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. 2024 Nov 13;19(11):e0310826. doi: 10.1371/journal.pone.0310826

Understanding public health risk from unsafe dry fish consumption in Bangladesh

Mahdi Al Hasan Rahat 1,#, Anik Saha 1,#, Mehedy Hasan Abir 1,2,#, A S M Nafis Sadekeen 1, Shahneaz Ali Khan 3, Sukanta Chowdury 4,*
Editor: Charles Odilichukwu R Okpala5
PMCID: PMC11560022  PMID: 39536065

Abstract

Dried fish holds a significant place in the Bangladeshi diet particularly for people living in coastal regions. However, there is a growing concern regarding its adverse effects on human health, as it contains high levels of illegal preservatives, heavy metals, and other harmful substances. In this study, we aimed to explore the current knowledge, attitudes, and practices regarding health hazards due to unsafe dried fish consumption among people across the country. We conducted a cross-sectional study among consumers to assess their knowledge, attitudes, and practices about the health risks associated with consuming hazardous dried fish. We interviewed a total of 415 participants, of whom 52.8% were male; the majority were students (55.9%), aged between 18 and 30 years (63.9%), and living in urban areas (81.7%). Most of the participants (60.7%) had less accurate knowledge of the health hazards of unsafe dry fish, 92.8% had more positive attitudes to buying safe dry fish, and 26.8% used unsafe dry fish more frequently. Many respondents were unaware of the presence of harmful substances in dried fish, such as illegal pesticides (66.5%), microplastics (77.6%), and heavy metals (67.4%). A significant number of participants (13%) reported that they had a history of cancer in any of their family members. Many individuals (57.4%) were not familiar with the proper storage and preparation methods of dry fish. The majority of participants (81.4%) strongly prefer packed dried fish. Most of the respondents (67.7%) agreed to participate in awareness programs. Female consumers were more likely to have more accurate knowledge (AOR = 1.53; 95% CI = 1.03–2.29, p = 0.0.37) than males, and participants whose present residence were in rural were more likely to have accurate knowledge (AOR = 2.64; 95% CI = 1.30–5.36, p = 0.007) than those whose present residence were in urban or semi-urban areas. A targeted education campaign focused on improving awareness of the risks associated with eating unsafe dry fish is needed, particularly in coastal areas.

Introduction

Dry fish serves as a source of protein and it is a typical food item in many places, especially in areas where fresh fish are poorly available [1]. In Bangladesh, the fish sector contributes to the country’s economy, nutrition, and earning money from abroad [2]. More than 20% of locally caught fish are sun-dried and consumed locally [3]. Adequate sanitation and hygiene are not maintained properly during drying process of fish. Bug infestation, the presence of dirt, and pesticide residue are commonly found in dried fish [1]. The fish processors are responsible for drying, packing, and distributing fish to markets and they do not have adequate training in sanitation practices [4]. The dried fish is often kept close to the seaside town in a warehouse. Due to blowfly infestation and longer drying times might result in spoilages and quality losses of dry fish. Bug infestation can cause dried fish to lose 30–40% of their weight [4]. There is also the issue that many fish processors prioritize profit over quality, leading to improper drying practices that make insects more prevalent more quickly [5]. Dry fish processors typically used several insecticides such as dichlorodiphenyltrichloroethane (DDT), aldrin, acephate, diazinon and dimethoate to prevent insects [6, 7]. Pesticides and the low microbiological quality of dried fish are concerning for Bangladeshi consumers since they may cause prolonged sickness and provide long-term harm to human health [8].

Pesticide-contaminated dried fish is unsafe for consumption that poses health risks to humans. Pesticide residues in dried fish may cause cancer, epilepsy, liver and kidney damage, leukemia, decreased fertility, genetic damage, and immune system suppression [9, 10]. The deposition of heavy metals and microplastics from the aquatic environment effect on the quality of dried fish [11]. Upon consumption, heavy metals present in dry fish pose moderate-to-high health concerns to the human body, and dried fish polluted with Pb and Cd pose a cancer risk to consumers [7, 12].

The attitudes and behaviors of food consumers and handlers influence their degree of knowledge of food safety [13]. Consumers’ mindset is a key element that affect their behavior and practices with regard to food safety [14, 15]. A positive outlook can help to suggest safety information to ensure that everyone has access to safe food [16]. There have been no previously published KAP studies on consumers in Bangladesh about food safety problems resulting from the consumption of unsafe dry fish. This study focused on exploring the current knowledge and practices regarding health hazards due to unsafe dried fish consumption. This study’s findings help to understand consumers’ knowledge gaps and identify risky practices.

Methods

Study sites, population and study period

A cross-sectional study was carried out throughout different divisions in Bangladesh, with a special focus on the coastal division Chattogram due to its significant quantity of dried fish production and consumption [17], between November and December 2022. A combination of in-person and online interviews were conducted, with the majority of interviews being conducted in person (373 in-person interviews). For the in-person interview, we selected participants purposively who were aged above 18 years. The study team visited a dry fish market every day and invited visitors or buyers or consumers to participate in this study. Participants were interviewed, and data was collected using a structured questionnaire administered by an interviewer, who asked questions and recorded the information. Additionally, a web-based questionnaire was distributed via social media platforms (Facebook, Instagram, LinkedIn, and Gmail) to collect online data, with participants given instructions before filling out the form themselves.

Data collection

We collected data on socio-demographics, health indicators, consumption habits of dry fish, knowledge, attitudes, and practices on health hazards about unsafe dry fish and associated practices.

  1. Socio-demographic data: Socio-demographic information included age, gender, religion, division, marital status, education, occupation, monthly family income (BDT), family size, place of present address, place of permanent residence, and frequency of living with the family.

  2. Health indicators: The history of family health included nine questions to know whether there were previous risks associated with dried fish consumption.

  3. Knowledge, attitudes, and practices: A total of 39 questions were included to assess the level of knowledge, attitudes, and practices.

After completion of the initial draft of the questionnaire, the questionnaire was validated and adopted by knowledgeable academics and experts. After coordination and consensus from the experts, the questionnaire has been finalized for data collection. The calculated sample size was 385 individuals based on 95% confidence interval, a 5% margin of error, and the assumption that 50% of the respondents are at risk from consuming unhealthy dried fish [18]. The formula is as follows:

n0=Z2pqe2

Here, n0 = Sample size; Z = Z-score = 1.96 for 95% level of confidence; p = Estimated proportion of the attribute that is present in the population = 50% (or 0.5); q = 1-p = 0.5; e = Margin of error = 5%.

In this study, the we collected data from 415 potential respondents. The reliability coefficient analysis was conducted for collected data. The Cronbach’s alpha coefficient of the knowledge, attitudes, and practices were 0.84, 0.70, and 0.70, respectively, and the overall Cronbach’s alpha of KAP questions was 0.84, which indicated acceptable internal consistency as described [19].

The knowledge section consisted of 13 questions and each of them had a possible response of “Yes”, “No”, and “Don’t know” (e.g., Do you think consuming preservative-free dried fish is good for health?). The correct answer (Yes) was coded as 1, while the wrong answer (No/ Don’t know) was coded as 0. The total score ranged from 0–13, with an overall greater score indicating “More Accurate” knowledge. A cut-off level of ≥10 was set for “More accurate” knowledge, 6–10 was set for “Moderate” knowledge, and ≤5 was set for “Less accurate” knowledge.

The attitudes section consisted of 15 questions, and each of them was indicated on a 3-point Likert scale as follows: 0 for “Disagree”, 1 for “Undecided”, and 2 for “Agree” (e.g., Consumers should have some knowledge on the hazards of dried fish before buying). The total score was calculated by summating the raw scores of the six questions ranging from 0 to 30, with an overall greater score indicating “More positive” attitudes. A cut-off level of ≥21 was set for “More positive”, 11–20 was set for “Moderate”, and ≤10 was set for “Less positive” attitudes.

The practices section included a total of 11 questions, and each of them had one of the responses of “Yes”, “No”, and “Sometimes” (e.g., Do you buy dried fish from a trusted seller/processor?). Practice items’ total score ranged from 0 to 22, with an overall greater score indicating “More frequent” practices. A cut-off level of ≥15 was set for “More frequent”, 8–14 was set for “Moderate”, and ≤7 was set for “Less frequent” practices.

Statistical analysis

We summarized participants’ demographic characteristics such as age, sex, marital status, education, occupation and residence status by descriptive analyses (frequency, mean, standard deviation, p-value and 95% confidence interval). Chi-square test was performed to examine the differences between categories for each categorical value with respect to their knowledge, attitudes, and practices. Multivariate logistic regression analysis was performed to identify the association between the variables and respondents’ knowledge, attitudes, and practices on health hazards due to unsafe dry fish through. We estimated the odds ratio (OR) and adjusted odds ratio (AOR) separately for knowledge, attitudes, and practices. All statistical analyses were performed in Stata 13 software (StataCorp LP, College Station, TX).

Ethical statement

The research review committee of Chattogram Veterinary and Animal Sciences University, Chittagong, Bangladesh reviewed and approved the study protocol. Written consent was obtained from selected participants before collecting data.

Results

A total of 415 participants were interviewed; 52.8% were male and the majority of the participants (63.9%) were between the ages of 18 and 30. Most of the participants (79.5%) were came from the Chattogram division. Students were predominant (55.9%) and 52% had bachelor’s degrees. More than 35% of the participants’ households had a monthly income of more than BDT 30,000 (Table 1).

Table 1. Demographic characteristics of the studied population (N = 415, November-December 2022, Bangladesh).

Variables n (%) 95% CI
Age (years)
18–30 265 (63.9) 59.0–68.4
31–50 101 (24.3) 20.3–28.8
51–65 44 (10.6) 7.8–14.0
>65 5 (1.2) 0.4–2.8
Gender
Male 219 (52.8) 47.8–57.7
Female 196 (47.2) 42.3–52.2
Division
Chattogram 329 (79.3) 75.1–83.1
Dhaka 55 (13.3) 10.1–16.9
Sylhet 11 (2.7) 1.3–4.7
Khulna 7 (1.7) 0.7–3.4
Rajshahi 4 (1.0) 0.3–2.4
Rangpur 4 (1.0) 0.3–2.4
Mymensingh 3 (0.7) 0.1–2.1
Barisal 2 (0.5) 0.1–1.7
Marital status
Married 160 (38.6) 33.8–43.4
Unmarried 248 (59.8) 54.9–64.5
Widowed 7 (1.7) 0.7–3.4
Education
No education 7 (1.7) 0.7–3.4
Primary (1–5) 2 (0.5) 0.1–1.7
Secondary (6–10) 36 (8.7) 6.1–11.8
Intermediate (11–12) 63 (15.2) 11.9–19.0
Bachelor 216 (52.0) 47.1–57.0
Higher education (above bachelor) 91 (21.9) 18.0–26.2
Occupation
Student 232 (55.9) 51.0–61.0
Govt. employee 17 (4.1) 2.4–6.5
Non-govt. employee 37 (8.9) 6.4–12.1
Businessman 26 (6.3) 4.1–9.0
Housewife 67 (16.1) 12.7–20.0
Unemployed 10 (2.4) 1.2–4.4
Others 26 (6.3) 4.1–9.0
Present residence
Urban 339 (81.7) 77.6–85.3
Semi-urban 26 (6.3) 4.1–9.0
Rural 50 (12.0) 9.1–15.6
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Health status of participants

A considerable number of respondents reported that they had a history of familial precedence of cancer (13%), ulcer (17.1%), neurological illnesses (9.9%), hypertension (67.2%) and asthma (27%). Familial infertility problems were reported by 4.3% of respondents, and 6.7% participants’ family members experienced vomiting or diarrhea immediately after dried fish consumption within the last 6 months (Table 2).

Table 2. History of family health of the studied population (N = 415, November-December 2022, Bangladesh).

Characteristics N (%) 95% CI
Any of your family members had cancer within the last 10 years?
Yes 54 (13.0) 9.9–16.6
No 361 (87.0) 83.4–90.1
Any of your family members had ulcer within the last 10 years?
Yes 71 (17.1) 13.6–21.1
No 344 (82.9) 78.9–86.4
Any of your family members had neurological disorder (autism/ seizure/ dementia/ epilepsy) by born?
Yes 28 (6.7) 4.5–9.6
No 387 (93.3) 90.4–95.5
Any of your family members currently have neurological disorder?
Yes 41 (9.9) 7.2–13.2
No 374 (90.1) 86.8–92.8
Any of your family members have hypertension at present?
Yes 279 (67.2) 62.5–71.7
No 136 (32.8) 28.3–37.5
Any of your family members have asthma at present?
Yes 112 (27.0) 22.8–31.5
No 303 (73.0) 68.5–77.2
Any of your married family members currently have infertility problem?
Yes 18 (4.3) 2.6–6.8
No 397 (95.7) 93.2–97.4
Any of your female family members experienced complications during pregnancy or childbirth (i.e., miscarriage)?
Yes 42 (10.1) 7.4–13.4
No 373 (89.9) 86.6–92.6
Any of your family members experienced vomiting or diarrhea immediately after eating dried fish within the last 6 months?
Yes 28 (6.7) 4.5–9.6
No 387 (93.3) 90.4–95.5
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Knowledge, attitudes, and practices towards health hazard associated with unsafe dried fish consumption

Overall, 7.2% of the respondents had accurate knowledge of health problems due to unsafe dry fish consumption, 92.8% had a more positive attitude to buying safe dry fish and 26.8% consumed unsafe dry fish more frequently (Table 3). Regarding the present place of residence, there was a significant variation in the knowledge levels among the population (p = 0.01). In terms of occupation, there was significant variation in attitude levels among the population (p = 0.034). Practice varied significantly within the population of different educational groups (p = 0.047) (Table 4).

Table 3. Overall knowledge, attitudes, and practices of the studied population (N = 415, November-December 2022, Bangladesh).

Factors n (%)
Knowledge
More accurate (>9) 30 (7.2)
Moderate (6–9) 133 (32.1)
Less accurate (<6) 252 (60.7)
Attitudes
More positive (>20) 385 (92.8)
Moderate (11–20) 28 (6.8)
Less positive (<11) 2 (0.5)
Practices
More frequent (>14) 111 (26.8)
Moderate (8–14) 254 (61.2)
Less frequent (<8) 50 (12.1)
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Table 4. Test of statistical significances of the variations in respondents’ knowledge, attitudes, and practices on health hazards due to unsafe dried fish consumption with their demographic characteristics (N = 415, November-December 2022, Bangladesh).

Variables Knowledge Attitudes Practices
More accurate
n (%)		 Moderate
n (%)		 Less accurate
n (%)		 p-value More positive
n (%)		 Moderate
n (%)		 Less positive
n (%)		 p-value More frequent
n (%)		 Moderate
n (%)		 Less frequent
n (%)		 p-value
Age (years)
18–30 20 (4.8) 91 (21.9) 154 (37.1) 0.255 245 (59) 18 (4.3) 2 (0.5) 0.920 76 (18.3) 154 (37.1) 35 (8.4) 0.362
31–50 5 (1.2) 25 (6.0) 71 (17.1) 95 (22.9) 6 (1.4) 0 (0.0) 22 (5.3) 69 (16.6) 10 (2.4)
51–65 5 (1.2) 14 (3.4) 25 (6.0) 40 (9.6) 4 (1.0) 0 (0.0) 13 (3.1) 26 (6.3) 5 (1.2)
>65 0 (0.0) 3 (0.7) 2 (0.5) 5 (1.2) 0 (0.0) 0 (0.0) 0 (0.0) 5 (1.2) 0 (0.0)
Gender
Male 19 (4.6) 78 (18.8) 122 (29.4) 0.078 204 (49.2) 13 (3.1) 2 (0.5) 0.325 59 (14.2) 128 (30.8) 32 (7.7) 0.211
Female 11 (2.7) 55 (13.2) 130 (31.3) 181 (43.6) 15 (3.6) 0 (0.0) 52 (12.5) 126 (30.4) 18 (4.3)
Religion
Islam 19 (4.6) 87 (20.9) 183 (44.1) 0.497 264 (63.6) 23 (5.5) 2 (0.5) 0.762 80 (19.3) 174 (41.9) 35 (8.4) 0.813
Hinduism 11 (2.7) 44 (10.6) 63 (15.2) 113 (27.2) 5 (1.2) 0 (0.0) 30 (7.2) 73 (17.6) 15 (3.6)
Buddhism 0 (0.0) 2 (0.5) 4 (1.0) 6 (1.4) 0 (0.0) 0 (0.0) 1 (0.2) 5 (1.2) 0 (0.0)
Others 0 (0.0) 0 (0.0) 2 (0.5) 2 (0.5) 0 (0.0) 0 (0.0) 0 (0.0) 2 (0.5) 0 (0.0)
Division
Chattogram 25 (6.0) 108 (26) 196 (47.2) 0.248 306 (73.7) 22 (5.3) 1 (0.2) 0.995 89 (21.4) 202 (48.6) 38 (9.1) 0.850
Dhaka 2 (0.5) 17 (4.1) 36 (8.7) 50 (12.1) 4 (1.0) 1 (0.2) 14 (3.4) 33 (8) 8 (1.9)
Sylhet 1 (0.2) 2 (0.5) 8 (1.9) 10 (2.4) 1 (0.2) 0 (0.0) 1 (0.2) 8 (1.9) 2 (0.5)
Khulna 0 (0.0) 1 (0.2) 6 (1.4) 6 (1.4) 1 (0.2) 0 (0.0) 2 (0.5) 3 (0.7) 2 (0.5)
Rajshahi 0 (0.0) 3 (0.7) 1 (0.2) 4 (1.0) 0 (0.0) 0 (0.0) 1 (0.2) 3 (0.7) 0 (0.0)
Rangpur 0 (0.0) 1 (0.2) 3 (0.7) 4 (1.0) 0 (0.0) 0 (0.0) 1 (0.2) 3 (0.7) 0 (0.0)
Mymensingh 1 (0.2) 1 (0.2) 1 (0.2) 3 (0.7) 0 (0.0) 0 (0.0) 2 (0.5) 1 (0.2) 0 (0.0)
Barisal 1 (0.2) 0 (0.0) 1 (0.2) 2 (0.5) 0 (0.0) 0 (0.0) 1 (0.2) 1 (0.2) 0 (0.0)
Education
No education 0 (0.0) 0 (0.0) 7 (1.7) 0.077 7 (1.7) 0 (0.0) 0 (0.0) 0.719 0 (0.0) 6 (1.4) 1 (0.2) 0.047
Primary (1–5) 0 (0.0) 0 (0.0) 2 (0.5) 2 (0.5) 0 (0.0) 0 (0.0) 0 (0.0) 2 (0.5) 0 (0.0)
Secondary (6–10) 0 (0.0) 11 (2.7) 25 (6) 33 (8) 3 (0.7) 0 (0.0) 5 (1.2) 27 (6.5) 4 (1.0)
Intermediate (11–12) 3 (0.7) 21 (5.0) 39 (9.4) 55 (13.2) 8 (1.9) 0 (0.0) 14 (3.4) 39 (9.4) 10 (2.4)
Bachelor 17 (4.1) 80 (19.3) 119 (28.7) 203 (48.9) 11 (2.7) 2 (0.5) 75 (18.0) 118 (28.4) 23 (5.5)
Higher education (above bachelor) 10 (2.4) 21 (5.0) 60 (14.5) 85 (20.5) 6 (1.4) 0 (0.0) 17 (4.1) 62 (14.9) 12 (2.9)
Occupation
Student 13 (3.1) 85 (20.5) 134 (32.3) 0.143 216 (52.0) 15 (3.6) 1 (0.2) 0.034 67 (16.1) 136 (32.8) 29 (7.0) 0.131
Govt. employee 2 (0.5) 5 (1.2) 10 (2.4) 15 (3.6) 2 (0.5) 0 (0.0) 7 (1.7) 8 (1.9) 2 (0.5)
Non-govt. employee 5 (1.2) 8 (1.9) 24 (5.8) 35 (8.4) 2 (0.5) 0 (0.0) 4 (1.0) 29 (7) 4 (1.0)
Businessman 1 (0.2) 12 (2.9) 13 (3.1) 23 (5.5) 3 (0.7) 0 (0.0) 10 (2.4) 12 (2.9) 4 (1.0)
Housewife 4 (1.0) 14 (3.4) 49 (11.8) 64 (15.4) 3 (0.7) 0 (0.0) 17 (4.1) 43 (10.4) 7 (1.7)
Unemployed 2 (0.5) 2 (0.5) 6 (1.4) 8 (1.9) 1 (0.2) 1 (0.2) 1 (0.2) 6 (1.4) 3 (0.7)
Others 3 (0.7) 7 (1.7) 16 (3.9) 24 (5.8) 2 (0.5) 0 (0.0) 5 (1.2) 20 (4.8) 1 (0.2)
Monthly family income (BDT)
<10,000 6 (1.4) 36 (8.7) 83 (20) 0.314 116 (28) 9 (2.2) 0 (0.0) 0.352 27 (6.5) 83 (20) 15 (3.6) 0.534
10,000–30,000 13 (3.1) 43 (10.4) 88 (21.2) 131 (31.6) 11 (2.7) 2 (0.5) 40 (9.6) 88 (21.2) 16 (3.9)
>30,000 11 (2.7) 54 (13.0) 81 (19.5) 138 (33.3) 8 (1.9) 0 (0.0) 44 (10.6) 83 (20) 19 (4.8)
Present residence
Urban 27 (6.5) 118 (28.4) 194 (46.8) 0.011 318 (76.6) 19 (4.6) 0 (0.0) 0.256 100 (24.1) 196 (47.2) 43 (10.4) 0.003
Semi-urban 3 (0.7) 4 (1.0) 19 (4.6) 24 (5.8) 2 (0.5) 0 (0.0) 8 (1.9) 15 (3.6) 3 (0.7)
Rural 0 (0.0) 11 (2.7) 39 (9.4) 43 (10.4) 7 (1.7) 0 (0.0) 3 (0.7) 43 (10.4) 4 (1.0)
Permanent residence
Urban 17 (4.1) 56 (13.5) 95 (22.9) 0.142 156 (37.6) 10 (2.4) 2 (0.5) 0.216 55 (13.3) 87 (21) 26 (6.7) 0.027
Semi-urban 5 (1.2) 14 (3.4) 40 (9.6) 52 (12.5) 7 (1.7) 0 (0.0) 15 (3.6) 38 (9.1) 6 (1.4)
Rural 8 (1.9) 63 (15.2) 117 (28.2) 177 (42.7) 11 (2.7) 0 (0.0) 41 (9.8) 129 (31.1) 18 (4.3)
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There was no significant difference in knowledge between males and females (S1 Table). In contrast, there was a significant difference in attitude toward buying dry fish that was stored in an airtight polythene pouch between males and females (S2 Table). Similarly, a significant difference in practice between males and females was found when buying dry fish that are free of flies, insects, or rodents (S3 Table).

Factors related to knowledge, attitudes and practices

In bivariate analysis, respondents aged between 31 and 50 years were more likely to have more accurate knowledge compared to those aged 18 to 30 years (OR 1.69; 95% CI: 1.04–2.75, p 0.034). Females were found to have more accurate knowledge compared to males (OR 1.57; 95% CI: 1.06–2.32, p 0.024), and housewives had a higher likelihood of having more accurate knowledge than students (OR 1.87; 95% CI: 1.03–3.39, p 0.039). Participants from rural and urban areas exhibited a significant difference, with the former showing a higher likelihood of having more accurate knowledge (OR 2.75; 95% CI: 1.37–5.53, p 0.004). Individuals living in rural areas exhibit a substantially greater tendency (OR 1.89; 95% CI: 1.06–3.36, p 0.03) to participate in more frequent practices compared to respondents living in urban areas (Table 5).

Table 5. Logistic regression analysis of the variables associated with respondents’ knowledge, attitudes, and practices on health hazards due to unsafe dried fish consumption (N = 415, November-December 2022, Bangladesh).

Variables Knowledge Attitudes Practices
OR, 95% CI, p Adjusted OR, 95% CI, p OR, 95% CI, p Adjusted OR, 95% CI, p OR, 95% CI, p Adjusted OR, 95% CI, p
Age (years)
18–30 Ref. Ref. Ref.
31–50 1.69, 1.04–2.75, 0.034 0.77, 0.30–1.97, 0.584 1.15, 0.73–1.82, 0.538
51–65 0.89, 0.47–1.69, 0.730 1.21, 0.39–3.73, 0.737 0.92, 0.49–1.84, 0.796
>65 0.64, 0.13–3.12, 0.578 Undefined 1.69, 0.33–0.59, 0.526
Gender
Male Ref. Ref. Ref. Ref. Ref.
Female 1.57, 1.06–2.32, 0.024 1.53, 1.03–2.29, 0.037 1.12, 0.53–2.34 0.773 0.87, 0.59–1.27, 0.463 0.72, 0.45–1.14, 0.161
Religion
Islam Ref. Ref. Ref.
Hinduism 0.67, 0.44–1.02, 0.059 0.47, 0.17–1.25, 0.128 1.10, 0.71–1.69, 0.657
Buddhism 1.26, 0.24–6.71,0.789 Undefined 1.03, 0.23–4.72, 0.966
Others Undefined Undefined 1.68, 0.13–21.53, 0.688
Division
Chattogram Ref. Ref. Ref.
Dhaka 1.33, 0.74–2.40, 0.338 1.35, 0.49–3.72, 0.561 1.16, 0.65–2.06, 0.617
Sylhet 1.70, 0.44–6.52, 0.442 1.32, 0.16–10.78, 0.793 2.25, 0.69–7.36, 0.180
Khulna 4.18, 0.50–34.88, 0.187 2.20, 0.25–18.95, 0.474 1.68, 0.33–8.56, 0.530
Rajshahi 0.41, 0.07–2.23, 0.299 Undefined 0.79, 0.12–5.07, 0.802
Rangpur 2.14, 0.22–20.34, 0.506 Undefined 0.79, 0.12–5.07, 0.802
Mymensingh 0.23, 0.24–2.24, 0.207 Undefined 0.18, 0.16–1.95, 0.158
Barisal 0.23, 0.01–5.66, 0.371 Undefined 0.34, 0.02–4.96, 0.431
Education
No education Ref. Ref. Ref. Ref.
Primary (1–5) Undefined Undefined 0.65, 0.03–12.15, 0.774 0.80, 0.40–15.85, 0.883
Secondary (6–10) Undefined Undefined 0.60, 0.13–2.79, 0.511 0.68, 0.14–3.31, 0.631
Intermediate (11–12) Undefined Undefined 0.52, 0.12–2.33, 0.394 0.49, 0.10–2.31, 0.369
Bachelor Undefined Undefined 0.28, 0.07–1.19, 0.084 0.19, 0.04–0.87, 0.032
Higher education (above bachelor) Undefined Undefined 0.54, 0.13–2.34, 0.412 0.44, 0.10–2.01, 0.290
Occupation
Student Ref. Ref. Ref. Ref.
Govt. employee 0.94, 0.35–2.52, 0.900 1.78, 0.38–8.48, 0.467 0.62, 0.23–1.68, 0.348 0.33, 0.11–0.97, 0.044
Non-govt. employee 1.15, 0.56–2.38, 0.699 0.77, 0.17–3.49, 0.733 1.76, 0.89–3.48, 0.107 0.98, 0.45–2.11, 0.954
Businessman 0.79, 0.37–1.70, 0.551 1.75, 0.47–6.44, 0.403 0.76, 0.33–1.74, 0.516 0.36, 0.14–0.90, 0.029
Housewife 1.87, 1.03–3.39, 0.039 0.63, 0.18–2.23, 0.475 1.07, 0.62–1.84, 0.815 0.62, 0.31–1.24, 0.178
Unemployed 0.84, 0.23–3.14, 0.799 3.77, 0.73–19.55, 0.113 3.45, 0.97–12.30, 0.056 2.69, 0.71–10.16, 0.144
Others 1.04, 0.46–2.38, 0.918 1.12, 0.24–5.16, 0.885 1.09, 0.50–2.39, 0.830 0.54, 0.23–1.28, 0.162
Monthly family income (BDT)
<10,000 Ref. Ref. Ref.
10,000–30,000 0.76, 0.46–1.25, 0.277 1.30, 0.53–3.14, 0.565 0.79, 0.49–1.27, 0.324
>30,000 0.64, 0.39–1.03, 0.066 0.75, 0.28–2.00, 0.563 0.76, 0.47–1.23, 0.269
Present residence
Urban Ref. Ref. Ref. Ref. Ref.
Semi-urban 1.82, 0.74–4.47, 0.190 1.90, 0.77–4.71, 0.164 1.25, 0.28–5.66, 0.770 0.71, 0.13–3.80, 0.688 0.92, 0.42–2.056, 0.847
Rural 2.75, 1.37–5.53, 0.004 2.64, 1.30–5.36, 0.007 2.43, 0.98–6.05, 0.056 4.62, 1.40–15.23, 0.012 1.89, 1.06–3.36, 0.030
Permanent residence
Urban Ref. Ref. Ref. Ref.
Semi-urban 1.59, 0.86–2.96, 0.143 1.72, 0.64–4.60, 0.279 1.82, 0.61–5.40, 0.282 1.11, 0.62–2.01, 0.722
Rural 1.35, 0.89–2.05, 0.152 0.80, 0.34–1.86, 0.602 0.43, 0.14–1.28, 0.128 1.24, 0.81–1.88, 0.317
Open in a new tab

Multivariate analysis showed that females had more accurate knowledge (AOR 1.53; 95% CI: 1.03–2.29, p 0.0.37) than males. Participants whose present residence was in rural areas were more likely to have more accurate knowledge (AOR 2.64; 95% CI: 1.30–5.36, p 0.007) than those whose present residence was in urban or semi-urban areas. Residents of rural areas exhibited significantly more positive attitudes (AOR 4.62; 95% CI: 1.40–15.23, p 0.012) compared to their counterparts in urban or semi-urban areas. Respondents with a bachelor’s degree were less likely to engage in frequent practices compared to those with no education (AOR 0.19; 95% CI: 0.04–0.87, p 0.032). Businessmen were also less likely to engage in more frequent practices compared to students (AOR 0.36; 95% CI: 0.14–0.90, p 0.029) (Table 5).

Discussion

The consumption of unhealthy dried fish can pose significant health risks to individuals and the overall well-being of the community. The study highlights a significant knowledge gap regarding the health risks of unsafe dried fish consumption, despite a positive attitude towards safe alternatives. Rural and urban populations have different knowledge and practices, highlighting the need for tailored public health campaigns. Older participants and females have higher knowledge levels, suggesting they can effectively disseminate information within their communities. The study indicates, a small number of respondents had accurate knowledge of health problems caused by unsafe dry fish consumption but most of the respondents showed positive attitudes toward hazards associated with unsafe dried fish consumption. Many respondents were aware of the potential dangers such as high levels of heavy metals, including mercury, cadmium, and lead in dry fish. Previous research has shown that the majority of heavy metals exhibit cumulative toxicity, accumulating in the human body with repeated exposure [20]. This underscores the importance of being aware of the detrimental effects of these heavy metals. Similar to previous study findings, this study observed a disparity in knowledge levels between rural and urban consumers, where demographic factors influence knowledge acquisition among rural people [21].

Most participants expressed positive attitudes towards purchasing dried fish packaged in airtight polythene pouches. A similar finding was found in another study conducted in Bangladesh, where 82% of consumers indicated a willingness to increase their dried fish consumption if packaging and quality were improved [22]. Training is effective in enhancing knowledge of food safety and hygiene [23]. The majority of participants in this study responded positively to participating in consumer awareness training programs about the health risks associated with unsafe dried fish consumption. It was noted that residents of rural areas exhibited significantly more positive attitudes compared to their counterparts in urban or semi-urban areas. This suggests that tailored interventions, such as targeted training and awareness programs, could be particularly effective in rural communities, where positive attitudes towards safe practices are already prevalent.

In this study, many participants reported moderate practices for purchasing dried fish free of flies, insects and rodents and for storing dried fish at home in airtight packets. There has been evidence that improper storage and handling can accelerate lipid rancidity in dried fish, resulting in unpleasant aromas, odors, and hazardous hydroperoxide molecule [24]. Following the purchase of dried fish, consumers should store them in a dry and clean environment. Approximately 77.6% of participants in this study adhere to this practice. Depending on packaging and storage conditions, dried fish can retain its quality for up to 3–6 months [25, 26]. Furthermore, the study revealed interesting patterns related to the frequency of these practices among different demographic groups. Respondents with a bachelor’s degree were less likely to engage in frequent practices compared to those with no education. This may suggest a gap in awareness or differing priorities regarding food safety practices among more educated individuals. Similarly, businessmen were less likely to engage in more frequent practices compared to students, which might be attributed to differences in lifestyle, time availability, or access to information about proper dried fish handling and storage.

Globally, one out of ten people get sick from foodborne diseases every year, according to the World Health Organization (WHO), leading to significant morbidity and mortality, with around 420,000 deaths attributed to foodborne diseases [27]. Better knowledge of food safety practices is essential for preventing foodborne illnesses and fostering positive attitudes toward food safety [28, 29]. Despite having awareness of the potential health risks of eating dry fish, some individuals continue to consume dried fish because of its taste, physical appearance, convenience, and trust in sellers [30]. The threats posed by foodborne diseases in developing countries like Bangladesh occur mostly due to improper handling, poor food storage, poor hygiene, inadequate monitoring, poor regulatory systems, and poor awareness [31]. A greater emphasis on public awareness and education campaigns is crucial to raise awareness. By addressing knowledge gaps and promoting safe alternatives, public health efforts can effectively reduce the health risks associated with consuming dried fish. Overall, the findings suggest that there is a need for targeted educational campaigns to improve food safety practices, particularly among younger individuals and students.

Limitation

This study has several limitations. Social desirability or reporting bias may be present, as individuals may not express their true beliefs or behaviors but instead conform to perceived expectations. The study samples were primarily drawn from populated or significant areas of Chattogram city, excluding several peripheral regions of Chattogram and other districts. The use of purposive sampling may have inadvertently introduced interviewer or selection bias.

Conclusion

This study highlights the importance of improving knowledge and awareness about the health hazards associated with consuming unsafe dried fish. Most participants in the study had less accurate knowledge about the health hazards of unsafe dried fish. This emphasizes the need for targeted interventions and educational programs to enhance knowledge, attitudes, and practices related to dried fish consumption. Targeted interventions should focus on specific demographic groups, such as women, older age groups, individuals from rural areas, and those with lower education levels. By addressing these factors, it is possible to promote healthier practices and reduce potential health risks associated with the consumption of dried fish.

Supporting information

S1 Table. Gender-wise knowledge regarding health hazard due to dried fish consumption (N = 415, November-December 2022, Bangladesh).

(DOCX)

pone.0310826.s001.docx (39.5KB, docx)
S2 Table. Gender-wise attitudes toward health hazard due to dried fish consumption (N = 415, November-December 2022, Bangladesh).

(DOCX)

pone.0310826.s002.docx (41KB, docx)
S3 Table. Gender-wise practices of dried fish consumption among the studied population (N = 415, November-December 2022, Bangladesh).

(DOCX)

pone.0310826.s003.docx (39.2KB, docx)
S1 Dataset. Dry fish.

(XLSX)

pone.0310826.s004.xlsx (344.5KB, xlsx)

Acknowledgments

The authors express their gratitude to all the participants who generously participated in this study and provided their valuable information.

Data Availability

All relevant data are within the manuscript and its Supporting Information file.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Charles Odilichukwu R Okpala

22 Jul 2024

PONE-D-23-40168Understanding Public Health Risk from Unsafe Dry Fish Consumption in BangladeshPLOS ONE

Dear Dr. Chowdhury,

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5. Review Comments to the Author

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Reviewer #1: 1. Keep keywords in alphabetical order in abstract.

2. In line number66, “There have been no research studies on Bangladesh's food safety problems resulting from the consumption of unsafe dry fish.” How can you say that no research studies yourself?

3. How was the sample size calculated in your study? Explain properly.

Reviewer #2: Introduction

In general the author does not follow the reference style of the journal.

The introduction does not clearly state the extent of the problem of dry fish contamination. It is important to specify the extent to which fish are contaminated with residues and other contaminants that pose public health risks. Relevant studies should be cited to support this information.

Lines 82-84

mention that the study was conducted on people living in Chattogram City, whereas lines 88-89 state, "the study was carried out across multiple divisions in Bangladesh, with a particular focus on the Chattogram division." Please clarify this discrepancy.

Methods

Lines 91-96:

• Were the interviews self-administered or interviewer-administered?

• How were respondents recruited for the web-based questionnaire? What criteria were used, and how was consent obtained?

• Which social media platform was used to administer the questionnaire? This paragraph needs revision for clarity.

Line 105:

• You would wish to use "Health indicators" rather than "Family health history/disease history," as mentioned earlier in line 98.

Lines 116-132:

• How were the cut-offs determined? Is there literature supporting the chosen cut-offs?

• Why did you use Yes/No or Sometimes in judging practices, and how does this translate into More, Moderate, and Less frequent?

Results

Line 149:

• How many participants were interviewed in person versus via the web-based questionnaire? Was there any difference in terms of their responses or data quality?

Lines 154-158:

• Besides vomiting and diarrhea, how are the other diseases related to risks attributed to the consumption of dry fish? If available, please provide relevant information.

Discussion

• Begin with a summary of your findings.

• The discussion section does not mention the results from the multivariate analysis. Critical parts of the results have been omitted from the discussion. Please ensure these results are included and discussed comprehensively.

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Reviewer #2: Yes: Agnes Abel Mpinga

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pone.0310826.s005.docx (12.4KB, docx)
PLoS One. 2024 Nov 13;19(11):e0310826. doi: 10.1371/journal.pone.0310826.r002

Author response to Decision Letter 0

2 Aug 2024

Thank for sharing the reviewers’ helpful comments and suggestions. Detail responses to each comment are given below for your consideration.

Reviewer: 1

Comment: Keep keywords in alphabetical order in abstract.

Response: Thanks for your comment. Keywords have been rearranged in alphabetical order (line number 37).

Comment: In line number 66, “There have been no research studies on Bangladesh's food safety problems resulting from the consumption of unsafe dry fish.” How can you say that no research studies yourself?

Response: I appreciate your concern. The statement, "There have been no research studies on Bangladesh's food safety problems resulting from the consumption of unsafe dry fish," is based on an extensive literature review conducted during the preparation of this manuscript. While there may be some studies done on dry fish handlers and fishermen, we found no knowledge, attitude, and practice (KAP) studies that have been conducted on consumers in Bangladesh. The sentence has been rewritten to make it clearer (line numbers 66-68).

Comment: How was the sample size calculated in your study? Explain properly.

Response: Thanks for the comment. The required sample size for this study was 385 individuals based on 95% confidence interval, a 5% margin of error, and the assumption that 50% of the respondents are at risk from consuming unhealthy dried fish. This has been now included in Data collection section (line numbers 100-110).

Reviewer: 2

Comment: In general, the author does not follow the reference style of the journal.

Response: Thank you so much for this comment. The reference style has been changed to “PLoS”.

Comment: The introduction does not clearly state the extent of the problem of dry fish contamination. It is important to specify the extent to which fish are contaminated with residues and other contaminants that pose public health risks. Relevant studies should be cited to support this information.

Response: Thank you for your insightful comment. I agree that the introduction should clearly state the extent of the problem of dry fish contamination in Bangladesh. To address this, I have added some information from past studies that indicates the harmful effects of dry fish consumption (line numbers 54-56) (line numbers 61-62).

Comment: Lines 82-84: mention that the study was conducted on people living in Chattogram City, whereas lines 88-89 state, "the study was carried out across multiple divisions in Bangladesh, with a particular focus on the Chattogram division." Please clarify this discrepancy.

Response: Thank you for pointing out the inconsistencies. I appreciate your thorough review. Bangladesh has eight divisions, including Chattogram. The study was conducted across these various divisions, with a focus on the coastal division, Chattogram, where the majority of dry fish is produced and consumed. However, dry fish is widely available across the country. To gain full knowledge, the study included these significant divisions. We have revised the content to make this information clear and avoid confusion (line numbers 80-82).

Comment: Lines 91-96: Were the interviews self-administered or interviewer-administered?

Response: Thank you for your comment. The data was collected using two different methods. For in-person interviews, data collection was interviewer-administered. One interviewer initiated the interview and recorded the information in the questionnaire form. In contrast, for online data collection, the process was self-administered. Participants were provided with instructions before filling out the form themselves. We clarified this in the manuscript to ensure the data collection methods are clearly understood (line number 85).

Comment: Lines 91-96: How were respondents recruited for the web-based questionnaire? What criteria were used, and how was consent obtained?

Response: The online questionnaire was distributed through a variety of platforms, including social media, email invitations, and community forums. The selection criteria were being a Bangladesh resident, being over the age of 18, and having completed university. Permission was gathered via a permission form at the start of the online questionnaire, which participants were required to read and agree to before continuing with the survey. Data was mostly collected from bachelor's degree holders who were aware of the importance of filling out forms correctly. Despite caution, social desirability bias or reporting bias can occur, as discussed in the limitations section (line numbers 88-93).

Comment: Lines 91-96: Which social media platform was used to administer the questionnaire? This paragraph needs revision for clarity.

Response: Thank you for your comment. The web-based questionnaire was distributed over several social media channels (Facebook, Instagram, LinkedIn), and Gmail. These platforms were chosen because of their extensive reach and active user base in Bangladesh. We have incorporated this information in the manuscript to help readers understand the recruitment process (line numbers 88-94).

Comment: Line 105: You would wish to use "Health indicators" rather than "Family health history/disease history," as mentioned earlier in line 98.

Response: Thank you for the suggestion. I have revised line 105 to use "health indicators" instead of "family health history/disease history" to ensure consistency with the terminology used in line 98 (line number 97 and 104).

Comment: Lines 116-132: How were the cut-offs determined? Is there literature supporting the chosen cut-offs?

Response: We are thankful for this observation. We have used some cutoff scores for knowledge, attitudes, and practice questions based on the answers of participants. The correct answer was denoted as 1 and the incorrect answer was denoted as 0, and then we have identified whether each individual’s knowledge is correct or incorrect, attitude is favorable or unfavorable, and practice is good or bad (details are discussed in the “Data collection” section). The cut-offs were set by adopting methods from past KAP studies and also modified to go with our study (please use- https://doi.org/10.21203/rs.3.rs-24562/v2 and doi: 10.12669/pjms.311.6317).

Comment: Lines 116-132: Why did you use Yes/No or Sometimes in judging practices, and how does this translate into More, Moderate, and Less frequent?

Response: Thank you for your comment. The use of "Yes," "No," and "Sometimes" responses was chosen to capture the frequency of specific practices in a simple and straightforward manner. Each response was assigned a numerical value: "Yes" = 2, "Sometimes" = 1, and "No" = 0. This scoring system allowed us to quantify the frequency of practices and calculate a total score for each respondent. The total score ranged from 0 to 22, with higher scores indicating more frequent engagement in the practices.

• Yes: Participants exercise this practice regularly.

• No: The practice is not followed at all.

• Sometimes: The practice is done irregularly.

The cut-off levels were determined as follows:

• More frequent (≥15): Respondents who scored 15 or above frequently engaged in the practices.

• Moderate (8-14): Respondents who scored between 8 and 14 showed moderate engagement.

• Less frequent (≤7): Respondents who scored 7 or below engaged in the practices less frequently.

This method provided a clear and measurable way to categorize the frequency of practices based on respondents' answers

Comment: Line 149: How many participants were interviewed in person versus via the web-based questionnaire? Was there any difference in terms of their responses or data quality?

Response: Thank you for your comment. The study involved 415 participants, including 373 in-person interviews and 42 completing a web-based questionnaire. Initially, we collected data from 435 participants; some of the data was not included in this study as there was incomplete information and they did not satisfy our inclusion criteria. This was done to maintain the integrity of the data.

Concerning the replies and data quality, the following observations were made:

In-person interviews enabled prompt clarification of questions, resulting in more detailed and consistent responses. Furthermore, the presence of an interviewer ensured that the questionnaire was completed thoroughly, reducing the number of missing data cases.

Web-based questionnaires provided convenience for participants, potentially increasing regional representation. The incomplete information was excluded from the study.

To address potential data quality issues, both datasets underwent rigorous review for completeness and consistency. Any discrepancies were managed according to predefined protocols to uphold the integrity of the data.

Comment: Lines 154-158: Besides vomiting and diarrhea, how are the other diseases related to risks attributed to the consumption of dry fish? If available, please provide relevant information.

Response: Thank you for your comment. Besides vomiting and diarrhea, there are other health effects that can be caused by unsafe dry fish consumption. Aflatoxin B1 (AFB1), T-2 toxin (T-2), ochratoxin A (OTA), and deoxynivalenol (DON) were mycotoxins found to be released by fungi in dried fish products, predominantly by Fusarium, Penicillium, and Aspergillus fungi. If consumed in excess, mycotoxins could cause major health problems like liver cancer, immune issues, and respiratory issues (please use- https://doi.org/10.3390/foods11192938). Moreover, Toxic metal poisoning tends to cause cancer, cardiovascular, brain, kidney, respiratory, reproductive, and neurological problems in human beings, with children been more susceptible to heavy metal toxicity (please use- https://doi.org/10.1016/j.envres.2017.08.051).

Comment: Begin with a summary of your findings. The discussion section does not mention the results from the multivariate analysis. Critical parts of the results have been omitted from the discussion. Please ensure these results are included and discussed comprehensively.

Response: Thank you for your helpful feedback. I revised the discussion section to start with an overview of our findings. Furthermore, I have incorporated the results of the multivariate analysis and ensured that all essential aspects of the results are thoroughly described.

The multivariate analysis findings were highlighted in the updated discussion, providing insights into the major factors impacting participants' views and habits toward dried fish intake. These findings, along with other significant findings, are now thoroughly explored to provide a more complete understanding of the study's consequences (line numbers 192-199) (line numbers 224-226, 234-240 and 252-256).

Attachment

Submitted filename: PONE-D-23-40168_Reviewers comments_responses.docx

pone.0310826.s006.docx (22.7KB, docx)

Decision Letter 1

Charles Odilichukwu R Okpala

8 Sep 2024

Understanding Public Health Risk from Unsafe Dry Fish Consumption in Bangladesh

PONE-D-23-40168R1

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Reviewer #1: Authors have addressed all comments as asked by the reviewers. I recommend this manuscript for the publication.

Reviewer #2: The authors should in the limitations include the challenges observed with the use of the web-based questionnaire

In line 154-158: Give brief clarification of the diseases that are indirectly caused by fish consumption such as immune issues, cardiovascular etc..

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Reviewer #1: No

Reviewer #2: Yes: AGNES ABEL MPINGA

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Acceptance letter

Charles Odilichukwu R Okpala

13 Sep 2024

PONE-D-23-40168R1

PLOS ONE

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Gender-wise knowledge regarding health hazard due to dried fish consumption (N = 415, November-December 2022, Bangladesh).

    (DOCX)

    pone.0310826.s001.docx (39.5KB, docx)
    S2 Table. Gender-wise attitudes toward health hazard due to dried fish consumption (N = 415, November-December 2022, Bangladesh).

    (DOCX)

    pone.0310826.s002.docx (41KB, docx)
    S3 Table. Gender-wise practices of dried fish consumption among the studied population (N = 415, November-December 2022, Bangladesh).

    (DOCX)

    pone.0310826.s003.docx (39.2KB, docx)
    S1 Dataset. Dry fish.

    (XLSX)

    pone.0310826.s004.xlsx (344.5KB, xlsx)
    Attachment

    Submitted filename: Plos one review.docx

    pone.0310826.s005.docx (12.4KB, docx)
    Attachment

    Submitted filename: PONE-D-23-40168_Reviewers comments_responses.docx

    pone.0310826.s006.docx (22.7KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information file.

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