Assessing the drivers of non-communicable diseases prevention activities in primary health care facilities in Ghana: a case study of some selected districts

Abstract

Background

Primary health care (PHC) is pivotal to equity and disease control in low- and middle-income countries. In Ghana, the rising burden of non-communicable diseases (NCDs) demands a shift from curative to preventive PHC. Evidence on the extent and drivers of PHC-led prevention, however, remains limited.

Methods

A cross‑sectional, facility‑based survey was conducted between November 2023 and March 2024, covering 210 primary‑health‑care facilities in 10 randomly selected districts of Greater Accra and the Eastern Region, Ghana. An adapted WHO‑SARA questionnaire was administered to facility heads or senior clinicians, capturing facility type, ownership, service‑readiness scores, and four self‑reported NCD‑prevention activities, community durbars, home visits, screening tests, and nutritional counselling. Multivariable logistic regression identified predictors of each activity, and a zero‑inflated negative‑binomial model evaluated the association between preventive engagement and NCD‑related outpatient‑department visits.

Results

Overall, 31% of facilities conducted Community Durbars, 63% Home Visits, 59% Screening Tests and 54% Nutritional Counselling. CHPS compounds (56% of facilities) were the most active—81% provided home visits, while only 8% of private clinics did so. CHPS status strongly predicted home visits (OR 26.89, 95% CI 1.20–604.85). Facilities offering Community Durbars (β 1.24, p < 0.01) or Nutritional Counselling (β 1.72, p < 0.01) recorded higher NCD-related OPD use. Higher district NHIS coverage was inversely associated with preventive engagement (OR 0.26, p < 0.01).

Conclusion

NCD prevention in Ghana is led by lower-tier, government PHC facilities, yet resource gaps and treatment-centred financing limit wider uptake. Re-aligning NHIS incentives, investing in CHPS infrastructure and tailoring outreach to underserved groups especially men are critical for shifting Ghana’s PHC from reactive care to sustainable NCD prevention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-026-14032-0.

Introduction

Primary healthcare (PHC) systems form the backbone of healthcare delivery in low- and middle-income countries (LMICs), providing accessible, affordable, and community-based services that prioritise disease prevention, health promotion, and early intervention [1–3]. By emphasising equity, universal access, and cost-effectiveness, PHC enables timely care that averts progression to severe complications and drives better health outcomes globally [4–7]. Historically, PHC has been pivotal in controlling infectious diseases such as malaria, tuberculosis, and HIV/AIDS, alongside improving maternal and child health through immunisation, antenatal care, and safe delivery programmes [8, 9]. Success stories across LMICs underscore its transformative impact, for example, Sri Lanka’s early-twentieth-century PHC reforms markedly reduced mortality and advanced public health [10], while Rwanda’s PHC-led pursuit of universal health coverage has been linked to sharp declines in under-five mortality and gains in maternal health [11].

Despite past successes, PHC systems now face the rising tide of non-communicable diseases (NCDs), which demands a shift from short-term, curative models toward continuous, preventive care. Hypertension, diabetes, cardiovascular disease, and mental health disorders increasingly dominate morbidity and mortality profiles, not only globally but also in Ghana, where NCDs account for roughly 3% of all deaths, with cardiovascular disease alone responsible for 9% [12–15]. Because NCDs require lifelong management, PHC must embed health promotion, early screening, and lifestyle-modification programmes into routine service delivery. In Ghana, for example, the integration of the WHO Package of Essential Noncommunicable Disease Interventions (WHO PEN) into PHC aims to strengthen early detection, prevention, and referral capacity, an essential strategy for reducing the long-term health and economic burden of NCDs [15].

Ghana’s primary-care delivery is organised as a three-tier district system comprising district hospitals, health centres, and Community-based Health Planning and Services (CHPS) compounds [16, 17]. District hospitals serve as referral hubs; health centres provide first-line curative services; and CHPS, decentralised health posts staffed by community health officers, conduct home visits, health education, and basic screening in the communities they serve, particularly in rural and peri-urban areas [18–20]. Through this outreach mandate, CHPS represents the frontline for preventive care and behaviour-change communication, offering an essential platform for early detection and control of NCD risk factors. Consequently, the extent to which CHPS and other PHC facilities implement NCD-related prevention activities is a critical indicator of Ghana’s readiness for the ongoing epidemiological transition [19, 21].

Consistent with experience in many LMICs [22–27], evidence from Ghana shows that preventive action remains the weakest link in PHC’s NCD response. Wellness Clinics concentrate on diagnosis and counselling with little community outreach, and district-level health-promotion efforts are sporadic, under-funded, and overshadowed by communicable-disease priorities [28, 29]. Where the WHO-PEN package has been introduced, facilities still face shortages of trained staff, diagnostics, and essential medicines, limiting early detection and follow-up care [30, 31]. Broader system audits corroborate these gaps: infection-control supplies, personal protective equipment, and laboratory tools are often absent, while high workloads leave minimal time for patient education or lifestyle counselling [32–34]. These intertwined constraints hamper the routine integration of NCD prevention into everyday PHC practice.

Despite growing literature on structural constraints and resource gaps, empirical knowledge of what PHC facilities actually do to prevent NCDs remains scant. Few studies have catalogued the specific preventive activities undertaken, such as screening programmes, community-based health promotion, or lifestyle-modification counselling, leaving Ghana’s true preparedness for the NCD surge largely unmeasured. Likewise, the facility- and district-level determinants of prevention are poorly understood, because most research stops at describing barriers rather than quantifying their influence. Crucially, no study has tested whether PHC facilities that invest in prevention record higher NCD-related outpatient visits, a plausible sign of earlier detection and linkage to care, compared with facilities that do little prevention. Filling these evidence gaps is essential for judging the effectiveness of PHC-driven prevention strategies and for closing the divide between preventive outreach and curative NCD management.

This study, therefore, aims to [1] examine the types of NCD-related preventive activities implemented by PHC facilities [2], identify the facility- and district-level factors influencing engagement in these activities, and [3] assess whether these preventive activities translate into increased OPD visits for NCD care. By addressing these gaps, this research will contribute to a more informed policy framework for strengthening PHC’s role in NCD prevention and management in Ghana.

Methods

Study design

This study employed a cross-sectional, facility-based survey conducted between November 14, 2023, and March 24, 2024. Data were collected in two phases: the Greater Accra Region was covered from November to December 2023, while the Eastern Region was surveyed from February to March 2024. A structured questionnaire was administered to facility heads or senior clinical staff. The questionnaire was adapted from the WHO Service Availability and Readiness Assessment (SARA) framework [35], focusing on service readiness for NCD prevention and management, with additional modules on preventive activities and associated outpatient utilization.

Study setting

The study was undertaken in two Ghanaian regions, Greater Accra and the Eastern Region; chosen to capture the spectrum of urban, peri‑urban, and rural health‑service contexts. Greater Accra, home to the national capital, is predominantly metropolitan, hosting several high‑volume facilities and advanced referral services. By contrast, the Eastern Region combines peri‑urban corridors with remote rural districts, where access to care and infrastructure is more variable. Ghana’s primary‑health‑care system follows a three‑tier district model that frames service delivery and referral. At the apex, district hospitals function as referral hubs for complex cases. Sub‑district health centres provide first‑line curative and limited preventive care. At the community base lie the CHPS decentralised health posts staffed by community health officers who conduct home visits, offer health education, and deliver basic screening and outreach, especially in underserved rural and peri‑urban areas. This tiered structure underpins our examination of how PHC facilities contribute to NCD prevention.

Sample estimation

Following the WHO SARA [35] sampling strategy for small country (district‑level) analyses, we employed a three‑stage design. In the first stage of sampling, two regions were purposively chosen to capture Ghana’s urban and rural diversity: the metropolitan Greater Accra Region and the Eastern Region, which contains a mix of urban, peri‑urban, and rural districts. In the second stage, a total of ten districts were then randomly selected—four from Greater Accra and six from the Eastern Region—while maintaining proportional representation of urban and rural areas (three urban and one rural district in Greater Accra, and two urban and four rural districts in the Eastern Region). In the final stage, we used district health directorate registries to compile a census of all functional PHC facilities in the ten selected districts. Every listed district hospital, health centre, and CHPS compound was included, producing a total sample of 210 facilities. This comprehensive capture provided the breadth of service capacities, ownership arrangements, and geographic contexts required for a rigorous analysis of factors influencing NCD‑prevention activities.

Data collection and variables

Data for this study were collected on three key domains: preventive activities, facility characteristics, and district-level characteristics. The primary aim was to assess the factors influencing the engagement of PHC facilities in preventive activities for NCDs. Data were collected via a structured questionnaire Table S2 (Supplementary File) that we developed for this study.

Preventive activities

Preventive healthcare activities assessed in this study were self‑reported by facility heads or senior clinical staff through the structured questionnaire. Such activities are essential for reducing the burden of NCDs by promoting early detection, awareness creation, and lifestyle modification. Recognising the need for structured prevention strategies, the questionnaire drew on key global and regional frameworks that emphasise the role of PHC in NCD prevention [36]. The WHO-PEN underscores screening, health education, and lifestyle modification as critical preventive measures in PHC settings [36, 37]. Additionally, the Ottawa Charter for Health Promotion advocates for community engagement, awareness creation, and the promotion of healthy living as fundamental components of public health [38].

As illustrated in Table 1, a range of preventive activities are undertaken across PHC facilities. These include community durbars, which serve as platforms for health education through outreach events; home visit campaigns, where health workers deliver NCD prevention messages directly to households; and screening tests, such as blood pressure and glucose checks, that enable early detection. Additionally, nutritional counseling is offered to guide patients on healthy eating habits and diet-related NCD prevention. Together, these activities form an essential component of the PHC response to the growing NCD burden.

Table 1.

Description of preventive activities

Preventive activity	Description
Community Durbars	Community-based health education and outreach events aimed at raising awareness about NCD prevention and management. These activities often include health fairs, mobile clinics, and public talks.
Home visits Campaigns	Health education conducted at the household level, where trained health workers visit homes to educate families on NCD risk factors, early detection, and preventive measures.
Screening Tests	Availability of routine screening services for NCDs, including blood pressure checks, blood glucose tests, cholesterol screenings, and BMI assessments to facilitate early detection and intervention.
Nutritional Counseling	Provision of nutrition counseling services at PHC facilities, educating patients on balanced diets, healthy food choices, and the impact of nutrition on preventing NCDs.

Facility characteristics

Beyond preventive activities, facility-level characteristics were examined to understand their influence on NCD prevention efforts. These included facility type, categorized into district hospitals, clinics, health centers, and CHPS compounds, representing different levels of service delivery. Managing authority classified facilities as government-run, private, or mission-based. The basic amenities score, basic equipment score, and diagnostic capacity score were computed based on the availability of essential resources, as outlined in Table S1 (Supplementary File). Additionally, the presence of NCD guidelines and NCD training for health workers were assessed to determine the availability of standardized protocols and the capacity of healthcare personnel to manage NCDs effectively.

District characteristics

District-level socio-economic characteristics were included in the analysis to examine their potential influence on NCD prevention activities within primary healthcare (PHC) facilities. These characteristics were sourced from the 2021 Population and Housing Census and provided insights into the broader contextual factors affecting health service delivery and preventive care engagement.

One key variable was the average age of the population, which reflects demographic trends that may influence disease prevalence and healthcare demand. Additionally, the educational attainment of the population was assessed through two indicators: the percentage of the population with primary education or less and the percentage of the population with secondary education. These measures capture literacy levels, which are crucial for understanding health awareness and the ability to engage with NCD prevention programs.

Economic indicators were also considered, including the unemployment rate (%), which serves as a proxy for financial stability and access to healthcare [39–42]. Housing conditions were captured through the percentage of households in formal housing, an indicator of living standards and infrastructure quality. District‑level healthcare access was proxied by the share of the population enrolled in the National Health Insurance Scheme (NHIS); although not a perfect measure, higher NHIS coverage is widely taken to indicate greater financial protection and affordability of care [43, 44]. These district-level factors provide a comprehensive context for evaluating PHC facility performance in NCD prevention.

Data analysis

The data analysis proceeded in three main stages: descriptive analysis, logistic regression for preventive activities, and zero-inflated negative binomial (ZINB) regression to examine the determinants of outpatient visits for NCD care.

Descriptive analysis

Using Stata 17, we generated basic summary statistics for each study variable. Frequencies and percentages were calculated for categorical measures (e.g., facility type, ownership, urban‑rural location, engagement in each preventive activity), while means with standard deviations were produced for continuous scores (basic amenities, equipment, diagnostic capacity and district socio‑economic indicators). Results were tabulated both overall and stratified by facility type, managing authority and location to illustrate patterns in resource distribution and preventive‑activity engagement across the 210 PHC facilities.

Logistic regression for preventive activities

To examine the determinants of preventive healthcare engagement, four separate logistic regression models were estimated for each preventive activity: [1] Community Durbars [2], Home visits campaigns [3], screening tests, and [4] Nutritional Counseling. The probability of a PHC facility engaging in each activity was modeled using the following logistic regression equation:

1

Where represents the probability that facility i engages in a specific preventive activity, is a vector of facility-level characteristics, including facility type, managing authority, and resource availability, represents district-level socio-economic characteristicsand and is the error term.

Zero-inflated negative binomial (ZINB) regression

Given that outpatient visits for NCD care (our dependent variable) exhibit an excess of zero counts, we employed a zero-inflated negative binomial (ZINB) model to account for two processes: [1] the binary decision of whether a facility records any outpatient NCD visits, and [2] the count of outpatient visits among facilities that do record visits [45].

The ZINB model consists of two components:

1. A logit model estimating the probability of a facility being in the zero-outcome group (i.e., no outpatient NCD visits).

2. A negative binomial count model estimating the number of outpatient visits among facilities providing NCD services.

The full ZINB specification is given as:

2

Where represents the count of outpatient NCD visits at facility I and is the probability of excess zeros, modeled using a logistic regression.

3

is the expected count of NCD outpatient visits for non-zero facilities, modeled using a negative binomial regression.

4

This modeling approach allows us to separately examine factors influencing whether a facility reports any NCD visits (logit model) and those affecting the frequency of visits (negative binomial model) [46]. Facility characteristics, preventive activities, and district-level factors were included in both components of the model. All statistical analyses were conducted using Stata, with robust standard errors to account for potential heteroskedasticity. Statistical significance was set at p < 0.01, p < 0.05 and p < 0.1.

Results

Descriptive analysis

Table 2 presents the distribution of primary healthcare (PHC) facilities included in the study. A total of 210 PHC facilities were surveyed, with the majority being CHPS compounds (56.19%), followed by health centers (30.95%), district hospitals/hospitals (6.67%), and clinics (6.19%). Regarding managing authority, 80.95% of facilities were government-run, while 17.62% were privately owned, and 1.43% were mission-based. The geographical distribution shows that 59.52% of facilities were located in rural areas, while 40.48% were in urban settings. The regional distribution of facilities varied, with Kwahu Afram Plains (18.10%) and Akyemansa (12.38%) having the highest concentration of facilities, while Ada East (6.19%), Ayawaso West (7.62%), Ga South (7.62%), and Tema West (7.62%) had the lowest.

Table 2.

Distribution of PHC facilities

Variables	Freq.	Percent
Type of PHC facility
District Hospitals/Hospitals	14	6.67
Clinics	13	6.19
Health Centers	65	30.95
CHPS	118	56.19
Managing Authority
Government	173	80.95
Private	37	17.62
Mission	3	1.43
Location
Rural	125	59.52
Urban	85	40.48
Districts
Upper West Akim	20	9.52
Nsawam Aboagye	16	7.62
Akyemansa	26	12.38
Yilo Krobo	21	10.00
Kwahu Afram Plains	38	18.10
Ayensuano	28	13.33
Ada East	13	6.19
Ayawaso West	16	7.62
Ga South	16	7.62
Tema West	16	7.62
Guidelines for NCD
Yes	124	59.05
No	86	40.95
Training for NCD
Yes	85	40.48
No	125	59.52

Regarding the availability of non-communicable disease (NCD) guidelines, 59.05% of facilities reported having standardized guidelines, while 40.95% did not. Additionally, 40.48% of facilities provided NCD-related training to healthcare workers, whereas 59.52% had no such training programs.

Table 3 summarizes the service availability characteristics of PHC facilities. The mean basic amenities score was 40.61 (SD = 23.42), indicating variability in infrastructure availability across facilities. The basic equipment score averaged 83.02 (SD = 18.59), suggesting that most facilities had essential medical equipment. The mean diagnostic capacity score was 53.15 (SD = 35.69), highlighting limitations in diagnostic service availability for NCDs.

Table 3.

Service availability variables

Panel A. Facility-level service-availability scores	Obs	Mean (0-100)	Std. Dev. (%)	Minimum (%)	Maximum (%)
Basic amenities	210	40.612	23.422	0	85.714
Basic equipment	210	83.016	18.587	0	100
Diagnostic capacity	210	53.155	35.688	0	100
Panel B. District-level socio-economic indicators	Obs	Mean (%)	Std. Dev. (%)	Minimum (%)	Maximum (%)
Average Age of Population	210	26.38	1.042	24.68	27.774
Percentage of Population with Primary Education or Less	210	38.305	9	17.632	48.329
Percentage of Population with Secondary Education	210	51.927	4.391	43.041	57.879
Unemployment Rate (%)	210	41.588	2.96	35.599	45.978
Percentage of Households in Formal Housing	210	79.344	9.938	58.654	92.979
Percentage of Population Covered by NHIS (National Health Insurance Scheme)	210	70.347	13.489	42.498	86.095

Note: Scores computed as the percentage of tracer items available at each facility (0 = none; 100 = all)

Average age expressed in years; all other Panel B variables are percentages.

At the district level, the average population age was 26.38 years (SD = 1.04). The mean percentage of the population with primary education or less was 38.31% (SD = 9.00), while those with secondary education accounted for 51.93% (SD = 4.39). The average unemployment rate was 41.59% (SD = 2.96). Formal housing coverage among households stood at 79.34% (SD = 9.94), while NHIS coverage was 70.35% (SD = 13.49%).

Table 4 provides details on the participation of PHC facilities in NCD-related preventive activities. Overall, 31% of facilities engaged in Community Durbars, 63% participated in Home visits campaigns, 59% offered screening tests, and 54% provided Nutritional Counseling.

Table 4.

Engagement in preventive activities by facilities

Variables	Community Durbars	Home visits	Screening Test	Nutritional Counseling
Engagement
Yes	31%	63%	59%	54%
No	69%	37%	41%	46%
Type of PHC facility
District Hospitals/Hospitals	7%	21%	64%	50%
Clinics	8%	31%	23%	38%
Health Centers	23%	45%	62%	42%
CHPS	42%	81%	61%	64%
Managing Authority
Government	37%	74%	61%	59%
Private	5%	8%	46%	32%
Mission	33%	100%	100%	67%
Location
Rural	41%	78%	67%	66%
Urban	18%	41%	47%	36%
Regions
Greater Accra	15%	26%	54%	44%
Eastern	38%	78%	61	58%

Engagement in preventive activities varied by facility type. CHPS compounds were the most active in Home visits campaigns (81%), while district hospitals had the highest rate of screening tests (64%). CHPS facilities also had the highest engagement in Community Durbar(42%) and Nutritional Counseling (64%).

Ownership type influenced preventive activity participation. Government-managed facilities reported the highest engagement in Home visits campaigns (74%), screening tests (61%), and Nutritional Counseling (59%). Private facilities had the lowest rates across all preventive activities, with only 8% participating in Home visits programs, 46% offering screening tests, and 32% providing dietary advice. Mission-based facilities showed 100% engagement in Home visits and screening tests, and 67% participation in Nutritional Counseling.

Location also played a role in preventive activity engagement. Rural facilities had higher participation in Home visits campaigns (78%), screening tests (67%), and Nutritional Counseling (66%), compared to urban facilities, which reported 41% participation in Home visits, 47% in screening tests, and 36% in dietary counseling.

Regionally, facilities in the Eastern Region had higher engagement in all preventive activities compared to those in Greater Accra. In Eastern Region, 78% of facilities conducted Home visits campaigns, 61% provided screening tests, and 58% offered dietary advice. In contrast, Greater Accra facilities reported 26% engagement in Home visits, 54% in screening tests, and 44% in dietary advice.

Determinants of facility-initiated preventive activities

Table 5 presents the results of the logistic regression analysis assessing the determinants of facility engagement in four preventive activities: Community Durbars, Home visits campaigns, screening tests, and Nutritional Counseling. The odds ratios (OR) and 95% confidence intervals (CI) are reported for each predictor variable.

Table 5.

Logistic‑regression determinants of facility‑initiated preventive activities (odds ratios [OR] with 95% confidence intervals in parentheses)

Variables	(1) Community Durbars	(2) Home visits	(3) Screening Test	(4) Nutritional Counseling
Facility Type
Clinics	1.99 (0.13–30.85)	2.81 (0.17–46.93)	0.28 (0.02–3.09)	1.09 (0.15–7.95)
CHPS	7.21 (0.64–81.81)	26.89 ** (1.20 -604.85)	3.02 (0.27–34.34)	0.87 (0.11–6.85)
Health Centre	7.41 ** (1.24–44.39)	7.71 (0.57 -104.88)	2.65 (0.36–19.75)	0.81 (0.17–3.90)
Managing Authority
Government	2.00 (0.39–10.19)	6.53 ** (1.24–34.31)	11.41 * (0.94 -138.93)	2.25 (0.46–10.94)
Area Type
Urban	43.58 (0.43 -4372.62)	6.78 (0.38 -120.83)	13.99 * (0.62 -316.98)	6.65 (0.21 -212.61)
Avaliability of Resources
Basic Amenities Score	0.98 * (0.95 -1.00)	1.03 * (1.00 -1.06)	1.00 (0.97–1.03)	1.00 (0.97–1.02)
Basic Equipment Score	1.01 (0.98–1.04)	0.97 ** (0.94 -1.00)	0.99 (0.96–1.01)	1.01 (0.98–1.04)
Diagnostic Capacity Score	0.97 ** (0.95–0.99)	0.99 (0.97–1.02)	1.01 (0.99–1.02)	0.98 ** (0.97 -1.00)
Essential Medicines Score	1.04 * (1.00 -1.08)	1.03 (0.98–1.09)	1.08 *** (1.03–1.13)	1.01 (0.97–1.05)
NCD Guidelines	2.14 (0.78–5.84)	4.10 * (0.97–17.26)	0.41 (0.11–1.51)	0.87 (0.25–3.05)
NCD Training	1.75 (0.66–4.61)	0.82 (0.19–3.53)	0.87 (0.27–2.83)	1.73 (0.55–5.42)
Workforce	1.00 (0.99–1.02)	0.99 (0.97–1.02)	1.00 (0.98–1.02)	1.00 (0.99–1.01)
Community Characteristics
Average Age of Population	0.68 (0.21–2.17)	0.24 ** (0.06–0.91)	0.66 (0.19–2.28)	0.39 ** (0.16–0.96)
Percentage of Male	3.75 * (0.92–15.27)	4.55 (0.70 -29.65)	1.22 (0.33–4.50)	9.58 *** (3.07–29.83)
Percentage of Population with Primary Education or Less	2.68 *** (1.39–5.16)	3.30 *** (1.95–5.57)	3.57 *** (2.03–6.27)	2.54 *** (1.65–3.89)
Percentage of Population with Secondary Education	1.20 (0.63–2.27)	1.48 (0.68–3.21)	0.63 (0.33–1.19)	1.63 (0.97–2.71) *
Unemployment Rate (%)	0.19 ** (0.05–0.74)	0.33 (0.07–1.56)	0.76 (0.23–2.44)	0.07 (0.03–0.21) ***
Percentage of Households in Formal Housing	3.32 ** (1.09–10.16)	1.81 (0.57–5.77)	1.28 (0.48–3.42)	8.22 *** (3.43–19.71)
Percentage of Population Covered by NHIS (National Health Insurance Scheme)	0.26 *** (0.12–0.58)	0.33 ** (0.12–0.89)	0.40 ** (0.20–0.79)	0.13 *** (0.07–0.26)
Constant	6.50e-18 (3.34e-42 -1.27e + 07)	1.01e-13 (5.30e-46 -1.91e + 19)	7.16e + 12 (1.52e-12 -3.38e + 37)	1.28e-29 (1.67e-50 -9.79e-09)
Number of observations	210	210	210	210
Wald chi²(19)	63.36	109.07	109.33	111.57
Prob > chi¬≤	0	0	0	0
Log pseudolikelihood	-83.98	-69.27	-89.46	-89.78
Pseudo R²	0.3575	0.5	0.3705	0.38

Note: Robust standard errors were used in all regressions. Statistical significance is indicated as follows: *p < 0.01, p < 0.05, and p < 0.1. The base category for facility type is District Hospital, for managing authority is Private, and for area type is Rural

Facility characteristics significantly influenced engagement in preventive activities. Compared to district hospitals, CHPS facilities had higher odds of conducting Home visits campaigns (OR = 26.89, 95% CI: 1.20–604.85, p < 0.05), while health centers were more likely to engage in Community Durbar(OR = 7.41, 95% CI: 1.24–44.39, p < 0.05). Government-managed facilities had greater involvement in Home visits campaigns (OR = 6.53, 95% CI: 1.24–34.31, p < 0.05) and screening tests (OR = 11.41, 95% CI: 0.94–138.93, p < 0.10) compared to private facilities.

Urban-based facilities had higher odds of conducting screening tests (OR = 13.99, 95% CI: 0.62–316.98, p < 0.10) than rural facilities. The average age of the population was negatively associated with Home visits campaigns (OR = 0.24, 95% CI: 0.06–0.91, p < 0.05) and Nutritional Counseling (OR = 0.39, 95% CI: 0.16–0.96, p < 0.05). A higher percentage of males was linked to Community Durbar(OR = 3.75, 95% CI: 0.92–15.27, p < 0.10) and Nutritional Counseling (OR = 9.58, 95% CI: 3.07–29.83, p < 0.01).

Basic amenities were positively associated with Community Durbar(OR = 0.98, 95% CI: 0.95–1.00, p < 0.10) and Home visits campaigns (OR = 1.03, 95% CI: 1.00–1.06, p < 0.10). Higher diagnostic capacity was negatively linked to Community Durbar(OR = 0.97, 95% CI: 0.95–0.99, p < 0.05) and Nutritional Counseling (OR = 0.98, 95% CI: 0.97–1.00, p < 0.05). Greater availability of essential medicines was associated with screening tests (OR = 1.08, 95% CI: 1.03–1.13, p < 0.01). Model fit statistics indicated strong performance, with Wald chi-square values ranging from 63.36 to 111.57 (p < 0.01) and pseudo R² values between 0.36 and 0.50.

Zero-inflated negative binomial regression results for NCD OPD attendance

Table 6 presents the results from the ZINB regression models assessing the determinants of NCD-related OPD visits across PHC facilities. The analysis examines the influence of facility characteristics, resource availability, engagement in preventive activities, and district-level factors on NCD service utilization.

Table 6.

Zero-Inflated negative binomial regression results for OPD

	Model 1	Model 2	Model 3	Model 4	Model 5
Community Durbars	0.789*	1.244***
	(0.442)	(0.374)
Home visits	-0.267		-0.463
	(0.370)		(0.401)
Screening Test	-0.304			0.0286
	(0.480)			(0.483)
Advice on Eating	1.356**				1.717***
	(0.541)				(0.488)
Facility type
District Hospital/Hospitals	0.103	-0.0367	0.750	0.372	0.0762
	(0.669)	(0.646)	(0.647)	(0.750)	(0.657)
CHPS	0.207	0.285	1.018	0.393	-0.0919
	(0.847)	(0.767)	(0.803)	(0.946)	(0.817)
Health Centre	0.00970	-0.0209	0.337	0.338	-0.0711
	(0.485)	(0.467)	(0.477)	(0.559)	(0.497)
Managing Authority
Private	-0.140	0.0869	0.173	-0.403	-0.121
	(0.330)	(0.333)	(0.325)	(0.434)	(0.342)
Location
Urban	1.350***	1.239***	2.381***	1.515***	1.636***
	(0.459)	(0.395)	(0.551)	(0.380)	(0.435)
Avaliability of Resources
Basic Amenities Score	0.0164*	0.0219**	0.00825	0.0153	0.0117
	(0.00943)	(0.0109)	(0.00892)	(0.0104)	(0.00906)
Basic Equipment Score	0.0147	0.00988	0.0113	-0.000744	0.0109
	(0.00948)	(0.00823)	(0.00910)	(0.00916)	(0.00883)
NCD Guidelines	0.443	0.359	0.409	0.853	0.719
	(0.489)	(0.513)	(0.568)	(0.578)	(0.447)
NCD Training	0.823**	0.759**	1.266***	0.949**	0.932***
	(0.366)	(0.379)	(0.426)	(0.456)	(0.348)
Average Age of Population	-0.0392	0.0542	0.730**		-0.0861
	(0.225)	(0.190)	(0.305)		(0.222)
Percentage of Male	-0.199**	-0.106	0.137		-0.230***
	(0.0793)	(0.0778)	(0.119)		(0.0713)
Percentage of Population with Primary Education or Less			0.138**
			(0.0594)
Percentage of Population with Secondary Education			-0.223***
			(0.0776)
Constant	12.08	5.469	-20.37*	2.388**	14.73*
	(8.367)	(7.588)	(11.63)	(0.930)	(7.850)
Inflation Model
District Hospitals/Hospitals	18.78	17.86***	18.65**	18.18***	17.73***
	(11.80)	(6.760)	(8.724)	(6.115)	(3.076)
CHPS	21.35*	20.44***	21.20**	20.82***	20.31***
	(11.17)	(7.226)	(8.618)	(6.328)	(3.069)
HC	18.39	17.47***	18.43**	17.75***	17.26***
	(11.18)	(6.749)	(9.319)	(6.351)	(2.545)
Constant	-20.17*	-19.25***	-19.93**	-19.65***	-19.19***
	(10.42)	(6.858)	(8.853)	(5.665)	(2.390)
lnalpha	0.661***	0.728***	0.589***	0.871***	0.748***
	(0.202)	(0.184)	(0.149)	(0.181)	(0.198)
Observations	209	209	209	209	209

Note: Standard errors in parentheses, ^*p < 0.10, ^**p < 0.05, ^***p < 0.01

Facility characteristics and preventive activities

Engagement in preventive activities was significantly associated with NCD-related OPD visits. Facilities conducting Community Durbar had significantly higher NCD-related OPD visits in Model 2 (β = 1.244, SE = 0.374, p < 0.01) and Model 1 (β = 0.789, SE = 0.442, p < 0.10). Additionally, facilities offering Nutritional Counseling recorded a significant increase in OPD visits (β = 1.356, SE = 0.541, p < 0.05, Model 1; β = 1.717, SE = 0.488, p < 0.01, Model 5). However, Home visits campaigns and screening tests were not significantly associated with NCD-related OPD visits.

Facility type did not show a significant effect on NCD-related OPD visits, with CHPS compounds and health centers displaying no statistically significant impact when compared to district hospitals. There were no significant differences in NCD-related OPD visits between government and privately managed facilities. However, facilities located in urban areas consistently reported higher NCD-related OPD visits across all models, with Model 1 showing β = 1.350, SE = 0.459, p < 0.01 and Model 5 reporting β = 1.636, SE = 0.435, p < 0.01.

Facilities with better basic amenities had a significant positive association with NCD-related OPD visits (β = 0.0164, SE = 0.00943, p < 0.10, Model 1; β = 0.0219, SE = 0.0109, p < 0.05, Model 2). However, diagnostic capacity was negatively associated with OPD visits (β = 0.97, SE = 0.95–0.99, p < 0.05, Model 1), suggesting that facilities with higher diagnostic capability may cater to fewer, more specialized NCD cases. NCD training for healthcare workers significantly increased OPD visits (β = 0.823, SE = 0.366, p < 0.05, Model 1; β = 1.266, SE = 0.426, p < 0.01, Model 3), highlighting the importance of workforce capacity in improving NCD service delivery.

Several district-level characteristics significantly influenced NCD-related OPD visits. The average age of the population was negatively associated with OPD visits (β = -0.0861, SE = 0.222, p < 0.05, Model 5). Additionally, a higher percentage of males in the district was negatively associated with NCD-related OPD attendance (β = -0.199, SE = 0.0793, p < 0.05, Model 1; β = -0.230, SE = 0.0713, p < 0.01, Model 5). Educational attainment also played a role, with a higher percentage of the population with only primary education significantly increasing NCD-related OPD visits (β = 0.138, SE = 0.0594, p < 0.05, Model 3). Conversely, a higher percentage of individuals with secondary education was negatively associated with OPD visits (β = -0.223, SE = 0.0776, p < 0.01, Model 3).

Inflation model

The inflation model, which accounts for excess zero NCD-related OPD visits, found that CHPS compounds had significantly higher odds of reporting zero visits across all models (β = 21.35, SE = 11.17, p < 0.10, Model 1; β = 20.31, SE = 3.069, p < 0.01, Model 5). Similarly, health centers exhibited higher odds of zero NCD-related OPD visits (β = 18.39, SE = 11.18, Model 1), suggesting that some PHC facilities had structural limitations leading to lower NCD service utilization. The ZINB regression models showed strong performance, with Wald chi-square values ranging from 63.36 to 111.57 (p < 0.01). The log pseudolikelihood values ranged from − 69.27 to -89.78, while pseudo R² values varied between 0.36 and 0.50, indicating moderate explanatory power. The significance of lnalpha across all models confirmed overdispersion in NCD-related OPD visit data.

Discussion

This study identified several factors that significantly influence the engagement of primary health care (PHC) facilities in NCD preventive activities. In particular, CHPS, facilities in rural areas, and government-managed PHC facilities were more likely to conduct NCD prevention interventions (such as screenings and health education) than urban or privately managed clinics. By contrast, district hospitals (higher-level facilities) and private clinics tended to prioritize curative services over prevention.

Community-based PHC facilities, particularly CHPS compounds, played a crucial role in NCD prevention. Designed to bring healthcare closer to communities, CHPS facilities prioritize preventive services, such as village health education, screenings for hypertension and diabetes, and follow-up visits. Their high engagement mirrors findings from other LMICs, where community health workers have successfully delivered lifestyle education and early NCD screening [28]. Similar initiatives in Nigeria highlight the effectiveness of integrating community health extension workers into PHC-led NCD prevention [47–49].

Private PHC facilities had significantly lower engagement in NCD preventive care, primarily due to financial and structural incentives. Unlike public facilities, private clinics in LMICs operate on a fee-for-service model, where revenue is tied to curative treatments rather than preventive outreach [50–52]. Ghana’s NHIS, for instance, reimburses treatments but does not cover general check-ups or community health education [53], disincentivizing private providers from investing in prevention. Similar trends are seen across Africa, where preventive services are largely funded by public health budgets or NGOs, while private sector participation in screenings, immunization, and health promotion remains limited [54, 55]. Historically, essential services such as family planning and health education in Ghana have relied on government or donor support [56, 57]. As a result, government-managed PHC facilities, which are mandated to provide public health services, take the lead in prevention. Addressing this imbalance requires financial reforms that incentivize private sector participation in NCD prevention.

A key paradox in this study is that higher-tier PHC facilities, such as district hospitals, focus less on prevention despite having more resources, while lower-tier facilities take on more preventive activities despite resource constraints. This trend is seen across LMICs, where hospitals prioritize curative care due to patient demand, while community-level clinics integrate prevention into their core mission [58]. Ghana’s CHPS compounds, like similar community-based models in Africa, emphasize screenings and outreach but often lack the resources for follow-up care. In several LMICs, while community-level screening initiatives have proven effective in increasing NCD detection and enrollment, inadequate resources, poor access to diagnostic tools and medications, and weak referral systems continue to hinder continuity and quality of care at the primary level [58].

The study found that districts with a higher percentage of males had greater engagement in NCD preventive activities, likely reflecting a strategic response by PHC teams to men’s lower healthcare-seeking behavior. In many African and LMIC contexts, men tend to underutilize routine health services due to cultural norms, work obligations, and a sense of invulnerability [59]. Studies in South Africa, for example, show that men often self-medicate or delay seeking care until symptoms become severe [60]. As a result, they are underrepresented in preventive health programs. This study suggests that PHC facilities in male-dominated communities are proactively reaching men through tailored outreach, such as screenings at workplaces, markets, or religious gatherings, and using male community leaders to promote health messages. Similar strategies have improved HIV testing uptake in other African settings, demonstrating that targeted interventions can increase male participation in health services [61]. Ghana’s PHC teams appear to be adapting similarly, ensuring men are reached where they are rather than waiting for them to visit clinics. Encouraging these adaptive strategies across all districts can enhance NCD prevention and improve early detection among both men and women.

The study found an inverse relationship between NHIS enrollment and preventive activity, suggesting that higher insurance coverage may shift PHC facilities’ focus toward curative care. Ghana’s NHIS has improved access by covering treatments for illnesses like hypertension and diabetes, leading to increased patient visits. However, as facilities become overwhelmed with insured patients seeking treatment, staff may deprioritize preventive activities like health education and screenings, which are not reimbursed by NHIS. This misalignment is reinforced by NHIS’s benefits package, which primarily covers curative services but excludes many preventive interventions, such as wellness check-ups and community screenings. Similar patterns have been observed in Kenya and Nigeria, where insurance contracts focus on treatment targets, leaving prevention underfunded [62–64]. Policy experts argue that NHIS should incentivize preventive care, possibly through equal capitation payments for prevention and treatment. Aligning NHIS financing with prevention is crucial to ensuring that insured districts excel not only in curative care but also in proactive NCD prevention efforts.

Strengths and limitaitons

This study offers several methodological strengths. First, it enumerated all 210 functional PHC facilities in ten randomly selected districts, providing a near‑census view of Ghana’s public, private, rural and urban service mix and reducing selection bias. Second, the survey instrument was built on the WHO‑SARA template and WHO‑PEN prevention domains, ensuring standardised, internationally comparable indicators for amenities, equipment, diagnostics and NCD‑specific outreach. Third, linking facility data to 2021 census variables allowed simultaneous assessment of health‑system capacity and district socio‑economic context, an evidence combination rarely reported for Ghana. Fourth, multivariable logistic and zero‑inflated negative‑binomial models—with district‑clustered robust errors, controlled for key confounders and accommodated excess zeros in utilisation data, yielding stable effect estimates.

The findings should be interpreted in light of several limitations. The cross‑sectional design precludes causal inference; observed associations may be bidirectional. Preventive activities were self‑reported by facility heads, introducing recall and social‑desirability bias. Although Greater Accra and Eastern Regions capture urban–rural diversity, results may not generalise to northern zones with different service footprints. Private facilities formed only 18% of the sample, producing wider confidence intervals for that subgroup. Finally, unemployment and NHIS coverage were used as district‑level proxies for affordability; household‑level economic hardship and informal employment were not measured.

Policy recommendation

The study’s findings highlight critical policy and programmatic implications for strengthening NCD prevention in Ghana’s PHC system. First, increasing resource allocation to lower-tier PHC facilities is essential. Clinics need basic tools (e.g., glucometers, BP monitors) and funding for outreach activities. Strengthening PHC infrastructure, as seen in Sri Lanka’s successful NCD model, can enhance prevention and early detection.

Financial incentives must be realigned, Ghana’s NHIS should reimburse preventive services, such as routine screenings and community education, to encourage both public and private providers to invest in prevention. Performance-based financing, rewarding districts that meet prevention targets, could also be considered.

Integrating NCD prevention into existing PHC programs, for example, adding BP and glucose checks to maternal clinics—ensures efficiency. Facilities should also target outreach to underserved groups, especially men, using tailored strategies like workplace screenings or community-based health events. Additionally, strengthening follow-up and referral systems through digital tracking and community health workers would improve care continuity. Lastly, ongoing monitoring and research should assess the long-term impact of prevention efforts, helping policymakers refine interventions. By adopting these strategies, Ghana’s PHC system can transition from reactive care to proactive NCD prevention, improving public health outcomes.

Conclusion

In conclusion, strengthening primary health care is essential for effective NCD prevention in Ghana. This study highlights the need to enhance resource allocation for PHC facilities, align NHIS incentives with prevention, integrate NCD services into routine care, and target underserved populations, especially men. Best practices from other LMICs show that strong PHC systems are key to sustainable NCD control. A shift from late-stage treatment to early detection and prevention is crucial. With supportive policies and adequate funding, Ghana’s PHC facilities can evolve into wellness hubs, reducing the NCD burden and improving long-term health outcomes across communities.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization of the study was led by MAA, TM, IA, and JA. Data analysis and interpretation were performed by MAA, DA and DKA under the guidance of JA. Review and oversight of the manuscript were provided by TM, IA and JA. All authors contributed to the review and revision of the final manuscript, ensuring accuracy and alignment with the study objectives.

Funding

This research was funded by the NIHR Global Health Research Centre for Non-communicable Disease Control in West Africa (STOP-NCD) (Grant Reference: NIHR203246) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK government.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethical approval

The Ghana Health Service Ethics Review Committee approved the study (GHS ERC 013/02/23). Written informed consent was obtained from all participants, and data collection adhered to Ghana Health Service guidelines and the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.