Mapping of Household Surveys to Measure Barriers to Access to Maternal and Child Health Services in India

Abstract

Objective:

To map the range of access barrier indicators for which data can be derived from the three most common health related household surveys in India.

Methods:

A mapping review study was conducted to identify access dimensions and indicators of access barriers for maternal and child health (MCH) services included in three household surveys in India: National Family Health Survey (NFHS), District Level Household and Facility Survey (DLHS) and Annual Health Survey (AHS).

Results:

The Tanahashi framework for effective coverage of health services was used in this study, and 12 types of access barriers were identified, from which 23 indicators could be generated. These indicators measure self-reported access barriers for unmet healthcare needs through delayed care, as well as forgone care, and unsatisfactory experiences during health service provision. Multiple barriers could be identified, although there was marked heterogeneity in variables included and how barriers were measured.

Conclusions:

This study identified tracer indicators that could be used in India to monitor the population that experiences healthcare needs but fails to seek and obtain appropriate healthcare, and determine what the main barriers are. The surveys identified are well validated and allow the disaggregation of these indicators by equity stratifiers. Given the variability of the frequency and methodologies used in these surveys, comparability could be limited.

Keywords

Household surveys maternal and child health barriers to access of care Tanahashi framework equity stratifiers delayed care forgone care

Introduction

Recent estimates indicate limited coverage of essential health services for at least half of the world’s population, and if the current trends continue, up to one-third of the world’s population would remain underserved by 2030 (The World Bank, 2017; United Nations, 2019). With 10 years left to reach the health-related Sustainable Development Goals (SDGs), one of the greatest persisting challenges facing national health authorities and the international health community is how to move from awareness of inequities in access to quality health services to sustained and concerted action to reduce these gaps (Plamondon et al., 2019; World Health Organization, 2019a, 2019b). The need for scaling up action on barriers to health services (and their drivers) has taken centre stage in global policy documents on universal health coverage (UHC). The declarations on UHC from the 2019 United Nations General Assembly and the Inter-Parliamentary Union clearly stress the importance of understanding who is being missed and responding through equity-oriented approaches (Inter-Parliamentary Union Assembly, 2019; United Nations, 2019). ‘The Global Action Plan for Healthy Lives and Well-being for All’ calls for tackling barriers as a critical means to leave no one behind (World Health Organization, 2019a). The ‘Alma Ata Declaration’ anchors action on barriers to health services as a vital part of strong primary health care (World Health Organization, 1978).

India accounted for almost 20% of the global maternal deaths in 2015 and is critically behind the proposed 2030 SDGs for global maternal and child health (MCH) indicators (Hamal et al., 2020). The 2018 national-level data reported 130 maternal deaths per 100,000 live births, almost double compared to the target of less than 70 deaths by 2030 (NITI Aayog, 2018). Similarly, the national under-5 mortality rate and neonatal mortality rate are significantly higher—34.3 and 21.7 deaths per 1,000 live births, respectively (The World Bank). Likewise, a country performance review of this goal demonstrates that MCH indicators for around eight Indian states/union territories (UTs) (of the 36) are grossly high as compared to the national average (NITI Aayog, 2018). Overall, the health service coverage indicators in India have shown consistent improvement in recent years; however, the available national-level data potentially mask the existing barriers to health services and the inequity in access to high-priority maternal, child and neonatal health services (Barik & Thorat, 2015).

Given the existing multitude of socio-demographic characteristics, values and perceptions within the Indian population, in addition to the rooted health inequities, obtaining adequate information on barriers to access for care is even more relevant today to ensure timely improvement in MCH indicators through increased access to quality health services (Barik & Thorat, 2015; Bhan et al., 2020). Attractive ways to measure access barriers are conceptually those that accurately capture these multiple factors influencing the ways in which access is realised (Levesque et al., 2013). The framework proposed by Tanahashi in 1978 examines service coverage as a series of dimensions that the beneficiary population must traverse in order to reach effective coverage and obtain the expected benefits (Tanahashi, 1978). Effective coverage is defined as: ‘people who need health services obtain them in a timely manner and at a level of quality necessary to obtain the desired effect and potential health gains’ (World Health Organization, 2015). Effective coverage is an important concept when considering UHC (Evans et al., 2013). The percentage of the target population with effective coverage depends on the coverage reached in the dimensions of availability, accessibility, acceptability, contact and, finally, effectiveness. The framework allows identification of a target population that is left behind at each step.

Efficient and regular health data are required to ascertain these gaps in access and to identify the associated barriers. Available tools for measuring access barriers typically rely on explicitly asking survey respondents whether there was a time they needed healthcare but did not receive it or whether they had to forgo healthcare, and what the main barriers were (OECD, 2020). This would require more clarity on concepts and sub-dimensions of access and its determinants and determination of whether it is possible to measure access barriers with the existing data available from household surveys (De Paz et al., 2017; Jacobs et al., 2012; Levesque et al., 2013; OECD, 2020). The National Family Household Survey (NFHS), District Level Household and Facility Survey (DLHS) and Annual Health Survey (AHS) are three population-based surveys that have been used extensively to gather information on fertility, mortality, family planning, MCH and some other aspects of health, nutrition and healthcare in India. In this study we review these three widely available health-related household surveys in India and map the barriers to access of MCH services, reflecting upon the strengths and weaknesses of the methodology used in these potential data sources. Our study builds on the Tanahashi framework to analyse the various dimensions of resource availability, accessibility and coverage and identify health access barrier indicators for each of these dimensions specific to MCH in India.

Methods

We examined the essential institution-based and population-based sources of health-related information for India. While NFHS and AHS are only population-based and incorporate self-reported household surveys, DLHS is a combination of both and collects data from both households and the health facilities such as district hospitals, community health centres and primary health centres (PHCs). All three surveys largely constitute information for MCH, a subject that has been at the forefront of India’s health system goals. Since the release timeline for each survey is invariably different, we downloaded the publicly available questionnaires available for the most recent round of survey—NFHS (2015–2016), AHS (2012–2013) and DLHS (2007–2008) (District Level Household & Facility Survey; National Family Health Survey; National Health Systems Resource Centre).

This article extensively analyses these questionnaires to identify and extract information related to MCH access barriers for each of the five Tanahashi dimensions. The five essential dimensions that result in a successful health intervention include: availability of resources (both physical infrastructure and a skilled workforce); accessibility to services (geographical and financial); acceptability of services provided; contact between the service provider and end user; and effective coverage, which relates to the ability to use quality health services when needed in a timely manner. The parameters used across the three surveys to record appropriate information with respect to the six dimensions (consequent to bisecting accessibility into two distinct dimensions, geographical and financial) of the Tanahashi framework were: (a) availability of healthcare facilities and skilled labour to provide maternal and child healthcare services (availability); (b) physical distance of the health facility (geographical accessibility) and financial resources for obtaining care (financial accessibility); (c) quality of service (acceptability); (d) participants’ willingness to pursue healthcare services (contact); and (e) the approach adopted to provide appropriate care, including treatment protocol (effective coverage) (Tanahashi, 1978).

The identified indicators for each of the six dimensions of access were also assessed for the measurement of unmet needs for healthcare services. While ‘delayed care’ incorporates data for the target population with a perceived healthcare need not receiving timely care, or receiving no care at all, ‘forgone care’ includes information for those with a perceived healthcare need not seeking appropriate care, or seeking no care at all.

Additionally, this article utilises equity stratifiers to determine potential health inequities within a population that is supposed to receive a designed health intervention. Elaborating on the significance of equity stratifiers, the World Health Organization (WHO) states, ‘stratifiers are used as proxy measures to measure the mechanisms for the distribution of resources, prestige or status, and discrimination in society’ (World Health Organization, 2016). For our study, we applied the PROGRESS framework to disaggregate population data according to eight stratifiers, including place of residence, religion, occupation, gender, race/ethnicity, education, socio-economic status and social capital (Chee et al., 2012).

Results

The household surveys generate a range of health information, including that on status of health infrastructure, contacts with health personnel, services during antenatal, delivery and postnatal care, child immunisation and utilisation of Integrated Child Development Services (ICDS). Additionally, most of these categories have a supplementary section on financial resources which offers insights on the financial tools that enable access to care. With respect to DLHS, since it primarily focuses on health facilities, more detailed and objective supply-side information is available, and DLHS incorporates questions on adequate supply of medical essentials, available human resources and infrastructure for each health facility type (village sub-centres, community health centres, PHCs and district hospitals).

We identified 12 types of health access barriers and mapped each of them to any one of the six dimensions of access (availability, geographical accessibility, financial accessibility, acceptability, contact and effective coverage) (Table 1). The availability dimension includes information that indicates the availability of a physical infrastructure/healthcare facility within the study/survey area, skilled workforce, essential medical supplies and types of healthcare services. While the geographical-accessibility dimension takes cognisance of transport and distance of the healthcare facility from the household, the financial-accessibility dimension attempts to provide insights on financial tools for accessing care, including out-of-pocket payments, insurance schemes and ancillary costs related to transport and consultation fee. For acceptability, perceived quality of service is a significant component and other awareness-related characteristics, such as the individual’s health literacy on certain health conditions, their personal perceptions on accessing care, such as the need for treatment, family norms, etc., are largely described by the contact dimension. Finally, effective coverage provides information on treatment protocols and aspects pertaining to the practice of seeking care from non–health providers.

While a few questions were specific, such as ‘Where did you receive antenatal care for this pregnancy?’, a few were implied, like ‘Whether any training programme was organized at PHC last year?’ (which was grouped under the ‘availability’ dimension). Therefore, the study also takes cognisance of the structure of the survey questionnaire(s) to map appropriate information.

Table 1.

Dimensions of Access, Types and Examples of Health Service Access Barriers in the National Family Health Survey, District Level Household and Facility Survey and Annual Health Survey

Dimensions of Access as Defined in the Tanahashi Framework	Types of Access Barriers	Examples of Barriers	Source
Dimensions of Access as Defined in the Tanahashi Framework	Types of Access Barriers	Examples of Barriers	Source
Availability (availability and sufficiency of resources for delivering comprehensive health services)	Inadequate availability of physical infrastructure	Facility not open Designated government building not available	NFHS, DLHS and AHS
	Inadequate availability of skilled health workforce	Unavailability/absence of specialists at the health centres Inadequate or no training to the staff in the last 5 years
	Insufficient stock of medical supplies	Unavailability of essential drugs/contraceptive supplies at the heath centre Inadequate supply or no supply of medical equipment at the health centre (e.g., cold chain equipment, lab requirements, surgical equipment)
Geographic accessibility (availability of quality health services within reasonable reach to those who need them)	Inaccessible healthcare facilities	The health centre providing healthcare service being too far away Inadequate modes of transport to reach the facility	NFHS, DLHS and AHS
Financial accessibility (ability to pay for services without financial hardship)	Ancillary and avoidable costs attached to treatment regime	Healthcare provider consultation costs Out-of-pocket payments for hospital stay, tests, etc. for pregnancy and childbirth-related conditions	NFHS, DLHS and AHS
	Absence of financial safety nets	No health insurance coverage—private or government Lack of government financial assistance for delivery care Non-utilisation by the health centres of ‘untied fund’ released by the government	NFHS, DLHS and AHS
Acceptability (willingness to seek services when they are perceived to be effective or when social and cultural factors do not discourage people from seeking services)	Poor service quality	Lack of trust in the providers ANC-provided services perceived as not being of expected quality	NFHS, DLHS and AHS
	Fear of stigma	Fear of stigma by provider, community	NFHS, DLHS and AHS
Contact (willingness to contact health services when they are available, accessible and acceptable)	Personal perceptions of illness	Reliance on self-treatment Avoidance of treatment since it is ‘non-customary’	NFHS, DLHS and AHS
	Health literacy	Ignorance about the facilities that offer specific services, e.g., HIV tests Lack of basic education and inability to read text messages	NFHS, DLHS and AHS
Effective coverage (ability to use health services when needed in a timely manner and at a level of quality necessary to obtain desired effect and potential health gains)	Gaps in treatment protocol	Irregular frequency of check-ups (e.g., gaps in follow-up after childbirth) Poor ability to communicate treatment instructions	NFHS, DLHS and AHS
	Seeking inappropriate care	Seeking of care from pharmacists	NFHS, DLHS and AHS

Note: ANC: Antenatal Care.

Ten indicators were mapped to determine unmet needs for healthcare service through delayed care and forgone care from the review of all three surveys (Table 2). Four additional indicators, though not directly related to delayed or forgone care, relate to self-reported barriers to effective coverage within the healthcare system. In this article, indicators for delay in receiving timely care correspond to DLHS only, as it includes level of preparedness and responsiveness to a woman and/or her child’s healthcare needs in health facility surveys within a given district. Forgone care has been linked to the woman’s personal decision to seek and/or utilise healthcare services, provided the appropriate services are available for use. Consequently, forgone care incorporates indicators only from the NFHS and AHS that gather information from the woman of the household.

The NFHS and AHS were found capable of allowing disaggregation using the following equity stratifiers in the PROGRESS framework: place of residence, race/ethnicity, occupation, gender, religion and education (Table 2). Both these surveys also have the component of socio-economic status, as it is commonly developed via proxy indicators, such as ownership of land/vehicle, access to the Internet, electricity, water and other household items. DLHS, on the other hand, could allow disaggregation using only four of the eight equity stratifiers. None of the three surveys provide information relating to the social-capital component of the PROGRESS framework.

Table 2.

Access Barriers and Unmet Need for Healthcare Services

	Unmet Needs for Healthcare Services
Dimensions of Access Barriers	Delayed Care (% of women with a perceived healthcare need not receiving timely care, or not receiving care at all)	Forgone Care (% of women with a perceived healthcare need not seeking appropriate care, or not seeking care at all)	Other Health Experience(s) (% of women responding to gaps within the healthcare system, specifically related to ancillary healthcare delivery factors)
Availability	% of women not receiving care due to unavailability of skilled workforce	% of women forgoing care due to unavailability of skilled workforce	% of women indicating inadequate availability of medical supplies
Geographical accessibility	% of women delaying care due to facility location/distance	% of women forgoing care due to facility location/distance	% of women who self-reported issues due to lack of transport
Financial accessibility	% of women delaying care due to financial reasons	% of women forgoing care due to financial reasons	Not available
Acceptability	Not available	% of women forgoing care due to inadequate quality of services	% of women who self-reported having a fear of stigma
Contact	Not available	% of women forgoing care due to personal perceptions of need for treatment	% of women indicating self-ignorance regarding offered services/care
Effective coverage	% of women delaying care due to non-adherence to treatment protocol	% of women seeking care from pharmacies
Corresponding PROGRESS equity stratifiers*	DLHS^^—P, O, G, E ^^DLHS for village, district hospital, CHC, PHC, Sub-center questionnaire	NFHS—PROGRES* AHS—PROGRES* DLHS^^—P, O, G, E, S* ^^DLHS for ever married women questionnaire	NFHS—PROGRES* AHS—PROGRES*
*PROGRESS (place of residence, race/ethnicity, occupation, gender, religion, education, socio-economic status, social capital)	*Note: O and E are only found in one DLHS questionnaire, i.e., for village, out of the five facility-based surveys considered for the study.	Notes: S indicates socio-economic status. The social capital (S) indicator is not available.*	Notes: S indicates socio-economic status. The social capital (S) indicator is not available.*

Source: Modified framework from Houghton et al. https://iris.paho.org/handle/10665.2/52573

Discussion

Based on the mapping of the three regularly conducted health surveys in India, our study contributes to the identification of metrics and indicators that can be used to measure progress towards the reduction of access barriers to unmet MCH needs. Most of the data collected to monitor the progress on health access goals in India have focused on intervention coverage (people using services they need) and financial-hardship indicators, which fail to capture those who are too vulnerable to even seek healthcare when needed in the first place (Boerma et al., 2014). This is particularly meaningful in identifying the population groups that fail to seek and obtain care and understanding the reasons why they are unable to obtain it. Our study contributes to the growing field of quantitative studies that analyse access barriers for the general population based on population surveys and have traditionally been almost non-existent outside of high-income countries (Houghton et al., 2020).

It is quite likely that the respondents’ awareness of the health condition and/or dimensions associated with effective coverage may not be adequate, resulting in incorrect self-reporting (Short et al., 2009). These inadequacies pose a serious challenge to identifying the precise gaps in health service delivery and the corresponding indicators to measure these gaps. However, short of more objective measures, accepting self-reported data as proxy indicators for dimensions of effective health service coverage can be an appropriate alternative.

Another problem with access barrier indicators is that there is significant variation in health conditions or services beyond the traditional focus on MCH. Quantifying access barriers for specific health conditions, such as non-communicable diseases, injuries, disability and others, is a critical challenge for access barriers’ measurement going forward. A new generation of surveys could collect information on the whole range of access barriers and health interventions, as India now faces a wide spectrum of health challenges beyond those included in the SDGs.

The surveys included in this study allow measurement of only initial contact with health services and associated reasons for forgoing healthcare, even though access barriers are found along the entire care-seeking pathway. Furthermore, the quantitative focus of survey questionnaires and its association with closed questions might limit users’ responses and probably does not allow them to explain the circumstances behind the reasons for forgoing care. Addressing these problems would require data from additional sources, such as qualitative information that can provide context to the statistical information captured by household surveys.

Comparing information available from the population-based surveys like NFHS and the institution-based surveys like DLHS may be helpful in recognising the areas of disparities in healthcare access and service delivery from the two complementary perspectives. Information received from the demand-side and supply-side can potentially be useful in designing appropriate intervention strategies that ensure continuum of quality healthcare. However, the variation in the frequency of surveys makes it practically difficult to compare information obtained from them. While the latest NFHS report is available for 2015–2016, the latest comprehensive DLHS report available is almost a decade old, that for 2007–2008. There are additional challenges associated with data comparison due to the inconsistencies in the geographical outreach of these three surveys. For example, AHS covers only nine Indian states low on the economic- and social-development index; therefore, its geographic coverage is entirely different from that of NFHS, which is a national-level household survey, and DLHS, which collects only district-level information.

Despite these concerns, the regular conduct of household- and district-level health facility surveys has many advantages, including accessibility of data, identification of health inequities across population subgroups and comparison of interstate differences. Since each survey is easily accessible through publicly available government websites, the surveys are instrumental in informing about the actions required to improve MCH, at the state and national levels. Additionally, the indicators can be disaggregated by equity stratifiers and enable identification of disproportionate distribution of access barriers within the target population and potentially allow the decision-makers to design necessary intervention strategies accordingly.

The methodological approach used in this study has limitations. First, while household survey mapping enables the critical review of a range of data sources for measuring MCH-related access barriers in India, this approach is limited in the appraisal of the quality and comparability of the available data. Second, the study was limited to only three surveys, which prevented the inclusion of other surveys conducted less frequently with different geographical scope and, in relation to this, identification of all potential data sources and metrics and indicators for measuring access barriers. Future studies exploring these gaps are necessary. Despite these limitations, this study allowed the identification of a set of tracer indicators that could be monitored across India.

Future work on barriers to access of maternal and child services in India could focus on in-depth secondary data analyses of selected access barrier indicators and highlight data advantages and limitations when using these indicators. These indicators of access barriers can be used to show average levels and inequalities on the path to the SDGs and universal health in India and can provide useful guidance to policymakers in identifying which segments of the population are being left behind and why. In furtherance to the notion that all SDGs, including SDG 3 (Good Health and Well-being) are inextricably linked to SDG 1 (no poverty) (Nilsson et al., 2018), Multidimensional Poverty Index (MPI) can provide an excellent approach to map the gaps that contribute to health inequities based on these data sets on MCH in India. Even though it is a concept related to poverty, it is an appropriate and a holistic tool in understanding the deprivations in health, education and living standards in any particular area. The concept of MPI thrives on the fact that not just income or socio-economic status but also multiple dimensions, such as health, education and living standards, can contribute to any particular household becoming multi-dimensionally poor even when it may have the necessary income to afford the respective services. MPI is a holistic concept, though not an exhaustive one, as this concept can be adapted and localised as per the prevailing indicators that need to be monitored. It includes the three varied-yet-interlinked dimensions of health, education and living standards (Alkire & Santos, 2011) and can be used to stratify barriers for each dimension in the Tanahashi framework, thereby providing a systematic approach for a localised MPI questionnaire, and improve the monitoring of equity in barriers to access across geography and time.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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