Evidence map›Paper›PMID 40775326›Full record

ArticleBMC public health2025

ARIMA based projection of infant mortality rate by the year 2030: a comparative analysis of India and Madhya Pradesh.

Abhinav Bahuguna, Akanksha Uniyal, Vidisha Vallabh

Abstract readComparative Study
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Abhinav BahugunaDepartment of Biostatistics, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, Uttarakhand, India.
Akanksha UniyalDepartment of Biostatistics, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, Uttarakhand, India. akankshauniyal97@gmail.com.
Vidisha VallabhDepartment of Community Medicine, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfant mortality is an important predictor of a government's commitment to its people. Global infant deaths have declined since past decades but at a pace that leaves much to be desired. India's declining pattern of trends is encouraging but the low performance of individual states like Madhya Pradesh (MP) indicates an urgent need for policy revision and implementation.

methodsThis paper forecasts the Infant mortality rate (IMR) of India and MP by the year 2030 through autoregressive integrated moving average (ARIMA) model after obtaining stationarity by differencing the series of IMR once. The Akaike's information criterion and Bayesian information criterion have been used for the selection of best ARIMA model amongst other existing choices. The model diagnostics through Ljung and Box test shows absence of autocorrelation in the residuals (p > 0.05).

resultsThe findings through ARIMA(3, 1, 0) foretell a declining IMR from 27 to 20 per thousand live births (from 2021 to 2030) in India. Similarly, MP is expected to experience reduction in infant deaths from 44 to 39 per thousand live births (from 2021 to 2030). The deployed model is well fitted as mean absolute percentage error lies below 5%. During 2010-20, India and MP witnessed a decadal reduction of 40% and 31% in IMR, respectively. From the year 2010 onwards, India experienced the highest annual reduction of 8.1% in IMR during 2015-16. Similarly, MP encountered a decrease of 6.5% in IMR recently (2019-20), which is the highest declining annual IMR in the state during past ten years.

conclusionsThe projected figures on IMR are satisfactory for policy makers at national level, but MP is still miles away to achieve acceptable IMR as compared to the country's IMR. The state requires more attention and focus on exploring reasons and identifying underlying factors responsible for higher IMR across its demographic structure including socio-economic characteristics.

Indexed as

Infant MortalityModels, StatisticalForecastingHumansIndiaInfantInfant, NewbornAkaike’s information criterionAutoregressive integrated moving averageBayesian information criterionDecadal growth rateIndiaInfant mortality rateMadhya PradeshMean absolute percentage error

Identifiers

PMID40775326
PMCPMC12330000

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.