Detecting geographical clusters of low birth weight and/or preterm birth in Japan

Md Obaidur Rahman1, Daisuke Yoneoka2,3, Yayoi Murano4

  • 1Center for Surveillance, Immunization, and Epidemiologic Research, National Institute of Infectious Diseases, Tokyo, Japan.

Scientific Reports
|January 31, 2023
PubMed

In Japan, mean birth weight has significantly decreased from 3152 g in 1979 to 3018 g in 2010 and the prevalence of preterm birth (PTB) has risen to 5.7% in the last thirty years. However, the presence and magnitude of geographical differences in low birthweight (LBW) and/or PTB in Japan is not well understood. We implemented spatial analysis to identify localized clusters and hot spots of LBW and/or PTB during 2012-2016. The Japan national birth database was used in this study. A total of 5,041,685 (male: 2,587,415, female: 2,454,270) births were used for spatial analysis using empirical Bayes estimates of the incidence rate of LBW and/or PTB and spatial scan tests to detect hot-spot areas with p values calculated from Monte Carlo iterations. The most and second likely clusters were located in two areas: (1) the small islands in south-west Japan (Amami and Okinawa, Relative risk = 1.09-1.67 with p < 0.001) and (2) the cities on the base of Mt. Fuji, stretching over three neighboring prefectures of Yamanashi, Shizuoka and Kanagawa (Relative risk = 1.10-1.55 with p < 0.001), respectively. We need to optimize the medical resource allocations based on the evidence in geographical clustering of LBW and/or PTB at specific locations in Japan.

Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
461
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
481
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
11.1K
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.1K