Impact of traffic congestion on spatial access to healthcare services in Nairobi

Abstract

Background: Geographic accessibility is an important determinant of healthcare utilization and is critical for achievement of universal health coverage. Despite the high disease burden and severe traffic congestion in many African cities, few studies have assessed how traffic congestion impacts geographical access to healthcare facilities and to health professionals in these settings. In this study, we assessed the impact of traffic congestion on access to healthcare facilities, and to the healthcare professionals across the healthcare facilities.

Methods: Using data on health facilities obtained from the Ministry of Health in Kenya, we mapped 944 primary, 94 secondary and four tertiary healthcare facilities in Nairobi County. We then used traffic probe data to identify areas within a 15-, 30- and 45-min drive from each health facility during peak and off-peak hours and calculated the proportion of the population with access to healthcare in the County. We employed a 2-step floating catchment area model to calculate the ratio of healthcare and healthcare professionals to population during these times.

Results: During peak hours, <70% of Nairobi's 4.1 million population was within a 30-min drive from a health facility. This increased to >75% during off-peak hours. In 45 min, the majority of the population had an accessibility index of one health facility accessible to more than 100 people (<0.01) for primary health care facilities, one to 10,000 people for secondary facilities, and two health facilities per 100,000 people for tertiary health facilities. Of people with access to health facilities, a sub-optimal ratio of <4.45 healthcare professionals per 1,000 people was observed in facilities offering primary and secondary healthcare during peak and off-peak hours.

Conclusion: Our study shows access to healthcare being negatively impacted by traffic congestion, highlighting the need for multisectoral collaborations between urban planners, health sector and policymakers to optimize health access for the city residents. Additionally, growing availability of traffic probe data in African cities should enable similar analysis and understanding of healthcare access for city residents in other countries on the continent.

Keywords: catchment model; healthcare accessibility; sub-Saharan Africa; traffic congestion; universal healthcare access.

Figure 1

The four categories of healthcare in Kenya. Source: Kenya Health Policy 2014–2030 (17).

Figure 2

Road network coverage and locations of health facilities in Nairobi County, Kenya source: OpenStreetMap, Ministry of Health. Shapefile source: GADM (18).

Figure 3

Accessibility index (population: facility ratios) for primary health care in Nairobi within 15-, 30- and 45-min drive during peak and off-peak traffic times. The white areas have no accessibility to health facilities. Shapefile source: GADM (18).

Figure 4

Accessibility index (population: facility ratios) for secondary healthcare in Nairobi within 15-, 30- and 45-min drive during peak and off-peak traffic times. The white areas have no accessibility to health facilities. Shapefile source: GADM (18).

Figure 5

Accessibility index (population: facility ratios) for tertiary health care in Nairobi within 15-, 30- and 45-min drive during peak and off-peak traffic times. The white areas have no accessibility to health facilities. Shapefile source: GADM (18).

Figure 6

Accessibility index (population: health care professional ratios) by facility tier for primary (PHC), secondary (SHC) and tertiary (THC) health care in Nairobi within 15-, 30- and 45-min drive during peak and off-peak traffic times. The white areas have no accessibility to health care professionals. Shapefile source: GADM (18).