| Literature DB >> 35189016 |
Philip J Batterham1, Kimberly Brown1, Angelica Trias1, Carmel Poyser1, Dominique Kazan1, Alison L Calear1.
Abstract
OBJECTIVE: Physical and natural environments might strongly influence mental health and well-being. Many studies have examined this relationship in urban environments, with fewer focused on rural settings. The aim of this systematic review was to synthesise quantitative evidence for the relationship between environmental factors (drought, climate and extreme weather events, land use/environmental degradation, green space/vegetation, engagement in natural resource management activities) and mental health or well-being in rural areas.Entities:
Keywords: biodiversity; degradation; drought; environment; farmers; mental health; rural
Mesh:
Year: 2022 PMID: 35189016 PMCID: PMC9303895 DOI: 10.1111/ajr.12851
Source DB: PubMed Journal: Aust J Rural Health ISSN: 1038-5282 Impact factor: 2.060
FIGURE 1PRISMA diagram of the review procedure
Summary of study characteristics
| Author | Year | Country of study | Setting/ design | Rurality description | Sample size | Participant details | Mean age (SD) | Sex | Mental health measures | Environmental measures |
|---|---|---|---|---|---|---|---|---|---|---|
| Studies of green space | ||||||||||
| Akpinar, Barbosa‐Leiker | 2016 | USA | Explore specific types of green spaces associated with mental and general health using data from Behavioral Risk Factor Surveillance System Survey and National Land Cover Data | State with a variety of eco‐zones (heavily forested, shrubland, grassland and both irrigated and dryland agriculture) | N = 5148 | Residents of Washington State | 52.40 | Male 39.2%; Female 60.8% | Mental health complaints (last 30 d); anxiety‐depression complaints (last 14 d) | Percentage of green space type by ZIP code |
| Alcock, White | 2015 | England | Examine relationships between types of green space and mental health using data from 18‐y longitudinal British Household Panel Survey linked with Land Cover Map (LCM) | Rural residential areas | N = 2020 (12 697obs) | Residents in English rural neighbourhoods | 47.59 | Female 52.4% | GHQ‐12; 2‐item second standard scoring method | 10 aggregate land cover classes in the LCM2007 |
| Losert, Schmauss | 2012 | Germany | Test environmental risk factors for mental illness in rural catchment area | Rural municipalities | N = 4198 | Psychiatric patients living in study region | NA | NA | Hospital admission data for schizophrenia and affective disorders from 2006 to 2009 | Data on proportion of forest and agricultural areas |
| Nishigaki, Hanazato | 2020 | Japan | Examine relationship between green space and depression among older adults living in rural (and urban) areas | Rural municipalities, nationwide | N = 33 823 | Older adults (age 65+) living in the community | NA | NA for rural sample (full sample: female 51.5%) | Geriatric Depression Scale (GDS) | Total green space, grass, tree and field ratios (tertiles) from satellite imagery, at the school district level |
| Studies of drought | ||||||||||
| Austin, Handley | 2018 | Australia | Examine drought‐related stress using data from longitudinal cohort study—Australian Rural Mental Health Study (ARMHS) | Non‐metropolitan New South Wales | N = 664 | Living or working on a farm | 55‐64 27.7% | Male 43.7%; Female 56.3% | K10; personal and community drought‐related stress | Drought conditions by comparing rainfall during prior 12 mo |
| Brew, Inder | 2016 | Australia | Determine whether farming is associated with poorer health using data from ARMHS study | Non‐metropolitan New South Wales | N = 1284 | Farmers and non‐farming workers (other rural workers and farm residents employed elsewhere) | 48.3 (11.9) | Female 57% | K10; PHQ‐9; item on self‐report overall mental health | Remoteness of location of residence (ARIA+); item on drought stress |
| Edwards, Gray | 2015 | Australia | Impact of drought on mental health using data from stratified random Rural and Regional Family Survey | Rural and regional ‐ agricultural | N = 8000 | Adults living in agricultural areas | 46.5 (10.91) | Female 54.2% | 5‐item Mental Health Inventory Form SF‐36 | Area‐based self‐report drought measure |
| Friel, Berry | 2014 | Australia | Association between drought exposure, food insecurity and mental health using data from longitudinal study Household, Income and Labour Dynamics in Australia (HILDA) Survey | Rural and urban | N = 5012 | Wave 7 survey participants aged 15+ | NA | NA | K10 | Monthly rainfall data from Australian Bureau of Meteorology |
| Guiney | 2012 | Australia | Examination of farming suicides during prolonged drought based on reports to State Coroner | NA | N = 110 | Farmers and primary producers in Victoria | 40‐49 22% | Male nearly 95% | Intentional self‐harm fatalities data obtained from National Coroners Information System for 7‐y period | NA |
| Gunn, Kettler | 2012 | Australia | Examination of psychological distress and coping in drought‐affected area | Rural farming | N = 309 | Farmers or spouses of farmers in South Australia | 51.81 (11.69) | Male 63.4%; Female 34.6% | K10 | NA |
| Hanigan, Butler | 2012 | Australia | Investigation of suicide in rural populations with a previously established climatic drought index | Rural and urban regions of New South Wales (NSW) | NA | Residents of 11 regions in NSW | NA | NA | Data on suicides 1970‐2007 | Hutchinson Drought Index |
| Hanigan, Schirmer | 2018 | Australia | Association between drought and distress using survey questionnaire | Rural area | N = 5312 | Residents of Victoria—farmers and non‐farmers | NA | Male 41.5%; female 57.7% | K10 | Hutchinson Drought Severity Index |
| Kelly, Lewin | 2011 | Australia | Individual and contextual factors influencing mental health within rural communities using data baseline sample from ARMHS | Non‐metropolitan regions of New South Wales | N = 2462 | Residents aged 18‐65 | 55.6 (14.5) | Female 59% | K10 | Data on drought severity and remoteness (ARIA+and ASGC) |
| Mann, Freyens | 2016 | Australia | Impact of natural and economic crises on structural change in farming sector using data from Australian Regional Well‐being Survey | Rural and regional | N = 2492 | Dryland farmers and irrigators | NA | NA | 1 item on happiness (in the last 4 weeks) | 1 item each on drought and other natural disaster (over the last 5 y) |
| O'Brien, Berry | 2014 | Australia | Quantitatively identify association between patterns of drought and mental health using HILDA Survey and rainfall data from Australian Bureau of Meteorology | Rural and urban | N = 5012 | People aged 15+ | 40‐55—rural 33.04 (0.02), urban 31.38 (0.01) | Male—rural 51.62 (0.01), urban 47.28 (0.01) | K10 | Drought patterns for 2001‐2008 |
| Parida, Dash | 2018 | India | Examine the effects of drought and flood on farmer suicides using state‐level panel data for 1995‐2011 | Agricultural | NA | Residents of 17 Indian states | NA | NA | Suicide data from annual report from National Crime Record Bureau | Flood data from Dartmouth Flood Observatory; Drought data from Department of Land Resources |
| Stain, Kelly | 2011 | Australia | Examine factors associated with drought impact | Rural and remote | N = 302 | Randomly selected residents of NSW aged 18+ | 53 | Female 57% | K10; Worry about Drought Scale | Drought status |
| Wheeler, Zuo | 2018 | Australia | Large‐scale assessment of Murray‐Darling Basin irrigators’ mental health | Irrigation districts | N = 1000 | irrigators | NA | NA | K10 | Items on drought, water availability |
| Studies of land degradation | ||||||||||
| Canu, Jameson | 2017 | USA | Examine relative risk for mental health diagnoses in areas with mountaintop removal (MTR) using data from State Emergency Department Database | Residential area | N = 1 380 394 | Kentucky State ED outpatients in a calendar year aged 18+ | 42.2 (18.19) | Female 58.1% | Rates of emergency department diagnosis for depressive disorders, substance use disorders and anxiety disorders in 2008 | ZIP code to determine active MTR area and rural status |
| Kallioniemi, Simola | 2016 | Finland | Stress among Finnish dairy farmers using cross‐sectional survey | Dairy farms | N = 265 | Finnish dairy farmers | 47.8 (10.35) | Men 56%; female 44% | MBI‐GS | Items on work and living environment resources |
| Morgan, Hine | 2016 | Australia | Examine contribution of coal seam gas (CSG) extraction to global stress burden and mental health of farmers | NA | N = 378 | Farmers or their partners | 53.08 (10.28) | Male 50.5%; female 49%; other 0.5% | DASS‐21 | Items on farm stress, that is weather, CSG concerns; engagement with CSG industry |
| Speldewinde, Cook | 2009 | Australia | Examine the effects of environmental degradation (dryland salinity) on mental health | Dryland agricultural areas | N = 2669 | Residents of southwest Western Australia | 20‐39 42% | Male 38%; Female 62% | Hospital cases (1st admission) for depression | Soil and landscape mapping as a measure of dryland salinity |
| Studies of climate conditions and extreme weather | ||||||||||
| Daghagh Yazd, Wheeler | 2020 | Australia | Longitudinal examination of whether area‐level climatic conditions and water scarcity were associated with poorer mental health for farmers | Rural areas | N = 235 | Active farmers living in the Murray–Darling Basin region of Australia | 49.7 (16.2) | Female 35%; Male 65% | MHI‐5 subscale | Water scarcity (measured through decreased rainy days; drought period; increased summer temperatures; reduced water allocations; lower soil moisture) |
| Howard, Ahmed | 2020 | USA | Impact of perception of climate change on mental health among rural agricultural populations using cross‐sectional survey | Rural agricultural | N = 125 | Farmers and ranchers aged 18+ from Montana | 35‐54 49.2% | Mostly male | Modified GAD‐7; PHQ‐9 | 3 items from Climate Change in the American Mind; 4 items from Climate Harm Scale |
| Pailler and Tsaneva | 2018 | India | Test effects of extreme weather and precipitation on psychological well‐being using data from World Health Survey (WHS) and Study on Global AGEing and Adult Health (SAGE) | Rural and urban | N = 16 227 | Adults aged 18‐60 | NA | Female—WHS 52%, SAGE 68% | Items on depression symptoms | Climate data using GPS coordinates—average monthly temperature and total monthly precipitation |
| Wind, Joshi | 2013 | India | Examine immediate impact of recurrent flood on mental health | Rural district | N = 615 | Affected population in Bahraich, Uttar Pradesh, compared with non‐affected group in the same region | Affected 46.03 (15.74); non‐affected 47.23 (13.92) | Affected—male 61%, female 39%; non‐affected male 54.9%, female 44.1% | HSCL‐25; SF‐12 | NA |
| Studies of engagement in natural resource management activities | ||||||||||
| Hounsome, Edwards | 2006 | Wales | Exploration of farmer health as a variable in adoption of agri‐environment schemes | Farm households | N = 111 | Farmers | NA | NA | SF‐36 | Involvement in agri‐environment schemes |
| Moore, Kesten | 2018 | Australia | Explore benefits gained by involvement in management of land for conservation using mixed methods | Rural regions in Victoria | N = 102 | Members of community‐based land management group and controls matched by age and sex | 45‐64 nearly 50% | Male 63%; female 37% | 1 item feel anxious; 1 item feel depressed | NA |
Abbreviations: ARMHS, Australian Rural Mental Health Study; DASS‐21, 21‐item Depression Anxiety and Stress Scale; ED, emergency department; GAD‐7, 7‐item Generalized Anxiety Disorder; GDS, Geriatric Depression Scale; GHQ‐12, 12‐item General Health Questionnaire; HILDA, Household; HSCL‐25, 25‐item Hopkins Symptom Checklist; Income and Labour Dynamics in Australia; K10, Kessler‐10 Distress Scale; MBI‐GS, Maslach Burnout Inventory—General Survey; MHI‐5, 5‐item Mental Health Inventory; NA, not applicable; PHQ‐9, 9‐item Patient Health Questionnaire; SF‐12, 12‐item Short‐Form Health Survey; SF‐36, 36‐item Short‐Form Health Survey.
Risk‐of‐bias assessment
| Inclusion criteria | Participants/settings detailed | Valid reliable measure of exposure | Objective criteria to measure MH | Confounding factors identified | Strategies to handle confounding | Valid reliable outcome measures | Appropriate stat analysis | Total criteria met | |
|---|---|---|---|---|---|---|---|---|---|
| Akpinar et al (2016) | Y | Y | Y | Y | Y | Y | U | Y | 7 |
| Alcock et al (2015) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Austin et al (2018) | Y | Y | U | Y | Y | Y | Y | Y | 7 |
| Brew et al (2016) | Y | Y | U | Y | Y | Y | Y | Y | 7 |
| Canu et al (2017) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Daghagh Yazd et al (2020) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Edwards & Hunter (2015) | Y | Y | U | Y | Y | U | Y | Y | 6 |
| Friel et al (2014) | Y | U | U | Y | Y | Y | Y | Y | 6 |
| Guiney et al (2012) | Y | Y | U | Y | N | N | Y | U | 4 |
| Gunn et al (2012) | Y | Y | U | Y | Y | U | Y | Y | 6 |
| Hanigan et al (2012) | Y | Y | Y | Y | U | U | Y | Y | 6 |
| Hanigan et al (2018) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Hounsome et al (2006) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Howard et al (2020) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Kallioniemi et al (2016) | Y | Y | U | Y | Y | Y | Y | Y | 7 |
| Kelly et al (2011) | Y | Y | U | Y | Y | Y | Y | Y | 7 |
| Losert et al (2012) | Y | Y | U | Y | Y | Y | Y | Y | 7 |
| Mann et al (2017) | U | U | U | U | Y | Y | U | Y | 3 |
| Moore et al (2006) | Y | Y | Y | U | Y | U | U | U | 4 |
| Morgan et al (2016) | Y | Y | U | Y | Y | Y | Y | Y | 7 |
| Nishigaki et al (2020) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| OBrien et al (2014) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Pailler (2018) | Y | Y | Y | U | Y | Y | U | Y | 6 |
| Parida et al (2018) | Y | U | Y | Y | U | Y | Y | Y | 6 |
| Speldewinde et al (2009) | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Stain et al (2011) | Y | Y | U | Y | Y | Y | Y | Y | 7 |
| Wheeler et al (2018) | U | Y | U | Y | Y | U | Y | U | 4 |
| Wind et al (2013) | Y | Y | Y | Y | Y | U | Y | U | 6 |
Abbreviations: MH, mental health; stat, statistical; U, unclear; Y, yes.
Summary of study findings
| Study—author (year) | Summary of outcomes |
|---|---|
| Studies of green space | |
| Akpinar et al (2016) | No significant associations between aggregated green space and mental and general health. Greater percentages of forest and more urban green space were associated with fewer days of mental health complaints, but not for agricultural lands |
| Alcock et al (2015) | Natural space in rural areas was positively related to good mental health when estimated from within‐individual differences. There is some evidence that different types of green and other natural space offer different degrees of benefit to well‐being |
| Losert et al (2012) | Hospital admission rates due to affective disorders decreased with an increase in percentage of total space covered by forest |
| Nishigaki et al (2020) | The middle tertile of grassland area ratio was associated with significantly lower levels of depression in older adults. The highest tertile of grassland ratio had no significant benefit. In addition, area ratios of total green space, trees and fields had no association with depression |
| Studies of drought | |
| Austin et al (2018) | Moderately dry, mild dry and moderately wet conditions were related to higher incidence of community drought‐related stress (CDS). Mild wet conditions were associated with greater incidence of psychological distress, personal drought‐related stress and CDS, suggesting drought‐related stress persists beyond the end of the drought |
| Brew et al (2016) | Farmers who lived more remotely had poorer self‐reported mental health than non‐farm workers living remotely and that this was not mediated by rural specific factors or vulnerabilities. Drought stress did not impact directly on mental health outcomes |
| Edwards & Hunter (2015) | Farmers had a higher rate of mental health problems and a lower level of mental health well‐being than those in non‐agricultural employment. Living in a drought‐affected area was estimated to significantly reduce mental health for farmers and farm workers |
| Friel et al (2014) | Drought mediated the association between food intake and mental health in rural areas |
| Guiney et al (2012) | No trend of increasing numbers of suicides coinciding with prolonged drought conditions |
| Gunn et al (2012) | Farmers or spouses of farmers in drought‐affected areas displayed significantly higher levels of distress than the broader national and rural populations |
| Hanigan et al (2012) | Drought increased the suicide rates for men aged 30‐49 (likely farmers or farmworkers) in rural communities. However, the risk for rural women aged >30 fell |
| Hanigan et al (2018) | All subgroups that were in drought had slightly higher average distress levels compared with those not in drought. Drought was estimated to have a negative impact on mental health in younger women but not in older women or men, and this pattern did not differ by farming status |
| Kelly et al (2011) | District‐level impacts, including severity of drought, on mental health were not evident in a rural sample |
| Mann et al (2017) | Crises, such as drought, reduced both profitability of farms and happiness of farmers, and both these factors reduced the likelihood of continuing the farming business |
| OBrien et al (2014) | Extreme dryness occurring within a drought itself affects mental health: drought was associated with increased distress in rural areas, while no consistent effects for drought were found in urban areas |
| Parida et al (2018) | Frequent occurrence of drought significantly increased farmer suicide due to crop failure. However, flood had almost no direct impact on the occurrence of farmer suicides. Incidence of farmer suicides was higher in cotton‐producing states as they experienced frequent drought conditions |
| Stain et al (2011) | Levels of psychological distress and drought worry were associated with factors reflecting the pragmatic impact of drought and environmental adversity on livelihood. Greatest psychological distress was associated with individual vulnerability factors and attenuation of community and social connectedness |
| Wheeler et al (2018) | Land–water use and drought were associated with psychological distress: some irrigators had higher levels of distress than dryland farmers or the Australian population, while horticulturists reported the highest levels of distress. Financial worry was the most important day‐to‐day stress for irrigators, but the results emphasise the integral nature of drought and water availability pressures |
| Studies of land degradation | |
| Canu et al (2017) | Emergency department visits by residents of areas with active mountaintop removal sites would be more likely to involve a psychological disorder, that is depressive disorder and substance use disorder |
| Kallioniemi et al (2016) | All dairy farmers were classified as having slight burnout symptoms. Work and living environment (including resource variables such as ‘work near nature’ ‘living environment’ and ‘farming lifestyle’) as a summary factor reduced the probability of burnout |
| Morgan et al (2016) | Potential coal seam gas (CSG) extraction impacted on health, community and the environment was a source of concern for farmers, and was significantly associated with increased symptoms of depression and stress reactivity |
| Speldewinde et al (2009) | An elevated risk of hospitalisations for depression was associated with residence in areas more affected by dryland salinity |
| Studies of climate conditions and extreme weather | |
| Daghagh Yadz et al (2020) | Farmer mental health was poorer if they were located in an area that had experienced reduced rainfall, markedly reduced water allocations and high mean daily maximum summer temperatures. These effects appeared to be moderated by reduced income, with lower income during drought having a considerable impact on worse mental health |
| Howard et al (2020) | Perceptions of climate risk and harm correlate positively with increased levels of both anxiety and distress. Organic farmers reported higher levels of anxiety than conventional farmers, and fruit/vegetable farmers reported higher levels of anxiety than grain/legume farmers |
| Pailler (2018) | Higher temperatures had a significant negative effect on psychological well‐being in rural, but not urban, areas. Controlling for precipitation, hot weather had a significant effect on increased depression symptoms. The adverse effects of extreme temperatures are partly due to reductions in agricultural output, which in turn reduce income and consumption |
| Wind et al (2013) | Recurrent flood‐affected group scored significantly higher on scales of anxiety and depression. They also scored significantly lower on the mental health component as an indicator of functioning |
| Studies of engagement in natural resource management activities | |
| Hounsome et al (2006) | Adoption of agri‐environment schemes by farmers was likely to be affected by their age, the size (effective area) of the farm and their health. Better mental health appears to improve the odds of farmers’ adoption |
| Moore et al (2006) | Members of land management groups reported that they experienced higher levels of anxiety and the same degree of depression as controls. However, members (especially men and those in older age groups) reported experiencing higher levels of health and well‐being |