Skip to main content

Table 3 Publication details and included factors of rurality indices

From: Systematic scoping review of factors and measures of rurality: toward the development of a rurality index for health care research in Japan

Author and publication year Name of the index Population yes/no Travel distance yes/no Travel time yes/no Travel cost yes/no
Department of Primary Industries and Energy, Department of Human Services and Health, 1994 [24] Rural, remote and metropolitan area (RRMA) yes (population size) no no no
Weinert et al., 1995 [25] MSU rurality index yes (population size) yes (distance to emergency care) no no
Leduc, 1997 [26] General Practice Rurality Index (GPRI) yes (population size) yes (distance to basic/advanced referral center) no no
Department of Health and Aged Care, 2001 [27] Accessibility/Remoteness Index of Australia (ARIA) no yes (distance to the nearest centre) no no
Australian Institute of Health and Welfare Canberra, 2004 [28] Australian Standard Geographical Classification (ASGC) no yes (distance to the nearest centre/the service town) no no
Swan et al., 2008 [29] Clinical peripherality indicator yes (population density) no yes (travel time to nearest specialist led hospital and to Health Board administrative headquarters) no
Kralj, 2008 [11] Rurality Index for Ontario (RIO) yes (population size and population density) no yes (travel time to nearest basic referral centre and to nearest advanced referral centre) no
McGrail et al., 2009 [30] Index of Rural Access yes: (population size) no no no
Han et al., 2012 [31] Rural PHCWA index yes (population density) no no no
Humphreys et al., 2012 [13] six-level geographical classification yes (population size) yes (geographical remoteness) no no
Steinhaeuser et al., 2014 [32] modified RRS-Germany (mRRS-G) no no yes (travel time from the practice to next major hospital, to the nearest general practitioner colleague at place of work, to the satellite clinic and to most distant boundary covered by the practice) no
Mao et al., 2015 [33] Individual-based rurality and well-being measures yes (population density) no no no
Zhu et al., 2015 [34] Rural taxonomy no no no no
Inagami et al., 2016 [35] IRR zip yes (population size and density) yes (distance to metropolitan statistical area/micropolitan statistical area) no no
Alasia et al., 2017 [36] index of remoteness yes (population size) no yes (travel time) yes
Calovi et al., 2018 [37] spatial accessibility index no yes (distance to outpatient clinics) no no
Doogan et al., 2018 [38] Isolation scale no yes yes no
Health care resources yes/no Health care needs yes/no Others Formula
no no level in urban hierarchy (small/large/metropolitan/capital city urban center) not applicable
no no   Four mathematical operations are performed as below:
1. Distance and population measures are transformed to make the distribution of the resulting index as normal as possible
2. The transformed distance and population measures are standardized so that each has a standard deviation of one
3. The standardized transformed distance and population measures are weighted to produce an initial index of rurality that assigns high scores to rural families and low scores to urban families
4. The initial index constructed in operation #3 is restandardized to have a mean of zero and a standard deviation of one
yes (number of general practitioners, number of specialists, presence of an acute care hospital) no   Sum the points for each of the following (maximum 100 points):
1. Remoteness from closest advanced referral centre (km) ÷ 50
2. Remoteness from closest basic referral centre (km)÷25
3. 20*(Drawing population÷2000)
4. (20 ÷ number of full-time GPs with main place of business within 25 km of the centre of the community
5. Number of specialists
6. Presence of an acute care hospital
no no   unweighted addition of the four (threshold-limited) ratio values for each of the four levels of service centre
no no   calculates distance to the nearest centre in each of five categories of service centre
no yes (number of patients on the practice list) Practice list size, ward population density and travel time to hospital were log transformed to achieve near normality. The relationships among the variables were assessed by matrix plots and correlation coefficients. This was further multiplied by 100 for the index to range from 0 to 100 with a midpoint of 50. Higher values represent greater peripherality.
no no   Sum the points for each of the following (maximum 100 points):
1. Measure of community population and population density
2. Measure of travel time to nearest basic referral centre
3. Measure of travel time to nearest advanced referral centre
yes (the number of full-time equivalent services at location and the population-to-provider ratio) yes (health needs (Disability Adjusted Life Years: DALYs)) mobility (households without a car, individuals of low personal mobility and public transport availability) \( {\sum}_j^{\left\{\mathrm{100,10}\mathit{\min}\right\}}f2(dij) \)*Rj*Mobi
f(dij): impedance function
Rj: the population-to-provider ratio for service j
Mobi: equal to one within the initial catchment (10 min), and is less than one in the secondary catchment for areas of low mobility
yes (primary health care worker density per 1000 farming population index) no   Rural PHCWA index of X province = primary health care worker density per 1000 farming population index of X province * population density index of X province.
no no   not applicable
yes (backup by a paramedic team within 15 min and numbers of GP which engaged in on-call duty) no   Sum the following six variables:
1. travelling time from the surgery to major hospital
2. on-call duty
3. receiving timely backup by a paramedic team
4. travelling time to nearest general practitioner colleague at place of work
5. travelling time to most distant practice boundary
6. satellite clinic
yes (density of health facilities/social service facilities) no number of different ethnic groups/degree of land development/mean household income/density of loads \( \sum \limits_{L=1}^n ProbL,i \)*RuralDegreeL/ \( \sum \limits_{L=1}^n ProbL,i \)
1. n is the total number of places within individual i’s activity space
2. L represents any one of these places
3. ProbL,i is the probability of visiting place L by individual i
4. the degree of rurality for all places (RuralDegreeL) were extracted with GIS database
yes (provider resources: primary care physicians, medical specialists, non-physician practitioners, dentists and facility resources: staffed hospital beds, provider resources, average daily census, Medicare/Medicaid certified nursing home beds) no economic resource, age distribution not applicable
no no   Step 1: Calculating maximum, minimum and range of each variable.
Step 2: transforming each variable so that it is measured on a scale from 0 to 1.
Step 3: calculating averages of the transformed variables
The included variables are below:
1. population size,
2. population density
3. distance to closest metropolitan area
no no   ln \( \sum \limits_{k=1}^n\left(\frac{Popk}{Ci,k}\right) \)
Pop: sizes of the population centres
C: travel cost
no no volumes of activity \( \sum \limits_{\begin{array}{c}j\in \left\{ dij\leqq d0\right\}\\ {}\end{array}} Rj \)
dij: the distance between i and j
Rj: supply-to-demand ratio at supply location j
no no   v(i,j) = ajδdij
ai = maxj[v(i,j)]
v: function
aj: neighbor’s resources
dij: distance
δ: parameter which chosen based on research purpose