Sociodemographic predictors of parenting stress among mothers in disadvantaged settings: evidence from rural and urban study sites in Kenya and Zambia

doi:10.21203/rs.3.rs-3590513/v1

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Sociodemographic predictors of parenting stress among mothers in disadvantaged settings: evidence from rural and urban study sites in Kenya and Zambia

https://doi.org/10.21203/rs.3.rs-3590513/v1

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Background: Parental stress occurs when parenting demands are greater than the resources available to cope with parenting. Previous research has identified household wealth, educational level, marital status, age, and number of dependent children as predictors of parental stress. However, limited evidence exists from sub-Saharan Africa (SSA). This study investigated the sociodemographic predictors of parenting stress among mothers in Kenya and Zambia.

Methods: Data were obtained from longitudinal nurturing care evaluation studies conducted in rural and urban study sites in Kenya and Zambia. Mean parental stress scores (PSS) were compared across study sites, and multiple regression modelling was used to examine associations between sociodemographic predictors (household income, educational level, marital status, maternal age, child age, number of children aged <5 years) and PSS, adjusting for clustering and other predictors.

Results: The mean PSS was lower in rural study sites and higher in urban sites (Kenya rural: 37.6 [SD=11.8], Kenya urban: 48.8 [SD=4.2], and Zambia rural: 43.0 [SD=9.1]). In addition, mothers’ income and educational level were associated with PSS (income: Kenya rural, β = -0.43; 95% CI[-16.07, -5.74]; P =.003^**; Kenya urban, β = -0.33; 95% CI[-6.69, -0.80]; P =.01^*; education: Kenya rural, β = -0.24; 95% CI[-8.97, -1.68]; P=.005^**).

Conclusion: Measures to increase education levels, alleviate poverty, and improve household incomes, such as subsidising childcare, improving parental stress levels, and positive parenting practices, lead to better growth and development of their children.

Trial registration: PACTR201905787868050 and PACTR20180774832663

parenting

predictors of parenting stress

childcare

Parental stress occurs when parenting demands are greater than the resources available to cope with parenting [1]. This negatively affects the overall functioning of the children and their parents, as stress negatively affects parents’ mental health and well-being, resulting in poor psychological and physical health [2–4]. Parental psychological stress may also affect their children’s well-being, that is, their psychological, health, and developmental outcomes. A distressed parent might not be actively engaged in stimulating interactions with their children, which increases the risk of maltreatment and adverse childhood experiences [5–8]. This makes it important to identify and address predictors of parental stress.

Previous research has identified a range of sociodemographic predictors of parental stress. Maternal characteristics such as household wealth, educational level, marital status, age, and the number of children under their care have been indicated as predictors of parental stress [9–13]. Poverty has been associated with poor mental health outcomes [13–15], and dealing with financial difficulties/hardships leads to high stress levels and negatively affects an individual's health [16]. Families living in poverty often face challenges such as inadequate financial and educational resources, food insecurity, relationship instability, and overall unpredictability [17, 18].

Lower education levels have also been shown to contribute to parenting stress [18]. However, other findings have also demonstrated higher parental stress among mothers with high education, and significantly lower parenting stress among mothers with intermediate education [11]. These findings could be attributed to the fact that even though education is related to greater resources, which could lead to less parental anxiety, it also facilitates alternative fulfilment, which may lead to a greater feeling of dissatisfaction with parenting [19]. In addition, it could also be attributed to ‘parents’ education anxiety’[20] that may result in experiences of tension, panic, worry, and other forms of negative emotions regarding whether their parenting practices positively affect their children’s developmental outcomes.

Regarding marital status, community support systems have been reported to lessen the parenting burden on mothers, especially support from their spouses [9]. Specifically, one study found that mothers reported that fathers’ involvement in parenting reduced the stressors that came with parenting [9]. Such findings have also been evident in other contexts, such as a study in the USA on mothers in low-income settings [21]. For mothers in low-income settings, marriage could be a great contributor to an improved household financial base, emotional support, and support in caring for the child(ren) [22]. Marriage quality can also contribute to stress and health outcomes [23].

Maternal age has also been reported as a predictor of parental stress. Specifically, mothers between 30 and 35 years of age reported less stress than mothers who were younger or older than this age[10]. In addition, the number of children under mothers’ care is also a predictor of parental stress, with mothers having more than one child below eight years reporting higher parental stress than those having one child [12]. These effects are particularly important for understanding contexts such as Kenya and Zambia, where the average number of children is estimated to be approximately four per family[24, 25]. However, limited evidence has explored these predictors in the sub-Saharan African (SSA) context, where factors such as the poverty index are very high compared with those in previous studies.

Using datasets from two previous studies conducted in Kenya and Zambia, we investigated the relationship between mothers’ demographic characteristics, such as age, income, education, marital status, number of children below five years of age, and parental stress level. The parental stress scale (aka parental stress scores, PSS) tool by Berry and Jones (1995) was used to obtain information from parents on their experience of undertaking day-to-day parental responsibility. This study aimed to investigate the association between mothers’ demographic characteristics and PSS scores.

Data and participants

Data were collected through longitudinal nurturing care evaluation studies conducted in Kisumu and Nairobi Counties in Kenya and Chisamba District in Zambia [25, 26]. Mothers recruited from three longitudinal nurturing care studies (rural Kenya, rural Zambia, and urban Kenya) were included in this study [25, 26].

In rural Zambia, the study team used a cluster-randomised controlled trial design with communities as clusters. We determined the sample size following Hemming and Girling's study [27], which resulted in 510 primary mothers (255 per arm). However, only 395 primary mothers met the inclusion criteria (children aged < 18 months or pregnant women in the third trimester). In rural Kenya, the study used a quasi-experimental design, with each village as a cluster nested within a ward. The sample size was calculated based on Hemming and Girling's study [27] with six clusters in each arm and a fixed number of clusters. The number of mothers per arm was 132, implying a total sample size of 264 for the two arms. Similarly, the Kenyan urban study had a quasi-experimental design, and the sample size was calculated using G*Power [28]; 254 households with children aged < 24 months were recruited. In our analysis, we only included 613 participants (Kenya rural, n = 134; Zambia rural, n = 291; and Kenya urban, n = 188) with data on the key variables, as shown in Fig. 1.

INSERT FIGURE 1

Measures

PSS outcome and sociodemographic predictor measures were included in the mothers’ questionnaires. The questionnaire was administered by trained field interviewers, and the average duration of each interview was estimated to be one hour. The questionnaire covered items on mothers’ socio-demographics, caregiving knowledge, attitudes, and practices, parental stress scores, health-seeking behaviours, and the Ages and Stages Questionnaire (ASQ).

Parental stress score outcome measure

This study focused on parental stress levels, measured using the parental stress score (PSS). The PSS tool was used to obtain information on parents’ feelings and perceptions of their parenting experience. Studies on the validity and reliability of PSS tools indicate good internal consistency, construct validity, convergent validity, and test-retest reliability [29–34]. Notably, Cronbach’s alpha coefficient for the internal consistency of the items in the Parental Stress Scale was 0.79. The mothers’ PSS responses were assigned scores based on a 5-point Likert Scale (1 = Strongly Disagree, 2 = Disagree, 3 = Not Sure, 4 = Agree, 5 = Strongly Agree). To compute the parental stress score, items 1, 2, 5, 6, 7, 8, 17, and 18 were reversed and scored as follows: (1 = 5) (2 = 4) (3 = 3) (4 = 2) (5 = 1). The item scores were then summed. A low score signifies a low level of stress, and a high score indicates a high level of stress. The overall possible scores on the scale ranged from 18 to 90.

Sociodemographic predictor measures

The predictors were the following sociodemographic characteristics: income, education level, marital status, mother’s age, child’s age, and number of children aged < 5 years. A structured questionnaire was used to collect information on the mothers’ demographics. Mothers were asked questions on the average household income per month, level of education completed, marital status, age, and number of children under their care aged below five years. Mothers’ incomes are reported in bands (below USD 50, between USD 50 and 100, and above USD 100 per month). Mothers’ level of education was categorised as primary, secondary, and postsecondary education, which included college and vocational education. Marital status was categorised as married (living together/cohabiting, legally married) or unmarried (single, divorced, separated, or widowed). They were also asked about the number of children aged < 5 years under care. This included all those living in the same household, including nonbiological children.

Statistical Analysis

Data cleaning and analysis were performed using R software and R Studio [35]. A multiple regression model was used to determine the association between mothers’ parenting stress scores and their demographic characteristics, with adjustment for other predictors and clustering. The full analysis code is available at the Open Science Framework (https://osf.io/yfxjc).

Descriptive statistics and mean parental stress scores

Descriptive statistics on sociodemographic characteristics are provided in Table 1. Mothers had a mean age of approximately 27 years in the two rural populations, whereas the urban population had a slightly higher mean age of approximately 29 years. More than 70% of the mothers reported that they had a marital relationship. Notably, most mothers reported that their educational level was secondary school or lower. Regarding their monthly incomes, most participants from the urban population reported a higher income (above USD 100) than the participants in the rural study sites. On the number of children younger than five years, slightly more than 60% of the participants reported that they had one child less than 5 years of age.

Table 1

Descriptive statistics on sociodemographic characteristics and mean PSS
	Rural Kenyan	PSS Mean (SD)	Rural Zambian	PSS Mean (SD)	Urban Kenyan	PSS Mean (SD)
	(n = 134)		(n = 291)		(n = 188)
Income
Below USD 50	32 (33.9)	47.7 (9.60)	25 (8.6)	40.1 (8.09)	11 (5.9)	50.4 (4.08)
USD 50–100	102(76.1)	37.3 (10.0)	261 (89.7)	42.6 (9.15)	147(78.2)	48.2 (3.95)
Above USD 100	0 (0)		5 (1.7)	39.6 (7.30)	30(16)	46.9 (4.66)
Education
Primary and below	53 (39.6)	43.8 (9.42)	175(60.1)	42.1 (9.21)	109 (57.4)	48.7 (3.69)
Secondary	76 (56.7)	37.1 (10.8)	116(39.9)	42.8 (8.83)	70 (37.2)	47.4 (4.59)
Above secondary	5 (3.7)	37.8 (13.6)			9 (4.8)	47.2 (4.58)
Marital status
Single parent	30 (22.4)	46.3 (10.3)	77(26.5)	42.9 (8.93)	27 (14.4)	47.5 (4.84)
Married	104 (77.6)	37.9 (10.3)	214(73.5)	42.2 (9.11)	161 (85.6)	48.2 (4.00)
Mother age
Below 20 years	16 (11.9)	39.8 (13.2)	35(12.0)	43.2 (8.61)	13 (6.9)	49.0 (2.92)
20–29	67 (50)	37.8 (10.2)	153(52.6)	42.0 (8.56)	88 (46.8)	47.4 (4.35)
30–35	41 (30.6)	41.2 (10.7)	80(27.5)	42.4 (9.60)	76 (40.4)	48.8 (3.92)
Above 35	10 (7.5)	46.7 (8.59)	23(7.9)	43.3 (11.2)	11 (5.9)	48.3 (4.24)
Child age (mean)	0.9	39.7 (10.8)	0.8	42.4 (9.05)	3.4	48.1 (4.12)
Number of children aged < 5
1	93 (69.4)	40.1 (11.1)	240 (82.5)	42.0 (8.85)	181 (96.1)	48.2 (4.06)
2	35 (26.1)	37.9 (9.53)	47 (16.2)	43.8 (9.92)	2 (2)	49.0 (4.24)
3 and 4	6 (4.5)	44.3 (13.2)	4 (1.4)	47.0 (9.83)	3 (1.6)	44.0 (6.53)

INSERT Table 1

The mean parental stress scores were higher in Kenya’s urban study site than in the two rural study sites, as shown in Fig. 2. Notably, in the Kenya rural study site, children’s mean age (in years) at preintervention was 0.8 (SD = 0.7) in the Kenyan rural site, and in the Zambia rural study site, children’s mean age at preintervention was 0.9 (SD = 0.6) in the Zambia site and 3.4 (SD = 0.4) in the urban Kenyan population.

INSERT FIGURE 2

Modelling results on sociodemographic predictors of parental stress scores

We also sought to establish associations between household income, educational level, marital status, maternal age, child age, number of children under five years of age, and mothers’ parental stress scores.

The results from unadjusted linear regressions showed that in Kenya’s rural study site, the mean PSS scores of mothers with income above USD 50 were 0.35% lower than those of mothers with monthly income less than USD 50 (β = -0.35; 95% CI[-12.89, -2.53]). A similar trend was observed in Kenya's urban study site, with a proportion of 0.27 (β = -0.27; 95% CI[-5.78, -0.22]).

When adjusted for confounders, the results from the Kenyan rural sites showed that the mean PSS scores of mothers with income above USD 50 were 0.43% lower than those of mothers with monthly income less than USD 50 (β = -0.43; 95% CI[-16.07, -5.74]). A similar trend was observed in Kenya's urban study site, with a proportion of 0.33 (β = -0.33; 95% CI[-6.69, -0.80]), as shown in Tables 2, 3 & 4. Notably, the income category ‘above USD 100’ was excluded from Kenyan rural study site modelling, as no participants were in this category. Similarly, the above secondary education was excluded from the Zambia rural study site model for the same reason.

The results from unadjusted linear regressions showed that in Kenya’s rural study site, the mean PSS scores of mothers with secondary education were 0.22% lower than those of mothers with primary education (β = -0.22; 95% CI[-8.01, -2.22]). A similar trend was observed in Kenya's urban study site, with a proportion of 0.13 (β = -0.13; 95% CI[-2.33, -0.02]). The results from the multiple regression model showed that in Kenya's rural study site, the mean PSS scores of mothers with secondary education were 0.24% lower than those with primary education (β = -0.24; 95% CI[-8.97, -1.68]). Notably, the association between the level of education and PSS was nearly significant in Kenya's urban study sites, as shown in Tables 2, 3 & 4.

The findings on other predictors, such as marital status, mother’s age, child age, and number of children aged < 5 years, showed no significant association with PSS across the study sites. However, the association between the number of children aged < 5 years and PSS was nearly significant at the rural Zambia study site. The results from the multiple regression model showed that in the Zambia rural study site, the mean PSS scores of mothers with two children aged < 5 years were 0.12% higher than those of mothers with one child aged < 5 years (β = 0.12; 95% CI[-0.23, 5.86] P = 0.07.) as shown in Tables 2, 3 & 4.

Table 2

Beta (B), standard errors (SE), probability values (P) and standardised betas (β) for unadjusted and adjusted models on associations between prenatal demographic characteristics and parental stress Kenya rural study site
		Unadjusted			Adjusted
Variable	Level	β	[95% CI]	p-value	β	[95% CI]	p-value
Income
	Below USD 50	Reference
	USD 50–100	-0.351	[-12.889, -5.534]	< 0.001***	-0.430	[-16.067, -5.744]	0.003**
Education
	Primary and below	Reference
	Secondary	-0.223	[-8.007, -2.223]	< 0.001***	-0.244	[-8.969, -1.675]	0.005**
	Above secondary	-0.038	[-10.470, 5.617]	0.6	-0.039	[-11.233, 6.819]	0.6
Marital status
	Single	Reference
	Married	-0.279	[-12.039. -4.751	< 0.001***	-0.042	[-6.078, 3.918]	0.6
Mother age
	Below 20 years	Reference
	20–29	-0.105	[-6.628, 1.890]	0.3	0.172	[-2.102, 9.507]	0.2
	30–35	0.055	[-3.233, 6.034]	0.6	0.204	[-1.398, 10.962]	0.1
	Above 35	0.206	[3.101, 16.953]	0.005**	0.063	[-5.271, 10.465]	0.5
Child age		-0.157	[-4.960, -0.451]	0.02*	-0.024	[-3.398, 2.482]	0.7
Number of children aged < 5
	1	Reference
	2	-0.004	[-5.537, 5.336]	0.9	0.015	[-4.316, 3.578]	0.8
	3	0.066	[-4.917, 13.287]	0.4	0.089	[-3.431, 12.711]	0.2
Note. Significant at P > .05. All the adjusted models were controlled for clustering. The Parental Stress Score tool ranges from 18 to 90, with a low score indicating a low parental stress level. Therefore, the B coefficients should be interpreted accordingly. Standardised betas (β) were additionally provided to allow for direct comparisons of effect sizes. Significant at P < 0.05, significant at P < 0.01 and *significant at P < 0.001.

Table 3

Beta (B), standard errors (SE), probability values (P) and standardised betas (β) for unadjusted and adjusted models on associations between prenatal demographic characteristics and parental stress Kenya urban study site
		Unadjusted			Adjusted
Variable	Level	β	[95% CI]	p-value	β	[95% CI]	p-value
Income
	Below USD 50	Reference
	USD 50–100	-0.194	[-4.430, 0.546]	0.1	-0.243	[-5.022, 0.186]	0.07.
	Above USD 100	-0.268	[-5.782, -0.218]	0.03*	-0.334	[-6.692, -0.800]	0.01*
Education
	Primary and below	Reference
	Secondary	-0.131	[-2.327, -0.024]	0.05*	-0.144	[-2.542, 0.097]	0.07.
	Above secondary	-0.097	[-4.593, 0.634]	0.1	-0.023	[-3.405, 2.531]	0.8
Marital status
	Single	Reference
	Married	0.052	[-0.954, 2.275]	0.4	0.024	[-1.554, 2.530]	0.8
Mother age
	Below 20 years	Reference
	20–29	-0.148	[-3.439, 0.942]	0.3	-0.171	[-3.953, 1.136]	0.3
	30–35	-0.017	[-2.363, 2.076]	0.9	-0.014	[-2.736, 2.506]	0.9
	Above 35	0.065	[-1.731, 3.674]	0.5	-0.067	[-4.609, 2.278]	0.5
Child age		0.061	[-0.053, 0.155]	0.3	0.022	[-0.101, 0.136]	0.8
Number of children aged < 5
	1	Reference
	2	0.086	[-6.454, 11.654]	0.6	0.062	[-7.451, 12.458]	0.6
	3	-0.089	[-12.241, 6.242]	0.5	-0.078	[-11.373, 6.917]	0.6
Note. All the adjusted models were controlled for clustering. The Parental Stress Score tool ranges from 18 to 90, with a low score indicating a low parental stress level. Therefore, the B coefficients should be interpreted accordingly. Standardised betas (β) were additionally provided to allow for direct comparisons of effect sizes. Significant at P < 0.05, significant at P < 0.01 and **significant at P < 0.001.

Table 4

Beta (B), standard errors (SE), probability values (P) and standardised betas (β) for unadjusted and adjusted models on associations between prenatal demographic characteristics and parental stress Zambia rural study site
		Unadjusted			Adjusted
Variable	Level	β	[95% CI]	p-value	β	[95% CI]	p-value
Income
	Below USD 50	Reference
	USD 50–100	0.084	[-0.810, 5.867]	0.1	0.097	[-1.080, 6.837]	0.1
	Above USD 100	0.008	[-7.508, 8.720]	0.8	-0.028	[-11.132, 7.201]	0.7
Education
	Primary and below	Reference
	Secondary	0.047	[-0.974, 2.745]	0.4	0.062	[-1.123, 3.409]	0.3
Marital status
	Single	Reference
	Married	0.055	[-0.844, 0.294]	0.3	-0.037	[-3.266, 1.746]	0.6
Mother age
	Below 20 years	Reference
	20–29	-0.161	[-5.680, -0.209]	0.04*	-0.098	[-5.178, 1.647]	0.3
	30–35	-0.160	[-6.376, -0.331]	0.03*	-0.060	[-4.930, 2.487]	0.5
	Above 35	-0.078	[-6.760, 1.405]	0.2	-0.022	[-5.740, 4.212]	0.8
Child age		0.083	[-0.028, 0.227]	0.1	0.088	[-0.041, 0.260]	0.2
Number of children aged < 5
	1	Reference
	2	-0.153	[-7.117, -0.561]	< 0.001***	0.124	[-0.219, 5.845]	0.07.
	3	-0.002	[-9.310, 9.019]	0.02*	0.060	[-4.490, 13.839]	0.3
Note. Significant at P > .05. All the adjusted models were controlled for clustering. The Parental Stress Score tool ranges from 18 to 90, with a low score indicating a low parental stress level. Therefore, the B coefficients should be interpreted accordingly. Standardised betas (β) were additionally provided to allow for direct comparisons of effect sizes. Significant at P < 0.05, significant at P < 0.01 and *significant at P < 0.001.

INSERT Table 2, 3 & 4

This study aimed to establish predictors of parental stress using datasets from baseline findings of longitudinal studies from three different African regions (rural Kenya, rural Zambia, and urban Kenya). The mean parental stress scores were slightly below the average in the rural study sites and slightly above the average in the urban study sites. Additionally, parental stress was associated with at least two factors: the mother’s income and educational level.

Parental stress scores mirror findings from other studies on parental stress in Canada [36, 37] and the USA [38, 39]. According to Winston et al. (2010), mothers of children aged 12 to 36 months reported a mean parenting stress score of 36 (SD = 7.3), while Shapiro and Stewart (2010) reported a mean parenting stress score of 38 (SD = 9.38). Other findings from a study in Ireland reported a slightly higher mean score than that reported in rural Zambia and Kenya but similar to that reported in Kenya’s urban population [40, 41].

Findings on high parental stress scores, especially in the poor urban population, could illuminate the challenges encountered by women in combining childcare and work in poor urban settings, as reported in other studies [42]. Such scores were not reported in rural Zambia and Kenya because of communal childcare practices. That is, the childcare burden is often spread among extended family members, such as grandparents and other relatives [43]. Mothers can leave their children to their grandparents or other relatives while attending to daily chores such as work and household chores. However, such practices are uncommon in urban settings, where mothers often take their children to childcare facilities as they go to work. Notably, the quality of such childcare services is often poor, which could cause parents to worry about whether their child is receiving the right services [44].

Second, the findings on the predictors established that the parental stress score was associated with the mothers’ income in Kenyan study sites. These findings extend those of other studies that have shown associations between household income and mothers’ parental stress scores [45]. Such findings were also observed in a study conducted on perceived stress related to COVID-19, in which mothers from low-income households reported more uncertainty about their health than their counterparts with higher incomes [46]. Notably, these studies have been carried out in other contexts that do not represent sub-Saharan Africa. Although the findings of the current study were not consistent across the study sites, it is notable that the mean difference in PSS was consistent across the study sites. Therefore, these findings highlight the need for maternal health and childcare interventions that focus on improving household incomes and reducing childcare-related expenditures, such as the provision of quality and affordable childcare services.

Findings on the association between parental stress scores and other factors, such as mothers’ education, were also observed, although not consistent across the study sites. These findings have also been observed in a study of sociodemographic predictors of chronic stress in mothers[21]. Studies have suggested improved parenting practices through engagement programs among mothers with low educational levels, thereby promoting children’s developmental outcomes [47]. Therefore, community parenting programs or social groups could assist parents in sharing their parenting challenges and coming up with potential solutions to such challenges, thereby reducing the stressors that come with parenting. Surprisingly, the number of children aged < 5 years under care was not associated with parental stress at the three study sites. As pointed out in other studies, mothers of many young children under their care are likely to have higher parenting stress scores [12]. In addition, other findings on parental burnout in similar settings have reported an association between the number of children and parental burnout [48].

This study aimed to determine the predictors of parental stress among mothers in low-resource SSA settings. The findings indicated that parental stress was related to at least two factors: the mother’s income and educational level. The policy implications of the findings from this study focus on improving maternal mental health through poverty-alleviation-related interventions, such as subsidising childcare services and improving household incomes in poor urban and rural settings. Additionally, policies should focus on parental engagement programs that have been successful in improving parental practices among mothers with low educational levels. Based on these findings, future studies should focus on understanding the cost implications of addressing predictors of parental stress.

Ethical consideration

Ethics approval and consent to participate

Permission to use these datasets was granted by the African Population and Health Research Centre (APHRC). The APHRC obtained ethical approval from the Institutional Review Boards (IRBs) of Kenya and Zambia to conduct these studies at the three study sites. Written informed consent was obtained from the study participants before data were collected. For respondents who could neither read nor write, a thumbprint was used as a signature in the presence of a witness. Consent was obtained at every round of data collection. Consent documents and the questionnaire were translated into Kiswahili (Kenya urban study site), Dholuo (Kenya rural study site), and Nyanja and Tonga (Zambia rural study site). Confidentiality of the data and the participants’ privacy were always observed during and after data collection. These rural and urban studies were registered under the trial registration numbers PACTR20180774832663 and PACTR201905787868050, respectively. All methods were performed in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. For example, respect for individuals, the right to make informed decisions, and the recognition of vulnerable groups.

Informed consent process

The data collectors sought informed consent from all study participants before they were interviewed. For those who were not able to read, the information sheet was read to them in their local language, and they were asked to provide a thumbprint to signify their consent. Ethical research committees in both countries approved the use of a thumbprint or signature. The ethical research committees in both countries approved the use of a thumbprint or signature (Amref Health Africa’s Ethics and Scientific Review Committee in Kenya and the ERES Converge in Zambia).

Patient and Public Involvement

To design and implement this study, national and regional stakeholders from the Ministry of Health and Education were involved as part of the study team. Data collectors were also recruited from the study community. Stakeholders participated in the selection of the study site based on health indicators.

Consent to Publication

Not applicable.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

Conflict of interest

The authors declare that they have no conflicts of interest.

Funding

These studies were funded by the Episcopal Relief and Development funded this study with grants from the Conrad N. Hilton Foundation, Grand Challenges, Canada, and Other Donors.

Acknowledgement

The financial support from Episcopal Relief & Development and Grand Challenges Canada for the research activities reported here is gratefully acknowledged. The authors of this paper would also like to thank the survey respondents, the Moments That Matter (MTM) program staff, and the various stakeholders who participated in this study. We also gratefully acknowledge the contributions of the data collectors in Kenya and Zambia.

Author contributions

All authors listed have made substantial, direct, and intellectual contributions to the work and approved it for publication.

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Sociodemographic predictors of parenting stress among mothers in disadvantaged settings: evidence from rural and urban study sites in Kenya and Zambia

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Abstract

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INTRODUCTION

METHODOLOGY

Data and participants

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Measures

Parental stress score outcome measure

Sociodemographic predictor measures

Statistical Analysis

RESULTS

Descriptive statistics and mean parental stress scores

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Modelling results on sociodemographic predictors of parental stress scores

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DISCUSSION

CONCLUSIONS AND POLICY IMPLICATIONS

Declarations

Ethical consideration

Informed consent process

Availability of data and materials

Conflict of interest

Funding

Acknowledgement

References

Additional Declarations

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