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Background: The focus of discussion in addressing the treatment gap is often on biomedical services. However, community resources can benefit health service scale-up in resource-constrained settings. These assets can be captured systematically through resource mapping, a method used in social action research. Resource mapping can be informative in developing complex mental health interventions, particularly in settings with limited formal mental health resources. Method: We employed resource mapping within the Programme for Improving Mental Health Care (PRIME), to systematically gather information on community assets that can support integration of mental healthcare into primary care in rural Ethiopia. A semi-structured instrument was administered to key informants. Community resources were identified for all 58 subdistricts of the study district. The potential utility of these resources for the provision of mental healthcare in the district was considered. Results: The district is rich in community resources: There are over 150 traditional healers, 164 churches and mosques, and 401 religious groups. There were on average 5 eddir groups (traditional funeral associations) per sub-district. Social associations and 51 microfinance institutions were also identified. On average, two traditional bars were found in each sub-district. The eight health centres and 58 satellite clinics staffed by Health Extension Workers (HEWs) represented all the biomedical health services in the district. In addition the Health Development Army (HDA) are community volunteers who support health promotion and prevention activities. Discussion: The plan for mental healthcare integration in this district was informed by the resource mapping. Community and religious leaders, HEWs, and HDA may have roles in awarenessraising, detection and referral of people with mental illness, improving access to medical care, supporting treatment adherence, and protecting human rights. The diversity of community structures will be used to support rehabilitation and social reintegration. Alcohol use was identified as a target disorder for community-level intervention.
This study received ethical approval from the College of Health Sciences Institutional Review Board, Addis Ababa University (084/11/PSY) as part of a baseline situational appraisal. Data collected was purely related to community assets or resources and no personal information was included. No information with potential negative impact on the community was collected. The study was conducted with the full agreement of the district. Because of this only oral consent was obtained from the key informants. This is consistent with other studies of asset mapping. However, participation in the study as key informant was voluntary. The study was conducted in Sodo district, Gurage zone, located in the Southern Nations, Nationalities and People’s Regional state of Ethiopia. Bui, the capital of the Sodo district is located about 100 km south of Addis Ababa, the capital of Ethiopia. Sodo district covers a geographic area of 830. 63 km2 and comprises 54 rural and 4 urban sub-districts (smallest administrative units). Most people live in one-roomed mud and straw houses and work as subsistence farmers. Only 21.5% of people are literate. The majority is orthodox Christian and the ethnic composition of the population is Gurage 85.3%, Oromo 11.6% and Amhara 1.5%. Malaria and cutaneous leishmaniasis are important endemic health problems of the district [33]. The current study is part of a wider study, the PRogramme for Improving Mental healthcare (PRIME) project, which aims to provide evidence to support the implementation and scale up of mental health care in primary care in five low and middle-income countries (Ethiopia, India, Nepal, South Africa and Uganda) [34]. The initial stage was to develop a comprehensive MHCP for Sodo district for selected priority mental and neurological problems: psychosis, depression, alcohol use, maternal mental disorders and epilepsy. The MHCP will later be implemented, evaluated and scaled up. The MHCP incorporates interventions at the three levels of the health system: community, health facility and health service organisation levels. A situational analysis was previously conducted to describe existing primary and community healthcare and specialist mental health services in Sodo [33]. It also attempted to describe the community environment, but because it relied on information available in the public sector, a full picture was not obtained. Moreover, the situational analysis did not attempt to map resources that could be engaged in the MHCP. To complement the situational analysis, in particular to develop the community level aspects of the MHCP, in the current study resource mapping was utilised to identify community resources. This allowed us to examine resources outside of the public sector, and also to understand the geographical spread of resources. The study design was a community-level cross-sectional survey. Quantitative data were collected using a community resource inventory, adapted from a hand book of community assessment developed in Canada [35], which is being used as a resource for developing community asset mapping templates in the School of Social Work of Addis Ababa University. Three 90 minute research team meetings were held to select the items relevant for the rural Ethiopian context and to exclude those that were not considered relevant (for example restaurants). Additional items that should be added, for example eddir groups, were also considered in the meetings. The original function of eddir was as a funeral association i.e. providing financial, practical and emotional support when a member dies. In the final version there were seven sections, excluding the section on general information, and 83 items, which assess various domains of community resources, including physical assets (for example forests), community associations, health facilities, education facilities, justice system, recreational venues, agriculture, religious institutions, and NGOs (See S1 File). The inventory includes open questions (for example ‘What is the main means of subsistence in this sub-district?’) and closed questions (for example ‘How many churches are there in this sub-district?’). The inventory was initially developed in English and translated into the local language, Amharic, for ease of administration by the interviewers. The inventory was back translated into English to check for conceptual equivalence. Health Extension Workers (HEWs), who are frontline community health workers, were trained for two days in data collection. The training covered what community assets are, the purpose of identifying them and how to identify them. Two HEWs were assigned to the sub-district where they usually work, with each one covering a pre-specified geographical catchment area. On average each HEW interviewed two key informants (people believed to have rich knowledge about the local community) to complete the inventory. In total 165 key informants were interviewed including local community leaders, sub-district chairpersons, district officials, teachers, and community elders. HEWs were collecting data in their usual place of work and residence; therefore they had good prior knowledge about appropriate key informants. Each HEW collated the information from the individual interviews to produce one complete inventory. Two inventories were therefore produced for each sub-district (one for each catchment area). The HEWs were also considered key informants; where they were unable to gather the information from community leaders, they were encouraged to fill in the outstanding information themselves. Prior to the analysis, the two inventories were combined to produce a sub-district level summary of community resources. As the HEWs covered distinct geographical catchment areas, overlap between the inventories was perceived to be unlikely. However the data was checked to ensure to ensure no resources were counted twice. The study investigators triangulated the information with district-level officials and community representatives. A simple descriptive analysis was used to summarise the data. The frequency and distribution of the different resources across the district was described. The data entry was completed using EpiData Version 3.1 then exported to Stata version 11 [36] for the analysis.
