Manual handling of burdens as a predictor of birth outcome—a Finnish Birth Register Study

Manual handling of burdens as a predictor of birth outcome—a Finnish Birth Register Study Abstract Background Negative effects of manual handling of burdens on pregnancy outcomes are not elucidated in Finland. This study examines the association between perinatal outcomes and occupational exposure to manual handling of burdens. Methods The study cohort was identified from the Finnish Medical Birth Register (MBR, 1997–2014) and information on exposure from the Finnish job-exposure matrix (FINJEM) 1997–2009. The cohort included all singleton births of mothers who were classified as ‘service and care workers’ representing the exposure group (n=74 286) and ‘clerks’ as the reference (n=13 873). Study outcomes were preterm birth (PTB) (<37 weeks), low birthweight (LBW) (<2500 g), small for gestational age (<2.5th percentile), perinatal death (stillbirth or early neonatal death within first seven days) and eclampsia. We used logistic regression analysis to calculate odds ratio (OR) and adjusted for maternal age, marital status, BMI, parity and smoking during pregnancy. Results The risks of PTB [OR 1.16, 95% confidence interval (CI) 1.06–1.27], LBW (OR 1.12, 95% CI 1.01–1.25) and perinatal death (OR 1.51, 95% CI 1.09–2.09) were significantly higher among the high exposure group than in the reference group. All adverse outcomes were statistically insignificant among primiparous women except perinatal death (OR=1.95, 95% CI 1.13–3.39). Conclusions The study indicates that the risk of adverse pregnancy outcomes might be more common among women that are highly exposed to occupational manual handling of burdens. The results should be interpreted with caution due to the use of occupational level exposure. Further studies with information on individual level exposure and start of maternity leave are recommended. Introduction Gestational age and birthweight are important measurements of perinatal outcomes. Preterm birth (PTB), being born before 37 completed weeks of gestation1,2 and low birthweight (LBW), birthweight below 2500 g3 are the most frequently used perinatal health indicators. A recent global report indicates that every year 15 million (11% of all livebirths) are born premature. The same study reported that in 2010, PTB rate in Finland was 5.5% compared to 8.6% for other developed countries.4 Although relatively low, there was no improvement in Finland’s PTB rate for two decades.1,4 Even though not all LBW babies are preterm, the positive correlation between the two has been reported.5 Similar to other European countries, the occurrence of PTB in Finland has been attributed to factors such as advanced maternal age, smoking, genital infection, obesity and assisted reproduction techniques among others.6,7 Strategies to substantially reduce PTB and LBW are lacking.2,8 For two decades only three (Croatia, Ecuador and Estonia) out of 65 countries were able to reduce the rate of PTB in the world.4 Physical exertion such as heavy lifting has been linked to the aetiology of PTB and other adverse birth outcomes.9–11 A study by Banerjee reported that the demand for increased blood supply by working muscles tend to reduce placental blood perfusion to the growing foetus. Also, the physiological haemo-dilution in pregnancy can be compromised by excessive sweating that characterizes physical activity while intra-abdominal pressure would be increased in the course of heavy lifting.12 Other studies have suggested that physically demanding work is associated with adverse pregnancy outcomes.13–15 Registry-based studies give retrospective understanding of the aetiology of most disease conditions. Among the Nordic countries, Finland has a good record of computerized national health information system16 including the Finnish Medical Birth Register (MBR) and the Finnish Information System on Occupational Exposure [Finnish job-exposure matrix (FINJEM)] developed by the Finnish Institute of Occupational Health (FIOH). These registers have been the basis for most population-based studies.17–19 However, effects of manual handling of burdens on pregnancy outcomes are not elucidated at the country level. The aim of this study is to investigate the association between birth outcomes and occupational group level exposure to manual handling of burdens within the Finnish population. Methods The data source for this study includes the Finnish MBR and information to define exposure categories was from the FINJEM. The MBR contains a nationwide data on both hospital and home deliveries since 1987 with regular maintenance by the National Institute for Health and Welfare (THL). The MBR is also linked to other registers such as the Central Population Register containing livebirths and the Cause-of-Death Register with information on stillbirths and infant death.17 The source population contains information on 1 050 889 women and their newborns between 1997 and 2014. Second, FINJEM is Finland’s quantitative job-exposure matrix established in the 1990s19 and has been regularly updated till 2010–12.20 It contains comprehensive information on all major occupations classified into 311 categories, and their corresponding job-exposure estimates that are time specific at the country level. The assessment for exposure to all major occupational risk factors has been explained in detailed elsewhere.19–21 In this study, the source data to define the two exposure groups were the quantitative estimates or subjective ratings of manual handling of burdens (MAHB) at the occupational level from 1997 to 2009. Maternal occupational codes have been collected into the MBR dataset. To select the study cohort we used the F-ISCO-88 codes in the FINJEM dataset to identify the mothers from the MBR. The study cohort included all singleton births of mothers working as practical nurses, homecare assistants and kitchen assistants. For the purposes of this study, these three occupations are collectively classified as ‘service and care workers’ representing the high exposure group to MAHB (N = 74 286). Mothers working as secretaries, account and book-keeping clerks and insurance clerks are collectively classified as ‘clerks’ (N = 13 873) in this study. Clerks were chosen as suitable reference group because their occupational level exposure to MAHB was low. The classification of occupations is done by the Statistics Finland’s Classification Services from 1997 to 2009 census based on EU standards. Occupational exposures In this study, we used information from the FINJEM dataset to identify and define the exposure levels of MAHB. The average exposure of the whole occupational group to MAHB was used. In the FINJEM dataset, MAHB is classified under ergonomic factors and was obtained from either a subjective ratings or observations of lifting and carrying of heavy burdens as essential feature of everyday work. Level of exposure to MAHB is categorized as follows; 0= no or very little lifting at work, 1= lifting or carrying of moderate burdens (10 kg) daily or reported fairly much lifting at work and 2 = lifting or carrying of heavy burdens (20 kg) over five times a day or reported very much lifting at work. Based on this information in the FINJEM, we selected practical nurses, homecare assistants and kitchen assistants (collectively as service and care workers) for the high exposure group and the reference category were secretaries, accounting and book-keeping clerks and insurance clerks (collectively as clerks) taking into account similarities of their socio-economic statuses. The exposure groups were derived based on the proportion of occupation exposed and the level of exposure among the exposed. Among the high exposure group, the average group exposure varied between 0.65 and 0.81 and the proportion of workers exposed was between 74 and 83%. The low exposure group had an average group exposure of 0.38–0.5 and the proportion of workers exposed was between 2 and 6.1%. Permission to use the MBR data in this study was granted in February 2016 by THL (THL/151/5.05.00/2016) as required by Finland’s data protection legislation. FINJEM is not an individual level database, and its use does not require ethical review or study permission. Statistical analysis We considered the following outcome variables; PTB defined as gestational age <37 weeks and LBW (<2500 g), perinatal death defined as stillbirth (from 22 weeks or 500 g) or early neonatal death within the first seven days, eclampsia (blood pressure above 140/90 mmHg, proteinuria (>0.3 g/day) and seizures at or after 20 weeks gestation, ICD-10 code O15)22 and small for gestational age (SGA, defined as birthweight below the 2.5th percentile based on Finnish sex-specific growth curves).23 SPSS version 21 was used to examine the prevalence of adverse pregnancy outcomes among the two exposure groups with 95% confidence intervals (CIs). We conducted logistic regression analysis to estimate the odds ratios (ORs) between the exposure levels and pregnancy outcomes. We also conducted generalized estimating equation (GEE) analysis so that in the event of several deliveries by the same mother within the study period, each delivery could be evaluated separately from the influence of factors pertaining to other deliveries. Based on evidence from literature, in both models we adjusted for parity, smoking status, marital status and BMI, all as categorical variables and maternal age as a continuous variable. Information about these potential confounders was obtained from the MBR. The recording of maternal height and weight started in 2004 therefore all data that were missing before this year were categorized as ‘0’ while ‘1’ included women with BMI ≤25 and ‘2’ women with BMI >25. We conducted sensitivity analysis to examine the outcomes based on parity. The assumption was that nulliparous women do not have to lift under age children at home, do not take childcare leave and therefore have a higher chance of working throughout the pregnancy till they go on statutory maternity leave. Based on this, we compared primiparous women with all other women who have previously given birth. Further sensitivity analysis was done by comparing the lowest exposure occupation in each exposure category to the rest of the group to determine homogeneity among the exposure categories (results available on request from authors). Results Table 1 compares baseline characteristics of the high and low exposure groups and the sex of the newborns. Differences in prevalence of girls were negligible in the compared groups. Fewer women (12.7%) were advanced in age (≥35 years) among the high exposure group than the low exposure category (29.8%). Grand multiparity (five or more pregnancies) was less common among the low exposure group (1.0%) compared to 3.4% within the high exposure category. The low exposure group was often married and smoked less during pregnancy compared to the high exposure group. Table 1 Sex of the child and characteristics of the study population Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Table 1 Sex of the child and characteristics of the study population Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Table 2 shows prevalence of the studied pregnancy outcomes between the exposure groups. The mean gestational age and birthweight were almost equal among newborns of the reference group (3.535 g and 39.34 weeks) and among the high exposure group (3.542 g and 39.35 weeks), respectively. The prevalence of PTB was 4.7% among the high exposure category and 4.8% among the reference group. The proportion of LBW newborns was slightly higher in the reference group (3.4%) than in the high exposure group (3.2%). All other pregnancy outcomes were quantitatively similar among the two groups. Table 2 Prevalence of adverse pregnancy outcomes (%) among newborns of the high exposure group and the low exposure group. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – Table 2 Prevalence of adverse pregnancy outcomes (%) among newborns of the high exposure group and the low exposure group. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – Table 3 displays comparison of the effect estimates between the two exposure groups. Adjusted OR of PTB (OR = 1.16, 95% CI 1.06–1.27), LBW (OR = 1.12, 95% CI 1.01–1.25) and perinatal death (OR = 1.51, 95% CI 1.09–2.09) were statistically significantly higher among the high exposure group. We found no significant difference between the two groups for the risk of SGA (OR = 0.91, 95% CI 0.81–1.02) and eclampsia (OR = 0.82, 95% CI 0.38–1.77). Table 3 Comparison of crude and adjusted odds ratio of pregnancy outcomes between newborns of service and care workers (high exposure group, n=74 286) and clerks (low exposure group, n=13 873) to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 a Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. b Generalized estimating equations analysis: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Table 3 Comparison of crude and adjusted odds ratio of pregnancy outcomes between newborns of service and care workers (high exposure group, n=74 286) and clerks (low exposure group, n=13 873) to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 a Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. b Generalized estimating equations analysis: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Results of sensitivity analysis by comparing outcomes among primiparous with multiparous women are presented in table 4. Prevalence of all the outcomes was higher among primiparous women. After adjusting for confounders, the risk of perinatal death (OR = 1.95, 95% CI 1.13–3.39) was higher among children born to primiparous mothers in the higher exposure group. Higher exposure was not associated with any other outcome in this stratum. The risks of PTB (OR = 1.20, 95% CI 1.06–1.36) and LBW (OR = 1.18, 95% CI 1.00–1.37) were significantly increased in multiparous women but the lower end of the effect estimate for LBW included unity. The risk of SGA was decreased among both primiparous women (OR = 0.96, 95% CI 0.83–1.12) and significantly decreased in multiparous women (OR = 0.83, CI 0.69–0.99) while risk of eclampsia was non-significant in both groups. Table 4 Sensitivity analysis of the risk of adverse pregnancy outcomes based on parity. Comparison of adjusted odds ratio of pregnancy outcomes between newborns of primiparous (n=33 865) and multiparous women (56 853) based on their exposure to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Table 4 Sensitivity analysis of the risk of adverse pregnancy outcomes based on parity. Comparison of adjusted odds ratio of pregnancy outcomes between newborns of primiparous (n=33 865) and multiparous women (56 853) based on their exposure to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Discussion Our study results suggest that an occupational high exposure to manual handling of burdens might be associated with PTB, LBW and perinatal deaths. The risk of SGA and eclampsia was similar among both exposure categories. However, sensitivity analysis shows that with the exception of perinatal death, the risk of all other adverse birth outcomes was weaker among primiparous women than in multiparous women. The risk of SGA was elevated among multiparous women although it could be due to chance. In Finland, by legislation mothers who have children under age three are entitled to take paid childcare leave.24 Thus, we assumed that some of the multiparous women might have been on childcare leave and thus not working during the study period. Also, lifting own children during pregnancy may constitute a risk for multiparous women. To consider these potential problems, we conducted a sensitivity analysis to examine differences in outcome between primiparous and multiparous women. The findings among primiparous women were weaker than in the entire data and statistically significant only for perinatal death. It can be argued that besides occupational lifting and carrying, there may be other factors within the home environment that render these women susceptible to adverse pregnancy outcomes. Another plausible explanation may be that, contrary to nulliparous women, multiparous mothers frequently lift or carry their under age children at home thereby negating the beneficial effects of the childcare leave policy. Another sensitivity analysis comparing the lowest exposure in each exposure category with the rest of the group, did not alter the results. The strengths of this study are that, it has a large sample size including all babies born in Finland by women in the six occupations between 1997 and 2014. The population-based approach and about 100% coverage of all birth events in Finland25 eliminates the possibility of selection bias. GEE analysis was conducted to mitigate effects of same women possibly giving birth several times within the study period. The processes of diagnosing all study outcomes were based on the best available standardized measurements. Gestational age was estimated by ultrasound examination and the diagnosis of SGA was based on age and sex-specific estimates. We adjusted for potential confounders including parity, maternal age, marital status, smoking status and BMI which have shown to have high quality in the Finnish MBR. Additionally, measures such as rigorous definition and description of occupations, agents and probable changes in exposure levels were taken into account in the construction of FINJEM to diminish exposure misclassification. In effect, updating FINJEM will not significantly change the current job-exposure categorization because the nature of work by the various occupations has not changed over the course of time.19 The use of whole occupational group exposure to manual handling of burdens instead of individual estimates is a limitation to the study because it does not take into consideration differences in exposure within each occupational group.19 Furthermore, different perception among occupations about potential hazards of lifting and carrying by pregnant women could lead to over-or-under reporting of the exposure levels and that can be a source of misclassification. Unfortunately, we could not adjust for factors outside participants’ working environment that could potentially affect the results. Information such as when mothers began their maternity leave was not included in the analysis and therefore, the exact trimester which is adversely affected by the exposure could not be confirmed. Aspects of MAHB estimates based on subjective ratings are at least partly likely to be affected by recall bias. Based on the study objectives, we identified occupations with similar exposure levels to MAHB for each exposure group. Nevertheless, each of the six occupations may have unmeasured peculiar occupational hazards that might have influenced the effect estimates. Among the high exposure group, prevalence of the studied adverse pregnancy outcomes was often higher among the newborns of kitchen assistants and practical nurses than the newborns of homecare assistants. Practical nurses may differ on exposure to chemical and biological agents and kitchen assistants to heat which are all known potential risk factors for adverse pregnancy outcomes.26,27 Similarly, infants of insurance clerks had less adverse birth outcomes compared to those of secretaries and account and book-keeping clerks within the low exposure group. Among the reference group, secretaries were more advanced in age and often smoked during pregnancy. We adjusted for these confounders in all the models. It can be argued that existing residual confounders, such as the extent of psychosocial and other physical stress factors, may differ among the occupations. However, we could not collect information on all potential risk factors due to the secondary source of our data. Moreover, in Finland pregnant employees are by legislation to be reassigned to safer tasks if work poses danger.28 The results corroborate findings from previous epidemiological studies and a review that occupational lifting is associated with adverse pregnancy outcomes.29–31 Juhl et al.29 reported in a recent Danish MBR study that younger women were more exposed to occupational lifting than older women. The authors found an elevated risk of decreased birth size among mothers who lifted 11–20 kg 10 times per day compared with less or no exposure. In a systematic review, van Beukering et al.31 reported a summary OR of 1.29 for PTB among mothers who lifted or carried burdens weighing more than 5 kg compared to those who lifted less. On the contrary, authors of a Finnish daycare workers’ study32 reported no increased risk of PTB, perinatal death and SGA among daycare workers despite their potential exposure to heavy lifting. The risk of adverse pregnancy outcomes was not elevated among women exposed to physically demanding work in North Carolina.33 In a recent updated review,34 Palmer et al. reported smaller or no association between birth outcomes and a range of occupational activities. In conclusion, we identified an elevated risk of PTB, LBW and perinatal death among service and care workers that were highly exposed to manual handling of burdens. The adverse pregnancy outcomes were more common among multiparous women. However, the use of occupational group level exposure requires caution in interpreting the results. Further studies with information on individual level of exposure and start of maternity leave are recommended. Acknowledgement We are grateful to Mr. Ari Voutilainen, a data analyst at the Institute of Public & Clinical Nutrition, University of Eastern Finland for his technical advice concerning analysis of the data. Funding The first author received 12 months working grant from the Jenny & Antti Wihuri Foundation [Grant number: 00160196]. Conflicts of interest: None declared. Key points PTB, LBW and perinatal death are pervasive among occupations with high exposure to manual handling of burdens. The adverse effects appear to be absent in primiparous women but more common in multiparous women. Protecting pregnant women against heavy lifting is justifiable and should be reinforced. Future study with prospective objective measurement of lifting and carrying is recommended. References 1 March of Dimes, PMNCH, Save the Children, WHO. Born too soon: The global action report on preterm birth. 2012 . 2 Goldenberg RL , Culhane JF , Iams JD , Romero R . Epidemiology and causes of preterm birth . Lancet 2008 ; 371 : 75 – 84 . Google Scholar CrossRef Search ADS PubMed 3 The World Bank , UNICEF. The state of the world's children, childinfo, and demographic and health surveys. 2016 . 4 Blencowe H , Cousens S , Oestergaard MZ . National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications . Lancet 2012 ; 379 : 2162 – 72 . Google Scholar CrossRef Search ADS PubMed 5 Tang W , Mu Y , Li X , et al. Low birthweight in China: evidence from 441 health facilities between 2012 and 2014 . J Matern Fetal Neonatal Med 2017 ; 30 : 1997 – 2002 . Google Scholar CrossRef Search ADS PubMed 6 Zeitlin J , Szamotulska K , Drewniak N , et al. Preterm birth time trends in Europe: a study of 19 countries . BJOG 2013 ; 120 : 1356 – 65 . Google Scholar CrossRef Search ADS PubMed 7 Jakobsson M , Gissler M , Paavonen J , Tapper A . The incidence of preterm deliveries decreases in Finland . BJOG Int J Obstet Gynaecol 2008 ; 115 : 38 – 43 . Google Scholar CrossRef Search ADS 8 Kwegyir-Afful E , Ijaz S , Räsänen K , Verbeek J . Randomized controlled trials are needed to close the evidence gap in the prevention of preterm birth . Scand J Work Environ Health 2014 ; 40 : 96 – 9 . Google Scholar CrossRef Search ADS PubMed 9 Croteau A , Marcoux S , Brisson C . Work activity in pregnancy, preventive measures, and the risk of preterm delivery . Am J Epidemiol 2007 ; 166 : 951 – 65 . Google Scholar CrossRef Search ADS PubMed 10 Launer LJ , Villar J , Kestler E , de Onis M . The effect of maternal work on fetal growth and duration of pregnancy: a prospective study . Br J Obstet Gynaecol 1990 ; 97 : 62 – 70 . Google Scholar CrossRef Search ADS PubMed 11 Omokhodion FO , Onadeko MO , Roberts OA , et al. Paid work, domestic work, and other determinants of pregnancy outcome in Ibadan, southwest Nigeria . Int J Gynaecol Obstet 2010 ; 111 : 165 – 70 . Google Scholar CrossRef Search ADS PubMed 12 Banerjee B . Physical hazards in employment and pregnancy outcome . Indian J Community Med 2009 ; 34 : 89 – 93 . Google Scholar CrossRef Search ADS PubMed 13 Lee B , Jung H . Relationship between handling heavy items during pregnancy and spontaneous abortion: a cross-sectional survey of working women in South Korea . Work 2012 ; 60 : 25 – 32 . 14 Loomans EM , van Dijk AE , Vrijkotte TGM , et al. Psychosocial stress during pregnancy is related to adverse birth outcomes: results from a large multi-ethnic community-based birth cohort . Eur J Public Health 2013 ; 23 : 485 – 91 . Google Scholar CrossRef Search ADS PubMed 15 Mozurkewich EL , Luke B , Avni M , Wolf FM . Working conditions and adverse pregnancy outcome: a meta-analysis . Obstet Gynecol 2000 ; 95 : 623 – 35 . Google Scholar PubMed 16 Langhoff-Roos J , Krebs L , Klungsøyr K , et al. The nordic medical birth registers - a potential goldmine for clinical research . Acta Obstet Gynecol Scand 2014 ; 93 : 132 – 7 . Google Scholar CrossRef Search ADS PubMed 17 Quansah R , Gissler M , Jaakkola JJ . Work as a physician and adverse pregnancy outcomes: a Finnish nationwide population-based registry study . Eur J Epidemiol 2009 ; 24 : 531 – 6 . Google Scholar CrossRef Search ADS PubMed 18 Lamminpää R , Vehviläinen-Julkunen K , Gissler M , et al. Pregnancy outcomes of overweight and obese women aged 35 years or older - a registry-based study in Finland . Obes Res Clin Pract 2016 ; 10 : 133 – 42 . Google Scholar CrossRef Search ADS PubMed 19 Kauppinen T , Toikkanen J , Pukkala E . From cross-tabulations to multipurpose exposure information systems: a new job-exposure matrix . Am J Ind Med 1998 ; 33 : 409 – 17 . Google Scholar CrossRef Search ADS PubMed 20 Kauppinen T , Uuksulainen S , Saalo A , et al. Use of the Finnish information system on occupational exposure (FINJEM) in epidemiologic, surveillance, and other applications . Ann Occup Hyg 2014 ; 58 : 380 – 96 . Google Scholar PubMed 21 Lavoue J , Pintos J , Van Tongeren M , et al. Comparison of exposure estimates in the Finnish job-exposure matrix FINJEM with a JEM derived from expert assessments performed in Montreal . Occup Environ Med 2012 ; 69 : 465 – 71 . Google Scholar CrossRef Search ADS PubMed 22 American College of Obstetricians and Gynecologists . Hypertension in pregnancy. Available at: https://www.acog.org/Clinical-Guidance-and-Publications/Task-Force-and-Work-Group-Reports/Hypertension-in-Pregnancy. Updated 2013 (3 April, 2018 , date last accessed). 23 Pihkala J , Hakala T , Voutilainen P , Raivio K . Characteristic of recent fetal growth curves in Finland . Duodecim 1989 ; 105 : 1540 – 6 . Google Scholar PubMed 24 International Labour Organization . National labour law profile: Republic of Finland. Available at: http://www.ilo.org/ifpdial/information-resources/national-labour-law-profiles/WCMS_158896/lang–en/index.htm. Updated 2017 (28 September, 2017, date last accessed). 25 Gissler M , Haukka J . Finnish health and social welfare registers in epidemiological research . Norsk epidemiol 2009 ; 14 : 113 – 120 . 26 Hoskins IA . Environmental and occupational hazards to pregnancy . Prim Care Update Ob Gyns 2003 ; 10 : 253 – 7 . Google Scholar CrossRef Search ADS 27 Halliday-Bell JA , Quansah R , Gissler M , Jaakkola JJ . Laboratory work and adverse pregnancy outcomes . Occup Med (Lond) 2010 ; 60 : 310 – 3 . Google Scholar CrossRef Search ADS PubMed 28 Perinatal statistics - parturients, delivers and newborns, 2015 . Available at: https://www.thl.fi/en/web/thlfi-en/statistics/statistics-by-topic/sexual-and-reproductive-health/parturients-deliveries-and-births/perinatal-statistics-parturients-delivers-and-newborns. Updated 2017 (3 August, 2017, date last accessed). 29 Juhl M , Larsen PS , Andersen PK , et al. Occupational lifting during pregnancy and child's birth size in a large csohort study . Scand J Work Environ Health 2014 ; 40 : 411 – 9 . Google Scholar CrossRef Search ADS PubMed 30 Ahlborg G , Bodin L , Hogstedt C . Heavy lifting during pregnancy - a hazard to the fetus? A prospective study . Int J Epidemiol 1990 ; 19 : 90 – 7 . Google Scholar CrossRef Search ADS PubMed 31 van Beukering MDM , van Melick MJGJ , Mol BW , et al. Physically demanding work and preterm delivery: a systematic review and meta-analysis . Int Arch Occup Environ Health 2014 ; 87 : 809 – 34 . Google Scholar CrossRef Search ADS PubMed 32 Riipinen A , Sallmén M , Taskinen H , et al. Pregnancy outcomes among daycare employees in Finland . Scand J Work Environ Health 2010 ; 36 : 222 – 30 . Google Scholar CrossRef Search ADS PubMed 33 Pompeii LA , Savitz DA , Evenson KR , et al. Physical exertion at work and the risk of preterm delivery and small-for-gestational-age birth . Obstet Gynecol 2005 ; 106 : 1279 – 88 . Google Scholar CrossRef Search ADS PubMed 34 Palmer KT , Bonzini M , Harris EC , et al. Work activities and risk of prematurity, low birth weight and pre-eclampsia: an updated review with meta-analysis . Occup Environ Med 2013 ; 70 : 213 – 22 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Public Health Oxford University Press

Manual handling of burdens as a predictor of birth outcome—a Finnish Birth Register Study

Loading next page...
 
/lp/ou_press/manual-handling-of-burdens-as-a-predictor-of-birth-outcome-a-finnish-nKRLhRnaWp
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
ISSN
1101-1262
eISSN
1464-360X
D.O.I.
10.1093/eurpub/cky081
Publisher site
See Article on Publisher Site

Abstract

Abstract Background Negative effects of manual handling of burdens on pregnancy outcomes are not elucidated in Finland. This study examines the association between perinatal outcomes and occupational exposure to manual handling of burdens. Methods The study cohort was identified from the Finnish Medical Birth Register (MBR, 1997–2014) and information on exposure from the Finnish job-exposure matrix (FINJEM) 1997–2009. The cohort included all singleton births of mothers who were classified as ‘service and care workers’ representing the exposure group (n=74 286) and ‘clerks’ as the reference (n=13 873). Study outcomes were preterm birth (PTB) (<37 weeks), low birthweight (LBW) (<2500 g), small for gestational age (<2.5th percentile), perinatal death (stillbirth or early neonatal death within first seven days) and eclampsia. We used logistic regression analysis to calculate odds ratio (OR) and adjusted for maternal age, marital status, BMI, parity and smoking during pregnancy. Results The risks of PTB [OR 1.16, 95% confidence interval (CI) 1.06–1.27], LBW (OR 1.12, 95% CI 1.01–1.25) and perinatal death (OR 1.51, 95% CI 1.09–2.09) were significantly higher among the high exposure group than in the reference group. All adverse outcomes were statistically insignificant among primiparous women except perinatal death (OR=1.95, 95% CI 1.13–3.39). Conclusions The study indicates that the risk of adverse pregnancy outcomes might be more common among women that are highly exposed to occupational manual handling of burdens. The results should be interpreted with caution due to the use of occupational level exposure. Further studies with information on individual level exposure and start of maternity leave are recommended. Introduction Gestational age and birthweight are important measurements of perinatal outcomes. Preterm birth (PTB), being born before 37 completed weeks of gestation1,2 and low birthweight (LBW), birthweight below 2500 g3 are the most frequently used perinatal health indicators. A recent global report indicates that every year 15 million (11% of all livebirths) are born premature. The same study reported that in 2010, PTB rate in Finland was 5.5% compared to 8.6% for other developed countries.4 Although relatively low, there was no improvement in Finland’s PTB rate for two decades.1,4 Even though not all LBW babies are preterm, the positive correlation between the two has been reported.5 Similar to other European countries, the occurrence of PTB in Finland has been attributed to factors such as advanced maternal age, smoking, genital infection, obesity and assisted reproduction techniques among others.6,7 Strategies to substantially reduce PTB and LBW are lacking.2,8 For two decades only three (Croatia, Ecuador and Estonia) out of 65 countries were able to reduce the rate of PTB in the world.4 Physical exertion such as heavy lifting has been linked to the aetiology of PTB and other adverse birth outcomes.9–11 A study by Banerjee reported that the demand for increased blood supply by working muscles tend to reduce placental blood perfusion to the growing foetus. Also, the physiological haemo-dilution in pregnancy can be compromised by excessive sweating that characterizes physical activity while intra-abdominal pressure would be increased in the course of heavy lifting.12 Other studies have suggested that physically demanding work is associated with adverse pregnancy outcomes.13–15 Registry-based studies give retrospective understanding of the aetiology of most disease conditions. Among the Nordic countries, Finland has a good record of computerized national health information system16 including the Finnish Medical Birth Register (MBR) and the Finnish Information System on Occupational Exposure [Finnish job-exposure matrix (FINJEM)] developed by the Finnish Institute of Occupational Health (FIOH). These registers have been the basis for most population-based studies.17–19 However, effects of manual handling of burdens on pregnancy outcomes are not elucidated at the country level. The aim of this study is to investigate the association between birth outcomes and occupational group level exposure to manual handling of burdens within the Finnish population. Methods The data source for this study includes the Finnish MBR and information to define exposure categories was from the FINJEM. The MBR contains a nationwide data on both hospital and home deliveries since 1987 with regular maintenance by the National Institute for Health and Welfare (THL). The MBR is also linked to other registers such as the Central Population Register containing livebirths and the Cause-of-Death Register with information on stillbirths and infant death.17 The source population contains information on 1 050 889 women and their newborns between 1997 and 2014. Second, FINJEM is Finland’s quantitative job-exposure matrix established in the 1990s19 and has been regularly updated till 2010–12.20 It contains comprehensive information on all major occupations classified into 311 categories, and their corresponding job-exposure estimates that are time specific at the country level. The assessment for exposure to all major occupational risk factors has been explained in detailed elsewhere.19–21 In this study, the source data to define the two exposure groups were the quantitative estimates or subjective ratings of manual handling of burdens (MAHB) at the occupational level from 1997 to 2009. Maternal occupational codes have been collected into the MBR dataset. To select the study cohort we used the F-ISCO-88 codes in the FINJEM dataset to identify the mothers from the MBR. The study cohort included all singleton births of mothers working as practical nurses, homecare assistants and kitchen assistants. For the purposes of this study, these three occupations are collectively classified as ‘service and care workers’ representing the high exposure group to MAHB (N = 74 286). Mothers working as secretaries, account and book-keeping clerks and insurance clerks are collectively classified as ‘clerks’ (N = 13 873) in this study. Clerks were chosen as suitable reference group because their occupational level exposure to MAHB was low. The classification of occupations is done by the Statistics Finland’s Classification Services from 1997 to 2009 census based on EU standards. Occupational exposures In this study, we used information from the FINJEM dataset to identify and define the exposure levels of MAHB. The average exposure of the whole occupational group to MAHB was used. In the FINJEM dataset, MAHB is classified under ergonomic factors and was obtained from either a subjective ratings or observations of lifting and carrying of heavy burdens as essential feature of everyday work. Level of exposure to MAHB is categorized as follows; 0= no or very little lifting at work, 1= lifting or carrying of moderate burdens (10 kg) daily or reported fairly much lifting at work and 2 = lifting or carrying of heavy burdens (20 kg) over five times a day or reported very much lifting at work. Based on this information in the FINJEM, we selected practical nurses, homecare assistants and kitchen assistants (collectively as service and care workers) for the high exposure group and the reference category were secretaries, accounting and book-keeping clerks and insurance clerks (collectively as clerks) taking into account similarities of their socio-economic statuses. The exposure groups were derived based on the proportion of occupation exposed and the level of exposure among the exposed. Among the high exposure group, the average group exposure varied between 0.65 and 0.81 and the proportion of workers exposed was between 74 and 83%. The low exposure group had an average group exposure of 0.38–0.5 and the proportion of workers exposed was between 2 and 6.1%. Permission to use the MBR data in this study was granted in February 2016 by THL (THL/151/5.05.00/2016) as required by Finland’s data protection legislation. FINJEM is not an individual level database, and its use does not require ethical review or study permission. Statistical analysis We considered the following outcome variables; PTB defined as gestational age <37 weeks and LBW (<2500 g), perinatal death defined as stillbirth (from 22 weeks or 500 g) or early neonatal death within the first seven days, eclampsia (blood pressure above 140/90 mmHg, proteinuria (>0.3 g/day) and seizures at or after 20 weeks gestation, ICD-10 code O15)22 and small for gestational age (SGA, defined as birthweight below the 2.5th percentile based on Finnish sex-specific growth curves).23 SPSS version 21 was used to examine the prevalence of adverse pregnancy outcomes among the two exposure groups with 95% confidence intervals (CIs). We conducted logistic regression analysis to estimate the odds ratios (ORs) between the exposure levels and pregnancy outcomes. We also conducted generalized estimating equation (GEE) analysis so that in the event of several deliveries by the same mother within the study period, each delivery could be evaluated separately from the influence of factors pertaining to other deliveries. Based on evidence from literature, in both models we adjusted for parity, smoking status, marital status and BMI, all as categorical variables and maternal age as a continuous variable. Information about these potential confounders was obtained from the MBR. The recording of maternal height and weight started in 2004 therefore all data that were missing before this year were categorized as ‘0’ while ‘1’ included women with BMI ≤25 and ‘2’ women with BMI >25. We conducted sensitivity analysis to examine the outcomes based on parity. The assumption was that nulliparous women do not have to lift under age children at home, do not take childcare leave and therefore have a higher chance of working throughout the pregnancy till they go on statutory maternity leave. Based on this, we compared primiparous women with all other women who have previously given birth. Further sensitivity analysis was done by comparing the lowest exposure occupation in each exposure category to the rest of the group to determine homogeneity among the exposure categories (results available on request from authors). Results Table 1 compares baseline characteristics of the high and low exposure groups and the sex of the newborns. Differences in prevalence of girls were negligible in the compared groups. Fewer women (12.7%) were advanced in age (≥35 years) among the high exposure group than the low exposure category (29.8%). Grand multiparity (five or more pregnancies) was less common among the low exposure group (1.0%) compared to 3.4% within the high exposure category. The low exposure group was often married and smoked less during pregnancy compared to the high exposure group. Table 1 Sex of the child and characteristics of the study population Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Table 1 Sex of the child and characteristics of the study population Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Characteristics Service and care workers Clerks (reference group) Total n % n % n % Total 74 286 – 13 873 – 88 159 – Sex Boy 37 798 50.9 7088 51.1 44 886 50.9 Girl 36 487 49.1 6785 48.9 43 272 49.1 Maternal age 19–34 64 819 87.3 9740 70.2 74 559 84.4 ≥35 9467 12.7 4133 29.8 13 600 15.4 Parity Primiparous 27 339 36.8 5428 39.2 32 767 37.2 1 24 104 32.5 5294 38.2 29 398 33.4 2 12 963 17.5 2276 16.4 15 239 17.3 3 5321 7.2 578 4.2 5899 6.7 4 1989 2.7 147 1.1 2136 2.4 ≥5 2534 3.4 133 1.0 2667 3.0 Missing 36 0.05 17 0.1 53 0.1 Marital status Married or registered partnership 37 948 51.1 9086 65.5 47 034 53.3 Unmarried 32 955 44.4 4110 29.6 37 065 42.0 Widow 65 0.1 15 0.1 80 0.1 Divorced 1273 1.7 248 1.8 1521 1.7 Missing 2045 2.7 414 3.0 2459 2.8 Smoking No 57 427 77.3 12 255 88.3 69 682 79.0 Quitted in first trimester 4223 5.7 314 2.3 4537 5.1 Yes 10 779 14.5 997 7.2 11 776 13.4 Missing 1857 2.5 307 2.2 2164 2.5 Table 2 shows prevalence of the studied pregnancy outcomes between the exposure groups. The mean gestational age and birthweight were almost equal among newborns of the reference group (3.535 g and 39.34 weeks) and among the high exposure group (3.542 g and 39.35 weeks), respectively. The prevalence of PTB was 4.7% among the high exposure category and 4.8% among the reference group. The proportion of LBW newborns was slightly higher in the reference group (3.4%) than in the high exposure group (3.2%). All other pregnancy outcomes were quantitatively similar among the two groups. Table 2 Prevalence of adverse pregnancy outcomes (%) among newborns of the high exposure group and the low exposure group. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – Table 2 Prevalence of adverse pregnancy outcomes (%) among newborns of the high exposure group and the low exposure group. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – High exposure group Low exposure group (Service and care workers) (Clerks) Pregnancy outcome N % Mean SD N % Mean SD Gestational age (weeks) – – 39.35 1.79 – – 39.34 1.80 Birthweight (g) – – 3542 551 – – 3538 556 Preterm birth (<37 weeks) 3463 4.7 – – 667 4.8 – – Low birthweight (<2500 g) 2350 3.2 – – 476 3.4 – – SGA (<2.5th percentile) 2322 3.1 – – 450 3.2 – – Eclampsia 46 0.1 – – 9 0.1 – – Perinatal deaths 287 0.4 – – 49 0.4 – – Table 3 displays comparison of the effect estimates between the two exposure groups. Adjusted OR of PTB (OR = 1.16, 95% CI 1.06–1.27), LBW (OR = 1.12, 95% CI 1.01–1.25) and perinatal death (OR = 1.51, 95% CI 1.09–2.09) were statistically significantly higher among the high exposure group. We found no significant difference between the two groups for the risk of SGA (OR = 0.91, 95% CI 0.81–1.02) and eclampsia (OR = 0.82, 95% CI 0.38–1.77). Table 3 Comparison of crude and adjusted odds ratio of pregnancy outcomes between newborns of service and care workers (high exposure group, n=74 286) and clerks (low exposure group, n=13 873) to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 a Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. b Generalized estimating equations analysis: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Table 3 Comparison of crude and adjusted odds ratio of pregnancy outcomes between newborns of service and care workers (high exposure group, n=74 286) and clerks (low exposure group, n=13 873) to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 Crude estimates Adjusted estimates Pregnancy outcome OR 95% CI P-value Adjusted ORa 95% CI P-value GEE analysis adjusted ORb 95% CI P-value Preterm birth (<37 weeks) 0.97 0.89–1.05 0.45 1.16 1.06–1.27 0.002 1.16 1.05–1.28 0.002 Low birthweight (<2500 g) 0.92 0.83–1.02 0.10 1.12 1.01–1.25 0.038 1.12 1.00–1.26 0.045 SGA (<2.5th percentile) 1.04 0.88–1.07 0.47 0.91 0.81–1.02 0.091 0.91 0.81–1.02 0.103 Eclampsia 0.95 0.47–1.95 0.90 0.82 0.38–1.77 0.608 1.23 0.60–2.51 0.578 Perinatal deaths 1.09 0.81–1.48 0.56 1.51 1.09–2.09 0.012 1.51 1.09–2.09 0.013 a Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. b Generalized estimating equations analysis: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Results of sensitivity analysis by comparing outcomes among primiparous with multiparous women are presented in table 4. Prevalence of all the outcomes was higher among primiparous women. After adjusting for confounders, the risk of perinatal death (OR = 1.95, 95% CI 1.13–3.39) was higher among children born to primiparous mothers in the higher exposure group. Higher exposure was not associated with any other outcome in this stratum. The risks of PTB (OR = 1.20, 95% CI 1.06–1.36) and LBW (OR = 1.18, 95% CI 1.00–1.37) were significantly increased in multiparous women but the lower end of the effect estimate for LBW included unity. The risk of SGA was decreased among both primiparous women (OR = 0.96, 95% CI 0.83–1.12) and significantly decreased in multiparous women (OR = 0.83, CI 0.69–0.99) while risk of eclampsia was non-significant in both groups. Table 4 Sensitivity analysis of the risk of adverse pregnancy outcomes based on parity. Comparison of adjusted odds ratio of pregnancy outcomes between newborns of primiparous (n=33 865) and multiparous women (56 853) based on their exposure to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Table 4 Sensitivity analysis of the risk of adverse pregnancy outcomes based on parity. Comparison of adjusted odds ratio of pregnancy outcomes between newborns of primiparous (n=33 865) and multiparous women (56 853) based on their exposure to manual handling of burdens. Finnish Medical Birth Register Data, 1997–2014 and Finnish Job-Exposure Matrix, 1997–2009 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Primiparous women Multiparous women Characteristics N % Adjusted OR 95% CI P-value N % Adjusted OR 95% CI P-value Total 33 865 – – – – 56 853 – – – – Preterm birth (<37 weeks) 1863 5.7 1.14 1.00–1.30 0.060 2262 4.1 1.20 1.06–1.36 0.005 Low birthweight (<2500 g) 1375 4.2 1.09 0.94–1.27 0.263 1449 2.6 1.18 1.00–1.37 0.044 SGA (<2.5th percentile) 1525 4.7 0.96 0.83–1.12 0.605 1245 2.2 0.83 0.69–0.99 0.033 Eclampsia 38 0.1 1.13 0.46–2.80 0.785 17 0.03 1.56 0.33–7.39 0.575 Perinatal deaths 119 0.4 1.95 1.13–3.39 0.017 216 0.4 1.33 0.89–1.98 0.167 Logistic regression: adjusted for BMI, marital status, smoking in pregnancy, maternal age and parity. Discussion Our study results suggest that an occupational high exposure to manual handling of burdens might be associated with PTB, LBW and perinatal deaths. The risk of SGA and eclampsia was similar among both exposure categories. However, sensitivity analysis shows that with the exception of perinatal death, the risk of all other adverse birth outcomes was weaker among primiparous women than in multiparous women. The risk of SGA was elevated among multiparous women although it could be due to chance. In Finland, by legislation mothers who have children under age three are entitled to take paid childcare leave.24 Thus, we assumed that some of the multiparous women might have been on childcare leave and thus not working during the study period. Also, lifting own children during pregnancy may constitute a risk for multiparous women. To consider these potential problems, we conducted a sensitivity analysis to examine differences in outcome between primiparous and multiparous women. The findings among primiparous women were weaker than in the entire data and statistically significant only for perinatal death. It can be argued that besides occupational lifting and carrying, there may be other factors within the home environment that render these women susceptible to adverse pregnancy outcomes. Another plausible explanation may be that, contrary to nulliparous women, multiparous mothers frequently lift or carry their under age children at home thereby negating the beneficial effects of the childcare leave policy. Another sensitivity analysis comparing the lowest exposure in each exposure category with the rest of the group, did not alter the results. The strengths of this study are that, it has a large sample size including all babies born in Finland by women in the six occupations between 1997 and 2014. The population-based approach and about 100% coverage of all birth events in Finland25 eliminates the possibility of selection bias. GEE analysis was conducted to mitigate effects of same women possibly giving birth several times within the study period. The processes of diagnosing all study outcomes were based on the best available standardized measurements. Gestational age was estimated by ultrasound examination and the diagnosis of SGA was based on age and sex-specific estimates. We adjusted for potential confounders including parity, maternal age, marital status, smoking status and BMI which have shown to have high quality in the Finnish MBR. Additionally, measures such as rigorous definition and description of occupations, agents and probable changes in exposure levels were taken into account in the construction of FINJEM to diminish exposure misclassification. In effect, updating FINJEM will not significantly change the current job-exposure categorization because the nature of work by the various occupations has not changed over the course of time.19 The use of whole occupational group exposure to manual handling of burdens instead of individual estimates is a limitation to the study because it does not take into consideration differences in exposure within each occupational group.19 Furthermore, different perception among occupations about potential hazards of lifting and carrying by pregnant women could lead to over-or-under reporting of the exposure levels and that can be a source of misclassification. Unfortunately, we could not adjust for factors outside participants’ working environment that could potentially affect the results. Information such as when mothers began their maternity leave was not included in the analysis and therefore, the exact trimester which is adversely affected by the exposure could not be confirmed. Aspects of MAHB estimates based on subjective ratings are at least partly likely to be affected by recall bias. Based on the study objectives, we identified occupations with similar exposure levels to MAHB for each exposure group. Nevertheless, each of the six occupations may have unmeasured peculiar occupational hazards that might have influenced the effect estimates. Among the high exposure group, prevalence of the studied adverse pregnancy outcomes was often higher among the newborns of kitchen assistants and practical nurses than the newborns of homecare assistants. Practical nurses may differ on exposure to chemical and biological agents and kitchen assistants to heat which are all known potential risk factors for adverse pregnancy outcomes.26,27 Similarly, infants of insurance clerks had less adverse birth outcomes compared to those of secretaries and account and book-keeping clerks within the low exposure group. Among the reference group, secretaries were more advanced in age and often smoked during pregnancy. We adjusted for these confounders in all the models. It can be argued that existing residual confounders, such as the extent of psychosocial and other physical stress factors, may differ among the occupations. However, we could not collect information on all potential risk factors due to the secondary source of our data. Moreover, in Finland pregnant employees are by legislation to be reassigned to safer tasks if work poses danger.28 The results corroborate findings from previous epidemiological studies and a review that occupational lifting is associated with adverse pregnancy outcomes.29–31 Juhl et al.29 reported in a recent Danish MBR study that younger women were more exposed to occupational lifting than older women. The authors found an elevated risk of decreased birth size among mothers who lifted 11–20 kg 10 times per day compared with less or no exposure. In a systematic review, van Beukering et al.31 reported a summary OR of 1.29 for PTB among mothers who lifted or carried burdens weighing more than 5 kg compared to those who lifted less. On the contrary, authors of a Finnish daycare workers’ study32 reported no increased risk of PTB, perinatal death and SGA among daycare workers despite their potential exposure to heavy lifting. The risk of adverse pregnancy outcomes was not elevated among women exposed to physically demanding work in North Carolina.33 In a recent updated review,34 Palmer et al. reported smaller or no association between birth outcomes and a range of occupational activities. In conclusion, we identified an elevated risk of PTB, LBW and perinatal death among service and care workers that were highly exposed to manual handling of burdens. The adverse pregnancy outcomes were more common among multiparous women. However, the use of occupational group level exposure requires caution in interpreting the results. Further studies with information on individual level of exposure and start of maternity leave are recommended. Acknowledgement We are grateful to Mr. Ari Voutilainen, a data analyst at the Institute of Public & Clinical Nutrition, University of Eastern Finland for his technical advice concerning analysis of the data. Funding The first author received 12 months working grant from the Jenny & Antti Wihuri Foundation [Grant number: 00160196]. Conflicts of interest: None declared. Key points PTB, LBW and perinatal death are pervasive among occupations with high exposure to manual handling of burdens. The adverse effects appear to be absent in primiparous women but more common in multiparous women. Protecting pregnant women against heavy lifting is justifiable and should be reinforced. Future study with prospective objective measurement of lifting and carrying is recommended. References 1 March of Dimes, PMNCH, Save the Children, WHO. Born too soon: The global action report on preterm birth. 2012 . 2 Goldenberg RL , Culhane JF , Iams JD , Romero R . Epidemiology and causes of preterm birth . Lancet 2008 ; 371 : 75 – 84 . Google Scholar CrossRef Search ADS PubMed 3 The World Bank , UNICEF. The state of the world's children, childinfo, and demographic and health surveys. 2016 . 4 Blencowe H , Cousens S , Oestergaard MZ . National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications . Lancet 2012 ; 379 : 2162 – 72 . Google Scholar CrossRef Search ADS PubMed 5 Tang W , Mu Y , Li X , et al. Low birthweight in China: evidence from 441 health facilities between 2012 and 2014 . J Matern Fetal Neonatal Med 2017 ; 30 : 1997 – 2002 . Google Scholar CrossRef Search ADS PubMed 6 Zeitlin J , Szamotulska K , Drewniak N , et al. Preterm birth time trends in Europe: a study of 19 countries . BJOG 2013 ; 120 : 1356 – 65 . Google Scholar CrossRef Search ADS PubMed 7 Jakobsson M , Gissler M , Paavonen J , Tapper A . The incidence of preterm deliveries decreases in Finland . BJOG Int J Obstet Gynaecol 2008 ; 115 : 38 – 43 . Google Scholar CrossRef Search ADS 8 Kwegyir-Afful E , Ijaz S , Räsänen K , Verbeek J . Randomized controlled trials are needed to close the evidence gap in the prevention of preterm birth . Scand J Work Environ Health 2014 ; 40 : 96 – 9 . Google Scholar CrossRef Search ADS PubMed 9 Croteau A , Marcoux S , Brisson C . Work activity in pregnancy, preventive measures, and the risk of preterm delivery . Am J Epidemiol 2007 ; 166 : 951 – 65 . Google Scholar CrossRef Search ADS PubMed 10 Launer LJ , Villar J , Kestler E , de Onis M . The effect of maternal work on fetal growth and duration of pregnancy: a prospective study . Br J Obstet Gynaecol 1990 ; 97 : 62 – 70 . Google Scholar CrossRef Search ADS PubMed 11 Omokhodion FO , Onadeko MO , Roberts OA , et al. Paid work, domestic work, and other determinants of pregnancy outcome in Ibadan, southwest Nigeria . Int J Gynaecol Obstet 2010 ; 111 : 165 – 70 . Google Scholar CrossRef Search ADS PubMed 12 Banerjee B . Physical hazards in employment and pregnancy outcome . Indian J Community Med 2009 ; 34 : 89 – 93 . Google Scholar CrossRef Search ADS PubMed 13 Lee B , Jung H . Relationship between handling heavy items during pregnancy and spontaneous abortion: a cross-sectional survey of working women in South Korea . Work 2012 ; 60 : 25 – 32 . 14 Loomans EM , van Dijk AE , Vrijkotte TGM , et al. Psychosocial stress during pregnancy is related to adverse birth outcomes: results from a large multi-ethnic community-based birth cohort . Eur J Public Health 2013 ; 23 : 485 – 91 . Google Scholar CrossRef Search ADS PubMed 15 Mozurkewich EL , Luke B , Avni M , Wolf FM . Working conditions and adverse pregnancy outcome: a meta-analysis . Obstet Gynecol 2000 ; 95 : 623 – 35 . Google Scholar PubMed 16 Langhoff-Roos J , Krebs L , Klungsøyr K , et al. The nordic medical birth registers - a potential goldmine for clinical research . Acta Obstet Gynecol Scand 2014 ; 93 : 132 – 7 . Google Scholar CrossRef Search ADS PubMed 17 Quansah R , Gissler M , Jaakkola JJ . Work as a physician and adverse pregnancy outcomes: a Finnish nationwide population-based registry study . Eur J Epidemiol 2009 ; 24 : 531 – 6 . Google Scholar CrossRef Search ADS PubMed 18 Lamminpää R , Vehviläinen-Julkunen K , Gissler M , et al. Pregnancy outcomes of overweight and obese women aged 35 years or older - a registry-based study in Finland . Obes Res Clin Pract 2016 ; 10 : 133 – 42 . Google Scholar CrossRef Search ADS PubMed 19 Kauppinen T , Toikkanen J , Pukkala E . From cross-tabulations to multipurpose exposure information systems: a new job-exposure matrix . Am J Ind Med 1998 ; 33 : 409 – 17 . Google Scholar CrossRef Search ADS PubMed 20 Kauppinen T , Uuksulainen S , Saalo A , et al. Use of the Finnish information system on occupational exposure (FINJEM) in epidemiologic, surveillance, and other applications . Ann Occup Hyg 2014 ; 58 : 380 – 96 . Google Scholar PubMed 21 Lavoue J , Pintos J , Van Tongeren M , et al. Comparison of exposure estimates in the Finnish job-exposure matrix FINJEM with a JEM derived from expert assessments performed in Montreal . Occup Environ Med 2012 ; 69 : 465 – 71 . Google Scholar CrossRef Search ADS PubMed 22 American College of Obstetricians and Gynecologists . Hypertension in pregnancy. Available at: https://www.acog.org/Clinical-Guidance-and-Publications/Task-Force-and-Work-Group-Reports/Hypertension-in-Pregnancy. Updated 2013 (3 April, 2018 , date last accessed). 23 Pihkala J , Hakala T , Voutilainen P , Raivio K . Characteristic of recent fetal growth curves in Finland . Duodecim 1989 ; 105 : 1540 – 6 . Google Scholar PubMed 24 International Labour Organization . National labour law profile: Republic of Finland. Available at: http://www.ilo.org/ifpdial/information-resources/national-labour-law-profiles/WCMS_158896/lang–en/index.htm. Updated 2017 (28 September, 2017, date last accessed). 25 Gissler M , Haukka J . Finnish health and social welfare registers in epidemiological research . Norsk epidemiol 2009 ; 14 : 113 – 120 . 26 Hoskins IA . Environmental and occupational hazards to pregnancy . Prim Care Update Ob Gyns 2003 ; 10 : 253 – 7 . Google Scholar CrossRef Search ADS 27 Halliday-Bell JA , Quansah R , Gissler M , Jaakkola JJ . Laboratory work and adverse pregnancy outcomes . Occup Med (Lond) 2010 ; 60 : 310 – 3 . Google Scholar CrossRef Search ADS PubMed 28 Perinatal statistics - parturients, delivers and newborns, 2015 . Available at: https://www.thl.fi/en/web/thlfi-en/statistics/statistics-by-topic/sexual-and-reproductive-health/parturients-deliveries-and-births/perinatal-statistics-parturients-delivers-and-newborns. Updated 2017 (3 August, 2017, date last accessed). 29 Juhl M , Larsen PS , Andersen PK , et al. Occupational lifting during pregnancy and child's birth size in a large csohort study . Scand J Work Environ Health 2014 ; 40 : 411 – 9 . Google Scholar CrossRef Search ADS PubMed 30 Ahlborg G , Bodin L , Hogstedt C . Heavy lifting during pregnancy - a hazard to the fetus? A prospective study . Int J Epidemiol 1990 ; 19 : 90 – 7 . Google Scholar CrossRef Search ADS PubMed 31 van Beukering MDM , van Melick MJGJ , Mol BW , et al. Physically demanding work and preterm delivery: a systematic review and meta-analysis . Int Arch Occup Environ Health 2014 ; 87 : 809 – 34 . Google Scholar CrossRef Search ADS PubMed 32 Riipinen A , Sallmén M , Taskinen H , et al. Pregnancy outcomes among daycare employees in Finland . Scand J Work Environ Health 2010 ; 36 : 222 – 30 . Google Scholar CrossRef Search ADS PubMed 33 Pompeii LA , Savitz DA , Evenson KR , et al. Physical exertion at work and the risk of preterm delivery and small-for-gestational-age birth . Obstet Gynecol 2005 ; 106 : 1279 – 88 . Google Scholar CrossRef Search ADS PubMed 34 Palmer KT , Bonzini M , Harris EC , et al. Work activities and risk of prematurity, low birth weight and pre-eclampsia: an updated review with meta-analysis . Occup Environ Med 2013 ; 70 : 213 – 22 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

The European Journal of Public HealthOxford University Press

Published: May 10, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off