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Estimating spatially specific demand and supply of dental services: a longitudinal comparison in Northern Germany
Author(s) -
Schwendicke Falk,
Jäger Ralf,
Hoffmann Wolfgang,
Jordan Rainer A.,
van den Berg Neeltje
Publication year - 2016
Publication title -
journal of public health dentistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 63
eISSN - 1752-7325
pISSN - 0022-4006
DOI - 10.1111/jphd.12142
Subject(s) - supply and demand , gini coefficient , population , agricultural economics , workforce , physician supply , distribution (mathematics) , business , economics , demographic economics , econometrics , economic growth , medicine , inequality , environmental health , microeconomics , mathematics , mathematical analysis , economic inequality
Objectives Assessing the spatial distribution of oral morbidity‐related demand and the workforce‐related supply is relevant for planning dental services. We aimed to establish and validate a model for estimating the spatially specific demand and supply. This model was then applied to compare demand‐supply ratios in 2001 and 2011 in the federal state of Mecklenburg–Vorpommern (Northern Germany). Methods The spatial units were zip code areas. Demand per area was estimated by linking population‐specific oral morbidities to working times via insurance claim data. Estimated demand was validated against the provided demand in 2001 and 2011. Supply was calculated for both years using cohort data from the dentist register. The ratio of demand and supply was geographically mapped and its distribution between areas assessed using the Gini coefficient. Results Between 2001 and 2011, a significant decrease of the general population (−7.0 percent), the annual demand (−13.1 percent), and the annual supply (−12.9 percent) was recorded. The estimated demands were nearly (2001: −4 percent) and completely (2011: ±0 percent) congruent with provided demands. The average demand‐supply‐ratio did not change significantly between 2001 and 2011 ( P > 0.05), but was increasingly unequally distributed. In both years, few areas were over‐serviced, while many were under‐serviced. Conclusions The established model can be used to estimate spatially specific demand and supply.