Journal of Preventive Medicine and Public Health

  • 제55권4호
  • /
  • Pages.407-413
  • /
  • 2022
  • /
  • 1975-8375(pISSN)
  • /
  • 2233-4521(eISSN)
대한예방의학회 (The Korean Society for Preventive Medicine)
권호 표지
DOI QR코드

DOI QR Code

Analysis of a Targeted Intervention Programme on the Risk Behaviours of Injecting Drug Users in India: Evidence From the National Integrated Biological and Behavioural Surveillance Survey

  • Sahu, Damodar (Indian Council of Medical Research (ICMR)-National Institute of Medical Statistics) ;
  • Ranjan, Varsha (Indian Council of Medical Research (ICMR)-National Institute of Medical Statistics) ;
  • Chandra, Nalini (Joint United Nations Programme on HIV/AIDS (UNAIDS)) ;
  • Nair, Saritha (Indian Council of Medical Research (ICMR)-National Institute of Medical Statistics) ;
  • Kumar, Anil (Indian Council of Medical Research (ICMR)-National Institute of Medical Statistics) ;
  • Arumugam, Elangovan (Indian Council of Medical Research (ICMR)-National Institute of Epidemiology) ;
  • Rao, Mendu Vishnu Vardhana (Indian Council of Medical Research (ICMR)-National Institute of Medical Statistics)
  • 투고 : 2022.03.30
  • 심사 : 2022.06.27
  • 발행 : 2022.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objectives: This study provides insights on the impact of a targeted intervention (TI) programme on behaviour change among injecting drug users (IDUs) in India. Methods: This paper examined the data from the Integrated Biological and Behavioural Surveillance 2014-2015 for IDUs in India. Logistic regression was performed to understand the factors (TI programme services) that affected injecting risk behaviours by adjusting for covariates. Propensity score matching was conducted to understand the impact of the TI programme on using new needles/syringes and sharing needles/syringes in the most recent injecting episode by accounting for the covariates that predicted receiving the intervention. Results: Participants who received new needles and syringes from peer educators or outreach workers were 1.3 times (adjusted odds ratio, 1.29; 95% confidence interval [CI], 1.09 to 1.53) more likely to use new needles/syringes during most recent injecting episode than participants who did not receive needles/syringes. The matched-samples estimate (i.e., average treatment effect on treated) of using new needles in the most recent injecting episode showed a 2.8% (95% CI, 0.0 to 5.6) increase in the use of new needles and a 6.5% (95% CI, -9.7 to -3.3) decrease in needle sharing in the most recent injecting episode in participants who received new needles/syringes. There was a 2.2% (95% CI, -3.8 to -0.6) decrease in needle sharing in the most recent injecting episode among participants who were referred to other services (integrated counselling and testing centre, detox centres, etc.). Conclusions: The TI programme proved to be effective for behaviour change among IDUs, as substantiated by the use of new needles/syringes and sharing of needles/syringes.

키워드

과제정보

The authors wish to thank the National AIDS Control Organization (NACO) for providing the 2014-2015 IBBS data used in the analysis. The views or opinions expressed in this paper are those of the authors and not of institutions.

참고문헌

  1. Armstrong G, Nuken A, Medhi GK, Mahanta J, Humtsoe C, Lalmuanpuaii M, et al. Injecting drug use in Manipur and Nagaland, Northeast India: injecting and sexual risk behaviours across age groups. Harm Reduct J 2014;11(1):27. https://doi.org/10.1186/1477-7517-11-27
  2. Solomon SS, Srikrishnan AK, Mehta SH, Vasudevan CK, Murugavel KG, Thamburaj E, et al. High prevalence of HIV, HIV/hepatitis C virus coinfection, and risk behaviors among injection drug users in Chennai, India: a cause for concern. J Acquir Immune Defic Syndr 2008;49(3):327-332. https://doi.org/10.1097/QAI.0b013e3181831e85
  3. Ray Saraswati L, Sarna A, Sebastian MP, Sharma V, Madan I, Thior I, et al. HIV, hepatitis B and C among people who inject drugs: high prevalence of HIV and hepatitis C RNA positive infections observed in Delhi, India. BMC Public Health 2015;15:726. https://doi.org/10.1186/s12889-015-2003-z
  4. National AIDS Control Organization. Annual report 2012-13; 2013 [cited 2022 Feb 18]. Available from: http://naco.gov.in/sites/default/files/Annual%20report%202012-13_English.pdf.
  5. National AIDS Control Organization. National integrated biological and behavioural surveillance (IBBS), India 2014-15; 2015 [cited 2022 Feb 18]. Available from: http://naco.gov.in/sites/default/files/IBBS%20Report%202014-15.pdf.
  6. Sharma D, Goel NK, Walia DK, Thakare MM, Gupta V, Mittal S. Prevalence and predictors of self-reported risk behaviors among male injecting drug users. Indian J Public Health 2019;63(2):114-118. https://doi.org/10.4103/ijph.ijph_279_18
  7. Tanwar S, Rewari BB, Rao CD, Seguy N. India's HIV programme: successes and challenges. J Virus Erad 2016;2(Suppl 4):15-19.
  8. Lucas GM, Solomon SS, Srikrishnan AK, Agrawal A, Iqbal S, Laeyendecker O, et al. High HIV burden among people who inject drugs in 15 Indian cities. AIDS 2015;29(5):619-628. https://doi.org/10.1097/qad.0000000000000592
  9. Panda S, Kumar MS, Lokabiraman S, Jayashree K, Satagopan MC, Solomon S, et al. Risk factors for HIV infection in injection drug users and evidence for onward transmission of HIV to their sexual partners in Chennai, India. J Acquir Immune Defic Syndr 2005;39(1):9-15. https://doi.org/10.1097/01.qai.0000160713.94203.9b
  10. Government of India. Annual report 2015-16; 2016 [cited 2022 Feb 19]. Available from: http://naco.gov.in/sites/default/files/Annual%20Report%202015-16.pdf.
  11. Sarkar S, Das N, Panda S, Naik TN, Sarkar K, Singh BC, et al. Rapid spread of HIV among injecting drug users in north-eastern states of India. Bull Narc 1993;45(1):91-105.
  12. Armstrong G, Humtsoe C, Kermode M. HIV risk behaviours among injecting drug users in Northeast India following scale-up of a targeted HIV prevention programme. BMC Public Health 2011;11(Suppl 6):S9. https://doi.org/10.1186/1471-2458-11-S6-S9
  13. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70(1):41-55. https://doi.org/10.1093/biomet/70.1.41
  14. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984;79(387):516-524. https://doi.org/10.1080/01621459.1984.10478078
  15. Okamoto M, Ishigami H, Tokimoto K, Matsuoka M, Tango R. Early parenting program as intervention strategy for emotional distress in first-time mothers: a propensity score analysis. Matern Child Health J 2013;17(6):1059-1070. https://doi.org/10.1007/s10995-012-1088-6
  16. Thavaneswaran A, Lix L. Propensity score matching in observational studies; 2008 [cited 2022 Feb 19]. Available from: https://umanitoba.ca/faculties/health_sciences/medicine/units/chs/departmental_units/mchp/protocol/media/propensity_score_matching.pdf.
  17. Goswami P, Medhi GK, Armstrong G, Setia MS, Mathew S, Thongamba G, et al. An assessment of an HIV prevention intervention among people who inject drugs in the states of Manipur and Nagaland, India. Int J Drug Policy 2014;25(5):853-864. https://doi.org/10.1016/j.drugpo.2014.04.016