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ORIGINAL ARTICLE
Year : 2020  |  Volume : 64  |  Issue : 2  |  Page : 130-134  

Analyzing the disparities in the coverage of maternal and child health services: A district-level cross-sectional analysis of Jammu and Kashmir


1 Assistant Professor, Department of Geography, Government PG College, Bhaderwah, Jammu and Kashmir; Ph.D Research Scholar, Jamia Millia Islamia, New Delhi, India
2 Ph.D Research Scholar, Jamia Millia Islamia, New Delhi, India
3 Professor, Department of Geography, Jamia Millia Islamia, New Delhi, India

Date of Submission28-Feb-2019
Date of Decision01-Jun-2019
Date of Acceptance15-Apr-2020
Date of Web Publication16-Jun-2020

Correspondence Address:
Mohd Taqi
Department of Geography, Government PG College, Bhaderwah - 182 222, Jammu and Kashmir
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijph.IJPH_103_19

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   Abstract 


Background: Improving overall coverage of maternal and child health (MCH) services is essentially required if India in general and Jammu and Kashmir state in particular have to attain the Sustainable Development Goals by the year 2030. Thus, the disparities in coverage of MCH services need to be assessed and addressed. Objectives: The objective of this study was to examine the variation in coverage rates for a key set of interventions in MCH services and to assess the relationship between coverage gap and socioeconomic development across the districts of Jammu and Kashmir. Methods: Data from the National Family Health Survey-4 (NFHS-4), 2015–2016, Census of India 2011, and Digest of Statistics Jammu and Kashmir were used to construct two composite indexes of coverage gap and socioeconomic development at district level. Cronbach's alpha was used to assess the internal consistency of indicators used in the two indexes. Results: The overall coverage gap in the state was 28.17%, and the size of coverage gap was largest for family planning interventions (55.8%), followed by treatment of sick children (26.95%) and maternal and newborn care (18.75%), and was smallest for immunization (10.5%). There is a moderate negative correlation between coverage gap and socioeconomic development (r = −0.63, P = 0.01). Conclusion: Coverage of MCH services and socioeconomic development has a significant disparity in the districts of Jammu and Kashmir. Resource-rich and more urbanized districts are much ahead of the poor and less urbanized districts in terms of the usage of MCH services.

Keywords: Coverage gap, Jammu and Kashmir, maternal and child health, socioeconomic development


How to cite this article:
Taqi M, Sarkar S, Khan MM. Analyzing the disparities in the coverage of maternal and child health services: A district-level cross-sectional analysis of Jammu and Kashmir. Indian J Public Health 2020;64:130-4

How to cite this URL:
Taqi M, Sarkar S, Khan MM. Analyzing the disparities in the coverage of maternal and child health services: A district-level cross-sectional analysis of Jammu and Kashmir. Indian J Public Health [serial online] 2020 [cited 2020 Oct 1];64:130-4. Available from: http://www.ijph.in/text.asp?2020/64/2/130/286805




   Introduction Top


Maternal and child health (MCH) remains an intimidating challenge to the health-care system worldwide.[1] The global consensus on the concerns of MCH highlighted in the recently developed Sustainable Development Goals (SDGs).[2],[3],[4] By the year 2030, SDG objective 3.1 aims to reduce maternal mortality rate to below 70/100,000 live births, objective 3.2 seeks to reduce neonatal mortality rate below 12/1000 live births and under-five mortality below 25/1000 live births, and SDG objective 3.7 aims to achieve universal sexual and reproductive health access.[4],[5] Countdown to 2030 tracks progress toward the achievement of these SDGs' objectives with a particular emphasis on country inequalities. India is also among the countdown list of 81 priority countries that account for 90% of all child deaths and 95% of all maternal deaths globally.[6]

Increasing the overall coverage of MCH services is extremely important if India in general and Jammu and Kashmir state in particular have to attain the SDGs by the year 2030. To achieve the targets of SDGs, an accelerated increase in the key coverage indicators of the MCH interventions is indispensable.[3],[4]

Despite several programs and efforts made by the central government, the quality of maternal and child health care in India in general and Jammu and Kashmir state is not up to the mark. Jammu and Kashmir state had an infant mortality rate of 39/1000 live births and under-five mortality rate of 43/1000 live births in 2012,[7] which is far behind the set objectives under SDGs. Disparities in MCH services remain a serious public health concern in India that have many socioeconomic implications. Such disparities are influenced by various factors, such as socioeconomic status.[4],[8] In Jammu and Kashmir, majority of the population are living in rural areas (72.62%)[9],[10] and are devoid of even basic health-care services provided by the government and require targeted interventions to the underserved population in the specific regions.[11] The present study, therefore, analyzes the district-level disparities in the coverage of MCH services using a robust coverage gap measure, i.e. coverage gap index (CGI) developed by the “Countdown 2008 Equity Analysis Group.”[12] The district-level socioeconomic development has also been analyzed using the socioeconomic development index (SEDI).[13],[14] This study also assessed the relationship between coverage gap and socioeconomic development across districts of Jammu and Kashmir.


   Materials and Methods Top


Data sources

Data used in the study have been taken from the National Family Health Survey-4 (NFHS-4) which was carried out in 2014–2015. NHFS is a large-scale, multiround repeated cross-sectional survey conducted in a representative sample of households all over India. For the first time, NFHS-4 provides district-level estimates for many important health indicators. Data from Census of India 2011 and Digest of Statistics 2015–2016 published by the government of Jammu and Kashmir have also been used for computation of district-level socioeconomic development.

The NFHS-4 procedures were approved by the International Institute for Population Sciences Ethical Review Board and Technical Advisory Committee appointed by the Ministry of Health and Family Welfare, Government of India. Both NFHS and census data have been also approved by ICMR in 2017 for any biomedical and health research. The study was exempted from any institutional review board approval due to the use of secondary analysis of data with no identifiers.

Measures and procedures

The present study used two summary indexes to assess the variation in coverage of MCH services and socioeconomic development at district level. The coverage gap in MCH services has been measured using a composite indicator known as CGI (Countdown 2008 Equity Analysis Group).[4],[12],[15],[16],[17] A coverage gap is defined as the percentage of people not receiving a particular intervention out of those who need it.[1],[4] The CGI reports the gap between the maximum service coverage (100%) and the service coverage achieved under a specific community setting.[4] A detailed description and definition of variables used for constructing the CGI is presented in [Table 1]. The CGI comprises a set of four intervention areas:[1],[4] family planning, maternal and newborn care, immunization, and treatment of sick children. One, two, or three indicators are selected in each intervention area. The CGI is presented as:
Table 1: Definition of indicators by intervention area used for calculation of coverage gap index

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where ORT represents oral rehydration therapy, ARI: acute respiratory infection, FP: family planning, SBA: skilled birth attendance, ANC: antenatal care; MSL: measles vaccination, DPT3: the three doses of diphtheria, pertussis, and tetanus vaccine, and BCG: Bacillus Calmette–Guérin vaccination.

The socioeconomic development status of each district has been assessed using the SEDI.[4],[13],[14] In the present study seven variables are selected to construct SEDI on the basis of existing literature, namely household with: electricity, using improved sanitation facilities, using clean fuel, female literacy, child under 5 whose birth was registered, urban population, and main workforce; all variables are expressed as percentage.[18],[19],[20],[21] To calculate the composite index (CI) for SEDI, methodology was adopted from the Human Development Report, 2010,[13],[22] which includes two steps for computation as mentioned:

Step 1: dimension index (DI) for each of the indicators considered for specific CI is computed using the following formula:



where Vi is the actual value of the indicator, Vmin is the minimum value, and Vmax is the maximum value of each indicator, respectively, as shown in [Table 2].
Table 2: Vmin and Vmax values of variables used to construct dimension index of each indicator

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Step 2: the final CI (FCI) of SEDI was computed assigning equal weights to each indicator considered in the CI using the following formula:

FCI = 1/N (Di1 + Di2 + Di3 + Di4+…. + Dn)

where Di represents the DI and N is the number of DIs incorporated in the FCI.

Statistical analysis

To ascertain the internal consistency of the indicators used in each index, Cronbach's alpha reliability coefficient was calculated.[1],[4] To test the nature of correlation between CGI and SEDI, the bivariate correlation and regression analysis were used where P < 0.05 is considered as significant.[4] The data were tabulated and analyzed using MS Excel 2016 (Microsoft Corporation, Redmond, USA) and the Statistical Package for the Social Sciences (SPSS-20) developed by IBM Corporation (IBM Corporation, Armonk, New York, USA), and maps were prepared using ArcGIS 10.0 software package (Environmental Systems Research Institute (ESRI), Redlands, California, USA) to illustrate the district-level variation in CGI and SEDI.


   Results Top


Cronbach's alpha reliability coefficient was 0.87 and 0.75 for full set of eight service coverage indicators of CGI and for full set of seven indicators of SEDI, respectively, suggesting high internal consistency among the variables, so none of the variables were removed in the calculation of index.

Coverage gap by intervention area

[Table 3] shows the mean coverage gap for the CGI and each of the four intervention areas with respective indicators for 22 districts of the state. The mean overall coverage gap was 28.17%, and the mean size of coverage gap was largest for family planning interventions (55.8%), followed by treatment of sick children (26.95%) and maternal and newborn care (18.75%), and was smallest for immunization (10.5%). The large mean coverage gap for oral rehydration therapy (31.4%), antenatal care (23.9%), and acute respiratory treatment sought (22.5%) was highly discouraging.
Table 3: The mean coverage gap index for four intervention areas with indicators within each area, Jammu and Kashmir 2015-2016

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District-wise overall coverage gap and level of socioeconomic development

[Figure 1] represents the estimates of CGI and SEDI variation across the districts of Jammu and Kashmir during 2015–2016. The overall coverage gap was estimated highest in Doda (48.95%) and Rajouri districts (44.61%), followed by Reasi (37.94%), Ramban (37.28%), Samba (34.86%), Bandipore (30.74%), and Kupwara (30.69%) compared to the state's average CGI (28.17%). The remaining districts which lie below the state's average CGI are Poonch (24.21%), Pulwama (25.88%), Leh (17.67%), and Srinagar (16.11%).
Figure 1: District-wise coverage gap index and socioeconomic development index in Jammu and Kashmir. (a) Coverage gap index and (b) socioeconomic development index. NA = Data not available.

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The level of socioeconomic development was highest in Srinagar district (0.92), followed by Jammu (0.85), Samba (0.66), Baramulla and Leh (0.62), Kathua (0.59), Pulwama (0.58), Kulgam (0.55), Budgam and Ganderbal (0.54), and Anantnag (0.50) compared to the state average SEDI (0.49).

However, the districts with lowest SEDI are Kupwara (0.28), Rajouri (0.29), Reasi (0.30), Kishtwar and Ramban (0.31), Doda (0.38), Bandipore and Poonch (0.40), and Shopian (0.46), respectively.

Coverage gap and socioeconomic development correlation

The average CGI was 28.17% and the average SEDI was 0.49 for Jammu and Kashmir, respectively. The Pearson coefficient of correlation for CGI and SEDI was −0.63 suggesting a strong negative correlation between the two indices.

Inverse relationship was found between coverage gap index (CGI) and socioeconomic development index (SEDI) in the state which can be expressed as: CGI = 43.98 − 31.67 × SEDI.


   Discussion Top


The coverage of MCH service interventions and the evaluation of their outcome are essential for monitoring the success rates of any health policy and health program, which is well established in the literature.[4],[23] The competence of CGI for coverage of health services which summarizes the coverage of range of service interventions has been shown in previous studies.[4],[24]

This study establishes that the CGI and SEDI are bidirectional and are negatively related to each other which is evident from [Figure 2]. The relationship between the two can be summarized as an increase in SEDI will decrease CGI further. The similar findings are also reported from other studies throughout the world.[4],[16],[25] Awasthi et al.[4] discussed the relationship between CGI and SEDI in a district-level cross-sectional analysis in high-focus states of India which are similar to the findings of this study.

The evidently large overall coverage gap was found for family planning indicator (55.8%), oral rehydration therapy (31.4%), antenatal care (23.9%), and acute respiratory infection treatment sought (22.5%). The maximum coverage gap was reported for family planning indicator in contradiction to the Equity Analysis Group which reported that the maximum coverage gap was in the indicator for the treatment of sick children; however, Awasthi et al.[4] and Kumar et al.[1] reported the maximum coverage gap in the family planning indicator.

This is the first study to show district-level disparities in the coverage of MCH services in Jammu and Kashmir and makes a strong case for overall improvement in the SEDI indicators in order to minimize the coverage gap. However, extensive and more significant study could have been done by estimating the coverage gap of MCH services for all districts of India which could not be possible due to unavailability of data.


   Conclusion Top


Coverage of MCH services and socioeconomic development has a significant disparity in the districts of Jammu and Kashmir. The level of socioeconomic development is highly influenced by the resource base, economic, and infrastructural development. Therefore, the socioeconomic development is highly localized in terms of spatial distribution in the state. Resource-rich and more urbanized districts are much ahead of the poor and less urbanized districts in terms of the usage of MCH services. Such disparities should be addressed to achieve universal health service coverage in Jammu and Kashmir state. District-level planning complemented by careful monitoring and evaluation of the ongoing health programs in terms of their implementation to ensure efficient and effective utilization of resources is necessary to improve MCH services in the districts of Jammu and Kashmir.

This study demonstrates the district-level disparities in MCH service indicators which urgently need to be minimized in order to achieve the objective 3.1, 3.2, and 3.7 of the SDGs. This study also exhibits the progress made by the districts of Jammu and Kashmir in minimizing the coverage gap and the gap to be bridged. Targeted intervention for further reducing the disparities is extremely important for the betterment of overall situation in the state. The districts of Jammu and Kashmir which are lying below the states average SEDI such as Kupwara, Rajouri, Reasi, Kishtwar, Ramban, Doda, Bandipore, Poonch, and Shopian invites much attention for socioeconomic development in order to reduce CGI.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

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    Figures

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  [Table 1], [Table 2], [Table 3]



 

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