Abstract
This brief analyses data on preterm births in Maharashtra as available from the HMIS (Health Management Information System) for the three pre-pandemic years— 2017-18 to 2019-20. These data are important, both as surrogate for maternal health as well as early warning system for handling the burden of child malnutrition. The study examines the consistency of the data, the incidence of preterm births, and the presence of regional clusters. While the top five districts in terms of incidence of preterm births are Gadchiroli, Nandurbar, Chandrapur, Amravati, and Brihan Mumbai warrant attention, districts like Thane and Pune also stand out districts having the highest absolute number of preterm births. Sub-districts with a particularly high burden of preterm births have also been identified. In particular, tribal districts like Nandurbar, Gadchiroli and Dhule, many of which have a high incidence of preterm births, call for a special focus.
Introduction
Preterm birth (premature birth) is a significant public health problem across the world because of associated neonatal (first 28 days of life) mortality. It is also a major cause of a child’s short- and long-term morbidity and disability in later life (Morniroli et al., 2023). Preterm is defined by World Health Organization (WHO) as babies born alive before 37 completed weeks of gestation or fewer than 259 days of gestation since the first day of a woman’s last menstrual period ). Normally, a pregnancy lasts about 40 weeks. The past decade has witnessed only a marginal reduction in the number of preterm births globally (WHO, 2023). According to WHO, every year about 15 million babies are born prematurely around the world; more than one in ten babies are born globally (Kinney et al., 2012). In India, which has the highest burden of preterm births in the world, out of 27 million babies born every year (2010 data), 3.5 million babies born are premature (Blencowe et al., 2012). Complications of preterm birth were the leading cause of death in children younger than five years of age globally in 2018, accounting for approximately 16% of all deaths, and 35% of deaths among new-born babies. Newborn deaths (those in the first month of life) themselves accounted for nearly half all deaths in children under five years of age in 2018 (UN IGME, 2019). Survival of premature babies also depends on where they are born. Over 9 in 10 extremely preterm babies survive in high-income countries because of enhanced basic care and parental awareness, in sharp contrast to only 1 in 10 extremely preterm babies in low-income countries (WHO, 2023).
Many studies have pointed out that malnutrition during childhood is actually a continuation of malnutrition at birth. Prematurity and intrauterine growth restrictions (IUGR) are the two underlying biological factors leading to LBW. Even despite catch‐up growth, a large proportion of low birthweight (LBW) infants fail to attain the expected weight and height during infancy. Compared with infants born with normal weight, LBW infants are also more prone to post‐natal growth faltering (weight or height <−2 SD of reference). Preterm and growth‐restricted infants are vulnerable to infections, and infection in turn leads to growth faltering, thereby creating a vicious circle of infection and undernutrition (Sania, et al., 2015).
Maharashtra has seen an increase in the incidence of severe wasting between the latest two rounds of NFHS: NFHS-4 and NFHS-5, even though there has been some decrease in the incidence of wasting (https://www.youtube.com/watch?v=0yOhuptGOJo Maharashtra Analysis NFHS4 and NFHS5 – Towards a Kuposhan Mukt Bharat ). Since preterm birth significantly impacts the nutritional status of a child apart from health and prospects of survival, it is important to investigate the burden of preterm births and adopt measures to reduce it. This study investigates the HMIS data of Maharashtra for three financial years from 2017-18 to 2019-20 to understand the trends and patterns of preterm births in the state.
Methodology
The Health Management Information System (HMIS) was established under the National Rural Health Mission (NRHM), a flagship healthcare program under the Ministry of Health and Family Welfare (MoHFW). The policy brief uses data from HMIS that was previously available in the public domain for all states from April 2017-18 to March 2019-20 (over three financial years); at district and sub-district levels as well (MOHFW, n.d.). For this brief, we rely on data on preterm birth, live births (male), live births (female), and number of preterm children born over the period 2017-18 to 2019-20. Table 1 summarizes the parameters and indicators used in this analysis:
| Parameter/Indicator | Definition | Numerator | Denominator |
| Children Born | Number of live births (male + female) | ||
| Preterm Births | Number of preterm newborns ( < 37 weeks of pregnancy) | ||
| Preterm Birth Rate (or Incidence) | Number of preterm newborns per 1000 children born | Preterm Births*1000 | Children Born |
Literature suggests that HMIS data might suffer from data duplication or data inconsistency due to misinterpretation of data elements and other systemic issues (Kumar, 2018). However, parameters related to number of children born, casualty and preterm deliveries are consistent and have less chances of human error. The reported preterm birth data would provide at least the lower bound of incidence and the numbers can further go up if unreported preterm births are added. The analysis that follows must be viewed in this light. Nevertheless, it can still provide a meaningful input for policy, implementation, and research.
Results
Number of preterm births and incidence (preterm births per 1000 children born) of preterm births have been shown in Table 2 for the districts of Maharashtra. Focus on incidence is required because it gives a clear idea of the regions which are having higher proportion of preterm births. The districts Gadchiroli, Nandurbar, Chandrapur, Amravati, Brihan Mumbai, Aurangabad and Pune have shown higher total number as well as incidence for all the three financial years, whereas Osmanabad, Yavatmal and Kolhapur has shown lower number and incidence. Sindhudurg has low numbers but high incidence. From the table, it is evident that the range of the number of preterm births have increased but the range for incidence has decreased over these three years.
Note: Years refer to corresponding financial years.
Source: MOHFW (n.d.)
To check the consistency of the data between different years, the birth data as well as the data on preterm births for 2018-19 and 2019-20 have been regressed on the 2017-18 data (Figure 1 and 2). Data are fairly consistent as seen from the linear fit which was obtained after regression. Few outliers are apparent (Figure 2), contributed by the data from the districts of Nandurbar, Solapur, Nashik ,and Brihan Mumbai.
Source: MOHFW (n.d.)
To find out if any district is disproportionately contributing to the total preterm births in the state, the relative contribution of different districts to children born as well as preterm births reported for the three-year period have been analysed using pie-charts as shown in Figure 3 and 4.
Source: MOHFW (n.d.)
From the above figures, it can be observed that the economically well-off districts of Pune, Brihan Mumbai, Thane, and Nashik have contributed to higher number of children born as well as preterm births. Interestingly, the districts Akola, Amravati, Nandurbar and Palghar have contributed to preterm births disproportionately compared to the number of children born.
It is instructive to see if the districts with high and low incidence of preterm births form any clusters. In case of preterm birth rate (2019-20), a belt of red colour in the eastern part of Maharashtra can be observed (Figure 5). In addition to this, two districts in the northern part and two districts in the southern coast are contributing towards the high incidence. Central Maharashtra seem to be in the mid-range whilst small patches of green depicting low burden districts can be seen.
Sources: MOHFW (n.d.); IIPS (2020)
Figure 6 shows the map for severely wasted children in Maharashtra as per NFHS-5. On comparing both the figures, it is interesting to note that these overlap suggesting that districts with higher incidence of preterm births also show higher percentage of severely wasted children. However, the maps do not completely coincide as there are few exceptions as shown below.
Sources: MOHFW (n.d.); IIPS (2020)
Since severe wasting and incidence of preterm births are seen to generally overlap, it was expected that district would also show similar behaviour for percentage of children under five years who are wasted as per NFHS-5. Spatial map for wasted children is shown in Figure 7. Interestingly, some of the districts which show higher percentage of children who are wasted also belong to districts with higher incidence of preterm birth rate.
Sources: MOHFW (n.d.); IIPS (2020)
Table 3 shows the preterm birth rates of the 15 highest burden sub-districts of Maharashtra for the year 2019-20. Most of these sub-districts belong to high burden districts such as Gadchiroli, Amravati, Brihan Mumbai, Bid and Wardha. Interestingly, the sub-district of Gagan Bawada belongs to Kolhapur which is a low burden district. Areas that report an incidence of 100 and more, and especially those that cross the unusually high mark of 200, need closer scrutiny at the Primary Health Centre (PHC) level.
Source: MOHFW (n.d.)
Conclusion
There are 15 districts in Maharashtra which have more than 40 preterm births per 1000 children born. Gadchiroli, Nandurbar, Chandrapur, Amravati and Brihan Mumbai are the top 5 districts having highest burden out of the 15 high burden districts for the year 2019-20. These five districts have been consistent in showing high preterm births as well as incidence for all the three years. It was also seen that the most of the districts contributing to high incidence also show higher percentages of severely wasted children below 5 years of age. The high incidence of preterm births and severe wasting among children, Nandurbar (69.3%), Gadchiroli (38.7%) and Dhule (31.6%) are predominantly tribal districts as per Census 2011.
At the sub-district level, Kurkheda and Bhamragad of Gadchiroli have highest preterm birth rates of 217 and 175 respectively. In Brihan Mumbai, sub-district Mumbai City has a high incidence of 125. In terms of number, Pune district reports the highest preterm births which is more than 6000 preterm births every year in the duration from April 2017 to March 2020. Thane is amongst the high incidence district but it has shown reduction of 10 points in the rates of preterm birth from 2018-19 to 2019-20. Raigarh, Osmanabad, Parbhani, Yavatmal and Kolhapur are the five districts which show low total numbers as well as incidence of preterm birth for the year 2019-20.
This policy brief, which describes the available statistics and identifies high burden zones, may help identify specific target areas in these zones. This evidence can be used for more efficient allocation of resources, engagement in learning from the approaches of low burden areas and accentuates the ongoing concerns of maternal and child health in tribal districts. As mentioned earlier, since preterm birth and early childhood nutrition challenges are highly correlated, tackling the issue of preterm births may be the necessary first step towards achieving better childhood nutrition outcomes in the state.
References
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Authors
Danyal Bin Islam, Dr. Sambuddha Chaudhuri, Prof. Satish B Agnihotri & Prof. Sarthak Gaurav
Danyal Bin Islam did his Masters in Technology and Development in CTARA IIT Bombay and also worked as CTARA Nutrition Fellow with Fellowship support from the UNICEF.
Dr. Sambuddha Chaudhuri, currently Associate Professor and Assistant Dean Outreach Jindal School of Public Health and Human Development andwas Post-Doctoral Fellow at the Centre for Policy Studies, IIT Bombay and works in the field of Public Health Policy.
Prof. Satish B. Agnihotri is an Emeritus Professor at CTARA, IIT Bombay. Formerly a career bureaucrat, he worked for Government of India and State Government of Odisha under several capacities.
Prof. Sarthak Gaurav is an Associate Professor at Shailesh J. Mehta School of Management, IIT Bombay.
Suggested citation: Bin Islam, D., Chaudhuri, S., Agnihotri, S. B., & Gaurav, S. (2024). Preterm delivery patterns in Maharashtra as revealed by HMIS data: 2017-18 to 2019-20. Nutrition Group, IIT Bombay.