Dataset Information

Does health service funding go where the need is? A prototype spatial access analysis for new urban contracts data.

ABSTRACT:

Background

Much of spatial access research measures the proximity to health service locations. We advance this research by focusing on whether health service funding is within walkable reach of neighborhoods with high hardship. This is made possible by a new administrative data source: financial contracts data for those human services that are delivered by nonprofits under contract with the government.

Methods

In a prototypical spatial access study we apply a classic 2-step floating area catchment model for walkable network access to analyze 2018 data about contracted nonprofit health services funded by the Chicago Department of Public Health (CDPH). CDPH collected the data for the purpose of this study.

Results

We find that the common container approach of aggregating contract amounts by provider headquarter locations in a given area (ignoring satellite service sites) underestimates the share of funding that goes to Chicago neighborhoods with higher hardship. Once service sites and spatial access are taken into account, a larger share of CDPH funds was found to be within walkable reach of Chicago's high hardship areas. This was followed by low hardship areas (which could be driven by more headquarter locations there that do serve areas throughout the city). Medium hardship areas trail both, perhaps warranting closer attention. We explore these results by program type and neighborhood with a spatial decision support system developed for the health department.

Conclusions

The typical approach for analyzing human service contracts based on headquarters is misleading -- in fact, we find that results are reversed when service sites and walkable access are taken into account. This prototype provides an alternative framework for avoiding these misleading results.

SUBMITTER: Koschinsky J

PROVIDER: S-EPMC8744308 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

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Project description:BackgroundConditions requiring emergency treatment disproportionately affect low- and middle-income countries (LMICs), where there is often insufficient prehospital care capacity. To inform targeted prehospital care development in Ghana, we aimed to describe spatial access to formal prehospital care services and identify ambulance stations for capacity expansion.MethodsCost distance methods were used to evaluate areal and population-level access to prehospital care within 30 and 60 minutes of each of the 128 ambulance stations in Ghana. With network analysis methods, a two-step floating catchment area model was created to identify district-level variability in access. Districts without NAS stations within their catchment areas were identified as candidates for an additional NAS station. Additionally, five candidate stations for capacity expansion (e.g., addition of an ambulance) were then identified through iterative simulations that were designed to identify the stations that had the greatest influence on the access scores of the ten lowest access districts.ResultsFollowing NAS inception, the proportion of Ghana's landmass serviceable within 60 minutes of a station increased from 8.7 to 59.4% from 2004 to 2014, respectively. Over the same time period, the proportion of the population with access to the NAS within 60-minutes increased from 48% to 79%. The two-step floating catchment area model identified considerable variation in district-level access scores, which ranged from 0.05 to 2.43 ambulances per 100,000 persons (median 0.45; interquartile range 0.23-0.63). Seven candidate districts for NAS station addition and five candidate NAS stations for capacity expansion were identified. The addition of one ambulance to each of the five candidate stations improved access scores in the ten lowest access districts by a total 0.22 ambulances per 100,000 persons.ConclusionsThe NAS in Ghana has expanded its population-level spatial access to the majority of the population; however, access inequality exists in both rural and urban areas that can be improved by increasing station capacity or adding additional stations. Geospatial methods to identify access inequities and inform service expansion might serve as a model for other LMICs attempting to understand and improve formal prehospital care services.