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. 2023 Feb 16;139:106059. doi: 10.1016/j.chiabu.2023.106059

Socio-economic and psychosocial determinants of violent discipline among parents in Asia Pacific countries during COVID-19: Focus on disadvantaged populations

Yunhee Kang a,⁎,1, Darien Colson-Fearon b,c,1, Myungsun Kim c, Soim Park a, Matthew Stephens d, Yunseop Kim e, Erica Wetzler d
PMCID: PMC9933874  PMID: 36805614

Abstract

Background

Mobility restrictions and economic downfall as a result of the COVID-19 pandemic may increase the risk of child maltreatment, including increased risk for violent discipline use by parents.

Objective

We examined the socio-economic and psychosocial determinants of violent discipline among parents against children in Asia Pacific countries.

Participants & settings

This secondary data analysis included 7765 parents with children 6–18 years old in eight Asia Pacific countries.

Methods

24 potential determinants were identified, including household demographic factors, parents' psychosocial status, and livelihood changes. The dependent variable was parental use of violent discipline (physical, severe physical, psycho-social aggression, and any violent discipline). Univariate and multivariable logistic regression analysis was conducted.

Results

A total of 41 % of households reported violent discipline. Parental demographic characteristics that were positively related to use of violent discipline were living in rural areas, not being a household head, female sex, age younger than 35 years, and large family size. Poor parental mental health status, loss of job or reduced income due to COVID-19, lack of food at household level, parent engagement in petty trade, and owning a business also predicted violent discipline. Mandatory curfew and receiving pandemic-related education materials were also positive predictors.

Conclusion

Some socio-demographic factors, economic hardship due to COVID-19, and poor mental health status of parents are associated with the use of violent discipline against children in the Asia Pacific region. These results highlight several potential target areas for child protection interventions by governmental and non-profit organizations, including economic, social, and mental health interventions.

Keywords: Violent discipline, COVID-19, Children, Asia Pacific, Economic hardship, Mental health

1. Background

With over 515 million confirmed cases and over 6 million deaths as of May 2022, the ongoing COVID-19 pandemic continues to be responsible for extraordinary social, economic, and emotional strain across the globe Johns Hopkins University, 2022). Partly manifesting as decreasing job, food, and health security, the impact of this pandemic has been felt universally, yet disproportionately by the world's most vulnerable populations. The Asia Pacific region is one area in which the negative consequences of the pandemic have been particularly pronounced (Fitzgerald & Wong, 2020). At the conclusion of 2020, there were 118 million more undernourished people globally than at the onset of the COVID-19 pandemic. This added to an estimated 768 million undernourished individuals globally, out of which 418 million were in Asia (FAO, IFAD, UNICEF, WFP, & WHO, 2021). These events, in conjunction with the numerous public health interventions aimed at controlling the spread of the virus (e.g., lockdowns), have resulted in increased psychosocial and physical strain on families across the Asia Pacific region. However, these incremental increases in adverse mental health outcomes (e.g., depressive symptoms) have been higher in households experiencing food insecurity and loss of income (Pitchik et al., 2021). More specifically, the COVID-19 pandemic has had a pronounced negative impact on the mental health of parents who, in addition to having to navigate their own health, safety, and financial well-being, also have to be a source of security to other more vulnerable individuals who are dependent on them. One study conducted in China during the pandemic reported that 33.4 % of parents had depressive symptoms and 24.6 % of parents expressed anxiety symptoms (Wu et al., 2020). Additionally, several factors associated with worsening mental health status have been identified, including low socioeconomic status, having younger children, and having quarantined (Black, Slep, & Heyman, 2001; Kumar & Kumar, 2020; Wu et al., 2020).

As defined by UNICEF, violent discipline includes any physical punishment and/or psychological aggression (UNICEF, 2018; Bhatia et al., 2020). Using this definition, violent discipline has been associated with numerous material factors, such as low socioeconomic status (Pelton, 2015). This has been evidenced by the high prevalence of reported physical aggression as a means of discipline observed in low- and middle- income countries (LMICs) even before the COVID-19 pandemic. One study conducted by UNICEF in over 35 LMICS found that three out of four children between the ages of 0 and 14 years experienced some form of violent discipline by their caregiver(s) (Cuartas et al., 2019). Therefore, in segments of the population where COVID-19 acutely threatens a household's financial security and contributes to the development of worsening mental health status, threats to child protection and safety may rise.

The threat of increased use of violent discipline, in the setting of COVID-19, is of particular concern given its association with several adverse outcomes, some of which take years to become apparent. In addition to increasing the risk of developing long term mental illness, childhood experiences of physical and/or psychological violence have also been related to increased risk for developing several leading chronic diseases (Hillis, Mercy, & Saul, 2017). Additionally, the experience of acute stress that has been felt as a result of the COVID-19 pandemic is increasingly being recognized as an “adverse childhood experience” (ACE) (Claypool & Moore de Peralta, 2021; Sanders, 2020). The ACEs, which have previously been divided into categories of abuse, neglect, and household dysfunction, have been linked to lifelong poorer health with exposure to one doubling the likelihood of poor health, and exposure to four or more increasing that likelihood by almost three times (Boullier & Blair, 2018).

Given this unique relationship between the use of violent discipline and several ongoing public health crises, and with the recognition of COVID-19 as an adverse childhood experience, targeted interventions are becoming increasingly necessary as the conditions of the ongoing pandemic (e.g., more time spent at home (Fig. 1 )) provide increased opportunity for the use of these punishment measures, and therefore accumulation of additional ACEs. However, in order to design these interventions, a more comprehensive understanding of the determinants of these practices, on both an individual and cultural level, is needed. This study aims to contribute to the growing body of knowledge on the social, economic, and emotional impact of the COVID-19 pandemic on parents and children residing in the Asia Pacific region and identify key predictors of violent parental discipline as a means to inform targeted interventions in this social context.

Fig. 1.

Fig. 1

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Conceptual framework.

2. Methods

2.1. Data sources

This study used secondary data collected from World Vision's (WV) Rapid Recovery Assessment Round 2 (April–July 2021) which was implemented as a needs assessment survey to prepare COVID-19 responses among WV supported areas. The assessment was implemented among households with children 0–18 years old in nine Asia-Pacific countries: Cambodia (n = 621), India (n = 797), Indonesia (n = 951), Sri Lanka (n = 684), Laos (n = 717), Myanmar (n = 678), Nepal (n = 602), Thailand (n = 702) and Vietnam (n = 3578). However, the present study excluded data from Thailand as parental use of violent discipline was not assessed in this country. The survey population included those residing in World Vision supported areas. Consequently, they were primarily socio-economically disadvantaged. The survey was largely conducted via mobile phone. All country offices used the Open Data Kit (ODK) to streamline data collection and improve data validation to minimize data entry errors. Data were pooled from 8628 households in eight countries with children of all ages; the present analysis was limited to 7765 households with children 6–18 years old.

2.2. Sampling methodology

The World Vision Rapid Recovery Assessment was conducted in World Vision Area Programs (APs), which are communities receiving support from World Vision. Within those APs, random sampling and convenience sampling were used to select households to complete the survey. In Cambodia, a total of 16 APs were selected, and in each AP, on average 39 households were randomly selected. In India, 79 APs were selected, and within each AP, 10 or 11 households were randomly selected for the assessment. In Indonesia, a total of 30 APs were chosen, and within each AP, about 32 households were conveniently selected, resulting in a sample size of 951. In Sri Lanka, 28 AP areas were included in the survey, and approximately 24 households per AP were selected. In Laos, a total of 10 APs were selected and for each AP, approximately 30 households were randomly selected. In Myanmar, all 36 APs in the country were included, and in each AP, largely 10 households were purposively sampled from households with young children, pregnant and lactating women, and/or children living with disabilities. Additionally, five households were selected from each of the 69 Vision Fund Myanmar (VFM) client groups. In Nepal, the study was conducted in 23 APs. On average, 26 households were selected from each AP. In Vietnam, about 95 households were randomly selected from 35 APs, and 114 households were selected from a 36th AP. The household and child survey questionnaires were developed by World Vision regional and country offices in the Asia Pacific region. The survey was conducted using mobile phones or person-to-person interviews that abided by social distancing policies.

2.3. Dependent variables: parental violent discipline

Parental violent discipline was assessed using the Child Discipline Module, which was administered in Multiple Indicator Cluster Surveys (MICS) in >30 countries (UNICEF, 2010). The Child Discipline Module was a shortened version of the Conflict Tactics Scale Parent-Child (CTSPC), which is a validated screening tool in US and other countries (Straus et al., 1998).

Survey respondents were asked if they had engaged in any of the following violent discipline behaviors within the past month: (1) shouted, yelled at child; (2) called child dumb, lazy, or another name (3) shook child; (4) hit or slapped child on the bottom with a bare hand; (5) hit child on the bottom with a hard object; (6) hit child on the hand, arm or leg; (7) hit them on the face or head; and (8) beat the child hard and repeatedly. According to UNICEF's definition of violent discipline, four relevant variables were generated (UNICEF, 2018): (1) Physical punishment: Shaking, hitting a child on the hand/arm/leg, hitting on the bottom or elsewhere on the body with a hard object, hitting on the bottom with a bare hand, hitting the face, head or ears, and hitting or beating hard and repeatedly; (2) Severe physical punishment: Hitting or slapping a child on the face, head or ears, and hitting or beating a child hard and repeatedly; (3) Psychological aggression: shouting, yelling, as well as calling a child offensive names; and (4) Violent discipline: any physical punishment and/or psychological aggression (Supplemental Table 1).

2.4. Predictor variables

Based on literature review and available WV's survey questions, the research team selected 24 variables as potential determinants. Demographic characteristics (n = 5) included residence (e.g., rural, urban), respondent's gender, age, whether a respondent was the head of household, and family size. COVID-19 affected livelihood characteristics (n = 8) included source of income such as salaried work, government aid, agriculture, casual labour, and petty trade/owning a business. This category also included experiences of income change (e.g., no change, resorted to secondary, reduced or loss of job), and availability of food stock (e.g., none, 1–4 weeks, >4 weeks). Mental health characteristics of parents (n = 4) included feelings of loneliness, depression, and/or hopelessness during the pandemic with three response options (e.g., same as before, little worse than before, and far worse than before) and overall mental health status (e.g., very good, good, fair, poor). Lockdown characteristics (n = 2) included current lockdown status (e.g., normal, lockdown, and curfew) and current school closure status. Finally, five types of available COVID-19 relief benefits, including psycho-social support (PSS), education materials, cash or voucher support, food security assistance, and livelihood physical assets were included in this analysis.

2.5. Statistical analysis

All independent and dependent variables were presented as percentages for categorical variables and as means and standard deviations (SD) for continuous variables. Some variables had missing observations. These included: respondent's gender n = 5; family size n = 44; feeling lonely n = 76; feeling hopeless n = 78. The proportion of missing data was 1 % or lower, and thus did not influence the study results. The prevalence of violent discipline was presented separately for each country and as a single statistic for the pooled eight-country population. Logistic regression analyses were performed to calculate odds ratios (ORs) and 95 % confidence intervals (CIs) for potential predictors of violent discipline. Initially, univariate logistic regression was conducted to identify variables associated with each outcome. Next, variables selected in the univariate regression, identified by p-value < 0.2, were included in multivariable regression analysis. Variables with adjusted OR having p-value < 0.05 in the multivariable model were considered to be significant predictors of the outcome. Country location was included as a dummy variable in all regressions. The range of the variance inflation factor for each logistic regression model was checked to ensure that it was acceptable. All statistical analyses were conducted using Stata version 17 (StataCorp LP, College Station, TX, USA).

2.6. Ethical approval

This study was deemed to be exempt from the need for institutional review board approval as it involved secondary data analysis.

3. Results

More than half of survey respondents identified as female (56.6 %) (Table 1 ). The 35- to 44-year-old age group contained the largest proportion of respondents (46.8 %). Additionally, more than half of respondents were the head of their household (57.4 %), and 81.1 % of study participants had a family size of less than or equal to six individuals. The vast majority of participants were rural residents (84.2 %). Fifty-two percent of respondents had experienced a loss of employment and/or reduced income during the pandemic (52.0 %). An additional 9.4 % of respondents had to resort to a secondary source of income. The majority of participants reported ongoing school closures due to the pandemic (69.1 %).

Table 1.

General characteristics among respondents with child age: 6–18 years old (n = 7765).1

Characteristics n %
Demographic characteristics
 Region
 Rural 6541 84.2
 Urban 1224 15.8
 Respondent age
 <25 y 170 2.2
 25–34 y 1690 21.8
 35–44 y 3631 46.8
 ≥45 y 2274 29.3
 Respondent is the household head
 Yes 4456 57.4
 No 3309 42.6
 Respondent's gender
 Female 4389 56.6
 Male 3371 43.4
Reported parent mental health characteristics
 Parent's mental health status
 Good 3659 47.1
 Poor 447 5.8
 Fair 2279 29.4
 Very good 1380 17.8
 Feeling loneliness
 Far worse than before 429 5.6
 Little worse than before 3231 42.0
 Same as before 4029 52.4
 Feeling hopeless
 Far worse than before 526 6.8
 Little worse than before 3630 47.2
 Same as before 3531 46.0
 Feeling depressed
 Far worse than before 566 7.4
 Little worse than before 3518 45.6
 Same as before 3618 47.0
Reported livelihood characteristics
 Income change during COVID-19
 Loss of job or reduced income 4036 52.0
 No change 2999 38.6
 Resorted to secondary alternate source 730 9.4
 Regular salary 1384 17.8
 Government aid/other support 416 5.3
 Agriculture/fishing 4613 59.4
 Daily labour/migration worker 3315 42.7
 Trade/selling small items 1184 15.3
 None 391 5.0
 Food stock
 None 1279 16.5
 For 1 week 2033 26.2
 For 2–4 week 2413 31.1
 For >1 month 2040 26.3
Lockdown characteristics
 Status
 Curfew 413 5.3
 Lockdown 858 11.1
 Normal 6494 83.6
 School close now
 Yes 4400 69.1
 No 1968 30.9
 Type of benefits
 Psycho-social support (PSS) 399 5.7
 Education materials 1016 14.4
 Cash or voucher support 709 10.1
 Food security assistance 1247 17.7
 Livelihood physical assets 530 7.5
 Country
 Cambodia 596 7.7
 India 690 8.9
 Indonesia 871 11.2
 Laos 570 7.3
 Sri Lanka 650 8.4
 Vietnam 3254 41.9
 Myanmar 537 6.9
 Nepal 597 7.7
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1

Missing numbers: respondent's gender n = 5; family size n = 44; feeling lonely n = 76; feeling hopeless n = 78.

Study participants reported varying states of mental and emotional well-being since the start of the COVID-19 pandemic. About 47.1 % of participants reported a good state of being, while 5.76 % characterized themselves as in poor mental/emotional health (Table 2 ). When describing the change in feelings of hopelessness, those that described themselves as feeling a little worse than before made up the largest proportion of respondents (47.2 %), followed by the proportion feeling the same as before (45.9 %). Regarding feelings of loneliness, 42.0 % reported feeling a little worse than before in comparison to 52.4 % of participants reporting feeling the same. Self-reported feelings of depression followed a similar pattern with 45.7 % reporting feeling a little worse than before and 47.0 % reporting feeling the same.

Table 2.

Parents' violent disciplines in households with children aged 6 to 11 years old in the past 7 days (n = 7753).

Characteristics n %
Violent discipline 3179 41.0
Physical punishment 1561 20.2
Severe physical punishment 234 3.0
Psychological aggression 2813 36.3
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3.1. Determinants of violent discipline

Overall, violent discipline of children, including physical and psychological aggression was reported by 41.0 % of parents. Specifically, physical punishment of children was reported by 20.2 % of respondents. Of the countries analyzed, reports of physical punishment were highest in Cambodia (48.4 %) and lowest in Sri Lanka (6.4 %). The reported use of severe physical punishment was low (3.0 %). Psychological aggression was reported by 36.3 % of study participants (Fig. 2 and Supplemental Table 2). Similar to physical punishment, psychological aggression was most common in Cambodia (78.3 %) and rarest in Sri Lanka (15.0 %).

Fig. 2.

Fig. 2

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Parents' violent discipline among Asia Pacific countries.

3.2. Any violent discipline

Multivariate regression depicted several factors associated with significantly lower odds of violent discipline.

Among demographic characteristics, living in an urban setting (OR = 0.63, 95%CI [0.54, 0.74) (Table 3 ) compared to a rural area, being the head of household (OR = 0.79, 95%CI [0.69, 0.90]), respondent age between 35 and 44 years old (OR = 0.76, 95%CI [0.67, 0.87]), and respondent age 45 years old or older (OR = 0.66, 95%CI [0.57, 0.77]) when compared to 25–34 years were associated with lower odds of violent discipline used against children.

Table 3.

Univariate and multivariable logistic regressions for parents' violent discipline.

Violent discipline (any physical and psychosocial)
Univariate
OR (95 % CI)		 p value Multivariate
OR (95 % CI)		 p value
Demographic
 Region (ref: rural) 1.00 1.00
 Urban 0.73 (0.53, 1.00) 0.05 0.63 (0.54, 0.74) <0.001
 Gender (ref: male) 1.00 1.00
 Female 1.47 (1.31, 1.65) <0.001 1.22 (1.07, 1.40) 0.004
 Family size (ref: <7) 1.00 1.00
 7 or more 1.39 (1.20, 1.61) <0.001 1.43 (1.25, 1.63) <0.001
 Respondent age (Ref: 25–34 y) 1.00 1.00
 <25 y 1.11 (0.77, 1.59) 0.59 1.09 (0.76, 1.55) 0.65
 35–44 y 0.72 (0.63, 0.83) <0.001 0.76 (0.67, 0.86) <0.001
 ≥45 y 0.62 (0.54, 0.72) <0.001 0.66 (0.57, 0.77) <0.001
 Household head (ref: no) 1.00 1.00
 Yes 0.66 (0.59, 0.74) <0.001 0.79 (0.69, 0.90) 0.001
 Lockdown status (ref: normal) 1.00 1.00
 Lockdown 1.16 (0.55, 2.45) 0.70 1.26 (0.94, 1.68) 0.12
 Curfew 2.02 (1.13, 3.58) 0.02 1.96 (1.48, 2.61) <0.001
 Salaried work with regular income (ref: no) 1.00 1.00
 Yes 0.78 (0.66, 0.91) 0.002 0.89 (0.77, 1.03) 0.11
 Govt aid or social security (ref: no) 1.00 1.00
 Yes 1.31 (0.94, 1.83) 0.12 1.19 (0.94, 1.49) 0.14
 Agriculture (livestock) (ref: no) 1.00
 Yes 1.03 (0.85, 1.24) 0.78
 Casual labor/migrant worker (ref: no) 1.00 1.00
 Yes 1.14 (0.99, 1.32) 0.08 1.06 (0.95, 1.18) 0.33
 Petty trade/own business (ref: no) 1.00 1.00
 Yes 1.15 (0.98, 1.35) 0.09 1.22 (1.05, 1.42) 0.01
 Parent's mental health (ref: fair) 1.00 1.00
 Poor 1.02 (0.76, 1.37) 0.88 0.98 (0.77, 1.25) 0.87
 Good 0.95 (0.80, 1.14) 0.61 1.07 (0.94, 1.21) 0.29
 Very good 0.61 (0.48, 0.77) <0.001 0.72 (0.61, 0.85) <0.001
 Loneliness (ref: same as before) 1.00 1.00
 Little worse than before 1.18 (1.02, 1.37) 0.03 0.82 (0.70, 0.95) 0.01
 Far worse than before 1.32 (1.03, 1.70) 0.03 0.80 (0.59, 1.08) 0.15
 Hopeless (ref: same as before) 1.00 1.00
 Little worse than before 1.41 (1.21, 1.65) <0.001 1.45 (1.24, 1.69) <0.001
 Far worse than before 1.72 (1.36, 2.17) <0.001 1.67 (1.26, 2.21) <0.001
 Depression (ref: same as before) 1.00
 Little worse than before 1.27 (1.10, 1.48) 0.002
 Far worse than before 1.48 (1.17, 1.87) 0.001
 Income source (ref: no change) 1.00 1.00
 Loss of job or reduced income 1.36 (1.16, 1.59) <0.001 1.20 (1.07, 1.35) 0.002
 Resorted to secondary 1.14 (0.83, 1.57) 0.41 1.04 (0.86, 1.25) 0.72
 Available food stock (ref: =4 wk) 1.00 1.00
 1 to <4 weeks 1.19 (0.99, 1.42) 0.06 1.18 (1.04, 1.33) 0.01
 No 1.71 (1.17, 2.50) 0.005 1.67 (1.41, 1.97) <0.001
 School closed (ref: no) 1.00 1.00
 Yes 1.13 (0.95, 1.33) 0.17 1.12 (0.99, 1.27) 0.07
 Benefit from PSS materials (ref: no) 1.00 1.00
 Yes 1.88 (1.50, 2.34) <0.001 1.89 (1.48, 2.42) <0.001
 Benefit from education materials (ref: no) 1.00 1.00
 Yes 1.40 (1.20, 1.63) <0.001 1.32 (1.04, 1.46) <0.001
 Benefit from cash and voucher materials (ref: no) 1.00
 Yes 1.13 (0.94, 1.35) 0.20
 Benefit from food security assistance (ref: no) 1.00 1.00
 Yes 1.11 (0.96, 1.27) 0.16 0.93 (0.80, 1.09) 0.37
 Benefit from livelihood physical assets (ref = no) 1.00
 Yes 1.11 (0.92, 1.34) 0.27
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Regarding mental health status, a self-reported “very good” mental health status (OR = 0.72, 95%CI [0.61, 0.85]) and having feelings of loneliness that were a little worse than before the start of the pandemic (OR = 0.82, 95%CI [0.70, 0.95]) were also associated with lower odds of violent discipline.

There were also several positive predictors of violent discipline identified. These included, being a female respondent when compared to males (OR = 1.22, 95%CI [1.07, 1.40]), having a family size of greater than seven (OR = 1.43, 95%CI [1.25, 1.63]), working in petty trade or in a family business (OR = 1.22, 95%CI [1.05, 1.42]), and experiencing feelings of hopelessness that were a little worse than before (OR = 1.45, 95%CI [1.24, 1.69]) or far worse than before (OR = 1.67, 95%CI [1.26, 2.21]) when compared to those who did not experience a change. A lack of food stock (OR = 1.67, 95%CI [1.41, 1.97]), having 1 to 4 weeks of food stock (OR = 1.18, 95%CI [1.04, 1.33]), job loss or reduced income (OR = 1.20, 95%CI [1.07, 1.35]), mandatory curfew (OR = 1.96, 95%CI [1.48, 2.61]), benefitting from pandemic-related education materials (OR = 1.32, 95%CI [1.04, 1.46]), and benefiting from PSS materials (OR = 1.89, 95%CI [1.48, 2.42]) when compared to those who did not receive these were also associated with increased odds of violent discipline.

Respondents who received government aid or social security support showed higher odds of physical punishment than those who did not (OR = 1.40, 95%CI [1.09, 1.80]) (Table 4 ). Working as a casual laborer or migrant worker was associated with severe physical punishment and physical punishment (OR = 1.23, 95%CI [1.09, 1.40] and (OR = 1.37, 95%CI [1.02, 1.84]) respectively). Benefits from cash and voucher support were negatively associated with severe physical discipline (OR = 0.47, 95%CI [0.28, 0.79]). The predictors of psychosocial aggression were the same as the previously discussed predictors of physical punishment, and no additional predictors were identified. Specific odds ratios for predictors of psychosocial aggression are provided in Table 4.

Table 4.

Univariate and multivariable regressions for parents' physical punishment and severe physical punishment.

Physical punishment
 Severe physical punishment
 Psychosocial aggression
Univariate
OR (95 % CI)		 p value Multivariate
OR (95 % CI)		 p value Univariate
OR (95 % CI)		 p value Multivariate
OR (95 % CI)		 p value Univariate
OR (95 % CI)		 p value Multivariate
OR (95 % CI)		 p value
Demographic
 Region (ref: rural) 1.00 1.00 1.00 1.00
 Urban 0.91 (0.70, 1.20) 0.51 0.97 (0.55, 1.71) 0.91 0.67 (0.49, 0.94) 0.02 0.58 (0.50, 0.68) <0.001
 Gender (ref: male) 1.00 1.00 1.00 1.00 1.00 1.00
 Female 1.61 (1.39, 1.85) <0.001 1.37 (1.16, 1.61) <0.001 1.36 (0.93, 2.00) 0.12 1.27 (0.93, 1.74) 0.13 1.39 (1.23, 1.57) <0.001 1.19 (1.03, 1.37) 0.02
 Family size (ref: <7) 1.00 1.00 1.00 1.00 1.00 1.00
 7 or more 1.45 (1.24, 1.69) <0.001 1.50 (1.29, 1.75) <0.001 1.70 (1.20, 2.41) 0.003 1.69 (1.22, 2.33) 0.002 1.30 (1.13, 1.50) <0.001 1.33 (1.16, 1.52) <0.001
 Respondent age (ref: 25–34 y) 1.00 1.00 1.00 1.00 1.00 1.00
 <25 y 0.98 (0.66, 1.46) 0.94 0.96 (0.64, 1.43) 0.84 1.74 (0.85, 3.54) 0.13 1.61 (0.80, 3.23) 0.18 0.98 (0.68, 1.42) 0.9 1.01 (0.70, 1.45) 0.97
 35–44 y 0.65 (0.56, 0.76) <0.001 0.68 (0.60, 0.79) <0.001 0.88 (0.60, 1.29) 0.50 0.93 (0.67, 1.30) 0.68 0.72 (0.63, 0.83) <0.001 0.76 (0.66, 0.86) <0.001
 ≥45 y 0.53 (0.45, 0.64) <0.001 0.55 (0.46, 0.65) <0.001 0.69 (0.45, 1.05) 0.09 0.73 (0.49, 1.10) 0.13 0.64 (0.55, 0.75) <0.001 0.69 (0.59, 0.79) <0.001
 Household head (ref: no) 1.00 1.00 1.00 1.00 1.00
 Yes 0.69 (0.60, 0.79) <0.001 0.90 (0.77, 1.05) 0.18 0.98 (0.69, 1.37) 0.89 0.70 (0.62, 0.79) <0.001 0.82 (0.72, 0.94) 0.004
 Lockdown status (ref: normal) 1.00 1.00 1.00 1.00 1.00 1.00
 Lockdown 1.19 (0.58, 2.45) 0.64 1.32 (0.93, 1.86) 0.12 0.68 (0.28, 1.67) 0.40 0.54 (0.23, 1.26) 0.15 1.13 (0.55, 2.33) 0.73 1.26 (0.93, 1.69) 0.13
 Curfew 1.82 (1.10, 3.02) 0.02 1.65 (1.19, 2.28) 0.003 1.44 (0.77, 2.68) 0.25 1.30 (0.67, 2.54) 0.44 2.03 (1.21, 3.41) 0.01 2.14 (1.61, 2.84) <0.001
 Salaried work with regular income (ref: no) 1.00 1.00 1.00 1.00 1.00 1.00
 Yes 0.87 (0.74, 1.02) 0.09 0.97 (0.83, 1.14) 0.74 0.73 (0.48, 1.11) 0.14 0.88 (0.59, 1.31) 0.52 0.77 (0.65, 0.91) 0.002 0.92 (0.80, 1.06) 0.26
 Govt aid or social security (ref: no) 1.00 1.00 1.00 1.00
 Yes 1.53 (1.12, 2.10) 0.01 1.40 (1.09, 1.80) 0.01 0.86 (0.43, 1.72) 0.67 1.15 (0.85, 1.56) 0.36
 Fishing for salary (ref: no) 1.00 1.00 1.00
 Yes 0.98 (0.83, 1.16) 0.84 0.85 (0.56, 1.28) 0.43 1.07 (0.88, 1.30) 0.52
 Casual labor/migrant worker (ref: no) 1.00 1.00 1.00 1.00 1.00 1.00
 Yes 1.32 (1.14, 1.54) <0.001 1.23 (1.09, 1.40) 0.001 1.43 (1.04, 1.97) 0.03 1.37 (1.02, 1.84) 0.04 1.12 (0.96, 1.30) 0.14 1.07 (0.95, 1.20) 0.27
 Petty trade/own business (ref: no) 1.00 1.00 1.00 1.00
 Yes 1.03 (0.86, 1.23) 0.77 1.22 (0.86, 1.74) 0.26 1.12 (0.95, 1.33) 0.18 1.25 (1.08, 1.46) 0.004
 Parent's mental health (ref: fair) 1.00 1.00 1.00 1.00 1.00
 Poor 0.95 (0.70, 1.31) 0.77 0.91 (0.71, 1.18) 0.49 1.58 (0.99, 2.53) 0.06 1.36 (0.85, 2.19) 0.20 1.04 (0.78, 1.39) 0.79 0.97 (0.76, 1.23) 0.79
 Good 0.79 (0.66, 0.93) 0.01 0.838(0.76, 1.02) 0.09 0.87 (0.60, 1.27) 0.48 1.02 (0.71, 1.47) 0.90 0.95 (0.79, 1.15) 0.63 1.04 (0.91, 1.18) 0.56
 Very good 0.59 (0.46, 0.76) <0.001 0.70 (0.57, 0.86) 0.001 0.95 (0.58, 1.54) 0.82 1.19 (0.76, 1.86) 0.46 0.57 (0.45, 0.74) <0.001 0.65 (0.55, 0.77) <0.001
 Loneliness, depression, (ref: same as before) 1.00 1.00 1.00 1.00 1.00 1.00
 Little worse than before 1.20 (1.02, 1.42) 0.03 0.78 (0.65, 0.93) 0.01 1.53 (1.06, 2.19) 0.02 1.07 (0.71, 1.61) 0.75 1.14 (0.98, 1.32) 0.09 0.80 (0.68, 0.94) 0.01
 Far worse than before 1.40 (1.09, 1.82) 0.01 0.78 (0.57, 1.09) 0.15 2.59 (1.59, 4.21) <0.001 1.65 (0.87, 3.11) 0.13 1.42 (1.11, 1.82) 0.01 0.89 (0.66, 1.20) 0.45
 Hopeless (ref: same as before) 1.00 1.00 1.00 1.00 1.00 1.00
 Little worse than before 1.51 (1.26, 1.80) <0.001 1.60 (1.34, 1.92) <0.001 1.72 (1.23, 2.40) 0.002 1.60 (1.05, 2.44) 0.03 1.37 (1.16, 1.60) <0.001 1.40 (1.19, 1.64) <0.001
 Far worse than before 1.85 (1.45, 2.36) <0.001 1.83 (1.35, 2.49) <0.001 2.42 (1.48, 3.99) <0.001 1.67 (0.86, 3.23) 0.13 1.76 (1.39, 2.23) <0.001 1.58 (1.19, 2.09) 0.002
 Depression (ref: same as before) 1.00 1.00 1.00
 Little worse than before 1.25 (1.06, 1.48) 0.01 1.53 (1.09, 2.16) 0.01 1.29 (1.11, 1.50) 0.001
 Far worse than before 1.51 (1.18, 1.93) 0.001 1.61 (0.94, 2.75) 0.08 1.63 (1.30, 2.06) <0.001
 Income source (ref: no change) 1.00 1.00 1.00 1.00 1.00 1.00
 Loss of job or reduced 1.31 (1.11, 1.54) 0.001 1.10 (0.96, 1.26) 0.17 1.07 (0.74, 1.55) 0.72 0.91 (0.66, 1.26) 0.59 1.40 (1.18, 1.66) <0.001 1.25 (1.11, 1.41) <0.001
 Resorted to secondary 1.21 (0.85, 1.74) 0.29 1.10 (0.88, 1.38) 0.41 1.10 (0.63, 1.94) 0.74 1.02 (0.64, 1.63) 0.94 1.04 (0.78, 1.40) 0.773 0.96 (0.79, 1.17) 0.69
 Foodstock availability (ref: ≥ 4 weeks) 1.00 1.00 1.00 1.00 1.00 1.00
 1–<4 weeks 1.23 (1.02, 1.48) 0.03 1.21 (1.04, 1.40) 0.02 0.92 (0.58, 1.46) 0.73 0.93 (0.66, 1.33) 0.70 1.17 (0.99, 1.40) 0.07 1.14 (1.01, 1.29) 0.04
 No 1.67 (1.27, 2.20) <0.001 1.55 (1.29, 1.88) <0.001 1.59 (0.97, 2.59) 0.06 1.43 (0.95, 2.15) 0.08 1.60 (1.07, 2.38) 0.02 1.46 (1.23, 1.73) <0.001
 Benefit from PSS materials (ref: no) 1.00 1.00 1.00 1.00 1.00
 Yes 1.57 (1.22, 2.01) <0.001 1.31 (0.99, 1.74) 0.06 0.79 (0.40, 1.59) 0.50 1.82 (1.46, 2.27) <0.001 1.84 (1.44, 2.36) <0.001
 Benefit from education materials (ref: no) 1.00 1.00 1.00 1.00 1.00 1.00
 Yes 1.56 (1.31, 1.85) <0.001 1.41 (1.16, 1.72) <0.001 1.29 (0.90, 1.83) 0.16 1.39 (0.96, 2.01) 0.08 1.27 (1.09, 1.48) 0.003 1.15 (0.97, 1.38) 0.11
 Benefit from cash and voucher materials (ref: no) 1.00 1.00 1.00 1.00
 Yes 1.10 (0.89, 1.36) 0.37 0.48 (0.29, 0.80) 0.01 0.47 (0.28, 0.79) 0.003 1.13 (0.94, 1.36) 0.20
 Benefit from food security assistance (ref: no) 1.00 1.00 1.00 1.00 1.00
 Yes 1.35 (1.14, 1.59) <0.001 1.09 (0.91, 1.30) 0.37 1.11 (0.77, 1.60) 0.56 1.13 (0.98, 1.30) 0.10 0.98 (0.84, 1.14) 0.77
 Benefit from livelihood physical assets (ref: no) 1.00 1.00 1.00
 Yes 1.21 (0.96, 1.51) 0.10 1.04 (0.82, 1.32) 0.73 1.00 (0.58, 1.72) 0.99 1.07 (0.88, 1.30) 0.50
 School closed (ref: no) 1.00 1.00 1.00
 Yes 1.02 (0.85, 1.23) 0.80 1.27 (0.84, 1.91) 0.255 1.02 (0.80, 1.30) 0.86
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4. Discussion

A caregiver's use of violent discipline was more likely among those who were female, younger than 35 years old, living in rural areas, and having a large family size. Additionally, caretaker reports of worsening mental health status demonstrated a close relationship with the use of violent discipline. Some of the specified mental health issues, including feelings of hopelessness, were positively associated with violent discipline. However, feelings of loneliness and a self-reported “very good” mental health status were negative predictors of caregiver's use of violent discipline. Lack of food, parents' engagement in petty trade, owning a small business, and living under curfew status also predicted violent discipline.

4.1. Demographic factors

As mothers, particularly in the of Asian cultures where gender roles in regards to childcare tend to be clearly differentiated, often spend more time with their children than fathers, they are more likely to have to be the primary disciplinarian and, thereby, more likely to use corporal punishment (Mehlhausen-Hassoen, 2021; Cui, Xue, Connolly, & Liu, 2016). Also, violent discipline is more likely to occur when parents are younger. Among the plausible explanations for this finding is that parents who are older have more resources and experience caring for their children (Henschel, de Bruin, & Möhler, 2014). Additionally, the association between family size and severe punishment is consistent with findings reported by UNICEF that having more children in a household increases the chance of severe physical punishment (UNICEF, 2018). One interpretation for this finding is that parents of larger families are more likely to use violent discipline than non-punitive discipline to gain immediate compliance (Kotlar, Gerson, Petrillo, Langer, & Tiemeier, 2021).

4.2. COVID-19 restrictions

The results of this study support the hypothesis that COVID-19 restrictions such as lockdowns and curfews, and pandemic-related stressors are linked with violent discipline of children. Food insecurity, job loss/reduced income, and school closures are highly associated with violent discipline and punishment of children by parents. This parallels with previous literature that has demonstrated increasing levels of domestic violence against women due to quarantine and lockdown protocols (Noman, Griffiths, Pervin, & Ismail, 2021). Additionally, this aligns with previously published estimates of increased violent child discipline in Nigeria, Mongolia, and Suriname during pandemic lockdowns (Fabbri et al., 2021). Our findings contribute to expanding the knowledge on risk of household violence in households under COVID-19 restrictions.

4.3. Mental health, economic hardship, and livelihoods

In this study self-reported “good” mental health and parental feelings of loneliness were associated with a decreased likelihood of violent discipline, while self-reported feelings of hopelessness were associated with an increased likelihood. This finding on feelings of loneliness occurs in the setting of previous studies that found loneliness to be a predictor of parental use of violent punishment in the setting of COVID-19 (Lee, Ward, Lee, & Rodriguez, 2022; Rodriguez, Lee, Ward, & Pu, 2021). Given that the association between parental loneliness and violent discipline remains unclear, further research is needed to verify this observation. This mixed trend was also found in economic hardship due to the pandemic. Households with reduced income reported more harsh discipline. Parents involved in petty trades and tending to family businesses were also significantly more likely to use violent discipline. Given that much of the work in this sector relies on physical proximity, one possible explanation is that these workers suffered from a higher degree of income loss as a result of lockdowns and social precautions due to COVID-19 (Belitski, Guenther, Kritikos, & Thurik, 2022; Fairlie, 2020), which led to violent discipline and neglect.

4.4. External support

More violent discipline was reported in families with psycho-social support, and more psychological aggression in families with education material support. The positive association between households receiving external resources and violent behaviors is likely to be explained by the fact that external support was given to households that were more economically vulnerable or more affected by the pandemic. Also, it is assumed that current economic stressors, either due to COVID-19 or innate stressors, outweigh financial and psychological compensation, but further studies are needed to elaborate this relationship.

4.5. Strengths & limitations

This study used data with a large sample size from multiple countries using the same survey tool in the Asia Pacific region. However, there are a few limitations. First, cross-sectional data cannot demonstrate a causal relationship between predictors and parental violent discipline. Second, the assessment was made among World Vision supported communities, which intentionally targets communities with higher rates of poverty and socioeconomically disadvantaged populations. Therefore, the results may not be generalizable at the national level. Indeed, the proportion of those using violent discipline in nationally representative samples were higher than the findings of this WV survey in some countries (Supplemental Table 2). In Vietnam, Nepal, Myanmar, and Laos, the proportion of violent discipline use reported by other surveys was 68 %, 82 %, 77 %, and 69 %, respectively (Minh, Hong, Long, & Dong, 2021; Bhatia et al., 2020; Ministry of Health and Sports MoHS, 2017; Lao Statistics, 2018). However, as the areas included in this study were World Vision supported communities, they would have been impacted by the organization's child advocacy activities. Third, parents' use of violent discipline was assessed only among parents/caregivers. Responses from parents do not necessarily equate to those that would be given by their children. There may be some social desirability bias in parental reporting, with parents more likely to underreport negative and violent discipline directed toward their children if they perceive that violent discipline may be looked upon negatively by their society and the interviewer. This would dilute the true associations between the outcomes of violent discipline and the predictor variables. The data used in this study did not provide any link between parental information and child data. Fourth, there are variables not assessed during the survey but theoretically associated with child punishment such as child sex, child age, number of caregivers in the home, understanding of what violent discipline consists of, and attitudes toward use of violent discipline (Cappa & Dam, 2014).

4.6. Conclusion & implications

This study adds to the large body of evidence to suggest that the use of violent discipline is intimately linked to excess economic strain and mental health burden. However, to our knowledge, it is one of the first to assess these relationships in the acute setting of the onset of the COVID-19 pandemic in the Asia Pacific region. Additionally, this study analyzed general markers of economic insecurity (e.g., change in income source), but also included analysis on specific areas of work commonly utilized in these communities (e.g. petty trade, fishing, migrant work), which has to this point been rarely described. Of note, outside of the setting of the COVID-19 pandemic there has been a number of economically focused interventions that have demonstrated significant declines in the use of violent discipline. For example, an analysis of 57 cash voucher programs throughout LMICs demonstrated that 20 % of those programs led to a significant reduction in violence against children (Peterman et al., 2020). The mechanism behind this reduction has been hypothesized to be multifold. Additionally, the 3-year evaluation of the “VSLA-Plus” (village savings and loan group + child protection messaging) program in Burundi also reduced harsh and verbal discipline (United States Government, 2012). Cash vouchers are believed to decrease parental stress and improve overall parental well-being (Peterman et al., 2020). Furthermore, cash vouchers are also believed to contribute to decreased risk of child violence through the intrahousehold conflict pathway (Peterman et al., 2020).

However, previous implementation of interventions aimed at addressing mental health concerns in these nations have been far rarer, potentially due to prevalent stigma against these issues held among many individuals within these countries. Currently, in several of the nations assessed in this study, there exists considerable stigma around mental illness diagnosis and treatment seeking (Kudva et al., 2020). Child protection interventions most often focus on providing mental health support to afflicted children without providing complementary support to parents (Shastri, 2009). Even among parenting programs implemented in high income nations, few aim to address parental mental health, and even fewer have shown to have a significant impact on parental mental health (Branco, Altafim, & Linhares, 2022).

Another commonly cited barrier to establishing adequate mental health care in LMICs is the associated financial burden (Alloh, Regmi, Onche, van Teijlingen, & Trenoweth, 2018). In LMICs, like the ones accessed in this study, the overall burden of mental health disorders is higher, yet the available workforce trained in these fields is significantly smaller (Alloh et al., 2018; Semrau et al., 2015). One study accessing the mental health care gap in LMICs across the world, including several nations assessed in this study, demonstrated that an additional 239,000 full time workers would be needed to meet the mental health care needs of these countries (Bruckner et al., 2011). However, the lack of quality mental health resources in combination with a similar lack of economic support for lower socioeconomic status families, and social stigma reduction against mental health disorders allows for the accumulation of adverse childhood experiences (Jorm & Mulder, 2018), which comes with its own financial costs. In fact, it has been demonstrated that a reduction in just 10 % of ACEs could equate to an annual savings of $105 billion in North America and Europe (Bellis et al., 2019). Therefore, in settings with increased burden, such as in LMICs, cost savings could be even higher, and may make ACE prevention through increase mental health care providers and services a cost-effective approach.

The results of this work demonstrate that violence against children remains prevalent, hence suggesting a need for additional social and systemic change. These results highlight several potential target areas for intervention by governmental and non-profit organizations, including economic factors, social factors, and mental health related factors. Of note, an approach that currently aims to target all of these areas is the WHO INSPIRE strategy recommended for implementation in South-East Asia (WHO, 2016). The INSPIRE strategy was originally launched in 2017 and consists of seven evidence-based approaches to eliminating violence against children, including “I for the implementation and enforcement of laws; N for norms and values; S for safe environments; P for parent and caregiver support; I for income strengthening; R for response and support services; and E for education and life skills.”(Fabbri et al., 2021) However, government support across different countries for each of the seven INSPIRE strategies varies widely across nations, and only 13 % of South-East Asian nations support providing mental health services for perpetrators of child violence, 25 % support interventions targeted at gender and social norms, and 38 % support targeted intervention for violence by school staff (Fabbri et al., 2021). In LMICs where national budget allocations are already stretched for child protection and welfare system investments, governments and civil society stakeholders need to take more data-driven decisions of which of the seven INSPIRE strategies to invest in—increase in MH services certainly seems to be well justified by the findings here.

While violent discipline against children remains a prevalent issue in the Asia Pacific region, this study provides insight into the areas potentially most amenable to change. With additional socioeconomic, mental health, and legal support, the goal of eliminating violence against children in the Asia Pacific region will become increasingly tangible.

List of funding sources

This study was funded by World Vision US. Darien Colson-Fearon's participation was supported by the Pre-Doctoral TL1 Award in conjunction with the Johns Hopkins Institute for Clinical and Translational Research (ICTR) which is funded in part by Grant Number TL1 TR003100 from the National Center for Advancing Translational Sciences (NCATS) a component of the National Institutes of Health, and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS, or NIH.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

All authors thank World Vision Asia Pacific Regional Office for kindly providing administrative support including data sharing. We acknowledge the World Vision National Offices (Cambodia, Bangladesh, India, Indonesia, Myanmar, Nepal, Philippines, Sri Lanka and Vietnam) for leading data collection, survey management, and review of the manuscript. We also thank Teresa Wallace and Christy Fellner from World Vision US for their technical review of the manuscript, and Hilary Williams and Philip James Ceriales for their administrative support to this research.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.chiabu.2023.106059.

Appendix A. Supplementary data

Supplemental Table 1

mmc1.docx (20.1KB, docx)

Data availability

The authors do not have permission to share data.

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Associated Data

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Supplementary Materials

Supplemental Table 1

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Data Availability Statement

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