Menu
Islam N, Patwary M, Hasan T, Qayyum Z. Common issues and challenges to collect socio-demographic and economic mHealth data by BRAC community health workers. HPHR. 2024;86. https://doi.org/10.54111/0001/HHHH1
Today, data-driven decision-making is perceived as one of the most crucial elements of an organization’s success. The health program of BRAC, the world’s largest non-governmental development organization based in Bangladesh introduced a new mHealth application in 2020 to digitize data collected from over 80 million individuals. Through extensive door-to-door health promotion, diverse facilities, programs, and dedicated workers, it reaches 80 million people across Bangladesh.
Covering 61 districts with 18 million households (HHs) in Bangladesh, our research employs a multi-method approach, incorporating quantitative analysis (randomly selecting 388 households) and qualitative insights gathered from in-depth interviews with 24 Community Health Workers (CHWs). The analysis highlights missing data and irregularities in indicators such as financial status and contact numbers.
CHWs encounter challenges in engaging reluctant community members, especially when it comes to sharing sensitive information. The study found 38% missing values in HH financial status, 84% missing value in NID or Birth ID, 39-70% missing values of assets information, and 77% missing values in household contact phone numbers. Interviews with CHWs highlight challenges in collecting accurate data due to reluctance from community members to share personal information, difficulties in accessing mobile numbers, NID, and providing intentional or unintentional misinformation.
In delving into these challenges, it becomes apparent that relying on housewives for data introduces risks. Gathering information from neighbors when household members are unavailable increases the likelihood of errors. Some CHWs adopt unconventional methods, like taking screenshots or using paper records, potentially leading to mistakes during data entry. Difficulty in submitting forms with missing values prompts CHWs to use guesswork, compromising data accuracy.
Despite the benefits of mHealth tools, challenges persist in ensuring accurate data collection. This study offers valuable insights for improving data collection strategies, particularly in low- to middle-income countries.
Today, data-driven decision-making is perceived as one of the most crucial elements of an organization’s success. Organizational leaders can plan, avert risks, and make decisions based on accurate data to boost revenues, save expenses, and improve the end-user experience. Instead of waiting for high-level outcomes like quarterly reports, correctly managed data can help identify and address problems in real-time. Organizational employees with accurate insight and knowledge about their tasks can solve issues more effectively and efficiently1.
Data is unequivocally essential for public health projects. Nowadays, massive amounts of data are generated or collected during daily community level public health interventions. This information is used to develop evidence-based decisions, allocate resources, and formulate health strategies. Data is also required for monitoring and evaluating program performance, service quality, overall scope, and ensuring equitable service provision for the community2.
Today, data is frequently collected at the individual level and referred to as “unit level data.” Individual records detailing interactions with health services are attempted to be handled in mHealth data. To increase programmatic efficiency, organizations are switching from paper-based to electronic or mHealth data. Public health decisionmakers can now obtain a more complete picture of the factors influencing community health outcomes with the aid of mHealth data systems. Additionally, it improves the coordination of care across sectors for people with complex social and medical need3.
One of the main elements of public health practice is the efficient use of Health data. Accurate, timely health information is needed for public health interventions like community health programme, outbreak investigations, disease prevention initiatives, and improvements to the healthcare system. As a result, various public and private organizations use electronic or mHealth data to learn more and quickly respond in better way to new public health issues4,5.
One of the most crucial aspects of public health data quality is data accuracy. This refers to whether the data values stored for a fact are accurate7. Generally, community health workers (CHWs) gather information from their communities for services sectors, survey data, and research. However, organizations do not have clear ideas about the quality of these data8. Furthermore, incorrectly entered data in electronic public health record systems can cause not only that person’s record to be inaccurate but also have a negative impact on summary data and decision-making based on that data9,10.
BRAC, based in Bangladesh, is the largest non-governmental development organization in the world. Currently, its community-driven healthcare initiative engages 4,300 women community health workers. These dedicated individuals provide essential primary healthcare services including reproductive, maternal, neonatal, child, adolescent, women, and adult health services within their respective areas, establishing crucial connections with formal healthcare providers. Utilizing door-to-door health promotion and service delivery, along with an array of other programmatic facilities, BRAC extends its reach to 80 million people across Bangladesh.14.
BRAC deployed a mHealth application in 2020 to digitize its health data system, enabling CHWs to monitor the health situation of millions of people throughout Bangladesh and provide necessary health support. Data quality is integral to since the data reflects the reality on the ground, given that various factors influence quality in this type of nationwide data in both community and facility settings. The most common issues include skipping sensitive or non-mandatory questions when filling out mHealth data forms, intentionally or unintentionally inputting the wrong data, resulting in a lack of trust in the mHealth data, and limited use of data for decision-making2. This study aims to identify the common issues or challenges faced by BRAC CHWs in collecting socio-demographic and economic data and gather insights behind the data-related issues.
In Bangladesh, BRAC Health Programme (BHP) is covering 80 million individual people within 61 districts out of 64 districts of Bangladesh through various interventions where 65 million people’s information is registered so far in its mHealth system. Community health workers’ (CHWs) household (HH) visit data from the BHP catchment area (61 districts) was collected for the purpose of this study.
This study involves the usage of a multi-method approach. In the first phase, secondary mHealth data was analyzed to find the common issues in the data. Subsequently, individual interviews of CHWs were conducted to gather insight about the issues in the data.
The target population were (1) members of the household who had got the HH visits within the last 2 months and (2) mHealth household data collectors such as CHWs or Shasthya Kormis.
Here 30 cluster sampling methods were used to select the households. In 1st step, 30 CHWs out of 4,162 active CHWs were selected at random, then in 2nd step 30 HHs in each CHWs’ coverage area were selected using systematic sampling from the list of members. In the BRAC health program 10 Shasthya Shemika or community health volunteers (CHVs) are working under each BRAC CHW. Here 3 HHs were selected for each CHV. The total sample size was 900 where 388 HHs get follow up HH visits. The ultimate sample size was 388 out of 18 million households for quantitative analysis.
In qualitative analysis, the primary plan was to take at least 10 in-depth interviews (IDIs) with community health workers. However, given it is a qualitative study, the sample size was kept flexible, and the interviews were continued till they reached the data saturation point. In this study 24 IDIs were conducted for data saturation.
The semi-structured IDI guidelines and relevant probing questions were developed in mHealth system. The tools were written in Bangla.
After that, the interview with CHWs was conducted at the field level. The interviews were conducted based on the time convenience of the interviewer and the interviewee.
In the data analysis, mHealth secondary data was analyzed to assess the quality of the data and the common errors in the data.
Based on the secondary outcomes, some a priori codes were developed based on the first phase. The codebook contained definitions and instructions on when to use the codes. After data collection, the interviews transcribed verbatim.
The data analyzed thematically after the data collection and transcription period. All the team members familiarized themselves with the data by going through the transcripts of prior analyses. During analysis, newly emerged codes were incorporated into the codebook.
The data display matrix contains the quotations that reflect the sub-codes under each code. After that, all the relevant codes were clustered into major themes, which were used to present the results section.
BRAC CHWs collect household and member information in the mHealth system by door-to-door household visits. In the household visit time, they fill up the household (HH) visit form, eligible couple form, adolescent form, pregnancy related care form and non-communicable disease care form. At that time, if they find any new HHs or members in their coverage area, they register them in the household registration form and the member registration form. As per the registered member list they have provided age-based counseling and delivery services (family planning, pregnancy related care, non-communicable disease care and eye care services) to the targeted people. In this study, however, the focus was solely on HH visit, HH registration and member form. Before this study, these forms and their logical relationship with other forms were also checked. Here the member registration form and HH visit form are related to the HH registration form. CHWs cannot fill in the member registration form and HH visit form before completing the HH registration form.
No system flaws were found in these three forms.
Table 1. Missing value
Characteristic | N = 388 |
HH Financial Status* | 240 (62%) |
Less than 6K | 86 (36%) |
6K to 7K | 88 (37%) |
7K to 12K | 54 (22%) |
More than 12K | 12 (5.0%) |
Missing Value | 148 (38%) |
|
|
HH head ID* |
|
NID or BID | 64 (16%) |
Missing Value | 324 (84%) |
|
|
Has Sanitary Latrine | 362 (93%) |
Missing Value | 26 (7%) |
|
|
Member Count* | 388 (100%) ; 4.00 (3.00, 5.00) |
Missing Value | 0 (0%) |
|
|
List of Assets | 236 (61%) |
Missing Value | 152 (39%) |
|
|
Homestead Land | 144(37%) ; 6 (5, 8) |
Missing Value | 244 (63%) |
|
|
Cultivable Land | 116 (30%) ; 2 (0, 42) |
Missing Value | 272 (70%) |
|
|
Contact Phone number | 90 (23%) |
Missing Value | 298 (77%) |
In the secondary data analysis in Table 1, many missing values were found in some indicators as well as some irregular values in data. There were around 38% missing values in HH financial status, 84% missing values in NID or Birth ID, 39-70% missing values of assets information and 77% missing values in household contact phone numbers. Some irregular values in household members’ number and amount of cultivable and homestead land were also found.
Based on these findings, IDIs guidelines were developed to find out the reason behind the missing values or irregular values in these indicators. In this study, 24 IDIs were conducted to investigate the CHWs’ knowledge, insights, and perception about how to properly fill up these three forms. It was found that CHWs have proper knowledge about the data points in the HH visits form. Furthermore, they informed that they have updated the household information (sociodemographic, economic and sanitation information) and member information (identification, person specific information, relationship with HH head and disability information) in their follow up visit. Generally, they update the member list according to new birth or death. However, they did not mention about the migrated people information, although the migration feature is also available on their app. CHWs can add new HHs when they have found a new HH in their coverage area or any HH divided into two parts. Yet, CHWs did not mention HH migration information during the interview.
“I record household information through my tab. Some of the information that goes there are micro finance Village Organizer (VO) or Non-VO status of HH, HH serial number, HH head name, HH head profession, Number of HH members, existence of sanitary latrine, HH financial condition, list of assets (i.e., television, fridge), construction material of house and floor, arable land etc.”
(CHW Mowshumi Akhter, IDI 05 Line 20)
BRAC CHWs are facing some issues and challenges in collecting the data regarding national ID (NID), birth ID, mobile number and house economy condition. Generally, the male members or household heads are not available at the daytime. For this reason, community health workers are not able to collect their mobile number from their household. Sometimes community people do not want to provide their mobile number, NID and Birth ID for fear of unknown problems. BRAC CHWs try to collect these data by counseling and assure them that people will not face any problems by providing their information.
“It is problematic to get information about a household when the HH members are not present. Sometimes they are reluctant to give me their phone number and I have to persuade them for that. It also becomes impossible to get the phone number when the HH head is away (with his phone).” (CHW Anita Rani, IDI 14 Line 18)
In the mHealth system, HH visit forms are not submitted without the mobile number, but some CHWs enter only zero instead of mobile number and submit the form. Some of CHWs informed us that if they are not able to collect the information on their HH visit, they will try to collect that information in their HH follow up visit.
It was also found that mobile phone numbers of community people are also connected with MFS or Mobile Financial Services (i.e., bKash, Nagad) and some of them get monetary benefits from different other sources using these MFS and are not willing to provide their bKash number. Sometimes some community people intentionally provide other phone numbers or wrong mobile numbers. Sometimes some of the people thought that they would get financial support from BRAC. BRAC CHWs try to provide them with the actual information and collect this data by counseling and explaining their purpose.
“I face a reluctance from the community people in giving mobile number and NID card-related information as they use bKash on their phone and NID card-related information is used for various purposes. Because of this, we have to sensitize them.” (CHW Shantana Rani, IDI 15 Line 34)
“Most of the community people are not able to provide actual information of their income and assets, so they try to provide an approximate figure.” (CHW Halima, IDI 21 Line 32)
During discussion, CHWs mentioned that they are not sure about the accuracy of data. Because most of the data comes from the village level housewives who have little knowledge on their total household income, assets and all the members’ information including mobile number. In the absence of HH members, sometimes CHWs are collecting the HHs and member information from neighbors which have a high chance of entering misleading or wrong information. A few CHWs inform that they collect the data by using screenshots and write on paper or diary, after the field visit, they enter that data in the mHealth system which has a high chance to get wrong input in the mHealth system. CHWs also informed that they are not able to submit the form for missing values in the mandatory field, at that time they enter some guess-based numbers and submit the form.
“Sometimes we have to leave certain household information blank in the absence of a Voter ID Card or Birth Certificate. For example, when we go to a house and don’t find anyone, we put an approximate age for the members.” (CHW Sonia, IDI 11 Line 43)
Most of the CHWs informed that they are not able to complete their activities within time. Nowadays they need to spend more time updating the mHealth data. They mentioned that they need almost 20-30 minutes to complete one HH visit. They mentioned that they entered data from their household or community volunteer’s household after completing their daily work schedule.
However, BRAC CHWs informed that the mHealth system has brought many helpful features for them. Nowadays they are able to check their daily and monthly targets and performance in their dashboard and also are able to make decisions based on their data. They can easily cross check their performance with area managers to compare their dashboard with the area manager dashboard.
“We used the register previously but now we have a tab which is very useful for us to provide information quickly. It lets us see our monthly performance at a glance.” (CHW Aysha, IDI 08 Line 56)
Public health data is crucial for decision-making and programme design. However, there are a lot of challenges to collect accurate data from the community. According to our study findings, some of the community people are not ready to provide their personal data. Organizing community sensitization programs about the mHealth system and the benefit of providing socio-demographic and public health data can be useful in this regard. BRAC needs to check the data collection process and reduce the data points for reducing data entry time. In this study, there was not enough scope to determine the proper intervention to increase the accuracy of data. It will be better to conduct further investigation to review the data quality issues and their solutions. Community health workers need more training and motivation to ensure the data quality. However, this study results cannot be generalized based on a very small dataset (388 out of 18 million HHs) for the community, community health workers, and data systems. But these outputs give us some idea about the issues and challenges to collect the sociodemographic and economic health data. This knowledge will be helpful for someone who wants to collect large-scale socio demographic health data from the community of low- and middle-income countries.
Naimul Islam is the Senior Manager, Data Science has 11 years of professional experience in organizations such as BRAC. Naimul Islam is a qualified public health data professional with dual Master’s degrees, one in Biostatistics and another in Public Health. With expertise spanning both statistical analysis and public health, he has over a decade of experience, showcasing proficiency in project management, monitoring and evaluation, and research. Naimul’s educational background and extensive experience underscore his commitment to leveraging data for informed decision-making, particularly in the dynamic fields of biostatistics and public health.
Monzur Morshed Patwary has 12 years of professional experience in organizations such as BRAC, The Task Force for Global Health, and CDC Foundation. Monzur represents Bangladesh on global platforms such as Paris Model World Health Assembly, Global Leadership Forum, and HPAIR Harvard Conference. Monzur completed his bachelors from the University of Minnesota-Twin Cities and the Hubert H. Humphrey fellowship from Emory University. He is also a PMI Certified Project Manager.
Dr Tanvir Hasan is an Assistant Professor and Co-Director of Centre of Excellence for Urban Equity and Health (CUEH) at BRAC James P Grant School of Public Health. He holds a PhD from the University of Queensland. He also holds a Master of Science degree in Applied Statistics from Dhaka University. He teaches Biostatistics and Quantitative Research Methods in the Master of Public Health programme. Besides teaching, he currently leads two research projects. His research work mainly focuses on maternal and child nutrition, social determinants of health and sexual and reproductive health.
Dr Zahidul Quayyum is a Professor, Co-Director of the Centre of Excellence for Urban Equity and Health, and the Technical Adviser to the Centre of Excellence for Health Systems and Universal Health Coverage at BRAC James P Grant School of Public Health. With over 20 years of experience in conducting health economics research and teaching in UK universities, He has worked for many international research projects, mainly economic evaluation models for safe motherhood interventions strategies and economic analysis of government interventions and policy changes for improving maternal and child health. Dr Quayyum was also a founding faculty member at the Institute of Health Economics of Dhaka University.
BCPHR.org was designed by ComputerAlly.com.
Visit BCPHR‘s publisher, the Boston Congress of Public Health (BCPH).
Email [email protected] for more information.
Click below to make a tax-deductible donation supporting the educational initiatives of the Boston Congress of Public Health, publisher of BCPHR.
© 2024 BCPHR: An Academic, Peer-Reviewed Journal