Burden of Neurological Disorders in Resource-Limited Settings: Lessons from Pakistan for Global Neurology

Abstract

Neurological disorders (NDs) are a leading global cause of disability and mortality, with their burden falling disproportionately on low- and middle-income countries (LMICs). Pakistan exemplifies the systemic challenges faced in resource-limited settings, including delayed diagnosis, limited specialist access, and fragmented care pathways. Despite this, comprehensive spectrum-based data remain scarce. This study was aimed to quantify the burden and characterize the spectrum of NDs presenting to a major Pakistani tertiary-care center and to identify associated predictors and geographic disparities. A hospital-based cross-sectional study was conducted from May 2023 to January 2024, enrolling 537 consecutive patients with confirmed NDs. Descriptive statistics and logistic regression models were employed. Neurodevelopmental disorders were most frequent (31%), followed by neurovascular (20%), epilepsy (14%), autoimmune neurological disorders (12%), neurodegenerative (8%), neuroinfectious (7%), and neuromuscular disorders (6%). Logistic regression revealed strong age- and sex-dependent patterns, including markedly higher odds of neurodegenerative disorders in males and increased odds of neurovascular disorders with older age. Geographic analyses demonstrated a high burden of autoimmune and neuromuscular disorders among patients traveling >100 km, particularly from Khyber Pakhtunkhwa (KPK) and Azad Jammu Kashmir (AJK), highlighting significant access inequities. This study provides comprehensive clinical snapshot of NDs in Pakistan, revealing distinct epidemiological patterns shaped by socioeconomic disparities, geographic inequities, and genetic factors, such as consanguinity. The findings underscore the urgent need for strengthened neurological services, decentralization of specialized care, improved diagnostic capacity, early-interventions, and national surveillance systems. Lessons from Pakistan mirror broader challenges across LMICs and highlight the global imperative to invest in equitable neurological healthcare.

Introduction

Neurological disorders (NDs) represent a paramount global health challenge. As the leading cause of disability and the second leading cause of death worldwide, these conditions encompass a large spectrum of over 600 diseases, from neurodevelopmental and neurodegenerative diseases to headache and seizure disorders [1,2]. These disorders do not discriminate by age; while some, like neurodevelopmental conditions, afflict in early life, others, like neurodegenerative diseases, become more prevalent with aging, impacting individuals across the lifespan [3,4].

The scale of this challenge is significant but variable across the nations. According to the 2021 Global Burden of Disease (GBD) study, NDs resulted in 443 million disability-adjusted life years (DALYs) and 11.1 million deaths annually. For instance, stroke is one of the largest contributor to this burden and is responsible for 7.3 million deaths alone, accounting for approximately 11% of global mortality [3,5].

This burden of NDs falls disproportionately on low- and middle-income countries (LMICs), where approximately 82% of global neurological deaths and 85% of DALYs occurred in 2021, with a significant increase in the last 5 years [1,3]. This disparity is driven by systemic barriers such as poverty, limited access to treatment, rehabilitation and management services, and a shortage of healthcare professionals [1,6]. The regional burden in Asia is particularly high, with neurological disorders causing 167 million DALYs and 5.79 million deaths in 2019 [6].

Within this context, Pakistan faces a significant yet understudied NDs burden. Although comprehensive epidemiological data is limited, existing studies consistently identify a recurring pattern. A multicenter survey of 28 845 adults found vascular diseases (20%), headache disorders (19%), and epilepsy (13%) to be the most common conditions [7]. This pattern was also evident in regional studies across Sindh, Quetta, Karachi, and Peshawar, Pakistan, where stroke and nerve root lesions had the highest presentations [8-11]. This consistent profile underscores the critical need for more detailed, nationwide research to inform effective public health and clinical responses.

Pakistan’s high burden of neurodevelopmental and neurological disorders is shaped by a unique confluence of variables including a young, rapidly urbanizing population, increased prevalence of consanguineous marriages, and the absence of national health registries or a dedicated neurological surveillance system. Clinical care and health management is further hindered by critical systemic gaps. There is a severe shortage of neurologists, and those available are concentrated in urban/metropolitan cities, which create pronounced urban-rural disparities in access [8-10]. Further, there is limited availability of essential diagnostic procedures, leading to frequent delays and missed diagnoses. Furthermore, referral pathways are not well defined and there is fragmented coordination between care centers. It is usually the case that the patients are presented to specialists with advanced disease. This systemic data gap is exacerbated by substantial underreporting from rural areas and wide variations in diagnostic practices across hospitals [7].

Existing research in Pakistan has focused predominantly on specific conditions like stroke or epilepsy, leaving a major gap in understanding the full spectrum of NDs in clinical practice [7,8]. A comprehensive, spectrum-based study is essential to capture co-occurring, mixed, and less common conditions within the same population.

This study addresses this critical evidence gap by providing the first comprehensive assessment of the full neurological spectrum across all age groups in a tertiary care hospital in Islamabad. The research aims to identify the prevalence patterns of NDs, and to examine the potential biodemographic factors associated with the most prevalent conditions. By integrating demographic, clinical, and comorbidity profiles, this study will generate holistic data essential for informed service planning and resource allocation within Pakistan’s resource-constrained health system.

Materials and Methods

Ethical Considerations

The study was approved by the Ethical Review Committee of Quaid-i-Azam University, Islamabad (DAS-19). All procedures adhered to international ethical standards for human research as outlined in the revised Declaration of Helsinki. Written informed consent was obtained from adult participants, and parental consent along with age-appropriate verbal assent was obtained for minors. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

Study Design and Setting

A hospital-based, cross-sectional study was conducted between May 2023 and January 2024 in the Department of Neurology, Pakistan Institute of Medical Sciences (PIMS), Islamabad, which is a major tertiary-care referral hospital serving a diverse urban and semi-urban population of the adjoining areas but also districts of far-flung localities.

Study Population: Inclusion Criteria

All new and follow-up patients presenting to neurology OPD, emergency, or inpatient departments during the study period were included. Patients of all age groups with a confirmed neurological disorder were included. Diagnosis was confirmed by a specialist neurologist based on clinical assessment and available investigations (neuroimaging, electrophysiology, metabolic tests).

Exclusion Criteria

Patients presenting solely with primary headache disorders (eg, migraine, tension-type) without other neurological involvement; acute traumatic injuries without chronic or lasting neurological sequelae; symptoms judged by neurologists to be of non-neurological origin (eg, somatic symptoms secondary to psychiatric conditions).

Sampling Strategy

A consecutive sampling approach was used in which all eligible patients presenting during the study period were invited to participate. This approach was chosen due to the high volume and diversity of neurology referrals and to minimize selection bias.

Data were collected using a structured questionnaire filled in prospectively through face-to-face interviews. The questionnaire consisted of questions regarding bio-demographic attributes of the patients, clinical details, comorbidities, family history and genetics-related variables. Information was obtained on parental consanguinity and three-generation pedigree was obtained for disorders suggestive of genetic etiology.

Classification of Disorders

Diagnosis was based on the review of at least two neurologists to ensure diagnostic accuracy. Disorders were grouped into major categories, including neurodevelopmental, neurodegenerative, neurovascular, epilepsy, neuroinfectious, neuromuscular, and autoimmune neurological disorders, along with “others” category that do not fit into these groups. The classification of neurological disorders in this study follows a framework adapted from de Sá-Caputo et al. (2021) and Khan et al. (2018) [2,12]. The standard definitions of the anomalies were adopted using the online databases, like Online Mendelian Inheritance in Man (OMIM) and the International Classification of Diseases (ICD-10; Ver. 2019). Detailed definitions of mental health and brain-related conditions were also obtained from DSM-5 diagnostic criteria [13].

Statistical Methods

The data were entered and stored in MS Excel and analyzed using GraphPad Prism v5 (GraphPad Prism version 5 for Windows, GraphPad Software, Boston, Massachusetts USA, www.graphpad.com) and STATA v13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LLC). Categorical variables were summarized using frequencies and percentages, and continuous variables using mean±standard deviation (SD). The proportions of each diagnostic category were calculated using the total number of patients as the denominator. The corresponding 95% confidence intervals (CIs) were computed. Associations between neurological disorder categories and biodemographic factors were assessed using Chi-square tests. Binary logistic regression was applied to identify predictors of selected major disorders. Multivariable analyses were conducted using logistic regression modeling. The disease category was treated as a dichotomous variable and the bio-demographic variables were taken as predictors. The results were expressed in adjusted OR (aOR). Due to sparse data and low event counts in some covariate categories, we performed sensitivity analyses using Firth penalized logistic regression.

Results

Sample Characteristics

A total of 537 patients with NDs were enrolled. The sample demonstrated a male predominance (58%), and the largest age group was children and adolescents (up to 19 years), representing 40% of all cases. The majority of patients originated from Punjab province (58%), with 57% residing in rural areas and 63% belonging to Punjabi-speaking families. Socioeconomic indicators showed that most participants (70%) fell into the poor or low-income quartiles (Table 1).

Table 1. Demographic Characteristics of Index Males and Females.

Variables	Male, No. (%)	Female, No. (%)	Total, No. (%)	OR#	P-value	95% CI
Age categories*
Up to 9	47 (67)	23 (33)	70 (13)	Ref.
>9-19	97 (67)	47 (33)	144 (27)	1.01	0.975	0.55-1.86
>19-29	33 (46)	38 (54)	71 (13)	0.42	0.014	0.21-0.84
>29-39	30 (52)	28 (48)	58 (11)	0.52	0.078	0.26-1.07
>39-49	18 (46)	21 (54)	39 (7)	0.42	0.034	0.19-0.94
>49	89 (57)	66 (43)	155 (29)	0.66	0.169	0.37-1.19
Total	314 (58)	223 (42)	537 (100)
Province
Punjab	177 (57)	132 (43)	309 (58)	Ref.
Islamabad	80 (61)	52 (39)	132 (25)	1.15	0.517	0.76-1.74
Azad Jammu Kashmir	28 (55)	23 (45)	51 (9)	0.91	0.751	0.50-1.65
Khyber Pakhtunkhwa	29 (64)	16 (36)	45 (8)	1.35	0.364	0.71-2.59
Rural/urban origin
Rural	172 (56)	136 (44)	308 (57)	0.77	0.152	0.55-1.10
Urban	142 (62)	87 (38)	229 (43)	Ref.
Caste system
Rajput	36 (52)	33 (48)	69 (13)	Ref.
Awan	35 (65)	19 (35)	54 (10)	1.69	0.16	0.81-3.51
Syed	29 (66)	15 (34)	44 (8)	1.77	0.152	0.81-3.87
Gujjar	12 (43)	16 (57)	28 (5)	0.69	0.407	0.28-1.67
Arain	15 (63)	9 (38)	24 (4)	1.53	0.383	0.59-3.96
Jatt	11 (48)	12 (52)	23 (4)	0.84	0.718	0.33-2.16
Others	176 (60)	119 (40)	295 (55)	1.36	0.257	0.80-2.30
First language
Punjabi	195 (58)	142 (42)	337 (63)	Ref.
Pashto	42 (70)	18 (30)	60 (11)	1.70	0.08	0.94-3.07
Urdu	24 (57)	18 (43)	42 (8)	0.97	0.929	0.51-1.86
Pahari	20 (54)	17 (46)	37 (7)	0.86	0.657	0.43-1.69
Hindko	21 (64)	12 (36)	33 (6)	1.27	0.522	0.61-2.67
Others	12 (43)	16 (57)	28 (5)	0.55	0.128	0.25-1.19
Economic quartile*
Poor	61 (62)	37 (38)	98 (18)	Ref.
Low	148 (53)	133 (47)	281 (52)	0.67	0.102	0.42-1.08
Low-mid	95 (66)	50 (34)	145 (27)	1.15	0.602	0.68-1.96
Upper-mid	12 (80)	3 (20)	15 (3)	2.02	0.308	0.52-7.82
Family type
Extended	105 (57)	78 (43)	183 (34)	0.93	0.711	0.65-1.34
Nuclear	209 (59)	145 (41)	354 (66)	Ref.

*P<0.05; differences in distribution in all other variables were statistically not significant; #, odds of affected males vs females.

Distribution of Major and Minor Neurological Disorder Categories

NDs were classified into eight major and at least 94 minor entities (Tables 2, 3; Figure 1). Neurodevelopmental disorders were the most prevalent, comprising 166 cases (31%). This was followed by neurovascular disorders (107 cases, 20%) and epilepsy (76 cases, 14%). Autoimmune disorders accounted for 66 cases (12%), whereas neurodegenerative disorders (42 cases, 8%), neuroinfectious diseases (37 cases, 7%), and neuromuscular disorders (34 cases, 6%) were less frequently observed. The “others” category contributed the smallest proportion, with 9 cases (2%).

Table 2. Major Categories of Neurological Disorders, their Proportions and 95% CI.

Major category*	Male, No. (%)	Female, No. (%)	Total, No. (%)	Proportion	95% CI
Neurodevelopmental disorders	110 (66)	56 (34)	166 (31)	0.309	0.270-0.348
Neurovascular disorders	52 (49)	55 (51)	107 (20)	0.199	0.165-0.233
Epilepsy	47 (62)	29 (38)	76 (14)	0.142	0.112-0.171
Autoimmune disorders	28 (42)	38 (58)	66 (12)	0.123	0.095-0.151
Neurodegenerative disorders	30 (71)	12 (29)	42 (8)	0.078	0.056-0.101
Neuroinfectious diseases	21 (57)	16 (43)	37 (7)	0.069	0.047-0.090
Neuromuscular disorders	20 (59)	14 (41)	34 (6)	0.063	0.043-0.084
Others	6 (67)	3 (33)	9 (2)	0.017	0.006-0.028
Total	314 (58)	223 (42)	537 (100)	1.000

*P<0.01.

Table 3. Major and Minor Entities of Neurological Disorders.

Major/minor category	Total	ICD-10	OMIM	Proportion	95% CI
Neurodevelopmental disorders	166			0.309	0.270-0.348
Cerebral palsy	75	G80	310200	0.139	0.110-0.169
Spastic CP	(43)	G80.1	612900	0.080	0.057-0.103
Quadriplegia CP	(23)	G80.0	612900	0.043	0.026-0.060
Hemiplegia CP (Rt:9; Lt:7)	(16)	G80.2	104290	0.030	0.015-0.044
Paraplegia CP	(4)	G80.8	613744	0.007	0.000-0.015
Athetoid CP	(27)	G80.3		0.050	0.031-0.068
Ataxic CP	(5)	G80.4	605388	0.009	0.001-0.017
Intellectual disability (ID)	35)	F70–F79	300243	0.064	0.044-0.085
Mild ID	(9)	F70	249500	0.016	0.006-0.027
Moderate ID	(8)	F71		0.015	0.005-0.025
Severe/ profound ID	(18)	F72 /F73	611091	0.033	0.018-0.048
Down syndrome	17	Q90.0–Q90.9	190685	0.031	0.016-0.045
Developmental delay	13	R62.0	618330	0.024	0.011-0.036
Autism spectrum disorder	9	F84.0	209850	0.016	0.006-0.027
Microcephaly	6	Q02	251200	0.011	0.002-0.020
Hydrocephalus	4	G91	307000	0.007	0.000-0.014
Schizophrenia	2	F20.x	181500	0.004	-0.001-0.009
Tuberous sclerosis	2	Q85.1	191100	0.004	-0.001-0.009
Spina bifida	2	Q05.x	182940	0.004	-0.001-0.009
ADHD	1	F90.0		0.002	-0.002-0.005
Neurovascular disorders	107			0.192	0.159-0.224
Ischemic stroke (Rt:23; Lt:21)	44	I63.x		0.079	0.056-0.101
Hemorrhagic stroke (Rt:11; Lt:5)	16	I61.x		0.029	0.015-0.042
MCA infarct (Rt:3; Lt:6)	9	I63.5		0.016	0.006-0.026
Intracerebellar bleed (Rt:2; Lt:3)	5	I61.4		0.009	0.001-0.017
CVST; stroke	4	I67.6		0.007	0.000-0.014
Parieto-occipital infarct	4	I63.3		0.007	0.000-0.014
Subarachnoid hemorrhage	4	I60.x		0.007	0.000-0.014
Thalamic bleed (Rt:1; Lt:3)	4	I61.0		0.007	0.000-0.014
Basal ganglia hemorrhage	2	I61.1		0.004	-0.001-0.008
Brain stem bleed	2	I61.3		0.004	-0.001-0.008
Cerebellar bleed	2	I61.4		0.004	-0.001-0.008
Cerebellar stroke	2	I63.8		0.004	-0.001-0.008
Parietal lobe hemorrhage (Rt:1; Lt:1)	2	I61.1		0.004	-0.001-0.008
Posterior circulation stroke	2	I63.5 / I63.6		0.003	-0.001-0.008
Internal capsular bleed, Lt	1	I61.1		0.002	-0.002-0.005
Intrapartum basal ganglia bleed	1	P52.4		0.002	-0.002-0.005
Medullary ischemic stroke, Lt	1	I63.8		0.002	-0.002-0.005
Posterior cerebellar bleed, Lt	1	I61.4		0.002	-0.002-0.005
Posterior fossa bleed	1	I61.4		0.002	-0.002-0.005
Epilepsy	76			0.131	0.104-0.159
Unspecified epilepsy	43	G40.9	604827	0.074	0.053-0.096
Generalized epilepsy	28	G40.3	254770	0.048	0.031-0.066
Focal epilepsy	5	G40.0	117100	0.009	0.001-0.016
Autoimmune disorders	66	G37.9		0.113	0.088-0.139
Guillain-Barré syndrome	32	G61.0		0.055	0.036-0.073
Myasthenia Gravis	11	G70.0	254200	0.019	0.008-0.030
NMOSD; Neuromyelitis optica spectrum disorders	5	G36.0	607914	0.009	0.001-0.016
CIDP (Chronic inflammatory demyelinating polyneuropathy)	4	G61.81		0.007	0.000-0.014
Multiple sclerosis	4	G35	126200	0.007	0.000-0.013
Transverse myelitis	4	G37.3		0.007	0.000-0.013
Autoimmune encephalitis	2	G04.81		0.003	-0.001-0.008
Demyelinating CNS infection	1	G37.9		0.002	-0.002-0.005
Demyelinating pseudotumor	1	G04.81 /G37.9		0.002	-0.002-0.005
Polyneuritis cranialis (oculopharyngeal subtype of GBS)	1	G61.0		0.002	-0.002-0.005
Rheumatic Chorea	1	I02.0	607250	0.002	-0.002-0.005
Neurodegenerative disorders	42	G31.9		0.071	0.050-0.091
Parkinson’s disease	26	G20	168600	0.044	0.027-0.060
Parkinsonism	5	G21.9		0.008	0.001-0.016
Spinocerebellar ataxia	4	G11.1	164400	0.007	0.000-0.013
Alzheimer’s disease	2	G30	104300	0.003	-0.001-0.008
Chorea	2	G25.5		0.003	-0.001-0.008
Essential tremors	2	G25.0	190300	0.003	-0.001-0.008
Cerebellar ataxia	1	G11.8	164400	0.002	-0.002-0.005
Neuroinfectious diseases	37	A89/ G04.9		0.061	0.042-0.081
Tuberculous meningitis	12	A17.0		0.020	0.009-0.031
Meningitis	8	G03.9		0.013	0.004-0.022
Viral encephalitis	7	A86		0.012	0.003-0.020
Meningoencephalitis	6	G04.90		0.010	0.002-0.018
Pyogenic/ bacterial meningitis	1	G00.9		0.002	-0.002-0.005
Rhinocerebral mucormycosis	1	B46.4		0.002	-0.002-0.005
Subacute sclerosing panencephalitis	1	A81.1	260470	0.002	-0.002-0.005
Viral meningitis	1	A87.9		0.002	-0.002-0.005
Neuromuscular disorders	34			0.056	0.037-0.074
Bells palsy	5	G51.0		0.008	0.001-0.015
Muscular dystrophy	5	G71.0	310200	0.008	0.001-0.015
Facial palsy	3	G51.9	617732	0.005	-0.001-0.010
Cervical dystonia	2	G24.3	128100	0.003	-0.001-0.008
Congenital myopathy	2	G71.2	161800	0.003	-0.001-0.008
Duchenne muscular dystrophy	2	G71.0	310200	0.003	-0.001-0.008
Dystonia	2	G24.9	128100	0.003	-0.001-0.008
Hereditary motor and sensory neuropathy	2	G60.0	118220	0.003	-0.001-0.008
Peripheral neuropathy	2	G62.9	118200	0.003	-0.001-0.008
Congenital facial palsy	1	Q07.81	134780	0.002	-0.002-0.005
Hemidystonia	1	G24.9		0.002	-0.002-0.005
Hereditary spastic paraplegia	1	G11.4	182601	0.002	-0.002-0.005
Polyneuropathy	1	G62.8/G62.9		0.002	-0.002-0.005
Sensory ataxia	1	G60.3	607458	0.002	-0.002-0.005
Severe sensorimotor polyneuropathy	1	G62.9		0.002	-0.002-0.005
Spinal muscular atrophy	2	G12.9	253300	0.003	-0.001-0.008
Syringomyelia	1	G95.0	186700	0.002	-0.002-0.005
Others	9			0.014	0.005-0.024
Wilson’s disease	2	E83.0	277900	0.003	-0.001-0.008
Bardet-Biedl syndrome	1	Q87.89	209901	0.002	-0.002-0.005
Carpal tunnel syndrome	1	G56.0		0.002	-0.002-0.005
Glioblastoma	1	C71.x	137800	0.002	-0.002-0.005
Glioma (left basal ganglia)	1	C71.8	137800	0.002	-0.002-0.005
Mucopolysaccharidosis type IIIB	1	E76.1	252920	0.002	-0.002-0.005
Niemann pick disease	1	E75.2	257200	0.002	-0.002-0.005
Round blue cell tumor	1	C49 / C80	612219	0.002	-0.002-0.005

ICD-10=International Classification of Diseases; OMIM=Online Mendelian Inheritance in Man; Lt=left; Rt=right.

Figure 1.

Sunburst chart depicting the distribution of major categories of neurological disorders. The inner ring represents the major categories, while the outer ring shows break down into minor entities.

Sex Distribution, Familial Occurrence, Consanguinity, and Clinical Presentation

Most disorder categories exhibited a male predominance. Male proportions were highest among neurodegenerative disorders (71%), neurodevelopmental disorders (66%), and epilepsy (62%) (Figure 2A). In contrast, autoimmune disorders showed a female predominance (58%), and neurovascular disorders displayed an almost equal sex distribution (49% male vs 51% female). Overall differences across categories were statistically significant (p=0.0082).

Figure 2.

Bar graph showing the comparison of major categories of CA with respect to sex of index case (A), familial/sporadic presentation (B), parental consanguinity (C), and isolated/syndromic nature (D).

Most cases were sporadic in nature (84%), while 16% had a familial occurrence (Figure 2B). Autoimmune and neuroinfectious disorders were exclusively sporadic. The highest frequencies of familial clustering were observed in neuromuscular disorders (35%) and the “others” category (22%). The distribution of familial vs sporadic cases differed significantly across disorder groups (p=0.0016).

Parental consanguinity was observed to be 52% in the overall cohort (Figure 2C). The highest consanguinity rates were evident among neurodegenerative disorders (74%) and the “others” category (60%). In contrast, consanguinity was least frequent in neuroinfectious disorders (26%).

Overall, syndromic presentation was more common than isolated (60% vs 40%, respectively; Figure 2D). Syndromic presentation was customary in neurodegenerative disorders (83%) and autoimmune disorders (73%). Conversely, an isolated presentation predominated in epilepsy (95%), neuroinfectious disorders (92%), and neurovascular disorders (90%) (p<0.0001).

Biodemographic Predictors of Neurological Disorders

Multivariate logistic analyses were carried out in order to elucidate the biodemographic predictors for five major categories of NDs and all dependent variables were included in the model (Table 4).

Table 4. Odds Ratios of Bio-Demographic Predictors of Neurological Disorders in Multivariable Logistic Models.

Variables	Neurodevelopmental disorders		Neurovascular disorders		Autoimmune disorders		Epilepsy		Neuro-degenerative disorders
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Gender
Male (female)	1.35	0.79-2.30	0.50*	0.25-0.99	0.29**	0.12-0.69	1.06	0.56-1.99	7.86***	1.96-31.47
Age categories
Up to 9	Ref.
>9-19	0.50	0.24-1.03	0.58	0.18-1.89	0.50	0.12-2.02	5.66***	1.79-17.88	0.00***	0.00-0.02
>19-29	0.18***	0.08-0.41	1.09	0.30-3.96	1.38	0.34-5.53	5.19***	1.50-17.98	0.03***	0.01-0.15
>29-39	0.09***	0.03-0.26	3.87*	1.15-13.06	1.93	0.45-8.35	5.85*	1.50-22.82	0.02***	0.00-0.23
>39-49	0.02***	0.00-0.21	3.23	0.73-14.34	1.82	0.29-11.59	3.62	0.72-18.27	0.12	0.01-1.03
>49	0.02***	0.00-0.06	4.39**	1.44-13.36	1.00		1.16	0.23-5.80	1.00	-
Province
Punjab	Ref.
Islamabad	0.90	0.43-1.87	0.90	0.31-2.63	0.69	0.17-2.84	0.77	0.32-1.84	2.89	0.66-12.70
Azad Jammu Kashmir	0.65	0.16-2.66	0.82	0.18-3.83	3.04	0.49-18.66	0.74	0.15-3.59	1.61	0.08-34.33
Khyber Pakhtunkhwa	0.34*	0.11-0.99	1.68	0.44-6.40	1.06	0.23-4.90	0.97	0.27-3.46	5.53	0.80-38.03
Rural / urban
Rural (urban)	0.63	0.34-1.18	2.17	0.88-5.36	2.54	0.80-8.08	0.98	0.47-2.07	0.57	0.15-2.23
Caste system
Rajput	Ref.
Awan	0.84	0.27-2.58	1.05	0.30-3.67	3.29	0.61-17.68	0.55	0.13-2.22	24.41**	1.50-398.25
Syed	7.32***	2.22-24.10	0.38	0.08-1.76	0.53	0.07-4.11	0.18	0.03-1.01	2.41	0.13-43.92
Gujjar	3.24	0.76-13.79	1.78	0.41-7.81	1.00	-	1.00	-	14.17	0.64-313.71
Arain	0.65	0.15-2.88	0.16	0.02-1.74	1.83	0.14-24.38	1.93	0.40-9.26	13.87	0.76-252.18
Jatt	0.86	0.15-4.83	0.61	0.11-3.46	1.62	0.13-20.37	1.38	0.21-9.16	2.64	0.12-56.22
Others	1.91	0.78-4.65	0.71	0.26-1.93	1.24	0.31-4.91	1.23	0.46-3.28	4.73	0.44-50.58
First language
Punjabi	Ref.
Pashto	0.60	0.25-1.45	0.65	0.16-2.67	3.17	0.69-14.61	2.24	0.82-6.11	0.25	0.03-2.21
Urdu	1.00	0.34-2.96	0.45	0.08-2.55	0.78	0.14-4.41	2.87	0.94-8.74	0.09	0.00-1.61
Pahari	0.39	0.08-1.81	2.26	0.43-11.90	0.62	0.08-4.91	1.68	0.35-8.20	0.59	0.02-21.57
Hindko	1.27	0.41-3.96	1.64	0.48-5.56	1.18	0.19-7.21	0.27	0.03-2.31	0.13	0.00-3.35
Others	0.42	0.12-1.41	0.30	0.05-1.77	6.53**	1.69-25.22	0.70	0.17-2.93	1.72	0.17-16.95
Economic quartile
Poor	Ref.
Low	1.06	0.53-2.14	1.14	0.47-2.74	1.43	0.43-4.68	1.06	0.47-2.38	0.16	0.03-0.78
Low-mid	0.91	0.41-2.00	0.51	0.16-1.58	1.72	0.43-6.80	1.30	0.53-3.19	1.24	0.24-6.37
Upper-mid	2.54	0.36-17.80	1.00	-	9.63	0.63-146.77	1.00		1.40	0.06-35.58
Family Extended (nuclear)	1.54	0.87-2.75	1.91	0.94-3.89	0.39	0.14-1.11	0.30***	0.14-0.68	3.09	0.84-11.30
Parental consanguinity (no)	0.97	0.57-1.64	1.30	0.64-2.63	0.96	0.40-2.28	0.89	0.48-1.62	1.39	0.38-5.02
-cons	2.14	0.54-8.55	0.09*	0.01-0.61	0.06*	0.01-0.55	0.07***	0.01-0.41	0.08	0.00-1.86

Note: Category in the parentheses is the reference group, otherwise mentioned. Statistical significance determined at *p<0.05, **p<0.01, ***p<0.001. (Penalized regression attenuated the wide OR and 95% CI in caste system and economic quartile, indicating that sparse-data bias may have inflated the conventional model).

Sex/gender showed a variable effect across neurological conditions. Male participants had significantly lower odds of neurovascular disorders (OR=0.50, 95% CI=0.25-0.99) and autoimmune neurological disorders (OR=0.29, 95% CI=0.12-0.69) compared with females (Table 4). In contrast, males demonstrated markedly higher odds of neurodegenerative disorders (OR=7.86, 95% CI=1.96-31.47).

A strong age gradient was evident across the age-categories. For instance, compared with children ≤9 years, the odds of neurodevelopmental disorders declined sharply across all older age groups (p<0.001). Further, adults >29 years had significantly higher odds of neurovascular disorders (OR=4.39, 95% CI=1.44-13.36 for >49 years). All older age groups had greatly increased odds of epilepsy (OR=range 3.62-5.85; p <0.01). Conversely however, age was strongly protective for neurodegenerative disorders, where odds approached zero in all age groups above 9 years.

Age-stratified analyses of NDs further revealed these patterns (Figure 3). Neurodevelopmental disorders declined with age and neurovascular disorders markedly increased with age. Epilepsy demonstrated a bimodal pattern, with the highest frequencies in the 9- to 19-year (24%) and 19- to 29-year (23%) age groups. The prevalence steadily declined thereafter, reaching 4% in individuals over 49 years.

Figure 3.

Temporal trends in neurological disorders across survey periods.

Compared with Punjab province, the participants from Khyber Pakhtunkhwa (KPK) had significantly lower odds of neurodevelopmental disorders (OR=0.34, 95% CI=0.11-0.99). No other regional differences emerged as statistically significant.

With respect to the caste system, belonging to the Syed caste was strongly associated with higher odds of neurodevelopmental disorders (OR=7.32, 95% CI=2.22-24.10). For neurodegenerative disorders, the Awan caste showed substantially elevated odds (OR=24.41, 95% CI=1.50-398.25), though the wide CI reflects small subsample sizes.

First/native language groups showed mixed associations. Individuals speaking “other” languages had significantly increased odds of autoimmune neurological disorders (OR=6.53, 95% CI=1.69-25.22). Economic quartiles showed no clear pattern for most neurological disorders. However, being in the low-income group significantly lowered odds of neurodegenerative disorders (OR=0.16, 95% CI=0.03-0.78). With respect to family structure, living in an extended family was significantly protective for epilepsy (OR=0.30, 95% CI=0.14-0.68). Parental consanguinity was not significantly associated with any of the neurological outcomes in this model.

Distance Traveled to Reach the Neurology Clinic

To understand the disparities, geographic inequity, and centralization of neurological care, we measured the distance traveled by the index cases to reach the neurology facility and mapped the patients’ origins by disease type (Figure 4).

Figure 4.

Disease and geographic distance. A. Bar graph depicting the average distance (km) travelled by the index patients with NDs types to reach the hospital (error bars representing standard error). Patients with autoimmune, neuromuscular, and neuroinfectious disorders experience the highest travel burden. B. Heatmap illustrating the frequency of different NDs across four geographic regions. C. Map of Pakistan depicting the strong geographic gradient in access to tertiary neurological care and the average distance travelled by patients. D. Staked bar chart showing the distribution of NDs types in four geographic rings (in C).

The highest average travel distance was covered by patients with autoimmune disorders (approximately 125-130 km), followed by patients with neuromuscular disorders (95-100 km), and neuroinfectious disorders (75-80 km) (Figure 4A). Furthermore, there was a high representation of patients with neurodevelopmental and neurovascular disorders from Punjab and the Federal area (Islamabad/Rawalpindi). A high influx of patients with autoimmune disorders came from Azad Jammu Kashmir (AJK), while cases from KPK showed a high intensity of patients with epilepsy, autoimmune, and neurodegenerative disorders (as depicted in the heatmap in Figure 4B).

To further elucidate the dynamics of distance and NDs, four geographic categories (rings) were defined: 50km, 100km, 200km, and >200 km (Figure 4C,D). Patients from KPK (≈200 km) and AJK (~150 km) face significant access barriers (Figure 4C).

It was observed that all categories of NDs were represented among patients originating from a short distance (ie, 50 km). However, there was a high representation of patients with autoimmune disorders among those originating from 100 km (likely from AJK) and >200 km (likely from KPK). The representation of patients with neurodevelopmental disorders decreased with increasing distance.

Discussion

This descriptive study of 537 NDs cases at a tertiary care center in Islamabad identified neurodevelopmental (31%) and neurovascular (20%) disorders as the most prevalent categories. Together, these accounted for more than half of all diagnoses, with cerebral palsy (n=75) and ischemic stroke (n=60) being the most frequently observed conditions in their respective groups. This pattern contrasts with national multicenter findings in which vascular diseases (20%), headache disorders (19%), and epilepsy (13%) were predominant [7]. Similar adult-dominated disease profiles which is characterized by headache and stroke have been reported from other regions of Pakistan like Sindh, Quetta, Karachi, and Peshawar [8-11]. These differences likely reflect uneven service availability and fragmented care pathways in Pakistan, where some centers primarily receive adults while others disproportionately manage pediatric neurological cases. This underscores the lack of integrated neurological services, which is a well-recognized challenge in many LMICs. As in other LMIC settings, childhood neurodevelopmental disorders remain underrepresented in national datasets because children with NDs are often evaluated in general pediatrics rather than neurology departments, leading to delayed diagnosis.

The multivariable models revealed a complex interplay of biodemographic factors underlying NDs in this resource-limited Pakistani cohort. Many of these associations demonstrated patterns observed across LMICs, where systemic barriers shape the epidemiology, recognition, and clinical outcomes of NDs.

In our cohort, male patients outnumbered females, consistent with trends reported in both international literature and national data from Karachi (52%), Peshawar (55%), and the multicenter study (53%) [7,9,10]. Evidence from Pakistan and neighboring developing countries indicates that girls are less often brought for early developmental or neurological assessment, contributing to systematic under-recognition of neurodevelopmental delays and epilepsy in female children. This aligns with broader observations that neurological disorders in LMICs are frequently underdiagnosed and underreported, especially among socioeconomically disadvantaged or marginalized groups [1].

A high rate of parental consanguinity was observed among individuals diagnosed with neuromuscular and neurodevelopmental disorders. Consanguinity increases the likelihood of autosomal recessive conditions, thereby contributing to the elevated burden of neurodevelopmental disorders in LMICs [14,15]. However, health systems in many LMICs lack the capacity to diagnose such conditions, as they require specialized genetic testing, metabolic evaluation, electrophysiology, and long-term follow-up, all of which are limited or inaccessible, creating a persistent diagnostic vacuum [16].

A high proportion of sporadic cases in this cohort likely reflects the predominance of acquired pediatric neurological conditions due to poor perinatal care, infections, and environmental exposures. This reinforces broader LMICs pattern in which immune-mediated neurological diseases are frequently missed until later stages [17,18]. Additionally, inadequate access to genetic testing, poor documentation of family history, and under-recognition of recessive inheritance patterns may contribute to inflated rates of sporadic classification.

Our analysis of travel distances to the neurology clinic reveals pronounced geographic inequities in access to specialized neurological care. It was observed that patients with autoimmune, neuromuscular, and neuroinfectious disorders experienced the highest travel burden. Traveling over 100 km is expensive, particularly for chronic diseases requiring repeated visits, which disproportionately difficult for low-income rural households. This is consistent with other observations from other LMICs [17,19].

Age-stratified analyses revealed distinct and informative trends. Neurodevelopmental disorders were highly prevalent in early childhood (up to 9 years, ~70%) but declined sharply in adulthood. This pattern is consistent with their early-onset nature but also reflects systemic LMICs barriers, including poor long-term follow-up, limited adult neurodevelopmental services, and elevated childhood mortality among children with severe disabilities [19].

Epilepsy showed a decline after middle age, likely indicating underrepresentation of late-onset epilepsy. This may reflect the population structure of the center, selective referral pathways, and LMIC-specific barriers such as limited diagnostic capacity, under-recognition of seizures in older adults, and reduced healthcare access among elderly populations [20].

Neurovascular disorders showed a progressive rise with age, becoming most common after 49 years. While this increase is consistent with accumulation of vascular risk factors, the absence of cases in very old adults may indicate LMIC health-system constraints such as insufficient stroke surveillance, inadequate emergency response capacity, and underreporting of stroke-related mortality at the community level.

Limitations

As a single-center study conducted in a major tertiary care hospital, our findings may not be generalizable to the broader population, particularly in remote and rural populations. This institutional bias is evident when comparing our data to other studies. Furthermore, our analysis was restricted to the descriptive observational study of NDs and did not extend to an investigation of their underlying etiologies due to resource-limited setting. Consequently, we are unable to provide insights into the potential genetic, environmental, infectious, or metabolic risk factors that may have contributed to the disease burden observed in this cohort. While neuroimaging supported clinical diagnoses in many cases, the absence of genetic testing limited the confirmation of suspected neurogenetic disorders. High consanguinity rates in some categories highlight the need for such resources in future studies. Exclusion of headache disorders might limit the scope of generalizability and may underrepresent common neurological conditions. Finally, in logistic regression modeling, some ORs have very wide confidence intervals, likely due to small sample size or insufficient data in specific categories.

Future Perspectives

Future research should take a multidisciplinary approach to better understand the causes of neurological disorders by examining genetic, environmental, nutritional, infectious, and socio-economic factors. Longitudinal studies in larger, more diverse populations are essential to elucidate disease progression and treatment outcomes. In resource-limited settings like Pakistan, though routine genetic testing remains costly and inaccessible for many, collaborative research focusing on high-risk groups such as consanguineous families can help us better understand genetic factors in Pakistani populations. Integrating digital registries and electronic medical records could significantly improve long-term tracking of disease progression and outcomes, thereby enhancing clinical management and research capabilities. Considering psychosocial and health system aspects in future studies is important for understanding their impact on patient outcomes and care.

Conclusion

This study highlights a distinct epidemiological pattern of NDs in a resource-limited Pakistani setting, shaped by socio-economic disadvantage, high consanguinity, and significant geographic barriers to specialist care. Neurodevelopmental and neurovascular disorders rendered the highest morbidity burden, while many patients particularly those with autoimmune and neuromuscular conditions had to travel long distances due to the scarcity of regional neurology services. These findings mirror broader challenges across LMICs, where NDs remain underdiagnosed and undertreated. Strengthening decentralized neurology services, improving diagnostic capacity, and integrating genetic counseling are essential steps toward reducing the growing neurological burden in Pakistan and similar settings globally. This study also establishes critical baseline data for future comparative research.

Glossary

aOR

adjusted Odd ratios

AJK

Azad Jammu Kashmir

CIs

confidence intervals

DALYs

disability-adjusted life years

GBD

Global Burden of Disease

KPK

Khyber Pakhtunkhwa

LMICs

low- and middle-income countries

NDs

Neurological disorders

OMIM

Online Mendelian Inheritance in Man

OPD

out-patient department

PIMS

Pakistan Institute of Medical Sciences

SD

standard deviation

STROBE

Strengthening the Reporting of Observational Studies in Epidemiology

Ethical statement

The study was approved by the Ethical Review Committee of Quaid-i-Azam University, Islamabad, Pakistan (DAS-19). All procedures adhered to international ethical standards for human research as outlined in the revised Declaration of Helsinki. Written informed consent was obtained from adult participants, and parental consent along with age-appropriate verbal assent was obtained for minors. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

Declarations

We declare that we used AI tool DeepSeek (version as of 2024) for syntax error removal and grammar checks. Following the use of this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Author Contributions

SMk: Conceived, designed, and supervised the study. SBF, MM, MB, and SMz: Data collection, curation, clinical evaluation, and statistical analysis. SMk: Responsible and accountable for the accuracy and integrity of data. All authors have read and approved final version of the manuscript.