Open Access

Occupational illnesses in the 2009 Zambian labour force survey

BMC Research Notes20103:272

DOI: 10.1186/1756-0500-3-272

Received: 10 March 2010

Accepted: 27 October 2010

Published: 27 October 2010

Abstract

Background

Occupational health has received limited research attention in the Southern African Development Community (SADC). Much of the published data in this region come from South Africa and little has been reported north of the Limpopo. The present study was conducted to estimate the burden of occupational illnesses in Zambia and assess factors associated with their occurrence.

Methods

Data were obtained from the Zambian Labour Force Survey of 2009. Frequencies were used to estimate the prevalence of occupational diseases. Logistic regression analyses were conducted to determine the associations between demographic, social and economic factors and reported illness resulting from occupational exposures. Odds ratios (OR) from bivariate analyses and adjusted odds ratios (AOR) from the multivariate analysis together with their 95% Confidence Intervals (CI) are reported.

Results

Data on 59,118 persons aged 18 years or older were available for analysis, of which 29805 (50.4%) were males. The proportions of the sample that reported to have suffered from an occupational illness were 12.7% among males and 10.4% among females (p < 0.001). Overall the proportions of respondents who reported suffering from fatigue, fever and chest infections were 38.8%, 21.7% and 17.1%, respectively. About two thirds (69.7%) of the study participants had stayed away from work due to the illness suffered at work; there was no sex differences (p = 0.216). Older age, being male, lower education level, married/cohabiting or once married (separated/divorced/widowed), and paid employee or employer/self employed were positively associated with having suffered from illness.

Conclusions

The findings from this study call for urgent effort for specific measures to prevent and mitigate the effects of occupational injuries. These interventions may include: public health campaigns, enforcement or change in work policies and regulations. Special attention may have to be made towards those who were more likely to suffer from occupational illnesses.

Background

Research on occupational illnesses and the pursuit for improved occupational health have largely been reported from high and middle income nations. Data from low income nations are often unavailable and when they do, are incomplete, unreliable or generally describe poor occupational health situations among workers.

There has been growing literature on occupational illnesses in health care workers and agriculture workforce [17]. Health workers are exposed to infections or diseases such as tuberculosis, Hepatitis B, human immunodeficiency virus and acquired immunodeficiency syndrome. Meanwhile workers in certain types of agriculture suffer from ill-health resulting from exposure to animals, micro-organisms, plant material dust or chemicals. This may be important in developing nations like Zambia where the majority of the population are in the agricultural sector.

Much of the data on occupational health and safety from the Southern African Development Community (SADC) are from South Africa. The SADC comprises the following countries: Angola, Botswana, Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe. There is paucity of data from the rest of the region, especially north of the Limpopo. Hence the negative impact of poor work conditions is unappreciated and the scientific basis for interventions and policy formulation is to a great extent absent. Loewenson [8], however, has argued that "While the share of world trade to the world's poorest countries has decreased, workers in these countries increasingly find themselves in insecure, poor quality jobs, sometimes involving technologies which are obsolete or banned in industrialized countries". Examples of obsolete technologies include unshielded dangerous machinery and hazardous substances known to cause increase in occupational diseases and accidents [9]. Loewenson [8] further argued that "The occupational illness which results is generally less visible and not adequately recognized as a problem in low income countries." The present study was carried out to assess the burden of occupational illnesses and associated factors in the Zambian workforce.

Methods

We obtained data from the Central Statistical Office (CSO) [Zambia] through the Work and Health in Southern Africa (WAHSA) Project. A detail on the methodology that is used in the Zambian labour force survey (LFS) is published elsewhere [10]. However, we briefly describe the methodology below.

Study design and setting

Cross sectional labour force surveys are conducted from time to time by Central Statistical Office of Zambia. The target population for LFS is persons of age 15 years or older. However, for our study we selected only persons of age 18 years or older and currently in employment (whether paid or not).

Sample size and sampling

The sample size aims to enroll households and this is designed in such a way as to have adequate power to produce estimates for the entire country, urban and rural areas, and for each province. Zambia has nine provinces which are: Central, Copper Belt, Eastern, Luapula, Lusaka, Northern, North-Western, Southern and Western. The administrative hub of the country is in Lusaka province in which the capital city, Lusaka, is situated. Hence, the major economic sector in Lusaka province is the Service sector. The Copper Belt province as the name implies is the seat of Zambia's copper mining efforts. Fishing is the main occupation in Luapula and Western provinces. Peasantry farming (mainly cultivating maize, cotton and groundnuts) is the major economic activity in the rest of the provinces.

A two stage cluster sampling technique is used to draw sampling units. The primary sampling units are Census Enumeration Areas (CEAs), identified from a sampling frame compiled from the 2000 population and housing census. In the second stage of sampling, households are systematically sampled in each CEA and all persons of age 15 years or older in the household are requested to participate in the survey.

Questionnaire

The composition of the questionnaire used in LFS varies from survey to survey. In the 2009, the Central Statistical Office incorporated questions on: health outcomes, work sector and conditions, work place facilities, work-related injuries and history of compensation from occupational injuries. The design of the questions and definitions used conform to the requirements set by international bodies such as the International Labour Organization (ILO). Questionnaires were administered in the homes of the survey participants by trained research assistants.

Data analysis

Analyses were conducted using SPSS version 11.5.0. Frequencies were used to estimate the prevalence of occupational illnesses. The Chi-square test was used to compare proportions. The cut off point for statistical significance was set at the 5% level. Logistic regression analyses (bivariate and multivariate) were conducted to determine the level of association between demographic, social and economic factors and occupational illnesses suffered. We used Deviation as the contrast, and the first category of the explanatory variables as the reference. Odds ratios (OR) from bivariate analysis and adjusted odds ratios (AOR) from a multivariate analysis (backward logistic regression) together with their 95% Confidence Intervals (CI) are reported.

Results

Socio-demographic description of the sample

Data on 59,118 study participants of age 18 years or older were available for analysis of which 29,805 (50.4%) were males. Sex was not recorded in 5 participants. The socio-demographic distributions of participants by sex are shown in Table 1. Overall, female participants tended to be younger, and once married (separated, divorced or widowed). More female (61.2%) than male (43.4%) participants had completed no more than 7 years of formal education. While more male (55.7%) than female (38.1%) respondents were self employed, a higher proportion of females (52.5%) were unemployed family workers compared to males (21.0%).
Table 1

Socio-demographic characteristics of study participants in the Zambia Labour Survey 2009

Characteristic

Male

Female

Total

 

n (%)

n (%)

n (%)

Age (years)

   

5-9

11654 (18.2)

11656 (17.8)

23310 (18.0)

10-14

11099 (17.3)

10958 (16.7)

22057 (17.0)

15-19

9837 (15.3)

9421 (14.4)

19258 (14.8)

20-24

6435 (10.0)

7585 (11.6)

14020 (10.8)

25-34

10399 (16.2)

11268 (17.2)

21667 (16.7)

35-44

6802 (10.6)

6699 (10.2)

13501 (10.4)

45-54

3715 (5.8)

3729 (5.7)

7444 (5.7)

55+

4178 (6.5)

4330 (6.6)

8508 (6.6)

Total

64119 (100)

65646 (100)

129765 (100)

Marital status

   

Never married

24364 (50.8)

19143 (38.7)

43507 (44.6)

Married

21501 (44.8)

22459 (45.4)

43960 (45.1)

Separated

476 (1.0)

1058 (2.1)

1534 (1.6)

Divorced

752 (1.6)

2369 (4.8)

3121 (3.2)

Widowed

821 (1.7)

4356 (8.8)

5177 (5.3)

Cohabiting

70 (0.1)

102 (0.2)

172 (0.2)

Total

47984 (100)

49487 (100)

97471 (100)

Completed years in formal school

   

0

348 (0.7)

373 (0.7)

721 (0.7)

1-3

10331 (19.5)

11828 (23.1)

22159 (21.3)

4-7

20239 (38.2)

22179 (43.3)

42418 (40.7)

8-9

10312 (19.5)

9060 (17.7)

19372 (18.6)

10-12

9820 (18.6)

6541 (12.8)

16361 (15.7)

13+

1887 (3.6)

1246 (2.4)

3133 (3.0)

Total

52937 (100)

51227 (100)

104164 (100)

Province

   

Central

6147 (9.6)

6165 (9.4)

12312 (9.5)

Copperbelt

10349 (16.1)

10397 (15.8)

20746 (16.0)

Eastern

7978 (12.4)

8085 (12.3)

16063 (12.4)

Luapula

5937 (9.3)

5970 (9.1)

11907 (9.2)

Lusaka

7162 (11.2)

7260 (11.1)

14422 (11.1)

Northern

8080 (12.6)

8208 (12.5)

16288 (12.6)

North-Western

4378 (6.8)

4407 (6.7)

8785 (6.8)

Southern

8485 (13.2)

8830 (13.5)

17315 (13.3)

Western

5603 (8.7)

6324 (9.6)

11927 (9.2)

Total

64119 (100)

65646 (100)

129765 (100)

Current* employment status

   

Self employed

17050 (42.3)

11565 (29.7)

28615 (36.1)

Employed

126 (0.3)

67 (0.2)

193 (0.2)

Paid employee

7641 (18.9)

3253 (8.4)

10894 (13.8)

Unpaid family worker

15406 (38.2)

23896 (61.5)

39302 (49.6)

Other

116 (0.3)

99 (0.3)

215 (0.3)

Total

40339 (100)

38880 (100)

79219 (100)

Current employer

   

Central government

1549 (3.8)

924 (2.4)

2473 (3.1)

Local government

217 (0.5)

126 (0.3)

343 (0.4)

Parastatal/State owned firm

352 (0.9)

95 (0.2)

447 (0.6)

Private

7382 (18.3)

3529 (9.1)

10911 (13.8)

NGO** or church

251 (0.6)

153 (0.4)

404 (0.5)

International organization

49 (0.1)

24 (0.1)

73 (0.1)

Household

30542 (75.7)

34032 (87.5)

64574 (81.5)

Total

40342 (100)

38883 (100)

79225 (100)

Current number of employees at place of work

   

5+

11907 (29.6)

8128 (21.0)

20035 (25.4)

Total

40162

38646

78808

Current employment activity

   

In paid employment/business

18489 (29.1)

10884 (16.7)

29373 (22.8)

In paid employment but temporarily not working due to illness, leave, industrial dispute or on study

235 (0.4)

151 (0.2)

386 (0.3)

Working without pay

11942 (18.8)

16991 (26.1)

28933 (22.5)

Not working but looking for work/business

1533 (2.4)

1118 (1.7)

2651 (2.1)

Not working and not looking for work but available for work/business

4647 (7.3)

5300 (8.1)

9947 (7.7)

Housewife/Homemaker

244 (0.4)

4580 (7.0)

4824 (3.7)

Retired

169 (0.3)

53 (0.1)

222 (0.2)

In school (full time student)

16479 (25.9)

15448 (23.7)

31927 (24.8)

Too old to work

654 (1.0)

1113 (1.7)

1767 (1.4)

Too young to work

7361 (11.6)

7346 (11.3)

14707 (11.4)

Not working, not looking for work and not available for work for other reasons

1878 (3.0)

2184 (3.4)

4062 (3.2)

Total

63631 (100)

65168 (100)

128799 (100)

* status in the previous 7 days to the survey

** Non Governmental Organization

Note: Numbers not adding up due to missing information

Illnesses suffered at workplace

The proportions of males and females who reported to have suffered from any illness known or suspected to result from work in the past 12 months prior to the survey were 12.7% and 10.4%, respectively. Overall the proportions of respondents who reported suffering from fatigue, fever and chest infections were 38.8%, 21.7% and 17.1%, respectively. About two thirds (69.7%) of the study participants had stayed away from work due to the illness suffered at work; there was no sex differences (p = 0.216), see Table 2.
Table 2

Serious illness suffered at workplace in past 12 months prior to the survey.

 

Male

Female

Total

Characteristic

n (%)

n (%)

n (%)

Suffered from any illnesses due to work in past 12 months

   

Yes

3660 (12.0)

3027 (9.6)

6687 (10.8)

Total

30558

31525

62083

Most serious illness suffered from due to work in past 12 months

   

Skin problems

151 (4.1)

110 (3.6)

261 (3.9)

Respiratory problems

268 (7.3)

142 (4.7)

410 (6.1)

Allergies

41 (1.1)

43 (1.4)

84 (1.3)

Diarrhoea

99 (2.7)

139 (4.6)

238 (3.6)

Fatigue

1360 (37.2)

1235 (40.8)

2595 (38.8)

Chest infections

691 (18.9)

428 (14.1)

1119 (16.7)

Fever

738 (20.2)

731 (24.2)

1469 (22.0)

Other

312 (8.5)

198 (6.5)

510 (7.6)

Total

3660 (100)

3026 (100)

6686 (100)

Stayed away from work due to above Illness

   

Yes

2503 (68.4)

2133 (70.5)

4636 (69.4)

Total

3659

3025

6684

Days away from work due to above Illness

   

<7

1103 (44.4)

963 (45.6)

2066 (45.0)

7-13

735 (29.6)

604 (28.6)

1339 (29.1)

14-20

322 (13.0)

250 (11.8)

572 (12.4)

21+

325 (13.1)

293 (13.9)

618 (13.4)

Total

2485 (100)

2110 (100)

4595 (100)

Received compensation from work as a result of the above illness

   

Yes

202 (5.6)

63 (2.1)

265 (4.0)

Total

3635

3011

6646

Note: Numbers not adding up due to missing information.

Table 3 shows the proportions of serious illnesses suffered in relation to work conditions. Fatigue was the most common illness among persons exposed to vibrations (31.7%), breathing in smoke, fumes, powder or dust (40.1%), pesticide (37.6%), skin contact with chemicals (38.4%), handling infectious materials or waste (26.0%), and lifting heavy objects (39.5%). Chest infections were common among persons exposed to temperatures causing perspiration (26.8%), breathing in vapours from other chemicals such solvents and thinners (27.0%), noise (24.2%), and radiation (21.8%). Fever was most common among persons exposed to low temperatures (26.8%).
Table 3

Demographic, social and economic factors associated with serious illnesses

 

Suffered serious illness

Bivariate

Multivariate

Factor

Total

n (%)

OR (95%cCI)

AOR (95%CI)

Age (years)

    

<15

45385

69 (0.2)

1

1

15-19

19262

221 (1.1)

0.55 (0.49, 0.62)

0.69 (0.60, 0.81)

20-24

14023

508 (3.6)

1.78 (1.63, 1.95)

1.69 (1.51, 1.89)

25-34

21669

1458 (6.7)

3.41 (3.18, 3.67)

1.95 (1.76, 2.17)

35+

29459

2382 (8.1)

4.16 (3.89, 4.45)

2.03 (1.83, 2.26)

Sex

    

Female

65646

2131 (3.2)

1

1

Male

64119

2507 (3.9)

1.01 (1.07, 1.13)

1.11 (1.07, 1.16)

Completed years in school

    

<3

22983

466 (2.0)

1

1

4-7

42508

1878 (4.4)

1.33 (1.27, 1.40)

1.24 (1.18, 1.31)

8-9

19405

825 (4.3)

1.28 (1.20, 1.36)

0.98 (0.92, 1.05)

10+

19527

647 (3.3)

0.99 (0.92, 1.05)

0.60 (0.56, 0.65)

Current marital status

    

never married

43512

577 (1.3)

1

1

married/cohabiting

44134

3336 (7.6)

1.87 (1.79, 1.95)

1.24 (1.17, 1.32)

separated/divorced/widowed

9833

700 (7.1)

1.75 (1.65, 1.86)

1.17 (1.08, 1.27)

Province

    

Western

11928

200 (1.7)

1

1

Copperbelt

20748

661 (3.2)

0.97 (0.90, 1.04)

1.19 (1.09, 1.30)

Eastern

16065

295 (1.8)

0.55 (0.49, 0.61)

0.47 (0.41, 0.53)

Luapula

11907

1071 (9.0)

2.90 (2.72, 3.09)

2.77 (2.56, 2.99)

Lusaka

14446

375 (2.6)

0.78 (0.71, 0.86)

0.88 (0.79, 0.99)

Northern

16288

672 (4.1)

1.26 (1.17, 1.36)

1.13 (1.03, 1.23)

North-Western

8788

597 (6.8)

2.14 (1.97, 2.32)

2.19 (1.99, 2.42)

Southern

17316

431 (2.5)

0.75 (0.68, 0.82)

0.81 (0.73, 0.89)

Central

12312

336 (2.7)

0.82 (0.74, 0.91)

0.81 (0.72, 0.90)

Current employment status

    

unpaid formal worker

39383

1204 (3.1)

1

1

paid employee

10915

767 (7.0)

1.23 (1.17, 1.30)

1.36 (1.27, 1.46)

employer/self employed

28843

2561 (8.9)

1.59 (1.52, 1.65)

1.03 (0.98, 1.08)

suffered at workplace

Note: Numbers not adding up due to missing information.

Multivariate analysis of factors associated with illnesses suffered at workplace

All the factors that were significantly associated with having suffered from illness arising from place of work in bivariate analyses remained significant in a multivariate analysis (Table 3). Older age, male, lower education level, married/cohabiting or once married (separated/divorced/widowed), and paid employee or employer/self employed were positively associated with having suffered from illness.

Discussion

The labour force survey is one of the largest studies conducted in Zambia. Female respondents tended to be less educated, married or were once married and unpaid family workers. More than 10% of workers reported illness they considered to be work related, for which 70% of those affected stayed away from work.

We found that respondents with more education were less likely to suffer from illnesses compared to respondents with little or no education. In a study conducted among Nigerian welders, Sabitu et al (2009) reported that only 20% of those who had no formal education were aware of occupation hazards and safety measures compared to 77.6% among those who has primary education and 85.0% among those who had secondary education [11]. People with education are more knowledgeable to avoid harmful exposures, and as a result may be less likely to fall ill. Educated workers may also be employed in more skilled but less hazardous jobs, and as a result may be less likely to suffer from illnesses.

We also found that workers who were self employed had missed more workdays as a result of work-related illness compared to those employed by others. There are several possible reasons why this may be the case. Firstly, it is possible that self-employed workers are less likely to pay attention to safe work environments as they may be accountable only to themselves. As a consequence, they may be more likely to suffer occupation associated illnesses. Secondly, it is possible the self-employed persons have more opportunity to excuse themselves from work due to illness while it may be harder for those who are employed by others.

Results from our study suggest that males are more likely to suffer from serious illnesses than females. Men may be more likely to work in harsher environment than females, and this may partly explain the observed sex difference in the proportion of serious illnesses suffered.

Limitations of the study

There are several limitations for this present study. Data were collected through self-reports, and our results may be biased to the extent that the participants mis-reported either intentionally or unintentionally. Since the design of the data collection was cross sectional, it is not possible to assign causation to any of the explanatory variables. We did not have information on the underlying medical conditions [12], and stress [13] to verify the illnesses reported by the respondents as resulting from their workplaces. Information on how the sample size was determined or the participation rate was not obtained from the CSO.

Conclusion

The prevalence of work-related illness was high in Zambia, and associated with significant levels of absence from work. The data provide good social and socioeconomic grounds to motivate for improvements to working conditions to prevent these occurrences as well as a baseline on which to base statistical targets for improvement. There was geographic variation in the distribution of reported disease, with higher reported prevalence in specific provinces. This information could be useful to the Ministry of Labour to identify areas in specific need of attention, especially in terms of surveillance, enforcement or revision of work policies.

Declarations

Acknowledgements

The data used in this study was made available to us from Central Statistical Office, Government of the Republic of Zambia. We thank Amanda Ryan for her input into the design of the questionnaire and suggestions on how to analyze the data.

Authors’ Affiliations

(1)
Department of Public Health, Division of Community Health, College of Medicine, University of Malawi
(2)
Division of Epidemiology and Biostatistics, Graduate School of Public Health, San Diego State University
(3)
Department of Community Medicine, School of Medicine, University of Zambia

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Copyright

© Muula et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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