KAP COVID-19

Introduction

Title: Knowledge, attitudes, and practices (KAP) Towards COVID-19 among Migrant workers in Phuket province

Institute of Preventive Medicine, Department of Disease Control, Ministry of Public Health, Thailand

This project focuses on creating a registry system for promoting health equity and tracking on migrant workers’ health status in the South Andaman Coast of Thailand, pilot in Phuket province.

Objective

1. To investigate the level of migrant workers on knowledge, attitudes, and practices toward COVID-19.
2. explores how COVID-19 knowledge is related to practices and relation by demographic factors.
3. Identified which population has low levels of knowledge about COVID-19.

Methods

Participants

The target population is migrant workers in the South Andaman Coast of Thailand; pilot in Phuket provinces, including migrant workers in other cities. The target area is selected according to the objectives of the funders (Centers for Disease Control and Prevention (CDC)). A target of four hundred migrant workers to enroll and participate in the survey of knowledge, attitudes and practices toward COVID-19 study. Sample size was determined by the one proportion formula; n = Z2 P(1-P)/d2. At the maximum proportion of 0.5, p = 0.05 and d = 0.05, the minimum number of sample size will be 384. Prepare 5% for drop out or missing value, the total sample size will be 400 participants Participants in this study have the following qualifications; - is a migrant worker Burmese, Cambodian and Lao nationality; - is 18 years old or over age at the date of participation in the study; - be able to communicate in Thai

Measures

KAP survey questionnaire was built with 6 sections, socio-demographic information (i.e. gender, age, education etc.), COVID-19 Situation, Knowledge, Altitude, Practice, and Mental health. This is an KAP survey document. For more details on KAP survey see http://www.migranthealthvolunteer.com/redcap. Data collection separate into 3-time period (May-July, 2022) via paper and online form. All questionnaires are collected in digital form by Redcap software.

Data analysis

In this research (survey part), the qualitative data analysis will used content analysis, while the data on knowledge, attitude and practice of migrant workers towards the situation of COVID-19 disease will be analyzed by both descriptive statistic such as percentages and Cross-Tabulation tables. We also proceed statistical analyses using RStudio 2022.07.01 with multivariate linear regression analysis.

Redcap software has the ability not only to create forms to collect the data but also to generate reports and dashboards. This is an example of a report generated by Redcap. Demographic information. However, Redcap also supports access to data by API. All you have to do is just create a token for the form that need to access. In this case, we tried to pull and analyze data via R programming.

This is some processing to shown summary of KAP survey.

Load library

Set Connection to Redcap projects

This step is create connection to the project on server install Redcap. First of all generate apt token for the project that need to access and pull data. Then create url and token variable for data pulling.

The data dictionary describing 90 fields was read from REDCap in 0.8 seconds.  The http status code was 200.

779 records and 1 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Starting to read 779 records  at 2023-08-13 10:46:49.

Reading batch 1 of 8, with subjects 1 through 100 (ie, 100 unique subject records).

100 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Reading batch 2 of 8, with subjects 101 through 200 (ie, 100 unique subject records).

100 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Reading batch 3 of 8, with subjects 201 through 300 (ie, 100 unique subject records).

100 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Reading batch 4 of 8, with subjects 301 through 400 (ie, 100 unique subject records).

100 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Reading batch 5 of 8, with subjects 401 through 500 (ie, 100 unique subject records).

100 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Reading batch 6 of 8, with subjects 501 through 600 (ie, 100 unique subject records).

100 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Reading batch 7 of 8, with subjects 601 through 700 (ie, 100 unique subject records).

100 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

Reading batch 8 of 8, with subjects 701 through 779 (ie, 79 unique subject records).

79 records and 181 columns were read from REDCap in 0.4 seconds.  The http status code was 200.

── Column specification ────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  input_name = col_character(),
  staying_time = col_character(),
  job_orthers = col_character(),
  not_q_reason = col_character(),
  attitude_09_other = col_logical(),
  attitude_11_other = col_character(),
  vaccinated_1st = col_date(format = ""),
  vaccine_name_orther = col_character(),
  vaccinated_2nd = col_date(format = ""),
  vaccine_name_orther_2 = col_character(),
  vaccination_concern_reas = col_character(),
  vaccination_decision_no_rs = col_character(),
  behavior_08_yes_orther = col_character()
)
ℹ Use `spec()` for the full column specifications.

Data Cleaning

You can also clean data, for example:

# filter valid data
df_valid = df %>% filter(age_kap > 17)
df_valid = df_valid %>% drop_na(education, 
                                income, 
                                from_nation,
                                staying_time,
                                house_members,
                                stay_with_family)

knitr::kable(df_valid[1:5, ], caption = "A KAP Survey (sample data)")

A KAP Survey (sample data)

record_id	input_name	gender	age_kap	education	income	job_status	from_nation	staying_time	amphoe	moving_reason	works_permit	job_desc	job_orthers	house_members	stay_with_family	house_members_number	religion	other_covid19	no_family_covid19_detected	no_family_covid19_died	contact_covid19	quarantine_14day	not_quarantine_res	not_q_reason	test_covid19	testing_after_covid19	testing_type_covid19___1	testing_type_covid19___2	testing_type_covid19___3	testing_type_covid19___4	testing_type_covid19___5	testing_type_covid19___6	result_test_covid19	testing_result_others	detected_relation___1	detected_relation___2	detected_relation___3	detected_relation___4	detected_relation___5	detected_relation___6	detected_treatment	family_death_covid19	knowledge_01	knowledge_02	knowledge_03	knowledge_04	attitude_01	attitude_02	attitude_03	attitude_3_1	attitude_04	attitude_05	attitude_06	attitude_07	attitude_08	attitude_09	attitude_09_other	attitude_10___1	attitude_10___2	attitude_10___3	attitude_10___4	attitude_10___5	attitude_10___6	attitude_10___7	attitude_10___8	attitude_10___9	attitude_10___10	attitude_11	attitude_11_other	attitude_12___1	attitude_12___2	attitude_12___3	attitude_12___4	attitude_12___5	attitude_12___6	attitude_12___7	attitude_12___8	attitude_13___1	attitude_13___2	attitude_13___3	attitude_13___4	attitude_13___5	attitude_13___6	attitude_13___7	attitude_13___8	vaccination	vaccinated_1st	vaccine_name	vaccine_name_orther	vaccinated_2nd	vaccine_name_2	vaccine_name_orther_2	no_vaccinated	vaccination_concern	vaccination_concern_reas	vaccination_decision	vaccination_decision_no	vaccination_decision_no_rs	behavior_01___1	behavior_01___2	behavior_01___3	behavior_01___4	behavior_01___5	behavior_01___6	behavior_01___7	behavior_01___8	behavior_01___9	behavior_01___10	behavior_01___11	behavior_01___12	behavior_01___13	behavior_01___14	behavior_01___15	behavior_01___16	behavior_01___17	behavior_01___18	behavior_01___19	behavior_01___20	behavior_02	behavior_03	behavior_04	behavior_05	behavior_06	behavior_07	behavior_08	behavior_08_yes	behavior_08_yes_orther	behavior_09___1	behavior_09___2	behavior_09___3	behavior_09___4	behavior_09___5	behavior_09___6	behavior_09___7	behavior_09___8	behavior_09___9	behavior_09___10	behavior_09___11	behavior_09___12	behavior_09___13	behavior_09___14	behavior_09___15	behavior_09___16	behavior_09___17	behavior_09___18	behavior_09___19	behavior_09___20	behavior_09___21	behavior_09___22	behavior_09___23	bahavior_10	mental_01	mental_02	mental_03	mental_04	impact_01	impact_01_yes	impact_02	impact_03	impact_04	impact_05	inforamtion_01___1	inforamtion_01___2	inforamtion_01___3	inforamtion_01___4	inforamtion_01___5	inforamtion_01___6	inforamtion_01___7	inforamtion_01___8	inforamtion_01___9	inforamtion_01___10	inforamtion_01___11	inforamtion_01___12	inforamtion_01___13	inforamtion_01___14	inforamtion_01___15	inforamtion_01___16	inforamtion_01___17	inforamtion_01___18	form_1_4c55c8_complete
1	NA	2	28	3	1	2	1	3	1	1	1	4	NA	10	3	NA	1	2	1	2	2	4	3	NA	1	6	0	1	0	0	0	0	2	2	0	0	0	0	0	0	NA	4	3	2	1	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	0	0	0	0	0	0	NA	NA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	NA	2	28	3	1	2	1	2	1	1	1	4	NA	10	3	NA	1	2	1	2	2	4	3	NA	1	6	0	1	0	0	0	0	2	2	0	0	0	0	0	0	NA	2	1	1	1	3	3	1	1	NA	1	1	1	4	3	5	NA	1	0	0	0	0	0	1	0	0	0	3	NA	0	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	1	NA	2	NA	NA	3	NA	NA	1	NA	1	NA	NA	1	0	1	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	1	1	1	1	2	1	1	1	NA	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	NA	1	4	1	4	2	NA	1	1	1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0
5	NA	1	37	3	2	1	1	0	1	1	1	3	NA	3	1	0	1	2	2	2	1	2	2	NA	1	1	0	0	0	0	0	1	2	2	0	0	0	0	0	0	NA	NA	1	1	1	2	1	1	1	4	4	1	1	1	1	6	NA	1	0	0	0	0	0	0	0	0	0	3	NA	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	NA	1	NA	NA	3	NA	NA	1	NA	1	NA	NA	1	0	0	0	0	1	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	3	1	1	1	1	1	1	NA	1	0	1	0	1	1	1	0	1	1	0	1	1	0	1	0	1	0	1	0	0	0	0	1	1	6	1	1	2	NA	2	2	1	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0
6	NA	1	37	3	2	1	1	15	1	1	1	8	NA	0	1	0	1	1	3	3	2	4	2	NA	1	1	0	1	0	0	0	0	1	2	0	0	0	0	0	0	NA	2	1	1	1	1	1	2	2	2	2	1	1	1	3	4	NA	1	0	0	0	0	0	0	0	0	0	4	NA	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	0	1	2021-06-20	1	NA	2021-07-11	1	NA	NA	1	NA	1	NA	NA	1	1	1	0	0	0	0	0	0	1	1	0	1	1	1	0	0	0	0	0	1	3	1	1	1	1	1	3	NA	1	1	0	1	1	1	1	1	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	1	1	1	1	6	1	2	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
7	NA	2	43	2	3	1	1	25	2	2	1	5	NA	4	1	4	1	1	1	2	2	2	1	NA	1	5	0	1	0	0	0	0	2	1	0	0	1	0	0	0	3	2	1	1	1	1	1	1	1	1	1	1	1	3	1	4	NA	1	0	0	0	0	0	0	0	0	0	4	NA	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	NA	1	NA	NA	1	NA	NA	1	NA	1	NA	NA	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	1	1	1	1	4	1	2	NA	NA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	NA	1	1	1	1	1	2	2	4	2	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0

# summary by gender and age
age_sex = df_valid %>%
  count(age_kap, gender)
knitr::kable(age_sex[1:5, ], caption = "A Summary by Gender & Sex")

A Summary by Gender & Sex

age_kap	gender	n
18	1	6
18	2	5
19	1	7
19	2	4
19	3	1

Data processing

df_group <- df_valid %>% mutate(age_group = dplyr::case_when(
  age_kap >= 18 & age_kap <= 29 ~ "18-29",
  age_kap >= 30 & age_kap <= 39 ~ "30-39",
  age_kap >= 40 & age_kap <= 49 ~ "40-49",
  age_kap >= 50 & age_kap <= 59 ~ "50-59",
  age_kap > 60          ~ "> 60"
),
age_group = factor(
  age_group,
  level=c("18-29","30-39","40-49","50-59","> 60")
)
)

# Sum by each group
df_sum = summarize(group_by(df_group, age_group), total_age_group = n())
df_gender = summarize(group_by(df_group, gender), total_gender=n())
df_amphoe = summarize(group_by(df_group, amphoe), total_amphoe=n())
df_edu = summarize(group_by(df_group, education), total_edu=n())
df_income = summarize(group_by(df_group, income), total_incomde=n())
df_nation = summarize(group_by(df_group, from_nation), total_nation=n())
df_housemember = summarize(group_by(df_group, house_members), total_hmember=n())

Results

Socio-demographic information

• 751 individuals responded to the survey, and after excluding individuals with missing data, 555 individuals were including to final analysis (Table 1-6)

• Total respondents

[1] "Total Respondents = 555"

Gender

Table 1 A Summary by Gender

gender	total_gender	Percentage
Female	291	52.4%
Male	261	47.0%
Prefer not to answer	3	0.5%

Age Group

• Age statistic

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  18.00   27.00   34.00   34.46   42.00   57.00 

[1] "Age SD =  9.18"

Table 2 A Summary by Age Group

age_group	total_age_group	Percentage
18-29	193	34.77%
30-39	195	35.14%
40-49	129	23.24%
50-59	38	6.85%

Country

Table 3 A Summary by Country

from_nation	total_nation	percentage
Burmese	548	98.74%
Lao	3	0.54%
Cambodia	4	0.72%

Education

Table 4 A Summary by Education

education	total_edu	percentage
Not study	82	14.77%
Elementary school	149	26.85%
Middle school	212	38.20%
High school	89	16.04%
Bachelor Degree	17	3.06%
Master Degree	1	0.18%
Do not know	1	0.18%
Prefer not to answer	4	0.72%

Monthly household income (Bath)

Table 5 A Summary of Income

income	total_incomde	percentage
< 5,000	88	15.9%
5,000 - 10,000	313	56.4%
10,000 - 15,000	141	25.4%
> 15,000	13	2.3%

Family

Table 6 A Summary of Family

stay_with_family	total_stay	percentage
Yes	410	74%
No	131	24%
Prefer not to answer	14	3%

House member

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    2.00    3.00    3.55    4.00   12.00 

[1] "House member SD = "

[1] 1.853569

Table 7 A Summary by House Member

house_members	total_hmember	percentage
0	1	0.18%
1	38	6.85%
2	144	25.95%
3	131	23.60%
4	115	20.72%
5	57	10.27%
6	23	4.14%
7	20	3.60%
8	16	2.88%
9	2	0.36%
10	7	1.26%
12	1	0.18%

COVID-19 History

COVID-19 History Summary by Redcap

Knowledge

      gender      age_kap    education       income staying_time knowledge_01 
    "factor"    "numeric"     "factor"     "factor"    "numeric"     "factor" 
knowledge_02 knowledge_03 knowledge_04  attitude_01  attitude_02  attitude_03 
    "factor"     "factor"     "factor"     "factor"     "factor"     "factor" 
attitude_3_1  attitude_04  attitude_05  attitude_06  behavior_02  behavior_04 
    "factor"     "factor"     "factor"     "factor"     "factor"     "factor" 
 behavior_07  behavior_08    kap_score     kl_score 
    "factor"     "factor"    "numeric"    "numeric" 

knowledge	Yes	No	DoNotKnow
COVID-19 can spread from person-to-person when people are in close contact with each other.	84.8%	8.1%	7.05%
A person infected with COVID-19 can transmit the virus to others even if they do not have symptoms.	82.2%	12.1%	5.73%
Isolating people infected with COVID-19 is an effective way to reduce the spread of the virus.	91.4%	5.1%	3.52%
Older adults and those with chronic illness, such as heart or lung disease, are at increased risk of having serious COVID-19 symptoms.	85.9%	6.6%	7.49%

Altitude

Face Masks or Coverings

att_mask	Yes	No	DoNotKnow	PreferNotAnswer
It is important for everyone to wear a mask when in INDOOR public spaces (businesses, schools, places of worship etc.).	88.3%	4.9%	1.8%	5%
It is important for everyone to wear a mask when there are other people nearby in OUTDOOR public spaces (businesses, schools, places of worship etc.).	84.7%	7.6%	3.8%	4%

Physical Distancing

att_physical	Yes	No	DoNotKnow	PreferNotAnswer
It is important for everyone to practice physical distancing (i.e. limiting contact & keeping 2 meters between people) when they are INDOORS in public spaces even if wearing a mask.	85.2%	7.21%	4.50%	3.1%
It is important for everyone to practice physical distancing (i.e. limiting contact & keeping 2 meters between people) when they are OUTDOORS in public spaces even if wearing a mask.	84.1%	7.03%	4.68%	4.1%

Cleaning or Sanitizing

clean_sani	Yes	No	DoNotKnow	PreferNotAnswer
It is important to clean and sanitize doorknobs/handles, countertops and other surfaces to prevent the spread of COVID-19.	86%	6%	3%	4%

Hand Hygiene

clean_hand	Yes	No	DoNotKnow	PreferNotAnswer
It is important to wash your hands with soap and water for 20 seconds after you have been in a public place.	90%	2%	3%	5%

Health conditions

health_cond	Yes	No	DoNotKnow	PreferNotAnswer
Older adults (65+) and people who are overweight or with diabetes, high blood pressure, or cancer are at-risk for becoming so sick from COVID-19 that they need to go to a health facility.	85%	4%	6%	5%

Testing

cov_testing	Likely	Neutral	Unlikely	Dontknow_Unsure	PreferNottoAnswer
How likely are you to get tested for COVID-19 if you had symptoms?	76%	6%	6.7%	9.0%	2.5%
Would you be more likely, about as likely, or less likely to get tested for COVID-19 if you could easily get a Ministry of Health approved pill to reduce the chance that you would get really sick?	61%	18%	9.4%	10.5%	0.9%

cov_testing	HomebyMyself	HomebyComHealthWorker	Clinic	Hospital	Other	Dontknow_Unsure	PreferNottoAnswer
Some new pills for COVID-19 work best if people who are at-risk for getting really sick are tested and get treatment soon after symptoms start. Where would you prefer to get tested?	14%	9%	10%	62%	2%	2%	1%

Treatment

Treatment Summary by Redcap

Vaccines

Vaccines Summary

vaccination	Yes	No
Are you vaccinated for COVID-19?	95%	5%
Do you have a concern about any kind of vaccine or preference?	14%	86%

Determinants of attitudes toward COVID-19

Practice

Behavior summmary by Redcap

beh_1	Yes	No	DoNotKnow	PreferNotAnwser
In the last month, have you ever worn a face mask to reduce the spread of COVID-19?	92.1%	3.2%	0.90%	0.18%
In the past month, do you try to maintain physical distance between yourself and persons who do not live in your household to prevent the spread of COVID-19?	86.1%	7.9%	1.98%	0.00%
Since March 2020, have you practiced self-isolation (staying away from others, including household members) because you were diagnosed with or thought that you had COVID-19?	56.4%	36.8%	2.88%	0.00%
Since March 2020, have you practiced quarantine (separating and restricting your movements) because you had been exposed to COVID-19?	43.8%	47.7%	4.68%	0.00%

beh_2	Always	Often	Sometimes	Seldom	Never	NotApplicable	DontKnow	PreferNotAnwser
In the past month, how often have you worn a face mask when in public spaces?	82.0%	3.6%	6.85%	0.36%	1.6%	0.36%	0.90%	4.32%
In the past month, how often did you practice physical distancing when in indoor public spaces?	74.2%	6.7%	7.39%	1.08%	4.1%	0.00%	1.26%	5.23%

Determinants of preventive behaviors toward COVID-19

Mental health

In the past 2 weeks,

mental	NotAtAll	Several	MorethanHalfDay	NearlyEverday	DontKnow	PreferNotAnwser
how often have you had little interest or pleasure in doing things?	67.39%	5.95%	6.31%	1.98%	9.55%	8.83%
how often have you been bothered with feeling down, depressed or hopeless?	59.82%	9.55%	5.59%	2.70%	10.81%	11.53%
how often have you felt nervous, anxious or on edge?	68.47%	4.32%	6.85%	2.52%	9.55%	8.29%
how often have you not been able to stop or control worrying?	59.46%	6.13%	7.21%	7.03%	9.01%	11.17%

Impact of COVID-19

Since March 2020,

impact_1	Yes	No	Unsure	PreferNotAnswer
Have you or anyone in your household experienced a loss of income?	61%	30%	2%	7%

impact_2_3	NotAtAll	Little	Somewhat	Greatly	PreferNotAnswer
In general, how worried are you about not having enough food?	45.4%	32.6%	6.31%	6.85%	8.83%
Since March 2020, how often have you run out of food, and you didn’t have a way to get more?	65.6%	15.7%	7.75%	4.14%	6.85%
Since March 2020, do you ever cut the size of your meals or skip meals because there wasn’t enough money for food for your household?	64.0%	18.6%	5.59%	3.42%	8.47%

COVID-19 Sources of Information

COVID-19 Sources of Information