Article Text

Original research
Success of health cell approach in improving knowledge, attitude and practice related to COVID-19: difference-in-differences analyses of a community-based quasi-experimental trial
  1. Subhasish Das1,2,
  2. Md Golam Rasul1,
  3. Ar-Rafi Khan1,
  4. Shah Mohammad Fahim1,
  5. Kazi Istiaque Sanin1,
  6. Tahmeed Ahmed1
  1. 1Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
  2. 2Liggins Institute, University of Auckland, Auckland, New Zealand
  1. Correspondence to Dr Subhasish Das; subhasish.das{at}icddrb.org

Abstract

Objectives There remain hesitations and miscommunication regarding appropriate public health behaviours and conceptions related to COVID-19. We tested the effectiveness of the community-based health cell approach in improving knowledge, attitude and practice (KAP) related to COVID-19.

Setting Households of the Bauniabadh slum area in Mirpur, Dhaka, Bangladesh.

Participants Household heads (HHs) and homemakers (HMs) of intervention (n=211) and comparison households (n=209).

Interventions Behaviour change communication delivered at the community level in a quasi-experimental manner through small-scale community meetings and home visits.

Outcome variables and methods The outcomes of interest were before–after mean and per cent changes in KAP scores. Data were collected from HHs and HMs before and after the intervention and difference-in-differences (DID) analysis technique was applied.

Results We found statistically significant (p<0.05) before–after differences in the responses to the KAP questions made by the intervention groups. The DID models estimated the improvements in COVID-19-related KAP of HHs by 16.58 (95% CI: 14.05, 19.12), 20.92 (95% CI: 18.17, 23.67) and 28.45 (95% CI: 23.84, 33.07) per cent points, respectively. The DID estimates of KAP in HMs were 17.8 (95% CI: 15.09, 20.51), 22.33 (95% CI: 19.47, 25.19) and 28.06 (95% CI: 23.18, 32.93) per cent points, respectively. Overall, 20.91 (95% CI: 18.87, 22.94) and 21.81 (95% CI: 19.68, 23.94) per cent points of improvement were observed among HHs and HMs, respectively. The DID estimates of before–after mean changes in different KAP domains ranged from 2.24 to 2.68 units and the overall changes in KAP scores among HHs and HMs were 7.11 (95% CI: 6.42, 7.8) and 7.42 (95% CI: 6.69, 8.14) units.

Conclusion Scientifically valid information disseminated at the community level using the health cell approach could bring positive changes in KAP related to COVID-19.

  • COVID-19
  • Public health
  • Epidemiology

Data availability statement

Data are available upon reasonable request.

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STRENGTHS AND LIMITATIONS OF THIS STUDY

  • The educational programme was delivered at community level via well-equipped health cells.

  • Knowledge, attitude and practice data were collected using a pretested questionnaire.

  • Though we did not deliver any behaviour change communication interventions to the control group, they might have received similar information from other sources which potentially confounded the findings of our study.

  • Application of difference-in-differences analysis technique might have adjusted the effect for such confounding.

Introduction

The emergence of COVID-19 has shown the importance of certain human behaviours such as social distancing, hand cleansing and effective use of face covering in controlling the pandemic, particularly when it was decided that ‘lockdowns’ or ‘zero covid’ policies are not sustainable. The director-general of the World Health Organisation recognised the importance of human behaviour in his comments made on 29 June 2020, in which he stated: “Every individual must understand that they are not helpless—there are things everyone should do to protect themselves and others. Your health is in your hands.” But still there remain hesitancy as well as a lack of understanding regarding practising appropriate preventive behaviours such as proper use of face masks, appropriate social distancing and hand cleansing practice.1 As a result, even after widespread vaccination coverage, new COVID-19 cases are emerging in every geography and are impacting the population’s overall health, well-being and economic status.

COVID-19, even as an endemic disease, can create imbalances in healthcare service delivery by burdening routine essential services, at least in countries with low-resource health systems, such as Bangladesh. Hence, making mass people aware of practising the preventative measures might prove fruitful in thwarting the re-emergence of COVID-19 as an endemic, if not a pandemic. Educational campaigns have the potential to provide correct information regarding health and diseases. If well adapted to a community setting, such campaigns can encourage people to bring changes to their behaviour and practice for preventing a disease.2 Translating and delivering scientifically valid information through a community-based health cell approach might be a good inclusion to the health system. But only a few community-based interventions were being put in place to modify the behavioural practices of community members by engaging themselves in a knowledge-based community intervention for preventing COVID-19. Such approach was fruitful in improving vaccine uptake and preventing unnecessary use of antibiotics in the healthcare setting and in the community, and transmission of sexually transmitted diseases in high-risk groups when the traditional epidemiological and microbiological interventions were not enough to address the critical threats.3 Moreover, it also has been shown that behaviour change communication (BCC) can effectively reduce the risk of HIV/AIDS and improve hand hygiene practice.4 5

Based on such empirical evidence, we developed and tested a model of a community-based health cell approach for improving knowledge, attitude and practice (KAP) related to the prevention of COVID-19. In addition, another objective of this approach was to deliver accurate information to prevent the ‘infodemic’ or overabundance of scientifically invalid and potentially harmful information that was spreading during the pandemic.

Methods

Study site and participants

We conducted a community-based quasi-experimental trial among residents of Bauniabadh and the adjacent slum areas in Mirpur, Dhaka, Bangladesh. The Bauniabadh slum area has six different zones named A, B, C, D, and E blocks and Kalabagan areas. A total of 203 households from the B block and Kalabagan area received the intervention and another 203 households from the D block were selected as the comparison cohort. We used a sampling frame and a simple random sampling technique to select the participating households. A buffer zone was maintained between the intervention and control areas to avoid information spillover. The A and C blocks were kept as the buffer zone that covers approximately 400 m of distance between the intervention and control areas. Field research assistants (FRAs) visited the households and, based on the predefined inclusion (participants living within the geographical location and were willing to consent for participation) and exclusion (receiving similar interventions from other sources) criteria, they screened the families. If a family was eligible to participate, the FRAs gathered written consent from the participants (household heads (HHs) and homemakers (HMs) of the selected households) using a consent form developed according to their literacy level. Participants got the opportunity to review the consent forms thoroughly before consenting to participate in the study.

Sample size

We used the G*Power (V.3.0.10) software for calculating the sample size. We assumed that, at baseline, the interviewed participants from intervention households would be able to answer 50% of the KAP questions correctly and the interventions would enable them to answer 80% of the questions correctly at the end of the intervention. Therefore, the effect size we considered was 30%. Additionally, we considered the level of significance as 0.05 and power as 90% for the sample size calculation. Considering all these assumptions, each group’s calculated sample size (number of households to be included) was 183. Then, taking account of an attrition rate of 10% points, a minimum sample size of 203 was required in each of the groups, and a total of 420 participating households (211 intervention and 209 control) were enrolled in the study.

Design and functionality of the health cells

We set up three health cells in Bauniabadh and adjacent slum areas of Mirpur, Dhaka. Each health cell was equipped with two well-trained health service providers from International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b; one FRA and one health worker), BCC materials, sphygmomanometers, thermometers, glucometers, pulse oximeters and face masks. We used standard BCC materials developed by icddr,b and World Health Organisation. Members of the health cells visited the households to recruit the participants. These service providers were well trained in providing BCC messages and were well-known to the community.

BCC message was provided through small-scale community meetings with HMs (mother or lead female member of a household) and HHs (lead earning member of a household). These meetings were conducted in the courtyard of the participants and strict social distancing was maintained. Eight to 10 members from the selected households were present in the meeting. Each session was 20–30 min long and each household joined the sessions twice a month. Leaflets and posters on effective use of face covering, hand cleansing and social distancing were also distributed during the educational sessions. The messages included simple but scientifically proven information on how, when and where to wear face covering, maintain social distancing and clean hands. The health workers demonstrated the household members how to make soapy water—a simple, cost-effective hand cleansing solution invented by icddr,b.6 Households of comparison group received face masks and hand cleansing solutions. No educational interventions or BCC materials were provided to them. We referred the participant from the intervention and comparison households to appropriate health facilities in case of any illness they reported.

Data collection

We reviewed the KAP surveys conducted in different parts of the world and selected a set of questionnaires on COVID-19-related KAP.7–9 The questionnaire was then pretested to check the applicability in local context. The pretesting was done in the A and C blocks of the Bauniabadh slum area of Mirpur, Dhaka. A total of 25 participants answered the questions asked to them and no issues were found regarding understanding or application of the questionnaire. The pretested version of the questionnaire was used to document the before–after changes in KAP of the household members regarding different preventive measures of COVID-19. Data were collected before and after the intervention period from the HHs and HMs to document the effect of the delivered intervention on KAP related to COVID-19.

Outcome variables

The questionnaire’s KAP sections had 14, 12 and 8 questions, respectively. Answers to 34 questions (online supplemental table 1) were sought to assess the participant’s KAP before and after the delivered intervention. The total number and per cent of questions a responder could answer correctly were the outcome variables for the analyses.

Data analysis

We report the overall and cohort-specific socioeconomic, household characteristics and KAP using mean, SD, frequency and percentages. Student’s t-test was performed for group-wise comparisons of quantitative symmetric variables. Group-wise comparison of the quantitative asymmetric data was made using the Kruskal-Wallis test. The Χ2 or Fisher’s exact test was done for qualitative variables. The correlation between KAP scores before and after the intervention was measured, and Spearman rank correlation coefficients with corresponding p values were reported.

The difference-in-differences (DID) analysis, a quasi-experimental data analysis technique, was used to measure the effect of the delivered intervention on KAP of the household members. In the regression models, a p value less than 0.05 was considered statistically significant and regression coefficients with 95% CIs were calculated. The multivariable DID models were statistically adjusted for WAMI index (Water, sanitation, hygiene, Asset, Maternal education and Income index), HH’s age, sex, total years of education, study group and timing of data collection. WAMI index (ranging from 0 to 1)—a socioeconomic status index that includes access to improved water and sanitation, eight selected assets (separate room for a kitchen, household bank account, mattress, TV, refrigerator, people per room, table, chair or bench), maternal education and household income—was used as a representative of socioeconomic status of the households.10 A higher WAMI index means a better socioeconomic status.

DID modelling technique measures the true effect of the intervention on the outcome and its before–after changes with the following formula: DID: [(B−A)−(D−C)], where:

A=baseline values in intervention group.

B=endline values in intervention group.

C=baseline values in comparison group.

D=endline values in comparison group.11

To assess the true effect of the intervention, we applied a regression model with a generalised estimating equation as follows: Yit=β0+β1Time+β2Group+δ (Time×Group)+β3X+ε (2), where:

Yit=outcome variable of interest for individual i at time t.

Time=(1) if endline and (0) if baseline.

Group=(1) if intervention group and (0) if comparison group.

δ=the effect of the delivered intervention, X=other covariates and ε=error term.

Patient and public involvement

Patients or the public WERE NOT involved in the design, or conduct, or reporting, or dissemination plans of our research.

Results

The median (IQR) age of the HHs was 40 years (15 years) for all households. Years of education of HHs and HMs were comparable over the intervention and control households. A median number of three people lived per room of the participating households. The median monthly income of the intervention household was US$141.51 (IQR 70.75); for the control family, it was 176.89 (117.92). Also, the median WAMI score was 0.53 (0.17) vs 0.52 (0.19) for intervention and control groups, respectively. Among our study households, most HHs were involved in small businesses (intervention: 29.38% vs control: 20.1%). Two-thirds of the HMs were not involved in any economic activities and stayed home. In the intervention group, 31.75% of the households were from the poorest economic conditions, 24.17% from the middle, 21.8% from the rich and 21.33% from the richest economic quantile where this prevalence was 44.02%, 20.1%, 15.31% and 16.75%, respectively, in the control group. Nearly 41% of women from the intervention group and 49% from the control group always washed their hands after cleaning their child’s bottom after defecation. Most of the women from intervention and control households always washed their hands after using the toilet. Also, 37.44% of women from the intervention group and 30.62% from the control group always washed their hands with soap before preparing food. The detailed sociodemographic characteristics of the study participants are presented in table 1.

Table 1

Socioeconomic status of the participating households

Figures 1 and 2 present the correlation coefficients with corresponding probability values between KAP scores before and after the intervention in HHs and HMs, respectively. The figures show weak correlations between the variables before intervention. However, the correlations became stronger and statistically significant after the intervention.

Figure 1

Scatter plots showing correlation, correlation coefficients with corresponding probability values, and regression lines with 95% CIs between knowledge, attitude and practice scores before and after the intervention in HHs.

Figure 2

Scatter plots showing correlation, correlation coefficients with corresponding probability values, and regression lines with 95% CIs between knowledge, attitude and practice scores before and after the intervention in HMs.

Online supplemental table 1 presents the absolute number and percentages of correct responses to KAP questions made by the HHs and HMs before and after the intervention. The table reports statistically significant (p<0.05) changes in the responses to all the questions made by the HHs and HMs of intervention groups. Expectedly, before–after changes among the HHs and HMs of the control group were not statistically significant. Most importantly, the delivered BCC intervention was successful in eradicating wrong perceptions regarding the transmission of virus from person to person and household pets to person, and the use of antibiotics or other medicines for the prevention of COVID-19.

Table 2 describes the per cent of correct responses in specific components of KAP questionnaire before and after the intervention and associated DID estimates in HHs and HMs. HHs in intervention groups correctly responded to 75% (95% CI: 72.80%, 76.60%) of knowledge, 78% (95% CI: 76.19%, 79.61%) of attitude and 66% (95% CI: 62.84%, 68.76%) of practice-related questions at baseline. At the endline, the proportion of correctly answered questions increased by around 19, 21 and 30 per cent points in KAP components, respectively. On the other hand, before–after per cent point changes in different KAP components were around 1.5–2% among the HHs of the control group. The DID models estimate the improvements in COVID-19-related KAP by 16.58 (95% CI: 14.05, 19.12; p<0.001), 20.92 (95% CI: 18.17, 23.67; p<0.001) and 28.45 (95% CI: 23.84, 33.07; p<0.001) per cent points, respectively. Overall, a change of 20.91 (95% CI: 18.87, 22.94; p<0.001) per cent points was observed among the HHs after adjusting for WAMI index, HH’s age, sex and total years of education, study group and timing of data collection. A similar scenario was seen among the HMs. HMs from the intervention group correctly responded to 78%, 75%, and 67% of KAP-related questions correctly at baseline, whereas at endline, they correctly responded to 98–99% of the questions. Contrary to this, the before–after changes among HMs of the control group were not significantly different. The DID regression also echoed this as the β coefficient values for before–after changes of KAP were 17.8 (95% CI: 15.09, 20.51), 22.33 (95% CI: 19.47, 25.19) and 28.06 (95% CI: 23.18, 32.93), respectively. Overall, an improvement of 21.81 (95% CI: 19.68, 23.94) per cent points was observed among the HMs.

Table 2

Proportion of correct responses in specific components of KAP questions before and after the intervention and the associated DID estimates

Table 3 presents the before–after mean changes in different domains of KAP among HHs and HMs. The changes in different domains range from 2.24 to 2.68 units, and the overall changes in KAP among HHs and HMs were 7.11 (95% CI: 6.42, 7.8) and 7.42 (95% CI: 6.69, 8.14), respectively.

Table 3

Before–after mean changes in different domains of knowledge, attitude and practice among the study participants

Discussion

COVID-19 can be prevented by following a few public health measures such as vaccination, wearing masks, avoiding crowded places, washing hands, etc. We developed and tested a model of a community-based health cell approach for improving KAP related to the prevention of COVID-19. We also wanted to deliver accurate information to prevent the ‘infodemic’ or overabundance of scientifically invalid and potentially harmful information that was spreading during the pandemic. In the present study, we found a lack of knowledge and attitude regarding COVID-19 and inadequate practice of preventive behaviours was also reported. The study was conducted in a crowded slum of Dhaka city. The baseline characteristics of the participants were comparable. We found statistically significant positive changes in KAP and total scores post-intervention. With the help of the provided intervention, the HHs and HMs of the intervention group successfully attained knowledge regarding COVID-19-related behaviour, which modified their attitude and practice. KAP scores were more strongly correlated with each other after the intervention than before. Though the knowledge regarding COVID-19 increased significantly after the intervention, the largest changes were found in the questions related to symptoms, transmission modality and use of antibiotics or other medications to prevent it. The overall changes in knowledge also modified the three main preventive behaviours: avoiding crowded places, wearing a mask and washing hands for at least 20 s. Our findings demonstrate that knowledge delivered through a community-based BCC programme can bring positive changes in attitude towards and practice of preventive behaviours related to COVID-19. Similar associations were reported in a few other KAP surveys, but these studies were limited to their cross-sectional designs.12

Though a good proportion of participants had accurate knowledge about COVID-19, we also found a high prevalence of misinformation regarding KAP related to COVID-19. Cross-sectional studies done in similar settings echo our findings. A study done in Pakistan reported that even though participants had a high level of literacy regarding the pandemic, certain myths were also prevalent.13 According to another cross-sectional survey done in India, nearly 26% of the medical and allied health science students thought that antibiotics were effective in preventing or treating COVID-19. One-fifth of them even believed that herbal medicine and garlic could prevent the disease.14 Similar reports were available from South Africa, Nigeria, Ghana and other countries in sub-Saharan Africa.15–18 The COVID-19-related misinformation has spread so widely that even the high-income countries of Europe and America had to fall for this infodemic.19 20 The internet brings us a wide range of verified and unverified information from different sources, and sometimes, it is difficult for an unprepared population to get the correct information. Hence, we need to focus on delivering knowledge-based accurate information to the community to prevent the spread of COVID-19. The objective of our study was to do the same through the health cell approach, and the results we report here indicate this effort’s success. But only a few studies were done with similar objectives. Banerjee proposed a toolkit for psychological preparedness for COVID-19.21 In Vietnam, an educational intervention that was developed and provided to agricultural communities improved awareness of H5N1 and accessibility of healthcare.22

We did not deliver any BCC interventions to the control group. However, they might have received similar information from other sources, which confounded the findings of our study. This is a potential limitation of our study. The educational programme was delivered at the community level via a well-equipped health cell approach, and KAP data were collected using a pretested questionnaire. Hence, we believe we successfully captured the true changes in COVID-19-related KAP scores in the study participants after receiving the intervention we delivered.

Conclusion

Information developed based on scientific evidence and disseminated at the community level using the health cell approach can bring positive changes in KAP related to prevention of COVID-19. Hence, we call to adopt the health cell approach to the existing health system and conduct the BCC interventions continuously to prevent at least two hazards: COVID-19 as an epidemic and the infodemic of misinformation.

Data availability statement

Data are available upon reasonable request.

Ethics statements

Patient consent for publication

Ethics approval

This study involves human participants and was approved by the Institutional Review Board of International Centre for Diarrhoeal Disease Research, Bangladesh (protocol no: 20120). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We express our sincere thanks to the study participants. icddr,b is also grateful to the Governments of Bangladesh, Canada, Sweden and the UK for providing core/unrestricted support.

References

Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Footnotes

  • Correction notice This article has been corrected since it was published. The middle name of author KIS has been corrected.

  • Contributors SD conceived the idea. SD and MGR participated in the design of the study. SD led the protocol. SD, MGR, SMF and KIS were involved in the sample and data collection. SD, A-RK and MGR were involved in data analysis. SD interpreted the results. SD, MGR, SMF, A-RK, KIS and TA were involved in manuscript writing. SD is responsible for the overall content as the guarantor. All the authors read, reviewed and approved the final version of the manuscript.

  • Funding This study secured funding from Global Affairs Canada (award/grant no: NA).

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.