) | 4.50(0.21) | 2.53(0.47) | 2.82(0.42) | 2.03(0.57) |
| Highest completed education level | ||||||
| Primary | 98(12.5) | 33(9.4) | 90(13.1) | 60(11.1) | 25(15.2) | 5(4.6) |
| Secondary | 583(74.3) | 251(71.5) | 502(73.3) | 390(72.4) | 15(69.7) | 77(70.6) |
| Tertiary | 99(12.6) | 64(18.2) | 87(12.7) | 85(15.8) | 23(13.9) | 25(22.9) |
| χ2 (p value) | 24.23(0.00)* | 4.52(0.21) | 16.42(0.00)* | 0.15(0.99) | 3.37(0.34) | 11.09(0.01)* |
| Employment status | ||||||
| Employed | 679(86.5) | 316(90.0) | 593(86.6) | 476(88.3) | 147(89.1) | 101(92.7) |
| Unemployed | 106(13.5) | 36(10.0) | 92(13.4) | 63(11.7) | 18(10.9) | 8(7.3) |
| χ2 (p value) | 4.56(0.03)* | 2.67(0.10) | 2.48(0.39) | 0.39(0.54) | 0.35(0.56) | 2.78(0.09) |
| Number in a household | ||||||
| 5 persons and more | 295(37.6) | 138(39.3) | 264(38.5) | 204(37.8) | 71(43.0) | 40(36.7) |
| Less than 5 persons | 490(62.4) | 213(60.7) | 421(61.5) | 335(62.2) | 94(57.0) | 69(63.3) |
| χ2 (p value) | 0.01(0.93) | 0.63(0.43) | 0.68(0.41) | 0.02(0.89) | 2.42(0.12) | 0.05(0.83) |
| Age of respondents | ||||||
| 18–27 | 225(28.7) | 83(23.7) | 198(28.9) | 150(27.9) | 38(21.0) | 40(36.7) |
| 28–37 | 374(47.2) | 200(57.0) | 325(47.5) | 259(48.1) | 90(54.5) | 44(40.4) |
| 38–47 | 163(20.8) | 58(16.6) | 137(20.0) | 113(21.0) | 31(18.8) | 23(21.1) |
| 48–57 | 20(2.9) | 10(2.7) | 23(3.4) | 17(3.0) | 4(2.4) | 2(1.8) |
| ≥ 58 | 3(0.4) | 0(0.0) | 2(0.2) | 0(0.0) | 2(1.2) | 0(0.0) |
| χ2 (p value) | 11.00(0.14) | 20.77(0.00)* | 1.38(0.99) | 8.36(0.30) | 18.50(0.01)* | 9.10(0.25) |
Table 6 shows the association between perceived supply-side challenges and sociodemographic characteristics of health providers. Some perceived supply side challenges were found to be statistically significant with the following sociodemographic variables: type of provider (0.00), training status of the provider with regards to the services they provide (0.001), registration with any government agency (0.001) and health provider’s staff size (0.00).
Table 6.
Association between perceived supply-side challenges and sociodemographic characteristics of health providers
| Demographic characteristics | Poor patients/clients with no money to pay for services | Lack of supportive supervision | Low level of availability of good quality drugs | Insecurity of health facility | Lack of good access road | High cost of treatment | Lack of basic amenities in health facilities |
|---|---|---|---|---|---|---|---|
| n(%) | n(%) | n(%) | n(%) | n(%) | n(%) | ||
| Type of provider | |||||||
| Formal | 8(3.9) | 0(0) | 2(5.3) | 4(15.4) | 7(8.5) | 4(2.4) | 4(12.9) |
| Informal | 197(96.1) | 24(100) | 36(94.7) | 22(84.6) | 75(91.5) | 166(97.6) | 27(87.1) |
| χ2 (p value) | 12.8(0.00)* | 1.89(0.17) | 0.14(0.70) | 3.54(0.04)* | 0.68(0.41) | 15.25(0.00) * | 2.21(0.14) |
| Training status of provider for type of work done | |||||||
| Trained | 167(81.5) | 13(54.2) | 33(86.8) | 26(100) | 63(76.8) | 143(84.1) | 25(80.6) |
| Untrained | 38(18.5) | 11(45.8) | 5(13.2) | 0(0) | 19(23.2) | 27(15.9) | 6(19.4) |
| χ2 (p value) | 7.15(0.01)* | 8.81(0.00)* | 2.02(0.16) | 8.15(0.00)* | 0.10(0.75) | 10.99(0.00) * | 0.14(0.71) |
| Registration with any government agency | |||||||
| Registered | 87(42.4) | 11(45.8) | 23(60.5) | 13(50.0) | 43(52.4) | 77(45.3) | 14(45.2) |
| Not registered | 118(57.6) | 13(54.2) | 15(39.5) | 13(50.0) | 39(47.6) | 93(54.7) | 17(54.8) |
| χ2 (p value) | 0.69(0.41) | 0.24(0.63) | 6.90(0.01)* | 0.93(0.34) | 6.33(0.01)* | 3.57(0.06) | 0.23(0.63) |
| Health provider’s staff size | |||||||
| 1–3 persons | 190(92.7) | 23(95.8) | 34(89.5) | 21(80.8) | 78(95.1) | 158(92.9) | 29(93.5) |
| 4–6 persons | 11(5.4) | 1(4.2) | 3(7.9) | 1(3.8) | 1(1.2) | 9(5.3) | 2(6.5) |
| 7 and more | 4(1.9) | 0(0.0) | 1(2.6) | 4(15.4) | 3(3.7) | 3(1.8) | 0(0.0 |
| χ2 (p value) | 3.07(0.22) | 0.92(0.63) | 0.33(0.85) | 17.4(0.00)* | 5.02(0.08) | 2.22(0.33) | 1.01(0.61) |
*p < 0.05
Table 7 shows that the consumers’ perceived high cost of treatment as a challenge to accessing healthcare services are explained by employment status (OR 0.53, CI 1.02–2.83). Health providers’ perceived challenges of lack of funds to pay for healthcare and high cost of providing health services was predicted by the type of health provider’s facility (OR -1.69, CI 0.07–0.51) and the training received by the health service provider (OR -0.91, CI 0.21–0.79), while perceived lack of quality drugs as a challenge was predicted by health providers being registered with a government agency (OR -0.93, CI 0.18–0.88).
Table 7.
Logistic regression of the demand- and supply-side challenges and respondents’ characteristics
| Explanatory variables (Demand side) | OR | Z | p-value | 95% CI |
|---|---|---|---|---|
| High cost of treatment | ||||
| Gender | -0.05 | 0.02 | 0.89 | 0.46–1.97 |
| Age | -0.14 | 0.79 | 0.37 | 0.64–1.18 |
| Formal education | -0.64 | 0.76 | 0.38 | 0.13–2.23 |
| Household size | 0.12 | 0.55 | 0.46 | 0.83–1.52 |
| Employment status | 0.53 | 4.11 | 0.04* | 1.02–2.83 |
| Marital status | -0.49 | 2.20 | 0.14 | 0.32–1.17 |
| Lack of money to pay for treatment | ||||
| Gender | -0.22 | 0.39 | 0.53 | 0.41–1.59 |
| Age | -0.16 | 1.38 | 0.24 | 0.65–1.11 |
| Formal education | 0.38 | 0.22 | 0.64 | 0.29–7.32 |
| Household size | ||||
| Employment status | 0.29 | 1.79 | 0.18 | 0.87–2.04 |
| Marital status | 0.33 | 2.46 | 0.12 | 0.92–2.12 |
| Lack of drugs in government facilities | ||||
| Gender | -0.50 | 2.36 | 0.12 | 0.32–1.15 |
| Age | 0.13 | 1.05 | 0.31 | 0.88–1.46 |
| Formal education | -0.13 | 0.03 | 0.85 | 0.22–3.53 |
| Household size | 0.05 | 0.14 | 0.71 | 0.82–1.35 |
| Employment status | 0.08 | 0.17 | 0.68 | 0.74–1.59 |
| Marital status | -0.12 | 0.39 | 0.54 | 0.61–1.29 |
| Explanatory variables (Supply-side) | ||||
| Clients with no money to pay for services | ||||
| Type of respondents’ facility | -1.69 | 10.72 | 0.00* | 0.07–0.51 |
| Trained for the job | -0.91 | 6.88 | 0.01* | 0.21–0.79 |
| Years of formal education received for the health work you provide | -0.08 | 2.42 | 0.12 | 0.84–1.02 |
| Registered with a government agency | -0.30 | 0.84 | 0.36 | 0.39–1.41 |
| Staff size | -0.05 | 0.48 | 0.49 | 0.82–1.09 |
| Lack of quality drugs | ||||
| Type of respondents’ facility | -0.76 | 0.91 | 0.34 | 0.10–2.21 |
| Trained for the job | -0.24 | 0.18 | 0.67 | 0.26–2.39 |
| Years of formal education received for the health work you provide | -0.02 | 0.29 | 0.59 | 0.89–1.06 |
| Registered with a government agency | -0.93 | 5.15 | 0.02* | 0.18–0.88 |
| Staff size | 0.03 | 0.18 | 0.67 | 0.88–1.21 |
| High cost of providing health services | ||||
| Type of respondents’ facility | -2.61 | 17.52 | 0.00* | 0.02–0.25 |
| Trained for the job | -0.99 | 8.11 | 0.00* | 0.19–0.73 |
| Years of formal education received for the health work you provide | 0.05 | 1.99 | 0.16 | 0.98–1.14 |
| Registered with a government agency | -0.58 | 2.83 | 0.09 | 0.28–1.10 |
| Staff size | -0.21 | 6.22 | 0.01* | 0.68–0.95 |
OR = Odds ratio; CI = Confidence Interval *p < 0.05
Table 8 shows that subsidizing (63.5%) or providing free health care services (71.6) and improving the quality of health services provided by the existing government facilities (56.4) are opportunities to improve access to and utilize health care services by urban poor populations. Ensuring the availability of drugs in government health facilities (55%), improving the capacity of informal healthcare providers (43%), and providing government health facilities in urban slums (39%) are opportunities to improve the provision of quality healthcare services for urban slum dwellers.
Table 8.
Demand- and supply-side perceived solutions to improve access to and provision of quality healthcare services in Enugu and Onitsha urban slums
| Variables (Solutions) N = 1025 | n (%) |
|---|---|
| Suggested solutions for improving demand for healthcare services | |
| Providing free health care services | 734 (71.6) |
| Subsidizing healthcare services | 651 (63.5) |
| Use of health insurance payment strategy | 113 (11.0) |
| Provision of more public health centres | 135 (13.2) |
| Construction of more private hospitals | 41 (4.0) |
| Improving the quality of health services in existing facilities | 578 (56.4) |
| Employment of more health workers in public health facilities | 164 (16.0) |
| Addressing the attitude of health workers | 180 (17.6) |
| Suggested solutions for improving the provision of services ( N = 255) | |
| Building of government health centers in a slum neighborhood | 100 (39.2) |
| Ensuring the availability of drugs in government health facilities | 141 (55.3) |
| Improving the quality of treatment of drug sellers and informal providers | 112 (43.9) |
| Provision of easy transportation facilities for going to health facilities | 67 (26.3) |
| Provision of free essential health services for pregnant women and children | 63 (24.7) |
| Do not know | 15 (5.9) |
| Not bothered | 23 (9.0) |
| Others (e.g. provision of security) | 39 (15.3) |
Summary of findings as adapted from andersen’s framework
| Level | Andersen Component | Shown in the Study | Key Findings |
|---|---|---|---|
| Contextual (Community) | Predisposing |
Rapid urbanization, poverty, slum density Cultural trust in IHPs |
80% cite poverty as barrier - High reliance on informal providers |
| Enabling |
Availability of formal/informal facilities Cost, subsidies, insurance |
High cost of treatment (76%) - Lack of govt facilities (p = 0.004) | |
| Need | Perceived illness burden | High demand for basic curative care | |
| Individual (Demand-Side) | Predisposing | Age, gender, education, and employment | Employment status predicts cost perception (OR 0.53, p = 0.003) |
| Enabling | Income, ability to pay, social support | 80% cannot afford services | |
| Need | Self-reported health status | Caregivers seek care for children | |
| Provider (Supply-Side) | Predisposing | Type of provider (formal/informal), training | Training status predicts provision barriers (OR -0.91) |
| Enabling | Registration, staff size, drug supply, supervision |
Registration, Better drug quality (OR -0.93) - Lack of supervision (9.4%) |
|
| Need | Perceived service demand | IHPs report high workload | |
| Health Behavior | Personal Health Practices | Care-seeking from IHPs vs. formal | 70% first contact with IHPs |
| Use of Health Services | Utilization Index | High cost (p = 0.001) | |
| Outcomes | Perceived Health Status | -Satisfaction, trust | Improved if the cost is reduced |
| Consumer Satisfaction | Willingness to pay, recommendations | 71.6% want free care |
Discussion
This study identified several challenges to accessing and utilizing healthcare services by urban slum dwellers. It also elaborated on the challenges to the provision of health services by both formal and informal urban slum healthcare providers in southeast Nigeria. The opportunities to address the challenges were also identified, which, when adopted, could lead to improved access and utilization of health services in urban slums.
The finding that socioeconomic status was the most perceived challenge to accessing healthcare, and that poverty-driven inability to pay dominates demand-side constraints, is similar to findings documented by other studies [15–18]. Sociodemographic stratification further underscores equity gaps: women, youth, the uneducated, and the unemployed perceive demand-side barriers more acutely. Also, the consequent financial inaccessibility to health services featured prominently in most of the studies on healthcare access for residents in vulnerable urban settlements [15–18].
The constraint from socio-economic factors points to the need to use innovative financing mechanisms that will reduce the economic burden of payment to the consumers, examples are conditional cash transfers [19, 20], health equity funds [21, 22], introducing pre-payment schemes, such as an adoption model of social health insurance strategy [23], and prioritizing the use of government-owned primary health centres due to their reduced cost of health services [24].
The finding that lack of medicines, especially in government-owned hospitals, was identified as a major challenge to both provision and utilization of healthcare services is consistent with previous studies that highlighted lack of medicine as a major barrier to both utilization and provision of quality healthcare services [25–27]. Hence, the need for the government’s attention on this, introduction of partnership schemes that can help revive the drug revolving fund schemes in such places and the inclusion of the health centres located in urban slums in the current basic health care provision fund program nationwide.
The finding that community factors such as lack of basic amenities, poverty, insecurity, and non-availability of government health facilities were the greatest challenges to the utilization of healthcare by slum dwellers shows that issues from non-health sectors affect the provision and use of health services in urban slums. Some of these factors have been identified in previous studies as major barriers to healthcare utilization [28–32]. Similarly, lack of security has been particularly known to pose a major barrier to accessing healthcare in other urban slums in Nigeria, due to the high rate of crimes in such settlements [33–35]. This has implications regarding attracting government and private partnerships for health interventions, thus increasing health inequities and jeopardizing efforts to achieve UHC in Nigeria.
Untrained, unregistered, and small-scale informal providers report disproportionately higher supply constraints. Improving the quality of health services provided by both the formal and informal health providers in the urban slums was also stated as an opportunity to improve the provision and utilization of health care services by the urban slum dwellers. This corroborates with similar strategies identified in previous studies that have been found successful in improving health outcomes [20, 33, 36]. Advocating to policymakers on the need to build the capacity of the informal providers and intermittently refresh the knowledge of the formal health providers is essential in improving the quality of care provided by these diverse groups of essential health workers. This is important as informal health workers have been found to play a vital role in the urban slums due to limited access to formal healthcare facilities [37, 38].
The limitation of this study is that it did not tease out the various complex relationships between the numerous determinants and their interlinkages with the multiple contexts of urban slums, as this would need in-depth analysis to harmonize such evidence. This study would have benefited from a qualitative lens that would have explored the reasons behind these quantitative findings. Also, broadening the sample to include equal numbers of male and female caregivers will provide a richer analysis of the impact of gender on access to and utilization of health services in urban slums. However, this study has some merits as it provides multivariate evidence that unemployment, not just income, drives cost-related healthcare avoidance in Nigerian urban slums, while provider registration status significantly predicts perceived drug quality, which suggests targeted job-linked health subsidies. The use of dual lenses of demand and supply in the same study, and the study also quantifies the magnitude of poverty and establishes it as the dominant demand-side constraint, surpassing distance, awareness, or trust. This will serve as a useful tool for advocacy and lobbying to attract health interventions to the urban slum settings.
Conclusion
This study provides insights into the complex landscape of challenges that health providers and slum dwellers navigate to access and use healthcare services anchored in Andersen’s Behavioral Model of Health Services Use. Findings reveal that poverty-driven inability to pay dominates demand-side constraints, while poor drug availability and lack of supportive supervision are the leading supply-side bottlenecks. Multivariate regression confirms that employment status is the strongest predictor of perceived cost barriers among consumers, whereas facility type and provider training significantly shape supply-side challenges. These associations highlight enabling resources such as cost, regulation of informal health providers and capacity building as the critical leverage points for improving access and quality in underserved urban settings.
Sociodemographic stratification further underscores equity gaps: women, youth, the uneducated, and unemployed perceive demand-side barriers more acutely, while untrained, unregistered, and small-scale informal providers report disproportionately higher supply constraints. Respondents prioritize free healthcare, subsidies, and improved government facility quality as high-impact opportunities, thereby indicating a clear preference for direct cost relief over provider-focused interventions alone.
Recommendation
Coordinated provision of health services across formal and informal providers is required to improve health service provision in slum communities in Nigeria. This study recommends the introduction of free or heavily subsidized essential health packages in urban slums through innovative measures such as conditional cash transfers, health equity funds, and providing state or community-based health insurance. Other recommendations include establishing mandatory registration and regular training for informal health providers; deploying monthly supportive supervisory visits with performance-based incentives to motivate the formal health providers; and upgrading the functional PHCs in these major slum clusters, ensuring that they are secure and that they have affordable quality medicines. These could help address demand and supply side challenges to affordable health care in underserved urban settings. Efforts at implementing the suggested solutions towards providing healthcare services will need partnerships with civil society organizations and non-governmental organizations, as well as engaging the policymakers to attract interventions based on the urban slum dwellers’ felt needs and to advocate for National Urban Slum Health Policy.
These recommendations, grounded in Andersen’s model, target enabling resources at contextual, individual, and provider levels. Their implementation could reduce financial barriers and increase service utilization in the underserved urban settings.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
CHORUS study is funded by UK Aid, from the UK Government, Grant 301132. We are grateful to all our respondents.
Author contributions
C.O contributed to the study design, material preparation, data collection and analysis, and drafting and review of the manuscript. U.E participated in material preparation, data collection and analysis, and review of the manuscript. B.E participated in material preparation and review of the manuscript. O.O contributed to the study conception and design, material preparation and review of the manuscript.
Funding
This study in Nigeria was part of the larger CHORUS Research Consortium, that was funded by the UK Aid from the UK Government, Grant 301132. The views expressed do not necessarily reflect the UK government’s official policies.
Data availability
The dataset(s) supporting the conclusions of this article is(are) included within the article.
Declarations
Ethics approval and consent to participate
The Ethical approval for this study was obtained from the University of Leeds School of Medicine Research Ethics Committee (MREC 21–009) and the Health Research Ethics Committee of the University of Nigeria Teaching Hospital, Enugu (NHREC/05/01/2008B-FWA00002458-1RB00002323). The study was performed per the Declaration of Helsinki. Written informed consent to participate in the study was obtained from all participants before the questionnaires were administered. Participation in this study was voluntary, and all participants were informed of the purpose of the study and their rights, including that they could withdraw their participation at any time they wished during data collection and before data analysis commenced.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The dataset(s) supporting the conclusions of this article is(are) included within the article.