Data sources
This study is a secondary analysis of data drawn from a set of surveys of adolescent children conducted by World Vision (WV) as part of a multi-country “Rapid Recovery Assessment”. The assessment was carried out in 402 of the communities in which WV works across 13 Asia Pacific countries: Bangladesh, Cambodia, India, Indonesia, Laos, Mongolia, Myanmar, Nepal, Philippines, Sri Lanka, Thailand, Timor-Leste and Vietnam in May 2020. The assessment focused on the socio-economic impact of COVID-19 on vulnerable households and their children in order to guide WV’s continued programming to best support recovery from the effects of the pandemic.
The study uses data sets from the adolescent surveys in six of the 13 countries, which contains data of adolescents aged 10–18, who largely attended schools, from both urban and rural area, and excluding any respondents from 0–9 years of age. These surveyed a total of 12,232 adolescents (n = 5,552 males; n = 6,680 females): 1,599 from Bangladesh, 5,595 from India, 812 from Indonesia, 386 from Myanmar, 421 from the Philippines, and 3,419 from Vietnam (Table 1). The other countries were excluded for various reasons. The Nepal survey collected significantly different information than other countries. The sample sizes in Cambodia (n = 238), Laos (n = 72), and Mongolia (n = 47) were too small. The Thailand survey was only administered to rural households. However, the present study considered the location of residence as a major confounding variable. The surveys in Sri-Lanka and Timor-Leste did not include adolescents.
Table 1
Demographic characteristics in Asia Pacific countries during early COVID-19
| Bangladesh (n = 1599) | India (n = 5595) | Indonesia (n = 812) | Myanmar (n = 386) | Philippines (n = 421) | Vietnam (n = 3419) |
| n (%) | n (%) | n (%) | n (%) | n (%) | n (%) |
Type of Community | | | | | | |
Rural | 1247 (77.99) | 3871 (69.19) | 733 (90.27) | 80 (20.73) | 311 (73.87) | 3013 (88.13) |
Urban | 352 (22.01) | 1724 (30.81) | 79 (9.73) | 306 (79.27) | 110 (26.13) | 406 (11.87) |
Gender | | | | | | |
Male | 701 (43.84) | 2565 (45.84) | 292 (35.96) | 164 (42.49) | 179 (42.52) | 1651 (48.29) |
Female | 898 (56.16) | 3030 (54.16) | 520 (64.04) | 222 (57.51) | 242 (57.48) | 1768 (51.71) |
Age | | | | | | |
Younger Age (10–14 y) | 485 (30.33) | 2993 (53.49) | 476 (58.62) | 230 (59.59) | 216 (51.31) | 1986 (58.09) |
Older Age (15–18 y) | 1114 (69.67) | 2602 (46.51) | 336 (41.38) | 156 (40.41) | 205 (48.69) | 1433 (41.91) |
Age, Mean (SD) | 15.33 (1.69) | 14.31 (2.15) | 13.70 (2.18) | 14.09 (1.76) | 14.43 (1.73) | 14.27 (1.50) |
Rural category included “Rural”, “Other” and “Tribal” communities. Urban category included “Urban”, “Semi-Urban” and “Slum” communities. |
COVID-19 preventive measures in Asia Pacific region
In the Asia Pacific region, lockdown measures varied considerably between countries. In the first half of 2020, Bangladesh, the Philippines, and Vietnam had strict measures throughout the country; while in Indonesia and Myanmar, the measures were less strict and varied by locations [13, 15, 28]. In Myanmar, curfews were adopted by several regions, and a supplementary stay-at-home order was imposed by seven townships[15]. The Philippines experienced a long period of stay-at-home orders from mid-March to May and then again in August [5]. According to United Nations Educational, Scientific and Cultural Organization (UNESCO) [58], schools were closed country-wide until September 2020 in Bangladesh, India, Indonesia, Myanmar, and the Philippines. As a result, school-aged adolescents in the Asia-Pacific region faced substantial challenges and difficulties in daily life and to pursing education, though these differed between countries. One of the biggest issues, was availability and accessibility of temporary or emergency remote learning in lieu of physical school attendance. Governments in the Asia Pacific region responded with different strategies to mitigate the disadvantages brought by school closures. In Indonesia, India and Bangladesh, public broadcasters, using radio and television, were utilized to broadcast educational context to compensate the K-12 education [36, 41]. In Vietnam, adolescents returned to school from the beginning of May 2020, after more than three months’ social distancing measures [21]. In Myanmar, school started to reopen from July, with free face masks and shields provided to teachers and students [55]; however, with the second wave of COVID-19 cases, students were sent home again at the end of August 2020 [56]. UNESCO [60] estimates that on average 22 weeks’ school attendance has been lost so far across Eastern and South-Eastern Asia countries.
Sampling methods
In all countries, the sampling frame was the population of children living within the boundaries of the Area Programmes (APs) of the WV office, which is the basic organizational unit for WV’s programming. It is a geographic area that consists of a community or set of communities within which WV collaborates for its long-term partnering to support sustainable development. All adolescent data came from a cross-sectional survey of households, within which a sub-set of adolescents were surveyed.
In Bangladesh, 50 households with children under 18 years old were selected randomly from each of 53 APs across 52 Upazilas of 24 districts from four regions. About 30 school going children (12–18 years of age) per AP were selected randomly for child survey. Thus total 1590 children were planned in this survey. If more than one child in a household, only the one enrolled in the Child Sponsorship Programming by World Vision would be interviewed.
In India, about 50 households from each of 111 APs and 30 household per seven special project areas were selected using non-probability convenience sampling. For the child survey, only households with children aged 10–18 years were included. Only one child per household was interviewed. If a household had more than one child, the interviewer interviewed a random child based on availability and comfort of that child.
In Indonesia, 247 out of 594 APs were selected, and a total of 20 adolescents were targeted in each AP in a way to get 10 respondents for households with 6–11 years of age and 10 respondents for households with 12–18 years of age. Only one child per household was interviewed. If a household has more than one child, interviewer asked for permission and willingness to the caregiver and the child about whom to be interviewed.
In Myanmar, a total of 31 APs from 46 districts in 13 of 14 States and Regions were randomly selected. Ten households were purposively sampled in each AP, with households hosting most vulnerable children (12–17 years old), children under five years old, pregnant and lactating women, children living with disabilities and Vision Fund Myanmar clients. If more than one child in households, the interview was made for older child; and if there were boys and girls in households, a boy was selected for the interview, according to the previous survey result that girls are mostly found than boys in a household.
In Vietnam, about 95 households (or 114 households based on AP who have 5 to 6 supervision areas) were randomly selected from 35 APs. If the interviewed household does not have any child aged 12–18 years or the child is absent from the survey location during the assessment, the enumerator selected a child from the nearest household in the COVID-19 Response participant list. If one household have more than one child aged 12–18 years, the interviewers interviewed both two children as they may have different opinions even they live in the same household.
In the Philippines, a total of 28 APs in Luzon, Visayas and Mindanao covering 229 barangays (the smallest administrative division in the Philippines) were selected. Only one child per household was interviewed. If more than one child in a household, the interview was made for a random child aged 12–17 years old based on availability and comfort.
Data collection
The surveys used structured questionnaires that collected information from adolescents about their demographic characteristics (gender, age, residence); study and leisure activities; psychosocial status (feelings of happiness, unhappiness, isolation, stress, and concerns; and reasons for these); parental discipline; sources of COVID-19 information; perceptions of COVID-19; and their plans for after the lockdown.
Questionnaires were created in standard electronic document format (Microsoft Word or Excel) and translated into local languages. There was a master questionnaire, but additionally contextualized questions were included in each country. These were then transformed into electronic data-collection format using Kobo Toolbox. Data were then collected by interview and entered directly into Android tablets mostly through telephone conversation, or in-person when feasible, with appropriate COVID-19 protocols regarding social distancing and using personal protective equipment.
In each country, World Vision AP staff or non-AP staff were engaged in conducting phone surveys. The data collectors underwent training prior to collecting the information to become acquainted with the survey tools, using smartphones for data collection, and to understand the key objectives and aims of the assessment. Following the training, the questionnaire was piloted in the project areas.
Available variables
Independent variables. The independent variable is adolescent gender (male vs. female).
Dependent variables. Questions assessing the impact of the lockdown on the adolescent mental health included whether the adolescent was feeling isolated or stressed and understanding any specific concerns the adolescent might have due to the stay-at-home orders. This included 1) study and playing activity during restriction; 2) negative psychosocial status; and 3) information sources of COVID-19.
The study and playing activity during restriction included 1) studying (i.e., all types of study activities), 2) remote education by school, 3) online courses, 4) playing-physically, 5) sleeping during daytime, 6) watching TV, and 7) playing games on TV, phones, and tablets. Since most schools were closed across these six countries when the data were collected in May 2020, variables describing the main daily activities of adolescents were collected. Adolescents were asked about how many hours they spent on each activity throughout the day.
Assessment of psychosocial status for being required to stay at home during COVID-19 included the following areas: 1) feeling isolated/stressed, 2) worrying about getting sick, 3) concerns about not going to school, 4) concerns about missing friends, 5) concerns about household income or food security, and 6) feeling unsafe or insecure.
The types of information source about COVID-19 included 1) internet/social media, 2) mobile phone (phone call/text), 3) friends, 4) family, and 5) TV.
Confounding variables. The confounding variables included in the regression models are type of residence (rural or urban), and young age (10–14 y) versus older age (15–18 y).
Statistical Analysis
Exploratory data analysis was conducted to present proportions for categorical variables and mean (standard deviation) for continuous variables. Odds ratios (OR) and 95% confidence intervals (CI) were calculated through univariate and multivariable logistic regression of 1) The studying and leisure activity during restriction; 2) psychosocial status; and 3) information source on COVID-19 by adolescent gender. The logistic regression adjusted for type of residence (rural vs. urban) and adolescent age, and clustering at project area unit in each country. Stata 16.0 (Stata Corporation, College Station, TX, USA) was used for data management and statistical analysis.
Ethical clearance
Interviews with adolescents were conducted only after obtaining informed consent from their parents or legal guardians, and then the individuals themselves. All subjects completed the surveys voluntarily and the information collected via the survey was anonymized to maintain confidentiality. This secondary data analysis was deemed to have exemption of ethical review from Johns Hopkins School of Public Health.