Research article

Social and cultural determinants of health; understanding the persisting Alcohol Use Disorder (AUD) in the rural populations in central Kenya

  • Received: 26 September 2019 Accepted: 04 December 2019 Published: 23 December 2019
  • Excessive alcohol use is a significant public health problem globally. Alcohol use typically begins in adolescence or early adult life, and effective prevention strategies focused on this age group are needed to avoid development of Alcohol Use Disorder (AUD). AUD is a worldwide problem resulting in millions of deaths, including hundreds of thousands of young lives lost. It is not only a causal factor in many diseases, but also a precursor to injury and violence. Furthermore, its’ negative impacts can spread throughout a community or a country, and beyond, by influencing levels and patterns of alcohol consumption across borders [1]. This study sought to ascertain the influence of socio-cultural factors in AUD among adults. The study adopted a descriptive cross-sectional study design. Stratified random sampling techniques were used to sample alcohol users across the county. Both descriptive (frequencies and percentages) and inferential (chi-square test) statistics were employed in data analysis. Content analysis was used to identify emerging themes in the interviews conducted. The study established that 65% of alcohol users in Muranga County have symptoms of AUD. Socio-cultural factors were found to influence AUD. Based on the findings, it was recommended that the Ministry of health and NACADA should organize sensitizations and awareness drives on alcohol abuse on the worrying trends of AUD together with their associated morbidities. The study also recommended deliberate efforts towards implementation of sound policies aimed at curbing the growth of the AUD.

    Citation: Danny Mungai, Ronnie Midigo. Social and cultural determinants of health; understanding the persisting Alcohol Use Disorder (AUD) in the rural populations in central Kenya[J]. AIMS Public Health, 2019, 6(4): 600-611. doi: 10.3934/publichealth.2019.4.600

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  • Excessive alcohol use is a significant public health problem globally. Alcohol use typically begins in adolescence or early adult life, and effective prevention strategies focused on this age group are needed to avoid development of Alcohol Use Disorder (AUD). AUD is a worldwide problem resulting in millions of deaths, including hundreds of thousands of young lives lost. It is not only a causal factor in many diseases, but also a precursor to injury and violence. Furthermore, its’ negative impacts can spread throughout a community or a country, and beyond, by influencing levels and patterns of alcohol consumption across borders [1]. This study sought to ascertain the influence of socio-cultural factors in AUD among adults. The study adopted a descriptive cross-sectional study design. Stratified random sampling techniques were used to sample alcohol users across the county. Both descriptive (frequencies and percentages) and inferential (chi-square test) statistics were employed in data analysis. Content analysis was used to identify emerging themes in the interviews conducted. The study established that 65% of alcohol users in Muranga County have symptoms of AUD. Socio-cultural factors were found to influence AUD. Based on the findings, it was recommended that the Ministry of health and NACADA should organize sensitizations and awareness drives on alcohol abuse on the worrying trends of AUD together with their associated morbidities. The study also recommended deliberate efforts towards implementation of sound policies aimed at curbing the growth of the AUD.



    Acknowledgments



    We would like to thank our colleagues at the Department of Public Health at Great Lakes University of Kisumu for their support during this project. The researchers performed this study as a dissertation for a master's degree at the Great Lakes University of Kisumu and would thus acknowledge academic supervisors: Dr. Isaac Okeyo and Dr. Boaz Otieno. We further acknowledge the support given to us by our colleagues at the Department of Public Health; Mohamed Sheikh Omar, Benson Maina Njoroge, Danny Kariuki Mungai and Lydia Pendo Mutsumi.

    Conflict of interest



    Any recommendations put forth in this paper are the views of the authors and do not necessarily represent the views of the Ministry of Health in Kenya nor the National Authority for the Campaign Against Drug Abuse.

    [1] Waller R, Murray L, Shaw DS, et al. (2019) Accelerated alcohol use across adolescence predicts early adult symptoms of alcohol use disorder via reward-related neural function. Psychol Med 49: 675–684. doi: 10.1017/S003329171800137X
    [2] World Health Orgnization (2016) World Health Statistics 2016: Monitoring health for the SDGs. Available from: https://www.who.int/gho/publications/world_health_statistics/2016/en/.
    [3] World Health Orgnization (2014) Global Status Report on Alcohol and Health 2014. Available from: https://www.who.int/substance_abuse/publications/alcohol_2014/en/.
    [4] Degenhardt L, Hall W (2012) Extent of illicit drug use and dependence, and their contribution to the global burden of disease. Lancet 379: 55–70. doi: 10.1016/S0140-6736(11)61138-0
    [5] McKay JR (2017) Making the hard work of recovery more attractive for those with substance use disorders. Addict 112: 751–757. doi: 10.1111/add.13502
    [6] Lundin A, Hallgren M, Balliu N, et al. (2015) The use of alcohol use disorders identification test (AUDIT) in detecting alcohol use disorder and risk drinking in the general population: validation of AUDIT using schedules for clinical assessment in neuropsychiatry. Alcohol Clin Exp Res 39: 158–165. doi: 10.1111/acer.12593
    [7] Liu W, Li R, Zimmerman MA, et al. (2019) Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use. Addict Behav 94: 147–155. doi: 10.1016/j.addbeh.2018.12.024
    [8] McNeely J, Cleland CM, Strauss SM, et al. (2015) Validation of self-administered single-item screening questions (SISQs) for unhealthy alcohol and drug use in primary care patients. J Gen Intern Med 30: 1757–1764. doi: 10.1007/s11606-015-3391-6
    [9] Babor TF, Robaina K (2016) The Alcohol Use Disorders Identification Test (AUDIT): A review of graded severity algorithms and national adaptations. Int J Alcohol Drug Res 5: 17–24. doi: 10.7895/ijadr.v5i2.222
    [10] Morris A (2015) Documenting the effects of the media on alcohol consumption in central Kenya.
    [11] Mutindi M (2016) Effects of alcohol abuse on the wellbeing of urban household in Kenya: a case of Mlolongo township in Athi River division in Machakos County Kenya.
    [12] Krejcie RV, Morgan DW (1970) Determining sample size for research activities. Educ Psychol Meas 30: 607–610. doi: 10.1177/001316447003000308
    [13] Germain CB (1991) Human behavior in the social environment. Found Soc Work Knowle, 88–121.
    [14] Bandura A (1989) Human agency in social cognitive theory. Am Psychol 44: 1175. doi: 10.1037/0003-066X.44.9.1175
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