- Record: found
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Trends and seasonality in cause-specific mortality among children under 15 years in Guangzhou, China, 2008–2018
research-article
Author(s):
Xiao-Han Xu
1 ,
Hang Dong
2 ,
Li Li
1 ,
Wen-Hui Liu
2 ,
Guo-Zhen Lin
2
,
,
Chun-Quan Ou
1
,
Publication date (Electronic):
16 July 2020
Journal:
BMC Public Health
Publisher:
BioMed Central
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Abstract
Background
This study analyzed the trends and seasonality in mortality among children aged 0–14 years in Guangzhou, China during 2008–2018. Understanding the epidemiology of this public health problem can guide policy development for children mortality prevention.
Methods
A population-based epidemiological retrospective study was conducted. Seven thousand two hundred sixty-five individual data of children mortality were obtained from the Guangzhou Center for Disease Control and Prevention (CDC). The Poisson regression was used to quantify the annual average reduction rate and the difference in mortality rate between sex and age groups. Incidence ratio with 95% confidence interval (CI) was estimated to determine the temperaol variations in mortality by month, season, school term, day of the week and between holidays and other days.
Results
Between 2008 and 2018, the children mortality rate in Guangzhou decreased from 54.0 to 34.3 per 100,000 children, with an annual reduction rate of 4.6% (95% CI: 1.1%–8.1%), especially the under-5 mortality rate decreased by 8.3% (95% CI: 4.8%–11.6%) per year. Decline trends varied by causes of death, even with an upward trend for the mortality of asphyxia and neurological diseases. The risk of death among males children was 1.33 times (95% CI: 1.20–1.47) of that of females. The distribution of causes of death differed by age group. Maternal and perinatal, congenital and pneumonia were the top three causes of death in infants and cancer accounted for 17% of deaths in children aged 1–14 years. Moreover, the injury-related mortality showed significant temporal variations with higher risk during the weekend. And there was a summer peak for drowning and a winter peak for asphyxia.
Conclusions
Guangzhou has made considerable progress in reducing mortality over the last decade. The findings of characteristics of children mortality would provide important information for the development and implementation of integrated interventions targeted specific age groups and causes of death.
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Most cited references21
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Child Mortality Estimation: Estimating Sex Differences in Childhood Mortality since the 1970s
Cheryl Chriss Sawyer (2012)
Introduction Sex is a key variable for disaggregation of childhood mortality rate estimates, both for monitoring and analytical purposes and as an input to other demographic estimates such as sex-specific life tables or population size and structure. However, the task of estimating trends in sex-specific child mortality, particularly for countries without reliable death registration statistics, is far from straightforward, and sex-specific data are frequently more limited or noisier than those available for both sexes combined. In this paper I outline the challenges in the estimation and interpretation of sex-specific childhood mortality rates, and develop simple methods to take advantage of available data on under-five or infant mortality by sex. The analysis updates previous United Nations work on sex differences in childhood mortality [1],[2]. Challenges in the Estimation of Mortality Trends Disaggregated by Sex Reliably estimating even overall trends in childhood mortality—that is, without taking into account differences by sex—is a difficult task in many developing countries. In the absence of complete and reliable vital registration systems in much of the developing world, estimation of mortality rates for children primarily relies upon data from certain questions in household sample surveys and population censuses [3]. These questions elicit information from female respondents about their childbearing history, either in detail or in summary, and the survival status of their children. Estimates based on these questions are subject to sampling errors (for surveys) and non-sampling errors (for both surveys and censuses), with the outcome that multiple inquiries may produce quite different estimates for the same time period. Since 2004, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) has reconciled inconsistent data on overall (both-sexes) under-five mortality for each country using regression models to produce a best estimate of trends from the 1960s to the present [3]. The UN IGME also produces time series of infant mortality estimates. One reason that the UN IGME has not taken sex-specific data into account to date is that some of the censuses and surveys on which United Nations estimates of both-sexes mortality are based did not collect the relevant data by sex. If estimates by sex were to be produced by fitting trend lines to sex-specific data, the estimates might not be consistent with estimates for both sexes combined that incorporate more data sources. A further complicating factor is that sampling error for mortality estimates from surveys—which is often large already for both sexes combined due to the relatively small number of child deaths even in a large sample—is increased when mortality estimates are disaggregated by sex or any other variable. For countries with high-quality data from vital registration, computing mortality rates by sex from annual data is uncomplicated, but there is a further consideration pertaining to the analysis of sex differentials in mortality. In countries with low levels of mortality, ratios of male to female under-five and infant mortality can fluctuate substantially from year to year because of small numbers of deaths. For purposes of analysis and cross-national comparisons, some form of smoothing is desirable. Interpreting Sex Differentials in Childhood Mortality Boys and girls have different probabilities of death due to biological factors, and these differences vary between infancy and early childhood. If sex-disaggregated estimates are to be used for monitoring or advocacy purposes, it must be clearly explained to users (1) what the expected differences are, (2) when a given difference might indicate excessive disadvantage for one sex or the other, and (3) how to understand changes. The under-five mortality rate, also denoted in the literature as U5MR or 5 q 0, is the probability of dying between birth and exact age 5 y. The components of the under-five mortality rate examined here are the infant mortality rate (the probability of dying between birth and exact age 1 y, denoted 1 q 0) and the child mortality rate (the probability of dying between exact ages 1 and 5 y, denoted as 4 q 1). The under-five mortality rate and its components are related as follows: (1) The measures of sex differences employed are the ratios of male to female rates of infant, child, and under-five mortality, multiplied by 100 for ease of presentation. Equity in survival between females and males does not imply equal mortality rates (that is, male-to-female ratios equal to 100). Under circumstances where boys and girls have the same access to resources such as food and medical care, boys have higher mortality rates than girls during childhood, and the examined ratios would overall be expected to be greater than 100. Newborn girls have a biological advantage in survival over newborn boys, with lesser vulnerability to perinatal conditions (including birth trauma, intrauterine hypoxia and birth asphyxia, prematurity, respiratory distress syndrome, and neonatal tetanus), congenital anomalies, and such infectious diseases as intestinal infections and lower respiratory infections [4]. However, beyond early infancy, girls do not enjoy the same advantage in relation to certain infectious diseases, which are the primary causes of death in later infancy and early childhood in settings where overall mortality is high [5],[6]. Thus, the sex ratio of child mortality (that is, mortality at ages 1–4 y) is generally lower than the sex ratio of infant mortality (Figure 1). The sex ratio of under-five mortality is intermediate between the two, and will depend on the relative mortality levels of the infant and child age groups. 10.1371/journal.pmed.1001287.g001 Figure 1 Historical change in the male-to-female ratio of mortality as under-five mortality declined in selected developed countries. As living conditions improve, an “epidemiological transition” occurs during which infectious diseases recede as a cause of death [7]. As the decline in infectious disease proceeds, perinatal and congenital causes form an increasing share of total mortality among infants, while external causes, more typically affecting boys, form an increasing share of mortality for children between ages 1 and 5 y [8],[9]. Hence, as overall levels of mortality fall, female advantage in infant and child mortality would normally increase, assuming no sex-specific changes in the treatment of children. Figure 1 shows the historical change in sex ratios of infant, child, and under-five mortality for several developed countries where access of children to resources was not believed to differ greatly by sex [9]. The female advantage in survival, however, can be eroded if girls are deprived relative to boys in access to health care or to proper nutrition. If such deprivation occurs, the sex ratio of mortality might be substantially below the values shown in Figure 1 for a given level of mortality. Because of the biologically based characteristics of differential survival by sex, it is difficult to construct a precise target of gender equity in survival in childhood. However, situations in which the survival of girls is lower than that of boys—that is, the sex ratio of mortality is less than 100—suggest that differential treatment or access to resources may be putting girls at a disadvantage. Earlier studies have found numerous countries in many regions of the world, particularly southern Asia, eastern Asia, and northern Africa/western Asia, where mortality at ages 1–4 y was higher for girls than for boys [1],[10],[11]. India and China in particular have a long-documented history of female disadvantage in mortality that is covered in an extensive literature [12]–[25]. The present study addresses the challenges outlined above in the estimation and interpretation of sex differentials. I estimate smoothed trends in the sex ratio of mortality for infants and young children to evaluate changes over time in the relative mortality of males and females for individual countries and world regions. I also examine regional differences in the relationship between the overall level of under-five mortality and sex ratios of infant and child mortality. Methods Data Sources The three indicators of childhood mortality can be estimated directly or indirectly from demographic data sources. Direct estimates of 1 q 0, 4 q 1, and 5 q 0 are calculated from reported deaths and information on the population exposed to the risk of death, and do not require the use of models for their derivation. Direct estimates may be based either on vital events data—normally from a vital registration system or in some cases from information about household deaths collected in a census or large survey—or on survey questions posed to adult women about their childbearing histories. The childbearing histories collected in surveys comprise the date of each live birth and the age at death of children who have died before the survey date. Period-specific probabilities of dying calculated from birth histories are based on reported deaths and the numbers of children at risk of dying during a specific period, such as the 5 y preceding the survey [26]. Indirect estimates of mortality in childhood are derived from summary data on the total number of children ever born and number surviving to women of reproductive age. The method used to derive indirect estimates (also known as the Brass method) is described in detail in a United Nations manual [27]. A large number of censuses and surveys have collected the required data, although the data are not always collected or published by sex (that is, the questionnaires do not always ask about sons and daughters separately, or, if they do, the separate tabulations may not be published). The Brass method translates proportions of children surviving classified by age of mother at the time of data collection into measures of survival to various childhood ages, which in turn can be transformed into standard indicators of childhood mortality using model life tables. Under-five mortality is the preferred indicator derived via the Brass method, because 5 q 0 is more robust to the choice of model life table than 1 q 0, which can vary considerably according to the model selected. For this reason, indirect methods do not provide a satisfactory basis for estimating sex ratios of 1 q 0, since the sex ratios obtained for this indicator through indirect methods are more affected by the choice of mortality model used than are differentials in 5 q 0. The dataset used for this study builds upon datasets [28]–[30] that were developed for a 1998 United Nations publication on sex differentials in childhood mortality [1] and expanded for a 2011 report [2] (data collection for [2] was completed in 2010; the present study incorporates additional or revised data obtained through November 2011). Microdatasets from Demographic and Health Surveys (DHS) were processed to produce a time series from each survey of direct estimates of 5-y mortality rates by sex, extending back to a period 20–24 y before each survey. In addition, tabulations of children ever born and children surviving by age of the mother were calculated by sex of the child for each DHS survey to produce indirect estimates of 5 q 0. An important new source of data since the mid-1990s is the Multiple Indicator Cluster Survey program, conducted by the United Nations Children's Fund, which has yielded additional sets of indirect estimates by sex, many for countries that had very limited data by sex from other sources. For other survey programs (including the World Fertility Survey, the Reproductive Health Survey, and the Pan Arab Project for Family Health), surveys not affiliated with the major survey programs, and censuses, the data used here are any direct or indirect estimates by sex available in published sources, or calculated from tabulations available therein. In addition, the number of data points from vital registration was greatly expanded. A large dataset of infant and under-five mortality by sex calculated from vital registration data was provided by the World Health Organization. These data were supplemented with registration data from the Human Mortality Database, the United Nations Demographic Yearbook, and other sources of life tables. Table S1 lists the data sources considered for each country. Data Issues Vital registration data Data derived from the complete registration of births and deaths are the ideal basis for the estimation of mortality, since they cover the full set of events of interest and permit the estimation of trends. Unfortunately, in most developing countries the coverage and completeness of registration by vital registration systems is insufficient to produce accurate estimates of the level of childhood mortality. However, in the absence of evidence that reporting of births and deaths differs by sex of the child in a way that would affect the ratio of male to female mortality, such ratios derived from vital registration may usefully inform trends of sex differentials. The sex differentials in 1 q 0 and 5 q 0 calculated from vital registration data were used without adjustment, even when overall births and child deaths were known to be under-registered, on the assumption that under-registration in vital registration systems did not differ by sex of the child. More study is required to assess whether this assumption is valid. For most countries, however, sex differentials estimated from vital registration are consistent with those calculated from survey birth history data and often have considerably less variability. The same assumption of sex-neutral underreporting was made for data from census or survey questions on household deaths. Survey data Compared to most measures estimated by sample surveys, deaths of children are relatively rare events. The sample sizes of typical household surveys are not large enough to produce very precise estimates of childhood mortality, even for both sexes combined at the national level. In a study of 50 DHS surveys, Curtis [31] showed that relative standard error for estimated infant and under-five mortality over a 5-y period for both sexes at the national level ranged from 0.04 to 0.08, implying that the 95% confidence interval ranged from 8% to 16% on each side of the point estimate. For child mortality, relative standard errors were higher, in the range of 0.06 to 0.15, because fewer deaths occur at ages 1 to 4 y. Such large sampling errors, which are even larger when estimates are disaggregated for a subset of the sample, complicate the assessment of trends in differential mortality by sex. For example, the male-to-female ratio of infant mortality calculated from birth histories for the Haiti 2000 DHS survey was 141 for 1991–1995 and 93 for 1996–2000, while the corresponding ratios for child mortality were 88 and 105. If taken at face value, the reported ratios would imply that the situation in Haiti changed from one in which there was excess male mortality under age 1 y and excess female mortality between ages 1 and 4 y to a reverse situation in only 5 y. The trend estimates derived in this study smooth out such fluctuations through the application of regression techniques described below. Some important potential non-sampling biases in survey reports of childbearing histories include errors in the dating of births and deaths or omission of events from the birth history. Incorrect assignment of dates to events—for example, the heaping of date of death on 12 mo of age—can have an effect particularly on the relative levels of 1 q 0 and 4 q 1. Fortunately, for the purposes of the present study, such misdating is unlikely to occur differentially for the deaths of boys and girls, so it is unlikely to have a major impact on the sex differentials in either of these indicators. Omission of children from the birth history, on the other hand, might be more likely to differ by sex of the child. In most cases, however, there was insufficient data from alternative sources to assess whether sex-differential omission from survey birth histories was occurring. The exception was in India, where examination of sex ratios of infant mortality (SR1) from the Sample Registration System and from the National Family Health Surveys revealed systematic differences in the sex ratio of infant mortality between the two sources, with SR1 estimates from the National Family Health Surveys being consistently higher than those from the Sample Registration System. For the sex ratio of child mortality, in contrast, the two sources produced consistent estimates. The discrepancy in SR1 could be due either to defects in the sample registration system that understate male mortality, or to omission from the survey birth histories of girls who died, thus inflating survey estimates of excess male mortality. The assessment was made that the difference in SR1 was most likely due to underreporting in the National Family Health Surveys birth histories of babies who died shortly after birth, with daughters who died more likely to be omitted than sons who died [2],[32]. Availability of recent data A final caveat refers to the availability of data for the 2000s. In many cases, the last available data point refers to 2005 or earlier (Table S1), and the estimates for the latter part of the decade are a projection of the earlier trend. Estimation Methods The estimation of sex differentials in under-five, infant, and child mortality proceeded in three basic steps: (1) estimate trend in the sex ratio of 5 q 0 (SR5); (2) estimate, and adjust if appropriate, trend in the sex ratio of 1 q 0 (SR1); and (3) apply those trends to both-sexes estimates of 5 q 0 and 1 q 0 to derive estimate and sex ratio of 4 q 1 (SR4). In the first step, a weighted trend line SR5 t was fitted to all available SR5 estimates. The weights for data points from surveys, censuses, and vital registration were determined using a weighting scheme used in previous work by the UN IGME [33],[34]. This weighting scheme assigns progressively lower weights to direct estimates from birth histories that refer to 5-y time periods more distant from the survey date, on the assumption that recall errors may affect distant periods more strongly. For indirect data, low or zero weights are assigned to indirect estimates that are based on reports of women in the early and late childbearing years, on the assumption that these estimates may be of lower quality or subject to systematic biases. Because of variations between countries in the amount and consistency of data available, three different methods were ultimately employed to estimate sex ratios of mortality. Initially, loess regression was tested for all countries. The loess method fits a series of polynomials to localized subsets of the data centered on each point of the dataset. The weight of each data point in the localized regression is determined by its distance from the center. A bandwidth, denoted alpha, selected by the user, determines the proportion of the dataset used to fit each local regression. A number of different alpha values were tested, to impose varying degrees of smoothing. For countries where estimates were based on a time series of vital registration data, it was found that the loess with an alpha of 0.75 captured changes in trend without being overly sensitive to short-term variation. In addition, a re-descending M estimator with Tukey's biweight function was applied in the loess procedure in R (family = “symmetric”) to reduce the influence of more extreme data points. The case of Bulgaria is shown in Figure 2A to illustrate the loess fitting method. 10.1371/journal.pmed.1001287.g002 Figure 2 Examples of data and fits for sex ratios of under-five and infant mortality using different methods. (A) Loess method, applied to Bulgaria. (B) Linear method, applied to the Dominican Republic. (C) Average method, applied to Lao People's Democratic Republic. Points shown in gray were assigned zero weight in the weighting scheme. The fitted SR1 was adjusted in the linear and average methods to account for the exclusion of indirect data. VR, vital registration. For countries where the primary sources of data were sample surveys, however, the degree of smoothing required to achieve plausible results with the loess often was so strong that the results differed little from a simpler linear regression. The linear regression line had the additional benefit of being more straightforward to adjust for SR1, as described below. Therefore, for many developing countries the results are based on robust linear regression (implemented with the rlm function in the R MASS package and hereafter referred to as the “linear method”), shown for the Dominican Republic in Figure 2B. There were a number of countries where neither loess nor robust linear regression fitted to all data points was able to produce a result that was satisfactory for purposes of analysis or disaggregation. In a few of these countries, where time series of vital registration data were available to fit a stable trend and survey data had high sampling error, the decision was made to exclude the survey data and fit the loess or robust linear regression to the vital registration data only. In countries where such a stable time series was not available, a simple weighted average of all available SR5 data was computed (as in Figure 2C for Lao People's Democratic Republic). Such an average may be useful for disaggregating both-sexes estimates if no other method is available, but it does not give any information on time trends in SR5. For this reason, results from the average method are not analyzed at length, and countries where it was used are excluded from the time trend analysis for regional aggregations presented below. Table S2 indicates which of the methods—loess, linear, or average—was used to produce estimates of sex differentials for each country. The second step of the estimation process was to fit a trend line SR1 t to available data on SR1. As was noted in the previous section on data sources, indirect data on SR1 were not included in the analysis because SR1 is less robust than SR5 to the choice of model life table. Yet, using only direct data for SR1 while using both direct and indirect data for SR5 could cause inconsistency between time series fitted for SR1 and SR5. This was particularly the case in countries where a number of surveys had collected indirect data only. Therefore, in order to best exploit the available information, direct and indirect estimates of SR5 were used to adjust direct estimates of SR1 in the linear and average methods. A preliminary trend line, SR1 t *, was fitted to SR1 from direct data using the same fitting method that had been selected for SR5 t . If no indirect data had been used to fit SR5 t , then SR1 t * was adopted as the final estimate SR1 t . If both direct and indirect data had been used to fit SR5 t , an additional trend line, SR5 t *, was fitted to SR5 points coming from direct data only. The ratio of SR5 t /SR5 t * was used to adjust SR1 t *, producing the final estimate SR1 t . Figure 2B and 2C show the results of this adjustment for the Dominican Republic and Lao People's Democratic Republic, respectively. Predicted SR5 t and SR1 t were applied to estimates of 5 q 0 and 1 q 0 for both sexes to produce time series of infant and under-five mortality levels by sex. Levels of 5 q 0 by sex for time period t were derived from the both-sexes estimates using the formulas (2) and (3) where SRB is the sex ratio at birth as estimated for each country for the period 2000–2005 in World Population Prospects: The 2010 Revision [35]. Corresponding formulas were applied for infant mortality. Then, 4 q 1male and 4 q 1female were derived via the relationship in Equation 1. The resulting 4 q 1male and 4 q 1female were used to compute estimates of SR4 t . These derived estimates of SR4 t were compared to direct data on SR4 from surveys or vital registration and generally found to be consistent. Estimates of under-five and infant mortality rates for both sexes combined were taken from two United Nations sources, World Population Prospects: The 2010 Revision, produced by the Population Division [35], and Levels & Trends in Child Mortality: Report 2011, produced by the UN IGME [36],[37]. The estimates from these two sources are generally quite similar for 5 q 0—the indicator coordinated by the UN IGME—but can differ somewhat more for 1 q 0, usually because of the use of different model life tables. The both-sexes estimates for 5 q 0 and 1 q 0 from World Population Prospects [35] were used in this report for most developing countries (noting that for 5 q 0 the estimates referring to periods prior to 1980 are unpublished). The choice of which series of both-sexes estimates to use does not affect the estimated trends SR5 t or SR1 t , as those come from the data, but it does affect estimated trends in SR4 t because the trend in the sex ratio of 4 q 1 derived from estimated 5 q 0 and 1 q 0 is dependent on the relative levels of 5 q 0 and 1 q 0 as well as the sex differentials in each. There were only a few cases where the choice of both-sexes estimate made an appreciable difference in SR4 t . For countries of the more developed regions, estimates from Levels & Trends in Child Mortality [36] were used because levels of 5 q 0 and 1 q 0 from this set of estimates are taken directly from annual vital registration. For countries where averages of SR5 and SR1 were employed, these average ratios were applied to the whole series of both-sexes estimates. It should be noted that applying constant SR5 t and SR1 t to changing both-sexes estimates results in SR4 t values that change over time. However, these changes in SR4 t should not be interpreted as trends and will not be presented as such. As noted above, estimates for countries where the average method was used are not included in the aggregated trends for regions and development groups presented below. Estimates were attempted for all countries or areas (hereafter referred to as countries) that had a population of 1 million or more in 2010. Out of the 156 countries with such a population, estimates were generated for 153 countries (Table 1). Of these, 113 were in less developed regions, comprising Africa, Asia excluding Japan, Latin America/Caribbean, and Oceania excluding Australia and New Zealand (the lists of countries located in the less developed regions and more developed regions as well as the other geographical groupings used for this study—sub-Saharan Africa, northern Africa/western Asia, eastern/southeastern Asia, Commonwealth of Independent States [CIS] Asia, Latin America/Caribbean, and developing Oceania—are shown in Table S3). Ninety-two countries in the less developed regions, containing 92% of the population of those regions, had sufficient data to apply the methods developed for trend analysis. For an additional 21 countries, holding 6% of the population of the less developed regions, enough data were available to estimate average sex differentials in under-five or infant mortality, which were assumed to apply to the entire time span under consideration. 10.1371/journal.pmed.1001287.t001 Table 1 Number of countries or areas and percentage of population covered in the study. Measure Region or Development Group Grand Total Included Not Included Using Trend Estimate Method Using Average Method Total Insufficient Data Less than 1 Million Population in 2010 Number of countries or areas World 229 131 22 153 3 73 Less developed regions 173 92 21 113 3 57 Sub-Saharan Africa 49 32 9 41 0 8 Northern Africa/western Asia 23 13 5 18 3 2 Eastern/southeastern Asia (excluding Japan) 17 11 4 15 0 2 Southern Asia 9 5 2 7 0 2 CIS Asia 8 8 0 8 0 0 Latin America/Caribbean 46 23 0 23 0 23 Developing Oceania (excluding Australia and New Zealand) 21 0 1 1 0 20 More developed regions 56 39 1 40 0 16 Population in 2010 (thousands) World 6,895,889 6,452,879 368,629 6,821,508 57,674 16,706 Less developed regions 5,659,989 5,223,246 364,869 5,588,115 57,674 14,200 Sub-Saharan Africa 811,887 761,961 46,688 808,649 0 3,237 Northern Africa/western Asia 425,248 350,255 39,578 389,833 34,458 956 Eastern/southeastern Asia (excluding Japan) 2,040,849 1,850,332 166,359 2,016,691 23,216 943 Southern Asia 1,704,146 1,597,719 105,385 1,703,105 0 1,042 CIS Asia 77,358 77,358 0 77,358 0 0 Latin America/Caribbean 590,024 585,621 0 585,621 0 4,403 Developing Oceania (excluding Australia and New Zealand) 10,477 0 6,858 6,858 0 3,619 More developed regions 1,235,900 1,229,633 3,760 1,233,394 0 2,506 Percentage of population World 93.6 5.3 98.9 0.8 0.2 Less developed regions 92.3 6.4 98.7 1.0 0.3 Sub-Saharan Africa 93.9 5.8 99.6 0.0 0.4 Northern Africa/western Asia 82.4 9.3 91.7 8.1 0.2 Eastern/southeastern Asia (excluding Japan) 90.7 8.2 98.8 1.1 0.0 Southern Asia 93.8 6.2 99.9 0.0 0.1 CIS Asia 100.0 0.0 100.0 0.0 0.0 Latin America/Caribbean 99.3 0.0 99.3 0.0 0.7 Developing Oceania (excluding Australia and New Zealand) 0.0 65.5 65.5 0.0 34.5 More developed regions 99.5 0.3 99.8 0.0 0.2 Trends were estimated for 39 countries in the more developed regions (comprising Europe, northern America, Japan, Australia, and New Zealand), while for one developed country (Bosnia and Herzegovina), only average sex differentials could be estimated. The methods and results presented in this article were developed as an analytical study separate from the production of United Nations mortality estimates published in Levels & Trends in Child Mortality [36] or World Population Prospects [35]. The mortality estimates by sex presented here may differ from estimates in forthcoming editions of those publications due to differences in data availability or other methodological considerations. Results Trends in the sex ratios of under-five, infant, and child mortality are summarized as decade averages for the 1970s, 1980s, 1990s, and 2000s. Two different approaches to examining global and regional trends are examined: first, considering each country as a unit of analysis for computing median country-specific ratios (Table 2) and, second, weighting country mortality rates by number of births to produce weighted regional averages (Table 3). The country results on which the regional analyses are based may be found in Table S2. Only the 131 countries for which trend estimates were produced were included in the computation of regional medians and averages. 10.1371/journal.pmed.1001287.t002 Table 2 Median sex ratios of infant, child, and under-five mortality by region, 1970s–2000s. Region or Development Group Number of Countries with Trend Estimates Median Ratio of Male to Female Mortality (per 100) Infant Mortality Child Mortality Under-Five Mortality 1970sa 1980s 1990s 2000s Change from 1970s to 2000s 1970sa 1980s 1990s 2000s Change from 1970s to 2000s 1970sa 1980s 1990s 2000s Change from 1970s to 2000s World 131 121 122 122 121 0 106 109 111 116 9 115 117 119 120 5 Less developed regions 92 118 118 119 120 2 100 103 107 112 12 111 113 114 117 6 Sub-Saharan Africa 32 115 116 117 117 2 102 103 105 107 5 109 111 111 113 4 Northern Africa/western Asia 13 112 115 117 116 5 94 99 105 122 28 107 109 113 116 10 Eastern/southeastern Asia 11 124 124 123 121 −3 100 106 110 118 18 116 117 117 117 1 Southern Asia 5 111 112 113 114 3 84 83 82 93 9 101 101 102 106 4 CIS Asia 8 — 124 127 131 3 — 103 107 109 2 — 118 123 126 3 Latin America/Caribbean 23 122 122 123 122 1 106 111 114 117 11 117 120 121 122 5 More developed regions 39 129 129 126 123 −6 125 126 124 124 −1 128 128 125 123 −6 a Estimates for the 1970s exclude the following countries that are included for subsequent decades: Albania, Armenia, Azerbaijan, Belarus, Croatia, Czech Republic, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Mongolia, Qatar, Republic of Moldova, Serbia, Sierra Leone, Slovakia, Slovenia, Somalia, Tajikistan, the former Yugoslav Republic of Macedonia, Timor-Leste, and Turkmenistan. 10.1371/journal.pmed.1001287.t003 Table 3 Regional average estimates of male, female, and both-sexes infant, child, and under-five mortality, and sex ratios of infant, child, and under-five mortality, 1970s–2000s. Region or Development Group Decadea Infant Mortality Rate (Deaths under Age 1 y per 1,000 Live Births) Child Mortality Rate (Deaths at Ages 1–4 y, per 1,000) Under-Five Mortality Rate (Deaths under Age 5 y, per 1,000 Live Births) Ratio of Male to Female Mortality (per 100) Male Female Both Sexes Male Female Both Sexes Male Female Both Sexes Infant Mortality Child Mortality Under-Five Mortality World 1970s 85 77 81 45 50 47 125 122 123 111 90 103 1980s 70 64 67 32 37 34 99 97 98 110 89 102 1990s 60 56 58 28 30 29 85 84 84 107 91 101 2000s 49 46 47 22 24 23 69 68 69 107 94 103 World excluding China and India 1970s 91 77 84 47 49 48 132 120 126 118 98 110 1980s 74 63 69 37 38 38 107 98 103 117 98 110 1990s 65 55 60 34 34 34 95 86 91 117 101 111 2000s 53 46 49 27 26 27 78 70 74 117 103 111 Development group Less developed regions 1970s 95 86 91 51 57 54 141 138 139 110 90 102 1980s 78 71 75 37 42 39 111 109 110 109 88 102 1990s 66 62 64 31 34 32 94 93 93 107 90 101 2000s 54 51 53 25 26 26 77 75 76 107 94 102 Less developed regions excluding China and India 1970s 112 95 104 61 63 62 165 151 158 118 97 109 1980s 91 77 84 47 48 47 132 120 126 117 98 110 1990s 77 66 72 41 41 41 114 103 108 117 101 110 2000s 63 54 58 33 32 32 92 83 88 117 103 111 More developed regions 1970s 22 17 19 4.2 3.5 3.8 26 20 23 129 122 127 1980s 16 12 14 3.4 2.7 3.1 19 15 17 129 124 128 1990s 11 8.4 9.6 2.3 1.8 2.1 13 10 12 127 125 127 2000s 7.5 6.0 6.7 1.6 1.3 1.4 9.0 7.2 8.1 125 124 124 Region Sub-Saharan Africa 1970s 134 115 125 100 98 99 219 201 210 116 102 109 1980s 122 105 114 87 85 86 198 180 189 116 103 110 1990s 115 98 107 81 78 79 185 169 177 116 103 110 2000s 97 83 90 63 61 62 153 139 146 116 103 110 Northern Africa/western Asia 1970s 125 113 119 51 56 54 169 162 166 111 91 105 1980s 87 78 82 33 35 34 117 110 114 112 94 107 1990s 62 54 58 22 22 22 82 74 78 114 99 110 2000s 42 36 39 13 13 13 54 48 51 117 104 113 Eastern/southeastern Asia 1970s 57 49 53 33 34 34 89 81 85 118 97 109 1980s 42 39 41 18 19 18 59 57 58 108 93 103 1990s 30 33 31 9.8 10 9.9 40 42 41 92 98 94 2000s 23 26 24 6.1 5.7 5.9 29 31 30 91 107 94 Eastern/southeastern Asia excluding China 1970s 84 66 75 36 36 36 116 99 108 126 99 117 1980s 59 47 53 22 22 22 79 68 74 124 102 117 1990s 41 33 37 13 12 13 53 45 49 123 107 119 2000s 29 24 27 8.5 7.4 8.0 37 31 34 122 115 120 Southern Asia 1970s 117 116 117 55 74 64 166 181 173 101 75 92 1980s 95 92 93 37 52 44 129 138 133 103 73 93 1990s 76 74 75 28 38 33 102 109 105 103 75 94 2000s 58 58 58 20 25 22 77 81 79 101 79 95 Southern Asia excluding India 1970s 140 122 131 70 85 78 200 196 198 115 82 102 1980s 110 96 103 48 58 53 153 148 151 114 83 103 1990s 87 77 82 33 37 35 117 111 114 113 89 105 2000s 65 58 62 21 21 21 85 78 81 113 100 109 CIS Asia 1970s — — — — — — — — — — — — 1980s 79 62 71 18 17 18 95 78 87 128 102 122 1990s 67 51 59 15 14 15 81 65 73 130 106 125 2000s 50 38 44 11 10 10 60 47 54 131 110 127 Latin America/Caribbean 1970s 83 68 76 34 33 33 114 98 106 122 105 116 1980s 58 47 53 17 16 17 74 63 68 123 105 118 1990s 39 32 35 11 10 11 50 42 46 123 109 120 2000s 26 21 24 6.9 6.1 6.5 33 27 30 123 113 121 Selected countries China 1970s 47 42 44 32 34 33 78 74 76 112 96 105 1980s 36 36 36 16 18 17 51 53 52 99 90 96 1990s 26 33 29 8.3 8.8 8.5 34 41 37 79 94 82 2000s 20 27 23 4.8 4.7 4.8 25 31 28 76 102 80 India 1970s 110 114 112 50 70 60 155 176 165 96 72 88 1980s 90 90 90 34 49 41 121 135 128 100 69 89 1990s 73 73 73 26 38 32 97 108 102 100 70 90 2000s 56 58 57 19 26 23 74 82 78 97 74 90 Countries weighted by number of births. a Estimates for the 1970s exclude the following countries that are included for subsequent decades: Albania, Armenia, Azerbaijan, Belarus, Croatia, Czech Republic, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Mongolia, Qatar, Republic of Moldova, Serbia, Sierra Leone, Slovakia, Slovenia, Somalia, Tajikistan, the former Yugoslav Republic of Macedonia, Timor-Leste, and Turkmenistan. Table 2 shows that in the less developed regions the median sex ratio of under-five mortality increased from the 1970s to the 2000s. For the 92 countries in the less developed regions for which trends were estimated in this study, the median sex ratio of under-five mortality increased from 111 in the 1970s to 117 in the first decade of the 2000s. Thus, in the majority of developing countries, females have an advantage in survival to age 5 y, and this advantage has increased, as expected from the historical experience of developed countries as described above, as mortality has declined. This increase is due primarily to increases in the sex ratio of mortality at ages 1–4 y in many countries, while increases in the sex ratio of the infant component of under-five mortality have been smaller. However, when countries are weighted according to the number of births, no such rise in the sex ratio of under-five mortality is seen. On average, the sex ratio of under-five mortality in the less developed regions remained nearly constant, around 101 to 102, from the 1970s to the 2000s (Table 3; Figure 3). This difference between the median trend and the birth-weighted trend occurs because the sex ratios of under-five mortality estimated for the two most populous countries, China and India, constitute important exceptions to the rising trend. Estimated sex ratios of under-five mortality for the first decade of the 2000s were below 100 in both countries (Table 3), indicating substantial excess female mortality. In China, the sex ratio of under-five mortality declined between the 1970s and the 2000s, while in India it remained roughly constant, suggesting that even though mortality rates were falling in both countries, girls did not share in survival improvements to the expected extent. 10.1371/journal.pmed.1001287.g003 Figure 3 Trends in the male-to-female ratio of under-five mortality by level of under-five mortality. Dashed line is the historical sex ratio of under-five mortality for selected developed countries from Hill and Upchurch [9]. Moreover, China and India were the only two countries in the world where female infant mortality was higher than male infant mortality in the 2000s. In China, the sex ratio of infant mortality fell from 112 in the 1970s to 76 in the 2000s (Table 3), that is, from a situation where infant mortality was 12% higher for boys than for girls to one where infant mortality was 24% lower for boys. In India, female infant mortality was roughly equal to or slightly higher than male infant mortality throughout the decades examined, but girls' survival disadvantage was particularly acute in the 1–4-y age group. In the 2000s, the ratio of male to female child mortality was estimated at 74 (Table 3), meaning that girls' mortality between ages 1 and 5 y was more than 30% higher than boys'. While the estimates suggest that the sex ratio of child mortality may have increased somewhat in India since the 1980s, girls remain disadvantaged in mortality, compared both to the sex differences found in other parts of the developing world and to the historical experience of developed countries at the same level of mortality. The lower relative survival of girls to age 5 y in China and India has a large impact on estimates of average sex differentials for their respective regions of Asia, as well as on the average for the less developed regions. The average sex ratio of under-five mortality for eastern and southeastern Asia declined from 109 in the 1970s to 94 in the 2000s (Table 3). However, the average for the countries of the region apart from China rose from 117 to 120. The sex ratio of under-five mortality in southern Asia rose slightly from 92 to 95, but increased more steeply, from 102 to 109, in the countries of the region other than India. The estimates in this study suggest that the survival disadvantage of girls has lessened more in other countries of southern Asia than in India, with the exception of Nepal. In many of the less developed regions, girls' past disadvantage in mortality at ages 1–4 y appears to be easing. The regions of northern Africa/western Asia, eastern/southeastern Asia, southern Asia, CIS Asia, and Latin America/Caribbean all experienced increases in the average sex ratio of child mortality of 6 or more percentage points (Table 3). In sub-Saharan Africa, however, there was essentially no change in the average sex differential of child mortality, with increasing ratios in many countries offset by decreasing ratios in others. For the less developed regions on average (excluding China and India), girls went from a situation of slight disadvantage in mortality at ages 1–4 y in the 1970s to a slight advantage in the 2000s. However, the average ratios of child mortality in all regions of the developing world remain below those expected based on the historical experience of some developed countries at similar levels of mortality (Figure 4). 10.1371/journal.pmed.1001287.g004 Figure 4 Trends in the male-to-female ratio of child mortality (ages 1–4 y) by level of under-five mortality. Dashed line is the historical sex ratio of child mortality for selected developed countries from Hill and Upchurch [9]. The rising regional average sex ratios of child mortality mask a number of cases where the estimates suggest continued or worsening female disadvantage in mortality at ages 1–4 y. While the case of India was highlighted above because of its weight in regional and world averages, there are many other countries where mortality in this age group was higher for girls than for boys in the 2000s. The countries where excess female child mortality was apparent in the 2000s are indicated in Figure 5. While countries with excess female mortality can be found in most regions of the developing world, there are notable concentrations in southern Asia and in the western and middle regions of sub-Saharan Africa, as well as several countries in northern Africa/western Asia. While data quality issues may affect the reliability of these estimates, countries with apparent female disadvantage merit further study to see if differential treatment is an issue. 10.1371/journal.pmed.1001287.g005 Figure 5 Countries where excess female child mortality (ages 1–4 y) was found in the 2000s. Among infants under age 1 y, girls continue to have the advantage in survival in all countries apart from China and India. However, the female survival advantage in infancy in most of the developing world is not as great as would be expected based on the historical experience of some developed countries at similar levels of mortality (Figure 6). It cannot be stated with certainty whether this finding is due to differences in the treatment of girls and boys, to factors such as differences in cause-of-death patterns or the rollout of medical interventions in different locations at a given level of mortality, or to issues with the quality of the data for some countries that affect the estimates in a systematic way. 10.1371/journal.pmed.1001287.g006 Figure 6 Trends in the male-to-female ratio of infant mortality by level of under-five mortality. Dashed line is the historical sex ratio of infant mortality for selected developed countries from Hill and Upchurch [9]. In the more developed regions, ratios of male to female infant mortality have been declining in recent decades (Tables 2 and 3; Figure 6), meaning that the male disadvantage in infant mortality is becoming smaller. This trend started in certain countries in the 1970s and has since spread to almost all of the developed countries and to a number of countries in the less developed regions that have relatively low levels of mortality. The change in trend may be attributable to improvements in neonatal care that have decreased deaths from prematurity and respiratory distress, causes that have a greater impact on male infants [38]. However, further study is required to confirm the causes of this trend. Several countries had findings of unusually high sex ratios of infant mortality (greater than 130), suggesting a greater than expected degree of male disadvantage in survival. These countries, found in both more developed and less developed regions, include many of the European and Asian countries of the former Union of Soviet Socialist Republics (Figure 7). The high ratios could be due to lack of access to the advances in medical care that have led to declining sex ratios of infant mortality in most of the more developed regions, but again, more detailed examination of causes of infant deaths by age and sex is required. 10.1371/journal.pmed.1001287.g007 Figure 7 Countries where excess male infant mortality was found in the 2000s. Discussion Our estimates of long-term trends in the sex ratios of infant, child, and under-five mortality show that in the majority of countries in the less developed regions, the ratio of male to female under-five mortality has increased since the 1970s. This is due primarily to increases in the sex ratio of mortality at ages 1–4 y, while changes in the sex ratio of infant mortality have been smaller. There remain, however, a number of developing countries where girls have higher mortality than boys at ages 1–4 y, with concentrations in middle and western sub-Saharan Africa, northern Africa/western Asia, and southern Asia. Estimated infant mortality was higher for girls than for boys in only two countries, India and China. Meanwhile, in the more developed regions, a reversal of the historically rising trend in sex ratios of infant mortality has been observed as countries approach very low levels of mortality. Estimates of under-five mortality levels are receiving intense focus as the world nears the 2015 target date for the Millennium Development Goals. The target for Millennium Development Goal 4 calls for reducing under-five mortality by two-thirds from its 1990 level, and efforts to strengthen child survival programs are intensifying. In many areas of the world, advances in survival appear to be accruing relatively equitably to girls and boys, in line with the changes in sex differentials expected given the changing cause-of-death patterns that accompany mortality decline. However, this is not universally the case. Findings of low or declining sex ratios of infant or child mortality in a number of countries that still have relatively high mortality may merit concern, as they suggest that girls in these countries may not be sharing fully in the recent improvements in survival. Further study is needed to confirm these findings, to identify why girls' relative survival may not be keeping pace in some countries, and to assess interactions with other barriers to care such as poverty or marginalization. Studying Regions with Excess Female Mortality Regions where concentrations of excess female child mortality were found would benefit from in-depth cross-national or sub-national studies of cause-specific mortality and mortality determinants by sex. Regions such as middle and western sub-Saharan Africa, northern Africa/western Asia, and southern Asia each have a number of countries with excess female child mortality, but also have countries where mortality is (or has become) higher for boys. Case studies from countries that have been successful in reducing inequalities in the survival of girls and boys—whether this was a conscious policy choice or an indirect outcome of generally expanded access to interventions—could provide useful insights and guidance for the planning of child health interventions and health system improvements. The situation of girls in China and India, already well documented in the literature, merits continued study, as there is evidence that girls are not benefiting as much as boys from the mortality declines in these countries. The interaction of strong son preference and declining fertility has continued implications for the health and survival of girls in these countries. Both countries have implemented policies and programs intended to improve the status of girls and women as well as directly influence families' treatment of girls [2], but the available data, which refer most recently to 2005 for China and 2009 for India (Table S1), do not indicate significant change as yet in girls' relative survival to age 5 y, and trends in these countries should be reassessed as new data become available. In both countries, media and policy attention in recent years have concentrated largely on sex-selective abortion—that is, prenatal discrimination—but differences in postnatal treatment still have mortality consequences for large numbers of girls, particularly in India, where relatively high infant and child mortality rates mean that a significant number of excess deaths still occur. The methods presented here have the benefit of producing comparable results for countries in a wide variety of data situations. They are valuable for making sense of noisy data from the often patchy collection of survey, census, and vital registration sources for many developing countries. Yet, they are also useful for analyzing sex differentials even in situations of good data quality and when mortality is low, because year-to-year fluctuations in the sex ratio of mortality can be substantial when numbers of infant and child deaths are low, as is the case even in very large countries with low mortality levels. Limitations The methods are subject to a number of limitations. Despite the use of robust regression methods to limit the influence of extreme data points that are due to sampling error, the nature of the data—which for many countries can be variable, sparse, or from sources for which data collection quality cannot be adequately assessed—does not permit strong conclusions. Particularly in the case of countries with only a few sources of data, addition of a new source may change the results substantially. Future work should aim to quantify the uncertainty of the estimates. Another limitation, from an analytical standpoint, is that sex differentials in overall under-five mortality cannot be explained well without a nuanced understanding of mortality in the component age groups. The reliance on the sex ratio of 4 q 1 as an important indicator of sex differentials in this study is somewhat problematic. This ratio is calculated from sex-specific estimates of 4 q 1 that are derived after fitted trends in the sex ratios of infant and under-five mortality are used to disaggregate both-sexes estimates of infant and under-five mortality. Thus, the sex ratio of 4 q 1 is sensitive both to the fitted sex ratios of infant and under-five mortality, and to the relative levels of both-sexes infant and under-five mortality used as inputs. However, in cases where direct data on 4 q 1 by sex were available for comparison, the derived trends in the sex ratio of 4 q 1 were generally consistent with the empirical trends. The estimates derived here will be useful for incorporation into life tables for the estimation of mortality and population change from the 1970s until today. In this way, they represent an advance over earlier studies of sex differentials in child mortality. However, the usefulness of the fitted trends for the projection of sex differentials in individual countries may be limited, particularly in cases where the estimated increases or decreases are steep and such rapid change cannot sensibly be projected into the future. Conclusions The results obtained here could be a first step in developing a model based on the experience of countries with low mortality, or on regional trends, to blend with the estimates, for purposes of projection. Such a model will also be useful for countries with little or no information on which to base estimates of sex differentials in mortality. However, I believe there is merit in using country-specific data to the extent possible, especially if the intent is to have policy-relevant monitoring of sex differences in mortality. Models based strictly on levels of mortality or regional dummies could mask findings that should prompt further investigation, such as the different trends in sex differentials among countries within regions such as northern Africa/western Asia and western Africa. Despite the limitations detailed above, the methods developed here may be useful for the international community. The methods can be easily implemented by researchers or national authorities working with standard registration or survey data in their own countries. Applied cross-nationally, the methods can advance understanding of the dynamics of childhood mortality by sex around the world. Supporting Information Table S1 Data sources. (XLS) Click here for additional data file. Table S2 Sex ratios and levels of infant, child, and under-five mortality for countries, 1970s–2000s. (XLS) Click here for additional data file. Table S3 Regional groupings used in the study. (XLS) Click here for additional data file.
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Estimating the completeness of death registration: An empirical method
Tim Adair, Alan Lopez (2018)
Introduction Many national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. Existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. This paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level. Methods Random-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970–2015 using the Global Burden of Disease 2015 database. Predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. Models were developed separately for males, females and both sexes. Findings All variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (R-squared 0.85). Mean error is highest at medium levels of observed completeness. The models show quite close agreement between predicted and observed completeness for populations outside the dataset. There is high concordance with the Hybrid death distribution method in Brazilian states. Uncertainty in the under-five mortality rate, assessed using the dataset and in Colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level. Conclusions The method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. The method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. Its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. The method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.
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Predictive model and determinants of under-five child mortality: evidence from the 2014 Ghana demographic and health survey
Justice Moses K Aheto (2019)
Background Globally, millions of children aged below 5 years die every year and some of these deaths could have been prevented. Though a global problem, under-five mortality is also a major public health problem in Ghana with a rate of 60 deaths per 1000 live births. Identification of drivers of mortality among children aged below 5 years is an important problem that needs to be addressed because it could help inform health policy and intervention strategies aimed at achieving the United Nations SDG Goal 3 target 2. The aim of this study is to develop a predictive model and to identify determinants of under-five mortality. Method The 2014 Ghana Demographic and Health Survey data was used in this study. Analyses were conducted on 5884 children. The outcome variable is child survival status (alive or dead). Single level binary logistic and multilevel logistic regression models were employed to investigate determinants of under-five mortality. The fit of the model was checked using Variance Inflation Factor and Likelihood Ratio tests. The Receiver Operating Characteristic curve was used to assess the predictive ability of the models. A p-value< 0.05 was used to declare statistical significance. Results The study observed 289 (4.91%) deaths among children aged below 5 years. The study produced a good predictive model and identified increase in number of total children ever born, number of births in last 5 years, and mothers who did not intend to use contraceptive as critical risk factors that increase the odds of under-five mortality. Also, children who were born multiple and residing in certain geographical regions of Ghana is associated with increased odds of under-five mortality. Maternal education and being a female child decreased the odds of under-five mortality. No significant unobserved household-level variations in under-five mortality were found. The spatial map revealed regional differences in crude under-five mortality rate in the country. Conclusion This study identified critical risk factors for under-five mortality and strongly highlights the need for family planning, improvement in maternal education and addressing regional disparities in child health which could help inform health policy and intervention strategies aimed at improving child survival. Electronic supplementary material The online version of this article (10.1186/s12889-019-6390-4) contains supplementary material, which is available to authorized users.
Author and article information
Contributors
Guo-Zhen Lin: xwkgzcdc@126.com
Chun-Quan Ou:
ORCID: http://orcid.org/0000-0001-6866-7213
ouchunquan@hotmail.com
Journal
Journal ID (nlm-ta): BMC Public Health
Journal ID (iso-abbrev): BMC Public Health
Title:
BMC Public Health
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BioMed Central
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ISSN
(Electronic):
1471-2458
Publication date
(Electronic):
16
July
2020
Publication date PMC-release: 16
July
2020
Publication date Collection: 2020
Volume: 20
Electronic Location Identifier: 1117
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Author information
Chun-Quan Ou
http://orcid.org/0000-0001-6866-7213
Article
Publisher ID: 9189DOI: 10.1186/s12889-020-09189-0
PMC ID: 7364532
PubMed ID: 32678015
SO-VID: 54ce2ef9-ca91-462a-a638-e57b4d44c1f8
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Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
History
Date
received
: 1
April
2020
Date
accepted
: 1
July
2020
Funding
Funded by: National Nature Science Foundation of China
Award ID: 81973140
Award Recipient
:
Chun-Quan Ou
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ScienceOpen disciplines: Public health