Despite the recognised importance of health-system inputs and financing, the presence of medicines, healthcare personnel, medical equipment and infrastructure does not necessarily result in effective UHC service provision. Public health facilities may possess these inputs but still experience difficulties in maintaining continuous and quality service delivery when financing is inadequate or unpredictable. Financial constraints can affect medicine procurement, staff recruitment and retention, equipment maintenance, infrastructure development and routine facility operations. Consequently, the effectiveness of one health-system input may depend partly on the financial resources available to utilise and sustain it.

Finance may therefore strengthen or weaken the relationship between health-system inputs and UHC provision. For example, medical equipment is likely to contribute more effectively to healthcare delivery when funds are available for maintenance, repairs, electricity and consumables. Similarly, adequate healthcare personnel may improve service delivery when sufficient funds are available for recruitment, training, supervision and staff motivation. Finance may also support the continuous procurement of medicines and the maintenance of hospital infrastructure. Although health-system resources and health financing are widely recognised as important determinants of healthcare delivery, limited attention has been given to whether facility-level finance changes the strength of the relationships between these inputs and UHC provision in Lusaka Province.

The study therefore examined the direct relationships between four health-system inputs and UHC provision and assessed whether facility-level finance moderates these relationships in public health facilities in Lusaka Province. This distinction is important because finance may contribute directly to UHC provision while also changing the extent to which available medicines, personnel, equipment and infrastructure are translated into healthcare services.

The study was guided by the following objectives:

To examine the relationship between drug availability and the provision of UHC services.To assess the relationship between healthcare personnel and the provision of UHC services.To determine the relationship between medical equipment and the provision of UHC services.To examine the relationship between health facility infrastructure and the provision of UHC services.To assess the moderating effect of finance on the relationships between health-system inputs and UHC service provision.

The study tested the following hypotheses:

H1: Drug availability is significantly associated with UHC service provision.

H2: Healthcare personnel are significantly associated with UHC service provision.

H3: Medical equipment is significantly associated with UHC service provision.

H4: Health facility infrastructure is significantly associated with UHC service provision.

H5: Finance significantly moderates the relationships between drug availability, healthcare personnel, medical equipment, health facility infrastructure and UHC service provision.

Universal Health Coverage is concerned with ensuring that individuals and communities receive the health services they need without suffering financial hardship. The WHO identifies access, quality, equity and financial protection as essential elements of UHC ([12]). The [14] further emphasise that progress towards UHC must be assessed through both service coverage and financial protection. A health system may provide a wide range of services but still fail to achieve UHC if those services are inaccessible, unaffordable or of poor quality.

At the facility level, UHC provision can be reflected in the availability of essential services, affordability of care, equitable treatment, patient satisfaction, service quality, efficiency and protection from excessive out-of-pocket expenditure. These dimensions depend on the capacity of facilities to maintain adequate resources and organise service-delivery processes effectively.

Essential medicines are central to the prevention, diagnosis and treatment of disease ([12]). Drug availability involves the consistent presence of required medicines, reliable procurement and supply systems, adequate stock levels and the ability of patients to obtain prescribed medicines. Medicine shortages may lead to delayed treatment, referrals, avoidable complications and increased out-of-pocket expenditure ([14]).

From a systems perspective, medicines are one component of a broader service-delivery system. Their contribution depends on the availability of qualified health-care personnel, diagnostic capacity, storage facilities, procurement systems and operational financing ([11]). Consequently, drug availability is expected to have a positive relationship with UHC provision. However, the strength of this relationship may depend on finance because funds are needed for procurement, storage, distribution and stock management ([12]).

Healthcare personnel include doctors, nurses, midwives, clinical officers, pharmacists, laboratory staff, community health workers and other cadres involved in the provision of health services. The availability and adequacy of personnel affect waiting times, continuity of care, quality of treatment, patient communication and the range of services that a facility can provide ().

Staff shortages may increase workload, reduce morale and compromise the quality of care. Conversely, adequate staffing may improve service availability, responsiveness and patient satisfaction. Healthcare personnel also require financial support through salaries, allowances, training, supervision and appropriate working conditions. Finance may therefore strengthen the relationship between staffing and UHC provision by enabling facilities to retain personnel, provide training and support effective service delivery ([12]; [8]).

Medical equipment supports diagnosis, treatment, monitoring and emergency care. Its contribution to healthcare delivery depends on availability, functionality, reliability, maintenance and the presence of personnel who can operate it ([13]). Equipment that is unavailable or non-functional may reduce the quality and range of services provided. Medical equipment is also financially demanding. Facilities require funds for procurement, installation, calibration, repairs, replacement parts, electricity and staff training. Thus, the mere presence of equipment does not guarantee improved UHC provision. Finance may strengthen the relationship between equipment and UHC by ensuring that equipment remains functional and integrated into routine service delivery ([12]).

Health facility infrastructure includes buildings, wards, consultation rooms, waiting areas, laboratories, pharmacies, water systems, sanitation, electricity, waste-management facilities and other physical structures supporting healthcare delivery. Adequate infrastructure promotes patient safety, privacy, infection prevention and service accessibility ([13]). Inadequate infrastructure may result in overcrowding, poor sanitation, long waiting times and reduced patient satisfaction. Infrastructure improvements may also be ineffective when adequate personnel, medicines, equipment and operational funds do not accompany them. Finance is therefore expected to moderate the infrastructure-UHC relationship by supporting construction, rehabilitation, utilities, maintenance and the operationalisation of infrastructure investments ([11]; [12]).

Health financing refers to the mobilisation, allocation, management and utilisation of financial resources for healthcare delivery. It includes government funding, health insurance arrangements, donor resources, internally generated funds and other financing mechanisms. The [12] argues that health financing should promote access to needed services while protecting households from financial hardship.

Finance can influence UHC through at least three mechanisms. First, it provides resources for purchasing health-system inputs. Second, it supports the operation and maintenance of facilities. Third, it determines whether available inputs can be effectively converted into healthcare services. In this study, finance was therefore conceptualised not only as a direct predictor of UHC but also as a moderator of the relationships between health-system inputs and UHC provision ([1]; [5]).

Systems Theory, associated with [11], views organisations as interconnected systems whose performance emerges from the interaction of their components. A public health facility is an open system that receives inputs from its environment, transforms those inputs through administrative and clinical processes, and produces healthcare services and outcomes ([7]). The theory is relevant because medicines, personnel, equipment, infrastructure and finance are not independent resources. A shortage in one component may reduce the effectiveness of the others. For example, healthcare personnel may be unable to provide appropriate treatment when essential medicines are unavailable. Similarly, equipment may remain unused when trained operators or maintenance funds are absent. Systems Theory, therefore, supports the expectation that the four health-system inputs will be positively associated with UHC provision and that finance may influence the strength of these relationships.

The Input-Process-Output model, developed by systems theorists and widely associated with the systems approach to organisational analysis, explains how organisational resources are transformed into outputs through operational processes ([11]; [6]). In the present study, drugs, healthcare personnel, medical equipment and infrastructure represent inputs. Clinical, administrative and public-health activities constitute the transformation processes, while UHC service provision represents the intended output. The model suggests that inputs create the potential for service delivery, but this potential must be realised through effective organisational processes. Finance supports the transformation process by providing resources for procurement, maintenance, staffing, utilities and management ([12]). The model therefore provides a basis for examining finance as a factor that conditions the relationship between health-system inputs and UHC service provision.

[3] framework distinguishes between structure, process and outcome. Structural elements include human resources, equipment, buildings and other organisational resources. Processes refer to the activities involved in providing care, while outcomes refer to the effects of healthcare delivery. The four independent variables in this study are primarily structural conditions. UHC provision represents an outcome-oriented construct incorporating accessibility, affordability, equity, quality, efficiency, service availability, patient satisfaction and financial protection. Finance supports both structure and process by enabling facilities to acquire, maintain and utilise resources.

The study adopted a positivist research philosophy. Positivism was considered appropriate because the study sought to measure relationships among clearly defined variables and test hypotheses using quantitative statistical techniques. A quantitative explanatory cross-sectional design was employed. The explanatory orientation was appropriate because the study examined the relationships between health-system inputs and UHC provision and assessed whether finance changed the strength of those relationships ([2]).

The study was conducted in Lusaka Province, Zambia, within facilities forming part of the public health delivery system. The sampling frame comprised 194 public health facilities operating at primary/community, intermediate and hospital levels. The analytical unit was the individual healthcare professional. Eligible respondents were healthcare professionals whose roles provided direct knowledge of facility operations, resource availability, financing and service delivery. The frame comprised facilities owned by the Ministry of Health together with mission and faith-based facilities operating under formal Ministry of Health partnership arrangements, reflecting their role in the public health delivery system.

The facility was the sampling unit. Applying [15] formula to the 194 facilities at a 5% margin of error gave a sample of 130 facilities, allocated proportionally across the five facility levels as shown in Table 1, with simple random sampling applied within each stratum. At each selected facility, one eligible healthcare professional completed the questionnaire, drawn from the clinical, pharmaceutical, laboratory or administrative functions on the basis of direct knowledge of facility operations, resource availability, financing and service delivery. The study retained 104 completed questionnaires, representing an 80% response rate. Because each facility was represented by a single respondent, the 104 responses correspond to 104 separate facilities.

Table 1.Sampling framework.

^aTwo eligible questionnaires were returned from the Level 3 stratum although the proportional target was one; both were retained in the achieved respondent sample.

The achieved sample comprised 104 respondents, representing an 80% response rate. Two eligible questionnaires were returned from the Level 3 stratum although the proportional target was one, and both were retained as fieldwork over-recruitment. The 104 respondents were drawn from 104 separate facilities, one respondent per facility. No respondents were clustered within facilities, responses are therefore independent across facilities, and no clustering or multilevel adjustment was required. The effective sample size for the regression analysis is 104.

Data were collected using a structured self-administered questionnaire adapted from the WHO Health Systems Building Blocks Framework and the WHO—World Bank UHC Monitoring Framework. The questionnaire contained 60 construct items distributed equally across six constructs: 10 items for drug availability, 10 for healthcare personnel, 10 for medical equipment, 10 for health facility infrastructure, 10 for finance and 10 for UHC provision. Items were scored on a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. Construct scores were obtained by aggregating the corresponding items. Three items were worded so that agreement indicates a resource constraint rather than a favourable condition: one in the drug availability construct, one in the medical equipment construct and one in the finance construct. These items were reverse-scored before aggregation. Higher construct scores therefore indicate more favourable conditions.

Drug availability was measured with 10 items concerning essential medicine availability, stock levels, procurement reliability, access to prescribed medicines and disruption caused by shortages. Healthcare personnel were measured with 10 items covering staffing adequacy, training, professionalism, communication, motivation, workload, commitment and timely service provision. Medical equipment was measured with 10 items covering availability, functionality, diagnostic usefulness, maintenance, reliability and treatment delays. Health facility infrastructure was measured with 10 items covering wards, consultation rooms, waiting areas, sanitation, utilities, physical condition, overcrowding and contribution to service delivery.

Finance was measured with 10 facility-financing items concerning adequacy, availability, predictability, allocation and utilisation of financial resources for procurement, staffing, equipment, infrastructure and routine operations. UHC provision was measured with 10 outcome-oriented items covering access, affordability, equity, quality, efficiency, patient satisfaction, service availability and financial protection. The finance and UHC item sets were distinct: no identical questionnaire item was included in both constructs. Nevertheless, some finance items refer to service access or quality, while UHC items measure those outcomes directly. This conceptual proximity is acknowledged; finance was operationalised as the availability and management of facility-level financial resources, whereas UHC was operationalised as the reported service-delivery outcome. In particular, UHC financial-protection and affordability items were not used as measures of facility finance. Because the finance and UHC constructs were measured through the same respondent and showed a relatively high bivariate correlation (r = 0.758), the manuscript treats their conceptual proximity as a measurement limitation rather than assuming complete construct separation.

Drug availability was measured through 10 items concerning essential medicine supplies, stock levels, procurement reliability, access to prescribed medicines and the extent to which medicine shortages disrupted treatment. Healthcare personnel were measured through 10 items covering staffing adequacy, training, professionalism, communication, motivation, workload, commitment and timely service provision. Medical equipment was measured through 10 items covering availability, functionality, diagnostic usefulness, maintenance, reliability and equipment downtime. Health facility infrastructure was measured through 10 items covering wards, consultation rooms, waiting areas, sanitation, utilities, physical condition, overcrowding and contribution to service delivery. Finance was measured through 10 facility-level financing items concerning adequacy, availability, predictability, allocation and utilisation of financial resources for procurement, staffing, utilities, maintenance and routine operations.

The UHC provision was measured through 10 outcome-oriented items covering accessibility, affordability, equity, quality, efficiency, patient satisfaction, service availability and financial protection. The finance and UHC item sets were distinct: affordability and financial-protection items were used to measure UHC provision, not facility finance. Thus, finance represents the financial resources and financing conditions available to facilities, whereas UHC provision represents the reported service-delivery outcome. The wording of the UHC items was designed to capture service-delivery outcomes rather than the availability of financial resources to facilities; nevertheless, the conceptual proximity between finance and UHC is acknowledged as a limitation.

A pilot study involving 15 respondents was conducted to assess clarity, relevance, sequencing, completion burden and preliminary internal consistency. Feedback from the pilot informed revisions to wording, sequencing and questionnaire flow before the main survey. The final instrument retained 60 construct items. Cronbach’s alpha coefficients were 0.89 for drug availability, 0.91 for healthcare personnel, 0.87 for medical equipment, 0.88 for hospital infrastructure, 0.92 for finance and 0.90 for UHC provision. The overall instrument alpha was 0.90. These coefficients indicate high internal consistency ([10]).

Since all study constructs were measured using one self-report instrument at one point in time, common method variance (CMV) was considered. The study used Harman’s single-factor test together with procedural remedies. The first unrotated factor accounted for less than 50% of total variance. This result reduces, but does not eliminate, concern about common method bias; Harman’s test has recognised limitations and is therefore treated as a diagnostic rather than definitive proof that CMV is absent ([4]; [9]).

Data were analysed using SPSS. Descriptive statistics and Pearson’s product-moment correlations were used to summarise the constructs and examine bivariate associations. Moderation was tested through hierarchical multiple regression in three explicitly specified steps. All continuous predictors were mean-centred before interaction terms were calculated.

Step 1—Health-system inputs model:

Step 2—Health-system inputs plus finance:

Step 3—Full moderation model:

where UHC denotes UHC service provision; Drugs denotes drug availability; Personnel denotes healthcare personnel; Equipment denotes medical equipment; Infrastructure denotes health facility infrastructure; Finance denotes facility-level finance; and ε denotes the error term. Step 1 estimates the contribution of the four health-system inputs. Step 2 estimates the incremental contribution of finance after controlling for those inputs. Step 3 tests whether finance changes the strength of the four input-UHC relationships. Moderation is assessed from the interaction terms and their incremental contribution to explained variance, while the finance main-effect coefficient represents the direct association of finance conditional on the other predictors.

Normality, multicollinearity and residual independence were assessed before regression analysis. The reported diagnostic results indicated no significant departures from normality. The Durbin-Watson statistics were 1.89 for Step 1 and 1.92 for the full model, suggesting no substantial autocorrelation in the residuals.

The correlation analysis (Table 2) indicated positive and statistically significant

Table 2.Pearson correlation coefficient.

**Correlation significant at 0.01 level (2-tailed).

relationships between all the predictor variables and UHC provision. Finance had the strongest correlation with UHC provision, followed by healthcare personnel, medical equipment, drug availability and hospital infrastructure. The results suggest that facilities reporting better availability of medicines, personnel, equipment, infrastructure and finance also tended to report stronger UHC provision.

The correlations among the independent variables were moderate to strong but did not indicate a reason to exclude any of the predictors from the regression analysis.

Step 1: Health-system inputs model. The four health-system inputs were entered simultaneously. The model produced R = 0.742 and R² = 0.551 (adjusted R² = 0.532), indicating that drugs, personnel, equipment and infrastructure jointly explained 55.1% of the variance in UHC provision. The model was statistically significant, F(4, 99) = 27.18, p < 0.001. The coefficient results were: drug availability, B = 0.214, β = 0.238, t = 3.01, p = 0.003; healthcare personnel, B = 0.268, β = 0.281, t = 3.88, p < 0.001; medical equipment, B = 0.192, β = 0.201, t = 2.59, p = 0.011; and hospital infrastructure, B = 0.173, β = 0.182, t = 2.40, p = 0.018.

Finance was entered after the four health-system inputs, allowing its direct contribution to be distinguished from the interaction effects entered in Step 3. The five-predictor model explains R² = 0.729 (adjusted R² = 0.715), compared with R² = 0.551 in Step 1, an increase of ΔR² = 0.178. The corresponding standardised finance coefficient is β = 0.219 in this five-predictor specification. As noted beneath Table 3, the Step 2 fit statistics are derived from the correlation matrix and n = 104.

Table 3.Hierarchical regression model fit.

Note: Step 2 fit statistics are derived from the correlation matrix and n = 104.

Step 3: Full moderation model. The four interaction terms were added to the Step 2 model. The full model produced R = 0.864, R² = 0.747 and adjusted R² = 0.720. Relative to Step 2, the interaction block contributes ΔR² = 0.018. Relative to Step 1, finance and the four interaction terms together contribute ΔR² = 0.196, ΔF(5, 94) = 14.68, p < 0.001. The evidence for moderation is assessed from the individual interaction coefficients and the conditional slopes reported in Section 6.5. The full model was statistically significant, F(9, 94) = 27.67, p < 0.001.

In the full nine-predictor model, finance had a positive direct association with UHC provision (B = 0.312, SE = 0.075, β = 0.298, t = 4.16, p < 0.001; variance inflation factor (VIF) = 2.40). This coefficient represents the direct finance effect conditional on the four health-system inputs and the interaction terms.

The four interaction terms were positive and individually statistically significant in the full model. Drug availability × Finance: B = 0.118, SE = 0.045, β = 0.142, t = 2.62, p = 0.010; Healthcare Personnel × Finance: B = 0.135, SE = 0.048, β = 0.155, t = 2.81, p = 0.006; Medical Equipment × Finance: B = 0.109, SE = 0.047, β = 0.128, t = 2.32, p = 0.022; and Health Facility Infrastructure × Finance: B = 0.121, SE = 0.046, β = 0.139, t = 2.63, p = 0.010. The positive interaction coefficients indicate that the relationships between the four health-system inputs and UHC provision become more positive as finance increases.

At low finance (−1 standard deviation [SD]), mean finance, and high finance (+1 SD), the estimated slopes for drug availability were 0.032, 0.142, and 0.252, respectively; for healthcare personnel, they were 0.069, 0.195, and 0.321, respectively; for medical equipment, they were 0.030, 0.131, and 0.232, respectively; and for health facility infrastructure, they were 0.007, 0.120, and 0.233, respectively. Each slope was computed as the full-model input coefficient ± (interaction coefficient × SD of finance), with SD of finance = 0.93.

Table 4 presents the interaction effects between facility-level finance and the four health-system inputs. The interaction between drugs and finance was positive and statistically significant (B = 0.118, SE = 0.045, β = 0.142, t = 2.62, p = 0.010), indicating that higher levels of finance strengthened the positive relationship between drug availability and UHC provision. Similarly, the personnel × finance interaction was positive and statistically significant (B = 0.135, SE = 0.048, β = 0.155, t = 2.81, p = 0.006), demonstrating that finance strengthened the relationship between healthcare personnel and UHC provision.

Table 4. Interaction effects.

Note: B = unstandardised coefficient; SE = standard error; β = standardised coefficient.

The equipment × finance interaction was also positive and statistically significant (B = 0.109, SE = 0.047, β = 0.128, t = 2.32, p = 0.022), indicating that the contribution of medical equipment to UHC provision increased with higher levels of facility-level finance. Finally, the infrastructure × finance interaction was positive and statistically significant (B = 0.121, SE = 0.046, β = 0.139, t = 2.63, p = 0.010), showing that finance strengthened the relationship between health facility infrastructure and UHC provision.

Overall, all four interaction terms were positive and statistically significant, demonstrating that facility-level finance significantly strengthened the relationships between drugs, healthcare personnel, medical equipment, health facility infrastructure and UHC service provision. The largest standardized interaction coefficient was observed for healthcare personnel × finance (β = 0.155), followed by drugs × finance (β = 0.142), infrastructure × finance (β = 0.139) and equipment × finance (β = 0.128).

The results support the moderating hypothesis at the level of the individual interaction terms: all four input × finance coefficients were positive and statistically significant. The interaction results therefore indicate that the strength of each input-UHC association varies with the level of finance, while the direct finance coefficient indicates a separate positive association with UHC provision.

The study contributes to Systems Theory by demonstrating that the effectiveness of health-system inputs depends not only on their availability but also on the financial conditions under which they are utilised. Drugs, healthcare personnel, medical equipment and infrastructure may create the potential for service delivery; however, their contribution to UHC provision is strengthened when adequate financial resources support procurement, staffing, maintenance and daily operations. This extends the systems perspective by positioning finance as an enabling and moderating condition within the relationship between health-system inputs and service outcomes ([11]; [6]).

The findings also reinforce the structure-process-outcome perspective of health-care quality. Although the four resource variables were positively associated with UHC provision, their effects were stronger when supported by finance. This suggests that structural resources do not automatically produce improved outcomes unless appropriate operational processes are adequately funded ([3]). The study therefore provides a basis for integrating financial conditions more explicitly into theoretical explanations of health-service delivery and UHC attainment.

The findings imply that health authorities should allocate resources for both the acquisition of health-system inputs and their continued operation. Procurement of medicines and equipment should be accompanied by adequate funding for stock management, maintenance, repairs, consumables and technical support. Similarly, staffing policies should provide sufficient resources for recruitment, retention, training, supervision and staff welfare. Infrastructure investments should also include budgets for utilities, rehabilitation, sanitation and routine maintenance ([12]; [8]).

The study further highlights the need for predictable and coordinated health financing. Delayed or fragmented funding may prevent facilities from converting available resources into effective services. Government allocations, health insurance resources, donor support and facility-level revenues should therefore be aligned with facility needs and UHC priorities. Strengthening financial planning, monitoring and accountability at facility level may improve the utilisation of resources and support more equitable access to quality healthcare services ([13]; [14]).

The Ministry of Health and relevant financing institutions should improve the predictability and timeliness of funds released to public health facilities. Delayed disbursements may disrupt procurement, utilities, maintenance and routine service delivery.

Capital investments in buildings and medical equipment should be accompanied by realistic budgets for maintenance, utilities, consumables and technical support. This would reduce the risk of infrastructure and equipment becoming underutilised or non-functional.

Financing plans should provide for recruitment, retention, professional development, supervision and staff welfare. Adequate personnel financing may improve service continuity, staff motivation and the quality of patient care.

Financial resources should be allocated to strengthen procurement planning, stock monitoring, storage and distribution systems. Facilities should also be supported to identify and respond quickly to medicine shortages.

Government, the National Health Insurance Management Authority (NHIMA), cooperating partners and other financing institutions should improve coordination to reduce duplication, funding gaps and uncertainty. Financing mechanisms should be aligned with facility-level service-delivery priorities.

Dedicated maintenance budgets should be established for medical equipment, buildings, water systems, electricity and sanitation facilities. Maintenance should be treated as a core component of health financing rather than an occasional activity.

Public health facilities should strengthen financial planning, reporting, monitoring and accountability. Transparent financial management may improve the utilisation of available resources and ensure that funding is directed towards essential service-delivery needs.