Qualitative Analysis of Factors Influencing the Use of DHIS2 for Tuberculosis Surveillance: A Case Study in Guinea
Abstract:
Tuberculosis (TB) is
a major public health problem in Guinea, where many cases are undetected and untreated.
A robust health information system is needed to improve TB case detection and treatment
outcomes. DHIS2 (District
Health Information Software 2) is a web-based system that collects,
analyses and reports data on TB indicators. However, its use and use in Guinea is
affected by various factors. We explored these factors using a qualitative survey
with health workers and managers who use DHIS2 for TB surveillance. We collected
data through a survey with open-ended questions and analysed them using classical
content analysis. We conducted a qualitative survey with 35 health workers and managers
who use DHIS2 for TB surveillance at different levels of the health system in Guinea.
We collected data through an online survey with open-ended questions and analysed
them using classical content analysis with NVivo software. We identified four main
themes: technical issues (such as internet connection, data synchronisation, and
validation rules), data quality issues (such as data validation, data aberrations,
and data completeness), training and support issues (such as data analysis techniques,
online training, orientation on DHIS2), and organisational issues (such as integration
of community data, standardisation of data elements, meetings for data validation).
We discussed how these findings could inform the improvement of DHIS2 for TB surveillance
in Guinea and other similar settings.
Keywords: DHIS2, Health information system, Surveillance, Tuberculosis, Qualitative study.
References:
[1] WHO W.
WHO | Definitions and reporting framework for tuberculosis [Internet]. WHO.
2013 [cited 2019 Jun 29]. Available from: https://www.who.int/tb/publications/definitions/en/.
[2] WHO
COVID-19 Case definition [Internet]. [cited 2021 Jun 28]. Available from: https://www.who.int/publications-detail-redirect/WHO-2019-nCoV_Surveillance_Case_Definition-2020.2.
[3] WHO.
Global tuberculosis report [Internet]. 2021 [cited 2022 Jul 28]. Available
from: https://www.who.int/publications-detail-redirect/9789240037021.
[4] Programme
National de lutte anti-tuberculeuse. Rapport Annuel de Lutte Antittuberculeuse.
2022.
[5] WHO:
WHO Global tuberculosis report [Internet]. WHO. 2018 [cited 2019 Sep 10]. Retrieved
from: http://www.who.int/tb/publications/global_report/en/.
[6] WHO.
WHO treatment guidelines for drug-resistant tuberculosis. 2016.
[7] Dehnavieh
R, Haghdoost A, Khosravi A, Hoseinabadi F, Rahimi H, Prsh A, et al. The
District Health Information System (DHIS2): A literature review and
meta-synthesis of its strengths and operational challenges based on the
experiences of 11 countries. Health Inf Manag J. 2018 Jun 13;
48:183335831877771.
[8] Dehnavieh
R, Haghdoost A, Khosravi A, Hoseinabadi F, Rahimi H, Poursheikhali A, et al.
The District Health Information System (DHIS2): A literature review and
meta-synthesis of its strengths and operational challenges based on the
experiences of 11 countries. Health Inf Manag J. 2019 May 1;48(2):62–75.
[9] Kiberu
VM, Matovu JK, Makumbi F, Kyozira C, Mukooyo E, Wanyenze RK. Strengthening
district-based health reporting through the district health management
information software system: the Ugandan experience. BMC Med Inform Decis Mak.
2014 May 13; 14:40.
[10] Reynolds
E, Martel LD, Bah MO, Bah M, Bah MB, Boubacar B, et al. Implementation of DHIS2
for Disease Surveillance in Guinea: 2015–2020. Front Public Health [Internet].
2022 [cited 2022 Feb 11]; 9. Available from: https://www.frontiersin.org/article/10.3389/fpubh.2021.761196.
[11] Karuri
J, Waiganjo P, Orwa D. Determinants of Acceptance and Use of DHIS2 in Kenya:
UTAUT-Based Model. J Health Inform Dev Ctries [Internet]. 2017 Dec 10 [cited
2021 Aug 16];11(2). Available from: https://www.jhidc.org/index.php/jhidc/article/view/167.
[12] Moungui
HC, Nana-Djeunga HC, Nko’Ayissi GB, Sanou A, Kamgno J. Mixed-methods evaluation
of acceptability of the District Health Information Software (DHIS2) for
neglected tropical diseases program data in Cameroon. J Glob Health Rep. 2021
Aug 9;5: e2021071.
[13] Maina
JK, Macharia PM, Ouma PO, Snow RW, Okiro EA. Coverage of routine reporting on
malaria parasitological testing in Kenya, 2015-2016. Glob Health Action.
2017;10(1):1413266.
[14] Hsieh
HF, Shannon S. Three Approaches to Qualitative Content Analysis. Qual Health
Res. 2005 Dec 1; 15:1277–88.
[15] Data
Quality - DHIS2 Documentation [Internet]. [cited 2022 Feb 23]. Available from: https://docs.dhis2.org/en/use/user-guides/dhis-core-version-235/collecting-data/data-quality.html.
[16] McHugh
ML. Interrater reliability: the kappa statistic. Biochem Medica. 2012 Oct
15;22(3):276–82.
[17] NVivo -
Lumivero [Internet]. [cited 2023 Jul 20]. Available from: https://lumivero.com/products/nvivo/.
[18] Njeru
I, Kareko D, Kisangau N, Langat D, Liku N, Owiso G, et al. Use of technology
for public health surveillance reporting: opportunities, challenges and lessons
learnt from Kenya. BMC Public Health. 2020 Jul 13; 20:1101.
[19] Sahay
S, Rashidian A, Doctor HV. Challenges and opportunities of using DHIS2 to
strengthen health information systems in the Eastern Mediterranean Region: A
regional approach. Electron J Inf Syst Dev Ctries. 2020;86(1): e12108.
[20] Muhoza
P, Tine R, Faye A, Gaye I, Zeger SL, Diaw A, et al. A data quality assessment
of the first four years of malaria reporting in the Senegal DHIS2, 2014–2017.
BMC Health Serv Res. 2022 Jan 2; 22:18.
[21] Githinji
S, Oyando R, Malinga J, Ejersa W, Soti D, Rono J, et al. Completeness of
malaria indicator data reporting via the District Health Information Software 2
in Kenya, 2011–2015. Malar J. 2017 Aug 17; 16:344.