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 [1–7]. 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
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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.
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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
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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.