Comparing absolute and relative distance and time travel measures

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

Comparing absolute and relative

distance and time travel measures of

geographic access to healthcare facilities

in rural Haiti

Kayleigh Pavitra Bhangdia ,

1,2

Hari S Iyer,

3,4

Jean Paul Joseph,

Rubin Lemec Dorne,

Joia Mukherjee,

6,7

Temidayo Fadelu

3,6

To cite: BhangdiaKP, IyerHS,

JosephJP, etal. Comparing

absolute and relative distance

and time travel measures of

geographic access to healthcare

facilities in rural Haiti. BMJ Open

2022;12:e056123. doi:10.1136/

bmjopen-2021-056123

► Prepublication history and

additional supplemental material

for this paper are available

online. To view these les,

please visit the journal online

(http://dx.doi.org/10.1136/

bmjopen-2021-056123).

Received 11 August 2021

Accepted 10 April 2022

For numbered afliations see

end of article.

Correspondence to

Kayleigh Pavitra Bhangdia;

kbhangdia@ gmail. com

Original research

employer(s)) 2022. Re- use

permitted under CC BY- NC. No

commercial re- use. See rights

and permissions. Published by

BMJ.

ABSTRACT

Introduction While travel distance and time are important

proxies of physical access to health facilities, obtaining valid

measures with an appropriate modelling method remains

challenging in many settings. We compared ve measures of

geographic accessibility in Haiti, producing recommendations

that consider available analytic resources and geospatial

goals.

Methods Eight public hospitals within the ministry of public

health and population were included. We estimated distance

and time between hospitals and geographic centroids of

Haiti’s section communes and population- level accessibility.

Geographic feature data were obtained from public

administrative databases, academic research databases

and government satellites. We used validated geographic

information system methods to produce ve geographic

access measures: (1) Euclidean distance (ED), (2) network

distance (ND), (3) network travel time (NTT), (4) AccessMod

5 (AM5) distance (AM5D) and (5) AM5 travel time (AM5TT).

Relative ranking of section communes across the measures

was assessed using Pearson correlation coefcients, while

mean differences were assessed using analysis of variance

(ANOVA) and pairwise t- tests.

Results All ve geographic access measures were highly

correlated (range: 0.78–0.99). Of the distance measures,

ED values were consistently the shortest, followed by

AM5D values, while ND values were the longest. ND

values were as high as 2.3 times ED values. NTT models

generally produced longer travel time estimates compared

with AM5TT models. ED consistently overestimated

population coverage within a given threshold compared

with ND and AM5D. For example, population- level

accessibility within 15 km of the nearest studied hospital in

the Center department was estimated at 68% for ED, 50%

for AM5D and 34% for ND.

Conclusion While the access measures were highly

correlated, there were signicant differences in the absolute

measures. Consideration of the benets and limitations of

each geospatial measure together with the intended purpose

of the estimates, such as relative proximity of patients or

service coverage, are key to guiding appropriate use.

BACKGROUND

Longer distance to health services has been

associated with lower service utilisation rates,

increased health expenditure and poor health

outcomes across multiple diseases.

1–4

Studies

in low- income and middle- income countries

(LMICs) have described poor geographic

access leading to lower rates of facility- based

deliveries, increased childhood mortality and

worse outcomes in communicable diseases

such as HIV as well as in non- communicable

diseases (NCDs) such as cardiovascular

disease and breast cancer.

4–16

Hence, distance

to health services has been adopted as a popu-

lation measure of health equity, with travel

time a proxy for equitable physical access.

Patient travel distance and travel time

measures can be used to assess disparity and

STRENGTHS AND LIMITATIONS OF THIS STUDY

⇒ We compared ve measures of geographic access

based on distance and travel time, using simple

models, such as Euclidean distance, and more com-

plex geographic information system models that

accounted for geographic features in the study area.

⇒ Our ndings highlight that there are signicant dif-

ferences in the absolute estimate values of time and

distance produced by each approach, and we pro-

vide generalisable recommendations to guide inves-

tigators when choosing between measures.

⇒ To our knowledge, this is the rst study to provide

comparisons between measures of geographic ac-

cessibility to healthcare in the Caribbean region, and

it provides guidance for conducting similar compar-

isons in other resource- limited settings.

⇒ By revealing the stark differences in population

coverage estimates obtained using different geo-

graphic access measures, our study demonstrates

the need to carefully consider the geospatial model

used to estimate access when considering absolute

thresholds.

⇒ This study did not use a true gold standard mea-

surement of travel time; our analysis relied on key

assumptions that patients used motorised transport,

followed national speed limits and did not account

for possible nancial barriers.

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

to describe the trade- offs health systems make between

equity of access and efficiency.

18–23

Further, being able to

contextualise the accessibility of particular health service

in relation to relative disease burden is critical for overall

health system efficiency. A 2- hour, one- way travel time

threshold has been proposed for hospital services such

as emergency care, obstetrics and general surgical inter-

ventions and is the threshold often used by policymakers

examining population- level access.

However, a realistic

threshold may vary based on condition or required care,

and a lower threshold may be indicated for more basic

health services provided at health centres, such as malaria

care or immunisations.

Despite the importance of distance and time measure-

ments, there are challenges with obtaining accurate

estimates in LMICs. Historically, Euclidean distance

(ED), or straight- line distance, has been most commonly

used in many LMICs. However, when compared against

distance measures from more sophisticated geographic

information system (GIS) modelling techniques and

patient- reported estimates of travel time, straight- line

distances often result in substantial underestimates,

inaccurately capturing travel burden and erroneously

estimating catching areas of health facilities.

1 7

Increas-

ingly however, health ministries and service providers

are adopting GIS across resource- limited settings.

Auto-

mated platforms such as AccessMod 5 (AM5) use publicly

available geographic feature databases including road

networks and local topography to produce valid estimates

of distance and travel time.

26 27

Additionally, raw world-

wide geographic data are becoming more widely avail-

able through platforms such as Google Earth Engine and

Open Street Map (OSM), which, taken together, allow for

more sophisticated modelling techniques to be leveraged

by LMICs to accurately measure population- level access

and ultimately improve health outcomes.

28–30

The primary aim of this study is to compare different

GIS measures used to estimate the geographic accessi-

bility of services at seven non- governmental organisa-

tion (NGO) supported public health facilities and the

main national referral hospital in Haiti (figure 1A). The

majority of health services in Haiti are concentrated in

the capital region and distance is a known access barrier

for many Haitians, particularly in rural regions.

31 32

While

historically there has been substantial variation in quality

of services across the primary care system in Haiti, there

is ongoing strengthening of the healthcare system with a

national goal of achieving universal health coverage by

2030.

32 33

As previous studies in Haiti have used ED as a

measure to estimate patients’ physical access to care,

34 35

we sought to compare access measures obtained using

different analytic approaches in this setting given the

increasing availability and usability of these modelling

techniques. This analysis set out to compare increas-

ingly sophisticated models for estimating travel time

and distance from patient residence to healthcare facil-

ities, using both raster and vector approaches. Our study

highlights the importance of appropriately measuring

accessibility and how research questions can inform the

choice of methods. We provide empirical estimates of

geographic accessibility in Haiti, but more importantly

provide guidance to policymakers in other resource-

limited settings seeking to estimate geographic access to

answer public health questions.

METHODS

Study setting

Haiti is a country in the Caribbean region with a popula-

tion of nearly 11 million, covering a land area of 27 750

km. The country is irregularly shaped and about 80%

of the land area is covered by mountains.

36 37

Classi-

fied as a low- income country, many Haitians (65%) live

below the national poverty line and the country ranks

poorly relative to neighbouring countries on many

health indicators.

36–39

The healthcare system in Haiti

Figure 1 (A) Map of Haiti with seven Zanmi Lasante (ZL)/

Partners In Health supported hospitals throughout the Center

and Artibonite departments. (B) Map of Haiti with road

network by road category and all Zanmi Lasante/Partners In

Health supported hospitals included in analysis.

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

is divided into three levels—primary, secondary and

tertiary, managed by the ministry of public health and

population (MSPP). Nearly half of the healthcare facil-

ities are located in the capital, Port- Au- Prince, while

service coverage is poorer in rural areas where many

lack access to essential health and nutrition services.

MSPP collaborates with many NGOs to support care

delivery and emergency response in these rural areas.

Zanmi Lasante (ZL) together with Partners In Health

(PIH) has worked closely with MSPP to provide high-

quality care in Haiti for over 30 years, helping to estab-

lish 16 health centres and public hospitals throughout

the country, and serving as one of the largest health-

care providers outside of the government. These health

facilities are concentrated within the Center and Arti-

bonite departments, two of the most remote and under-

privileged regions in the country.

Given the unique

geography and topography of the country, comparing

the utility of different measures of distance and time

may inform selection of the most appropriate measure

given available resources and precision required to

guide health systems planning efforts.

Data management and data sources

Information regarding geographic and population

features was obtained from publicly available databases.

The basemap of Haiti’s administrative boundaries was

obtained from the Humanitarian Data Exchange.

Road files were acquired from OSM updated in 2018.

Modelled population estimates were obtained from

WorldPop database for 2019.

Measures of geographic access

The administrative level used for this analysis was the

section commune level, the smallest administrative divi-

sion in Haiti with consistently available geographic data

(online supplemental appendix figure 1). There are a

total of 570 section communes ranging in size from 5 to

318 km

with a mean area of 53 km

and average popu-

lation of 19 160 individuals. The geographic centroids of

each section commune were calculated and represent

the 570 origins of our analysis. Two tertiary hospitals,

University Hospital Mirebalais (HUM) and State Univer-

sity Hospital (HUEH) and the six hospitals supported

by ZL/PIH, were chosen as destinations for our analysis.

Geocodes for these hospitals were extracted from Google

Maps.

We used GIS to produce five different measures

of geographic access: (1) ED, (2) network distance (ND),

(3) network travel time (NTT), (4) AM5 distance (AM5D)

and (5) AM5 travel time (AM5TT).

ED estimation

ED was calculated using geocodes corresponding to the

origin (section commune centroids) and destinations

(HUM, HUEH and six ZL/PIH hospitals), using the

near distance tool in ArcMap V.10.6.1 (Esri, Redlands,

California).

ND and time

First, roads were extracted within the Haiti national

border from a road shapefile obtained for the entire

island of Hispaniola.

A topology profile was added to the

road file using widely accepted topology rules: must not

overlap, must be single part and must not have pseudo

nodes. Using this methodology, errors in topology were

corrected using the following commands: subtracting

shorter road, exploding roads and merging roads.

47 48

Based on Haitian government classification and on prior

published studies, roads were reclassified into five catego-

ries: pedestrian path, residential streets, minor highways,

medium highways and major highways (figure 1B).

49 50

The associated road speeds were based on a previous study

by Mathon et al

and were stored as attributes; they can

be found in online supplemental appendix table 1.

A network dataset was built with the road file and accu-

mulation cost attributes were added for length and times.

The section commune centroids were added as the origin

and the location of the health facilities as the destina-

tions. Using the network dataset an Origin Destination

(OD) cost matrix analysis was performed with time as

the impedance factor. The resultant network was vali-

dated using Google Maps by randomly sampling 30 pairs

(5%) of origin and destination points and comparing the

distance and times from the OD cost matrix with Google

Maps driving directions. Cases which had no connection

network found between an origin point and the destina-

tion were manually identified (eg, located within a water

barrier, located far from road network). After identifying

these cases, in order to assign complete road networks

to these section communes, a road segment was built

connecting centroids located away from existing roads

and walking speeds were assumed for those segments.

The final resultant OD cost matrix for length and time

was exported for data analysis. All spatial data manage-

ment and adjustments to complete the road network, ED

estimations and ND and NTT estimations were done in

ArcMap.

AM5 raster model for distance and time

AM5 was developed in 2005 as a suite of GIS tools to

allow countries to evaluate health service coverage using

an algorithm based on least- cost paths and accumula-

tive cost surface ultimately determining the most effi-

cient path between two points on the surface.

26 51

AM5

enables estimation of a travel time surface that covers a

geographic area of interest, assigning travel times to each

raster grid cell using the geographic access tool. Using

this travel time surface, AM5 also can estimate referral

times between lower- level health facilities and hospitals

using the referral analysis tool. AM5 uses road networks

and travel speeds similar to the network dataset described

above; however, AM5 further accounts for surface wide

geographic features evaluating the entire study setting

which in turn allows for travel off roads. By incorporating

additional geographic features beyond just road networks,

AM5 estimates may better capture the extent to which

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

physical distance impedes access to care in low- resource

settings where private car- based travel is less common.

26 27

The inputs into AM5 consisted of a digital elevation model

obtained from DIVA- GIS with a 1000 m resolution, a land

cover raster file from Google Earth Engine with a 500 m

resolution, rivers and lakes shapefile obtained from

OCHA Services, the road shapefile described previously,

hospital locations and section commune centroid loca-

tions.

52 53

Speeds for all land cover types can be found in

online supplemental appendix table 1. After generating a

merged land cover from the input layers listed above in

AM5, 61 (11%) section commune centroids were located

on top of barriers. Facility locations were corrected in

AM5 by using the Interactive Map tool to preview raster

layers and manually move facility locations to the nearest

cell that did not contain a defined barrier (rivers and

lakes). In order to calculate the distance and time from

the section communes to the health facilities, we used the

referral analysis tool to estimate travel time and distance

between section commune geographic centroids and the

hospitals. This table was then exported into Microsoft

Excel V.16.3 (Microsoft, Redmond, Washington) and the

shapefile was exported into ArcMap for visualisations.

Statistical analysis plan

We sought to compare five measures of geographic access

representing travel times and distance between section

communes to closest hospital. Measures of geographic

access are generally used for two purposes in health

services research: (1) obtaining estimates of the propor-

tion of population within a given access threshold and (2)

ranking participants or geographic areas by how far they

are from care.

Obtaining accurate measures of coverage

proportion requires accurate absolute estimate values,

while ranking participants only requires that the relative

ordering of estimate values is preserved. Analysis of vari-

ance (ANOVA) and pairwise t- tests were used to assess

concordance between absolute measures of geographic

access and correlations to assess concordance of ranking

of geographic access measures assigned to section

communes.

After estimating the distance between section commune

centroids within the Center and Artibonite departments

and the seven ZL/PIH hospitals based on three distance

measures: ED, ND and AM5D, one- way ANOVA was used

to test for a global difference between means, and then

pairwise t- tests with equal variances to determine which

measures differed from each other. Similarly, travel time

between the section communes and health facilities were

summarised based on two measures: NTT and AM5TT

and a pairwise t- test was performed to assess differences

between the measures. Following an assessment of the

distribution of the different measures, ANOVA and t- test

were chosen based on the relatively normal distribution

of distance and time measurements.

Given that healthcare planners often implement

services at varying levels of the healthcare system, with

some targeting smaller catchment areas and others rolled

out at a national level, we repeated these analyses at the

national level. Examining geographic accessibility across

Haiti from all section communes to the nearest of the two

tertiary hospitals included in our analysis, we were able

to compare the correlations and absolute differences

between measures at two spatial scales. In our experience,

some specialised health services such as cancer treatment

may only be available in one facility in the country, and

patients of these two hospitals will often travel from every

part of the country to reach these specialised services. In

addition, the population surrounding these two tertiary

hospitals are vastly different with HUM located in a rural

mountainous area and HUEH located in the urban

capital city. Of note, 15 observations (3%) located on

islands off the cost of mainland Haiti were excluded from

this analysis since the distance and time for these section

communes could not be calculated in AM5. In order to

compare relative ranking of section communes across

the five measures, we examined correlations between

the five geographic access measures of distance and time

estimates from section commune centroid nationally to

HUM using Pearson correlation coefficients and 95% CIs.

Lastly, we estimated population- level accessibility

within the Center and Artibonite departments capturing

the proportion of the catchment population that has

geographic access to their nearest ZL/PIH supported

hospital. We report these findings stratified by time and

distance intervals.

All statistical analyses were done in R statistical software

(V.4.0.3). All statistical tests were two sided and p values of

<0.05 were considered statistically significant.

Patient and public involvement

There was no direct patient involvement in the design

and conduct of this analysis. However, the development

of the research question was motivated by patient experi-

ences traveling far distances to reach healthcare services

in Haiti.

RESULTS

Geographic characteristics

Online supplemental appendix table 2 summarises

geographic, health and economic characteristics of each

department. The Center department, home to 701 205

individuals, is composed of 35 section communes and

has four ZL/PIH hospitals including HUM. The Artibo-

nite department, composed of 63 section communes, has

a population of 1 684 599 and has three ZL/PIH hospi-

tals. The Artibonite department is both larger in size and

more densely populated than the Center department.

Comparing mean distances and times across geographic

access measures

The results of the distance and time estimates from each

section commune to selected ZL/PIH- supported hospi-

tals, HUM and Saint Marc Hospital (SM), the largest

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

facilities within the Center and Artibonite departments,

respectively, are summarised in table 1.

Table 1 shows that within both the Center and Artibo-

nite departments, ED is significantly shorter than both

ND and AM5D yet ND and AM5D are comparable. For

example, the mean ND to SM was 20.65 km (48%) longer

than ED (p=<0.001) and the mean AM5D to SM was

16.59 km (39%) longer than ED (p=0.001). Findings were

similar for all ZL/PIH supported hospitals in this study as

displayed in online supplemental appendix table 3. The

largest absolute difference between distance measures

appeared for travel to Belladere hospital, where ED was

33.3 km and ND was 77.4 km (2.3 times ED).

NTT and AM5TT were also different, with AM5

producing significantly shorter estimates than NTT. For

example, among section communes in the HUM catch-

ment department, there was a statistically significant

32.5 min (95% CI: 19.7 to 45.3) shorter average time to

HUM when using AM5TT compared with NTT. These

measures followed a similar pattern in the SM catchment

department in addition to the other ZL/PIH supported

hospitals (see table 1 and online supplemental appendix

table 3).

Figure 2A displays choropleth maps of travel times to

the nearest ZL/PIH hospital within the Artibonite and

Center departments in 30 min time intervals comparing

the NTT and AM5TT estimation techniques. The dark

blue colour represents the section communes with the

shortest travel time and the dark red represents longest

travel times. While the maps are generally consistent,

more section communes were classified as having longer

travel times with NTT compared with AM5TT.

The nationwide distance and time estimations from

section communes to the two referral hospitals are

outlined in online supplemental appendix table 4,

expanding the spatial scale of the analysis and comparing

hospitals in both rural and urban settings. Based on the

estimation technique used, the national mean distance to

HUM, averaging over all of the section communes in the

study, ranged from 111.9 (ED) to 167.3 km (ND) and the

national mean distance to HUEH ranged from 108.3 (ED)

to 161.6 km (ND). The findings followed a similar pattern

to the results from the departmental difference with ED

being very different from ND and AM5D; however, in the

national analysis, AM5D was also significantly different

from ND in a pairwise test (p<0.001).

However, comparing the proportional differences from

the national comparison to HUM with the earlier depart-

mental comparison, we see that ND is only 50% longer

than ED nationally compared with 76% longer when

restricting to the Center department. This analysis also

found significant differences in the mean travel time to

these referral hospitals between the estimates generated

from the two techniques used. On average, NTT was 30.4

min (95% CI: 27.4 to 33.4) longer than AM5TT to HUM

(p<0.0001), with a similar pattern observed at HUEH.

Figure 2B displays national choropleth maps of travel

times to the nearest tertiary referral hospital in 1- hour

Table 1 Comparison of mean distance (in km) and time (in min) from section communes to selected hospitals in Center and

Artibonite departments

Distance and time estimates to HUM (N=35) Distance and time estimates to SM (N=63)

Absolute measures

Measure Mean distance (SD) Mean distance (SD)

ED 29.4 (15.7) 42.5 (19.4)

ND 51.7 (30.4) 63.1 (30.1)

AM5D 43.8 (26.2) 59.1 (28.3)

Measure Mean time (SD) Mean time (SD)

NTT 98.7 (61.0) 97.4 (56.8)

AM5TT 66.2 (33.2) 75.8 (34.8)

Distance pairwise comparison

Mean difference (95% CI) T- test

p value

Mean difference (95% CI) T- test

p value

ND–ED 22.33 (8.19 to 36.47) <0.001 20.65 (9.57 to 31.73) <0.001

AM5D–ED 14.44 (0.30 to 28.58) 0.04 16.59 (5.51 to 27.67) 0.001

ND–AM5D 7.89 (−6.25 to 22.02) 0.38 4.06 (−7.02 to 15.14) 0.66

Time pairwise comparison

Mean difference (95% CI) T- test

p value

Mean difference (95% CI) T- test

p value

NTT–AM5TT 32.5 (19.7 to 45.3) <0.001 21.7 (13.8 to 29.5) <0.001

AM5D, AccessMod 5 distance; AM5TT, AccessMod 5 travel time; ED, Euclidean distance; HUM, University Hospital Mirebalais; ND, network

distance; NTT, network travel time; SM, Saint Marc Hospital.

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

time intervals comparing the NTT and AM5TT estima-

tion techniques. Similarly, maps are generally consistent,

though AM5 produced shorter travel times in the Center

and Western departments relative to estimates derived

from network calculations.

Correlation between geospatial estimation measures

Table 2 includes Pearson correlations between the five

geographic access measures (travel time and distance

from each section commune centroid to HUM). We

found that the three distance and the two travel time esti-

mation techniques produced values that were all highly

correlated (range: 0.78–0.99) meaning that the rank

ordering of geographic access by section commune is

preserved across most measures.

The strongest correla-

tions were between the various distance measures with

each of the three correlation coefficients >0.96. The

weakest correlation was between ED and NTT (0.78).

Population-level comparison

Online supplemental appendix figure 2 presents

population- level accessibility comparisons between

the three distance estimation techniques across both

the Center and Artibonite departments. The relative

percentage of the catchment population living within

a particular threshold from the nearest hospital varies

based on the estimation technique used, with the differ-

ences being more pronounced in the Center department.

Population- level accessibility comparisons between the

two travel time estimation techniques are also presented

in online supplemental appendix figure 3 stratified by

30 min intervals and follow a similar pattern to distance

techniques. For example, within the Center department,

it was estimated that 54% of the catchment population

lives within 30 min of the closest hospital when using

AM5TT, while only 33% are estimated to live within this

radius when using NTT. In the Artibonite department,

35% of the population lives within 30 min of the closest

hospital when using AM5TT, while only 26% are esti-

mated to live within this radius when using NTT.

DISCUSSION

Our results indicate that while distance and travel time

estimates were highly correlated, there were differences

in absolute measures across the five approaches. ED

models estimated significantly shorter distance travelled

compared with ND and AM5D models that incorporated

roads and other geographic features. AM5D estimates

were longer than ED but shorter than ND; this pattern was

anticipated as AM5 raster referral analysis allows for travel

off roads. The absolute differences between ND and ED

were more pronounced when the analysis was restricted

to one department as compared with the national anal-

ysis; however, we find that patterns are similar regardless

of spatial scale. Given the increasing focus on tracking

geographic accessibility indicators to reach Universal

Figure 2 (A) Comparison of NTT and AM5TT to nearest

Zanmi Lasante (ZL)/Partners In Health supported hospitals

by 30 min intervals throughout the Artibonite and Center

departments. (B) National comparison of NTT and AM5TT

to nearest tertiary hospitals by 60 min intervals. AM5,

AccessMod 5; AM5TT, AM5 travel time; NTT, network travel

time.

Table 2 Matrix of Pearson correlation and 95% CIs of distance and time values to University Hospital Mirebalais by different

geospatial estimation techniques

ED ND AM5D NTT AM5TT

ED 1.00 – – – –

ND 0.97 (0.97 to 0.97) 1.00 – – –

AM5D 0.98 (0.98 to 0.99) 0.99 (0.99 to 0.99) 1.00 – –

NTT 0.78 (0.75 to 0.81) 0.83 (0.80 to 0.85) 0.81 (0.78 to 0.84) 1.00 –

AM5TT 0.88 (0.86 to 0.90) 0.91 (0.89 to 0.92) 0.91 (0.89 to 0.92) 0.93 (0.92 to 0.94) 1.00

All values are signicant at the 0.05 level.

AM5D, AccessMod 5 distance; AM5TT, AccessMod 5 travel time; ED, Euclidean distance; ND, network distance; NTT, network travel time.

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

Health Coverage and the Sustainable Development Goals,

accurate estimates of travel distance and times are crucial

to inform progress.

55 56

Recognising we did not present

a gold standard of directly observed patient travel times

and routes,

our analysis highlights possible trade- offs

between ED estimates and more sophisticated estimation

techniques, providing generalisable lessons regarding the

implications for each. These findings contribute to the

international literature by demonstrating the feasibility

of computing multiple measures of geospatial access in

resource- limited settings. Our study showcases the impor-

tance of choosing the appropriate accessibility measure-

ment and methods depending on the research question.

This work provides empirical estimates of geographic

accessibility in Haiti, but more importantly, provides guid-

ance to policymakers in other resource- limited settings

seeking to estimate geographic access to answer public

health questions.

To our knowledge, no earlier studies have provided

comparisons between measures of geographic accessi-

bility to hospitals in Haiti or the Caribbean region. While

few studies have assessed the impact of geographic health

accessibility, they have all used ED estimation. Wang

and Mallick

estimated the extent to which women’s

contraceptive use is associated with the available method

choices in the health facilities throughout Haiti using ED

to link Demographic and Health Surveys (DHS) clusters

with facilities.

However, they note that mountainous

terrain and road conditions may have led to misclassifica-

tion of accessibility by inaccurately classifying which facil-

ities were more easily accessed. Kwan et al

evaluated the

poverty distribution among patients with NCD at HUM,

using ED to explore the relationship between poverty

and distance to HUM. They found that among those who

presented at the health facility, those who lived further

were less poor than those living closer, speculating that

those who live further and are more poor face high

barriers to accessing care and therefore were underrepre-

sented in their sample.

Our findings are consistent with other studies from

LMICs; they reinforce the concept that the choice of

geographic accessibility measure may influence conclu-

sions about absolute accessibility. Noor et al illustrated

this issue by comparing multiple spatial models in

Kenya. Their results revealed that ED models incor-

rectly classified roughly 6 million individuals as being

within 1 hour of government health services.

Recently,

van Duinen et al compared two geospatial models

against patient- reported travel times and found that

the more conservative travel time estimates, with lower

travel speeds, better estimate patient- reported travel

times, highlighting how critical the input variables are

to producing valuable spatial model outputs.

Simi-

larly, Rudolfson et al found that standard AM5 gener-

ated travel times underestimated patient- reported

travel times for emergency obstetric care in Rwanda;

however, when the GIS models were adjusted to model

patients travel path passing through health centres

enroute to the hospital, modelled times were much

closer aligned to the patient- reported times.

Banke-

Thomas et al compared modelled travel time to compre-

hensive emergency obstetric care in Lagos, Nigeria, to

measured times from replicated patient journeys. Their

findings confirmed that existing geospatial model-

ling methods underestimate actual travel times when

using a gold standard validation, which these authors

did through actual replication of travel by two inde-

pendent drivers.

These findings further underscore

the importance of understanding true travel patterns

of patient populations. It is critical to note this anal-

ysis is focused on geographic or physical accessibility

to health services and we acknowledge that there

are additional dimensions of healthcare access not

addressed by this study. Although a region may appear

to have geographic access to a health facility, this does

not mean services are available, affordable, accept-

able or that there is appropriate accommodation—all

dimensions that contribute to overall access.

Similar to our findings, Nesbitt et al compared a variety

of spatial models of delivery care access in Ghana and

found that the models were highly correlated with each

other including ED.

Despite its known limitations, ED

may offer a reasonable proxy for other spatial measures,

especially when rank preservation rather than coverage

estimation is the goal of the analysis. Nonetheless, our

findings support growing consensus that ED may severely

overestimate health facility coverage within a given

threshold distance, and therefore should be used with

caution when estimating coverage.

Careful consideration of the benefits and limita-

tions of each measure can help guide appropriate use

and are summarised in table 3. Based on our findings,

ED is acceptable when estimating the relative prox-

imity of patients, with NTT and AM5TT being prefer-

able. However, when estimating coverage, ED results in

underestimates of health services coverage and therefore

would be unfavourable, while NTT or AM5TT would be

preferred. Furthermore, since ND and NTT estimates use

road networks, they would be the preferred options when

approximating actual patient travel routes. However,

it is key to note that ED requires the least amount of

researcher time and effort as well as the least amount of

data inputs, while ND and NTT require the most research

time and effort and AM5D and AM5TT require the most

data inputs.

Given the lack of a true gold standard measure of

travel time from a patient’s address to their health

facility, which would have required greater investments

of resources and time than our study allowed such as

detailed surveys or GIS trackers, our analysis relied on

key assumptions. We assumed that all patients travelled

using motorised transport and followed national speed

limits, which may not accurately reflect the true trans-

portation patterns used by patients to reach care. Our

analysis also assumes uninterrupted travel between two

points; however, we recognise patients may use multiple

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

forms of transportation, which may lead to additional

waiting times. Further, all estimates are measured from

geographic centroids and therefore do not reflect the

distribution of the population within each section

commune nor capture variation in travel patterns

among residents of the same section commune. In

addition, we do not account for travel costs which may

also influence the routes and mode of transportation.

While geospatial estimates may be helpful, assessing

the true burden of travel distance and time frequently

requires detailed survey of patients and the use of GIS

trackers. Future studies in Haiti and beyond should

endeavour to validate geospatial estimation techniques

using patient- reported methods. Lastly, this analysis

focused on a subset of hospitals throughout Haiti

focusing predominantly in the Center and Artibonite

departments. We recommend that future studies in

Haiti and the region explore a wider range of hospitals

and resident locations in order to confirm generalis-

ability of our findings.

Producing the geographic measures presented in this

study requires specialised geospatial analytic skills. Given

the limited published data on healthcare accessibility in

Haiti, we have stored the data and analytic files used in a

publicly available GitHub repository.

This resource will

enable researchers at ZL/PIH and other organisations

in Haiti and elsewhere to produce their own geographic

access measures, identify gaps and underserved areas,

inform allocation of resources and strengthen evidence-

based policy decisions.

CONCLUSION

Geographic access is a modifiable dimension of acces-

sibility and one where research can inform policy and

programmes such as travel support, strengthening

referral networks, decentralising care or building addi-

tional facilities to improve accessibility. Longer travel

times place an increased burden on patients—especially

the poorest—and may result in delays in treatment, higher

out of pocket expenditures and ultimately increased

mortality.

14 15

Understanding and measuring geographic

barriers to accessing care is of critical importance, espe-

cially in settings like Haiti where care may be inacces-

sible to many. Our study highlights the advantages and

trade- offs of different geographic accessibility measures

and provides guidance to researchers and policymakers

on choosing between measures when making relative or

absolute population comparisons.

Author afliations

Department of Global Health and Population, Harvard University T H Chan School of

Public Health, Boston, Massachusetts, USA

Institute for Health Metrics and Evaluation, Seattle, Washington, USA

Division of Population Science, Department of Medical Oncology, Dana- Farber

Cancer Institute, Boston, Massachusetts, USA

Department of Epidemiology, Harvard T H Chan School of Public Health, Boston,

Massachusetts, USA

Partners In Health/Zanmi Lasante, Mirebalais, Haiti

Department of Global Health and Social Medicine, Harvard Medical School, Boston,

Massachusetts, USA

Partners In Health, Boston, Massachusetts, USA

Twitter Temidayo Fadelu @temidayo

Acknowledgements The authors gratefully acknowledge Partners In Health for

providing administrative support and provision of analysis software. The authors

also express gratitude for technical support from Jeffrey Blossom, MA, and the

team at the Center for Geographic Analysis at Harvard.

Contributors KPB and TF: Concept, data analysis, interpretation, draft manuscript,

review of manuscript and approval, guarantor. HSI: Concept, data analysis,

interpretation, draft manuscript, review of manuscript and approval. JPJ: Concept,

interpretation, review of manuscript and approval. RLD: Concept, review of

manuscript and approval. JM: Concept, review of manuscript and approval.

Funding This work was supported by the Center for Global Cancer Medicine at

Dana- Farber Cancer Institute, the Breast Cancer Research Foundation 2019 Young

Investigator Award, grant number 16209, and the National Institutes of Health, grant

number T32 CA009001.

Map disclaimer The inclusion of any map (including the depiction of any

boundaries therein), or of any geographic or locational reference, does not imply

Table 3 Summary of recommendations on application of geospatial estimation measures based on goals of geospatial

analysis and analytic resources

Goals of geospatial analysis Analytic resource

Relative accessibility/

proximity

Absolute

coverage

Estimating patient travel

route

Researcher time and

effort

Required data

inputs

ED ++ + + ### ###

ND ++ ++ +++ # ##

NTT +++ +++ +++ # ##

AM5D ++ ++ + ## #

AM5TT +++ +++ ++ ## #

+ Unfavorable.

++ Acceptable.

+++ Preferred.

# High requirement.

## Medium requirement.

### Low requirement.

AM5D, AccessMod 5 distance; AM5TT, AccessMod 5 travel time; ED, Euclidean distance; ND, network distance; NTT, network travel time.

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

the expression of any opinion whatsoever on the part of BMJ concerning the legal

status of any country, territory, jurisdiction or area or of its authorities. Any such

expression remains solely that of the relevant source and is not endorsed by BMJ.

Maps are provided without any warranty of any kind, either express or implied.

Competing interests None declared.

Patient consent for publication Not applicable.

Ethics approval This study was reviewed and received Institutional Review Board

(IRB) exemption from both the Zanmi Lasante IRB Committee and the Dana Farber

Cancer Institute IRB Committee.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement All data were obtained from publicly available

sources and do not include individual level or identiable data. Data are available in

a public, open access repository. The full dataset developed for this study has been

made available through GitHub, with open access.

Supplemental material This content has been supplied by the author(s). It has

not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been

peer- reviewed. Any opinions or recommendations discussed are solely those

of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and

responsibility arising from any reliance placed on the content. Where the content

includes any translated material, BMJ does not warrant the accuracy and reliability

of the translations (including but not limited to local regulations, clinical guidelines,

terminology, drug names and drug dosages), and is not responsible for any error

and/or omissions arising from translation and adaptation or otherwise.

Open access This is an open access article distributed in accordance with the

Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which

permits others to distribute, remix, adapt, build upon this work non- commercially,

and license their derivative works on different terms, provided the original work is

properly cited, appropriate credit is given, any changes made indicated, and the use

is non- commercial. See:http://creativecommons.org/licenses/by-nc/4.0/.

ORCID iDs

Kayleigh PavitraBhangdia http://orcid.org/0000-0003-4235-0685

TemidayoFadelu http://orcid.org/0000-0002-1323-4119

REFERENCES

1 Noor AM, Amin AA, Gething PW, etal. Modelling distances travelled

to government health services in Kenya. Trop Med Int Health

2006;11:188–96.

2 Amooti- Kaguna B, Nuwaha F. Factors inuencing choice of delivery

sites in Rakai district of Uganda. Soc Sci Med 2000;50:203–13.

3 Schwartz JB, Akin JS, Popkin BM. Economic determinants of

demand for modern infant- delivery in low- income countries: the

case of the Philippines. In: Health economic research in developing

countries. Oxford Medical Publications, 1993.

4 Knapp GC, Tansley G, Olasehinde O, etal. Geospatial access

predicts cancer stage at presentation and outcomes for patients

with breast cancer in Southwest Nigeria: a population- based study.

Cancer 2021;127:1432–8.

5 Kapwata T, Manda S. Geographic assessment of access to health

care in patients with cardiovascular disease in South Africa. BMC

Health Serv Res 2018;18:197.

6 Kohler PK, Akullian A, Okanda J, etal. Distance to HIV and antenatal

care: a Geospatial analysis in Siaya County, Kenya. J Assoc Nurses

AIDS Care 2019;30:548–55.

7 Okwaraji YB, Cousens S, Berhane Y, etal. Effect of geographical

access to health facilities on child mortality in rural Ethiopia: a

community based cross sectional study. PLoS One 2012;7:e33564.

8 Schoeps A, Gabrysch S, Niamba L, etal. The effect of distance to

health- care facilities on childhood mortality in rural Burkina Faso. Am

J Epidemiol 2011;173:492–8.

9 Kadobera D, Sartorius B, Masanja H, etal. The effect of distance to

formal health facility on childhood mortality in rural Tanzania, 2005-

2007. Glob Health Action 2012;5:19099.

10 Mayer CM, Owaraganise A, Kabami J, etal. Distance to clinic is a

barrier to Pr EP uptake and visit attendance in a community in rural

Uganda. J Int AIDS Soc 2019;22:e25276.

11 Siedner MJ, Lankowski A, Tsai AC, etal. GPS- measured distance

to clinic, but not self- reported transportation factors, are associated

with missed HIV clinic visits in rural Uganda. AIDS 2013;27:1503–8.

12 Mamo Y, Dukessa T, Mortimore A, etal. Non- Communicable disease

clinics in rural Ethiopia: why patients are lost to follow- up. Public

Health Action 2019;9:102–6.

13 Joseph NK, Macharia PM, Ouma PO, etal. Spatial access inequities

and childhood immunisation uptake in Kenya. BMC Public Health

2020;20:1407.

14 Puttkammer NH, Zeliadt SB, Baseman JG. Patient attrition from

the HIV antiretroviral therapy program at two hospitals in Haiti.. Rev

Panam Salud Publica Pan Am J Public Health 2014;36:238–47.

15 Hanson C, Cox J, Mbaruku G, etal. Maternal mortality and

distance to facility- based obstetric care in rural southern Tanzania:

a secondary analysis of cross- sectional census data in 226 000

households. Lancet Glob Health 2015;3:e387–95.

16 van Duinen AJ, Adde HA, Fredin O, etal. Travel time and perinatal

mortality after emergency caesarean sections: an evaluation of

the 2- hour proximity indicator in Sierra Leone. BMJ Glob Health

2020;5:e003943.

17 Meara JG, Leather AJM, Hagander L, etal. Global surgery 2030:

evidence and solutions for achieving health, welfare, and economic

development. The Lancet 2015;386:569–624.

18 Daniels N, Bryant J, Castano RA. Benchmarks of Fairness for health

care reform: a policy tool for developing countries.. Health Syst

2000;11:740–50.

19 Macinko JA, Stareld B. Annotated bibliography on equity in health,

1980- 2001. Int J Equity Health 2002;1:1.

20 The World Health Report. Health systems: improving performance.

Geneva: World Health Organization, 2000.

21 Nesbitt RC, Gabrysch S, Laub A, etal. Methods to measure

potential spatial access to delivery care in low- and middle-

income countries: a case study in rural Ghana. Int J Health Geogr

2014;13:25.

22 Delamater PL, Messina JP, Shortridge AM, etal. Measuring

geographic access to health care: raster and network- based

methods. Int J Health Geogr 2012;11:15.

23 Iyer HS, Flanigan J, Wolf NG, etal. Geospatial evaluation of trade-

offs between equity in physical access to healthcare and health

systems efciency. BMJ Glob Health 2020;5:e003493.

24 Ouma PO, Maina J, Thuranira PN, etal. Access to emergency

hospital care provided by the public sector in sub- Saharan Africa in

2015: a geocoded inventory and spatial analysis. Lancet Glob Health

2018;6:e342–50.

25 Molla YB, Rawlins B, Makanga PT, etal. Geographic

information system for improving maternal and newborn health:

recommendations for policy and programs. BMC Pregnancy

Childbirth 2017;17:26.

26 Ray N, Ebener S. AccessMod 3.0: computing geographic coverage

and accessibility to health care services using anisotropic movement

of patients. Int J Health Geogr 2008;7:63.

27 Rudolfson N, Gruendl M, Nkurunziza T, etal. Validating the global

surgery geographical accessibility indicator: differences in modeled

versus patient- reported travel times. World J Surg 2020;44:2123–30.

28 Weiss DJ, Nelson A, Vargas- Ruiz CA, etal. Global maps of travel

time to healthcare facilities. Nat Med 2020;26:1835–8.

29 Google Earth Engine. Available: https://earthengine.google.com

[Accessed 1 Mar 2021].

30 Open Street Map. Open street MAP. Available: https://www.

openstreetmap.org/ [Accessed 1 Mar 2021].

31 Haiti Health Fact Sheet, 2016. Available: https://www.usaid.gov/

documents/1862/haiti-health-fact-sheet-2016 [Accessed 4 Mar

2019].

32 Gage AD, Leslie HH, Bitton A, etal. Assessing the quality of primary

care in Haiti. Bull World Health Organ 2017;95:182–90.

33 Hashimoto K, Adrien L, Rajkumar S. Moving towards universal health

coverage in Haiti. Health Syst Reform 2020;6:e1719339.

34 Tansley G, Schuurman N, Amram O, etal. Spatial access to

emergency services in low- and middle- income countries: a GIS-

based analysis. PLoS One 2015;10:e0141113.

35 Kwan GF, Yan LD, Isaac BD, etal. High poverty and hardship

nancing among patients with noncommunicable diseases in rural

Haiti. Glob Heart 2020;15:7.

36 CountryProle. Available: https://databank.worldbank.org/data/

views/reports/reportwidget.aspx?Report_Name=CountryProle&Id=

b450fd57&tbar=y&dd=y&inf=n&zm=n&country=HTI [Accessed 4 Mar

2019].

37 Haiti. Available: https://www.paho.org/salud-en-las-americas-2017/?

p=4110 [Accessed 18 Nov 2020].

38 WHO |. Haiti: country prole. who.. Available: https://www.who.int/

hac/crises/hti/background/prole/en/ [Accessed 18 Jan 2021].

39 The DHS Program. Haiti: standard DHS, 2016- 17. Available: https://

dhsprogram.com/methodology/survey/survey-display-503.cfm

[Accessed 02 May 2021].

40 Haiti. Partners in health. Available: https://www.pih.org/country/haiti

[Accessed 18 Nov 2020].

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from

BhangdiaKP, etal. BMJ Open 2022;12:e056123. doi:10.1136/bmjopen-2021-056123

Open access

41 Haiti. Administrative Boundary levels 0- 3 - Humanitarian Data

Exchange. Available: https://data.humdata.org/dataset/hti-polbndl-

adm1-cnigs-zip [Accessed 04 Mar 2019].

42 Geofabrik Download Server. Available: http://download.geofabrik.de/

central-america/haiti-and-domrep.html [Accessed 04 Mar 2019].

43 WorldPop. Population. Available: https://www.worldpop.org/geodata/

summary?id=129 (accessed 4 Mar 2019)

44 Partners in Health Hospital. Google maps. Available:

https://www.google.com/maps/place/Partners+in+Health+

Hospital/@18.8362879,-72.11685,15z/data=!4m5!3m4!1s0x0: 0xd94

2862086e97771!8m2!3d18.8362879!4d-72.11685 [Accessed 17 Jan

2021].

45 Near—Help. ArcGIS for desktop. Available: https://desktop.arcgis.

com/en/arcmap/10.3/tools/analysis-toolbox/near.htm [Accessed 21

Jun 2021].

46 Haiti Road Network. WFP GeoNode. Available: https://geonode.wfp.

org/layers/ogcserver.gis.wfp.org%3Ageonode%3Ahti_trs_roads_

osm

47 Hameed SM, Schuurman N, Razek T, etal. Access to trauma

systems in Canada. J Trauma 2010;69:1350–61.

48 ArcGIS for Desktop. Geodatabase topology rules and topology error

xes—Help. Available: http://desktop.arcgis.com/en/arcmap/10.3/

manage-data/editing-topology/geodatabasetopology-rules-and-

topology-error-xes.htm [Accessed 04 Mar 2019].

49 Mathon D, Apparicio P, Lachapelle U. Cross- Border spatial

accessibility of health care in the north- east department of Haiti. Int J

Health Geogr 2018;17:36.

50 Haiti. Network Road - Humanitarian Data Exchange. Available:

https://data.humdata.org/dataset/haitiroads[Accessed 18 Nov

2020].

51 AccessMod 5 user manual. User manual (English) - Conuence.

Available: https://docaccessmod.unepgrid.ch/display/EN/

AccessMod+5+user+manual [Accessed 18 Nov 2020].

52 DIVA- GIS. Download data by country. Available: https://www.diva-

gis.org/gdata [Accessed 18 Nov 2020].

53 OCHA Haiti. Humanitarian data exchange, 2020. Available: https://

data.humdata.org/search?q=Haiti%20lakes&ext_page_size=25

[Accessed 18 Nov 2020].

54 Mukaka MM. Statistics corner: a guide to appropriate use

of correlation coefcient in medical research. Malawi Med J

2012;24::69––71..

55 Jamison DT, Alwan A, Mock CN, etal. Universal health coverage and

intersectoral action for health: key messages from disease control

priorities, 3rd edition. Lancet 2018;391:1108–20.

56 UN General Assembly. Transforming our world: the 2030 agenda for

sustainable development., 2015. Available: .https://www.refworld.

org/docid/57b6e3e44.html [Accessed 1 Mar 2021].

57 Banke- Thomas A, Wong KLM, Ayomoh FI, etal. "In cities, it's not far,

but it takes long": comparing estimated and replicated travel times to

reach life- saving obstetric care in Lagos, Nigeria. BMJ Glob Health

2021;6:e004318.

58 Wang W, Mallick L. Understanding the relationship between family

planning method choices and modern contraceptive use: an analysis

of geographically linked population and health facilities data in Haiti.

BMJ Glob Health 2019;4:e000765.

59 Penchansky R, Thomas JW. The concept of access: denition and

relationship to consumer satisfaction. Med Care 1981;19::127––40.

60 Bhangdia K. Haiti_accessibility_outputs. GitHub. Available: https://

github.com/kbhangdia/Haiti_accessibility_outputs [Accessed 24 Feb

2021].

on August 2, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-056123 on 24 May 2022. Downloaded from