Abstract
Speech delay can affect a child’s academic performance, communication skills, and social interactions later in life. Despite its prevalence, gaps remain in understanding the risk factors associated with speech delay, particularly in diverse populations. This study aimed to identify sociodemographic, health, and environmental determinants of speech delay in children. This was an age-matched case–control study. A total of 282 responses were collected from the case group and 409 responses were received from the control group in the period from September 2022 to May 2023. Descriptive analysis was used to determine the characteristics of the cases and controls. Logistic regression analysis was employed to examine the association between speech delay and various factors, adjusting for potential confounders, such as age, sex, family history, and health conditions. Boys were significantly more likely to have speech delay than girls (odds ratio [OR], 3.05; 95% CI: 2.18–4.29, P < .001). Children with a family history of speech delay (OR: 2.38, 95% CI: 1.32–4.29, P = .004) were more than twice as likely to have speech delay. Lower educational levels of both fathers (OR: 4.21, 95% CI: 1.35–14.24, P = .015) and mothers (OR: 2.45, 95% CI: 1.27–4.77, P = .008) were significantly associated with a higher risk of speech delay. Moreover, nursery attendance and health conditions, including autism, attention deficit hyperactivity disorder (ADHD), and hearing loss, were strongly associated with speech delay (P < .05). However, family income and parental employment did not show a strong association with delayed speech. This study highlights key factors such as gender, parental education, family history, and certain health conditions (autism, ADHD, and hearing impairment) as strong and significant predictors of speech delay in children. Findings underscore the need for targeted interventions and early screenings to mitigate risks, especially for children in vulnerable groups. Future research should explore the interplay of environmental and genetic factors and refine prevention strategies.
Keywords: case–control, children, Jazan, risk factors, Saudi Arabia, speech delay
1. Introduction
Speech is a fundamental human skill, and spoken language remains the most widely used means of social communication. The ability to communicate thoughts, intentions, and emotions is critical in social interactions, as is understanding and responding to others.[1] Language serves as a measure of cognition, while speech reflects a child’s overall developmental and intellectual capabilities.[2] Language and speech development result from a complex interplay of biological and environmental factors. This study draws on Bronfenbrenner’s Ecological Systems Theory, which emphasizes how environmental systems–from family interactions to societal norms: affect child development.[3] Additionally, the interactionist theory of language development highlights the dynamic relationship between biological predispositions and environmental inputs, such as parental engagement and screen time, in shaping language acquisition.[4]
Speech delays are generally categorized as primary or secondary. Primary delays include conditions such as receptive language problems, expressive language disorders, and developmental speech delays.[5] Secondary delays arise from underlying conditions, including selective mutism, intellectual disabilities, autism spectrum disorder, physical speech impairments, or hearing loss.[5,6] Children with expressive language disorders, for example, face challenges in effectively communicating and expressing themselves. While some children with delayed language milestones eventually catch up, untreated speech and language impairments can increase the risk of behavioral, social, emotional, and cognitive difficulties.[7]
Despite its potential impact, speech delay is often perceived as harmless, leading to a “wait-and-watch” approach that delays diagnosis and intervention.[8] This underscores the need for reliable language assessment methods and monitoring strategies to identify developmental delays early. Tools such as caregiver reports, clinical assessments, and multiple language evaluations are vital for prompt detection and interventions.[9,10] Additionally, abnormal language development can serve as an early indicator of other physical or developmental issues.[9]
Speech delay and language delay differ conceptually.[11] Language delay impacts comprehension and the ability to connect words to create meaning, whereas speech delay refers to difficulties producing sounds necessary for verbal communication. Studies estimate that 40 to 60% of children with untreated speech and language delays continue to struggle into adulthood, facing challenges in behavioral, social, and cognitive domains.[11,12] Early intervention is crucial to mitigate these long-term effects. Parents should be promptly notified when speech or language delays are detected, and referrals to audiologists and speech-language pathologists should be made. Speech-language therapy, particularly for expressive language disorders, is an essential part of the treatment plan.[13,14]
Several factors may increase the risk of language delays in children, including male gender, first-born birth order, and a family history of speech delay.[15] Other contributing factors include the mother’s educational level, prolonged separation from parents, reduced parent-child interaction, excessive screen time (over 2 hours daily), and certain medical conditions such as seizures, oropharyngeal deformities, and birth asphyxia.[10,16] Autism, often accompanied by intellectual disabilities, is strongly associated with language delays.[5] Modern parenting practices, including the frequent use of electronic devices for entertainment or education, further exacerbate the issue.[17] These findings highlight the importance of monitoring and limiting screen time to support healthy language development.
Although many children with early language delays eventually overcome them, some continue to struggle without proper intervention. Early screening for high-risk groups, such as boys and children with a family history of speech delay, alongside community-based parental education programs, can enhance early detection and intervention.[18] Research confirms that early identification of risk factors and active parental involvement are crucial for improving outcomes.[19,20]
In Saudi Arabia, the prevalence of speech delay among preschoolers is reported at 24.5%, significantly higher than the global rate of 2.53%.[15,20] This discrepancy may be due to sociocultural norms, increased parental awareness, high rates of screen time, and a higher prevalence of consanguineous marriages, which are associated with genetic conditions affecting speech development.[15,21,22] The robust healthcare system in Saudi Arabia also facilitates earlier recognition and diagnosis compared to countries with less developed infrastructures.
Despite this high prevalence, limited studies have been conducted in the Jazan region to investigate the specific risk factors for speech delay. Focusing on Jazan is essential, as its unique cultural and socioeconomic factors, such as high consanguinity rates and rural healthcare access challenges, may significantly influence speech development.[23,24] These factors, coupled with socioeconomic disparities like lower parental education levels, underscore the need for region-specific research. By addressing these determinants, this study aims to provide actionable insights to inform targeted interventions and improve outcomes for children with speech delay.
2. Methodology
2.1. Study design and setting
This study used an age-matched case–control design to examine the factors associated with speech delay among children from the Jazan region, located in the southwestern part of Saudi Arabia, in the period from September 2022 to May 2023. The Jazan region comprises 17 governorates, with Jazan City serving as the capital. The age-matched case–control design ensures a focused and equitable analysis of factors influencing speech delay while accounting for the natural variability in developmental stages. This approach maximizes the validity of findings and allows for the identification of actionable risk factors relevant to the population studied.
2.2. Sample size
The sample size for this study was determined using the OpenEpi sample size calculator for case–control studies. Based on previous regional and global literature, the prevalence of speech delay was anticipated to be approximately 24.5% among children in Saudi Arabia, compared to a global prevalence of 2.53% in the general population. To detect a moderate effect size, an odds ratio (OR) of 2.0 was assumed, reflecting significant associations with key risk factors observed in prior research.
To ensure sufficient power, the study was designed with an 80% power level and a 5% significance level (2-tailed). A case-to-control ratio of 1:2 was selected to enhance statistical power while allowing for variability in exposure distributions. With these parameters, the minimum required sample size was calculated to be 144 cases and 288 controls.
To account for potential non-response or incomplete data, the study aimed to recruit a larger sample. Ultimately, 282 cases and 409 controls were included, exceeding the minimum requirement and ensuring robustness in the analysis.
2.3. Eligibility criteria
Speech delay in this study was defined based on established diagnostic criteria consistent with the guidelines provided by the Ministry of Health in Saudi Arabia.[25] Children were classified as having speech delay if they failed to meet age-appropriate developmental milestones for speech and language acquisition as outlined in the Ministry of Health’s developmental assessment framework. Eligible cases were recruited from the support services unit for persons with disabilities, under the Ministry of Human Resources and Social Development (MHRSD).[26] The MHRSD categorizes speech delay into mild, moderate, and severe intellectual disabilities as well as autism spectrum disorders.
The control group was matched by age was selected using a multistage cluster sampling method to ensure representativeness of the general population in Jazan. First, 3 governorates were randomly chosen from the 17 in the region to capture diverse urban, rural, and semi-urban populations. Next, 4 primary schools were randomly selected from each governorate, stratified by location and gender to reflect the demographic distribution. All children aged 6 to 9 years in the selected schools were eligible, and questionnaires were distributed to parents through the schools. This process yielded 409 responses from the control group, ensuring a broad and representative sample. The random selection of governorates and schools minimized bias, enhancing the generalizability and methodological rigor of the study.
2.4. Data collection
The CDC developmental milestones checklist[27] was selected as the basis for the data collection tool due to its wide acceptance and applicability in assessing speech and language development. However, modifications were made to align the checklist with the specific objectives of this study and to ensure cultural and contextual relevance for the Jazan region.
2.5. Modifications to the checklist
Questions related to speech and language milestones were refined to focus on the age range of interest (6–9 years), ensuring their relevance to the study population. Additional items were added to assess environmental factors, such as screen time and parental interaction, as well as health-related factors, such as family history of speech delay and known developmental disorders. Language and phrasing were adjusted to ensure clarity and comprehension for parents in the local population. The checklist was translated into Arabic, reviewed by bilingual experts, and culturally adapted to reflect common expressions and terminologies used in Saudi Arabia.
2.6. Pilot testing
A pilot study was conducted with 30 participants to evaluate the clarity, cultural appropriateness, and reliability of the adapted questionnaire. Feedback from participants led to modifications, such as simplifying language, refining culturally irrelevant items, and expanding questions on screen time and environmental factors. The pilot study confirmed the questionnaire’s reliability, with a Cronbach’s alpha of 0.85, indicating high internal consistency. Data from the pilot study were excluded from the final analysis to maintain methodological rigor and avoid bias.
2.7. Statistical analysis
Data were analyzed using SPSS version 27 (SPSS Inc., Chicago). Descriptive statistics were used to summarize the characteristics of both the case and control groups. Categorical variables were compared using cross-tabulation and the Chi-squared test for independence. Multivariate logistic regression analysis was conducted to examine the associations between speech delay and various predictors, including sociodemographic, health-related, and environmental factors. Potential confounding variables, such as sex, parental education, and family history, were included in the model as covariates to adjust for their influence. By controlling for these variables, the model isolated the independent effects of each predictor on the outcome. Adjusted ORs with 95% confidence intervals were reported to provide a clear interpretation of the findings. This approach enables the simultaneous assessment of multiple predictors, including gender, family history, and parental education, while controlling for confounders, and offers flexibility in managing categorical variables and non-linear relationships. Statistical significance was set at P < .05. This threshold indicates a <5% likelihood that the observed associations occurred by chance, providing confidence in the results. In the context of identifying risk factors for speech delay, statistically significant p-values highlight variables with meaningful associations, such as gender, parental education, and health conditions like autism or attention deficit hyperactivity disorder (ADHD). These findings guide clinical and public health efforts by prioritizing high-risk groups for early screening and intervention, ensuring resources are targeted effectively to mitigate speech delay in vulnerable populations.
2.8. Ethical consideration
This study adhered to rigorous ethical standards to ensure the rights, safety, and wellbeing of participants. Ethical approval was obtained from the Standing Committee for Scientific Research – Jazan University (HAPO-10-Z-001) (Reference No.: REC-44/06/457) on May 02, 2022. The review process carefully evaluated the study’s design, emphasizing the vulnerability of child participants and the need to minimize risks, ensure voluntary participation, and protect confidentiality. Permission for data collection was also secured from the Faculty of Medicine, further reinforcing institutional oversight.
Parents were fully informed about the study’s objectives, procedures, potential risks, and benefits through detailed consent forms provided in Arabic using plain and accessible language. These forms explicitly stated the voluntary nature of participation and the right to withdraw their child from the study at any time without penalty. To address questions or concerns, parents were encouraged to contact the research team through provided contact details, and one-on-one sessions were offered as needed. Verbal explanations of key points were also provided during the consent process to ensure clarity and understanding.
Given the involvement of children, additional safeguards were implemented to prioritize their wellbeing. These included minimizing risks during data collection and ensuring that participation did not disrupt routine activities or cause discomfort. Strict measures were taken to maintain participant anonymity and confidentiality. Personal identifiers were removed from collected data and replaced with coded identifiers, and data were securely stored in password-protected files and locked cabinets accessible only to authorized researchers.
Cultural sensitivity was a key consideration in the study’s design. Local cultural norms and expectations were respected, with input from community advisors to ensure procedures were aligned with societal values regarding research involving children. The use of culturally appropriate language and accessible communication ensured parents were fully informed.
This study complied with the ethical principles outlined in the Declaration of Helsinki and the Belmont Report, emphasizing respect for persons, beneficence, and justice. These guidelines informed every aspect of the research process, ensuring that participants were treated equitably, risks were minimized, and their rights and privacy were safeguarded throughout.
3. Results
The comparison of demographic and socioeconomic factors between children with and without speech delay are provided in Table 1. A significant difference was observed in the sex of children with speech delay (P = .000), with boys accounting for 69.5% of the children (196 out of 282). No significant difference was observed in the order of the children in the family, whether they were the first child or more (P = .895), or place of residence (P = .318).
Table 1.
Comparison of demographic and socioeconomic factors between children with and without speech delay (n = 691).
| Variables | Total, N (%) | Children without speech delay, N (%) | Children with speech delay (%) | P-value |
|---|---|---|---|---|
| Sex | ||||
| Boys | 372 (53.8) | 176 (43.0) | 196 (69.5) | .000 |
| Girls | 319 (46.2) | 233 (57.0) | 86 (30.5) | |
| Birth order | ||||
| 1st | 236 (34.2) | 139 (34.1) | 97 (34.4) | .895 |
| 2nd | 138 (20.0) | 84 (20.6) | 54 (19.1) | |
| 3rd and more | 316 (45.8) | 185 (45.3) | 131 (46.5) | |
| Place of residence | ||||
| Urban | 393 (56.9) | 239 (58.4) | 154 (54.6) | .318 |
| Rural | 298 (43.1) | 170 (41.6) | 128 (45.4) | |
| Marital status | ||||
| Married | 634 (91.8) | 379 (92.7) | 255 (90.4) | .480 |
| Divorced | 52 (7.5) | 28 (6.8) | 24 (8.5) | |
| Widowed | 5 (0.7) | 2 (0.5) | 3 (1.1) | |
| Education of the father | ||||
| Read and write | 20 (2.9) | 6 (1.5) | 14 (5.0) | .004 |
| Primary school | 37 (5.4) | 24 (5.9) | 13 (4.6) | |
| Middle school | 56 (8.1) | 30 (7.3) | 26 (9.2) | |
| High school | 220 (31.8) | 125 (30.6) | 95 (33.7) | |
| Diploma | 90 (13) | 46 (11.2) | 44 (15.6) | |
| Bachelor | 228 (33) | 147 (35.9) | 81 (28.7) | |
| Graduate studies | 9 (3.2) | 31 (7.6) | 9 (3.2) | |
| Education of the mother | ||||
| Read and write | 19 (2.7) | 8 (2.0) | 11 (3.9) | .005 |
| Primary school | 64 (9.3) | 31 (7.6) | 33 (11.7) | |
| Middle school | 60 (8.7) | 26 (6.4) | 34 (12.1) | |
| High school | 159 (23) | 94 (23.0) | 65 (23.0) | |
| Diploma | 48 (6.9) | 36 (8.8) | 12 (4.3) | |
| Bachelor | 320 (46.3) | 200 (48.9) | 120 (42.6) | |
| Graduate studies | 21 (3) | 14 (3.4) | 7 (2.5) | |
| Father’s employment status | ||||
| Government employee | 326 (47.2) | 199 (48.7) | 127 (45.0) | .259 |
| Private sector employee | 91 (13.2) | 59 (14.4) | 32 (11.3) | |
| Private business owner | 28 (4.1) | 18 (4.4) | 10 (3.5) | |
| Student | 159 (23) | 83 (20.3) | 76 (27.0) | |
| Unemployed | 87 (12.6) | 50 (12.2) | 37 (13.1) | |
| Mother’s employment status | ||||
| Government employee | 171 (24.7) | 108 (26.4) | 63 (22.3) | .203 |
| Private sector employee | 28 (4.1) | 20 (4.9) | 8 (2.8) | |
| Private business owner | 10 (1.4) | 8 (2.0) | 2 (0.7) | |
| Student | 19 (2.7) | 11 (2.7) | 8 (2.8) | |
| Unemployed | 463 (67) | 262 (64.1) | 201 (71.3) | |
| Family income (Saudi Riyal) | ||||
| <5000 | 172 (24.9) | 97 (23.7) | 75 (26.6) | .641 |
| 5000–9999 | 213 (30.8) | 123 (30.1) | 90 (31.9) | |
| 10,000–19,999 | 235 (34) | 144 (35.2) | 91 (32.3) | |
| 20,000 | 71 (10.3) | 45 (11.0) | 26 (9.2) | |
N = number, % = percentage.
Regarding birth order, no statistically significant association with speech delay was found (P = .895). Among children with speech delay, 34.4% were first-born, 19.1% were second-born, and 46.5% were third-born or later. Similarly, among children without speech delay, 34.1% were first-born, 20.6% were second-born, and 45.3% were third-born or later.
There was no significant difference in the marital status (P = .480) or employment status (P = .259 for the father and P = .203 for the mother) of the parents. Furthermore, no significant difference was observed in terms of family income (P = .641).
Children whose parents had attained higher education were less likely to experience speech delay. Specifically, only 28.7% of fathers of children with speech delay held a bachelor’s degree compared to 35.9% among fathers of children without speech delay (P = .004). Similarly, 42.6% of mothers of children with speech delay had a bachelor’s degree, compared to 48.9% among mothers of children without speech delay (P = .005). These findings highlight the protective effect of higher parental education against the risk of speech delay.
The associations between health conditions, family history, and screen time with speech delay are presented in Table 2. Children with speech delay had a higher prevalence of family history of speech delay (P = .000). Moreover, a significant correlation was observed between the following conditions and speech delay: autism (P = .000), Down syndrome (P = .000), mental retardation (P < .005), ADHD (P = .000), and hearing impairment (P = .000). Regarding parents consanguineous in marriage, the P-value of 0.056 indicates a trend towards significance but does not meet the conventional threshold (0.05). The p-values for children with cleft palate and those who underwent neonatal emergency resuscitation exceeded the conventional threshold of 0.05, indicating no statistically significant association between these conditions and speech delay in the present dataset. The P-value for screen time and its association with speech delay was <.05, indicating a significant association between screen time and speech delay. At 0 h, a higher percentage of children with speech delay (6%) had no screen time than those who did not (1.7%). A similar trend was observed with a screen time of 1 hour, with a slightly higher percentage of children with speech delay (16.7%) with a screen time of 1 hour compared to those without speech delay (13.9%). With 2 hours screen time, the percentage of children with speech delay (17%) was lower than that of those without speech delay (23.7%). The percentage was nearly the same in both groups (60.3% for children with speech delay vs 60.6% for those without), with screen time > 3 hours, suggesting that high screen time is common across both groups.
Table 2.
Comparison of health, family history and screen times and their association with speech delay (n = 691).
| Variables | Total, N (%) | Children without speech delay, N (%) | Children with speech delay, N (%) | P-value |
|---|---|---|---|---|
| Family history of speech delay | ||||
| No family history | 619 (89.6) | 383 (93.6) | 236 (83.7) | .000 |
| Yes (father, mother, or siblings) | 37 (5.4) | 13 (3.2) | 24 (8.5) | |
| Yes (grandparents, uncle or aunt) | 35 (5.1) | 13 (3.2) | 22 (7.8) | |
| Parents consanguineous in marriage | ||||
| No | 339 (49.1) | 213 (52.1) | 126 (44.7) | .056 |
| Yes | 352 (50.9) | 196 (47.9) | 156 (55.3) | |
| Health status of the child | ||||
| Autism | ||||
| No | 617 (89.4) | 403 (98.8) | 214 (75.9) | .000 |
| Yes | 73 (10.6) | 5 (1.2) | 68 (24.1) | |
| Down syndrome | ||||
| No | 670 (97.0) | 409 (100.0) | 261 (92.6) | .000 |
| Yes | 21 (3.0) | 0 (0.0) | 21 (7.4) | |
| Mental retardation (mild) | ||||
| No | 660 (95.5) | 408 (99.8) | 252 (89.4) | .000 |
| Yes | 31 (4.5) | 1 (0.2) | 29 (10.3) | |
| Mental retardation (moderate) | ||||
| No | 669 (96.8) | 409 (100.0) | 260 (92.2) | .000 |
| Yes | 22 (3.2) | 0 (0.0) | 22 (7.8) | |
| Mental retardation (sever) | ||||
| No | 686 (99.3) | 409 (100.0) | 277 (98.2) | .011 |
| Yes | 5 (0.7) | 0 (0.0) | 5 (1.8) | |
| Attention deficit hyperactivity disorder (ADHD) | ||||
| No | 602 (87.1) | 400 (97.8) | 202 (71.6) | .000 |
| Yes | 89 (12.9) | 9 (2.2) | 80 (28.4) | |
| Hearing impairment | ||||
| No | 670 (97.0) | 406 (99.3) | 264 (93.6) | .000 |
| Yes | 21 (3.0) | 3 (0.7) | 18 (6.4) | |
| Epileptic seizure or convulsion | ||||
| No | 681 (98.6) | 405 (99.0) | 276 (97.9) | .214 |
| Yes | 10 (1.4) | 4 (1.0) | 6 (2.1) | |
| Cleft palate | ||||
| No | 688 (99.6) | 409 (100.0) | 279 (98.9) | .068 |
| Yes | 3 (0.4) | 0 (0.0) | 3 (1.1) | |
| Other diseases | ||||
| No | 674 (97.5) | 405 (99.0) | 269 (95.4) | .002 |
| Yes | 17 (2.5) | 4 (1.0) | 13 (4.6) | |
| Neonatal emergency resuscitation and stabilization | ||||
| No | 577 (83.5) | 367 (89.7%) | 210 (74.5%) | .930 |
| Yes | 114 (16.5) | 42 (10.3%) | 72 (25.5%) | |
| Limited screen time (TV, iPad, phone, etc) | ||||
| 0 | 24 (3.5) | 7 (1.7%) | 17 (6%) | .004 |
| 1 h | 104 (15.1) | 57 (13.9%) | 47 (16.7%) | |
| 2 h | 145 (21) | 97 (23.7%) | 48 (17%) | |
| ≥3 h | 418 (60.5) | 248 (60.6%) | 170 (60.3) | |
N = number, % = percentage.
The association between prenatal and maternal health factors and speech delay are provided in Table 3. Maternal factors such as diabetes, gestational diabetes, hypertension, and gestational hypertension had p-values > .05, indicating no significant difference between the children with and without speech delay. The P-value of .053 for complications during pregnancy was just above the conventional threshold for significance (.05), suggesting a potential association, but it was not statistically significant. The presence of other diseases or conditions (P = .000) was significantly associated with speech delay. This means that children with speech delay are more likely to have other health conditions or diseases than are those without speech delay.
Table 3.
Comparison of prenatal and maternal health factors between children with and without speech delay (n = 691).
| Variables | Total N (%) | Children without speech delay N (%) | Children with speech delay N (%) | P-value |
|---|---|---|---|---|
| Diabetes | ||||
| No | 677 (98) | 398 (97.3) | 279 (98.9) | .136 |
| Yes | 14 (2) | 11 (2.7) | 3 (1.1) | |
| Gestational diabetes | ||||
| No | 666 (96.4) | 393 (96.1) | 273 (96.8) | .618 |
| Yes | 25 (3.6) | 16 (3.9) | 9 (3.2) | |
| Hypertension | ||||
| No | 660 (95.5) | 397 (97.1) | 269 (95.4) | .53 |
| Yes | 31 (4.5) | 20 (4.9) | 11 (3.9) | |
| Gestational hypertension | ||||
| No | 666 (96.4) | 389 (95.1) | 271 (96.1) | .246 |
| Yes | 25 (3.6) | 12 (2.9) | 13 (4.6) | |
| Premature birth | ||||
| No | 648 (93.8) | 387 (94.6) | 261 (92.6) | .269 |
| Yes | 43 (6.2) | 22 (5.4) | 21 (7.4) | |
| Mother suffering from epilepsy | ||||
| No | 688 (99.6) | 408 (99.8) | 280 (99.3) | .570 |
| Yes | 3 (0.4) | 1 (0.2) | 2 (0.7) | |
| Complications | ||||
| No | 556 (80.5) | 339 (82.9) | 217 (77.0) | .053 |
| Yes | 135 (19.5) | 70 (17.1) | 65 (23.0) | |
| Others | ||||
| No | 671 (97.1) | 405 (99.0) | 266 (94.3) | .000 |
| Yes | 20 (2.9) | 4 (1.0) | 16 (5.7) | |
| State of health of the mother after childbirth | ||||
| Normal | 650 (94.1) | 385 (94.1) | 265 (94.0) | .930 |
| Suffered complications | 41 (5.9) | 24 (5.9) | 17 (6.0) | |
N = number, % = percentage.
Key risk factors for speech delay, as identified through logistic regression, are summarized in Table 4. In this logistic regression analysis, we examined the association between various independent variables, including sex, marital status, maternal and paternal education, income, family history of speech delay, and nursery attendance, and speech delay. Boys were 3 times more likely to have speech delay than girls (OR: 3.05, 95% CI: 2.18–4.29, P < .001). Children with a family history of speech delay were more than twice as likely to have a speech delay, indicating a strong familial influence (OR: 2.38, 95% CI: 1.32–4.29, P = .004). Children of fathers without a bachelor’s degree had a 4.21-fold higher risk of speech delay (OR: 4.21; 95% CI: 1.35–14.24; P = .015), while children of mothers without a bachelor’s degree had a 2.45-fold increased risk (OR: 2.45; 95% CI: 1.27–4.77; P = .008). These findings indicate that lower educational levels of both parents were significantly and independently associated with an increased risk of speech delay. Attending nurseries was associated with a more than 3-fold increase in the likelihood of speech delay, which could reflect the developmental concerns or specific needs of children attending nurseries (OR: 3.22, 95% CI: 2.09–5.05, P < .001).
Table 4.
Multiple logistic regression analysis of health and environmental factors associated with speech delay.
| Predictors | Speech delay | ||
|---|---|---|---|
| OR | 95% CI | P | |
| Gender (reference: female) | |||
| [Male] | 3.05 | 2.18–4.29 | <.001 |
| Father education (reference: bachelor and higher degree) | |||
| [No bachelor] | 4.21 | 1.35–14.24 | .015 |
| Mother education (reference: bachelor and higher degree) | |||
| [No bachelor] | 2.45 | 1.27–4.77 | .008 |
| Marital status (reference: unmarried) | |||
| [Married] | 0.71 | 0.37–1.37 | .304 |
| [Divorced/widow] | 0.94 | 0.12–8.49 | .952 |
| Income (reference: <5000 SR) | |||
| [Between 5000 and 9999 SR] | 1.05 | 0.68–1.63 | .821 |
| [Between 10,000 and 15,000 SR] | 1.01 | 0.58–1.78 | .964 |
| [>15,000 SR] | 1.15 | 0.61–2.18 | .661 |
| Father employment (reference: unemployed) | |||
| [Employed] | 1.75 | 0.78–1.13 | .765 |
| Mother employment (reference: unemployed) | |||
| [Employed] | 1.12 | 0.89–1.78 | .231 |
| Residence (reference: urban) | |||
| [Rural] | 0.99 | 0.70–1.41 | .976 |
| Speech delay in the family (reference: No) | |||
| [Yes] | 2.38 | 1.32–4.29 | .004 |
| Autism (reference: No) | |||
| [Yes] | 14.21 | 6.28–38.24 | <.001 |
| ADHD (reference: No) | |||
| [Yes] | 11.72 | 5.76–26.62 | <.001 |
| Hearing impairment (reference: No) | |||
| [Yes] | 8.31 | 2.50–37.57 | .002 |
| Gestational DM (reference: No) | |||
| [Yes] | 0.54 | 0.20–1.32 | .190 |
| Gestational HTN (reference: No) | |||
| [Yes] | 1.57 | 0.67–3.69 | .292 |
| Preterm labor (reference: No) | |||
| [Yes] | 0.95 | 0.48–1.87 | .888 |
| Maternal seizure during pregnancy (reference: No) | |||
| [Yes] | 4.21 | 0.36–97.89 | .263 |
| Nursery (reference: no) | |||
| [Yes] | 3.22 | 2.09–5.05 | <.001 |
| Observations | 691 | ||
| R2 | 0.29 | ||
ADHD = attention deficit hyperactivity disorder, DM = diabetes mellitus, HTN = hypertension, OR = odds ratio.
4. Discussion
Speech delay is a common developmental problem in early childhood, affecting children globally due to a wide variety of genetic, environmental, and health-related factors. Our study assessed factors associated with speech delay among children in Jazan, Saudi Arabia. Below, we discussed the findings under key themes, emphasizing their implications for public health and early intervention strategies.
4.1. Sociodemographic and family factors
Our study revealed a significant variation in the prevalence of speech delay between boys and girls, with more than half (69.5%) of the affected children being boys, compared to only 30.5% being girls. This finding is consistent with multiple studies that have reported a higher prevalence of speech delay among boys.[8,10,11] Biological factors, such as slower maturation of language-related brain regions in boys, may contribute to this difference. Additionally, boys may be more vulnerable to certain developmental disorders, such as autism or ADHD, which are closely associated with speech delays.
The significant association between gender and speech delay highlights the importance of early identification and tailored interventions for boys. Public health initiatives should consider screening programs targeting boys as a high-risk group to ensure timely diagnosis and intervention.
Regarding family dynamics, our study also explored the impact of birth order. While some studies have suggested that first-born children are at a higher risk for speech delay due to a lack of sibling interaction,[11,28] our findings did not indicate a significant association between birth order and speech delay (P = .895). This aligns with other studies,[19,29] suggesting that cultural factors in the Jazan region, such as communal caregiving practices within extended families, may buffer any potential disadvantages associated with birth order.
These findings underline the complex interplay of sociodemographic and familial factors in speech development, emphasizing the need for context-specific strategies to address these influences.
A significant association was found between parental education levels and speech delay. Children of fathers with lower educational levels were 4.21 times more likely to have speech delay, while children of mothers with lower educational levels were 2.45 times more likely to experience it. These findings align with previous research, which consistently demonstrates that higher parental education fosters a language-rich environment and greater awareness of developmental milestones.[19,28] Conversely, families with lower educational levels may lack access to resources or awareness of strategies that promote early language development, such as reading and interactive play. However, Mondal et al did not find a significant association between these variables.[5] The statistically significant association between lower parental educational levels and speech delay (P < .05) underscores the practical importance of targeting interventions in families where educational attainment is limited. The effect size, reflected in the ORs (fathers: OR = 4.21; mothers: OR = 2.45), indicates that children of parents with lower educational levels are at a significantly higher risk of speech delay. These finding have practical implications for public health initiatives. For example, interventions could prioritize parental education programs that emphasize the importance of early communication, interaction, and language stimulation in a child’s development. Community-based workshops, especially in regions with lower literacy rates, could teach parents strategies to foster language development, such as reading, storytelling, and interactive play. Additionally, integrating speech development resources into existing maternal and child health programs could ensure that parents from diverse educational backgrounds have access to these critical tools.
Interestingly, family income did not show a significant association with speech delay in our study, while it has been reported to be associated with speech delay in previous studies, contrary to our findings.[30,31] This may reflect the complex relationship between income and speech development, which is often mediated by other factors such as parental education, caregiving practices, and resource allocation. While higher income can facilitate access to educational resources, its impact may be diminished without active parental involvement. Additionally, cultural factors in the Jazan region, such as extended family support and subsidized healthcare, might buffer the effects of income disparities on speech development.
These findings underscore the importance of addressing parental education in public health interventions. Programs focused on educating parents about early childhood language development and promoting active engagement with their children, regardless of income level, could help mitigate the risk of speech delay. Future studies should explore how income interacts with other factors, such as parental involvement and access to developmental resources, to provide a more comprehensive understanding of their impact on speech delay.
Our study did not find a significant association between the father’s employment status and speech delay. To the best of our knowledge, limited research directly explores this relationship. Previous studies examining parental employment status have generally focused on its indirect effects, such as time availability, stress levels, and quality of parent-child interactions.[32] The absence of a direct association in our findings may reflect the complexity of how employment impacts child development, which is often mediated by other factors like parental education, socioeconomic status, and caregiving dynamics. Future research could explore the interplay between paternal employment and speech delay by assessing the indirect effects of employment status on access to healthcare, educational resources, and early interventions, particularly in lower-income settings.
4.2. Prenatal and maternal health factors
Although our study did not find statistically significant associations between prenatal factors (e.g., gestational diabetes, hypertension, premature birth) and speech delay, these findings should be interpreted in the context of existing literature. Some studies have similarly reported no direct association between specific maternal health conditions and speech development, suggesting that these factors may act indirectly or require interaction with other risks to manifest their effects.[14,29,33] Consistent with our findings, Scime et al (2021) reported no statistically significant association between gestational hypertension and developmental delays in children at 36 months of age.[33]
Conversely, other studies have demonstrated associations between gestational diabetes and preeclampsia and adverse developmental outcomes, including delayed speech and cognitive impairments.[34–36] These contradictions may reflect differences in study design, populations, or unmeasured confounders, such as the severity and timing of maternal health issues or access to prenatal care.
The discrepancy in findings underscores the complexity of maternal health’s influence on speech development. While our results did not reveal significant associations, maternal health remains crucial for optimal developmental outcomes. For instance, gestational diabetes and hypertension may exert indirect effects via preterm birth or low birth weight, factors not comprehensively explored in this study. Additionally, these conditions may interact with other environmental or genetic risks, complicating their relationship with speech delay.
From a public health perspective, ensuring maternal health through comprehensive prenatal care programs remains essential. Early management of maternal conditions and education about their potential developmental impacts can reduce risks for a range of challenges, including speech delays. Future research employing longitudinal designs is needed to disentangle these complex relationships and determine how maternal health factors influence long-term speech and language outcomes.
4.3. Genetic and familial factors
Our study demonstrated that children with a family history of speech delay are at a significantly higher risk of developing speech delay themselves. This finding aligns with the established understanding of the genetic and familial components of language development. Several studies have highlighted the role of genetic predispositions in influencing speech and language disorders, with heritable factors often contributing to delays in speech acquisition.[5,14,34] In contrast, our study did not find significant associations between speech delay and consanguineous parents, despite the increased prevalence of consanguinity in Saudi Arabia.[37] This finding aligns with other studies that have not demonstrated a direct link between consanguinity and speech delay.[10,38] While consanguinity is associated with an increased risk of certain genetic disorders, speech delay may not result solely from single-gene mutations or inherited traits common in consanguineous marriages. Instead, speech delay often arises from a combination of genetic, environmental, and neurodevelopmental factors, which may dilute the isolated effect of consanguinity.
Additionally, certain cultural practices in regions with high consanguinity, such as Jazan, including strong extended family support systems and communal caregiving traditions, may help mitigate some of the potential developmental risks associated with consanguineous marriages.[37] For example, extended family support and shared caregiving responsibilities may help create enriched linguistic environments for children, mitigating the effects of any genetic predispositions linked to consanguinity. Furthermore, speech delay is a multifactorial condition, and factors such as parental education, early interventions, and health conditions may have a more significant impact than consanguinity alone.
The significant association between family history and speech delay highlights the need for family-centered interventions, including genetic counseling and targeted support for families with a history of developmental delays. Meanwhile, the lack of a significant link with consanguinity suggests that genetic risks related to speech delay are more complex and multifactorial, requiring further research to understand the interplay of genetic and environmental influences.
4.4. Health-related factors
Our study found strong associations between certain health conditions and speech delay. Autism, ADHD, and hearing impairment were among the most significant contributors, consistent with previous research. For example, 68 of 73 children with autism and 80 of 89 children with ADHD in our study had speech delays, reinforcing their established links with language and communication challenges. These findings align with studies showing that neurodevelopmental conditions such as autism and ADHD disrupt language acquisition and speech processing pathways.[38–40]
Similarly, hearing impairment was strongly associated with speech delay, as it limits auditory input critical for language development. This is consistent with findings from earlier studies emphasizing the importance of early detection and intervention for hearing loss to prevent speech and language delays.[41]
We also observed a significant association between mental retardation (across all grades) and speech delay, highlighting speech delay as a common early manifestation of intellectual disabilities. However, cleft palate, often cited as a potential risk factor, did not show a significant association in our study. This result may reflect the small number of children with cleft palate in our sample (n = 3), limiting the statistical power to detect an association.
These findings underscore the need for targeted early screening and intervention programs for children with these conditions. Public health initiatives should prioritize integrating speech and language therapy into broader care frameworks for children with neurodevelopmental or sensory impairments.
4.5. Technology use
Our study observed a nuanced relationship between screen time and speech delay. Interestingly, children with no screen time exhibited higher rates of speech delay compared to those with moderate screen time (up to 2 hours per day). However, prolonged screen time (>2 hours) was consistently linked to speech delays, potentially due to reduced caregiver-child interaction and passive engagement. These findings align with studies suggesting that limited, balanced screen time, particularly when interactive and paired with caregiver involvement, may not adversely affect speech development. In some cases, interactive screen time–such as using educational apps or engaging in co-viewing activities: has been shown to support language development by exposing children to new vocabulary and fostering shared learning experiences.[33,42] Conversely, excessive or passive screen use, such as solitary viewing of non-educational content, may replace vital opportunities for caregiver-child interaction and hinder language acquisition. Modern parenting practices often involve the use of digital devices for entertainment or educational purposes, making it essential to strike a balance between screen use and direct interaction. Structured guidelines promoting quality screen use, combined with active caregiver engagement, could mitigate risks associated with excessive screen exposure while leveraging its educational potential. Public health interventions should include educational campaigns for parents, emphasizing the importance of moderation and encouraging the use of screens as a complement to, rather than a replacement for, interactive language-building activities.
Future research should explore the differential impacts of interactive versus passive screen time on speech development. Structured guidelines promoting quality screen use, combined with parental involvement, could mitigate risks associated with excessive screen exposure while leveraging its educational potential.
4.6. Future directions
Further research is needed to explore additional factors influencing speech delay, including genetic predispositions, perinatal factors, and the role of early intervention programs. Longitudinal studies tracking speech and language development from infancy through early childhood in the Saudi population would provide deeper insights into causal relationships and critical periods for intervention. Moreover, integrating culturally sensitive early screening programs could enhance early detection and support services for at-risk children in regions with high consanguinity rates.
4.7. Strengths and limitations
The inclusion of confounding variables, such as gender, parental education, and family history of speech delay, strengthens the validity of our findings. These variables were chosen based on robust evidence from previous studies and theoretical reasoning, emphasizing their relevance to speech development. For instance, male gender and lower parental education are consistently reported as significant risk factors, while family history reflects genetic predispositions. By incorporating these confounders into the logistic regression model, we adjusted for their influence, enabling a clearer understanding of the independent associations between speech delay and other predictors. This approach aligns with established frameworks like Bronfenbrenner’s Ecological Systems Theory, which highlights the interaction between individual and environmental factors in child development.
While key confounders were addressed, some variables, such as cognitive development, quality of early childcare, and parent-child interaction, were not included due to data availability. These unmeasured confounders may have influenced the observed associations, underscoring the need for future research to include a broader range of factors to provide a more comprehensive analysis.
The case–control design is another key strength, enabling precise comparisons between children with and without speech delays while controlling for age and developmental milestones. This design is particularly effective for identifying associations between predictors and outcomes in conditions like speech delay. However, as with any retrospective study, it is important to acknowledge the inability to establish causation. For example, while lower parental education was associated with an increased risk of speech delay, it remains unclear whether this relationship is causal or mediated by other factors, such as reduced parent-child interaction. Longitudinal studies or randomized controlled trials are needed to explore these causal pathways further.
The study’s focus on autism, ADHD, and hearing impairment reflects their strong documented links with speech delay. However, the exclusion of other neurodevelopmental conditions, such as intellectual disabilities, may limit the comprehensiveness of the analysis. Future studies should explore a broader spectrum of conditions to better understand their contributions to speech delay.
Despite these limitations, the findings have significant public health and educational implications. Targeted interventions, such as early screening for high-risk groups (e.g., boys and children with a family history of speech delay), parental education programs, and structured screen time guidelines, could mitigate the impact of modifiable risk factors. Policymakers and practitioners can use these insights to design and implement strategies that enhance early speech and language development in children.
5. Conclusion and recommendation
Our study identified several sociodemographic and medical factors that were significantly associated with speech delay in 1st- and 2nd-year children. These factors include being male, family history of speech delay, nursery attendance, parents’ educational level, and amount of time spent using electronic devices. Interestingly, our findings suggest that a family history of speech delay may increase the likelihood of a child having this condition. Furthermore, the higher the parents’ educational level, the lower the incidence of speech delay, likely due to their awareness of the problem. However, further investigation is warranted to confirm these findings. Moreover, medical risk factors, such as autism, mental retardation, ADHD, hearing loss, and Down syndrome, have shown a significant correlation with speech delay. Therefore, parents should seek medical advice as soon as possible, if one or more of these risk factors are present. Although income was not significantly associated with speech delay in this study, it is important to recognize that socioeconomic status can influence other developmental outcomes. Future studies could explore the complex interaction between income, access to health services, and early childhood education, which might indirectly affect speech development. The lack of a significant association between parental employment and speech delay suggests that employment status alone does not directly impact speech development. However, future research could investigate the quality of parental engagement and caregiving practices, which might vary based on working hours, job stress, or work-life balance.
Our findings indicate the need for targeted screening programs to detect children at-risk of speech delay at an early stage, particularly boys and offspring of mothers with pregnancy complications. If the family physician identifies a family history of speech delay, they should educate the family and caregivers of young children, providing them with resources and support to help their children prevent future learning problems.
Acknowledgments
The authors are grateful to the parents for their generous support and participation in this study. The authors also gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project number: JU-20250268-DGSSR-RP-2025.
Author contributions
Conceptualization: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Data curation: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Formal analysis: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Funding acquisition: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Investigation: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Methodology: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Project administration: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Resources: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Software: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Supervision: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Validation: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Visualization: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Writing – original draft: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Writing – review & editing: Suhaila A. Ali, Maged El-Setouhy, Osama Albasheer, Ahmad Y. Alqassim, Mohammed A. Muaddi, Mohammad A. Jareebi, Anwar M. Mkeen, Khlood Khalid M. Alattas, Ghadh Moheedin A. Alshareef, Remas Fahad I. Koko, Ohuod Mohammed H. Masmali, Faya Mohammed A. Julajil, Fatimah Eissa L. Dalak, Rahf Ali Hakami, Abdullah A. Alharbi.
Abbreviations:
- ADHD
- attention deficit hyperactivity disorder
- CDC
- centers for disease control and prevention
- MHRSD
- Ministry of Human Resources and Social Development
- MOH
- Ministry of Health
This project is funded by the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project number: JU-20250268-DGSSR-RP-2025.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Ali SA, El-Setouhy M, Albasheer O, Alqassim AY, Muaddi MA, Jareebi MA, Mkeen AM, Alattas KKM, Moheedin A Alshareef G, Koko RFI, Masmali OMH, Mohammed A Julajil F, Dalak FEL, Hakami RA, Alharbi AA. Sociodemographic, health, and environmental determinants of speech delay in children: A case–control study. Medicine 2025;104:34(e43817).
Contributor Information
Suhaila A. Ali, Email: suali@jazanu.edu.sa.
Maged El-Setouhy, Email: maged.a.elsetouhy@gmail.com.
Ahmad Y. Alqassim, Email: aalqassim@jazanu.edu.sa.
Mohammed A. Muaddi, Email: mothman@jazanu.edu.sa.
Mohammad A. Jareebi, Email: mjareebi@jazanu.edu.sa.
Anwar M. Mkeen, Email: Dr.makeen@gmail.com.
Khlood Khalid M. Alattas, Email: Khloodalattas40@gmail.com.
Ghadh Moheedin A. Alshareef, Email: Ghadalshrif9@gmail.com.
Remas Fahad I. Koko, Email: Rmaaaas@gmail.com.
Ohuod Mohammed H. Masmali, Email: ohoud.masmali@gmail.com.
Faya Mohammed A. Julajil, Email: 1422fa@gmail.com.
Fatimah Eissa L. Dalak, Email: fatimhdalak@gmail.com.
Rahf Ali Hakami, Email: rahafhakami888@gmail.com.
Abdullah A. Alharbi, Email: Aaalharbi@jazanu.edu.sa.
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