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Infant Mortality Rates for Farming and Unemployed Households

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Infant Mortality Rates for Farming and Unemployed Households Journal of Epidemiology Original Article J Epidemiol 2021;31(1):43-51 Infant Mortality Rates for Farming and Unemployed Households in the Japanese Prefectures: An Ecological Time Trend Analysis, 1999–2017 Mariko Kanamori1, Naoki Kondo1, and Yasuhide Nakamura2 1Department of Health and Social Behavior and Department of Health Education and Health Sociology, The University of Tokyo, Tokyo, Japan 2School of Nursing and Rehabilitation, Konan Women’s University, Hyogo, Japan Received May 10, 2019; accepted December 9, 2019; released online February 1, 2020 ABSTRACT Background: Recent research suggests that Japanese inter-prefecture inequality in the risk of death before reaching 5 years old has increased since the 2000s. Despite this, there have been no studies examining recent trends in inequality in the infant mortality rate (IMR) with associated socioeconomic characteristics. This study specifically focused on household occupation, environment, and support systems for perinatal parents. Methods: Using national vital statistics by household occupation aggregated in 47 prefectures from 1999 through 2017, we conducted multilevel negative binomial regression analysis to evaluate occupation=IMR associations and joinpoint analysis to observe temporal trends. We also created thematic maps to depict the geographical distribution of the IMR. Results: Compared to the most privileged occupations (ie, type II regular workers; including employees in companies with over 100 employees), IMR ratios were 1.26 for type I regular workers (including employees in companies with less than 100 employees), 1.41 for the self-employed, 1.96 for those engaged in farming, and 6.48 for unemployed workers. The IMR ratio among farming households was 1.75 in the prefectures with the highest population density (vs the lowest) and 1.41 in prefectures with the highest number of farming households per 100 households (vs the lowest). Joinpoint regression showed a yearly monotonic increase in the differences and ratios of IMRs among farming households compared to type II regular worker households. For unemployed workers, differences in IMRs increased sharply from 2009 while ratios increased from 2012. Conclusions: Inter-occupational IMR inequality increased from 1999 through 2017 in Japan. Further studies using individual- level data are warranted to better understand the mechanisms that contributed to this increase. Key words: infant mortality; health inequality; occupation; farmer; unemployed worker Copyright © 2020 Mariko Kanamori et al. This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. and economic differences might also be affecting the IMR and INTRODUCTION increasing regional inequalities.6,9 The infant mortality rate (IMR), the number of deaths of children The health of pregnant women and infants is also greatly under 1 year of age per 1,000 live births in the same year, is an affected by their socioeconomic status, including household important measure of population health and serves as an indicator income and occupation. Sidebotham et al have suggested for of the effect of economic and social conditions on the health of instance, that factors affecting child and adolescent mortality mothers and newborns.1,2 Japan, like many other wealthy, in high-income countries “can be conceptualized within four developed countries, has a low IMR.3 Similarly, the mortality domains—intrinsic (biological and psychological) factors, the risk between 0 and 5 years old was the lowest level in the world at physical environment, the social environment, and service the time.4 However, these risks are not necessarily homogeneous delivery. The most prominent factors are socioeconomic across the regions of Japan. For example, Nagata et al (2017) gradients…”.7 If a pregnant woman is working, job stress and recently reported that inter-prefecture inequality in child mortality the number of hours worked, as well as the work environment, had increased since the 2000s.5 This increase in inequality in available medical services, and workplace support might all affect regional child mortality may be linked to changes in some of the her health and that of her unborn child. For example, in Japan social determinants of child mortality observed across high- paid maternity leave and childcare leave benefits depend on income countries that include relative poverty, income inequality employment conditions; moreover, there is no paid maternity and social policies, such as workplace maternal leave policies.6–8 leave for self-employed people and farmers.10 In relation to this, As the relative poverty rate for children in Japan increased by 1.5 in an earlier study Nishi and Miyake (2007) calculated IMR ratios times from 1985 to 2012, it is possible that an expansion in social across occupations at the national level and found that the IMR in Address for correspondence. Naoki Kondo, MD, PhD, Department of Health and Social Behavior and Department of Health Education and Health Sociology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan (e-mail: [email protected]). DOI https://doi.org/10.2188/jea.JE20190090 HOMEPAGE http://jeaweb.jp/english/journal/index.html 43 Occupation and Infant Mortality in Japan unemployed households was 4.2 times higher than the rate in all with more than 100 employees, executives, and government employed Japanese households from 1995 to 2004.11 However, officials. The “Self-employed” comprise those who are engaged since Nishi and Miyake’s study, to the best of our knowledge, in running their own companies=businesses (ie, working free- there has been no research on the occupation-IMR association lance). The “Farming” category refers to workers either engaged that has covered more recent years or focused on smaller solely in agriculture or both in agriculture and other professions. geographical units, such as prefectures. In addition, there have The “Other” category consists of workers employed for a con- been no studies that have examined how the regional character- tinuous period of less than one year. The “Unemployed” category istics of each prefecture and social factors of each household includes households in which nobody is employed. Missing data interact to affect the IMR. This regional influence is potentially were included in the “unknown” category. very important, as the environment of pregnant women and Other variables infants may differ depending on regional characteristics, even We used population density as a proxy measure of rurality in each when such women are working in the same occupation. prefecture. As the IMR in farming households was higher than for Therefore, in this ecological study, which uses time-trend data other occupations when we calculated the descriptive statistics, for Japanese prefectures from 1999 through 2017, we aimed to we also evaluated the relative predominance of farming in each clarify the trends in inter-occupational inequality in infant prefecture, measuring farm density (number of farming house- mortality and generate hypotheses about the relationship between holds per 100 households) and used it as a proxy measure of the macro-level social status and the IMR. To clarify the association industrial structure in each prefecture. In order to make the between regional characteristics and inequality in the IMR, we regression analysis estimates comparable, we standardized these also examined the interaction effects between rurality and industry two measures by their overall means and standard deviation. structure, such as the prevalence of farming and of different household occupations within each prefecture. We hypothesized Statistical analysis that the association between infant mortality and household Thematic maps occupation would vary in relation to a prefecture’s level of rurality We created a visual representation of the nationwide regional and regional industry structure, as this was likely to be reflected IMR distribution by household occupation using thematic maps in differing social circumstances, such as the availability of of prefectural data. We calculated the IMR by dividing total accessible medical resources that could potentially affect the link infant mortality for the years 1999 through 2017 by the total between infant mortality and household occupation. number of births for the same period. We used Arc GIS 10.5 (Esri, Redlands, CA, USA) to create the thematic maps.15 Calculation of IMR by household occupation METHODS We calculated IMR ratios by household occupation using Data multilevel negative binomial regression analysis that took into We obtained prefectural data on infant births and deaths account the hierarchical structure of the time series data. aggregated by household occupation.12 These vital statistics data Goodness-of-fit statistics and residual plots of the Poisson or are publicly available for the entire Japanese population between negative binomial regression models confirmed that the IMR 1999 and 2017. When an infant is born or dies, a parent or followed a negative binomial distribution. The level 1 variable household member must notify the infant’s municipality of was the yearly IMR by household occupation (N =7+ 19 + 47 = residence or the place where the infant was born or died. We also 6,251) between 1999 and 2017, while the level 2 variable was the used government prefectural summary statistics from 2000, 2005,
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