DOI: https://doi.org/10.21203/rs.3.rs-2163225/v1
Rigorous assessment of disparities in the coverage of crucial reproductive, maternal, new-born, and child healthcare (RMNCH) services is becoming more and more important in order to ease the path towards the Sustainable Development Goals (SDGs). The goal of this study is to measure the extent of disparities in RMNCH service coverage. Children and women are vulnerable groups in regards to health, and they are significantly influenced by the effects of economic imbalances across multiple dimensions. The main goal of this strategy is to reduce the neo-natal mortality rate to 16 per 1000 up to 2025 and under 5 mortality rates to 23 per 1000 up to 2025. Urban areas are assumed to have greater socioeconomic indicators, as well as maternal and child health indicators, than remote places. Because of this view, health policies were introduced that are oblivious of intra-urban health disparities. Several intervention schemes, such as the 'Janani Suraksha Yojana' (JSY) and others, have raised consumption of antenatal care services at the national and provincial levels. Tackling the discrepancy in the coverage of mother and infant and child healthcare services across different socio-economic segments of the society and throughout state is a critical component of modern health policy of India.
Aim of the study is to measure the inequality in the maternal and child health care coverage though the RMNCH strategy across distinct locality of the society (i.e., rural and urban) for different states in India.
We are using secondary data for this study and CCI index is calculated using the eight indicators that identify the maternal and child health coverage. We collected data from the National Family Health Survey (2015–16) and (2019–2021) for 23 states to assess the RMNCH coverage through cumulative indicators, especially the Composite Coverage Index (CCI) indicator, is developed. The absolute Index of Inequality (Q2-Q1) and Relative Index of Inequality (Q2/Q1) is also calculated to evaluate inequalities in the dispersion of RMNCH coverage. The descriptive statistics is calculated to analysis the characteristics of the data. Then paired t- test is calculated to see the mean difference between for two data set of the same variable that are separated by time that is composite coverage index for two quartile we defined for the two different year 2015-16 and for the year 2019-21. The time series plot according to locality is shown.
The result show that the coverage of key indicators has improved over the years but still there is inequality in the coverage of RMNCH indicators across different quartile. The absolute difference in coverage has reduced for the year 2019-21 as compared to 2015-16. The mean coverage in rural area is 63.52 percent and urban area is 69.09 percent in 2015-16 and 69.69 percent in rural area and 71.60 percent in urban area in 2019-21 in India. For the urban area 79.38 coverage for Punjab is highest and for the rural area highest coverage state is Punjab with 80.825 in the year 2015-16.
WHO states that “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”(Health and Well-Being, n.d.). Health is a “critically significant constituent of human capabilities” which means equality in availability of healthcare is crucial for social justice since it is an essential enabling component for a person to flourish as a prosper human being. Thus, inequalities in health services and care are a prime concern which is a barrier for developing nation(Banerjee & Roy Chowdhury, 2020). In terms of improving equality, eliminating poverty, and promoting social capital, maternal health is critical for every nation’s development(REPRODUCTIVE, MATERNAL, NEWBORN, CHILD AND ADOLESCENT HEALTH PROGRAMME, n.d.). In recent years, there has been a lot more emphasis on children's challenges and improve their health and wellbeing. Children are gaining significant attention as they are sensitive to disease and require special care, and also for what they will be in future in building a sound human capital, families, fuelling the workforce, and assuring the viability of democracy and help in the development of the country(Council (US) & Medicine (US), 2004). The millennium development goal endorsed in UN summit of 2000 for the year 2015 the goal 4 and 5 focused on to reduce child mortality and improve maternal health(Affairs, 2016). The MDG saw significant progress in health targets for improving the quality of care for the maternal and child health with a 44 percent decrease in maternal mortality rate, of which approximately 22 percent of them are living in South Asian countries(Akseer et al., 2017). Thereafter the sustainable development goal 2030 agenda was adopted in 2015 whose goal 3 clearly focus on ensuring healthy lives for all and promote well- being for all ages. The goal of WHO is to reduce the global maternity mortality ratio to less than 70 per 100000 live births by 2030(Affairs, 2019)... After independence the first national health policy came into effect in 1983 which focused on preventive, primitives and rehabilitative aspects of health also a progressive, time-bound programme for establishing a well-distributed network of complete primary healthcare, connected with extension and health education program, and planned in the context of the reality on the ground that individuals can solve basic health problems on their own(Nhp_1983.Pdf, n.d.). In 2002 the second health policy was launched with aim of improving the Indian public’s health to an acceptable level by improving infrastructure in existing institutions and the public health system can be decentralised for better outcome(National Health Policy 2002 (India), n.d.). The context has changed in the since the last health policy of India came 14 years before, and the primary aim of the National Health Policy, 2017, is directed to advise, clearly articulate, solidify, and prioritising the authority role in building health sector infrastructure in every direction like investment, medical management, disease prevention and promotion through cross-sectoral activities, access to technologies, human resource development, and encouraging medical diversity. Improve health status by unified policy action across all sectors, and expand preventative, promotive, curative, palliative, and rehabilitative services provided by the public health sector with a focus on quality and regaining trust in public health services(National_health_policy_2017.Pdf, n.d.).WHO stated universal health coverage as “that all individuals and communities receive the health services they need without suffering financial hardship also includes the full spectrum of essential, quality health services, from health promotion to prevention, treatment, rehabilitation, and palliative care across the life course”. One of the goals that countries around the world established when they adopted the SDGs in 2015 was to achieve universal health coverage. It also supports the WHO’s purpose of the right to the best possible health, Health for All, and the Sustainable Development Goals. By 2030, nearly 18 million additional health workers will be required to satisfy the SDGs' and UHC objectives' health workforce requirements. Low income and lower-middle income countries have the major supply and demand gaps of the health employees. By 2030, the increased demand for health personnel is expected to contribute an estimated 40 million jobs to the world economy(Universal Health Coverage (UHC), n.d.). During 1990 and 2019, the new born death rate was remarkably low, falling from 37 deaths per 1000 live birth to 18 deaths per 1000 live births, while the worldwide maternal mortality rate declined by around 38% during 2000 and 2017. Nearly every day, over 810 maternal individuals die, many of which are preventable and treatable. Every day 6,700 children under 1 month age die due to lack of proper antenatal and prenatal care(UN-IGME-Child-Mortality-Report-2020.Pdf.Pdf, n.d.). During year 2019, around 2.4 million infants have died globally, with approximately 1.9 million of births were stillborn. UHC definition in India is “ ensuring equitable access to cheap, accountable, appropriate health services of assured quality (motivational, preventative, curative, and rehabilitative) for all Indian residents living in any area of the country, regardless of income level, social status, gender, caste, or religion, as well as public health services addressing the health determinants available to individuals, with the government working as sponsor and facilitator but also not only provider of health services”(Universal Health Coverage | National Health Portal Of India, n.d.).
Advancing toward the Millennium Development Goals (MDGs) and then the Sustainable Development Goals (SDGs) to improving reproductive maternity, new-born, and child health (RMNCH) has gained recognition on the world level(Kothavale & Meher, 2021). Measuring coverage of reproductive, maternal, new-born, and child health (RMNCH) services is required to monitor achievement of the Sustainable Development Goals (SDGs), which succeeded the Millennium Development Goals (MDGs). The accomplishment of universal coverage of health interventions is the major focus of these goals. Universal health coverage (UHC) implies equality in health services as well as an increase in the total coverage of RMNCH interventions, and it is claimed that coverage measurements play a pivotal role in identifying policy approaches directed at accomplishing universal coverage and the SDGs(Https://Doi.Org/10.1371/Journal. Pone.0258244, n.d.).
The central aim of national health goal under the national rural health mission and SDGs is the improvement of the maternal and child healthcare. Indian government’s “call to action summit” which was held in February 2013 organised by the ministry of health and family welfare set in motion the reproductive, maternal, new born and adolescent healthcare strategy to trigger the main initiatives of reducing maternal deaths and child mortality. This strategy is built on the concept of continuous care and is very comprehensive in nature and design which enclose all the assessment that is to bring reproductive, maternal, child and adolescent health care all under one roof and aiming on the well thought out wheel of life approach. This strategy main aim is to increase and improve the child survival rate in India through the coverage of overall life. The characteristics of this strategy is to strengthen the health infrastructure, demand supply chain, management of health structure and building sound human resource.
To know the magnitude of the problem in the maternal and child health care that needs to be focused is important. Around 2,87,000 maternal deaths were recorded in 2010 globally and the global maternal mortality rate is 210 deaths per 100000live births. The sub-Saharan region and south Asia comprise of the 85% of the total burden of the world of maternal death in 2010. According to country level India constitutes of the 19% (that is around 56000 in absolute terms) of the total maternal deaths. In India nearly 20% of total global child death is recorded and it is the highest number of deaths of under 5-year child of 15.8 lakhs of all country and child mortality is also high 59 per 1000 births. There is a rural-urban measurable difference in under-five mortality, which levels at 28 percent; nevertheless, the good trend is that rural child death has declined faster than urban. There is also a 9-point gender disparity in the under-five category (female: 64; male: 55), highlighting the importance of addressing socioeconomic determinants of health such as the position of women and the girl child, female literacy, and women's socioeconomic empowerment.
For the planning and policy implication on the area where condition is worse it is important to know the main reason of poor maternal and child health and death in India. the reason of maternal mortality can be identified as medical, social, economic and due to poor public health system. Medical reasons might be either direct or indirect. According to SRS (2001–03), the most prevalent direct medical leading cause of maternal death are postnatal haemorrhage (37%), sepsis due to infection during pregnancy labour, and postnatal period (11%), illegal abortions (8%), hyperparathyroidism (5%), and premature labour (1%). (5 percent). These illnesses are typically preventable and treated when detected. Anaemia and malaria are the most common 'indirect causes' of maternal mortality. Children who die under age of 5 the main reason is infection which is curable. According to WHO-CHERG 2012 the reason of child death in India are (a) neonatal causes (52%), (b) pneumonia (15%), (c) toxic megacolon disease (11%), (d) measles (3%), (e) injury (4%), and (f) unspecified causes (15 percent). Premature birth (18%), that is, childbirth before 37 completed weeks of gestation, infectious diseases (16%) such as pneumonia and septicaemia, and asphyxia (10%), that is, incapacity to maintain breathing soon after birth and hereditary reasons, are the primary causes of neonatal mortality (5 percent) of which the pre mature birth is main cause of new born death. Majority of death of mother and child is due to three delays: first delay in determining to look for care second delay in getting to proper health institution and third is delay in getting treatment in the hospital. The RMNCH strategy is adopted to look inti the institutional drawbacks and improve the death caused by three delays(RMNCH + A_Strategy.Pdf, n.d.).
In 2010–11, the Annual Health Survey was conducted in the 9 high empowered and focused states (Assam, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, Uttar Pradesh, Uttarakhand) that gives a clearer image of the maternal and child healthcare status in states with a high incidence of maternal and child mortality. An examination of data from 284 districts in these nine states reveals significant inter-district difference. Like, Madhya Pradesh is a state recorded with a high under-five mortality rate, the inter-district variation ranges from 89 count between Indore (51) and Panna (140), whereas Uttar Pradesh with a 90-point variation range between Kanpur Nagar (52) and Sherawat (140)(Analysis Annual Health Survey AHS 2010-11(1).Pdf, n.d.).
There is a study that focused on inequality in coverage of maternity, neonatal, and child health (MNCH) care services across household wealth quintiles in India and its states examined Composite Coverage Index (CCI) in MNCH care using the District Level Household and Facility Survey conducted in 2007-08. At the national level, a mean overall coverage of 45 percent was calculated, ranging from 31 percent for the lowest quintile to 60 percent for the wealthiest quintile. Furthermore, the mean overall CCI showed a significant state-by-state variance among wealth quintiles. Almost half of Indian states and union territories had a 55 percent coverage rate in MNCH care services, which requires special attention(Singh et al., 2013). In Afghanistan this is the first thorough, systematic evaluation of RMNCH, nourishment and health benefits, and contextual factors of RMNCH results(Akseer et al., 2016). In Ethiopia a low-income country coverage of these intervention is less, a cross sectional study was done to analyse the continued utilisation of health care services of different wealth quartile(Tiruneh et al., 2022). The aim of this study was to use nationwide survey of Nepal Health Facility Survey (NHFS) 2015 data to evaluate the interconnected services of family planning and maternal and child health (FPMCH), which are essential elements of health institutions to provide quality maternal and neonatal care, which can be helpful in meeting the sustainable development goals (SDGs)(Health Facility Readiness to Provide Integrated Family Planning, Maternal and Child Health (FPMCH) Services in Nepal: Evidence from the Comprehensive Health Facility Survey, n.d.). The previous study has used CCI index intensively to measure the coverage of maternal and child health care.
This study aims to measure the inequality in the coverage of RMNCH strategy for different states according to place of residence. We will analyse the difference in utilisation of health care by comparing the NHFS-4 (2015-16) and NHFS-5(2019-21) data for the people residing in rural and urban area.
For the analytical work we collected the secondary data from National Family Health Survey (NHFS) that was lead in year 2015-16 and 2019-21. The NFHS-4 carried a two-stage stratified random sample in 640 districts in India and approx. 572,000 household as a sample. The rural sample was selected in two phases, with villages acting as Primary Sampling Units (PSUs) in the first phase and 22 families selected at random in every PSU next. In urban areas, a two- stage survey structure was used, with Census Enumeration Blocks (CEB) determined and a simple random of 22 houses in every CEB chosen next. Following a complete modelling techniques and household enumeration functioned in the designated first stage of units, households in both urban area and rural areas were chosen in the second stage. As of March 31, 2017, the NFHS-5 was intended for 707 districts in India, with a sample size of 610,000 households. The rural area sample was chosen using a two-stage sample design, with villages used as Primary Sampling Units (PSUs) in the first selection stage and 22 families randomly selected in every PSU in the second stage. In urban areas, a two-stage sample design was used, with Census Enumeration Blocks (CEB) chosen at the first phase and a randomly 22 homes were selected in each CEB chosen in the second phase. After performing a complete modelling and household enumeration techniques in the selected first-stage units, households were chosen at the second stage in both urban and rural areas(National Family Health Survey, n.d.).
We defined a broad set of RMNCH indicators and develop summative index, CCI. By minimising complexity, this indicator gives comprehensive information about RMNCH initiatives. The CCI focuses on four areas: reproductive health services, maternity and infant care, immunisation and child healthcare, and sick children health treatment. The CCI is created to evaluate coverage in RMNCH care. The four specified subgroups of interference coverage indicators cover all phases of the healthcare system for reproductive, prenatal, neonatal, and children's health, and that's been the central focus of the 'Countdown to 2015 for Maternal, Newburn, and Child Survival' initiative((PDF) Measuring Coverage in MNCH: Challenges and Opportunities in the Selection of Coverage Indicators for Global Monitoring, n.d.). The indicators are defined below:
| Indicators for Composite Coverage Index | Name of the indicators | Definition | Source |
|---|---|---|---|
| Reproductive health | |||
| FPS | Family Planning | Percentage of women that use the modern contraceptives | National Family Health Survey 2015-16, 2019-21 |
| Maternal health care services | |||
| 4ANC | Antenatal Care | Percentage of pregnant women with at least 4 antenatal care visits | National Family Health Survey 2015-16, 2019-21 |
| SBA | Skilled Birth attendance | Percentage of pregnant women whose birth is performed by medical professional | National Family Health Survey 2015-16, 2019-21 |
| New born and child health care vaccination | |||
| DPT3 | Diphtheria, Pertussis and tetanus vaccine | Percentage of children aged from 12–23 months who have received the three doses of DPT vaccine | National Family Health Survey 2015-16, 2019-21 |
| MSL | Measles vaccine | Percentage of children between 12–23 months who received the measles vaccination | National Family Health Survey 2015-16, 2019-21 |
| BCG | Bacilli Calmette Guerin vaccination | Percentage of children between 12–23 months who received BCG vaccination | National Family Health Survey 2015-16, 2019-21 |
| Utilisation of healthcare services among children | |||
| ORT | Oral Rehydration Therapy | Percentage of children under 5 years diagnosed with diarrhoea who received oral rehydration therapy and other fluids. | National Family Health Survey 2015-16, 2019-21 |
| ARI | Acute Respiratory Infection | Percentage of children under 5 years with fever and symptoms of acute respiratory infection within two weeks taken to a health facility | National Family Health Survey 2015-16, 2019-21 |
The formula for calculating the CCI index is:
The formula given represents a weighted average of eight important initiatives by allocating identical weight to every element(Singh et al., 2013). The data collected on the 8 indicators are divided in two quartiles according to the location and weights are given. The first quartile (Q1) is the rural area and second quartile (Q2) is the urban area and for each quartile individual data is considered to calculate the CCI index. In this study we will use absolute and relative inequality methods to signify the inequality in the coverage of RMNCH strategy. The absolute inequality (Q2-Q1) shows the extent of difference in the coverage of maternal and child healthcare between the rural and urban quartile. While the relative inequality (Q2/Q1) explains the ratio of difference in the rural and urban quartile in coverage of maternal and child health care. These indices have the advantage of easy interpretation. Here the rural household are classified in quartile 1 and urban household is classified in quartile 2. The descriptive statistics of the index is calculated such as mean, minimum, maximum, standard deviation and the quartiles. Mean is used to tell us about the data from a single value which signify the centre of the data. Minimum is the smallest value of data and maximum is the largest value in the data which is used to detect any outlier in the data or data entry error. Standard Deviation analyse how dispersed data is from the average value, and also estimate the overall variation in the data. Quartiles divide the whole in four parts that is first quartile is 25% second quartile is 50% and third quartile is 75%. The line graph of the CCI index calculated for two-year report for the rural and urban quartile is plotted to show the difference in the two years data. Paired t-test statistics a parametric test is used to measure that the mean difference between the pairs of measurement is zero or not. To statistically test the difference in the coverage of RMNCH indicators for NFHS-4 and NFHS-5 and show the inequality in rural and urban area. All the data analysis is done in excel.
The paired t- test is applied on two sets of data of which one is for the urban quartile which have data for year 2015-16 and 2019-21 and other is for the rural quartile which shows the data of year 2015-16 and 2019-21.
The hypothesis for t- statistics for urban population is:
H0: µU1 = µU2
Which means that the mean difference between the data of urban quartile of NFHS-4 and NFHS-5 is zero. U1 is for the data of urban quartile of NFHS-4 data and U2 is for the data of urban quartile ofNFHS-5.
H1: µU1 ≠ µU2
Which means that the mean difference between the data of urban quartile of NFHS-4 and NFHS-5 is not zero. U1 is for the data of urban quartile of NFHS-4 data and U2 is for the data of urban quartile ofNFHS-5.
The hypothesis for t- statistics for rural population is:
H0: µR1 = µR2
Which means that the mean difference between the data of rural quartile of NFHS-4 and NFHS-5 is zero. R1 is for the data of rural quartile of NFHS-4 data and R2 is for the data of rural quartile of NFHS-5.
H1: µR1 ≠ µR2
Which means that the mean difference between the data of rural quartile of NFHS-4 and NFHS-5 is not zero. R1 is for the data of rural quartile of NFHS-4 data and R2 is for the data of rural quartile of NFHS-5.
Now here Table 1 shows the percentage of coverage of indicators 4ANC, SBA, FPS, BCG4, DPT3, ORS and ARI for the states classified according to the region for urban and rural area for the NFHS-4 and NFHS-5.
| Region | State | Year | NFHS | Place of Residence | 4ANC | SBA | FPS | BCG4 | DPT3 | MSL | %ORS | ARI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SOUTHERN REGION | Andhra Pradesh | 2015-16 | 4 | RURAL | 75 | 90.7 | 70 | 97.1 | 90.6 | 90.6 | 45.3 | 78.6 |
| URBAN | 79.6 | 96 | 68.1 | 97.7 | 84.9 | 92 | 54.9 | 73.9 | ||||
| 2019-21 | 5 | RURAL | 67.9 | 95.2 | 71.1 | 95.6 | 89.9 | 88.6 | 61.9 | 70.6 | ||
| URBAN | 67.2 | 98.3 | 70.3 | 92.4 | 84.8 | 83.7 | 64.2 | 69.2 | ||||
| Karnataka | 2015-16 | 4 | RURAL | 70.7 | 94.4 | 54.3 | 95.2 | 82.1 | 83.8 | 58.7 | 76.4 | |
| URBAN | 69.4 | 92.5 | 47.1 | 89.2 | 72.7 | 80.7 | 44.9 | 77.8 | ||||
| 2019-21 | 5 | RURAL | 70.6 | 92.5 | 67.7 | 97.5 | 92.5 | 92.5 | 67.5 | 67.8 | ||
| URBAN | 71.2 | 96.2 | 68.8 | 96.6 | 91.3 | 89 | 79.1 | 60.8 | ||||
| Kerala | 2015-16 | 4 | RURAL | 91.7 | 100 | 50.1 | 97.9 | 90.3 | 88.6 | 54.9 | 89.9 | |
| URBAN | 88.4 | 99.9 | 50.6 | 98.3 | 90.5 | 90.3 | 40.5 | 90.2 | ||||
| 2019-21 | 5 | RURAL | 78 | 100 | 54.8 | 97 | 84.3 | 90.1 | 56.8 | 86.7 | ||
| URBAN | 79.3 | 99.9 | 50.6 | 98.2 | 86.1 | 86.4 | 65.9 | 85.7 | ||||
| Tamil Nadu | 2015-16 | 4 | RURAL | 81 | 99 | 51.6 | 93.9 | 83.1 | 84.4 | 58.7 | 81.1 | |
| URBAN | 81.3 | 99.5 | 53.5 | 96.2 | 86.3 | 85.9 | 65 | 83.4 | ||||
| 2019-21 | 5 | RURAL | 90.8 | 99.7 | 66.8 | 98.2 | 96.3 | 96.8 | 55.9 | 67 | ||
| URBAN | 88.8 | 100 | 64 | 96.9 | 93 | 94.7 | 51 | 68 | ||||
| Telangana | 2015-16 | 4 | RURAL | 72.3 | 88 | 55.7 | 97.2 | 86.4 | 89.4 | 52.5 | 72.1 | |
| URBAN | 77.8 | 95 | 58.3 | 97.7 | 89.5 | 90.7 | 61.8 | 82.1 | ||||
| 2019-21 | 5 | RURAL | 70 | 92.9 | 66.5 | 95.3 | 91.9 | 92.7 | 54.1 | 73.7 | ||
| URBAN | 71.7 | 94.6 | 66.9 | 90.4 | 84.3 | 86.7 | 61.8 | 76.8 | ||||
| EASTERN REGION | Bihar | 2015-16 | 4 | RURAL | 13 | 68.9 | 22 | 91.7 | 80.2 | 79.6 | 43.8 | 60.1 |
| URBAN | 26.3 | 79 | 32.1 | 91.5 | 79.3 | 77.3 | 62.1 | 57 | ||||
| 2019-21 | 5 | RURAL | 24 | 78.3 | 43.9 | 95.6 | 85.3 | 86 | 58.4 | 69.6 | ||
| URBAN | 32.4 | 83.1 | 47 | 95.3 | 83 | 84.2 | 56.7 | 67.8 | ||||
| Chhattisgarh | 2015-16 | 4 | RURAL | 55.7 | 75.1 | 53.6 | 98.7 | 91 | 93.3 | 67.8 | 68.2 | |
| URBAN | 71.1 | 89.3 | 57.3 | 97.1 | 93.2 | 96.3 | 68.3 | 78.6 | ||||
| 2019-21 | 5 | RURAL | 59.6 | 87.2 | 60.8 | 96.6 | 88.4 | 90.9 | 67 | 63.7 | ||
| URBAN | 62.2 | 95.5 | 64.9 | 95.8 | 84 | 87.7 | 68.8 | 63.2 | ||||
| Jharkhand | 2015-16 | 4 | RURAL | 24.7 | 65.6 | 35.8 | 95.1 | 81.3 | 82 | 44 | 64.7 | |
| URBAN | 52 | 86.8 | 42.5 | 98.7 | 87.1 | 85.4 | 49.1 | 76 | ||||
| 2019-21 | 5 | RURAL | 36.4 | 80.5 | 48.9 | 95.4 | 86.7 | 87.8 | 55.7 | 58.2 | ||
| URBAN | 48.5 | 92.6 | 51.4 | 93 | 79.8 | 81 | 55.2 | 68.4 | ||||
| Odisha | 2015-16 | 4 | RURAL | 60.5 | 85.9 | 44.8 | 94.2 | 89.6 | 88.5 | 68.6 | 72.7 | |
| URBAN | 69.7 | 89.7 | 48.3 | 93.3 | 87.4 | 84.7 | 68.6 | 74.3 | ||||
| 2019-21 | 5 | RURAL | 77.4 | 91.3 | 49.1 | 97.1 | 94 | 95.6 | 66.5 | 64.8 | ||
| URBAN | 80 | 94.8 | 47.2 | 98.2 | 98.4 | 97.7 | 61.1 | 68.9 | ||||
| West Bengal | 2015-16 | 4 | RURAL | 75.8 | 79 | 58.7 | 98.5 | 94.7 | 94.5 | 62.8 | 72 | |
| URBAN | 78.1 | 88.4 | 53 | 95.1 | 87.8 | 88.4 | 69.6 | 78.2 | ||||
| 2019-21 | 5 | RURAL | 73.8 | 93.7 | 60.6 | 99 | 95.6 | 95.1 | 74.8 | 69.5 | ||
| URBAN | 81.2 | 95.2 | 61 | 97.5 | 93.1 | 92.4 | 76.9 | 77.3 | ||||
| WESTERN REGION | Maharashtra | 2015-16 | 4 | RURAL | 69.4 | 88.1 | 64.2 | 89.8 | 74.7 | 82.9 | 58.8 | 83 |
| URBAN | 75.6 | 95 | 60.7 | 90.3 | 75 | 82.6 | 63.8 | 87 | ||||
| 2019-21 | 5 | RURAL | 68.7 | 92.2 | 64.7 | 95.1 | 84.8 | 86.2 | 57.3 | 75.1 | ||
| URBAN | 72.2 | 95.9 | 62.7 | 92 | 81.5 | 82.7 | 64.3 | 81.2 | ||||
| Gujarat | 2015-16 | 4 | RURAL | 63 | 83.6 | 44.6 | 85.9 | 69.1 | 73.7 | 44 | 66.5 | |
| URBAN | 80.5 | 92.2 | 41.2 | 90.6 | 77.6 | 76.7 | 49.7 | 76.3 | ||||
| 2019-21 | 5 | RURAL | 73.3 | 91.1 | 53.3 | 94.2 | 86.6 | 85.8 | 65.4 | 74.8 | ||
| URBAN | 82.4 | 96.8 | 54 | 95.6 | 85.2 | 88.5 | 69.8 | 76 | ||||
| Rajasthan | 2015-16 | 4 | RURAL | 34 | 84.9 | 52.1 | 87 | 69.8 | 75.8 | 53.2 | 81.6 | |
| URBAN | 53.8 | 92.8 | 57.9 | 95.3 | 78.4 | 86.5 | 64.6 | 85.8 | ||||
| 2019-21 | 5 | RURAL | 53.9 | 95 | 61.8 | 95.1 | 88.8 | 90.5 | 64 | 71.7 | ||
| URBAN | 60.6 | 98 | 63.2 | 97.4 | 91.6 | 93.5 | 65.7 | 68.8 | ||||
| NORTHERN REGION | Haryana | 2015-16 | 4 | RURAL | 42.6 | 84.4 | 62.3 | 92.3 | 79.2 | 79.1 | 57.1 | 80 |
| URBAN | 49.3 | 85 | 55.1 | 93.8 | 71.6 | 78.8 | 67 | 80.2 | ||||
| 2019-21 | 5 | RURAL | 59.2 | 94 | 61.3 | 94.6 | 88.3 | 89.4 | 44.4 | 74.6 | ||
| URBAN | 63.1 | 95.5 | 59 | 95.9 | 88.9 | 89.4 | 52.2 | 70.7 | ||||
| Punjab | 2015-16 | 4 | RURAL | 67.8 | 95 | 67.1 | 98.5 | 95.7 | 93.3 | 67.3 | 90.7 | |
| URBAN | 69.4 | 92.7 | 65.3 | 97.7 | 92.6 | 92.7 | 64.9 | 89.7 | ||||
| 2019-21 | 5 | RURAL | 58.4 | 96.6 | 51.1 | 95.1 | 89 | 89.4 | 55.3 | 56.8 | ||
| URBAN | 60.8 | 93.7 | 49.4 | 95.7 | 87.5 | 85.9 | 67.5 | 58.5 | ||||
| Uttarakhand | 2015-16 | 4 | RURAL | 25.7 | 66.3 | 49.8 | 93.8 | 79.4 | 81.6 | 52.3 | 74.9 | |
| URBAN | 41.2 | 81.5 | 48.5 | 90.4 | 81 | 77.7 | 63.8 | 86.5 | ||||
| 2019-21 | 5 | RURAL | 57.3 | 82.4 | 57.1 | 96.5 | 88.7 | 90.3 | 52 | 71.4 | ||
| URBAN | 71 | 86.6 | 59.5 | 92.5 | 91.8 | 91.1 | 62.6 | 70.3 | ||||
| CENTRAL REGION | Madhya Pradesh | 2015-16 | 4 | RURAL | 29.6 | 73.8 | 49.8 | 90.3 | 70.7 | 77.6 | 52.5 | 68.3 |
| URBAN | 21.6 | 90.4 | 49 | 95 | 80.8 | 85.1 | 62.8 | 79.6 | ||||
| 2019-21 | 5 | RURAL | 55.6 | 88.4 | 66.1 | 95.4 | 86.9 | 87.7 | 64.4 | 62.4 | ||
| URBAN | 63.3 | 92.5 | 63.8 | 95.3 | 89.3 | 89.1 | 67.6 | 69.6 | ||||
| Uttar Pradesh | 2015-16 | 4 | RURAL | 21.7 | 69 | 29 | 87.4 | 65.9 | 70.8 | 35.6 | 69.7 | |
| URBAN | 43.3 | 75.8 | 39.8 | 88.3 | 68.8 | 70.8 | 47.4 | 77.6 | ||||
| 2019-21 | 5 | RURAL | 39.6 | 83.8 | 43.2 | 93.6 | 81.5 | 83.9 | 51.5 | 62.1 | ||
| URBAN | 52.3 | 88.4 | 48.6 | 92 | 78.4 | 81 | 47.5 | 67.2 | ||||
| NORTH-EASTERN REGION | Manipur | 2015-16 | 4 | RURAL | 62 | 69.5 | 12.6 | 89.1 | 74.3 | 70.4 | 60.1 | 35.6 |
| URBAN | 81.7 | 92.4 | 12.9 | 95.5 | 84.9 | 81.8 | 60.4 | 45.6 | ||||
| 2019-21 | 5 | RURAL | 74.5 | 80.8 | 17.5 | 73.9 | 78.4 | 73.3 | 71.2 | 39.3 | ||
| URBAN | 88.8 | 95.6 | 19.3 | 79.6 | 87.8 | 83.7 | 66.7 | 44.8 | ||||
| Meghalaya | 2015-16 | 4 | RURAL | 46.3 | 48.1 | 20.6 | 84.3 | 71.7 | 69.6 | 77.3 | 72.7 | |
| URBAN | 71.3 | 90.8 | 27.6 | 96.2 | 88.1 | 86.6 | 77.6 | 87.3 | ||||
| 2019-21 | 5 | RURAL | 49.6 | 61.2 | 22.9 | 88.7 | 73.7 | 72.8 | 75.5 | 74 | ||
| URBAN | 67.5 | 82.4 | 21 | 93.1 | 69.6 | 70.4 | 60.9 | 66.6 | ||||
| Mizoram | 2015-16 | 4 | RURAL | 42.9 | 68.5 | 31.6 | 71.5 | 60.9 | 62.2 | 63.3 | 30 | |
| URBAN | 77.5 | 97.9 | 38.4 | 79.2 | 63 | 60.4 | 76.3 | 62.8 | ||||
| 2019-21 | 5 | RURAL | 45 | 76 | 33.2 | 85.2 | 80 | 81.9 | 86.5 | 45.8 | ||
| URBAN | 70.3 | 99.1 | 28.6 | 81.5 | 81.6 | 79.9 | 59.9 | 56.8 | ||||
| Arunachal Pradesh | 2015-16 | 4 | RURAL | 23.5 | 45.6 | 27.6 | 68 | 49 | 51.9 | 62.8 | 33.9 | |
| URBAN | 37.3 | 82.8 | 23.5 | 80.4 | 60 | 63.4 | 76.6 | 48.7 | ||||
| 2019-21 | 5 | RURAL | 34.6 | 80.3 | 47.6 | 87.1 | 76.8 | 80.1 | 63.1 | 45.9 | ||
| URBAN | 47.8 | 93 | 44.8 | 93.4 | 83.2 | 85.1 | 59.6 | 54 | ||||
| Assam | 2015-16 | 4 | RURAL | 44.8 | 72.1 | 36.8 | 81 | 64.6 | 69.7 | 50.9 | 45.1 | |
| URBAN | 60.3 | 93.9 | 38.4 | 94.3 | 82.8 | 86.1 | 58.7 | 58 | ||||
| 2019-21 | 5 | RURAL | 49.2 | 85.1 | 45.8 | 92.5 | 82 | 83.6 | 68.4 | 50.8 | ||
| URBAN | 62.6 | 94.9 | 42.3 | 92.6 | 79.7 | 77.3 | 81 | 55 |
Table 2 shows the composite coverage for the above indicators in the states and it is clear that for the urban area 79.38 coverage for Punjab is highest in the year 2015-16 preceding with Andra Pradesh with 77.54 coverage index, Telangana (77.012), Tamil Nadu (76.69), Chhattisgarh (76.47), Kerala (75.625). and the lowest coverage record is in the Arunachal Pradesh with 53.037, Bihar (56.53), Uttar Pradesh (59.006), Manipur (59.93). For the rural area highest coverage state is Punjab with 80.825, Kerala (77.53), Andra Pradesh (76.75), west Bengal (77.45), Tamil Nādu (74.40) and the lowest in Arunachal Pradesh with 41.24 coverage, 49.45 in Meghalaya and Bihar, Uttar Pradesh (49.87) we can say that Punjab is a state with better health facility in both rural and urban area with highest coverage index in India and Arunachal Pradesh with lowest coverage in both rural and urban area showing poor maternal health care and child care. Now for the year 2019-21 in urban areas west Bengal coverage was highest at 80.25, Karnataka (78.625) and with approx. of 77 of Andra Pradesh, Gujarat, Kerala, and Telangana. The lowest coverage in urban area is of 58.84 of Meghalaya, followed by 62.99 of Manipur, 63.2 of Mizoram, Bihar (63.34), Arunachal Pradesh (64.55). in rural area in 2019-21 the highest coverage index was of Tamil Nādu (80.1), west Bengal (78.20), Karnataka (77.66), Andra Pradesh (77.47), and Kerala (76.11).
| STATES | URBAN | RURAL | ||
|---|---|---|---|---|
| NFHS 4 | NFHS 5 | NFHS 4 | NFHS 5 | |
| CCI | CCI | |||
| Andhra Pradesh | 77.54375 | 76.54375 | 76.75625 | 77.475 |
| Arunachal Pradesh | 53.0375 | 64.55625 | 41.24375 | 59.9375 |
| Assam | 65.0875 | 67.84375 | 53.30625 | 64.39375 |
| Bihar | 56.5375 | 63.34375 | 49.45625 | 61.775 |
| Chhattisgarh | 76.475 | 74.40625 | 70.125 | 72.65625 |
| Gujarat | 67.79375 | 76.28125 | 61.9 | 73.475 |
| Haryana | 68.7 | 72.63125 | 69.2 | 71.8875 |
| Jharkhand | 66.00625 | 66.7875 | 55.05625 | 63.3625 |
| Karnataka | 67.05625 | 78.625 | 72.55 | 77.6625 |
| Kerala | 75.625 | 76.3 | 77.53125 | 76.11875 |
| Madhya Pradesh | 65.40625 | 75.2625 | 59.80625 | 72.68125 |
| Maharashtra | 75.53125 | 75.98125 | 73.59375 | 74.76875 |
| Manipur | 59.93125 | 62.99375 | 50.80625 | 56.6 |
| Meghalaya | 70.2125 | 58.84375 | 54.28125 | 57.56875 |
| Mizoram | 65.5125 | 63.2 | 49.45625 | 60.40625 |
| Odisha | 71.9125 | 74.44375 | 69.78125 | 73.56875 |
| Punjab | 79.3875 | 69.7 | 80.825 | 68.81875 |
| Rajasthan | 72.7625 | 75.81875 | 63.6375 | 73.725 |
| Tamil Nadu | 76.69375 | 78.075 | 74.40625 | 80.1 |
| Telangana | 77.125 | 76.44375 | 72 | 76.2 |
| Uttarakhand | 66.88125 | 74.1375 | 60.7375 | 69.925 |
| Uttar Pradesh | 59.00625 | 64.6875 | 49.875 | 61.70625 |
| West Bengal | 74.98125 | 80.08125 | 74.775 | 78.20625 |
The absolute inequality in the coverage of health indicator between rural and urban area according to the data of indicators from the NHFS- 4 report is Mizoram with 16.05 points followed by Meghalaya with 15.93-point difference and Arunachal Pradesh and assam with difference of 11.79 points approximately. Karnataka, Haryana, Punjab and Kerala have the negative absolute difference which indicates that rural area has greater coverage than the urban area. Andra Pradesh with the difference of 0.7 points show less than 1 difference in coverage of health care thus showing equality in urban and rural area RMNCH indicators. The absolute inequality in the coverage of health indicator between rural and urban area according to the data of indicators from the NHFS- 5 report is Manipur with 6.39 inequality points, 4.61 points absolute difference in Arunachal Pradesh, Uttarakhand (4.21), Jharkhand with 3.42, Uttar Pradesh, Mizoram, Gujrat, Madhya Pradesh, Rajasthan with approximately 2.50 points inequality. Tamil Nādu and Andra Pradesh has negative absolute difference showing higher coverage in rural area than urban area. The relative difference between rural and urban coverage index is approximately 1 for all the states for the NFHS-4 data same for the NFHS-5 data. These are shown in the Table 3.
| STATES | CCI | |||
|---|---|---|---|---|
| NFHS-4 | NFHS-5 | |||
| (Q2-Q1) | (Q2/Q1) | (Q2-Q1) | (Q2/Q1) | |
| Andhra Pradesh | 0.7875 | 1.01026 | -0.93125 | 0.98798 |
| Arunachal Pradesh | 11.79375 | 1.285952 | 4.61875 | 1.077059 |
| Assam | 11.78125 | 1.221011 | 3.45 | 1.053577 |
| Bihar | 7.08125 | 1.143182 | 1.56875 | 1.025395 |
| Chhattisgarh | 6.35 | 1.090553 | 1.75 | 1.024086 |
| Gujarat | 5.89375 | 1.095214 | 2.80625 | 1.038193 |
| Haryana | -0.5 | 0.992775 | 0.74375 | 1.010346 |
| Jharkhand | 10.95 | 1.198888 | 3.425 | 1.054054 |
| Karnataka | -5.49375 | 0.924276 | 0.9625 | 1.012393 |
| Kerala | -1.90625 | 0.975413 | 0.18125 | 1.002381 |
| Madhya Pradesh | 5.6 | 1.093636 | 2.58125 | 1.035515 |
| Maharashtra | 1.9375 | 1.026327 | 1.2125 | 1.016217 |
| Manipur | 9.125 | 1.179604 | 6.39375 | 1.112964 |
| Meghalaya | 15.93125 | 1.293495 | 1.275 | 1.022147 |
| Mizoram | 16.05625 | 1.324656 | 2.79375 | 1.046249 |
| Odisha | 2.13125 | 1.030542 | 0.875 | 1.011894 |
| Punjab | -1.4375 | 0.982215 | 0.88125 | 1.012805 |
| Rajasthan | 9.125 | 1.14339 | 2.09375 | 1.028399 |
| Tamil Nadu | 2.2875 | 1.030743 | -2.025 | 0.974719 |
| Telangana | 5.125 | 1.071181 | 0.24375 | 1.003199 |
| Uttarakhand | 6.14375 | 1.101153 | 4.2125 | 1.060243 |
| Uttar Pradesh | 9.13125 | 1.183083 | 2.98125 | 1.048314 |
| West Bengal | 0.20625 | 1.002758 | 1.875 | 1.023975 |
Table 4 presents that the mean coverage in rural area is 63.52 percent and urban area is 69.09 percent in 2015-16 and 69.69 percent in rural area and 71.60 percent in urban area in 2019-21 in India.
| NFHS-4 | NFHS-5 | |||
|---|---|---|---|---|
| RURAL | URBAN | RURAL | URBAN | |
| MEAN | 63.52636 | 69.09592 | 69.69647 | 71.6081522 |
| MIN | 41.24375 | 53.0375 | 56.6 | 58.84375 |
| MAX | 80.825 | 79.3875 | 80.1 | 80.08125 |
| SD | 11.30427 | 7.260085 | 7.316395 | 6.18934644 |
| Q1 | 53.79375 | 65.45938 | 62.56875 | 65.7375 |
| Q2 | 63.6375 | 68.7 | 72.65625 | 74.40625 |
| Q3 | 73.07188 | 75.57813 | 75.44375 | 76.290625 |
Graph 1 shows that the coverage in high in urban area than rural area except for the Haryana, Karnataka, Kerala, Punjab and west Bengal. Graph 2 presents in Kerala and Punjab the rural area has more coverage in health care availability for mother and child than urban area. Rest in all states urban area has more coverage. This shows inequality between rural and urban areas as urban area has more infrastructure, human capital and better education level than rural areas.
It is clear from the graph 3 that the coverage in rural area has improved according to NFHS-5 in comparison to NFHS-4 for the states except Punjab. Thus, showing the improvement in the maternal and child health care. For Punjab (80.825) it has already high coverage index in 2015-16 which have led to improvement in condition of the maternal and child health.
From graph 4 for the urban quartile the coverage in urban area has improved according to CCI index calculated for the NFHS-5 data in comparison to NFHS-4 for the states except Punjab. Thus, showing the improvement in the maternal and child health care. For Punjab (79.38) it has already high coverage index in 2015-16 which have led to improvement in condition of the maternal and child health.
The Table 5 and Table 6 shows the result of paired t- statistics by which we can interpret the result. For the urban population the p value is less than 0.05, thus it is statistically significant at 95% level of confidence at 22 degrees of freedom for both single tailed and two- tailed test and we can reject the null hypothesis that the mean difference is zero. Thus, we can say that the coverage of indicators for urban residence in 2019-21 has changed in comparison to 2015-16. For the rural population the p value is less than 0.01 for the two-tailed t-test that is statistically significant at 99% level of confidence with 22 degrees of freedom. Thus, we reject the null hypothesis. we can say that the coverage of indicators for rural residence in 2019-21 has changed in comparison to 2015-16.
| t-Test: Paired Two Sample for Means (urban) | ||
|---|---|---|
| 2015-16 | 2019-21 | |
| Mean | 69.09592391 | 71.60815217 |
| Variance | 52.70883739 | 38.30800936 |
| Observations | 23 | 23 |
| Pearson Correlation | 0.641844824 | |
| Hypothesized Mean Difference | 0 | |
| df | 22 | |
| t Stat | -2.08679209 | |
| P(T < = t) one-tail | 0.024353884 | |
| t Critical one-tail | 1.717144374 | |
| P(T < = t) two-tail | 0.048707767 | |
| t Critical two-tail | 2.073873068 | |
| t-Test: Paired Two Sample for Means (rural) | ||
|---|---|---|
| 2015-16 | 2019-21 | |
| Mean | 63.5263587 | 69.69646739 |
| Variance | 127.7864558 | 53.52963717 |
| Observations | 23 | 23 |
| Pearson Correlation | 0.856610209 | |
| Hypothesized Mean Difference | 0 | |
| df | 22 | |
| t Stat | -4.70100192 | |
| P(T < = t) one-tail | 5.44656E-05 | |
| t Critical one-tail | 1.717144374 | |
| P(T < = t) two-tail | 0.000108931 | |
| t Critical two-tail | 2.073873068 | |
This study examined the coverage of 8 RMNCH indicators and measured the inequality in coverage that is classified by area of residence (rural, urban) for Indian states using the 2015-16 and 2019-21 data of National Family Health survey. The indicators used for measuring coverage are according to countdown 2030 initiatives(Countdown 2030 – Maternal, Newborn & Child Health Data – Countdown to 2030, n.d.). to analyse the advancement in the RMNCH indicator (continuum of care) for countries separately and globally. In this research, we reported coverage and inequality trends over time across four different stages of the RMNCH care process reproductive health, maternity care, child immunizations, and treatment of child diseases by area of residence quintiles. We investigated advances in RMNCH coverage by using well-established absolute and relative indicators of inequality(Inequalities in the Coverage of Reproductive, Maternal, Newborn, and Child Health Interventions in Mali, n.d.).
This paper focus on the inequality in the coverage of health facility according to place of residence. Accessibility of health-care measures is well recognised in the literature as an essential characteristic and vital component of any policies to monitor the improvement of healthcare programs(Reducing Child Mortality: Can Public Health Deliver? - The Lancet, n.d.). Study showed a CI's ability in covering health services by summarising the coverage of a number of interventions. Lozano et al established a comprehensive index of health system accessibility to compare health system performance across Mexican states. This metric was built on 14 child and adult health interventions for which state-level participation estimates were available. This strategy enabled for both curative and preventive measures to be included(“Knowledge into Action for Child Survival,” 2003).
Our CCI analysis showed that just about 70% of the population receives the eight essential RMNCH interventions, with coverage perhaps lower within and between rural households. The research results disclosed that women in the urban quartiles were much more certain to have coverage for RMNCH initiatives than those in the rural quintile. Although rural women's mean coverage increased by only 3% from 2015-16 to 2019-21, and urban women's mean coverage increased by only 2%, a coverage gap remained between 2015-16 and 2019-21. There is seen an inequality between the rural and urban area in coverage of maternal and child health care. The t- statistics used to study the difference in coverage of urban and rural health care show that there is significant difference in two-year coverage index and from the graphical presentation and time series plot we can say that there is increase in coverage index of RMNCH indicators for data in the NFHS-5 from NFHS-4. This can be tested by seeing the graph 5 shows that there exists a significant inequality between the two quartiles but for NFHS-4 the inequity is more and for NFHS-5 inequality decreased but still exist some level of inequality between urban and rural area.
This study makes a significant impact to public health, and the research results are especially relevant as we begin the journey to achieve the SDGs. The availability of RMNCH interventions differed by quartiles and state group. States with stronger healthcare infrastructure and comprehensive distribution channels accomplished well in terms of absolute coverage and relative coverage as well as coverage based on the area of residence. Thus, in this paper we can conclude that there exists an inequality in coverage of the indicators among the rural and urban area. Our findings show that coverage levels for the 8 intervention studies gotten better from 2015-16 to 2019-21. We are concerned, however, that disparities in -health consequences and coverage are widening. The result shows the absolute inequality for different states and relative inequality of approx. 1 for all states from 2015-16 to 2019-21.
In general, our findings indicate that much work is necessary to accomplish universal coverage of the RMNCH care continuum intervention strategies and for India to meet its pledge to the SDG principle of "leaving no one behind." The Government of India require a set of recommendations with programme reactions to address disparities in coverage of RMNCH approaches between the rich and the poor and the rural and the urban households. some effective policy action should be taken to improve reduced coverage of certain measures among the poor and rural household.
A comprehensive study quantifying the levels of inequality at the district level could be the focus of future research. In addition to place of residence indices, investigations could look into the interplay between wealth indices to recommend more complex approaches. Future research can look deeper into additional characteristics of health inequality, such as cultural and racial criteria, to create a comprehensive picture of the disparities that occur across different societal boundaries. The + that is included in RMNCH strategy can also be focus area to show the extent of coverage in this area. Finally, identifying the degree of disparity in the effective usage of child medical care would be highly informative.
Based on the results presented above, we recommend the following policy recommendations for enhancing cumulative usage of RMNCH services and start reducing inequalities along all geographical boundaries. In this regard, government policymakers could consider introducing policy recommendations.
To begin, the centrally controlled agency can encourage, endorse and supervise awareness campaigns related to the eight indicators beginning from the grassroot or village levels and ensure wider reach. Second, as a precondition for receiving monetary incentives under established programme initiatives, basic health care check-up services can be incorporated (such as JSY). This study's findings also show that interventions involving multiple visits for prenatal and antenatal care, as well as coverage in rural areas, are required. It is possible to promote strong and transparent governance, as well as the use of technically advanced measures to track mother and child health status. the central should seek and involve state government who are facing the shortage of resources to improve health conditions and also the national, state and local body authority should increase health expenditure.
ACKNOWLEDGEMENT
I am deeply indebted to my professor of health economics Dr. MANOJ KUMAR for his guidance and suggestions in completing this project gave me the golden opportunity to do this wonderful project on Inequality in RMNCH (Reproductive, Maternal and Child health care) coverage in rural and urban area across all states in India, which also helped me do a lot of research. I came to know about so many new things I am really thankful to them.
Secondly, I would also like to thank my seniors and batchmates who helped me a lot in finalizing this project within the limited time frame.
INFORM CONSENT
There is no formal inform consent.
COMPETING INTERESTS
The authors declare no competing interests