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Open AcceResearch articleService network analysis for agricultural mental healthJeffrey D Fuller*1, Brian Kelly2, Susan Law2,4, Georgia Pollard2 and Lyn Fragar3

Address: 1Department of Rural Health, University of Sydney and Southern Cross University, Lismore, NSW, Australia, 2Centre for Rural and Remote Health, University of Newcastle, Orange, NSW, Australia, 3Australian Centre for Agricultural Health and Safety, University of Sydney, Moree, NSW, Australia and 4North Coast Area Health Service, NSW, Australia

Email: Jeffrey D Fuller* - jeffrey.fuller@ncahs.health.nsw.gov.au; Brian Kelly - Brian.Kelly@newcastle.edu.au; Susan Law - susan.law@ncahs.health.nsw.gov.au; Georgia Pollard - gpollard@gwahs.health.nsw.gov.au; Lyn Fragar - lyn.fragar@hnehealth.nsw.gov.au

* Corresponding author

AbstractBackground: Farmers represent a subgroup of rural and remote communities at higher risk of suicideattributed to insecure economic futures, self-reliant cultures and poor access to health services. Earlyintervention models are required that tap into existing farming networks. This study describes servicenetworks in rural shires that relate to the mental health needs of farming families. This serves as a baselineto inform service network improvements.

Methods: A network survey of mental health related links between agricultural support, health and otherhuman services in four drought declared shires in comparable districts in rural New South Wales,Australia. Mental health links covered information exchange, referral recommendations and programdevelopment.

Results: 87 agencies from 111 (78%) completed a survey. 79% indicated that two thirds of their clientsneeded assistance for mental health related problems. The highest mean number of interagency linksconcerned information exchange and the frequency of these links between sectors was monthly to threemonthly. The effectiveness of agricultural support and health sector links were rated as less effective bythe agricultural support sector than by the health sector (p < .05). The most highly linked across all areasof activity were Rural Financial Counsellors, the Department of Primary Industry Drought SupportWorkers and Community Health Centres. Hence for a mental health service network targeting farmingfamilies these are three key agencies across the spectrum of case work to program development. Thestudy limitations in describing service networks relate to the accuracy of network bounding, self reportbias and missing data from non participants.

Conclusion: Aligning with agricultural agencies is important to build effective mental health servicepathways to address the needs of farming populations. Work is required to ensure that these agriculturalsupport agencies have operational and effective links to primary mental health care services. Networkanalysis provides a baseline to inform this work. With interventions such as local mental health trainingand joint service planning to promote network development we would expect to see over time an increasein the mean number of links, the frequency in which these links are used and the rated effectiveness ofthese links.

Published: 29 May 2009

BMC Health Services Research 2009, 9:87 doi:10.1186/1472-6963-9-87

Received: 19 December 2008Accepted: 29 May 2009

This article is available from: http://www.biomedcentral.com/1472-6963/9/87

© 2009 Fuller et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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BackgroundFarmers represent a subgroup of rural and remote com-munities at higher risk of various health problems [1,2].Suicide, in particular, continues to be a major cause ofdeath by injury among farmers in Australia [3]. The healthrisk of farmers is influenced by their occupational, envi-ronmental and social conditions, which includes pooraccess to health care services in rural and remote areas ingeneral [4], and by their own specific cultural barriers tohealth care [5,6]. Financial and business pressures in agri-culture in recent years have compounded isolation andhave added further economic disincentives to these barri-ers in access to health services [3,5].

The current very prolonged drought affecting large areasof south-eastern Australian has contributed accumulatedadversity to rural and remote communities, especiallythose most dependent on agriculture. The attendantsocio-economic strain and social impact of drought forpeople in farming [7] highlights the need to consider waysto effectively meet their mental health needs [8]. Animportant improvement in access to mental health care issuicide prevention and the most effective strategies forsuicide prevention include those that improve access toappropriate services (such as health services) and improvethe quality of response received from those services [9].

A focus on potentially unmet mental health needsrequires close consideration of the existing patterns ofhelp-seeking. Caldwell et al [10] report that there arefewer presentations to primary care clinicians for mentalhealth problems in rural areas than in urban areas. Self-reliant characteristics, attitudes towards mental healthproblems and perceived stigma are claimed to inhibithelp seeking from professional mental health services.Australian data suggest that many common forms of dis-tress experienced by farmers can be attributed chiefly tofinancial pressures and trusted support workers for whomno stigma-related barriers exist, such as rural financialcounsellors, have been identified as important initial con-tact points for help [11-13]. While these workers have thespecific role of assisting farmers in managing financialissues (such as seeking drought-related assistance & nego-tiating benefits), they can play a key role in improvingtheir clients' access to health services. Rural financialcounsellors have identified the emotional distress of theirclients as a major concern. They have also identified theirown need for improved skills in recognising and respond-ing to this distress, and for improved local referral path-ways [12].

The inclusion of "front-line" agricultural workers in thebroad health care network of a rural community is war-ranted in view of their role as an important and trustedsource of advice to farmers. A strategy should identify the

range of services available in a community and developclear local pathways to care. Such an approach supportshealth services personnel to recognise the role that other"front-line" agencies can play in the broader tasks of gain-ing care and completing recovery. This recognitionincludes identifying the benefits of on-farm contact andthe support that can be provided from community organ-isations, such as the rural financial counsellors, in assist-ing with financial stressors that contribute to mentaldistress [14].

Based on earlier work that established the "front line"human service contact of agricultural support agencies,the aim of the study was to describe the mental healthservice links between these agencies and local health andhuman services [12]. This description would establishbaseline measures of strength, weakness and opportunityin the local service network that could then inform a sub-sequent network improvement strategy.

MethodsFour shires in rural New South Wales were purposivelyselected that had not recently received any mental healthservice development strategy. Shires were chosen betweena population size of 3000 and 20,000 so they would belarge enough to have a service network but not so large anetwork to make the analysis unmanageable. Our experi-ence in a pilot study was that a network size of up to 30agents was satisfactory for descriptive network analysis,whereas a larger network would make data collectionquite resource intensive when this was being done byinterview [15].

We adapted a bounded network analysis technique fromProvan et al who used this method to both describe andthen inform the improvement of local community servicepartnerships [16]. This technique surveys each member ofa defined network about their links with all other mem-bers in that network. Two sets of measures are generated,measures which each member reports about their linkswith others (that member's OutDegree), and those whichall others report about their links with that member (thatmember's InDegree). For more information about net-work analysis the article by Hawe at al provides an easy toread introduction [17].

The technique first required the construction of a relevantagency list for each shire (the bounded network) as thesewere the agencies that each were surveyed about. Con-structing a bounded network list is relatively easy fordefined groups, such as a classroom or partnerships whereformal agreements exists, where people or agency namesare recorded and where membership is clearly defined. Ina local community health network, where some relation-ships may be informal, that may occur opportunistically

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and where members may change, constructing such a listis not so clear cut. In order to maximise capture of the rel-evant agencies in each shire on a list, we identified threeknowledgeable key informants per shire from differentservice sectors. We asked them which other agencies theyconsidered had some involvement in the mental healthrelated needs of farming families, either through individ-ual case work, group work or community and agriculturaldevelopment. We prompted these key informants to con-sider agencies from the three sectors of agricultural sup-port, health (mental health & other health) and otherhuman services. Using three key informants from differ-ent service sectors maximised the spread of other agencieslisted and minimised a biased focus on any one sector.

Agencies were delimited to those with a physical presencein the shire, meaning that services such as statewide tele-phone based support agencies were not included. A finalagency list per shire was created as an amalgam of whatthe three informants described plus a check against whatwe researchers theoretically considered should be listed(ie the local GP, the nearest mental health team etc).While this did not eliminate the possibility that an agencywould have been left off the list, the process did minimisethis possibility. Each of the listed agencies wasapproached and interviewed through the most senior rel-evant service worker. As the agencies differed in size andfunction (some were one person operations and othersmuch larger), then the actual agency informants variedfrom the sole service provider in some cases to the localservice manager in others.

The survey involved standardised administration by inter-view of an instrument that covered interagency links to dowith the following activities: (1) information exchangeabout providing mental health related help to clients, (2)recommending clients to other agencies for further help,and (3) working together in other ways (than casework)such as on advocacy or program development. Surveyquestions asked informants to recall the presence of alink, the frequency in which a link was used and the effec-tiveness of that link. Rather than specify a recall timeframe(say the last 3 months) we simply asked informants torecall whether they linked with a listed agency, then "ingeneral" how often they linked and how effective theyrated that link. This approach is consistent with the meth-odological literature that suggests informant recall is morereliable with typical events over general timeframes thanspecific events over defined timeframes [18]. The surveyinstrument was adapted from that used by Provan at al,with changes to reflect our context which we had tested forface validity in an earlier pilot study in two other shires[15]. The pilot study helped us to refine the wording ofour questions and the associated definitions that weincluded in order to minimise uncertainty and ambiguity

about what was being asked. Reliability was also increasedby the use of only two interviewers who were trainedtogether in the survey administration. The survey instru-ment is included as Additional file 1.

Data were entered into the UCINET v6 program fordescriptive analysis and then exported to STATA v8 for fur-ther analysis [19,20]. The mean number of agencies thatwere linked was calculated per activity per shire, with theUCINET program applying a normalisation algorithm.This normalisation enables networks of different size tobe compared, otherwise difference in means could simplybe an effect of the different network size. Across the fourshires overall we tested the difference in the mean linksper activity (using one way ANOVA). Then for the scaleddata (frequency and effectiveness scores) we extracted thescores from all of the four shires that were either agricul-ture agencies reporting links to health agencies or viceversa. Where the scale could be considered interval (effec-tiveness) then we calculated an overall mean score peractivity, and where only ordinal (frequency) then we cal-culated medians. To test for significant difference betweenthese scaled data we used the non-parametric Mann Whit-ney test because the data were either skewed or ordinal.We then analysed the network position of specific agen-cies per shire by allocating a rank score from the mosthighly linked (score 1) to the least linked and then to getan overall score we calculated the median of each agency'srank scores across the four shires. This enabled us to iden-tify which agencies were the highly and least connected inthe local rural mental health service network for farmers.

The University of Newcastle Human Research EthicsCommittee approved the study. The surveys were con-ducted between May to October 2007.

ResultsFour shires were identified across comparable agriculturalregions of New South Wales and all were drought declaredat the time of the interview. Table 1 outlines the character-istics of these communities (eg population). Because wewanted to establish a baseline prior to the testing of a net-work improvement strategy (to be reported in laterpapers), we selected those shires that had not yet receivedany of the Mental Health First Aid training that was pro-gressively being provided through various national andstate government mental health responses to the drought[21].

The shire population size ranged from 6000 to 13,000and they were located between 300 to 600 kilometresfrom Sydney (the state capital).

Between 24 to 32 agencies were identified per shire with21 to 22 per shire agreeing to participate. Of the 111 agen-

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cies identified, 87 completed a survey (78%). While therewas variability within each shire, the distribution of inter-viewed agencies overall across the three agency types wasfairly even, with 27 agricultural support agencies, 28health and 32 other human service agencies (table 2).Hence, potential bias of a particular service sector is less-ened when we aggregated data from across the four shires.

NeedSeventy nine percent (79%) of the interviewed agenciesindicated that at least two thirds of their clients were inneed of assistance for mental health related problems andmost agencies (77%) indicated that they do recommendthat farming clients see other workers for help in these sit-uations. Well under a half (38%) indicated that they meetregularly as a group or network with other agencies intheir shire and this varied per shire from between 33–45%.

Agency links per activityThere was no consistency between the shires on whichactivity had the highest number of linked agents and therewas no statistically significant difference, the overallordering was from information exchange, then makingrecommendations to working together in other ways(table 3).

Frequency & effectiveness of linksWe were particularly interested in the links between theagricultural support sector and the health sector and so weexamined the frequency and effectiveness that each sectorrated their links with each other (tables 4 and 5).

Overall the median frequency in which the reported linkswere used between the agricultural support and healthand other human services was between monthly to threemonthly and there was no significant difference foundbetween sectors across all of the activities.

In terms of effectiveness, the agricultural support sectorrated giving information as significantly less effective thandid the health sector. This difference was similar forreceiving information although the significance was justoutside the conventional level of probability. Effective-ness data on making recommendations was not collectedbecause informants may not have known if a client actedon a recommendation and so could not rate effectivenessof this. There was no statistically significant differencebetween the agricultural support and health sector in theeffectiveness rating of working together, although thetrend here was the same, with the agricultural sector giv-ing a less effective rating than did the health sector.

Main agenciesAcross all activities the highest linked agency came fromagricultural support (the rural financial counsellors) withthe Department of Primary Industry drought supportworkers and the community health centres the secondhighest linked. The community mental health teams, theGPs and the Centrelink rural support officers were rela-tively well linked at fourth and fifth highest (table 6).

As well as the number of links, the strength of an agencyin the network can also be measured by the extent inwhich their overall relationship with others is embedded.For instance an agency that is linked to another on all

Table 1: Shire details

D E F G

population 6400 13100 6500 10138

drought declared Yes Yes Yes Yes

location (relative to Sydney) 400 kms SW 300 kms W 600 kms SW 600 kms W

Table 2: Agency category details

Agency type D E F G total

agricultural support 5 7 7 8 27

health 9 6 8 5 28

other human services 8 9 6 9 32

total interviewed 22 22 21 22 87

bounded list size 32 24 31 24 111

Table 3: Activities: normalised mean link scores

D E F G overall mean

info give 17.24 18.11 16.98 19.74 18.02

info receive 12.6 17.75 13.54 20.1 15.99

recommend 10.35 19.2 14.51 20.29 16.09

work together 14.92 17.93 8.38 16.66 14.46

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activities will have their overall relationship maintainedeven if some of those activities were to cease. This embed-dedness is measured by the multiplexity score, and in thisnetwork the highest possible score is 4 (number of activi-ties). A score of 4 would indicate that an agency waslinked to all other agencies on all activities. The agencieswith the highest scores were rural financial counsellors(1.58), the Department of Primary Industry drought sup-port workers (1.05) and the community health centres(1.03).

DiscussionThe extent of need evident in agency responses does indi-cate that mental health issues were prominent in thesefour shires, which includes responses from some agenciesfor whom we would not have considered mental health ascore business. This does support our rationale that devel-oping a local service network that includes this range ofagencies could improve both access to and the quality ofmental health care in rural locations.

The most highly linked and multiplex status of the ruralfinancial counsellors indicates that they were the mostprominent and embedded (in network terms) as serviceproviders and as intermediaries in mental health servicenetworks for farming families. This prominence andembedded status is consistent with their reported trustedhelper status and also signifies a probable financial and acounselling component of farmers mental health need.

This contact with rural financial counsellors offers a win-dow of opportunity for early intervention links that couldform an important part of suicide prevention for thisgroup. The other prominent and embedded agencies werethe DPI drought support workers and community healthcenters. Our earlier work identified that rural financialcounsellors do want better links with mental health andother social counsellors, training in mental health issuesand also the development of appropriate and effectivereferral processes to health professionals [12].

As agencies to whom information was given and agenciesto whom clients were recommended, general practitionerswere the most prominent after the rural financial coun-selors, and equal with community centres, the mentalhealth teams and drought support workers. However, gen-eral practitioners were not as highly linked on workingtogether in other ways as would be expected given theirclinical focus. Of interest is the relative high link status ofthe Farmers Association in working together in other waysand it is in this activity and through such agencies wherecommunity mental health promotion might occur. Therelatively low status of the Drought Mental Health Assist-ance Program might be explained by the recency of thisprogram and also by the non-clinical role of programworkers. The status of this program might be expected toincrease over time, particularly on working together inother ways.

Table 4: Overall median frequency scores

ag to hlth hlth to ag ag to OHS OHS to ag

give info 3 4 3 4

rec info 3 4 3 3.5

recommend 3 4 3 4

work together 3 4 4 3

Data summarised using OutDegree rather than in other sections where InDegree used.Scale 5 = weekly; 4 = monthly; 3 = three monthly; 2 = half yearly; 1 = less often that half yearlySignificant difference calculated using Mann-Whitney Test

Table 5: Overall mean effectiveness scores (agricultural support & health)

ag to hlth – mean (n) hlth to ag – mean (n) p

give info 3.04 (10) 4.2 (15) 0.007

rec info 3.5 (12) 4.31 (13) 0.057

work together 3.65 (12) 4.10 (14) 0.32

Data summarised using OutDegree rather than in other sections where InDegree used.Scale 5 = very good; 4 = good; 3 = neither; 2 = poor; 1 = very poorSignificant difference calculated using Mann-Whitney Test

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The lower rating of linkage effectiveness by the agricul-tural support sector does make sense given that agricul-tural support workers are not trained in mental health andso would be expected to rate their communication aboutmental health issues as less effective. However, this find-ing, which is about effectiveness of linking with the healthsector, could also reflect a dissatisfaction of rural commu-nities with their access to mental health services in general[22,23].

Given the potential to include agricultural support agen-cies in a local mental health service network and also theindicators of room for improvement, we suggest that strat-egies to increase local capacity are warranted. This capac-ity could include early recognition of mental healthdistress, first level responding skills and knowledge about

and confidence in referral options to professional mentalhealth care. Mental Health First Aid training is a provenprogram to increase recognition skills and through thiscurrent research we are studying whether networkingstrategies, that include a community mental health devel-opment worker along with service network meetingsincreases referral capacity [24]. Such developmental workaround early recognition rather than crisis respondingwould require local service planning to establish the dif-ferent capacities and potential functions of local agricul-tural support and human service agencies and this workcould come under the auspice of community mentalhealth services. Given that financial issues faced by agri-cultural communities may also lead to family tension andrelationship problems, then a network that provides fam-ily support and social counselling would be ideal, as well

Table 6: Rank link scores across all activities (all shire aggregated)

g_info r_info rec w_tog median rank multiplexity

Catchment Management Authority 8 7 8 7 7.5 0.40

Centacare 6 6 5 6 6 0.80

Centrelink (local office) 5 4.5 3.5 5.5 4.75 0.85

Centrelink Rural Support Officer 4 4 5 4 4 0.78

Community Health Centre 3 3 3 4 3 1.03

Dept Community Services 7 6 6 9 6.5 0.50

DPI Drought Support Worker 3 3 4 2 3 1.05

Drought MH Assist Prog/Liaison 7 5 4.5 5 0.49

Farmers Assoc 7 6 8 4 6.5 0.73

GP 3 5 3 8 4 0.77

Hospital 7 5 8 7.5 7.25 0.44

Community Mental Health Team 3 4 3 5 3.5 0.93

Psychologist/Counsellor 6 6 6 6 6 0.49

Rural Assistance Authority 7 7 7 6 7 0.59

Rural Financial Counsellor 1 1 1 1 1 1.58

Rural Lands Protection Board 6.5 5.5 6.5 4.5 6 0.61

Salvation Army 6 5 6 8 6 0.70

St Vincent de Paul 4 6 5 5 5 0.64

Agencies only included if ranked in at least 3 shires – hence some rank scores missing. Rank scores: 1 = highest linked

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as those services for people who have developed a mentalillness. With a range of agencies in a network, then a rangeof services can be provided, and in a more coherent andcoordinated way. Agencies such as the church based wel-fare organisations (Centrecare), community health cen-tres and psychologists do currently provide counsellingservices and they do join with agricultural agencies to con-duct family support functions such as Farm Family Gath-erings.

LimitationsThe study does have limitations, both to do with themethod and also the conclusions that can be drawn fromthe findings. First the data were generated from selfreports rather than records of actual communications andso may contain the biases associated with such reports,such as recall and social desirability. We sought to mini-mise recall accuracy and hence bias by asking about com-munications in general rather than over specifictimeframes.

We do not know whether the network lists that we createdwere accurate or whether some relevant agencies mayhave been left off the lists. Such omission were unlikely,however, as the lists were created from independent inputfrom three separate key informants per location. In addi-tion, the nature of the data collection by face to face inter-view did generate considerable anecdotal data duringwhich any key unlisted agencies would most likely havebeen named. Some non listed agencies were mentionedbut only by a few respondents and these were not consid-ered to be all that relevant as "network players". Also thedata on links were not complete, as 22% of agencies over-all did not participate in the survey and so did not providetheir own information about their links. While this wouldhave some effect on the results, the impact of this effect isdifficult to ascertain. This problem was overcome to someextent as we used InDegree data and so we do have infor-mation about the links to all the 111 agencies across thefour shires, but these come from the responses of the 87participating agencies rather than from the 111.

The reasons for the different probability levels for givingand receiving information and the slightly higher meaneffectiveness scores for receiving information over givinginformation are not clear. These may simply be an artefactrelated to the study power or because these were differentquestions on the survey with different psychometric prop-erties. A response to "giving information" might focus therespondent more on their own effectiveness capabilities"to give", whereas response to "receiving information"might focus the respondent more on the effectivenesscapability of the other party to "give to them". Anotherexplanation may relate to expectations, where giving

information to another agency may hold an expectationthat the other agency will use that information effectively,while receiving information may hold that same expecta-tion, but only about one's self. Detailed analysis of thenuances of cross sector agency communication was notwithin the scope of this study, but clearly improvementsin communication between services sectors would benefitfrom such an understanding.

ConclusionMental health program developers can use network infor-mation to see who are the most and least linked agenciesand to establish a baseline on the rated frequency andeffectiveness of these links. Highly linked agencies are"powerful" in network terms and can be used as the serv-ice point to add resources or support so that farmers arelinked via these highly networked agencies to the profes-sional health agents. In addition, these highly linkedagencies can be influential in promoting change, such asto make service network improvements.

Networks that service the mental health needs of farmingfamilies should include agencies in close human servicecontact with them. This requires work to ensure that localservice links are operational and effective and networkdescriptions provide a baseline for this work. The nextphase of our work in this area involves the use of commu-nity mental health development officers to promote thisnetwork development, which includes the training of agri-cultural support workers in the early recognition of men-tal health distress in others and some basic first levelresponding skills.

Competing interestsThe authors declare that they have no competing interests.

Authors' contributionsJF coordinated the network research component of thestudy, conducted the analysis and wrote the drafts of thispaper. BK coordinated the overall study and contributedto drafts of this paper. SL and GP collected the networkdata and contributed to drafts of this paper. LF helped todesign the study and contributed to drafts of this paper.All authors read and approved the final manuscript.

Additional material

Additional file 1Appendix. Building Mental Health Awareness and Service Networks in Rural Australian Communities – A Service Delivery Evaluation.Click here for file[http://www.biomedcentral.com/content/supplementary/1472-6963-9-87-S1.pdf]

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AcknowledgementsThe study was funded under a grant from the Australian Rotary Health Research Fund.

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