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Learn more: PMC Disclaimer | PMC Copyright Notice PLOS Glob Public Health . 2026 Apr 20;6(4):e0006037. doi: 10.1371/journal.pgph.0006037 Search in PMC Search in PubMed View in NLM Catalog Add to search Depressive and anxiety symptomatology among caregivers of children 0-3 years in Nairobi City County: Community-based prevalence study Esther Jebor Chongwo Esther Jebor Chongwo 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya 2 Department of Clinical, Neuro- and Developmental Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing Find articles by Esther Jebor Chongwo 1, 2, * , Japheth Adina Japheth Adina 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing Find articles by Japheth Adina 1 , Kevinson Mwangi Kevinson Mwangi 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing Find articles by Kevinson Mwangi 1 , Tabitha Shali Tabitha Shali 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Methodology, Supervision, Writing – review & editing Find articles by Tabitha Shali 1 , Edwin Dzoro Edwin Dzoro 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Methodology, Supervision, Writing – review & editing Find articles by Edwin Dzoro 1 , Cynthia Shitote Cynthia Shitote 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Methodology, Supervision, Writing – review & editing Find articles by Cynthia Shitote 1 , Vibian Angwenyi Vibian Angwenyi 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing Find articles by Vibian Angwenyi 1 , Caroline Ngunu Caroline Ngunu 3 Department of Health, Nairobi City County Government, Nairobi, Kenya Investigation, Methodology, Validation, Writing – review & editing Find articles by Caroline Ngunu 3 , Judy Macharia Judy Macharia 3 Department of Health, Nairobi City County Government, Nairobi, Kenya Investigation, Validation, Writing – review & editing Find articles by Judy Macharia 3 , Naomi Kigani Naomi Kigani 3 Department of Health, Nairobi City County Government, Nairobi, Kenya Methodology, Validation, Writing – review & editing Find articles by Naomi Kigani 3 , Rachel Odhiambo Rachel Odhiambo 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Conceptualization, Data curation, Methodology, Writing – review & editing Find articles by Rachel Odhiambo 1 , Margaret Kabue Margaret Kabue 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing Find articles by Margaret Kabue 1 , Amina Abubakar Amina Abubakar 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya 4 Centre for Geographic Medicine Research (Coast), Kenya Medical Research Institute/ Wellcome Trust Research Programme, Kilifi, Kenya Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing Find articles by Amina Abubakar 1, 4 Editor: Julia Robinson 5 Author information Article notes Copyright and License information 1 Institute for Human Development, Aga Khan University, Nairobi, Kenya 2 Department of Clinical, Neuro- and Developmental Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands 3 Department of Health, Nairobi City County Government, Nairobi, Kenya 4 Centre for Geographic Medicine Research (Coast), Kenya Medical Research Institute/ Wellcome Trust Research Programme, Kilifi, Kenya 5 PLOS: Public Library of Science, UNITED STATES OF AMERICA ✉ * E-mail: [email protected] The authors have declared that no competing interests exist. Roles Esther Jebor Chongwo : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing Japheth Adina : Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing Kevinson Mwangi : Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing Tabitha Shali : Methodology, Supervision, Writing – review & editing Edwin Dzoro : Methodology, Supervision, Writing – review & editing Cynthia Shitote : Methodology, Supervision, Writing – review & editing Vibian Angwenyi : Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing Caroline Ngunu : Investigation, Methodology, Validation, Writing – review & editing Judy Macharia : Investigation, Validation, Writing – review & editing Naomi Kigani : Methodology, Validation, Writing – review & editing Rachel Odhiambo : Conceptualization, Data curation, Methodology, Writing – review & editing Margaret Kabue : Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing Amina Abubakar : Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing Julia Robinson : Editor Received 2025 Aug 27; Accepted 2026 Mar 31; Collection date 2026. © 2026 Chongwo et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13094971 PMID: 42008469 Abstract Caregivers of young children in low-resource urban settings face multiple stressors, which can affect their mental health. There is limited population-based evidence on the prevalence and correlates of depression and anxiety among caregivers of young children in these contexts. This study assessed the prevalence and associated factors of depressive and anxiety symptoms among caregivers of young children in Nairobi City County. We conducted a cross-sectional household survey with 2,903 primary caregivers of children 0–3 years. Depressive and anxiety symptoms were assessed using validated Swahili versions of the Patient Health Questionnaire-9 and the Generalised Anxiety Disorder-7, respectively, with a cut-off score of ≥10 indicating clinically relevant symptoms. Random intercept logistic regression models were fitted to assess the factors associated with depressive and anxiety symptoms while accounting for clustering within Nairobi City County’s sub-counties. Approximately 13.8% of caregivers had depressive symptoms, and 8.0% had anxiety symptoms. Stronger paternal involvement in childcare and parenting responsibilities was associated with lower odds of depressive and anxiety symptoms (adjusted odds ratio (aOR)=0.95 and aOR=0.93, both P < 0.001). Factors associated with higher odds of depressive and anxiety symptoms were pregnancy-related complications (depression: aOR=2.36, P < 0.001; anxiety: aOR=1.61, P = 0.003), and moderate household food insecurity relative to a food secure status (depression: aOR=3.67; anxiety: aOR=4.59; both P < 0.001). Higher wealth status was associated with lower odds of depressive and anxiety symptoms. A child’s history of hospital admission was additionally associated with higher odds of depressive symptoms (aOR=1.56, P = 0.018), while tertiary level education was associated with lower odds (aOR=0.65, P = 0.049). The noted prevalence of depressive and anxiety symptoms among caregivers in low-resource urban settings elucidates the need to integrate mental health services into the existing maternal and child health programmes in Kenya. Introduction Mental health disorders are a major contributor to the global disease burden [ 1 , 2 ]. Depression and anxiety rank among the predominantly prevalent common mental health conditions, disproportionately affecting women [ 3 ]. A global systematic review estimated the prevalence of postnatal depression among women globally at 17.2% [ 4 ], with higher rates reported in low and middle-income countries (LMICs) [ 5 ], where socioeconomic factors, gender-based violence, and limited access to mental healthcare contribute to the elevated burden [ 6 – 8 ]. Notably, a recent review on the burden of postnatal depression in Sub-Saharan Africa (SSA) reported a pooled prevalence of 22.1%, with estimates ranging widely from 3.8% to 69.9% [ 9 ]. This is higher compared to estimates of 5–20% in high-income countries [ 10 ]. Other studies have similarly highlighted the high burden in the SSA region [ 6 , 11 ]. Similarly, global estimates suggest that approximately 18–25% of women experience anxiety during pregnancy, and around 15% affected during the postnatal period, with higher rates reported in LMICs [ 12 ]. The burden of mental health issues could be worse in urban settings due to a host of stressors, including economic hardship, inadequate housing, insecurity, barriers to quality and affordable health and social services, which may further increase the psychological burden on caregivers [ 13 , 14 ]. Existing evidence from Kenya reveals concerning trends. A study among 250 pregnant women in Nairobi’s informal settlements reported that 26.9% experienced antenatal depression and 6.4% had anxiety [ 15 ]. Similarly, a hospital-based study found that 16.2% of pregnant women had depressive symptoms and 6.6% exhibited anxiety symptoms [ 16 ]. The burden of depression and anxiety extends beyond pregnancy into the postnatal period. Depressive symptoms have been reported in 18.7% [ 17 ] and 27.1% [ 18 ] of mothers of young infants from low-resource settings attending maternal and child health clinics. Among post-natal mothers of severely malnourished children, the prevalence (64.4%) was concerning [ 19 ]. The variation in prevalence rates of depressive symptoms likely reflects differences in study design, sample size, screening tools, and different cut-off thresholds. Anxiety is also common during this period; in a recent study among mothers of pre-term infants in Kenya, 35.1% of caregivers screened positive for anxiety symptoms [ 20 ]. These high rates are driven by several risk factors, ranging from biological, psychological, and social factors [ 21 ]. Socioeconomic stressors such as poverty, food insecurity, and education [ 6 , 22 – 24 ], as well as psychosocial issues such as domestic violence and limited social support, are significantly linked to mental health disorders during the perinatal period [ 21 , 25 , 26 ]. Other factors have been reported, such as a history of mental illness, obstetric and perinatal complications, and poor child health [ 21 , 26 – 28 ]. Cultural beliefs and stigma surrounding mental illness present additional barriers to care-seeking and treatment, more so in low-resource settings [ 29 ]. In urban settings like Nairobi, added stressors may further heighten these risks. Nairobi City County’s rapid urbanisation has placed immense pressure on housing, healthcare, and social infrastructure, exacerbating vulnerabilities among low-income populations [ 30 , 31 ]. Data from the 2022 Kenya Demographic Health Survey indicate that approximately 30% of women (15–49 years) had experienced physical violence [ 32 ]. The combination of urban stressors—including economic hardship, overcrowding, gender-based violence, and high rates of unemployment further increases the risk of mental health disorders among caregivers [ 33 , 34 ]. Given these challenges, Nairobi presents a critical setting for understanding the prevalence and correlates of depressive and anxiety symptoms, particularly in caregivers of young children in both formal and informal settlements. Caregiver mental health is important to study, as it has been shown to influence child outcomes [ 35 – 41 ]. Existing evidence on the burden of mental health in urban contexts, such as Nairobi, has mainly focused on the pre-natal populations, and is characterised by limited geographic coverage and reliance on facility-based assessments [ 17 , 18 , 42 ]. Beyond these, data on the impact of the COVID-19 pandemic on caregivers’ mental health remains scarce [ 43 ]. Although there is a high burden of mental health issues, services remain underdeveloped in SSA [ 44 , 45 ]. In Kenya, mental health remains under-prioritised, with limited integration into the essential package of reproductive, maternal, newborn, and child health services (RMNCH). A shortage of specialists, limited research, stigma, and inadequate screening further hinder early intervention [ 46 , 47 ]. This study adds to the literature by conducting the first population-based assessment of the mental health of caregivers with children aged 0–3 years across all sub-counties of Nairobi, encompassing diverse socio-economic strata and including both informal and formal urban settings. The findings will inform targeted interventions and policies to strengthen mental health services. This study aimed to assess the prevalence and correlates of depressive and anxiety symptoms in caregivers of young children (0–3 years) in Nairobi City County. Methods Study design This study employed a cross-sectional household survey design. It was part of an initiative to evaluate the state of early childhood development (ECD) in Nairobi City County. Study setting The study was conducted in Nairobi City County, Kenya’s capital and largest urban centre, which is home to an estimated 4.8 million people [ 48 ]. Approximately 60% (2 million) of Nairobi residents live in densely populated informal settlements [ 49 ]. The county is characterised by stark socioeconomic inequalities, with pockets of affluence alongside widespread poverty, food insecurity, and inadequate access to essential services [ 50 ]. Nairobi is administratively divided into 17 sub-counties and 85 wards, all of which were included in the survey sampling frame to ensure broad geographic and socioeconomic representation, as shown in Fig 1 . Fig 1. Map of Kenya showing the location of Nairobi City County. Open in a new tab Map of Kenya showing the location of Nairobi City County. The map on the right shows the 17 sub-counties within Nairobi City County. The sub-counties are: 1 Dagoretti North, 2 Dagoretti South, 3 Embakasi Central, 4 Embakasi East, 5 Embakasi North, 6 Embakasi South, 7 Embakasi West, 8 Kamukunji, 9 Kasarani, 10 Kibra, 11 Langata, 12 Makadara, 13 Mathare, 14 Roysambu, 15 Ruaraka, 16 Starehe, and 17 Westlands. The map was author-created in R statistical software version 4.4.1. and shapefiles were obtained from the “geodata” package [ 51 ]. Sample and sampling strategy A total of 2903 primary caregivers with children (0–3) years old were recruited for the study. Primary caregivers included biological parents, or in their absence, adults who stay with the child on a full-time basis and were responsible for their daily care [ 52 ]. Notably, 95.8% of the participants were biological mothers of the children. Eligible participants were primary caregivers of young children between 0–3 years residing in Nairobi County and who could provide informed consent. Of those approached, 30 eligible participants refused to participate. Participants unavailable during the interview, unable to understand Swahili or English, or serving as temporary child caregivers were excluded. Nairobi City County comprises 17 sub-counties, all of which were included, with sample sizes allocated proportionate to their estimated population sizes. Households were selected using a multi-stage cluster sampling approach, with sub-counties serving as the clusters. Within each sub-county, households with at least one child aged 0–3 years were randomly selected from routinely updated community health unit household listings maintained by the Nairobi City County community health strategy. In multi-household compounds, eligible households were enumerated, and one was randomly chosen. When multiple eligible caregivers or caregiver–child dyads were present within a household, one dyad was randomly selected to minimise intra-household clustering. Recruitment and data collection procedures The initial step was study entry and engagement meetings with both county and sub-county officials to introduce the study and discuss the data collection approach. The community health services coordinators, including the community health services personnel (county and sub-county), community health assistants (CHAs), and community health promoters (CHPs), supported the identification and entry to households with eligible participants. Forty-two trained field enumerators (25 males and 17 females) collected the data under the supervision of six field supervisors . The six supervisors (4 females and 2 males) had training in nursing (n = 2), public health (n = 1), statistics (n = 1), nutrition (n = 1), and sociology (n = 1). One supervisor was a diploma-trained registered nurse, while the remaining five held bachelor’s degrees. All had at least five years of research experience. The field enumerators held a degree or diploma-level qualification in social sciences, health, or a related field, with prior experience conducting survey research. They underwent five days of training covering survey objectives, survey tool administration, research ethics, including consent procedures, community entry, and navigating field challenges. They were trained on psychological first aid to deal with participants in need of mental health support. Emphasis was on survey tool administration and electronic data collection through the Open Data Kit (ODK) software in tablet offline mode. The training was led by the study investigators, including the principal investigator, and supported by team supervisors with extensive experience in maternal, child health and mental health research. To ensure clarity and appropriateness of the survey instruments, a one-day field pre-test was conducted among primary caregivers of young children in a neighbouring informal settlement not included in the main study site. On the survey administration date, CHPs who work in and are part of the community accompanied field enumerators to households and introduced them to eligible participants. The field enumerators obtained written informed consent and collected the data in private spaces within the homes. Participants who screened positive for severe depressive or anxiety symptoms, expressed suicidal ideation, or disclosed safety concerns received immediate psychological first aid, after which the enumerator confirmed with the participant the need for referral. The participant’s information was then forwarded to the field supervisor, who coordinated with the CHAs to ensure referral to available mental health and psychosocial support services within Nairobi City County, including the nearest government-approved health facilities and community-based mental health resources. Data were collected between 25 September 2024 and 25 October 2024 electronically using ODK software (v2022.2.3) on password-protected tablets. Study measures Socio-demographic and health questionnaire: A structured, interviewer-administered questionnaire was used to collect data such as respondents’ age, occupation, marital status, number of household members, education level, and settlement type. The health history of the caregiver-child dyad was also asked. For example, the history of child admission, attendance of antenatal clinics, and whether the caregiver experienced complications during the pregnancy of the index child. We used stunting and underweight to assess children’s nutritional status. The heights and weights of children were measured following the World Health Organisation (WHO) recommended procedures [ 53 , 54 ]. Weight was measured using mobile-calibrated SECA digital scales to the nearest 0.1 kg. Length/height was measured using a SECA length board to the nearest 0.1 cm, with recumbent length taken for children below two years and standing height for those above two years. The “zscorer” package in R statistical software was used to calculate height-for-age z-scores (HAZ) and weight-for-age z-scores (WAZ) [ 55 ] . We defined stunting and underweight as HAZ and WAZ scores 2 standard deviations below the median of the WHO 2006 Growth Standards Reference population [ 53 ]. An asset index scale was administered as a proxy for socio-economic status. We used variables employed by the Demographic Health Survey (DHS), including household asset ownership, house characteristics and quality, water and sanitation, in a principal component analysis framework [ 56 ] and categorised participants as having a wealth index status of either low, medium, or high. Caregivers’ mental health: The Patient Health Questionnaire (PHQ-9) [ 57 ] and Generalised Anxiety Disorder (GAD-7) scales [ 58 ] were administered to screen for depressive and anxiety symptomology, respectively. Swahili versions of these tools have undergone adaptation and validation among the adult population in Kenya [ 59 , 60 ]. A total score of 10 or higher (≥10) was set as the threshold for a positive screen for depressive and anxiety symptoms based on previous validations in East Africa [ 61 ]. The internal consistency in the current study was PHQ-9, a = 0.81; GAD-7, a = 0.85. Household Food Insecurity Access Scale (HFIAS): The HFIAS tool [ 62 ], consists of nine questions that explore food-related experiences over the previous four weeks, covering worries about food supply, inadequate food variety, and reduced food consumption and its physical effects [ 62 ]. Respondents indicated whether they experienced each situation and, if so, how frequently. Scores ranged from 0 to 27, with higher scores signifying more severe food insecurity. The HFIAS showed an excellent internal consistency in the study (a = 0.91). We further classified HFIAS into four categories: secure, mildly insecure, moderately insecure, and severely insecure [ 62 ] and used the categorised variable in the regression analyses. Paternal involvement: Paternal involvement was assessed using a 9-item scale designed to evaluate the extent of male caregiver support in childcare and parenting responsibilities. The tool was adapted from the UNICEF Multiple Indicator Cluster Survey (MICS) paternal involvement module [ 63 ] and customised for a study focusing on paternal engagement in Nairobi’s informal settlements, where it demonstrated acceptable internal consistency (Cronbach’s alpha = 0.77) [ 64 ]. This scale explored various domains of involvement, including engagement in playing and communication with the child, support for breastfeeding and infant feeding, contribution to household expenses and children#39;s education, accompaniment to perinatal clinics, and provision of psychological support. Male participants responded regarding their own involvement in caregiving and parenting responsibilities. The participants rated the frequency of their spouse#39;s/child#39;s father#39;s/male caregiver#39;s support for each item using a four-point Likert scale rating from never ‘0’ to always ‘3’. Responses were totaled to compute the paternal involvement scores, ranging from 0 to 27, with higher scores reflecting greater paternal involvement. The internal consistency in the current study was excellent (a = 0.89). Ethical considerations Ethics approval was granted by the Institutional Ethics and Review Committee at Aga Khan University (ISERC) [Ref:2024/ISERC-136(V4)] and research authorisation from the National Commission for Science, Technology & Innovation (NACOSTI) [Ref: NACOSTI/P/24/40313]. Additionally, the participants gave written informed consent. Data analysis Statistical analyses were conducted using R software version 4.4.1 [ 65 ]. Continuous variables were summarised using mean (standard deviation (SD), while frequencies (%) were used to describe categorical variables. The 95% confidence interval (CI) were computed to describe the prevalence of depressive and anxiety symptoms and comorbid depressive and anxiety symptoms in our sample, presented in summary tables. Two separate multivariable mixed effects logistic regression models with random intercepts for sub-counties were used to assess the factors associated with a positive screen for depressive and anxiety symptoms. We used mixed-effects models to account for clustering within sub-counties. The models included child, caregiver, and household characteristics as the explanatory variables. Statistical significance was set at p-value <0.05. We report adjusted odds ratios (aOR). It has been shown that it is appropriate to use PHQ-9 during the postnatal period [ 66 ]. However, our study sample included caregivers of children 0–3 years, and there was a chance for an inflated prevalence of a positive screen for depressive symptoms independent of affective symptoms due to the overlap between some PHQ-9 items with typical postnatal experiences, especially among mothers of infants. We therefore conducted a sensitivity analysis by stratifying prevalence by child age bands to assess whether the prevalence of a positive screen for depressive symptoms was elevated among mothers of infants relative to mothers of older children. Results Missing values There were no missing values for either of the study’s outcome variables. However, some predictor variables had missing values as shown in Table 1 . The proportion of missingness for these predictor values was < 5% and ranged between 0% to 2.5%. As a result, we conducted a complete case analysis. Table 1. Participant sociodemographic characteristics. Characteristic N = 2,903 Child age (months), Mean (SD) 15.12 (9.8) Child gender, n (%) Female 1,458 (50.2) Male 1,445 (49.8) Mother attended any ANC, n (%) 2,844 (98.0) Missing 2 Place of delivery, n (%) Home 62 (2.1) Hospital/clinic 2,834 (97.9) Missing 7 Child ever admitted to a hospital, n (%) 270 (9.3) Missing 1 Child birthweight, n (%) Normal 2,635 (91.8) Low 236 (8.2) Missing 32 Mother had problems during pregnancy, n (%) 718 (24.8) Missing 9 Stunted, n (%) 623 (22.0) Missing 73 Underweight, n (%) 284 (10.0) Missing 60 Caregiver#39;s age (years), Mean (SD) 29.13 (7.0) Missing 23 Relationship to the child, n (%) Aunt/ Uncle 10 (0.3) Biological father 62 (2.1) Biological mother 2,782 (95.8) Brother/ Sister 2 (0.1) Grandparent 46 (1.6) Step parent 1 (0.0) Marital status, n (%) Single 403 (13.9) Married/cohabiting 2,230 (76.8) Separated/divorced/widowed 270 (9.3) Education level, n (%) Primary or less 820 (28.2) Secondary 1,586 (54.6) Tertiary 497 (17.1) Employment status, n (%) Not working 1,597 (55.0) Working 1,306 (45.0) Wealth status, n (%) Low 707 (24.4) Middle 1,481 (51.0) High 715 (24.6) Settlement type, n (%) Formal 1,142 (39.3) Informal 1,761 (60.7) No. of children ≤ 3 years, n (%) 1 2,524 (88.0) 2+ 343 (12.0) Missing 36 Food security status, n (%) Secure 671 (23.1) Mild insecure 464 (16.0) Moderately insecure 1,668 (57.5) Severely insecure 100 (3.4) Household has a child living with disability, n (%) 54 (1.9) Missing 11 Paternal involvement score, Mean (SD) 14.41 (8.2) Open in a new tab Participants characteristics Table 1 summarises sociodemographic characteristics of the 2903 child-caregiver dyads in the study. Most participants (95.8%) were the biological mothers of children, with a mean (SD) age of 29.1 (7.0) years, married or cohabiting (76.8%), and had attained secondary education (54.6%). Households were predominantly from informal settlements (60.7%), experienced moderate food insecurity (57.5%), and had a middle wealth index status (51.0%). The mean age of the children in the study was 15.1 months (SD = 9.8), with 50.2% being female. Most children (97.9%) were delivered in hospital or clinic settings. Prevalence of depressive and anxiety symptoms Fig 2 shows the proportion (95% CI) of participants in our sample with depressive symptoms, anxiety symptoms, and comorbid depressive and anxiety symptoms. Approximately 402 (13.8%; 95% CI: 12.6% to 15.2%) and 232 (8.0%; 95% CI: 7.0% to 9.0%) caregivers had depressive (PHQ-9 sum scores ≥10) and anxiety (GAD-7 sum scores ≥10) symptoms, respectively. Only 155 (5.3%; 95% CI: 4.5% to 6.2%) caregivers had comorbid depressive and anxiety symptoms. Approximately 281 (9.7%), 91 (3.1%), and 30 (1.0%) of the participants had mild, moderately severe, and severe depressive symptoms, respectively, while 169 participants (5.8%) had moderate anxiety symptoms, and 63 (2.2%) had severe anxiety symptoms ( Table 2 ). Our sensitivity analysis suggested that the prevalence of a positive screen for depressive symptoms was relatively stable across child age bands with overlapping confidence intervals, with no indication of higher prevalence among mothers of infants (see S1 Fig ). Fig 2. Prevalence of depressive and anxiety symptoms. Open in a new tab Prevalence (95% CI) of depressive (PHQ-9 sum score ≥10), anxiety (GAD-7 sum score ≥10), and comorbid symptoms. Table 2. Severity of depressive and anxiety symptoms. n/N Prevalence (%) (95% CI) Severity of depressive symptoms None (0–4) 1524/2903 52.5 (50.7 to 54.3) Mild (5–9) 977/2903 33.7 (31.9 to 35.4) Moderate (10–14) 281/2903 9.7 (8.6 to 10.8) Moderately severe (15–19) 91/2903 3.1 (2.5 to 3.8) Severe (20–27) 30/2903 1 (0.7 to 1.5) Severity of anxiety symptoms None (0–4) 1973/2903 68 (66.2 to 69.7) Mild (5–9) 698/2903 24 (22.5 to 25.6) Moderate (10–14) 169/2903 5.8 (5 to 6.7) Severe (15–21) 63/2903 2.2 (1.7 to 2.8) Open in a new tab Notes: The numbers in brackets represent the range of sum scores for PHQ-9 and GAD-7, respectively. Factors associated with depressive and anxiety symptoms Factors associated with depressive and anxiety symptom scores. Results from the multivariable logistic regression models of the factors associated with a positive screen for depressive and anxiety symptoms are presented in Table 3 . Caregivers of children ever admitted to a hospital had higher odds of depressive symptoms (aOR (adjusted odds ratio) =1.54; 95% CI:1.07 to 2.24, P = 0.018) relative to caregivers of children who had never been admitted. Tertiary education level was associated with lower odds of depressive symptoms (aOR =0.65; 95% CI: 0.42 to 0.99, P = 0.049) relative to primary education or less. Table 3. Results from multivariable logistic regression models of factors associated with depressive and anxiety symptoms. Depressive symptoms Anxiety symptoms Characteristic adjusted OR (95% CI) P-value adjusted OR (95% CI) P-value Child age (months) 1.0 (0.98 to 1.01) 0.427 1.00 (0.98 to 1.01) 0.884 Child gender Female — — Male 1.01 (0.80 to 1.28) 0.926 0.88 (0.66 to 1.19) 0.415 Mother attended any ANC No — — Yes 0.94 (0.42 to 2.36) 0.889 1.64 (0.55 to 7.08) 0.436 Place of delivery Home — — Hospital/clinic 1.27 (0.58 to 3.14) 0.569 1.67 (0.57 to 7.17) 0.412 Child ever admitted to a hospital No — — Yes 1.56 (1.07 to 2.24) 0.018 1.55 (0.98 to 2.39) 0.055 Child birthweight Normal — — Low 0.77 (0.49 to 1.18) 0.246 0.68 (0.37 to 1.18) 0.191 Maternal complications during pregnancy No — — Yes 2.36 (1.84 to 3.03) <0.001 1.61 (1.18 to 2.19) 0.003 Stunted No — — Yes 1.35 (1.00 to 1.81) 0.050 0.87 (0.58 to 1.28) 0.483 Underweight No — — Yes 1.11 (0.74 to 1.64) 0.617 1.42 (0.86 to 2.27) 0.157 Caregiver#39;s age (years) 1.01 (0.99 to 1.03) 0.280 0.98 (0.96 to 1.00) 0.097 Marital status Married/cohabiting — — Single 1.00 (0.65 to 1.52) 0.989 0.69 (0.40 to 1.16) 0.165 Separated/divorced/widowed 1.34 (0.87 to 2.04) 0.180 0.87 (0.51 to 1.47) 0.611 Education level Primary or less — — Secondary 0.88 (0.67 to 1.15) 0.337 0.85 (0.61 to 1.19) 0.339 Tertiary 0.65 (0.42 to 0.99) 0.049 0.83 (0.49 to 1.36) 0.466 Employment status Not working — — Working 1.09 (0.85 to 1.39) 0.507 1.07 (0.78 to 1.45) 0.679 Wealth status Low — — Middle 0.62 (0.47 to 0.81) <0.001 0.68 (0.49 to 0.96) 0.027 High 0.67 (0.46 to 0.97) 0.038 0.63 (0.39 to 1.02) 0.064 Settlement type Formal — — Informal 1.00 (0.77 to 1.31) 0.993 0.74 (0.54 to 1.02) 0.062 No. of children ≤ 3 years 1 — — 2+ 1.21 (0.84 to 1.71) 0.290 1.07 (0.67 to 1.65) 0.773 Food security status Secure — — Mild insecure 1.62 (0.93 to 2.87) 0.092 0.70 (0.26 to 1.73) 0.447 Moderately insecure 3.67 (2.39 to 5.87) <0.001 4.59 (2.64 to 8.70) <0.001 Severely insecure 1.61 (0.64 to 3.67) 0.277 0.76 (0.12 to 2.87) 0.722 Household has a child living with disability No — — Yes 1.57 (0.75 to 3.13) 0.211 1.47 (0.57 to 3.32) 0.382 Paternal involvement 0.95 (0.93 to 0.97) <0.001 0.93 (0.91 to 0.96) <0.001 Open in a new tab Abbreviations: CI = Confidence Interval, OR = Odds Ratio. Common factors associated with both depressive and anxiety symptoms included a history of maternal complications during pregnancy, wealth status, food insecurity status, and paternal involvement. A history of maternal complications during pregnancy for the index child was associated with higher odds of depressive (aOR = 2.36; 95% CI: 1.84 to 3.03, P < 0.001) and anxiety (aOR=1,61; 95% CI: 1.18 to 21.9, P = 0.003). Caregivers from households with a high (aOR = 0.67; 95% CI: 0.46 to 0.97, P = 0.038) and middle wealth status (aOR = 0.67; 95% CI: 0.46 to 0.97, P = 0.038) had lower odds of depressive symptoms while those from households with middle wealth status also had lower odds of anxiety symptoms (aOR=0.68; 95% CI: 0.49 to 0.96, P = 0.027) compared to those from low-wealth households. In addition, compared to those with a secure food insecurity status, the odds of depressive symptoms and anxiety symptoms were 3.67 (95% CI: 2.39 to 5.87, P < 0.001) and 4.59 (95% CI: 2.64 to 8.70, P < 0.001) times higher among those from moderately food insecure households, respectively. Finally, paternal involvement was associate with lower odds of depressive and anxiety symptoms with a unit increase in paternal involvement scores associated with 5.0% (95% CI: 3.0% to 7%), P < 0.001) and 7.0% (95% CI: 4.0% to 9%, P < 0.001) lower odds of depressive and anxiety symptoms, respectively ( Table 3 ). Discussion This study examined the prevalence and correlates of depressive and anxiety symptoms among caregivers of children 0–3 years in Nairobi, Kenya. Notably, 13.8% of caregivers screened positive for depressive symptoms and 8.0% for anxiety symptoms. The majority of those with depressive symptoms experienced mild to moderate severe symptoms, whereas most of those with anxiety symptoms reported mild to moderate levels of severity. Several factors were associated with caregiver mental health outcomes. For instance, pregnancy-related complications, lower household wealth status, household food insecurity, and low paternal involvement emerged as common correlates of both depressive and anxiety symptoms. Additional predictors specific to depressive symptoms included a history of child hospitalisation and a lower education level. The observed rates of depressive and anxiety symptoms in our study align with global estimates from other LMICs, where the prevalence of perinatal mental health issues is disproportionately higher [ 5 ]. The prevalence of anxiety symptoms observed in our study (8.0%) is comparable to previous estimates of 6.4% from a community-based cross-sectional analysis of pregnant women in their third trimester residing in an urban informal settlement in Nairobi City County [ 15 ] and 6.6%, from a cross-sectional study among urban-based pregnant women in their second and third trimesters attending antenatal care clinic [ 16 ]. This suggests a consistent pattern of anxiety symptoms during the perinatal period in Kenya. Interestingly, the prevalence of depressive (13.8%) and anxiety (8.0%) symptoms found in our study is lower than some previous estimates from Nairobi, particularly among specific high-risk groups. For instance, earlier studies reported higher rates of depression in pregnant women (26.9%) [ 15 ], and postnatal mothers (18.7%–64.4%) in urban informal settlements and public hospitals [ 17 – 19 ]. Although our estimates are somewhat lower, they still indicate a notable mental health burden among caregivers of young children in Nairobi. The observed difference could reflect our population-based sampling across both formal and informal urban settings, offering more generalizable estimates of the burden of depressive and anxiety symptoms within the general caregiving population in Nairobi than previous studies limited to clinical or high-risk groups. Caregivers of children who experienced complications during pregnancy reported significantly higher depressive and anxiety symptom scores, suggesting that perinatal maternal complications can have lasting effects on a caregiver’s psychological well-being, potentially heightening vulnerability to both depressive and anxiety symptoms after childbirth [ 67 ]. History of maternal complications during pregnancy, such as pre-existing health conditions or pregnancy-related complications, may contribute to both physical and emotional strain, influencing mental health outcomes [ 68 ]. In the present study, pregnancy-related complications were assessed using caregiver self-report of problems experienced during the index pregnancy and not specific clinical diagnoses. Similar associations between pregnancy-related health complications and poorer maternal mental health outcomes have been reported in Kenya [ 15 ]. These results underscore the need of addressing maternal health complications not only to improve physical outcomes, but also to protect the long-term mental health of caregivers and child outcomes. Future research would benefit from more detailed studies, including those from longitudinal designs, to elucidate the temporal pathways linking adverse pregnancy experiences to caregiver mental health. Marital status was not significantly associated with either depressive or anxiety symptoms in the multivariable analysis. Although caregivers who were separated, widowed, or divorced had higher odds of depressive symptoms compared to those who were married or cohabiting, these associations were not statistically significant, and no differences were observed for anxiety symptoms. These findings contrast with previous studies reporting a protective effect of marriage [ 69 ]. In urban informal settlements, high levels of gender-based violence, relationship conflict, and economic stress may undermine the potential mental health benefits of marriage [ 70 ]. Exposure to intimate partner violence has been consistently associated with increased risk of depression and anxiety among women, including during the perinatal and caregiving periods. As such, being married or cohabiting may not confer psychological protection where relationships are characterised by instability or violence [ 25 , 71 ]. However, this study did not collect data on gender-based violence or relationship quality, limiting our ability to directly assess whether these factors modified the association between marital status and caregiver mental health. Our findings suggest that marital status alone may be an inadequate proxy for psychosocial support, highlighting the need for future research to incorporate measures of intimate partner violence, relationship quality, and social support networks when examining caregiver mental health, particularly in urban settlements. Notably, we found that stronger male involvement in childcare and parenting responsibilities was associated with lower odds of depressive and anxiety symptoms. These results concur with previous studies from LMICs reporting that male involvement is linked to improved mental health outcomes [ 72 , 73 ]. Increased male involvement in caregiving responsibilities can support maternal mental health by strengthening coparenting relationships, improving the quality of the couple’s partnership, and redistributing childcare responsibilities, which may help reduce maternal stress by creating opportunities for women to rest, pursue personal interests, or engage in paid work, all of which can promote better psychosocial well-being [ 74 – 77 ]. Overall, these findings underscore that fostering greater male engagement in ECD may be a key strategy to support family well-being. In many low- and middle-income settings, including SSA, food insecurity, limited access to health care, poverty, and high caregiving burden frequently co-occur, contributing to psychological distress among caregivers [ 1 , 21 , 23 , 78 , 79 ]. We found that depressive and anxiety symptoms were significantly more common among caregivers experiencing moderate food insecurity compared with those from food-secure households. These findings align with prior research demonstrating that food insecurity and limited socioeconomic resources are associated with poorer mental health outcomes [ 80 – 83 ]. This may be explained by the fact that caregivers face psychological stress, including feelings of shame and inadequacy, when they are not able to provide the basic needs of their families, which in turn leads to symptoms of depression and anxiety [ 84 ]. Similarly, caregivers from higher-wealth households had lower odds of both depressive and anxiety symptoms, highlighting the protective role of economic resources in buffering against psychological distress. These results underscore the need for addressing socioeconomic vulnerabilities, including poverty and food insecurity, to support caregiver mental health [ 85 ]. Other factors that were significantly associated with depressive symptoms included recent child hospitalisation and caregiver education level. Caregivers whose children had been recently hospitalised reported higher depressive symptom scores, which is consistent with prior research [ 86 , 87 ], and may reflect the psychological and financial strain that they encounter when their children are unwell. Further, caregivers with tertiary-level education compared to those with primary or less had lower odds of depressive symptoms, consistent with previous research [ 88 ]. This suggests the potential protective role of education, possibly through improved health literacy, problem-solving skills, access to resources, and coping capacity. Implications for policy and practice The findings of this study have important implications for policy and practice in urban low‑resource settings such as Nairobi. First, the burden of depressive and anxiety symptoms among caregivers underscores the urgent need to integrate routine mental health screening, counselling, and referral services into existing maternal, newborn, and child health and ECD programmes, particularly at the primary health care and community levels. Given the strong associations observed with food insecurity and household wealth, mental health interventions should be embedded within broader social protection and poverty‑alleviation initiatives, including food assistance and cash‑transfer programmes, to address the underlying social determinants of caregiver mental health. In addition, the protective role of paternal involvement highlights the importance of policies and programmes that actively promote male engagement in caregiving and parenting, such as father‑inclusive antenatal and postnatal services, parenting programmes, and gender‑transformative interventions. Strengthening support for caregivers who experience pregnancy‑related complications or child hospitalisation, through enhanced follow‑up, psychosocial support, and linkages to mental health services, may further mitigate psychological distress. Collectively, these findings support a multisectoral approach that integrates mental health, social protection, and family‑centred interventions to improve caregiver well‑being and, ultimately, child development outcomes in urban settings. Strengths and limitations This is the first and the largest population-based survey of depressive and anxiety symptoms among primary caregivers with young children (0–3 years) in Nairobi, covering both formal and informal settlements. We had a large sample size which strengthens the representativeness and generalizability of the findings. The study also used adapted and validated tools to assess depression and anxiety ensuring greater reliability of the results. Furthermore, the inclusion of diverse sociodemographic and contextual variables, such as paternal involvement, maternal health, and food insecurity, provides further insights into the factors associated with caregiver mental health. However, the study also has its limitations. Its cross-sectional design prevents any causal inferences and limits the ability to determine the directionality of associations between the identified factors and caregiver mental health outcomes. Also, the utilisation of self-reported data may have introduced social desirability bias. Additionally, the lack of a follow-up clinical assessment limits the interpretation of the extent of depression and anxiety among caregivers, as symptom-based screening may not fully capture clinically significant cases. These results should therefore be interpreted with caution. Nonetheless, the study provides valuable insights for informing targeted interventions to support caregiver mental health in urban low-resource settings. Conclusion and recommendations Caregivers of young children in Nairobi experience a notably high burden of depressive and anxiety symptoms, with key factors such as maternal complications during pregnancy, low paternal involvement, marital disruption, and food insecurity emerging as significant predictors. Additionally, child hospitalisation, caregiver employment, and having a child with disability were associated with higher anxiety symptoms. These findings emphasise the need to prioritise caregiver mental health as part of ECD strategies. We recommend integrating mental health programmes into existing maternal and child health services by establishing routine screening, counselling, information-education sessions and referral systems. Doing so can help raise awareness, reduce stigma, and encourage caregivers to seek support. Strengthening caregiver well-being will also require scaling up parental counselling and coaching interventions delivered through health facilities, community groups, home visits, and digital platforms. Moreover, promoting male involvement and ensuring tailored support for caregivers facing complex challenges can enhance family resilience and child development outcomes. Finally, further research is needed to assess the long-term effects of caregiver mental health on child development and evaluate the impact of scalable, context-specific mental health interventions in urban low-resource settings. Supporting information S1 Fig. Prevalence of a positive screen for depressive symptoms among caregivers by child age band. (DOCX) pgph.0006037.s001.docx (136.8KB, docx) S2 Fig. Participant flow diagram. (DOCX) pgph.0006037.s002.docx (24.8KB, docx) S1 Table. STROBE Checklist. (DOCX) pgph.0006037.s003.docx (21.4KB, docx) Acknowledgments We would like to thank the caregivers and children of Nairobi City who generously participated in the study and provided the data upon which these findings are based. We are also grateful to the 42 field enumerators for their hard work and vital contribution to the data collection process. We acknowledge the Nairobi City County Government for granting approval to conduct the study and for supporting coordination efforts. Special thanks to the community health services personnel (county and sub-county), CHAs, and CHPs for their critical role in mobilising participants and facilitation of field activities. Lastly, we express our gratitude to the Aga Khan University, Institute for Human Development, and its dedicated teams—including administrative staff (Lorraine Asige, Moreen Mutiku, Rita Ochieng, Esther Peter, Caroline Kinyanjui), field supervisors (Eunice Njoroge, Kevin Wekesa, Martha Kaniala, Isaac Lihanda), data team (Anita Kerubo, John Mungai, Collins Akatch, Joseph Mbugua), and all involved personnel at Aga Khan University for their invaluable support in ensuring the successful implementation of this research. Data Availability The de-identified dataset used and analysed during the current study will be made available upon reasonable request, in due consideration of Aga Khan University’s data-sharing policies. Requests to access the dataset should be directed to the Research Office, Aga Khan University Kenya, via [email protected] or [email protected] . Funding Statement This work was supported by funding from BigWin Philanthropy in the UK to AA. EJC, JA, ED and AA also received support in part from the Science for Africa Foundation through grant number DEL-22-002, with funding from Wellcome Trust and the UK Foreign, Commonwealth & Development Office. This support is part of the EDCTP2 programme supported by the European Union. The funders did not play any role in the design of the study, its data collection, analysis, and interpretation of findings, or in writing this manuscript. References 1. Patel V, Saxena S, Lund C, Thornicroft G, Baingana F, Bolton P, et al. The Lancet Commission on global mental health and sustainable development. Lancet. 2018;392(10157):1553–98. doi: 10.1016/S0140-6736(18)31612-X [ DOI ] [ PubMed ] [ Google Scholar ] 2. GBD Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137–50. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. World Health Organization. Key facts: Depressive Disorder (Depression); 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/depression [ Google Scholar ] 4. Wang Z, Liu J, Shuai H, Cai Z, Fu X, Liu Y, et al. Mapping global prevalence of depression among postpartum women. Transl Psychiatry. 2021;11(1):543. doi: 10.1038/s41398-021-01663-6 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Woody CA, Ferrari AJ, Siskind DJ, Whiteford HA, Harris MG. A systematic review and meta-regression of the prevalence and incidence of perinatal depression. J Affect Disord. 2017;219:86–92. doi: 10.1016/j.jad.2017.05.003 [ DOI ] [ PubMed ] [ Google Scholar ] 6. Gelaye B, Rondon MB, Araya R, Williams MA. Epidemiology of maternal depression, risk factors, and child outcomes in low-income and middle-income countries. Lancet Psychiatry. 2016;3(10):973–82. doi: 10.1016/S2215-0366(16)30284-X [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Mutiso VN, Musyimi CW, Gitonga I, Tele A, Ndetei DM. Depression and Intimate Partner Violence (IPV) in mothers 6 weeks to 12 months post-delivery in a rural setting in Kenya. Transcult Psychiatry. 2024;61(4):596–609. doi: 10.1177/13634615231187259 [ DOI ] [ PubMed ] [ Google Scholar ] 8. Patel V, Kleinman A. Poverty and common mental disorders in developing countries. Bull World Health Organ. 2003;81(8):609–15. [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Nweke M, Ukwuoma M, Adiuku-Brown AC, Okemuo AJ, Ugwu PI, Nseka E. Burden of postpartum depression in sub-Saharan Africa: an updated systematic review. S Afr J Sci. 2024;120(1/2). doi: 10.17159/sajs.2024/14197 [ DOI ] [ Google Scholar ] 10. Nilaweera I, Doran F, Fisher J. Prevalence, nature and determinants of postpartum mental health problems among women who have migrated from South Asian to high-income countries: a systematic review of the evidence. J Affect Disord. 2014;166:213–26. doi: 10.1016/j.jad.2014.05.021 [ DOI ] [ PubMed ] [ Google Scholar ] 11. Dadi AF, Miller ER, Mwanri L. Postnatal depression and its association with adverse infant health outcomes in low- and middle-income countries: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2020;20(1):416. doi: 10.1186/s12884-020-03092-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Dennis C-L, Falah-Hassani K, Shiri R. Prevalence of antenatal and postnatal anxiety: systematic review and meta-analysis. Br J Psychiatry. 2017;210(5):315–23. doi: 10.1192/bjp.bp.116.187179 [ DOI ] [ PubMed ] [ Google Scholar ] 13. Owuor S. Urbanization and household food security in Nairobi, Kenya. In: Sustainable development in Africa: case studies; 2019. 161 p. [ Google Scholar ] 14. Omwenga M, editor. Nairobi-Emerging metropolitan region: development planning and management opportunities and challenges. ISOCARP CONGRESS; 2010. [ Google Scholar ] 15. Mulupi S, Abubakar A, Nyongesa MK, Angwenyi V, Kabue M, Mwangi PM, et al. Prevalence and correlates of depressive and anxiety symptoms among pregnant women from an urban informal settlement in Nairobi, Kenya: a community-based cross-sectional study. BMC Pregnancy Childbirth. 2025;25(1):213. doi: 10.1186/s12884-025-07339-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Adina J, Morawska A, Mitchell AE, Haslam D, Ayuku D. Depression and anxiety in second and third trimesters among pregnant women in Kenya: a hospital-based prevalence study. J Affect Disord Rep. 2022;10:100447. doi: 10.1016/j.jadr.2022.100447 [ DOI ] [ Google Scholar ] 17. Ongeri L, Wanga V, Otieno P, Mbui J, Juma E, Stoep AV, et al. Demographic, psychosocial and clinical factors associated with postpartum depression in Kenyan women. BMC Psychiatry. 2018;18(1):318. doi: 10.1186/s12888-018-1904-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Kariuki EW, Kuria MW, Were FN, Ndetei DM. Predictors of postnatal depression in the slums Nairobi, Kenya: a cross-sectional study. BMC Psychiatry. 2022;22(1):242. doi: 10.1186/s12888-022-03885-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Haithar S, Kuria MW, Sheikh A, Kumar M, Vander Stoep A. Maternal depression and child severe acute malnutrition: a case-control study from Kenya. BMC Pediatr. 2018;18(1):289. doi: 10.1186/s12887-018-1261-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Mutua J, Kigamwa P, Ng’ang’a P, Tele A, Kumar M. A comparative study of postpartum anxiety and depression in mothers with pre-term births in Kenya. J Affect Disord Rep. 2020;2:100043. doi: 10.1016/j.jadr.2020.100043 [ DOI ] [ Google Scholar ] 21. Fisher J, Cabral de Mello M, Patel V, Rahman A, Tran T, Holton S, et al. Prevalence and determinants of common perinatal mental disorders in women in low- and lower-middle-income countries: a systematic review. Bull World Health Organ. 2012;90(2):139G-149G. doi: 10.2471/BLT.11.091850 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Kivimäki M, Batty GD, Pentti J, Shipley MJ, Sipilä PN, Nyberg ST, et al. Association between socioeconomic status and the development of mental and physical health conditions in adulthood: a multi-cohort study. Lancet Public Health. 2020;5(3):e140–9. doi: 10.1016/S2468-2667(19)30248-8 [ DOI ] [ PubMed ] [ Google Scholar ] 23. Lund C, Breen A, Flisher AJ, Kakuma R, Corrigall J, Joska JA, et al. Poverty and common mental disorders in low and middle income countries: a systematic review. Soc Sci Med. 2010;71(3):517–28. doi: 10.1016/j.socscimed.2010.04.027 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Myers CA. Food insecurity and psychological distress: a review of the recent literature. Curr Nutr Rep. 2020;9(2):107–18. doi: 10.1007/s13668-020-00309-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Howard LM, Oram S, Galley H, Trevillion K, Feder G. Domestic violence and perinatal mental disorders: a systematic review and meta-analysis. PLoS Med. 2013;10(5):e1001452. doi: 10.1371/journal.pmed.1001452 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Gausia K, Fisher C, Ali M, Oosthuizen J. Antenatal depression and suicidal ideation among rural Bangladeshi women: a community-based study. Arch Women’s Ment Health. 2009;12:351–8. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Biaggi A, Conroy S, Pawlby S, Pariante CM. Identifying the women at risk of antenatal anxiety and depression: a systematic review. J Affect Disord. 2016;191:62–77. doi: 10.1016/j.jad.2015.11.014 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Agoub M, Moussaoui D, Battas O. Prevalence of postpartum depression in a Moroccan sample. Arch Womens Ment Health. 2005;8(1):37–43. doi: 10.1007/s00737-005-0069-9 [ DOI ] [ PubMed ] [ Google Scholar ] 29. Rahman A, Surkan PJ, Cayetano CE, Rwagatare P, Dickson KE. Grand challenges: integrating maternal mental health into maternal and child health programmes. PLoS Med. 2013;10(5):e1001442. doi: 10.1371/journal.pmed.1001442 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Kim J, Hagen E, Muindi Z, Mbonglou G, Laituri M. An examination of water, sanitation, and hygiene (WASH) accessibility and opportunity in urban informal settlements during the COVID-19 pandemic: Evidence from Nairobi, Kenya. Sci Total Environ. 2022;823:153398. doi: 10.1016/j.scitotenv.2022.153398 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Werlin H. The slums of Nairobi: explaining urban misery. World Aff. 2006;169(1):39–48. doi: 10.3200/wafs.169.1.39-48 [ DOI ] [ Google Scholar ] 32. KNBS, ICF. Kenya demographic and health survey 2022: key indicators report. Nairobi, Kenya; Rockville, Maryland, USA; 2023. [ Google Scholar ] 33. Madeghe BA, Kimani VN, Vander Stoep A, Nicodimos S, Kumar M. Postpartum depression and infant feeding practices in a low income urban settlement in Nairobi-Kenya. BMC Res Notes. 2016;9(1):506. doi: 10.1186/s13104-016-2307-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. African Population Health Research Center. Population and health dynamics in Nairobi#39;s informal settlements: report of the Nairobi cross-sectional slums survey (NCSS) 2000. Nairobi: African Population and Health Research Center; 2002. [ Google Scholar ] 35. Henderson JJ, Evans SF, Straton JAY, Priest SR, Hagan R. Impact of postnatal depression on breastfeeding duration. Birth. 2003;30(3):175–80. doi: 10.1046/j.1523-536x.2003.00242.x [ DOI ] [ PubMed ] [ Google Scholar ] 36. Paulson JF, Dauber S, Leiferman JA. Individual and combined effects of postpartum depression in mothers and fathers on parenting behavior. Pediatrics. 2006;118(2):659–68. doi: 10.1542/peds.2005-2948 [ DOI ] [ PubMed ] [ Google Scholar ] 37. Coyl DD, Roggman LA, Newland LA. Stress, maternal depression, and negative mother–infant interactions in relation to infant attachment. Infant Ment Health J. 2002;23(1–2):145–63. doi: 10.1002/imhj.10009 [ DOI ] [ Google Scholar ] 38. Kingston D, Tough S, Whitfield H. Prenatal and postpartum maternal psychological distress and infant development: a systematic review. Child Psychiatry Hum Dev. 2012;43(5):683–714. doi: 10.1007/s10578-012-0291-4 [ DOI ] [ PubMed ] [ Google Scholar ] 39. Abajobir A, Maina D, Wambui E, Sidze EM. Association between maternal mental health and early childhood development, nutrition, and common childhood illnesses in Khwisero subcounty, Kenya. PLoS One. 2025;20(1):e0317762. doi: 10.1371/journal.pone.0317762 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Laurenzi CA, Hunt X, Skeen S, Sundin P, Weiss RE, Kosi V, et al. Associations between caregiver mental health and young children’s behaviour in a rural Kenyan sample. Glob Health Action. 2021;14(1):1861909. doi: 10.1080/16549716.2020.1861909 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Rotheram-Fuller EJ, Tomlinson M, Scheffler A, Weichle TW, Hayati Rezvan P, Comulada WS, et al. Maternal patterns of antenatal and postnatal depressed mood and the impact on child health at 3-years postpartum. J Consult Clin Psychol. 2018;86(3):218–30. doi: 10.1037/ccp0000281 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Tele A, Kathono J, Mwaniga S, Nyongesa V, Yator O, Gachuno O, et al. Prevalence and risk factors associated with depression in pregnant adolescents in Nairobi, Kenya. J Affect Disord Rep. 2022;10:100424. doi: 10.1016/j.jadr.2022.100424 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Angwenyi V, Kabue M, Chongwo E, Mabrouk A, Too EK, Odhiambo R, et al. Mental health during COVID-19 pandemic among caregivers of young children in Kenya’s urban informal settlements. a cross-sectional telephone survey. Int J Environ Res Public Health. 2021;18(19):10092. doi: 10.3390/ijerph181910092 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Health WHODoM, Abuse S, Evidence WHOMH, Team R. Mental health atlas 2005. World Health Organization; 2005. [ Google Scholar ] 45. Rathod S, Pinninti N, Irfan M, Gorczynski P, Rathod P, Gega L, et al. Mental health service provision in low- and middle-income countries. Health Serv Insights. 2017;10:1178632917694350. doi: 10.1177/1178632917694350 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Ongeri L, Nyawira M, Kariuki SM, Theuri C, Bitta M, Penninx B, et al. Sociocultural perspectives on suicidal behaviour at the Coast Region of Kenya: an exploratory qualitative study. BMJ Open. 2022;12(4):e056640. doi: 10.1136/bmjopen-2021-056640 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Kumar M, Nyongesa V, Kagoya M, Mutamba BB, Amugune B, Krishnam NS, et al. Mapping services at two Nairobi County primary health facilities: identifying challenges and opportunities in integrated mental health care as a Universal Health Coverage (UHC) priority. Ann Gen Psychiatry. 2021;20(1):37. doi: 10.1186/s12991-021-00359-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. KNBS. Kenya population and housing census volume I: population by County and Sub county. 2019. 49. UN-Habitat. Bridging the affordability gap: towards a financing mechanism for slum upgrading at scale in Nairobi. 2019. 50. Da Cruz F. Nairobi urban sector profile. UN-HABITAT; 2006. [ Google Scholar ] 51. Hijmans RJ, Barbosa M, Ghosh A, Mandel A. Geodata: download geographic data. R package version 05-9. 2023. 52. Roth DL, Fredman L, Haley WE. Informal caregiving and its impact on health: a reappraisal from population-based studies. Gerontologist. 2015;55(2):309–19. doi: 10.1093/geront/gnu177 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards based on length/height, weight and age. Acta Paediatr Suppl. 2006;450:76–85. doi: 10.1111/j.1651-2227.2006.tb02378.x [ DOI ] [ PubMed ] [ Google Scholar ] 54. de Onis M, Onyango A, Borghi E, Siyam A, Blössner M, Lutter C, et al. Worldwide implementation of the WHO Child Growth Standards. Public Health Nutr. 2012;15(9):1603–10. doi: 10.1017/S136898001200105X [ DOI ] [ PubMed ] [ Google Scholar ] 55. Myatt M, Guevarra E. Zscorer: child anthropometry z-score calculator. 1st ed. Vienna, Austria: R Foundation; 2019. [ Google Scholar ] 56. Rutstein SO, Johnson K. The DHS Wealth Index. Calverton (MD): ORC Macro; 2004. Report No.: DHS Comparative Reports No. 6. [ Google Scholar ] 57. Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Thorofare (NJ): Slack Incorporated; 2002. p. 509–15. [ Google Scholar ] 58. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7. doi: 10.1001/archinte.166.10.1092 [ DOI ] [ PubMed ] [ Google Scholar ] 59. Mwangi P, Nyongesa MK, Koot HM, Cuijpers P, Newton CRJC, Abubakar A. Validation of a Swahili version of the 9-item Patient Health Questionnaire (PHQ-9) among adults living with HIV compared to a community sample from Kilifi, Kenya. J Affect Disord Rep. 2020;1:100013. doi: 10.1016/j.jadr.2020.100013 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. Nyongesa MK, Mwangi P, Koot HM, Cuijpers P, Newton CRJC, Abubakar A. The reliability, validity and factorial structure of the Swahili version of the 7-item generalized anxiety disorder scale (GAD-7) among adults living with HIV from Kilifi, Kenya. Ann Gen Psychiatry. 2020;19:62. doi: 10.1186/s12991-020-00312-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 61. Akena D, Joska J, Obuku EA, Stein DJ. Sensitivity and specificity of clinician administered screening instruments in detecting depression among HIV-positive individuals in Uganda. AIDS Care. 2013;25(10):1245–52. doi: 10.1080/09540121.2013.764385 [ DOI ] [ PubMed ] [ Google Scholar ] 62. Coates J, Swindale A, Bilinsky P. Household Food Insecurity Access Scale (HFIAS) for measurement of food access: indicator guide: version 3. 2007. 63. UNICEF G. Multiple indicator cluster survey (MICS). 2015. 64. Angwenyi V, Fletcher R, Mwangi PM, Kabue M, Odhiambo R, Mulupi S, et al. Engaging fathers(to-be): a pilot study on the adaptation and programme experience of SMS4baba intervention in Kenya’s informal settlements. BMC Public Health. 2024;24(1):3603. doi: 10.1186/s12889-024-21057-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Team RDC. R: a language and environment for statistical computing; 2010. [ Google Scholar ] 66. Wang L, Kroenke K, Stump TE, Monahan PO. Screening for perinatal depression with the Patient Health Questionnaire depression scale (PHQ-9): a systematic review and meta-analysis. Gen Hosp Psychiatry. 2021;68:74–82. doi: 10.1016/j.genhosppsych.2020.12.007 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Coussons-Read ME. Effects of prenatal stress on pregnancy and human development: mechanisms and pathways. Obstet Med. 2013;6(2):52–7. doi: 10.1177/1753495X12473751 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. Chen J, Cai Y, Liu Y, Qian J, Ling Q, Zhang W, et al. Factors associated with significant anxiety and depressive symptoms in pregnant women with a history of complications. Shanghai Arch Psychiatry. 2016;28(5):253–62. doi: 10.11919/j.issn.1002-0829.216035 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Ngocho JS, Watt MH, Minja L, Knettel BA, Mmbaga BT, Williams PP, et al. Depression and anxiety among pregnant women living with HIV in Kilimanjaro region, Tanzania. PLoS One. 2019;14(10):e0224515. doi: 10.1371/journal.pone.0224515 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 70. Kimuna SR, Djamba YK. Gender based violence: correlates of physical and sexual wife abuse in Kenya. J Fam Viol. 2008;23(5):333–42. doi: 10.1007/s10896-008-9156-9 [ DOI ] [ Google Scholar ] 71. Devries KM, Mak JY, Bacchus LJ, Child JC, Falder G, Petzold M, et al. Intimate partner violence and incident depressive symptoms and suicide attempts: a systematic review of longitudinal studies. PLoS Med. 2013;10(5):e1001439. doi: 10.1371/journal.pmed.1001439 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 72. Yargawa J, Leonardi-Bee J. Male involvement and maternal health outcomes: systematic review and meta-analysis. J Epidemiol Community Health. 2015;69(6):604–12. doi: 10.1136/jech-2014-204784 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. McCann JK, Freire S, de Oliveira CVR, Ochieng M, Jeong J. Father involvement is a protective factor for maternal mental health in Western Kenya. SSM Ment Health. 2024;5:100318. doi: 10.1016/j.ssmmh.2024.100318 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. McClain L, Brown SL. The roles of fathers’ involvement and coparenting in relationship quality among cohabiting and married parents. Sex Roles. 2017;76(5–6):334–45. doi: 10.1007/s11199-016-0612-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Doyle K, Levtov RG, Barker G, Bastian GG, Bingenheimer JB, Kazimbaya S, et al. Gender-transformative Bandebereho couples’ intervention to promote male engagement in reproductive and maternal health and violence prevention in Rwanda: findings from a randomized controlled trial. PLoS One. 2018;13(4):e0192756. doi: 10.1371/journal.pone.0192756 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. Maina LW, Kimani E, Azevedo A, Parkes A, Ndinda L. Gendered patterns of unpaid care and domestic work in the urban informal settlements of Nairobi, Kenya: findings from a household care survey–2019. 2019. 77. Stepanikova I, Acharya S, Abdalla S, Baker E, Klanova J, Darmstadt GL. Gender discrimination and depressive symptoms among child-bearing women: ELSPAC-CZ cohort study. EClinicalMedicine. 2020;20:100297. doi: 10.1016/j.eclinm.2020.100297 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 78. Gyasi RM, Obeng B, Yeboah JY. Impact of food insecurity with hunger on mental distress among community-dwelling older adults. PLoS One. 2020;15(3):e0229840. doi: 10.1371/journal.pone.0229840 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 79. Tsai AC, Bangsberg DR, Frongillo EA, Hunt PW, Muzoora C, Martin JN, et al. Food insecurity, depression and the modifying role of social support among people living with HIV/AIDS in rural Uganda. Soc Sci Med. 2012;74(12):2012–9. doi: 10.1016/j.socscimed.2012.02.033 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 80. Fawole W, Ilbasmis E, Ozkan B, editors. Food insecurity in Africa in terms of causes, effects and solutions: a case study of Nigeria. 2nd ICSAE 2015, International Conference on Sustainable Agriculture and Environment; 2015. Proceedings book. [ Google Scholar ] 81. Weaver LJ, Hadley C. Moving beyond hunger and nutrition: a systematic review of the evidence linking food insecurity and mental health in developing countries. Ecol Food Nutr. 2009;48(4):263–84. doi: 10.1080/03670240903001167 [ DOI ] [ PubMed ] [ Google Scholar ] 82. Chongwo EJ, Huizink AC, Chelangat D, Njoroge E, Aoko B, Kaniala M, et al. Food insecurity is associated with depressive and anxiety symptoms among caregivers of young children in arid and semi-arid regions of Kenya: a cross-sectional study. J Affect Disord. 2025;389:119754. doi: 10.1016/j.jad.2025.119754 [ DOI ] [ PubMed ] [ Google Scholar ] 83. Carter KN, Blakely T, Collings S, Imlach Gunasekara F, Richardson K. What is the association between wealth and mental health? J Epidemiol Community Health. 2009;63(3):221–6. doi: 10.1136/jech.2008.079483 [ DOI ] [ PubMed ] [ Google Scholar ] 84. Leung CW, Laraia BA, Feiner C, Solis K, Stewart AL, Adler NE, et al. The psychological distress of food insecurity: a qualitative study of the emotional experiences of parents and their coping strategies. J Acad Nutr Diet. 2022;122(10):1903-1910.e2. doi: 10.1016/j.jand.2022.05.010 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 85. Trudell JP, Burnet ML, Ziegler BR, Luginaah I. The impact of food insecurity on mental health in Africa: a systematic review. Soc Sci Med. 2021;278:113953. doi: 10.1016/j.socscimed.2021.113953 [ DOI ] [ PubMed ] [ Google Scholar ] 86. Rodríguez-Rey R, Alonso-Tapia J, Colville G. Prediction of parental posttraumatic stress, anxiety and depression after a child’s critical hospitalization. J Crit Care. 2018;45:149–55. doi: 10.1016/j.jcrc.2018.02.006 [ DOI ] [ PubMed ] [ Google Scholar ] 87. Franck LS, Mehra R, Hodgson CR, Gay C, Rienks J, Lisanti AJ, et al. Prevalence of depression and anxiety symptoms among parents of hospitalized children in 14 countries. Children (Basel). 2025;12(8):1001. doi: 10.3390/children12081001 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 88. Eriku GA, Bekele G, Yitayal MM, Belete Y, Girma Y. Depressive symptoms and its associated factors among primary caregivers of stroke survivors at Amhara regional state tertiary hospitals: multicenter study. Neuropsychiatr Dis Treat. 2023;19:1675–84. doi: 10.2147/NDT.S418074 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials S1 Fig. Prevalence of a positive screen for depressive symptoms among caregivers by child age band. (DOCX) pgph.0006037.s001.docx (136.8KB, docx) S2 Fig. Participant flow diagram. (DOCX) pgph.0006037.s002.docx (24.8KB, docx) S1 Table. STROBE Checklist. (DOCX) pgph.0006037.s003.docx (21.4KB, docx) Data Availability Statement The de-identified dataset used and analysed during the current study will be made available upon reasonable request, in due consideration of Aga Khan University’s data-sharing policies. Requests to access the dataset should be directed to the Research Office, Aga Khan University Kenya, via [email protected] or [email protected] . 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