Comparative study of heat-related symptoms among indoor and outdoor informal workers: A pilot study in Dhaka, Bangladesh

Abstract

Introduction

Rising temperatures and increasing extreme heat events pose severe health risks to informal workers, particularly those engaged in labor-intensive occupations. This pilot study investigates the comparative prevalence of heat-related symptoms among indoor and outdoor informal workers in Dhaka, Bangladesh, identifies key sociodemographic and occupational risk factors, and examines the association between heat stress and heat stroke.

Methods

A cross-sectional study was conducted among 416 informal workers using structured interviews. Temperature data were obtained from the Bangladesh Meteorological Department and satellite imagery. Heat-related symptoms were assessed using a validated questionnaire, and statistical analyses included independent t-tests, one-way ANOVA, and binary logistic regression to identify significant predictors of symptom severity and heat stroke risk.

Results

Weakness/tiredness (82.45 %), heavy sweating (94.47 %), and headaches (68.75 %) were the most commonly reported symptoms. Outdoor workers exhibited significantly higher symptom scores than indoor workers (p < 0.05), particularly for muscle cramps, weakness, and headaches, reflecting their greater exposure to extreme heat. Age (p = 0.544), education level (p = 0.003), and occupation (p = 0.034) were significantly associated with symptom severity. Older individuals, workers with no formal education, and those in physically demanding or exposed occupations such as day laborers, construction workers, and rickshaw pullers reported the highest symptom scores. A strong association between heat-related symptoms and heat stroke was found, particularly among outdoor workers (Exp(B) = 1.236, p < 0.001).

Conclusions

This pilot study underscores the urgent need for targeted heat adaptation strategies, including workplace cooling measures, rest breaks, and hydration access, to mitigate occupational health risks among informal workers in rapidly urbanizing regions.

Graphical abstract

1. Introduction

Climate change has markedly increased the frequency and intensity of heat waves, emerging as a major global public health threat. Heat waves are among the deadliest natural hazards, contributing significantly to weather-related mortality worldwide [[1], [2], [3], [4]]. Prolonged exposure to high temperatures compromises the body’s thermoregulation, resulting in heat-related illnesses such as heat exhaustion, heat stroke, and other severe health effects [5]. Symptoms may include dizziness, nausea, headaches, and muscle cramps, which can escalate to life-threatening complications like multi-organ failure [6]. Vulnerable populations such as older adults, children, and those with preexisting health conditions remain disproportionately affected [7,8].

Informal workers are particularly at risk due to limited occupational protections. According to the International Labour Organization (ILO), informal employment lacks legal or social protection, often with no formal contracts, benefits, or standardized working conditions [9]. This sector includes occupations such as street vending, construction labor, rickshaw pulling, and domestic work. Globally, informal workers account for over 60 % of the workforce and are particularly concentrated in developing nations like Bangladesh [10]. Their work environments typically lack heat-mitigating infrastructure, exposing them to heightened heat-related health risks [11].

Importantly, heat exposure varies within the informal sector. Outdoor workers such as rickshaw pullers and construction workers often perform strenuous labor under direct sunlight with limited access to rest, shade, or hydration. Conversely, indoor informal workers such as domestic workers, shopkeepers, or factory laborers may work in confined, poorly ventilated spaces, often with high ambient temperatures and minimal cooling [12,13]. In dense cities like Dhaka, the urban heat island (UHI) effect, resulting from concrete-heavy infrastructure and reduced green spaces, further intensifies indoor and outdoor heat exposure [14,15].

Dhaka, the capital of Bangladesh, exemplifies the confluence of urbanization, climate change, and occupational vulnerability. Rapid and unplanned urban growth has increased the city's ambient temperature and the frequency of extreme heat events [16]. Informal workers, already socioeconomically marginalized and with limited access to healthcare, insufficient knowledge regarding heat waves, poor preparedness and inadequate adaptation strategies are particularly vulnerable to climate-induced heat stress [17,18]. Despite their essential economic contributions, limited research has examined the differential vulnerabilities between indoor and outdoor informal workers in Dhaka.

Previous studies have highlighted the health impacts of heat on informal and outdoor workers globally. In India, for example, research has shown that outdoor laborers such as construction workers and street vendors suffer significant health and productivity losses due to direct sun exposure and high temperatures [19,20]. Xiang et al. reported a consistent link between outdoor settings and higher rates of heat-related illnesses [21], while Lundgren et al. emphasized the consequences of poor labor conditions and inadequate access to cooling in their global review [22]. Studies from Ghana and Mexico further show that lack of cooling infrastructure, protective gear, and socioeconomic disadvantage increase heat stress among informal workers [23,24]. Although global studies have addressed heat-related occupational health, critical gaps remain—particularly regarding distinctions between indoor and outdoor informal workers in urban environments. There is also limited evidence on how sociodemographic factors such as age, gender, education, and economic sufficiency intersect with occupational heat exposure in low- and middle-income megacities like Dhaka [25,26].

This pilot study addresses these gaps by comparing heat-related symptoms between indoor and outdoor informal workers in Dhaka, identifying key sociodemographic and occupational risk factors, and examining the association between heat-related symptoms and heat stroke. Specifically, the study aims to: (i) analyze temperature trends and extreme heat events in Dhaka city; (ii) assess and compare the prevalence of heat-related symptoms among informal workers in indoor and outdoor settings; (iii) explore group-level variations in symptom severity across sociodemographic and occupational categories; and (iv) explore the correlation between heat-related symptoms and heat stroke. Such insights are critical for developing targeted interventions, policies, and adaptive strategies tailored to the unique needs of these populations [27]. Findings will support evidence-based policymaking aligned with global frameworks, including Sustainable Development Goals (SDG) 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).

2. Methods and materials

2.1. Study design

The study employed a descriptive cross-sectional method to investigate comparative heat-related symptoms among indoor and outdoor informal workers in Dhaka, Bangladesh using both primary and secondary data. Primary data focused on symptom prevalence and risk factors, while secondary data, including temperature records and satellite imagery, assessed climate-induced heat risks in the city.

2.2. Study settings

Dhaka Metropolitan Area (DMA) was selected due to its vulnerability to climate-induced hazards, including heat waves and the Urban Heat Island (UHI) effect (Fig. 1). UHI intensifies heat exposure in densely populated cities due to dense infrastructure, lack of vegetation, and concentrated human activity [13,28]. With increasing frequency and intensity of heatwaves in Dhaka [[29], [30], [31]], the findings, while broadly relevant to other cities in South Asia, should be interpreted with caution given the unique socio-environmental context.

Fig. 1.

Map showing the study area: Dhaka Metropolitan Area (DMA), Bangladesh.

2.3. Sampling and sample size

A multi-stage sampling strategy was used. Four thanas (Shahbagh, Kamrangirchar, Mirpur, and Uttara) were purposively selected based on documented vulnerability to heat stress [32]. Within these, a range of informal occupational sites (e.g., markets, construction zones, households) were targeted, and participants were selected via convenience sampling due to the absence of a formal worker registry.

The sample size was determined using the standard formula for estimating proportions in cross-sectional studies. Assuming a 95 % confidence level (Z = 1.96), an expected proportion (p) of 0.5 to ensure maximum sample size, and a margin of error (d) of 5 %, the minimum required sample size was calculated as 384. To ensure robustness and compensate for potential non-response or data quality issues, the sample size was rounded up to 420. After data cleaning, 4 incomplete responses were excluded, resulting in a final analytic sample of 416 respondents. Those engaged in labor-intensive tasks under direct sunlight with limited cooling access were considered as outdoor workers [19] while indoor workers were those working in enclosed or semi-enclosed areas with partial protection from heat [21].

2.4. Instrument and data collection

Secondary temperature data (2001–2022) were obtained from the Bangladesh Meteorological Department (BMD). These included daily minimum and maximum temperatures recorded using standardized ground-based weather stations. Satellite-based Landsat 8 data (2014 and 2022) from the United States Geological Survey (USGS) were used to assess spatial distribution of heat exposure. While humidity exacerbates heat stress [26], it was not analyzed in this study.

Primary data were collected using a structured, self-administered questionnaire adapted from validated instruments [[33], [34], [35]]. The questionnaire was translated into Bengali, reviewed for cultural relevance, and pilot-tested among 35 informal workers to make necessary adjustments. The final tool assessed 20 heat-related symptoms (yes/no response). A detailed rationale for the selection and construction of the 20-item heat-related symptom scale has been provided in Supplementary Material 1. The questionnaire also captured data on sociodemographic characteristics, work setting (indoor/outdoor), and self-reported heat stroke history. Heat stroke was self-reported by participants in response to the question: ‘Have you ever experienced heat stroke (loss of consciousness, confusion, or hospitalization due to excessive heat) [5]?’ Clarification was provided when needed. Affirmative responses were recorded as self-reported cases of heat stroke. Data collection was conducted in July 2023, shortly after the summer season, to minimize recall bias and accurately capture heat-related experiences from the peak heat period. Trained enumerators conducted face-to-face interviews using the Kobo Toolbox digital platform, ensuring real-time data quality monitoring [36].

2.5. Data analysis

Temperature data were cleaned and polynomial interpolation was used in Stata [37] and RStudio [38] to address missing values, allowing smoother estimates than linear methods. Trends and variability from 2001 to 2022 were analyzed using linear regression, with days exceeding 36 °C—recognized by the Bangladesh Meteorological Department (BMD) as the threshold for heatwave classification [17]—used to identify “hot days,” and aligning with globally recognized thresholds for physiological heat stress [39]. Monthly and yearly hot day trends were also assessed. Land Surface Temperature (LST) maps for March 2014 and 2022 were developed in ArcMap 10.8 [40] to evaluate spatial shifts in heat exposure.

Primary data were analyzed using SPSS v27.0 [41]. Descriptive statistics (frequencies, percentages) described the respondents’ sociodemographic and occupational characteristics. Symptom scores were calculated for each respondent based on the number of heat-related symptoms they self-reported experiencing during the previous summer. Independent samples t-tests and one-way ANOVA were used to examine mean differences in symptom scores by demographic and occupational variables. Binary logistic regression was conducted to examine the association between heat-related symptom scores and the likelihood of experiencing self-reported heat stroke.

2.6. Ethical consideration

Ethical approval was obtained from a Research Ethics Committee of the Institute of Disaster Management and Vulnerability Studies, University of Dhaka (SN: ERC (EXT) – 19/282,024). Participation was voluntary, and informed consent was obtained from all respondents.

3. Results

3.1. Temperature-induced risk over dhaka city

The analysis shows a consistent upward trend in annual average temperature in Dhaka, modeled by the regression equation Yt = 25.85 + 0.0328t, indicating an annual rise of 0.0328 °C (Fig. 2). This warming trend is further supported by Land Surface Temperature (LST) data for 2014 and 2022. In 2014, minimum and maximum temperatures were 19.92 °C and 34.39 °C, respectively, which increased to 23.49 °C and 38.44 °C in 2022 (Fig. 3). High-temperature zones also expanded over time, shifting from areas like Mohammadpur and Tejgaon in 2014 to include Jatrabari and Kadamtali in 2022.

Fig. 2.

Linear Trend of annual mean temperatures in Dhaka, Bangladesh (2001–2022).

Fig. 3.

Land Surface Temperature (LST) maps of Dhaka in 2014 and 2022 showing urban heat pattern.

Between 2001 and 2022, Dhaka experienced 348 days with maximum temperatures exceeding 36 °C. The highest frequency occurred in 2014 with 45 hot days (Fig. 4). On average, hot days increased from 12 per year (2001–2011) to 20 per year (2012–2022), indicating growing heat stress.

Fig. 4.

Number of days with maximum temperatures exceeding 36 °c in Dhaka, Bangladesh (2001–2022).

Monthly analysis from March to October reveals April and May as the hottest months, accounting for 133 and 112 hot days, respectively (Fig. 5). The trend line suggests a continued increase in the number of hot days in the coming years. Notably, a post-2010 trend shows increased hot days during August to October, reflecting a seasonal shift and possible extension of the summer period in Dhaka (Fig. 6). This pattern differs from traditional seasonal expectations.

Fig. 5.

Seasonal distribution of days with extreme heat (>36 °c) in Dhaka (March–October 2001–2022).

Fig. 6.

Monthly trend of extreme heat days (>36 °c) in Dhaka during July to October (2001–2022).

3.2. Sociodemographic characteristics of the respondents

Table 1 summarizes the demographic and socioeconomic characteristics of 416 informal workers, disaggregated by work setting. Most respondents were male (86.1 %), with a higher proportion among outdoor workers (90.2 %) compared to indoor (73.3 %). Indoor work had more female participation (26.7 %).

Table 1.

Sociodemographic characteristics of informal workers surveyed in Dhaka, Bangladesh (N = 416).

Sociodemographic characteristics		Indoor Workers		Outdoor Workers		Total
		N	%	N	%	N	%
Gender
	Male	74	73.27	284	90.16	358	86.06
	Female	27	26.73	31	9.84	58	13.94
Age
	Up to 25 years	11	10.89	31	9.84	42	10.10
	26–35 years	24	23.76	83	26.35	107	25.72
	36–45 years	29	28.71	108	34.29	137	32.93
	46–55 years	22	21.78	48	15.24	70	16.83
	>55 years	15	14.85	45	14.29	60	14.42
Education
	No formal education	33	32.67	147	46.67	180	43.27
	Up to Primary/Equivalent	42	41.58	112	35.56	154	37.02
	Up to High School/Equivalent	15	14.85	48	15.24	63	15.14
	Up to HSC/Equivalent and above	11	10.89	8	2.54	19	4.57
Occupation
	Rickshaw/van puller	0	0	86	27.3	86	20.67
	Hawkers	0	0	41	13.02	41	9.86
	Day laborer	0	0	16	5.08	16	3.85
	Shopkeeper	36	35.64	8	2.54	44	10.58
	Street vendors	0	0	88	27.94	88	21.15
	Construction worker	0	0	9	2.86	9	2.16
	Domestic workers	17	16.83	0	0	17	4.09
	Guards	6	5.94	7	2.22	13	3.13
	Small store owner	21	20.79	8	2.54	29	6.97
	Others*	21	20.79	52	16.51	73	17.55
Socioeconomic class
	Lower Class	80	79.21	295	93.65	375	90.14
	Middle Class	19	18.81	19	6.03	38	9.13
	Upper Class	2	1.98	1	0.32	3	0.72
Level of sufficiency
	Not sufficient	31	30.69	141	44.76	172	41.35
	Sufficient without saving	35	34.65	131	41.59	166	39.90
	Sufficient with savings	35	34.65	43	13.65	78	18.75

^⁎“Others” include informal occupations not classified under predefined categories. For outdoor workers, this includes waste pickers, vehicle mechanics, electricians, welders, plumbers, and masons. For indoor workers, it includes laundry workers, ward boys, hotel/cafe attendants, and electronics repairers working in enclosed or semi-enclosed spaces. Classification into indoor/outdoor was based on direct field observation of heat exposure characteristics.

The largest age group was 36–45 years, and outdoor workers had a slightly higher share of older individuals (≥46 years). Education levels were lower among outdoor workers—46.7 % had no formal education, versus 32.7 % of indoor workers. In contrast, 10.9 % of indoor workers had completed higher secondary or more, compared to only 2.5 % of outdoor workers.

The “level of sufficiency” reflects respondents’ perceived financial status: whether their income is enough to meet household needs. A higher proportion of outdoor workers (44.3 %) reported financial insufficiency compared to indoor workers.

3.3. Heat-related symptoms

Table 2 outlines the prevalence of heat-related symptoms among respondents. The most common symptoms were heavy sweating (94.47 %), weakness/tiredness (82.45 %), dry mouth (71.39 %), and headache (68.75 %). Outdoor workers reported significantly higher levels of weakness (84.76 % vs. 75.25 %, p = 0.029), muscle cramps (49.21 % vs. 33.66 %, p = 0.006), and headaches (71.75 % vs. 59.41 %, p = 0.020) than indoor workers—likely reflecting greater physical exertion and direct sun exposure. However, symptoms like dry mouth, dizziness, and eye irritation were prevalent in both groups, suggesting that indoor environments—often lacking ventilation or involving heat-generating tasks—still pose health risks.

Table 2.

Self-reported prevalence of 20 heat-related symptoms among informal workers in Dhaka, Bangladesh (N = 416).

Physical Symptoms	Total		Indoor Workers		Outdoor Workers		P-value
	(n)	( %)	(n)	( % within group)	(n)	( % within group)
Heavy sweating	393	94.47	97	96.04	296	93.97	0.428
Weakness/tiredness	343	82.45	76	75.25	267	84.76	0.029*
Dry mouth	297	71.39	73	72.28	224	71.11	0.821
Headache	286	68.75	60	59.41	226	71.75	0.020*
Eye irritation	223	53.61	51	50.50	172	54.60	0.471
Dizziness	220	52.88	51	50.50	169	53.65	0.580
Loss of appetite	219	52.64	50	49.50	169	53.65	0.468
Dysuria/Yellow urine	207	49.76	45	44.55	162	51.43	0.229
Muscle cramps/pain	189	45.43	34	33.66	155	49.21	0.006*
Fever	178	42.79	43	42.57	135	42.86	0.960
Nose congestion	172	41.35	40	39.60	132	41.90	0.683
Rashes itchy skin	165	39.66	36	35.64	129	40.95	0.343
Cough	143	34.38	34	33.66	109	34.60	0.863
Dry, cracking skin	106	25.48	25	24.75	81	25.71	0.847
Nausea	87	20.91	21	20.79	66	20.95	0.973
Chest tightness	78	18.75	17	16.83	61	19.37	0.570
Wheezing	60	14.42	11	10.89	49	15.56	0.246
Swelling hands/feet	51	12.26	11	10.89	40	12.70	0.630
Fainting	44	10.58	10	9.90	34	10.79	0.800
Vomiting	45	10.82	8	7.92	37	11.75	0.281
Total number of cases	3506		793		2713

^⁎P < 0.05.

3.4. Mean differences of symptoms scores

Table 3 shows how average symptom scores varied across sociodemographic and occupational groups. Gender differences were not statistically significant, though male outdoor workers had slightly higher scores. Older respondents (≥55 years) had the highest symptom scores, reflecting age-related vulnerability, however not statistically significant. Education showed an inverse trend: those with no formal education reported the highest scores (8.95), while those with higher secondary education or more had the lowest (6.42; p = 0.003), suggesting greater awareness and adaptation among the educated.

Table 3.

Variation in heat-related symptom scores by occupation type and socioeconomic groups among informal workers in Dhaka.

Categories	Total				Indoor					Outdoor
	Mean	SD	F/t statistics	P values	Post Hoc	Mean	SD	F/t statistics	P values	Post Hoc	Mean	SD	F/t statistics	P values	Post Hoc
Gender
Male	8.50	3.78	1.003	0.317		7.73	3.60	−0.564	0.574		8.70	3.81	1.283	0.200
Female	7.97	3.82				8.19	3.57				7.77	4.06
Age
a. Up to 25 years	7.93	3.83	0.772	0.544		8.18	4.31	0.845	0.500		7.84	3.72	0.630	0.641
b. 26–35 years	8.31	4.37				7.46	3.13				8.55	4.65
c. 36–45 years	8.28	3.59				7.45	4.01				8.51	3.45
d. 46–55 years	8.61	3.44				7.64	2.97				9.06	3.58
e. >55 years	9.10	3.46				9.33	3.68				9.02	3.43
Education
a. No formal education	8.95	3.75	4.72	0.003**	a > c**	9.00	4.17	0.454	0.068		8.94	3.67	2.638	0.050
b. Up to Primary/Equivalent	8.50	3.52			a > d**	7.62	2.95				8.83	3.66
c. Up to High School/Equivalent	7.37	4.22			b > c*	7.40	3.18				7.35	4.53
d. Up to HSC/Equivalent and above	6.42	3.58			b > d*	5.91	3.67				7.13	3.56
Occupation
a. Rickshaw/van puller	9.23	3.55	2.035	0.034*	a > b*	–	–	1.232	0.302		9.23	3.55	1.632	0.115
b. Hawkers	7.76	4.42			c > b*	–	–				7.76	4.42
c. Day laborer	9.94	3.97			c > h**	–	–				9.94	3.97
d. Shopkeeper	7.98	3.94			e > h*	8.17	3.92				7.13	4.19
e. Street vendors	8.36	3.76			f > h**	–	–				8.36	3.76
f. Construction worker	10.11	3.95			g > h*	–	–				10.11	3.95
g. Domestic workers	9.00	3.62			j > h*	9.00	3.62				–	–
h. Guards	5.77	3.30			a > h**	5.67	3.67				5.86	3.24
i. small store owner	7.79	2.99				7.33	2.85				9.00	3.21
j. Others	8.26	3.70				7.52	3.47				8.56	3.78
Socioeconomic class
a. Lower Class	8.42	3.71	0.087	0.917		7.83	3.26	0.566	0.570		8.58	3.81	0.329	0.72
b. Middle Class	8.45	4.56				7.68	4.80				9.21	4.30
c. Upper Class	9.33	3.21				10.50	3.54				7.00
Level of sufficiency
a. Not sufficient	8.90	3.86	2.334	0.098		8.58	3.37	1.632	0.201		8.97	3.97	1.321	0.268
b. Sufficient without saving	8.13	3.78				7.03	3.78				8.43	3.74
c. Sufficient with savings	8.01	3.55				8.03	3.48				8.00	3.64
Work setting
Indoor	7.85	3.58	−1.763	0.079
Outdoor	8.61	3.84

*P < 0.05, **P < 0.01, *** P < 0.001.

Occupational patterns were notable. Day laborers (9.94), construction workers (10.11), and rickshaw pullers (9.23) had the highest scores—likely due to strenuous outdoor work. Surprisingly, some indoor jobs (e.g., domestic workers, shopkeepers) also showed high scores, likely due to long hours in enclosed, hot settings. Guards had the lowest score (5.77), though the small sample size limits interpretation. Socioeconomic class did not show significant variation, but those reporting insufficient income had higher symptom scores, suggesting limited access to coping strategies like hydration, rest, or shade.

The overall mean score of outdoor workers was higher (8.61) than the indoor workers (7.85), however the difference is not significant (p = 0.079).

3.5. Heat stroke and its association with heat-related symptoms

A total of 50 respondents (12.02 %) self-reported experiencing heat stroke among which 42 (13.23 %) were outdoor workers and 8 (8.08 %) were indoor workers. This difference was not statistically significant (p = 0.146). However, age showed a near-significant association with heat stroke (p = 0.064). The mean symptom score was significantly higher among those with heat stroke (10.76 vs. 8.11; p < 0.001). Among outdoor workers, the score was 11.21 for those with heat stroke, versus 8.21 for those without (Exp(B) = 1.236; p < 0.001). These results, detailed in Table 4, suggest that elevated symptom scores are strongly predictive of heat stroke risk among outdoor workers.

Table 4.

Association between cumulative heat-related symptom scores and self-reported heat stroke among informal workers in Dhaka.

Symptoms score	Total			Indoor workers			Outdoor workers
	Mean	Exp(B)	p-Value	Mean	Exp(B)	p-Value	Mean	Exp(B)	p-Value
No heat stroke	8.11	Ref	Ref	7.81	Ref	Ref	8.21	Ref	Ref
Had heat stroke	10.76	1.209	<0.001	8.38	1.046	0.665	11.21	1.236	<0.001

Note: The model was applied to the full sample and stratified by work setting (indoor/outdoor). No covariates were included due to the limited number of heat stroke cases (n = 50), to prevent overfitting. This unadjusted model aimed to examine association strength rather than prediction.

4. Discussion

4.1. Rising temperatures and changing seasonal patterns

Dhaka has experienced a clear warming trend over the past two decades, with a steady rise in annual temperatures and an increasing number of hot days. These patterns are consistent with prior research on the city’s urban heat island (UHI) effect, where rapid urbanization, loss of vegetation, and dense infrastructure contribute to heat retention, especially during nighttime hours [32,[42], [43], [44]]. Compared to other cities in Bangladesh, Dhaka is warming faster. Satellite-based LST maps (Fig. 3) confirm the spatial expansion of heat-prone areas between 2014 and 2022, particularly in neighborhoods like Badda, Kamrangirchar, and Biman Bandar. These zones overlap with dense informal work hubs, underscoring the link between urban surface heating and occupational heat exposure.

The rise in hot days (>36 °C) extending into monsoon and post-monsoon months signals a seasonal shift. This aligns with projections from the World Bank’s Climate Affliction Report, which anticipates a 1.4 °C average temperature increase in Bangladesh by 2050 [45]. Such seasonal extensions of heat pose serious risks to health, productivity, and urban resilience planning. Informal workers,often lacking rest breaks or protective infrastructure face heightened vulnerability from these prolonged heat exposures [34,42].

4.2. Heat-related symptoms and disparities between indoor and outdoor workers

Outdoor workers reported a significantly higher prevalence of heat-related symptoms,particularly weakness, muscle cramps, and headaches,reflecting the physiological strain from prolonged sun exposure and intense physical labor [34,[46], [47], [48], [49], [50]]. High-risk occupations such as rickshaw pulling, street vending, and construction involve long hours with limited access to rest or hydration, compounding vulnerability to heat illness [51,52].

Surprisingly, the difference in mean symptom scores between indoor and outdoor workers was smaller than expected. Many indoor workers operated in heat-retaining environments—tin-roofed shops, unventilated rooms, or cooking spaces—where poor airflow and lack of insulation led to significant thermal stress. These conditions mirror patterns observed in other dense, humid urban contexts [51].

Occupational context also influenced symptom patterns. For example, guards stationed in shaded areas reported low symptom scores, whereas those posted outdoors faced higher risks. Similarly, shopkeepers and small store owners in crowded, poorly ventilated spaces experienced elevated symptom burdens despite working indoors.

4.3. Associations between sociodemographic and occupational characteristics and symptom severity

Symptom severity varied across age, education, gender, and occupation, reflecting well-established vulnerabilities. Older respondents reported the highest symptom burden, supporting prior evidence that aging reduces thermoregulatory efficiency and heightens susceptibility to heat-related illnesses [53,54]. Education showed an inverse association: individuals with no formal education experienced more symptoms than those with higher education levels, likely due to limited awareness and access to adaptive knowledge and practices [[55], [56], [57]].

Gender disparities were minimal overall but context-specific. Outdoor male workers had higher symptom scores, reflecting physically demanding work under direct sun. Among indoor workers, female respondents showed marginally higher burdens, potentially due to tasks like cooking in enclosed, poorly ventilated settings and physiological differences in heat response [58].

Occupational role remained a critical determinant. Outdoor workers in labor-intensive jobs,such as rickshaw pullers and construction workers,had the highest symptom burdens, consistent with findings from similar urban heat studies [59,60]. Interestingly, some indoor occupations like shopkeeping and domestic work also reported moderate symptoms, reflecting the risks posed by long working hours in heat-retaining, poorly ventilated spaces.

Socioeconomic class did not show significant associations, likely due to sample homogeneity. However, respondents who perceived their income as insufficient experienced higher symptom burdens, echoing literature linking financial insecurity to reduced access to protective measures such as hydration, rest, and medical care [56,[61], [62], [63], [64]].

These findings underscore how structural vulnerabilities—age, education, occupation, and economic status—intersect to shape heat stress outcomes among informal workers, requiring targeted, equity-oriented adaptation responses.

4.4. Heat stroke and symptom severity

Our study confirmed a significant association between heat-related symptom burden and incidence of heat stroke, especially among outdoor workers. This reinforces the cumulative impact of prolonged thermal stress on vulnerable labor populations. These findings align with the biopsychosocial model of heat vulnerability, where physiological strain (e.g., dehydration), environmental exposure (sunlight, exertion), and social constraints (lack of rest or hydration) interact to elevate risk [5,65,66]. While fewer indoor workers reported heat stroke, enclosed, poorly ventilated spaces,especially during cooking or long work hours,may still result in harmful exposures. Underreporting is also plausible due to limited awareness or misrecognition of symptoms.

Comorbidities like cardiovascular or respiratory illnesses, though unassessed in this pilot study, may have influenced vulnerability and should be considered in future research [67,68]. Incorporating clinical assessments and objective heat indicators would enhance validity. Immediate policy responses are critical. Low-cost interventions—hydration stations, shaded rest areas, and mandatory cooling breaks—should be prioritized in high-risk sectors. Public health messaging must focus on early symptom recognition, especially among low-literacy groups [5,69,70].

Longer-term solutions involve integrating heat protections into labor policies and urban design. Models from India, Cambodia, and Vietnam demonstrate that scalable, cost-effective adaptations are feasible even in resource-limited settings [19,71]. In Bangladesh, protecting informal workers—often outside formal social safety nets—must be central to climate adaptation and occupational health reforms [3,72].

4.5. Limitations and future research scopes

This pilot study has some limitations. The purposive selection of high-risk areas in Dhaka restricts generalizability. While our sample captures relevant occupational diversity, the convenience sampling within clusters may introduce selection bias. Further, the cross-sectional design prevents causal conclusions, and reliance on self-reported symptoms raises potential recall or social desirability bias. Additionally, the absence of physiological or clinical assessments (e.g., core body temperature, hydration biomarkers) and wet-bulb temperature readings (which could have provided a more accurate measure of combined heat and humidity exposure relevant to heat stress) limits the medical precision of our findings. Longitudinal studies incorporating wearable sensors, environmental data loggers, and health follow-ups would strengthen causal inference and risk modeling. We also did not stratify indoor environments based on building material, ventilation, or internal heat or cooling sources — factors that may explain variability in indoor heat exposure.

Future research should use parameter-based assessments,including workplace temperature, humidity, ventilation, and physical exertion,to better quantify heat exposure. Incorporating multivariate analytical approaches will help disentangle the independent effects of overlapping factors such as age, education, and occupation. Longitudinal studies, broader geographic coverage, and physiological data can improve causal understanding. Evaluating existing adaptation strategies and exploring vulnerabilities among women, elderly workers, and community coping mechanisms—including social safety nets—will help guide sustainable occupational health and climate resilience policies.

4. Conclusion

This pilot study underscores the urgent need to protect informal workers from escalating heat stress risks, aligning with global climate and labor frameworks. The IPCC Sixth Assessment Report [73] warns that urban heat stress disproportionately affects low-income workers, a reality reflected in our findings where outdoor workers in Dhaka face severe heat-related symptoms and heightened heat stroke risks due to direct sun exposure and lack of protections. Addressing these risks is critical to achieving SDG 3 (Good Health and Well-being) by reducing heat-related morbidity, SDG 8 (Decent Work and Economic Growth) by ensuring safe working conditions, and SDG 13 (Climate Action) by integrating heat adaptation strategies into labor policies. The ILO’s Decent Work Agenda also recognizes heat stress as a labor rights issue, necessitating rest breaks, hydration access, and workplace protections. Without targeted interventions, informal workers will remain on the frontlines of climate-induced health crises, worsening socioeconomic inequalities. Moreover, strengthening policies that mandate climate-resilient interventions in workplaces, particularly for informal workers, is critically required to reduce heat-related health risks and improve occupational safety.

CRediT authorship contribution statement

Sheikh Mohiuddin Shahrujjaman: Writing – original draft, Software, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Bivuti Bhushan Sikder: Writing – review & editing, Supervision, Methodology, Conceptualization. Dilara Zahid: Writing – review & editing, Supervision, Conceptualization. Syed Irfan Uddin: Writing – original draft, Formal analysis, Data curation.

Declaration of competing interest

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