Estimates Of Infant Mortality Rates Research Articles

Estimating the stochastic uncertainty underlying sample-based estimates of infant mortality in the Philippines: a first-time application to a country in the Southeast Asia/Pacific Basin region

ABSTRACT Infant mortality is an important population health statistic that is often used to make health policy decisions. Unfortunately, these data are not available for all populations. A newly developed method is presented for accounting for the stochastic uncertainty found in infant mortality rates (IMRs) estimated from sample surveys and for the first time applied to a country in the Southeast Asian/Pacific Basin area, the Philippines. The method is founded on the fact that there are two sources of variation in sample-based estimates of IMRs: (1) sample size; and (2) variation of infant deaths. The approach is aimed at taking into account stochastic uncertainty while preserving information concerning the uncertainty due to sampling. In applying the method to the Philippines, the sample-based IMR estimates appear to perform well in terms of accounting for stochastic uncertainty. This finding is consistent with previous research assessing this approach in Africa and with variations, in Canada, Europe and the United States, which suggests that in the form presented here or in one of its variants, it could successfully be employed not only elsewhere in the Southeast Asia/Pacific Basin region but also in East Asia, North Asia, South Asia, and West Asia.

Household Environment Status and Infant Mortality: A Study of West Bengal

The factors influencing infant mortality have been documented in a number studies. A number of individual, biological and household environmental factors were found to be the significant predictor of infant mortality in any human setting. Most existing studies on early childhood mortality in India were based on census data. Using the data from second and third rounds of District Level Household and Facility Survey of India this paper assessed the pattern and degree of association of infant mortality with household environment status at the district level in West Bengal. The estimates of infant mortality rate for districts of West Bengal were derived based on birth history data. The household environment status for each district was determined based on three selected household environment factors, namely, availability of toilet facility, availability of safe drinking water and existence of pucca house. Key findings showed that the northern and western districts of this state were backward in terms of household environment status and mostly the districts of those regions had higher levels of infant mortality. The association of availability of toilet facility with infant mortality was found to be significant and stronger, compared to that of other two household factors.

Estimating Child Mortality at the Sub-national Level in Papua New Guinea: Evidence From the Integrated Health and Demographic Surveillance System.

BackgroundChild mortality is an important indication of an effective public health system. Data sources available for the estimation of child mortality in Papua New Guinea (PNG) are limited.ObjectiveThe objective of this study was to provide child mortality estimates at the sub-national level in PNG using new data from the integrated Health and Demographic Surveillance System (iHDSS).MethodUsing direct estimation and indirect estimation methods, household vital statistics and maternal birth history data were analysed to estimate three key child health indicators: Under 5 Mortality Rate (U5MR), Infant Mortality Rate (IMR) and Neonatal Mortality Rate (NMR) for the period 2014–2017. Differentials of estimates were evaluated by comparing the mean relative differences between the two methods.ResultsThe direct estimations showed U5MR of 93, IMR of 51 and NMR of 34 per 1000 live births for all the sites in the period 2014–2017. The indirect estimations reported an U5MR of 105 and IMR of 67 per 1000 live births for all the sites in 2014. The mean relative differences in U5MR and IMR estimates between the two methods were 3 and 24 percentage points, respectively. U5MR estimates varied across the surveillance sites, with the highest level observed in Hela Province (136), and followed by Eastern Highlands (122), Madang (105), and Central (42).DiscussionThe indirect estimations showed higher estimates for U5MR and IMR than the direct estimations. The differentials between IMR estimates were larger than between U5MR estimates, implying the U5MR estimates are more reliable than IMR estimates. The variations in child mortality estimates between provinces highlight the impact of contextual factors on child mortality. The high U5MR estimates were likely associated with inequality in socioeconomic development, limited access to healthcare services, and a result of the measles outbreaks that occurred in the highlands region from 2014-2017.ConclusionThe iHDSS has provided reliable data for the direct and indirect estimations of child mortality at the sub-national level. This data source is complementary to the existing national data sources for monitoring and reporting child mortality in PNG.

Estimating the infant mortality rate from DHS birth histories in the presence of age heaping.

BackgroundThe infant mortality rate (IMR) is a critical indicator of population health, but its measurement is subject to response bias in countries without complete vital registration systems who rely instead on birth histories collected via sample surveys. One of the most salient bias is the fact that child deaths in these birth histories tend to be reported with a large amount of heaping at age 12 months. Because of this issue, analysts and international agencies do not directly use IMR estimates based on surveys such as Demographic and Health Surveys (DHS); they rely instead on mortality models such as model life tables. The use of model life tables in this context, however, is arbitrary, and the extent to which this approach appropriately addresses bias in DHS-based IMR estimates remains unclear. This hinders our ability to monitor IMR levels and trends in low-and middle-income countries. The objective of this study is to evaluate age heaping bias in DHS-based IMR estimates and propose an improved method for adjusting this bias.Methods and findingsOur method relies on a recently-developed log-quadratic model that can predict age-specific mortality by detailed age between 0 and 5. The model’s coefficients were derived from a newly constituted database, the Under-5 Mortality Database (U5MD), that represents the mortality experience of countries with high-quality vital registration data. We applied this model to 204 DHS surveys, and compared unadjusted IMR values to IMR values adjusted with the log-quadratic model as well as with the classic model life table approach. Results show that contrary to existing knowledge, age heaping at age 12 months rarely generates a large amount of bias in IMR estimates. In most cases, the unadjusted IMR values were not deviating by more than +/- 5% from the adjusted values. The model life table approach, by contrast, introduced an unwarranted, downward bias in adjusted IMR values. We also found that two regions, Sub-Saharan Africa and South Asia, present age patterns of under-5 mortality that strongly depart from the experience represented in the U5MD. For these countries, neither the existing model life tables nor the log-quadratic model can produce empirically-supported IMR adjustments.ConclusionsAge heaping at age 12 months produces a smaller amount of bias in DHS-based IMR estimates than previously thought. If a large amount of age heaping is present in a survey, the log-quadratic model allows users to evaluate, and whenever necessary, adjust IMR estimates in a way that is more informed by the local mortality pattern than existing approaches. Future research should be devoted to understanding why Sub-Saharan African and South Asian countries have such distinct age patterns of under-five mortality.

Mortality in Tonga over three triennia, 2010\u20132018

BackgroundTonga is a South Pacific Island country with a population of 100,651 (2016 Census). This study examines Tongan infant mortality rates (IMR), under-five mortality rates (U5MR), adult mortality and life expectancy (LE) at birth from 2010 to 2018 using a recent collation of empirical mortality data over the past decade for comparison with other previously published mortality estimates.MethodsRoutinely collected mortality data for 2010–2018 from the Ministry of Health, national (Vaiola) hospital, community nursing reports, and the Civil Registry, were consolidated by deterministic and probabilistic linkage of individual death records. Completeness of empirical mortality reporting was assessed by capture-recapture analysis. The reconciled data were aggregated into triennia to reduce stochastic variation, and used to estimate IMR and U5MR (per 1000 live births), adult mortality (15–59, 15–34, 35–59, and 15–64 years), and LE at birth, employing the hypothetical cohort method (with statistical testing). Mortality trends and differences were assessed by Poisson regression. Mortality findings were compared with published national and international agency estimates.ResultsOver the three triennia in 2010–2018, levels varied minimally for IMR (12–14) and U5MR (15–19) per 1000 births (both ns, p > 0.05), and also for male LE at birth of 64–65 years, and female LE at birth 69–70 years. Cumulated risks of adult mortality were significantly higher in men than women; period mortality increases in 15–59-year women from 18 to 21% were significant (p < 0.05). Estimated completeness of the reconciled data was > 95%. International agencies reported generally comparable estimates of IMR and U5MR, with varying uncertainty intervals; but they reported significantly lower adult mortality and higher LE than the empirical estimates from this study.ConclusionsLife expectancy in Tonga over 2010–2018 has remained relatively low and static, with low IMR and U5MR, indicating the substantial impact from premature adult mortality. This analysis of empirical data (> 95% complete) indicates lower LE and higher premature adult mortality than previously reported by international agencies using indirect and modelled methods. Continued integration of mortality recording and data systems in Tonga is important for improving the completeness and accuracy of mortality estimation for local health monitoring and planning.

Inequalities in infant mortality in Brazil at subnational levels in Brazil, 1990 to 2015

BackgroundIn this study, infant mortality rate (IMR) inequalities are analyzed from 1990 to 2015 in different geographic scales.MethodsThe Ministry of Health (MoH) IMR estimates by Federative Units (FU) were compared to those obtained by the Global Burden of Disease (GBD) group. In order to measure the inequalities of the IMR by FU, the ratios from highest to lowest from 1990 to 2015 were calculated. Maps were elaborated in 2000, 2010, and 2015 at the municipality level. To analyze the effect of income, IMR inequalities by GDP per capita were analyzed, comparing Brazil and the FU to other same-income level countries in 2015, and the IMR municipal estimates were analyzed by income deciles, in 2000 and 2010.ResultsIMR decreased from 47.1 to 13.4 per 1000 live births (LB) from 1990 to 2015, with an annual decrease rate of 4.9%. The decline was less pronounced for the early neonatal annual rate (3.5%). The Northeast region showed the most significant annual decline (6.2%). The IMR estimates carried out by the GBD were about 20% higher than those obtained by the MoH, but in terms of their inequalities, the ratio from the highest to the lowest IMR among the 27 FU decreased from 4 to 2, for both methods. The percentage of municipalities with IMR higher than 40 per 1000 LB decreased from 23% to 2%, between 2000 and 2015. Comparing the IMR distribution by income deciles, all inequality measures of the IMR decreased markedly from 2000 to 2010.ConclusionThe results showed a marked decrease in the IMR inequalities in Brazil, regardless of the geographic breakdown and the calculation method. Despite clear signs of progress in curbing infant mortality, there are still challenges in reducing its level, such as the concentration of deaths in the early neonatal period, and the specific increases of post neonatal mortality in 2016, after the recent cuts in social investments.

Estimating the stochastic uncertainty in sample-based estimates of infant mortality in Ghana

Author(s): Kposowa, A; Baker, J; Swanson, DA | Abstract: The Infant Mortality Rate (IMR) is an important population health statistic often used as one of the indicators of the health status of a nation. In many countries lacking adequate vital registration systems, sample methods are used to estimate IMRs. However, evaluations of this approach are rare and the literature contains no assessments of the stochastic uncertainty underlying these estimated IMRs. Stochastic uncertainty reflects the fact that even where the underlying IMR is constant in a small population over time, there is a likelihood of yearly fluctuation in its empirical observations even if it is measured from a complete count of the events of interest. In this study a method is presented that can be used to assess this stochastic uncertainty. We use the country of Ghana as a case study for this purpose. The method, a beta-binomial model, is described, tested for validity, and illustrated using 2014 sample-based estimates of IMR for 13 sample regions in Ghana. As such, the approach we described regarding the revision of sample-based IMR estimates is aimed at taking into account of the stochastic uncertainty while preserving the information concerning the uncertainty due to sampling. In applying the method to Ghana, we find that the sample-based IMR estimates perform well in accounting for stochastic uncertainty and could be applied elsewhere.

County-level Variation in Infant Mortality Reporting at Early Previable Gestational Ages.

Infant mortality rate (IMR), or number of infant deaths per 1000 livebirths, varies widely across the US While fetal deaths are not included in this measure, reported infant deaths do include those delivered at previable gestations, or ≤20weeks gestation. Variation in reporting of these events may have a significant impact on IMR estimates. This retrospective analysis used US National Center for Health Statistics 2007-2013 data from 2391 US counties. Counties were categorised by US region, demographic characteristics, and state-level fetal death reporting requirements. County percentage of fetal deaths among all 17-20week fetal and infant deaths was evaluated using multivariable linear regression. County-level characteristics were then included in multivariable linear regression to determine the associated change in county IMR. County percentage of deaths at 17-20weeks reported as fetal ranged from 0% to 100% (mean 63.7%). Every 1 point increase in this percentage was associated with a 0.02 point decrease in county IMR (95% confidence interval (CI) 0.02, 0.03). When county IMRs were recalculated holding the percentage of fetal vs. infant deaths at 17-20weeks constant at 63.7%, results suggest that the predicted gap in county IMR between Northeast and Midwest regions would narrow by 0.45 points. Variable reporting of previable fetal and infant deaths may compromise the validity of county IMR comparisons. Improved consistency and accuracy of fetal and infant death reporting is warranted.

Does decentralization influence efficiency of health units? A study of opinion and perception of health workers in Odisha.

BackgroundHealth systems in low and middle income countries are struggling to improve efficiency in the functioning of health units of which workforce is one of the most critical building blocks. In India, Rogi Kalyan Samiti (RKS) was established at every health unit as institutions of local decision making in order to improve productive efficiency and quality. Measuring efficiency of health units is a complex task. This study aimed at assessing the perception (opinion and satisfaction) of health workers about influence of RKS on improving efficiency of peripheral decision making health units (DMHU); examining differences between priority and non-priority set-ups; identifying predictors of satisfaction at work; and discussing suggestions to improve performance.MethodsFollowing a cross-sectional, comparative study design, 130 health workers from 30 institutions were selected through a multi-stage stratified random sampling. A semi-structured questionnaire was administered to assess perception and opinion of health workers about influence of RKS on efficiency of decision making at local level, motivation and performance of staff, and availability of funds; improvement of quality of services, and coordination among co-workers; and participation of community in local decision making. Three districts with highest infant mortality rate (IMR), one each, from 3 zones of Odisha and 3 with lowest IMR were selected on the basis of IMR estimates of 2011. The former constituted priority districts (PD) and the latter, non-priority districts (NPD). Composite scores were developed and compared between PD and NPD. Adjusted linear regression was conducted to identify predictors of satisfaction at work.ResultsA majority of respondents felt that RKS was efficient in decision making that resulted in improvement of all critical parameters of health service delivery, including quality; this was significantly higher in PD. Further, higher proportion of respondents from PD was highly satisfied with the current set of provisions and manners of functioning of the sample health units. Active community engagement, participation of elected representatives, selection of a pro-active Chairman, and training to RKS members were suggested as the immediate priority action points for the state government. Mean scores differed significantly between PD and NPD with regard to: influence of RKS on individual-centric, organizational-centric and patient-centric performance, and the responsibilities to be entrusted with RKS. Absenteeism was strongly associated with satisfaction and local self-governance. Work-related factors, systemic factors, local accountability and patients’ involvement were found to be the key predictors of satisfaction of health workforce.ConclusionThe understanding on quality improvement strategies was found to be very poor among the health workers. Tailor-made capacity building measures at district and sub-district levels could be critical to equip the peripheral health units to achieve the universal health coverage goals. Work environment, systemic factors and accountability need to be addressed on priority for retention of health workforce. The hypothesized link between efficient local decision making, perception of health workers about efficiency of health units and the health status of population needs further investigation.

Mapping Geographic Variation in Infant Mortality and Related Black-White Disparities in the US.

In the US, black infants remain more than twice as likely as white infants to die in the first year of life. Previous studies of geographic variation in infant mortality disparities have been limited to large metropolitan areas where stable estimates of infant mortality rates by race can be determined, leaving much of the US unexplored. The objective of this analysis was to describe geographic variation in county-level racial disparities in infant mortality rates across the 48 contiguous US states and District of Columbia using national linked birth and infant death period files (2004-2011). We implemented Bayesian shared component models in OpenBUGS, borrowing strength across both spatial units and racial groups. We mapped posterior estimates of mortality rates for black and white infants as well as relative and absolute disparities. Black infants had higher infant mortality rates than white infants in all counties, but there was geographic variation in the magnitude of both relative and absolute disparities. The mean difference between black and white rates was 5.9 per 1,000 (median: 5.8, interquartile range: 5.2 to 6.6 per 1,000), while those for black infants were 2.2 times higher than for white infants (median: 2.1, interquartile range: 1.9-2.3). One quarter of the county-level variation in rates for black infants was shared with white infants. Examining county-level variation in infant mortality rates among black and white infants and related racial disparities may inform efforts to redress inequities and reduce the burden of infant mortality in the US.

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

Despite the increasing availability of current national censuses, these datasets are limited by their lack of small area demographic depth. At the same time, spatial microdata that include detailed demographic information are only available for limited geographies, thus limiting the complex analysis of population subgroups within and between small areas. Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internal validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.

Infant mortality and causes of infant deaths in rural Ethiopia: a population-based cohort of 3684 births.

BackgroundEthiopia has made large-scale healthcare investments to improve child health and survival. However, there is insufficient population level data on the current estimates of infant mortality rate (IMR) in the country. The aim of this study was to measure infant mortality rate, investigate risk factors for infant deaths and identify causes of death in a rural population of northern Ethiopia.MethodsLive births to a cohort of mothers under the Kilite Awlaelo Health and Demographic Surveillance System were followed up to their first birthday or death, between September 11, 2009 and September 10, 2013. Maternal and infant characteristics were collected at baseline and during the regular follow-up visit. Multiple-Cox regression was used to investigate risk factors for infant death. Causes of infant death were identified using physician review verbal autopsy method.ResultsOf the total 3684 infants followed, 174 of them died before their first birthday, yielding an IMR of 47 per 1000 live births (95 % CI: 41, 54) over the four years of follow-up. About 96 % of infants survived up to their first birthday, and 56 % of infant deaths occurred during the neonatal period. Infants born to mothers aged 15–19 years old had higher risk of death (HR = 2.68, 95 % CI: 1. 74, 4.87) than those born to 25–29 years old. Infants of mothers who attained a secondary school and above had 56 % lower risk of death (HR = 0.44, 95 % CI: 0.24, 0.81) compared to those whose mothers did not attend formal education. Sepsis, prematurity and asphyxia and acute lower respiratory tract infections were the commonest causes of death.ConclusionThe IMR for the four-year period was lower than the national and regional estimates. Our findings suggest the need to improve the newborn care, and empower teenagers to delay teenage pregnancy and attain higher levels of education.

Divergent mortality trends by ethnicity in Fiji.

To examine trends in infant mortality rate (IMR), adult mortality and life expectancy (LE) in the two major Fijian ethnic groups since 1975. Estimates of IMR, adult mortality (15-59 years) and LE by ethnicity are calculated from previously unreported Fiji Ministry of Health data and extracted from published sources. Over 1975-2008: IMR decreased from 33 to 20 deaths/1,000 live births in i-Taukei (Fiji Melanesians); and 38 to 18 in Fijians of Indian descent. Increased adult male mortality among i-Taukei and decline among Fijians of Indian descent led to an equal probability of dying in 2007 of 29%; while in female adults the probability trended upwards in i-Taukei to 25%, and declined in Fijians of Indian descent to 17%. Life expectancy in both ethnicities increased until 1985 (to 64 years for males; 68 for females) then forming a plateau in males of both ethnicities, and Fijian females of Indian descent, but declining in i-Taukei females to 66 years in 2007. Despite IMR declines over 1975-2008, LE for i-Taukei and Fijians of Indian descent has not increased since 1985, and has actually decreased in i-Taukei women, consistent with trends in adult mortality (15-59 years). Mortality analyses in Fiji that consider the entire population mask divergent trends in the major ethnic groups. This situation is most likely a consequence of non-communicable disease mortality, requiring further assessment and a strengthened response.

Mortality trends in Tonga: an assessment based on a synthesis of local data

BackgroundAccurate measures of mortality level by age group, gender, and region are critical for health planning and evaluation. These are especially required for a country like Tonga, which has limited resources and works extensively with international donors. Mortality levels in Tonga were examined through an assessment of available published information and data available from the four routine death reporting systems currently in operation.MethodsAvailable published data on infant mortality rate (IMR) and life expectancy (LE) in Tonga were sought through direct contact with the Government of Tonga and relevant international and regional organizations. Data sources were assessed for reliability and plausibility of estimates on the basis of method of estimation, original source of data, and data consistency. Unreliable sources were censored from further analysis and remaining data analysed for trends.Mortality data for 2001 to 2009 were obtained from both the Health Information System (based on medical certificates of death) and the Civil Registry. Data from 2005 to 2009 were also obtained from the Reproductive Health System of the Ministry of Health (MoH) (based on community nursing reports), and for 2005–2008, data were also obtained from the Prime Minister’s office. Records were reconciled to create a single list of unique deaths and IMR and life tables calculated. Completeness of the reconciled data was examined using the Brass growth-balance method and capture-recapture analysis using two and three sources.ResultsPublished IMR estimates varied significantly through to the late 1990s when most estimates converge to a narrower range between 10 and 20 deaths per 1,000 live births. Findings from reconciled data were consistent with this range, and did not demonstrate any significant trend over 2001 to 2009.Published estimates of LE from 2000 onwards varied from 65 to 75 years for males and 68 to 74 years for females, with most clustered around 70 to 71 for males and 72 to 73 for females. Reconciled empirical data for 2005 to 2009 produce an estimate of LE of 65.2 years (95% confidence interval [CI]: 64.6 - 65.8) for males and 69.6 years (95% CI: 69.0 – 70.2) for females, which are several years lower than published MoH and census estimates. Adult mortality (15 to 59 years) is estimated at 26.7% for males and 19.8% for females. Analysis of reporting completeness suggests that even reconciled data are under enumerated, and these estimates place the plausible range of LE between 60.4 to 64.2 years for males and 65.4 to 69.0 years for females, with adult mortality at 28.6% to 36.3% and 20.9% to 27.7%, respectively.ConclusionsThe level of LE at a relatively low IMR and high adult mortality suggests that non-communicable diseases are having a profound limiting effect on health status in Tonga. There has been a sustained history of incomplete and erroneous mortality estimates for Tonga. The findings highlight the critical need to reconcile existing data sources and integrate reporting systems more fully to ensure all deaths in Tonga are captured and the importance of local empirical data in monitoring trends in mortality.

All-cause mortality among young children in western Kenya. VI: the Asembo Bay Cohort Project.

Although all-cause mortality has been used as an indicator of the health status of childhood populations, such data are sparse for most rural areas of sub-Saharan Africa, particularly community-based estimates of infant mortality rates. The longitudinal follow-up of more than 1,500 children enrolled at birth into the Asembo Bay Cohort Project (ABCP) in western Kenya between 1992 and 1996 has provided a fixed birth cohort for estimating all-cause mortality over the first 5 yr of life. We surveyed mothers and guardians of cohort children in early 1999 to determine survival status. A total of 1,260 households were surveyed to determine the survival status of 1,556 live births (99.2% of original cohort, n = 1,570). Most mothers (66%) still resided but 27.5% had migrated, and 5.5% had died. In early 1999, the overall cumulative incidence of all-cause mortality for the entire 1992-1996 birth cohort was 26.5% (95% confidence interval, 24.1-28.9%). Neonatal and infant mortality were 32 and 176 per 1,000 live births, respectively. These community-based estimates of mortality in the ABCP area are substantially higher than for Kenya overall (nationally, infant mortality is 75 per 1,000 live births). The results provide a baseline description of all-cause mortality among children in an area with intense Plasmodium falciparum transmission and will be useful in future efforts to monitor changes in death rates attributable to control programs for specific diseases (e.g., malaria and HIV/AIDS) in Africa.