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Low Birth Weight Babies Classification Essay

Background: Although preterm delivery and low birth weight (LBW) have been studied in India, findings may not be generalisable to rural areas such as the Marathwada region of Maharashtra state. There is limited information available on maternal and child health indicators from this region. We aimed to present some local estimates of preterm delivery and LBW in the Osmanabad district of Marathwada and assess available maternal risk factors.
 
Methods: The study used routinely collected data on all in-hospital births in the maternity department of Halo Medical Foundation’s hospital from 1st January 2008 to 31st December 2014. Multivariable logistic regression analysis provided odds ratios (OR) with 95% confidence intervals (CI) for preterm delivery and LBW according to each maternal risk factor.
 
Results: We analysed 655 live births, of which 6.1% were preterm deliveries. Of the full term births (N=615), 13.8% were LBW (<2.5 kilograms at birth). The odds of preterm delivery were three times higher (OR=3.23, 95% CI 1.36 to 7.65) and the odds of LBW were double (OR=2.03, 95% CI 1.14 to 3.60) among women <22 years of age compared with older women. The odds of both preterm delivery and LBW were reduced in multigravida compared with primigravida women regardless of age. Anaemia (Hb<11g/dl), which was prevalent in 91% of women tested, was not significantly related to these birth outcomes.
 
Conclusions: The odds of preterm delivery and LBW were much higher in mothers under 22 years of age in this rural Indian population. Future studies should explore other related risk factors and the reasons for poor birth outcomes in younger mothers in this population, to inform the design of appropriate public health policies that address this issue.

Introduction

Birth weight is an important public health indicator as it is a strong predictor of neonatal as well as lifelong health outcomes1. Low birth weight (LBW) is defined as weight at birth of less than 2500 grams (<2.5 Kilograms)2, which is usually associated with preterm delivery (typically less than 37 weeks of gestation) or restricted intrauterine development3. Maternal factors such as nutrition, body mass index (BMI) and exposure to conditions such as malaria, tuberculosis and HIV may affect birth weight4. Globally more than 20 million LBW infants (15.5% of total births) are born every year, of which about 95% are from developing countries2,3. LBW babies have a 20 times higher risk of death than babies with normal birth weight, and have a higher probability of lifetime morbidity, irrespective of ethnic differences across populations internationally5.

In India it is estimated that 30% of babies are LBW, with nearly half being born full term3. Whilst LBW prevalence and associated risk factors have been studied using national survey data, the generalizability of previous findings is limited due to the considerable heterogeneity between communities, particularly in rural areas. There is a sizeable population for which these data are not documented, leaving a major gap in existing literature. The Marathwada region in the state of Maharashtra has limited data on birth outcomes for its population of approximately 18 million. A recently published study using Latur District Hospital records from the Marathwada region found a LBW prevalence of 26.7%6. However, no data are available for the more deprived districts of Marathwada, such as Osmanabad, which has a population of approximately 1.5 million and where the overall literacy rate is 67% (57% among females), 20% lower than the state average7. Approximately 18% of the district’s population belongs to scheduled castes and tribes, recognised as being particularly deprived by the Indian government, and only 16% of the total population resides in urban areas7. Healthcare access is not uniform across the region, creating further challenges in implementing routine data collection, particularly in rural and difficult to reach areas8. We conducted a study to provide local estimates of preterm delivery and LBW and investigate some key maternal risk factors using hospital data from a rural Marathwada region in Maharashtra state, India.

Methods

Halo Medical Foundation (HMF) is a non-governmental organisation (NGO) with a hospital in the Osmanabad district of Marathwada region that provides medical services to a population of nearly 100,000, spread across 60 villages8. All services are provided at less than 50% of the price charged by neighbouring urban hospitals, and the hospital is attended by patients from all socioeconomic groups8. We conducted a retrospective study using routinely collected data on all in-hospital births in the maternity department of HMF’s hospital from 1st January 2008 to 31st December 2014.

Birth weight was recorded for all live births immediately after birth under the direct supervision of an obstetrician. Low birth weight was defined as a weight of less than 2500 grams (<2.5 Kilograms) recorded immediately after birth3. Determination of gestational age was based on menstrual history, clinical examination and ultrasonography investigation conducted and recorded by an obstetrician. Deliveries occurring before 37 weeks were defined as preterm2. Maternal haemoglobin was measured prior to delivery by a qualified technician using the Sahli’s hemometer method (finger prick technique). This provides instant results, thus it is commonly used in the HMF hospital. Maternal anaemia was defined as haemoglobin levels of less than 11.0 g/dl10.

The study used HMF hospital data retrospectively, with no communication made with doctor, patients, or any other third party for the project. The data was freely available at HMF. Thus, external approval was not deemed necessary. The HMF governance board approved this project and gave permission to use anonymised data (Dataset 126 ). The study is reported in accordance with the STROBE guidelines (Supplementary Table 1)9.

We restricted analyses to singleton live births, and following an initial descriptive summary of the deliveries, logistic regression analysis was conducted to investigate the association of maternal factors (age [older or younger than the mean], gravidity [primigravida or multigravida] and anaemia) with preterm delivery and, among full-term deliveries only, having a LBW baby. Results are reported as unadjusted and adjusted odds ratios (OR) with 95% confidence intervals (CI). Statistical significance was ascertained based on a p value <0.05. All analyses used the licensed statistical software package IBM SPSS (version 20).

Dataset 1.HMF Hospital Delivery Data 2008–2014.

The attached dataset includes information on maternal age, gravidity, haemoglobin levels, delivery term, and birth weight of 655 study samples.

Results

Throughout the study period, 685 deliveries were carried out at the hospital. After excluding missing data (n=4), twin pregnancies (n=8) and stillbirths (n=18), we analysed 655 cases of singleton live births. For these 655 cases, mean maternal age at delivery was 22 years, with 93% normal vaginal deliveries and 7% caesarean sections. The sex ratio at birth was 1.07 (males n=340, females n=315), and none of the study participants had any systemic diseases such as hypertension or diabetes, or habits which may have influenced birth weight or delivery term, such as smoking. Table 1 summarises the descriptive details of the analysed live births, 6.1% of which were preterm deliveries. All preterm deliveries were natural and none were induced by the healthcare provider. Of the full term deliveries, 13.8% were LBW babies.

Table 1. Characteristics of singleton live births.

N=655 unless specified otherwise. SD: standard deviation.

CharacteristicClassificationParticipants
(N=655) (n, %)
Maternal ageMean years ± SD22.15 ± 3.17
GravidityPrimigravida337 (51.5%)
Multigravida318 (48.5%)
Haemoglobin
estimation
performed
on the day of
delivery
Yes391 (59.7%)
No264 (40.3%)
Mean haemoglobin
g/dl ± SD (N=391)
9.33 ± 1.14
Delivery termFull term615 (93.9%)
Preterm40 (6.1%)
Birth weight
among
full term
deliveries
(N=615)
Low birth weight
(<2.5 kg)
85 (13.8%)
Normal birth weight
(≥2.5 kg)
530 (86.2%)
Mean birth weight
kg ± SD
2.83 ± 0.44

Logistic regression analysis showed higher odds of preterm delivery in women younger than 22 years of age than in older women at the time of delivery (adjusted OR 3.23, 95% CI: 1.36 to 7.65, p=0.008) (Table 2). Gravidity was not associated with the odds of preterm delivery. Maternal anaemia, occurring in 91% (356) of the 391 women tested, was not associated with preterm delivery. Among full term deliveries, the odds of delivering a LBW baby was twice as high in mothers who were <22 years of age at the time of delivery (adjusted OR 2.03, 95% CI: 1.14 to 3.60, p=0.02) (Table 3). Primigravidas were two times more likely to deliver LBW babies compared with multigravidas (adjusted OR 2.87, 95% CI: 1.54 to 5.36, p=0.001). Maternal anaemia was not associated with having a LBW baby.

Table 2. Logistic regression analyses to assess risk factors for preterm delivery.

N=655 singleton live births, unless specified otherwise. Reference category for each variable is indicated as 1.

CharacteristicOutcomesCrude odds ratio^
(95% CI)
Adjusted odds ratio^
(95% CI)
p value for
adjusted OR
Preterm
delivery
N (%)
Full term
delivery
N (%)
Maternal age in years
(N= 655)
≥22 years
<22 years


10 (25.0)
30 (75.0)


318 (51.7)
297 (48.3)


1
3.21 (1.54 to 6.69)


1
3.23 (1.36 to 7.65)*



0.008
Gravidity (N=655)
Multigravida14 (35.0)304 (49.4)11
Primigravida26 (65.0)311 (50.6)1.82 (0.93 to 3.54)0.95 (0.43 to 2.11)+0.90
Maternal anaemia status
(N=391)
Not anaemic (Hb ≥ 11 g/dl)3 (13.0)32 (8.6)11
Anaemic (Hb < 11 g/dl)20 (87.0)336 (91.4)0.64 (0.18 to 2.25)0.61 (0.17 to 2.2)*+0.49

Table 3. Logistic regression analyses to assess risk factors for low birth weight.

N=615 full term singleton live births, unless specified otherwise. Reference category for each variable is indicated as 1.

CharacteristicOutcomesCrude odds ratio^
(95% CI)
Adjusted odds ratio^
(95% CI)
p value
Low birth
weight
N (%)
Normal birth
weight
N (%)
Maternal
age in years
(N=615)
≥22 years24 (28.2)294 (55.4)11
<22 years61 (71.8)236 (44.6)3.17 (1.92 to 5.23)2.03 (1.14 to 3.60)*0.02
Gravidity
(N=615)
Multigravida20 (23.5)284 (53.5)11
Primigravida65 (76.5)246 (46.5)3.75 (2.21 to 6.37)2.87 (1.54 to 5.36)+0.001
Maternal
anaemia
status (N=368)
Not anaemic
(Hb ≥ 11 g/dl)
5 (10.9)27 (8.4)11
Anaemic
(Hb < 11 g/dl)
41 (89.1)295 (91.6)0.75 (0.27 to 2.06)0.75 (0.27 to 2.1)*+0.59

Discussion

In summary, our results show a higher likelihood of preterm delivery and having a LBW baby in women of the Marathwada region younger than 22 years of age at the time of delivery. Gravidity and anaemia were not associated with these birth outcomes.

Strengths and limitations

This is the first study that uses data from a rural area of the Marathwada region to investigate maternal factors associated with both preterm delivery and LBW. The same obstetrician recorded all maternal health parameters and birth outcomes from in-hospital births throughout the study period. Preterm and full term deliveries were distinguished by the obstetrician through clinical examination and menstrual history and ultrasonography investigation at the time of admission. None of the study participants were diagnosed with hypertension, diabetes or other systemic conditions prior or during pregnancy, thereby limiting the influence of these confounders on our two main outcomes, LBW and preterm delivery.

The study hospital serves women across all social classes and, thus these estimates are likely to be representative of the local population in Marathwada region. However, our use of retrospective hospital records means that a detailed investigation of other maternal factors and probable confounders associated with birth outcomes is not feasible. Important factors including detailed medical history, birth spacing, maternal body mass index, education, socioeconomic status, healthcare access, knowledge and pregnancy complications which may have had important roles in our study population, were not available.

Comparison with other studies

A community-based prospective study involving 45 villages in the Pune district of Maharashtra in the early 1990s reported that 29% of babies in the study were LBW11. In the Pune study, LBW was significantly more prevalent in primiparae who were less than 20 years of age at the time of delivery than in mothers that were 21 to 25 years of age. A recent hospital based retrospective study from the southern western district of Maharashtra state investigated outcomes of teenage pregnancies (maternal age ≤19 years)12. The study showed that teenage mothers were three times more likely to deliver preterm (OR 2.97, 95% CI: 2.40 to 3.70), and twice as likely to deliver a LBW baby (OR 1.80, 95% CI: 1.50 to 2.20) compared to older mothers. Findings from both studies outlined above are in agreement with our results.

However, a case-control study by Mumbare et al from Marathwada region reported no association between maternal age and birth weight (OR 0.53, 95% CI: 0.24 to 1.19)6. The study found that a higher risk of LBW in full term delivery cases was associated with maternal weight (≤ 55 kilograms), maternal height (≤ 155 cm), weight gain during pregnancy (≤ 6 kilograms), and subsequent pregnancy spacing (<36 months). This case-control study6 obtained data from two centres; the Medical College Hospital of Latur city, based in Marathwada region, and the Medical College Hospital of Nasik city, based in western Maharashtra, which has higher socioeconomic profile compared to our study population (data from July 2009 to December 2009). In this study, the mean maternal age at delivery was 23.19 years (SD: 3.37), similar to the mean age of participants in our study (22.15 years, SD: 3.17). Authors of the case-control study stated that the high prevalence of LBW (26.8%) could be because both study hospitals were tertiary care centres located in the main city of their respective districts, where high-risk pregnancy cases are referred to from surrounding villages and blocks6,13. Unlike the Mumbare et al, our data came from a rural hospital with comparatively low risk pregnancies (no systemic diseases or tobacco consumption were observed in our participants)6.

Findings from other parts of the country also showed a higher risk of LBW and preterm delivery in younger mothers (typically defined as less than 20 years)14,15. Mean birth weight in our study was 2.83 kilograms, 16 grams higher than findings from the Karnataka study11. The Karnataka study had a larger sample size (n=1138) and reported a LBW prevalence of 23%, higher than in our study. LBW prevalence of 8% to 30% reported in other Indian studies varied mainly due to study locations, sample size, hospital type (primary health centres based in villages or district hospitals based in cities), and maternal characteristics such as diet, BMI and antenatal services16–21. The recent Indian National Family Health Survey (NFHS-3) reported 34% of LBW babies at national level, with higher prevalence in rural areas compared to urban regions22. Lastly, a very high prevalence of maternal anaemia (91%) among those tested was noted in our study, which is consistent with findings from other regions; however, no significant effect was seen on preterm delivery or birth weight in full term deliveries23. It should be taken into account that half of the participants were tested in the week preceding delivery and the rest were tested on the day of delivery.

Conclusion

The practice of early marriage followed by pregnancy is commonly observed in our study area. This is influenced by various factors such as parental education, financial resources, and willingness to support higher education for girls24. Though the current legal age for marriage is 18 years for girls in India, child marriage remains prevalent at both state and national level25. Following our observations, it may be advisable to plan the first pregnancy after 21 years of age. However this needs to be supported by necessary implementation of legislation on marriage age by the government authorities. Future studies should explore the reasons for poor birth outcomes in younger mothers in this population to inform the design of appropriate public health policies to address this issue.

Data availability

Dataset 1: HMF Hospital Delivery Data 2008–2014.

The attached dataset includes information on maternal age, gravidity, haemoglobin levels, delivery term, and birth weight of 655 study samples.

doi, 10.5256/f1000research.10659.d14985426

Author contributions

AA, LT, PM and AF conceptualized the study. AA obtained and validated the data and was responsible for project management, while SB conducted the data analysis. All authors contributed to the interpretation of study findings, manuscript write-up, and approved the final manuscript.

Competing interests

No competing interests were disclosed.

Grant information

Data collection activities using HMF hospital records were supported by Halo Medical Foundation India. Additional support for the publication was obtained from the Division of Epidemiology and Public Health, The University of Nottingham, UK.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgements

We thank HMF for providing institutional support for the study. We also acknowledge Ms Sandhya Rankhamb (employed by HMF) for providing support for data entry and verification.

Supplementary material

Supplementary material 1: STROBE Guidelines for cross-sectional studies.

The study is reported in accordance with the following checklist of STROBE guidelines.

Click here to access the data.

F1000 recommended

References

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From 1915 to 2008, the reported infant mortality rates (IMR) in the United States decreased from 99.9 deaths to 6.6 deaths per 1000 live births.1 Neonatal mortality rate (NMR) decreased from 20.5 deaths in 19502 to 4.3 deaths in 2008 per 1000 live births.1 Although the IMR and NMR in some other high-income countries have continued to decrease,3 IMR and NMR in the United States have not decreased notably from 2000 to the most recent data in 2008, ranging from 6.6–7.0 and 4.3–4.7 deaths per 1000 live births, respectively,1,4 thereby leading to a worsening international rank.5

The recent plateau in infant mortality may be related to the increasing proportion of preterm (<37 weeks gestation) and low birth weight (<2500 g) deliveries. From 1983 to 2005, preterm birth rates increased from 9.0% to 12.7%.6 Additionally, the percentage of low birth weight infant births increased from 6.8% in 1983 to 8.2% in 2005.6 Preterm birth and low birth weight are among the most frequent causes of infant and neonatal death in the United States.7 Between 2000 and 2005, total preterm births increased by 9%, which accounted for a large percentage of infant deaths.8 Increases in the Alabama and Delaware IMR have been attributed to a rise in very low birth weight infant mortality.8 In 1993, a similar study in Canada noted that increased reporting of live infants born weighing <500 g had negatively affected its IMR.10 An increase in preterm births may in part account for the lack of improvement in IMR and NMR, and thus analysis of nationwide data would be useful.

The purpose of this study was to examine the contribution of narrow birth weight categories (250-g and 500-g intervals) to the IMR and NMR of the United States. Specifically, we evaluated the effect of birth weight on the overall United States IMR and NMR. By examining narrow ranges of birth weights, it would be possible to determine their proportional contributions to IMR and NMR. We hypothesized that the increased number of infants born weighing <500 g has disproportionately influenced IMR and NMR.

Methods

We obtained data from the National Center for Health Statistics linked birth and infant death cohort data files6 for all the years available during the period 1983–2005. Years 1992–1994 were unavailable and were therefore excluded. To determine the birth weight–specific neonatal and infant mortality rates, data were analyzed by the following weight subgroups used in the database: ≥3500 g, 3000 to 3499 g, 2500 to 2999 g, 2000 to 2499 g, 1500 to 1999 g, 1250 to 1499 g, 1000 to 1249 g, 750 to 999 g, 500 to 749 g, and <500 g. Infants with unknown birth weight were excluded. For each year, the following calculations were performed for each weight subgroup: percentage of live births, percentage of infant deaths, percentage of neonatal deaths, IMR, and NMR. The same analysis was done for gestational age subgroups in the database: <28 weeks, 28 to 31 weeks, 32 to 35 weeks, 36 weeks, 37 to 39 weeks, 40 weeks, 41 weeks, and 42 weeks. By determining the percentages of births and deaths, we were able to observe how each subgroup’s contribution has changed over time.

Simple regression analysis was used to analyze the trends of IMR and NMR over time. We computed adjusted rates, which did not include infants born <500 g, as well as the actual rate, which included all birth weight subgroups. We evaluated and compared the trends over 2 time periods: 1983–1999 and 2000–2005. These periods were selected on the basis of recent analyses that support a lack of decrease in IMR during this the last decade.11 Analysis of this cut off allowed us to observe whether the mortality rates had changed significantly over time. We also analyzed the proportion of infants born <500 g to total births over the same 2 periods and over the entire period and their effect on IMR and NMR. This analysis allowed us to determine the contribution of these births to IMR and NMR. A P value of <.05 was considered significant.

Results

Over the study years, there was an increasing trend (P < .001) in the number of infants in the lower birth weight subgroups (<1500 g) with a corresponding decreasing trend (P < .001) in infants ≥3500 g (Fig 1). From 1983 to 2005, the contribution of very low birth weight infants (VLBW, <1500 g) to the total number of infant births increased from 1.2% to 1.5% (43 284–63 030), and the proportion of live birth infants <500 g increased from 0.12% to 0.18% (4444–7274), both P < .001, while the contribution of those >3500 g decreased from 40.1% to 35.1%. The contribution of VLBW infants to deaths increased from 42.9% to 54.8% during this period. Similarly, there was an increased proportion of preterm infants born, with a corresponding decrease in postterm and term infants (Fig 2). The various subgroups of VLBW infants and infants born at <28 weeks increasingly contributed to infant deaths (Figs 3 and 4).

FIGURE 1

Percentage contributions of infants in each VLBW subgroup to total births for 1983–2005. The contribution of infants in each VLBW subgroup increased during the study period.

FIGURE 2

Percentage contributions of infants in gestational age subgroups to total births for 1983–2005.

FIGURE 3

Percentage contribution of infants in each birth weight subgroup to infant mortality for 1983–2005. The subgroups that contributed the most to infant mortality were the VLBW subgroups despite their relatively small contribution to births. Infants <500 g contributed the most (>20%) to deaths in recent years.

FIGURE 4

Percentage contribution of infants in gestational age subgroups to infant mortality for 1983–2005. Infants, 28 weeks contributed the most to infant mortality, and their contribution increased over the years.

Both the IMR and NMR showed a significant decline from 1983 to 1999 and showed a nonsignificant declining trend from 2000 to 2005 (Fig 5). IMR and NMR decreased between 1983 and 2005 for all birth weight and gestational age subgroups. The analysis showed that during the period 1983–1999, the total IMR was declining significantly (P < .001), but during 2000–2005 it had no significant declining trend (P = .28). The gap between the IMR and NMR became smaller, as neonatal deaths contributed more to the IMR. When the IMR was adjusted so that it did not include births or deaths of infants <500 g, there was an improvement in the United States IMR and NMR (Fig 5). The adjusted IMR decrease declined during 2000–2005 (P = .006). The difference between the adjusted and unadjusted IMR widened in the recent years as more infants <500 g were being registered. NMR decreased significantly during 1983–1999 (P < .001) but not during 2000–2005 (P = .26). The adjusted NMR, however, decreased throughout both the first (P < .001) and the second (P = .002) analyzed time periods (Fig 4). Infants <500 g had a bigger impact on the NMR than infants in other weight subgroups.

FIGURE 5

Total IMR and NMR and adjusted (excluding infants <500 g) IMR and NMR for 1983–2005. Both IMR and NMR decreased significantly from 1983 to 1999, but there was a nonsignificant declining trend for 2000–2005 in total. However, adjusted IMR and adjusted NMR declined significantly during the most recent period (2000–2005).

Discussion

This study shows that IMR and NMR in the United States have not decreased recently, but this is due to increases in live birth registrations of smaller and more immature infants, particularly infants with birth weights <500 g. IMR and NMR have continued to decrease when birth weight– and gestational age–specific analyses are done. The narrowing gap between IMR and NMR is due to the increased proportion of extremely low birth weight and preterm infants.

This study has some intrinsic limitations. The data are retrospective and only published through 2005. Data collection of selected variables at the state level varied over this time period.12 There is possible misclassification between early infant deaths and fetal deaths12 but this is difficult to ascertain from the database. The unavailable cohort data from 1992 to 1994 could affect the statistical analysis of that time period.

The effect of VLBW infants on IMR has been reported individually for Delaware9 and Alabama7 as well as in Canada.10 In the Delaware study, investigators reported that the increased IMR was due to an increase in VLBW infant mortality, while in the Alabama study it was concluded that the increase in infant mortality was due to infants <500 g. In Canada, an increase in IMR noted from 1992 to 1993 was reported to be due to increased registration of infants <500 g.10

A previous national study in the United States identified preterm birth as the most frequent cause of infant death.13 The current analysis focused on birth weight, but birth weight and gestational age are highly correlated.14 The increasing proportion of low birth weight infant births and deaths suggests an increasing contribution of preterm births to IMR and NMR. In an in-depth analysis of causes of infant mortality, preterm birth was found to be the most frequent cause in the United States, accounting for at least 34% of the deaths in 2002 and twice as many as that determined using standard coding procedures.13 The current study suggests that the contribution of prematurity to IMR and NMR has been increasing in recent years, particularly due to the most preterm infants, and accounts for the significant declining trends in IMR and NMR in recent years.

In the United States, there has been a rise in preterm birth rates due to induced preterm birth while spontaneous preterm births have declined,15 but these data are not likely to be due to the infants <500 g, as they constitute a minute proportion (currently about 0.15%) of the births (Fig 1). The rise in induced preterm births may indicate obstetrical successes,15 but the benefits for perinatal mortality and morbidity need to be demonstrated. Importantly, 23% of late-preterm infants do not have a recorded indication for delivery noted on the birth certificate.16

In addition to prematurity, the long-standing racial and ethnic disparities in infant outcomes also account for the lack of decrease in infant mortality in the United States.11 The continuing increase in twin, triplet, and higher-order births in the United States16 accounts for some of the increase in lower birth weight and preterm infants. To our knowledge, this is the first birth weight–specific analysis that identifies the determinant contribution of infants <500 g to the IMR and NMR in the national data trends in the United States.

Although we focused the study on national linked birth and infant death cohort data, we also examined linked birth and infant death period data. The evaluations of the period linked set of data returned very similar results and verified that the changes in IMR and NMR from 1999 to 2005 were not significant unless infants <500 g were excluded.

It is possible that increased reporting accounts for the increased number of infants born weighing <500 g in the database. The World Health Organization defines live birth as the expelled product of conception showing evidence of life regardless of the duration of pregnancy. In contrast, in many countries, including many high-income countries, the definition of live birth requires a birth weight of 500 g or more.

In a study of live birth reporting in industrialized countries, the proportion of live births under 500 g varied from <1 per 10 000 live births in Belgium, Ireland, Latvia, Poland, and the Slovak Republic to >10 per 10 000 in Canada and the United States.17 These international differences compromise the validity of the rankings of neonatal and infant mortality. This also explains in part why the US IMR and NMR are higher than those of many other countries. The Born-Alive Infant Protection Act, which defines a live birth without regard to gestation, may have had a small impact after it became law in 2002, but the effect of the increased proportion of smaller and more immature infants occurred before.

A gestational age analysis indicates that the primary reason for the higher IMR in the United States appears to be the higher proportion of preterm births.5 A greater than 10-fold differential in the classification of fetal versus first 24-hour deaths has been reported between states in the United States. Twenty states had a specific tendency to classify these infants as either fetal deaths or live births/deaths.12 A trend for more births being classified as early neonatal deaths rather than fetal deaths could explain the increase in very low birth weight infants. The current study documents that there is an increase in the proportion of infants in the lower birth weight subgroups during the period of over 2 decades. Whether this is due to increased reporting or not cannot be determined by our study as it is difficult to ascertain from the national database. The 2009 US national data indicate that prematurity rates have decreased during the previous 3 years, and that overall low birth weight rates were not increasing.18

Conclusions

The current study shows that IMR and NMR have continued to decrease during recent years in the United States when birth weight–specific analysis is done. The increasing proportion of infants in the lower birth weight groups is preventing larger decreases in IMR and NMR. The extent to which the increased proportion of lower birth weight infants is due to reporting, versus actual increased births, cannot be determined from our analysis.

Footnotes

    • Accepted February 1, 2013.
  • Address correspondence to Waldemar A. Carlo, MD, Department of Pediatrics, University of Alabama at Birmingham, 9380 Women and Infants Center, 1700 6th Avenue South, Birmingham, AL 35249-7335. E-mail: wcarlo{at}peds.uab.edu
  • Ms Lau assisted in the conceptualization and design of the study, carried out the initial analysis, designed data collection instruments, collected data, critically reviewed the manuscript, and approved the final manuscript as submitted; Dr Ambalavanan designed data collection instruments, collected data, critically reviewed the manuscript, and approved the final manuscript as submitted; Dr Chakraborty assisted in the conceptualization and design of the study, carried out the initial analysis, designed data collection instruments, critically reviewed the manuscript, and approved the final manuscript as submitted; Dr Wingate assisted in the conceptualization and design of the study, critically reviewed the manuscript, and approved the final manuscript as submitted; and Dr Carlo conceptualized and designed the study, drafted the initial manuscript, designed data collection instruments, collected data, critically reviewed the manuscript, and approved the final manuscript as submitted.

  • FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose

  • FUNDING: Supported by a grant from the Perinatal Health and Human Development Program of the University of Alabama at Birmingham and the Children’s of Alabama Centennial Fund.

References

  • Copyright © 2013 by the American Academy of Pediatrics