INTRODUCTION
Globally, about 0.6 million deaths are attributable to secondhand smoke (SHS) each year. More than 33% of the population is often actively or passively exposed to cigarette smoke, and about 35% of all female non-smokers are exposed to SHS1. Tobacco smoke exposure and the resulting inhalation of SHS are among the most widespread forms of micro-environmental exposure (i.e. indoor). Women and children aged <5 years are most vulnerable to indoor air pollution. A retrospective study from 192 countries reported that 40% of children were exposed to SHS, higher than non-smoking men and women, who were only 33% and 35%, respectively. In contrast, 47% of deaths related to SHS occurred in women, almost twice as many as in children and men2. In addition, 36% of children have been exposed since they were in the womb3. At least 40% of children have become passive smokers because of SHS in their homes, and 31% of them die from the cigarette smoke they breathe every day2.
SHS during pregnancy among non-smoking women is associated with mortality and morbidity risks in their infants, including stillbirth, prematurity, miscarriage, and low birth weight (LBW)4. SHS is also associated with an increased risk of stunting, wasting, underweight among children, a smaller head circumference, and the susceptibility of being overweight simultaneously, in accordance with the country’s Gross National Income5. Evidence in Indonesia showed that compared to non-exposed pregnant women, the risk of having low birth weight infants was around three times higher in exposed ones. A similar risk was also drawn based on the number of active smokers at home, the number of cigarettes consumed, and the exposure duration on a daily basis6.
Based on Demographic and Health Survey data collected between 2008 and 2013, from 30 low- and middle-income countries (LMICs), daily SHS exposure accounted for a more significant population-attributable fraction of stillbirths than active smoking, which was 14% in Indonesia. This number is the highest among the other 30 LMICs7. Indonesia has compiled various regulations governing public protection from the dangers of exposure to cigarette smoke. One of them is the adoption of no-smoking zones in various public places and workplaces, especially in schools and hospitals. However, the World Health Organization (WHO) notes that regulations regarding smoke-free areas in public areas in Indonesia are still relatively low compared to other South-East Asian countries, and in accordance with the geographical distribution as well as socioeconomic disparity, in urban settings, the wealthier and more educated population were more likely to adopt a smoke-free policy8.
Given the implications for child mortality, a significant reduction in the prevalence of LBW is necessary to achieve the Sustainable Development Goals, and there is a similar need to strengthen the implementation of the Framework Convention on Tobacco Control (FCTC) of the WHO in all countries9. Only a few robust studies examined a clear association between exposure to SHS inside the house and birth outcomes, especially in Indonesia10,11. This study contributes to filling the knowledge gap in SHS exposure inside the house and low birth weight in Indonesia by using the evidence of large-scale population-based data and taking into account SHS frequency and LBW, neither of which have been presented in previous studies. This study assesses the prevalence, level, and frequency of SHS exposure inside the house and their associations with birth outcomes.
METHODS
Data sources
We used data from the latest 2017 Indonesia Demographic and Health Survey (IDHS) survey, a nationally representative, large-scale, and repeated cross-sectional household survey collecting population, health, and nutrition data. All ever-married women aged 15–49 years who had given birth in the last five years before the survey in sampled households are eligible for an interview using a standard self-reported questionnaire12. Women were chosen to give birth during the last five years before the survey to prevent bias in memory recall from mothers. The total sample size in the study was 19935. Respondents in the 2017 IDHS read a written informed consent statement before each interview. The statement also included voluntary participation, refusal to answer questions or termination of participation at any time, and confidentiality of identity and information.
Measurement
Two main independent variables were the exposure to SHS inside the house and the frequency of SHS exposure. The information about SHS inside the house is obtained from two types of 2017 IDHS questionnaires: the household questionnaire and the women’s questionnaire. The information regarding SHS exposure at home was derived from the question at the household questionnaire: ‘How often does anyone smoke inside your house? (daily, weekly, monthly, less than monthly, never)?’. To ascertain whether the mother in the household smoked or not, we linked smoking data from the household questionnaire to the women’s questionnaire through their unique identifier codes. In the women’s questionnaire, there are two questions related to smoking habits: 1) ‘Do you currently smoke?’ (every day, sometimes, not at all); 2) ‘In the past, have you ever smoked?’. (every day, sometimes, not at all.) All household members and mothers who answered ‘never’ or ‘not at all’ were included in the ‘non-exposed’ SHS group. Meanwhile, the SHS ‘exposed’ group consists of: 1) household members who do not smoke, but the mother smokes; 2) household members who smoke, but the mother does not smoke; and 3) all household members and mothers who answered ‘daily, weekly, monthly, less than monthly, every day, sometimes’. Then, a binary variable (not exposed vs exposed) was created to measure exposure to SHS inside the house, where one or more adults smoke commercial cigarettes, cigars (including kretek cigarettes or unfiltered cigarettes of Indonesian origin), and other country-specific smoking products (including pipes, cigars, shisha, chewing tobacco, and chewing betel nut with tobacco). The information regarding SHS exposure frequency at home was derived from the question on the household questionnaire in the 2017 IDHS: ‘How often does anyone smoke inside your house?’ (daily, weekly, monthly, less than monthly, never). SHS frequency was then classified as: not exposed, less than once a month, monthly, weekly, and daily. Two outcome variables related to the self-reported birth outcomes are LBW and birth weight. We treated LBW (<2500 g; compared to ≥2500 g) as a categorical variable. Birth weight (g) was treated as a continuous variable.
Potential covariates
Demographic and socioeconomic characteristics included maternal age, age at first birth, marital status, maternal education level, family size, mother’s occupation, husband’s education level, residence (urban or rural), parity, birth interval, birth order, wealth index, cooking fuel, and kitchen location. The wealth index is a composite measure of a household’s cumulative living standard or ownership of selected assets. The resulting combined wealth index has a mean of zero and a standard deviation of one. Once it is obtained, national-level wealth quintiles are obtained by assigning the household score to each de jure household member, ranking each person in the population by their score, and then dividing the ranking into five equal parts, from quintile one (lowest=poorest) to quintile five (highest=wealthiest), each having approximately 20% of the population. Cooking fuel consists of electricity or gas, kerosene, coal or lignite, charcoal, and wood or straw (including grass, shrubs, and plant residues). Clean cooking fuels include electricity or gas, while pollutant cooking fuels include kerosene, coal or lignite, charcoal, and wood or straw (including grass, shrubs, and plant residues).
Statistical analysis
The data were analyzed using SPSS version 25. The proportions and chi-squared tested the differences between SHS exposure and demographic and socioeconomic characteristics inside the house. Logistic regression analyses measured the relative odds of associations between SHS exposure and frequency inside the house and LBW. The general linear model assessed the relationships between SHS exposure and frequency inside the house and birth weight. All multivariable models were used to control for covariates. Backward elimination as the variable selection procedure retained critical confounding variables, resulting in a slightly richer model. The overall model was also evaluated using the goodness-of-fit test and the likelihood ratio test.
RESULTS
The characteristics of the participants are presented in Table 1. In all, 78.4% of the mothers were exposed to SHS in the household, with 7.2% of those with LBW being exposed to SHS. Compared with non-SHS exposure mothers, mothers exposed to SHS were aged 15–24 years, had their first birth before 20 years of age, were married, had a lower education, were non-workers, lived in a rural area, had grand multiparas, had pollution from cooking fuel, and cooked in a separate building. All the indicators were statistically significant at p<0.05, except for the husband’s occupation and birth interval, which were not different in exposure to SHS.
Table 1
Characteristics of participants by exposure to SHS inside the house based on secondary data analysis from the 2017 Indonesia Demographic Health Survey (N=19935)
| Characteristics | All (n=19935) n (%)* | SHS exposure | pa | |
|---|---|---|---|---|
| Not exposed (n=4305) n (%)* | Exposedb (n=15627) n (%)* | |||
| Birth weight (g) | 0.007 | |||
| Normal (≥2500) | 16287 (93.0) | 3529 (94.0) | 12758 (92.8) | |
| Low (<2500) | 1225 (7.0) | 225 (6.0) | 997 (7.2) | |
| Birth weight (g), mean (SD) | 3165.1 (506.6) | 3066.1 (551.7) | <0.001 | |
| Maternal age (years) | <0.001 | |||
| 15–24 | 3370 (16.9) | 470 (10.9) | 2899 (18.6) | |
| 25–34 | 10342 (51.9) | 2328 (54.1) | 8013 (51.3) | |
| >34 | 6223 (31.2) | 1507 (35.0) | 4715 (30.2) | |
| Maternal age at first birth (years) | <0.001 | |||
| <20 | 5712 (28.7) | 880 (20.4) | 4830 (30.9) | |
| 20–29 | 12950 (65.0) | 3067 (71.2) | 9882 (63.2) | |
| >29 | 1273 (6.4) | 358 (8.3) | 915 (5.9) | |
| Marital status | <0.001 | |||
| Unmarried | 31 (0.2) | 7 (0.2) | 24 (0.2) | |
| Married | 19297 (96.8) | 4101 (95.3) | 15193 (97.2) | |
| Divorced/widowed/separated | 607 (3.0) | 197 (4.6) | 410 (2.6) | |
| Maternal education level | <0.001 | |||
| None | 295 (1.5) | 47 (1.1) | 248 (1.6) | |
| Primary | 5109 (25.6) | 778 (18.1) | 4330 (27.7) | |
| Secondary | 11035 (55.4) | 2291 (53.2) | 8742 (55.9) | |
| Higher | 3496 (17.5) | 1189 (27.6) | 2307 (14.8) | |
| Family size, mean (SD) | 5.1 (1.8) | 5.7 (2.2) | ||
| Mother’s occupation | <0.001 | |||
| Not working | 9263 (46.5) | 1811 (42.1) | 7451 (47.7) | |
| Working | 10672 (53.5) | 2494 (57.9) | 8176 (52.3) | |
| Husband’s occupation | 0.821 | |||
| Not working | 151 (0.8) | 33 (0.8) | 118 (0.8) | |
| Working | 18976 (99.2) | 4003 (99.2) | 14970 (99.2) | |
| Residence | <0.001 | |||
| Rural | 9918 (49.8) | 1532 (35.6) | 8484 (54.3) | |
| Urban | 10017 (50.2) | 2773 (64.4) | 7143 (45.7) | |
| Parity | <0.001 | |||
| ≥5 | 1427 (7.2) | 240 (5.6) | 1186 (7.6) | |
| 3–4 | 5968 (30.1) | 1351 (31.5) | 4616 (29.7) | |
| 1–2 | 12456 (62.7) | 2698 (62.9) | 9757 (62.7) | |
| Birth interval | 0.163 | |||
| Short (<33 months) | 2845 (21.4) | 592 (20.5) | 2253 (21.7) | |
| Recommended (≥33 months) | 10435 (78.6) | 2298 (79.5) | 8135 (78.3) | |
| Birth order, mean (SD) | 2.3 (1.3) | 2.4 (1.5) | <0.001 | |
| Wealth index | <0.001 | |||
| Q1 (poorest) | 5466 (27.4) | 692 (16.1) | 4773 (30.5) | |
| Q2 | 3901 (19.6) | 611 (14.2) | 3290 (21.1) | |
| Q3 | 3635 (18.2) | 735 (17.1) | 2899 (18.6) | |
| Q4 | 3532 (17.7) | 944 (21.9) | 2587 (16.6) | |
| Q5 (richest) | 3401 (17.1) | 1223 (30.7) | 2078 (13.3) | |
| Cooking fuel | <0.001 | |||
| Clean | 13597 (68.2) | 3333 (77.5) | 10236 (65.7) | |
| Pollutant | 6334 (31.8) | 970 (22.5) | 5362 (34.3) | |
| Kitchen location | <0.001 | |||
| Separate building | 1482 (7.4) | 257 (6.0) | 1225 (7.8) | |
| In-house | 17930 (90.1) | 3929 (91.5) | 13998 (89.7) | |
| Outdoor | 496 (2.5) | 106 (2.5) | 390 (2.5) | |
a Chi-squared measure for the group difference of categorical variables, while continuous variables used generalized linear regression.
Table 2 shows that the mean birth weight was significantly associated with SHS exposure inside the house. After adjusting for the covariates, mothers exposed to SHS had children with a mean birth weight of 71.6 g (p<0.01) lower than that of mothers who were not exposed to SHS. Compared to non-SHS exposure, mothers who were exposed to SHS showed a 1.16-fold increase in the odds of having LBW children (AOR=1.16; 95% CI: 1.02–1.33, p<0.05). For SHS exposure frequency, mothers exposed to SHS daily had children with a mean birth weight of 63.4 g (p<0.01) lower than that of mothers who were not exposed to SHS. Compared to non-SHS exposure, mothers who were exposed to SHS weekly and daily showed an increase in the odds of having LBW children (AOR=1.33; 95% CI: 1.03–1.71, p<0.05 and AOR=1.18; 95% CI: 1.01–1.38, p<0.05, respectively).
Table 2
Association between SHS exposure and frequency inside the house, birth weight, and low birth weight, based on secondary data analysis from the 2017 Indonesia Demographic Health Survey (N=17400)
| SHS exposure | n | Birth weight (g) | Low birth weight | |
|---|---|---|---|---|
| Adjusted mean ± SEa | OR (95% CI) | AOR (95% CI)b | ||
| Inside the house | ||||
| Not exposed | 3724 | 3176.6 ± 11.11 | 1 | 1 |
| Exposed | 13676 | 3105.0 ± 5.76** | 1.21 (1.04–1.40)* | 1.16 (1.02–1.33)* |
| Frequency | ||||
| Not exposed | 3724 | 3140.8 ± 9.06 | 1 | 1 |
| Less than once a month | 2119 | 3074.6 ± 11.70 | 1.11 (0.90–1.38) | 1.10 (0.88–1.36) |
| Monthly | 281 | 3054.3 ± 32.03 | 1.19 (0.74–1.90) | 1.16 (0.72–1.86) |
| Weekly | 1124 | 3061.2 ± 16.02 | 1.37 (1.07–1.76)* | 1.33 (1.03–1.71)* |
| Daily | 10152 | 3077.4 ± 5.41** | 1.21 (1.04–1.41)* | 1.18 (1.01–1.38)* |
DISCUSSION
Our findings show that 78.4% of the study sample were exposed to SHS inside the house, which is remarkably higher than the prevalence of SHS exposure at home in other countries such as China (48.3%), Bangladesh (46.7%), and Thailand (46.8%)13-15. SHS is a significant source of indoor particulate matter pollution, which is further enhanced inside the houses by cigarette smoking. Cigarette smoke contains toxic substances and has detrimental effects on almost every organ in the body. Besides, tobacco smoke contains a large variety of poisonous gases and particles that are hazardous to smokers and those around them16.
SHS exposure in the house during pregnancy raised the risk of LBW, which was 7.2% more likely in households for babies whose mothers were exposed to SHS compared to babies whose mothers were not. As the prevalence of smoking is rising in some Asian countries due to a change in tobacco markets from high- to low-income countries17, LBW may be unavoidable. Regarding SHS exposure frequency, mothers who were exposed to SHS at a daily rate were at an increased risk of having LBW newborns and smaller infants. The results of other studies in different populations, such as the Netherlands, USA, and Greece, have also shown that exposure to SHS positively correlates with LBW risk levels4,18,19.
Previous retrospective and prospective cohort studies confirmed that the exposure to domestic cigarette smoke throughout pregnancy was significantly related to the lower adjusted mean birth weight and doubled the risk of having a smaller baby20. Voigt et al.21 also reported that the mean birth weight of the exposed group was significantly lower irrespective of the body mass index (BMI) of the mother. It was suggested that there was a 12.9 g reduction in birth weight related to exposure to each additional cigarette smoked, and the prevalence of LBW was twice higher in exposed women compared to non-exposed women20. Two pathways can explain the plausible mechanism for the increased risk of LBW, namely: 1) the elevation of Tumor Necrosis Factor-alpha triggered by SHS exposure, which is transmitted across the placenta; and 2) the increased secretion of TNF- and Vascular Cell Adhesion Molecule-1 as inflammatory markers. The abundant level of inflammatory markers in pregnant women leads to the damage of the placenta, and thus nutrients and oxygen cannot be optimally delivered to the fetus, resulting in suboptimal growth and LBW22.
There were 58% of women with daily exposure; these women were younger in maternal age (the proportion of women maternal age 15–24 year was 12.7% versus 19.9% with non-daily versus daily, or 1.6 times, which may drive the higher birth rates among daily). One study in China showed that birth weight increased by 16.204 g per year when maternal age was <24 years, increased by 12.051 g per year when maternal age ranged 24–34 years, and decreased by 0.824 g per year for those aged ≥34 years23. Smoking prevalence in men will raise the risk of SHS exposure among young women who have never smoked24. Since many women spend the majority of their time at home, the home is the most common source of SHS exposure for unemployed women. Well-educated and higher income women may have a greater understanding of the hazards of SHS exposure and may have more positive attitudes toward SHS exposure in the home, resulting in them becoming more conscious of avoiding SHS25. Also, because younger maternal age may tend to have lower education level, employability, income, and parity,
In terms of birth size, the exposure to SHS attenuated the risk of delivering a macrosomia baby (weight >4000 g) by almost 2 times among obese women (BMI of 30 kg/m2) and, on the other hand, increased the risk of delivering LBW infants among underweight women (BMI of 18 kg/m2) by almost 3-fold26. The nicotine exposure during pregnancy impaired the differentiation and proliferation of placenta cells named cytotrophoblasts (CTB), which reduced blood flow and created a pathological hypoxic environment in the womb27. Voigt et al.28 suggested that the prevalence of small gestational age, defined by birth weight, length, and head circumference, rises in accordance with the increasing exposure to cigarette smoke, up to 8% for each 7 additional cigarettes consumed daily.
Limitations
A self-reported questionnaire of SHS without measurement of the duration of maternal or pregnancy exposure and cotinine levels in the household may have underestimated the true impact of SHS in Indonesia. Also, we are unable to match the period of smoking experience (by anyone) and the period of pregnancy. Secondly, since we used a crosssectional dataset and worked under the presumption that SHS was even over time, the results should be interpreted with caution, and thus causality could not be determined. With reliable measurement of exposure to SHS toxins, a prospective cohort study would be suitable. Finally, we were unable to account for a variety of unmeasured confounders, such as biological influences, including food intake and nutritional status of pregnant women. Future studies may assess the associations between SHS smoke inside the house, the number of cigarettes exposed to per day, birth length, and head circumference, in addition to birth weight. Nonetheless, our research contributes to the body of knowledge on the effects of SHS with regard to birth weight. With a wide range of representative populations in Indonesia, this is the first study of its kind, thereby raising its generalizability to the Indonesian population, the largest cigarette consumer in the Asia Pacific region.
CONCLUSIONS
The homes of non-smokers living with smokers must be taken into account through the smoke-free homes policy. Therefore, it is not only important to enact regulations, but also to consider more public health strategies to raise awareness of the adverse health effects caused by SHS exposure. It is important to do further research to investigate the duration of SHS exposure and other biological parameters. Public health promotions that disseminate general recommendations for action are likely to have a positive impact on the health of mothers, newborns, and children.
