Back

Laboratory capacity assessment in a resource-limited health system, Savannah Region, Ghana; a descriptive cross-sectional study

Saeed, F. U.; Kubio, C.; Kutame, R.; Boateng, G.

2026-04-11 health systems and quality improvement
10.64898/2026.04.08.26350443 medRxiv
Show abstract

BackgroundLaboratory services are essential to the provision of health service delivery across the world. In resource-constrained settings such as in low- and middle-income countries like Ghana, maintenance of a strong capacity could be more challenging. This study assessed the capacity and gaps in laboratory service delivery in three districts of the Savannah Region of Ghana. MethodsThe WHO laboratory assessment tool (LAT) was adapted to collect data in 10 health facilities based on 11 operational system modules. Data were collected through interviews. Capacity was defined based on a 100-point score scale and interpreted as weak (<50%), moderate (50-80%) and strong (>80%). Differences in median scores were determined using Friedman and Kruska-Wallis tests. Statistical significance was set at p < 0.05. A scale (0-5) was used to identify the needs of the laboratory. ResultsOverall, capacity score was moderate, mean 50% {+/-} 25.7 with a median score of 52.5%, IQR: 30.0-68.5%. Testing module received the highest score, 71.5%, while document module scored the lowest, 14.5%. Scores did not differ significantly between system components after multiple comparisons, p>adjusted alpha. Hospital-level laboratories performed significantly higher than polyclinics (adjusted p = 0.044) and health centers (adjusted p<0.001). The biggest needs were biosafety, equipment maintenance, human and financial resources (median gap score: 3-4). ConclusionThe laboratory capacity in the health system of the Savannah Region was moderate, requiring improvements in all operational areas. The biggest needs include safety, equipment, human and financial support systems. Addressing these critical gaps would have direct impact on public health and patient outcomes.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.