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| JOURNAL SCAN |
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| Year : 2022 | Volume
: 12
| Issue : 3 | Page : 135-136 |
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Do checklist-based box system interventions improve post-natal care service utilisation?
Manish Taywade, Debkumar Pal, Dinesh Prasad Sahu
Department of Community Medicine and Family Medicine, AIIMS, Bhubaneswar, Odisha, India
| Date of Submission | 11-Apr-2022 |
| Date of Decision | 23-Apr-2022 |
| Date of Acceptance | 11-May-2022 |
| Date of Web Publication | 30-Jun-2022 |
Correspondence Address:
Dr. Debkumar Pal
Department of Community Medicine and Family Medicine, AIIMS, Bhubaneswar, Odisha
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/cmrp.cmrp_38_22
How to cite this article:
Taywade M, Pal D, Sahu DP. Do checklist-based box system interventions improve post-natal care service utilisation?. Curr Med Res Pract 2022;12:135-6 |
How to cite this URL:
Taywade M, Pal D, Sahu DP. Do checklist-based box system interventions improve post-natal care service utilisation?. Curr Med Res Pract [serial online] 2022 [cited 2022 Aug 12];12:135-6. Available from: http://www.cmrpjournal.org/text.asp?2022/12/3/135/349293 |
| Article Information | |  |
Andargie NB, Debelew GT. Effectiveness of checklist-based box system intervention (CBBSI) versus routine care on improving post-natal care utilisation in North-west Ethiopia: a cluster randomised controlled trial. Reprod Health 18, 234 (2021). https://doi.org/10.1186/s12978-021-01283-9.
| Background | | |
The maternal mortality ratio (MMR) is one of the most important indicators for assessing the status of health services in a country. The MMR of Ethiopia was reduced from 1030/1 lakh live birth to 401/1 lakh births in between 2000 and 2017.[1] However, still, Ethiopia is well lagging behind the Sustainable Development Goal's target 3.1.[2] The health-care delivery system is not only responsible for this situation, also service utilisation is also greatly responsible.
The higher maternal and infant mortality rate in Ethiopia can be attributed to less utilisation of maternal health services to a greater extent.[3] The cultural factors act as an important barrier to maternal service utilisation.[4],[5] The intervention for improvement of service utilisation should focus on individual problem intervention. In this study by Andargie et al., CBBSI is developed using the PRECEDE-PROCEED model, where one service utilisation checklist is used to track the drop-outs to improve service utilisation. The cluster randomised controlled trial aims to measure the effectiveness of CBBSI in people of the East Gojjam Zone of the Amhara region of the North-west region. Keble/health posts were taken as clusters. The sample size was estimated as 1200, where every 30 clusters will contain 40 participants.[6] In the baseline, the mothers who delivered 1 year ago were included. In the control arm, usual care is given, and in the intervention arm active screening of pregnancy, scheduling of health education and checkbox-based follow-up of mothers are done. End line assessment was done after three post-natal care service utilisation, the total time required for the study completion was 20 months in place of 12 months.
Descriptive analysis showed the service utilisation between baseline and end line in the control and intervention arm. Chi-square test was applied to find out the difference of post-natal care service utilization between the intervention and control arms. The service utilization was statistically significantly higher in the intervention group in comparison with the control arm. The difference in difference (DiD) method estimated the difference in the two arms as 14.8%, 95% confidence interval (5.4%–24.2%). Multilevel logistic regression was performed for different factors responsible for post-natal care service utilisation with four models. The influence of significant others, place of delivery and knowledge about danger signs of pregnancy were found significant besides CBBSI in post-natal service utilisation. Cluster level variation was also found to be a significant factor for service utilisation.
| Critical Analysis of the Study | | |
The ideal way of performing clustered randomised control is to analyse the data at a level of cluster or individual level or at both levels.[7] In this study, individual level analysis was performed. However, as two different groups of people were taken at end line and baseline assessment, a cluster-level analysis should be performed along with the individual level.[7] Although this study was mentioned as a blinded one, the mother who was previously pregnant residing in intervention clusters could create a bias by informing the mothers recruited in the intervention arm. The kebeles were not clearly demarcated geographically, so the chance of contamination is always there.[8] The DiD method is ideally used where end line and baseline assessment are done on the same individual. Hence, in this study, DiD should not be used.[9] The factors related to health-seeking behaviour patterns and the working status of health extension workers can be confounding factors that should be analysed.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
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| 2. | |
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| 4. | Fenta SM, Fenta HM. Risk factors of child mortality in Ethiopia: Application of multilevel two-part model. PLoS One 2020;15:e0237640. |
| 5. | Baraki AG, Akalu TY, Wolde HF, Lakew AM, Gonete KA. Factors affecting infant mortality in the general population: Evidence from the 2016 Ethiopian demographic and health survey (EDHS); a multilevel analysis. BMC Pregnancy Childbirth 2020;20:299. |
| 6. | Campbell MK, Thomson S, Ramsay CR, MacLennan GS, Grimshaw JM. Sample size calculator for cluster randomized trials. Comput Biol Med 2004;34:113-25. |
| 7. | Hooper R, Forbes A, Hemming K, Takeda A, Beresford L. Analysis of cluster randomised trials with an assessment of outcome at baseline. BMJ 2018;360:1121. |
| 8. | Multerer L, Glass TR, Vanobberghen F, Smith T. Analysis of contamination in cluster randomized trials of malaria interventions. Trials 2021;22:613. |
| 9. | Zhou H, Taber C, Arcona S, Li Y. Difference-in-differences method in comparative effectiveness research: Utility with unbalanced groups. Appl Health Econ Health Policy 2016;14:419-29. |
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